From f40e2e9185d14c733897f1ec08ad957e7932bc1e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 1 May 2025 19:15:46 +0000 Subject: [PATCH 001/219] Bump the dev-dependencies group with 3 updates Bumps the dev-dependencies group with 3 updates: [langchain-openai](https://github.com/langchain-ai/langchain), [pydantic](https://github.com/pydantic/pydantic) and [python-telegram-bot](https://github.com/python-telegram-bot/python-telegram-bot). Updates `langchain-openai` from 0.3.12 to 0.3.15 - [Release notes](https://github.com/langchain-ai/langchain/releases) - [Commits](https://github.com/langchain-ai/langchain/compare/langchain-openai==0.3.12...langchain-openai==0.3.15) Updates `pydantic` from 2.11.3 to 2.11.4 - [Release notes](https://github.com/pydantic/pydantic/releases) - [Changelog](https://github.com/pydantic/pydantic/blob/main/HISTORY.md) - [Commits](https://github.com/pydantic/pydantic/compare/v2.11.3...v2.11.4) Updates `python-telegram-bot` from 21.11.1 to 22.0 - [Release notes](https://github.com/python-telegram-bot/python-telegram-bot/releases) - [Commits](https://github.com/python-telegram-bot/python-telegram-bot/compare/v21.11.1...v22.0) --- updated-dependencies: - dependency-name: langchain-openai dependency-version: 0.3.15 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: pydantic dependency-version: 2.11.4 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: python-telegram-bot dependency-version: '22.0' dependency-type: direct:production update-type: version-update:semver-major dependency-group: dev-dependencies ... Signed-off-by: dependabot[bot] --- requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 5aaff28b..22c70839 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,15 +3,15 @@ cachetools==5.5.2 fastapi==0.115.12 langchain==0.3.24 langchain_core>=0.3.56,<1.0.0 -langchain_openai==0.3.12 +langchain_openai==0.3.15 langchain_text_splitters==0.3.8 langgraph==0.4.1 openai==1.76.2 pgvector==0.3.6 psycopg2==2.9.10 -pydantic==2.11.3 +pydantic==2.11.4 python-dotenv==1.1.0 -python-telegram-bot==21.11.1 +python-telegram-bot==22.0 python-twitter-v2==0.9.2 Requests==2.32.3 SQLAlchemy==2.0.40 From a0ed66514ae7d32e82b6e1ef41ebaa5e2f0dfd18 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Fri, 2 May 2025 23:16:14 -0700 Subject: [PATCH 002/219] reorg --- document_processor.py | 2 +- services/runner/tasks/dao_task.py | 4 +- services/schedule.py | 4 +- services/workflows/__init__.py | 65 +- services/workflows/base.py | 617 +++++++++--------- .../{vector_preplan_react.py => chat.py} | 126 +--- services/workflows/planning_mixin.py | 158 +++++ services/workflows/preplan_react.py | 481 -------------- services/workflows/proposal_evaluation.py | 25 +- services/workflows/react.py | 590 ----------------- services/workflows/vector_mixin.py | 180 +++++ services/workflows/vector_react.py | 443 ------------- services/workflows/web_search.py | 238 ------- services/workflows/web_search_mixin.py | 175 +++++ services/workflows/workflow_service.py | 55 +- tests/services/workflows/test_vector_react.py | 6 +- vector_react_example.py | 6 +- 17 files changed, 929 insertions(+), 2246 deletions(-) rename services/workflows/{vector_preplan_react.py => chat.py} (87%) create mode 100644 services/workflows/planning_mixin.py delete mode 100644 services/workflows/preplan_react.py delete mode 100644 services/workflows/react.py create mode 100644 services/workflows/vector_mixin.py delete mode 100644 services/workflows/vector_react.py delete mode 100644 services/workflows/web_search.py create mode 100644 services/workflows/web_search_mixin.py diff --git a/document_processor.py b/document_processor.py index e533d8a3..7e4cf7e3 100644 --- a/document_processor.py +++ b/document_processor.py @@ -24,7 +24,7 @@ TokenFilter, VoteFilter, ) -from services.workflows.vector_react import add_documents_to_vectors +from services.workflows.chat import add_documents_to_vectors # Load environment variables dotenv.load_dotenv() diff --git a/services/runner/tasks/dao_task.py b/services/runner/tasks/dao_task.py index 3f91e4e7..f33e0fbd 100644 --- a/services/runner/tasks/dao_task.py +++ b/services/runner/tasks/dao_task.py @@ -13,7 +13,7 @@ QueueMessageType, ) from lib.logger import configure_logger -from services.workflows import execute_langgraph_stream +from services.workflows import execute_workflow_stream from tools.tools_factory import filter_tools_by_names, initialize_tools from ..base import BaseTask, JobContext, RunnerConfig, RunnerResult @@ -181,7 +181,7 @@ async def _process_dao_message(self, message: QueueMessage) -> DAOProcessingResu logger.debug(f"DAO deployment parameters: {tool_input}") deployment_data = {} - async for chunk in execute_langgraph_stream( + async for chunk in execute_workflow_stream( history=[], input_str=tool_input, tools_map=self.tools_map ): if chunk["type"] == "result": diff --git a/services/schedule.py b/services/schedule.py index 0147033a..b172fcdb 100644 --- a/services/schedule.py +++ b/services/schedule.py @@ -10,7 +10,7 @@ from backend.models import JobBase, JobCreate, StepCreate, Task, TaskFilter from lib.logger import configure_logger from lib.persona import generate_persona -from services.workflows import execute_langgraph_stream +from services.workflows import execute_workflow_stream from tools.tools_factory import exclude_tools_by_names, initialize_tools logger = configure_logger(__name__) @@ -142,7 +142,7 @@ async def _process_job_stream( ["db_update_scheduled_task", "db_add_scheduled_task"], tools_map ) - stream_generator = execute_langgraph_stream( + stream_generator = execute_workflow_stream( history=history, input_str=task.prompt, persona=persona, diff --git a/services/workflows/__init__.py b/services/workflows/__init__.py index e2e72953..183c0607 100644 --- a/services/workflows/__init__.py +++ b/services/workflows/__init__.py @@ -6,20 +6,21 @@ BaseWorkflowMixin, ExecutionError, LangGraphError, - PlanningCapability, + MessageContent, + MessageProcessor, StateType, + StreamingCallbackHandler, StreamingError, ValidationError, - VectorRetrievalCapability, ) -# Enhanced ReAct workflow variants -from services.workflows.preplan_react import ( - PreplanLangGraphService, - PreplanReactWorkflow, - PreplanState, - execute_preplan_react_stream, +# Remove all imports from deleted files and import from chat.py +from services.workflows.chat import ( + ChatService, + ChatWorkflow, + execute_chat_stream, ) +from services.workflows.planning_mixin import PlanningCapability # Special purpose workflows from services.workflows.proposal_evaluation import ( @@ -30,15 +31,6 @@ # Core messaging and streaming components # Core ReAct workflow components -from services.workflows.react import ( - LangGraphService, - MessageContent, - MessageProcessor, - ReactState, - ReactWorkflow, - StreamingCallbackHandler, - execute_langgraph_stream, -) from services.workflows.tweet_analysis import ( TweetAnalysisWorkflow, analyze_tweet, @@ -47,19 +39,11 @@ TweetGeneratorWorkflow, generate_dao_tweet, ) -from services.workflows.vector_preplan_react import ( - VectorPreplanLangGraphService, - VectorPreplanReactWorkflow, - VectorPreplanState, - execute_vector_preplan_stream, -) -from services.workflows.vector_react import ( - VectorLangGraphService, - VectorReactState, - VectorReactWorkflow, +from services.workflows.vector_mixin import ( + VectorRetrievalCapability, add_documents_to_vectors, - execute_vector_langgraph_stream, ) +from services.workflows.web_search_mixin import WebSearchCapability # Workflow service and factory from services.workflows.workflow_service import ( @@ -76,7 +60,6 @@ "BaseWorkflowMixin", "ExecutionError", "LangGraphError", - "PlanningCapability", "StateType", "StreamingError", "ValidationError", @@ -96,22 +79,6 @@ "ReactState", "ReactWorkflow", "execute_langgraph_stream", - # PrePlan ReAct workflow - "PreplanLangGraphService", - "PreplanReactWorkflow", - "PreplanState", - "execute_preplan_react_stream", - # Vector ReAct workflow - "VectorLangGraphService", - "VectorReactState", - "VectorReactWorkflow", - "add_documents_to_vectors", - "execute_vector_langgraph_stream", - # Vector PrePlan ReAct workflow - "VectorPreplanLangGraphService", - "VectorPreplanReactWorkflow", - "VectorPreplanState", - "execute_vector_preplan_stream", # Special purpose workflows "ProposalEvaluationWorkflow", "TweetAnalysisWorkflow", @@ -120,4 +87,12 @@ "evaluate_and_vote_on_proposal", "evaluate_proposal_only", "generate_dao_tweet", + # Chat workflow + "ChatService", + "ChatWorkflow", + "execute_chat_stream", + # Mixins + "PlanningCapability", + "WebSearchCapability", + "add_documents_to_vectors", ] diff --git a/services/workflows/base.py b/services/workflows/base.py index 2259335e..1689b442 100644 --- a/services/workflows/base.py +++ b/services/workflows/base.py @@ -1,16 +1,19 @@ """Base workflow functionality and shared components for all workflow types.""" +import asyncio +import datetime import json +import uuid from abc import ABC, abstractmethod +from dataclasses import dataclass from typing import Any, Dict, Generic, List, Optional, TypeVar, Union from langchain.prompts import PromptTemplate -from langchain.schema import Document -from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from langchain_core.callbacks import BaseCallbackHandler +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage +from langchain_openai import ChatOpenAI from langgraph.graph import Graph, StateGraph -from openai import OpenAI -from backend.factory import backend from lib.logger import configure_logger logger = configure_logger(__name__) @@ -242,333 +245,361 @@ def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: pass -class PlanningCapability(BaseWorkflowMixin): - """Mixin that adds planning capabilities to a workflow.""" +@dataclass +class MessageContent: + """Data class for message content""" - async def create_plan(self, query: str, **kwargs) -> str: - """Create a plan based on the user's query. + role: str + content: str + tool_calls: Optional[List[Dict]] = None - Args: - query: The user's query to plan for - **kwargs: Additional arguments (callback_handler, etc.) - - Returns: - The generated plan - """ - raise NotImplementedError("PlanningCapability must implement create_plan") - - def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - """Integrate planning capability with a graph. - - This adds the planning capability to the graph by modifying - the entry point to first create a plan. - - Args: - graph: The graph to integrate with - **kwargs: Additional arguments specific to planning - """ - # Implementation depends on specific graph structure - raise NotImplementedError( - "PlanningCapability must implement integrate_with_graph" + @classmethod + def from_dict(cls, data: Dict) -> "MessageContent": + """Create MessageContent from dictionary""" + return cls( + role=data.get("role", ""), + content=data.get("content", ""), + tool_calls=data.get("tool_calls"), ) -class VectorRetrievalCapability(BaseWorkflowMixin): - """Mixin that adds vector retrieval capabilities to a workflow.""" +class MessageProcessor: + """Processor for messages""" - def __init__(self, *args, **kwargs): - """Initialize the vector retrieval capability.""" - # Initialize parent class if it exists - super().__init__(*args, **kwargs) if hasattr(super(), "__init__") else None - # Initialize our attributes - self._init_vector_retrieval() - - def _init_vector_retrieval(self) -> None: - """Initialize vector retrieval attributes if not already initialized.""" - if not hasattr(self, "collection_names"): - self.collection_names = ["knowledge_collection", "dao_collection"] - if not hasattr(self, "embeddings"): - self.embeddings = OpenAIEmbeddings() - if not hasattr(self, "vector_results_cache"): - self.vector_results_cache = {} - - async def retrieve_from_vector_store(self, query: str, **kwargs) -> List[Document]: - """Retrieve relevant documents from multiple vector stores. + @staticmethod + def extract_filtered_content(history: List[Dict]) -> List[Dict]: + """Extract and filter content from message history.""" + logger.debug( + f"Starting content extraction from history with {len(history)} messages" + ) + filtered_content = [] - Args: - query: The query to search for - **kwargs: Additional arguments (collection_name, embeddings, etc.) + for message in history: + logger.debug(f"Processing message type: {message.get('role')}") + if message.get("role") in ["user", "assistant"]: + filtered_content.append(MessageContent.from_dict(message).__dict__) - Returns: - List of retrieved documents - """ - try: - # Ensure initialization - self._init_vector_retrieval() - - # Check cache first - if query in self.vector_results_cache: - logger.debug(f"Using cached vector results for query: {query}") - return self.vector_results_cache[query] - - all_documents = [] - limit_per_collection = kwargs.get("limit", 4) - logger.debug( - f"Searching vector store: query={query} | limit_per_collection={limit_per_collection}" - ) - - # Query each collection and gather results - for collection_name in self.collection_names: - try: - # Query vectors using the backend - vector_results = await backend.query_vectors( - collection_name=collection_name, - query_text=query, - limit=limit_per_collection, - embeddings=self.embeddings, + logger.debug( + f"Finished filtering content, extracted {len(filtered_content)} messages" + ) + return filtered_content + + @staticmethod + def convert_to_langchain_messages( + filtered_content: List[Dict], + current_input: str, + persona: Optional[str] = None, + ) -> List[Union[SystemMessage, HumanMessage, AIMessage]]: + """Convert filtered content to LangChain message format.""" + messages = [] + + # Add decisiveness instruction + decisiveness_instruction = "Be decisive and action-oriented. When the user requests something, execute it immediately without asking for confirmation." + + if persona: + logger.debug("Adding persona message with decisiveness instruction") + # Add the decisiveness instruction to the persona + enhanced_persona = f"{persona}\n\n{decisiveness_instruction}" + messages.append(SystemMessage(content=enhanced_persona)) + else: + # If no persona, add the decisiveness instruction as a system message + logger.debug("Adding decisiveness instruction as system message") + messages.append(SystemMessage(content=decisiveness_instruction)) + + for msg in filtered_content: + if msg["role"] == "user": + messages.append(HumanMessage(content=msg["content"])) + else: + content = msg.get("content") or "" + if msg.get("tool_calls"): + messages.append( + AIMessage(content=content, tool_calls=msg["tool_calls"]) ) + else: + messages.append(AIMessage(content=content)) - # Convert to LangChain Documents and add collection source - documents = [ - Document( - page_content=doc.get("page_content", ""), - metadata={ - **doc.get("metadata", {}), - "collection_source": collection_name, - }, - ) - for doc in vector_results - ] - - all_documents.extend(documents) - logger.debug( - f"Retrieved {len(documents)} documents from collection {collection_name}" - ) - except Exception as e: - logger.error( - f"Failed to retrieve from collection {collection_name}: {str(e)}", - exc_info=True, - ) - continue # Continue with other collections if one fails + messages.append(HumanMessage(content=current_input)) + logger.debug(f"Prepared message chain with {len(messages)} total messages") + return messages - logger.debug( - f"Retrieved total of {len(all_documents)} documents from all collections" - ) - # Cache the results - self.vector_results_cache[query] = all_documents +class StreamingCallbackHandler(BaseCallbackHandler): + """Handle callbacks from LangChain and stream results to a queue.""" - return all_documents + def __init__( + self, + queue: asyncio.Queue, + on_llm_new_token: Optional[callable] = None, + on_llm_end: Optional[callable] = None, + ): + """Initialize the callback handler with a queue.""" + self.queue = queue + self.tool_states = {} # Store tool states by invocation ID + self.tool_inputs = {} # Store tool inputs by invocation ID + self.active_tools = {} # Track active tools by name for fallback + self.custom_on_llm_new_token = on_llm_new_token + self.custom_on_llm_end = on_llm_end + # Track the current execution phase + self.current_phase = "processing" # Default phase is processing + + def _ensure_loop(self) -> asyncio.AbstractEventLoop: + """Get the current event loop or create a new one if necessary.""" + try: + loop = asyncio.get_running_loop() + return loop + except RuntimeError: + logger.debug("No running event loop found. Creating a new one.") + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + return loop + + async def _async_put_to_queue(self, item: Dict) -> None: + """Put an item in the queue asynchronously.""" + try: + await self.queue.put(item) except Exception as e: - logger.error(f"Vector store retrieval failed: {str(e)}", exc_info=True) - return [] + logger.error(f"Failed to put item in queue: {str(e)}") + raise StreamingError(f"Queue operation failed: {str(e)}") - def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - """Integrate vector retrieval capability with a graph. + def _put_to_queue(self, item: Dict) -> None: + """Put an item in the queue, handling event loop considerations.""" + try: + loop = self._ensure_loop() + if loop.is_running(): + future = asyncio.run_coroutine_threadsafe( + self._async_put_to_queue(item), loop + ) + future.result() + else: + loop.run_until_complete(self._async_put_to_queue(item)) + except Exception as e: + logger.error(f"Failed to put item in queue: {str(e)}") + raise StreamingError(f"Queue operation failed: {str(e)}") - This adds the vector retrieval capability to the graph by adding a node - that can perform vector searches when needed. + def _get_tool_info( + self, invocation_id: Optional[str], tool_name: Optional[str] = None + ) -> Optional[tuple]: + """Get tool information using either invocation_id or tool_name. - Args: - graph: The graph to integrate with - **kwargs: Additional arguments specific to vector retrieval including: - - collection_names: List of collection names to search - - limit_per_collection: Number of results per collection + Returns: + Optional[tuple]: (tool_name, tool_input, invocation_id) if found, None otherwise """ - # Add vector search node - graph.add_node("vector_search", self.retrieve_from_vector_store) - - # Add result processing node if needed - if "process_vector_results" not in graph.nodes: - graph.add_node("process_vector_results", self._process_vector_results) - graph.add_edge("vector_search", "process_vector_results") + if invocation_id and invocation_id in self.tool_states: + return ( + self.tool_states[invocation_id], + self.tool_inputs.get(invocation_id, ""), + invocation_id, + ) + elif tool_name and tool_name in self.active_tools: + active_info = self.active_tools[tool_name] + return (tool_name, active_info["input"], active_info["invocation_id"]) + return None - async def _process_vector_results( - self, vector_results: List[Document], **kwargs - ) -> Dict[str, Any]: - """Process vector search results. + async def process_step( + self, content: str, role: str = "assistant", thought: Optional[str] = None + ) -> None: + """Process a planning step and queue it with the planning status. Args: - vector_results: Results from vector search - **kwargs: Additional processing arguments - - Returns: - Processed results with metadata + content: The planning step content + role: The role associated with the step (usually assistant) + thought: Optional thought process notes """ - return { - "results": vector_results, - "metadata": { - "num_vector_results": len(vector_results), - "collection_sources": list( - set( - doc.metadata.get("collection_source", "unknown") - for doc in vector_results - ) - ), - }, - } - - -class WebSearchCapability(BaseWorkflowMixin): - """Mixin that adds web search capabilities to a workflow using OpenAI Responses API.""" - - def __init__(self, *args, **kwargs): - """Initialize the web search capability.""" - # Initialize parent class if it exists - super().__init__(*args, **kwargs) if hasattr(super(), "__init__") else None - # Initialize our attributes - self._init_web_search() + try: + # Create step message with explicit planning status + current_time = datetime.datetime.now().isoformat() + step_message = { + "type": "step", + "status": "planning", # Explicitly mark as planning phase + "content": content, + "role": role, + "thought": thought + or "Planning Phase", # Default to Planning Phase if thought is not provided + "created_at": current_time, + "planning_only": True, # Mark this content as planning-only to prevent duplication + } - def _init_web_search(self) -> None: - """Initialize web search attributes if not already initialized.""" - if not hasattr(self, "search_results_cache"): - self.search_results_cache = {} - if not hasattr(self, "client"): - self.client = OpenAI() + logger.debug(f"Queuing planning step message with length: {len(content)}") + await self._async_put_to_queue(step_message) + except Exception as e: + logger.error(f"Failed to process planning step: {str(e)}") + raise StreamingError(f"Planning step processing failed: {str(e)}") + + def on_tool_start(self, serialized: Dict, input_str: str, **kwargs) -> None: + """Run when tool starts running.""" + tool_name = serialized.get("name") + if not tool_name: + logger.warning("Tool start called without tool name") + return + + invocation_id = kwargs.get("invocation_id", str(uuid.uuid4())) + + # Store in both tracking systems + self.tool_states[invocation_id] = tool_name + self.tool_inputs[invocation_id] = input_str + self.active_tools[tool_name] = { + "invocation_id": invocation_id, + "input": input_str, + "start_time": datetime.datetime.now(), + } - async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: - """Search the web using OpenAI Responses API. + logger.info( + f"Tool started: {tool_name} (ID: {invocation_id}) with input: {input_str[:100]}..." + ) - Args: - query: The search query - **kwargs: Additional search parameters like user_location and search_context_size + def on_tool_end(self, output: str, **kwargs) -> None: + """Run when tool ends running.""" + invocation_id = kwargs.get("invocation_id") + tool_name = kwargs.get("name") # Try to get tool name from kwargs - Returns: - List of search results with content and metadata - """ - try: - # Ensure initialization - self._init_web_search() - - # Check cache first - if query in self.search_results_cache: - logger.info(f"Using cached results for query: {query}") - return self.search_results_cache[query] - - # Configure web search tool - tool_config = { - "type": "web_search_preview", - "search_context_size": kwargs.get("search_context_size", "medium"), - } + # Try to get tool info from either source + tool_info = self._get_tool_info(invocation_id, tool_name) - # Add user location if provided - if "user_location" in kwargs: - tool_config["user_location"] = kwargs["user_location"] + if tool_info: + tool_name, tool_input, used_invocation_id = tool_info + if hasattr(output, "content"): + output = output.content - # Make the API call - response = self.client.responses.create( - model="gpt-4.1", tools=[tool_config], input=query + self._put_to_queue( + { + "type": "tool", + "tool": tool_name, + "input": tool_input, + "output": str(output), + "status": "processing", # Use "processing" status for tool end + "created_at": datetime.datetime.now().isoformat(), + } + ) + logger.info( + f"Tool {tool_name} (ID: {used_invocation_id}) completed with output length: {len(str(output))}" ) - logger.debug(f"Web search response: {response}") - # Process the response into our document format - documents = [] - - # Access the output text directly - if hasattr(response, "output_text"): - text_content = response.output_text - source_urls = [] - - # Try to extract citations if available - if hasattr(response, "citations"): - source_urls = [ - { - "url": citation.url, - "title": getattr(citation, "title", ""), - "start_index": getattr(citation, "start_index", 0), - "end_index": getattr(citation, "end_index", 0), - } - for citation in response.citations - if hasattr(citation, "url") - ] - - # Ensure we always have at least one URL entry - if not source_urls: - source_urls = [ - { - "url": "No source URL available", - "title": "Generated Response", - "start_index": 0, - "end_index": len(text_content), - } - ] - - # Create document with content - doc = { - "page_content": text_content, - "metadata": { - "type": "web_search_result", - "source_urls": source_urls, - "query": query, - "timestamp": None, - }, - } - documents.append(doc) + # Clean up tracking + if used_invocation_id in self.tool_states: + del self.tool_states[used_invocation_id] + del self.tool_inputs[used_invocation_id] + if tool_name in self.active_tools: + del self.active_tools[tool_name] + else: + logger.warning( + f"Tool end called with unknown invocation ID: {invocation_id} and tool name: {tool_name}" + ) - # Cache the results - self.search_results_cache[query] = documents + def on_tool_error(self, error: Exception, **kwargs) -> None: + """Run when tool errors.""" + invocation_id = kwargs.get("invocation_id") + tool_name = kwargs.get("name") # Try to get tool name from kwargs - logger.info(f"Web search completed with {len(documents)} results") - return documents + # Try to get tool info from either source + tool_info = self._get_tool_info(invocation_id, tool_name) - except Exception as e: - logger.error(f"Web search failed: {str(e)}") - # Return a list with one empty result to prevent downstream errors - return [ + if tool_info: + tool_name, tool_input, used_invocation_id = tool_info + self._put_to_queue( { - "page_content": "Web search failed to return results.", - "metadata": { - "type": "web_search_result", - "source_urls": [ - { - "url": "Error occurred during web search", - "title": "Error", - "start_index": 0, - "end_index": 0, - } - ], - "query": query, - "timestamp": None, - }, + "type": "tool", + "tool": tool_name, + "input": tool_input, + "output": f"Error: {str(error)}", + "status": "error", + "created_at": datetime.datetime.now().isoformat(), } - ] - - def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - """Integrate web search capability with a graph. + ) + logger.error( + f"Tool {tool_name} (ID: {used_invocation_id}) failed with error: {str(error)}", + exc_info=True, + ) - This adds the web search capability to the graph by adding a node - that can perform web searches when needed. + # Clean up tracking + if used_invocation_id in self.tool_states: + del self.tool_states[used_invocation_id] + del self.tool_inputs[used_invocation_id] + if tool_name in self.active_tools: + del self.active_tools[tool_name] + else: + logger.warning( + f"Tool error called with unknown invocation ID: {invocation_id} and tool name: {tool_name}" + ) - Args: - graph: The graph to integrate with - **kwargs: Additional arguments specific to web search including: - - search_context_size: "low", "medium", or "high" - - user_location: dict with type, country, city, region - """ - # Add web search node - graph.add_node("web_search", self.search_web) + def on_llm_start(self, *args, **kwargs) -> None: + """Run when LLM starts running.""" + logger.info("LLM processing started") - # Add result processing node if needed - if "process_results" not in graph.nodes: - graph.add_node("process_results", self._process_results) - graph.add_edge("web_search", "process_results") + def on_llm_new_token(self, token: str, **kwargs) -> None: + """Run on new token.""" + # Check if we have planning_only in the kwargs + planning_only = kwargs.get("planning_only", False) - async def _process_results( - self, web_results: List[Dict[str, Any]], **kwargs - ) -> Dict[str, Any]: - """Process web search results. + # Handle custom token processing if provided + if self.custom_on_llm_new_token: + try: + # Check if it's a coroutine function and handle accordingly + if asyncio.iscoroutinefunction(self.custom_on_llm_new_token): + # For coroutines, we need to schedule it to run without awaiting + loop = self._ensure_loop() + # Create the coroutine object without calling it + coro = self.custom_on_llm_new_token(token, **kwargs) + # Schedule it to run in the event loop + asyncio.run_coroutine_threadsafe(coro, loop) + else: + # Regular function call + self.custom_on_llm_new_token(token, **kwargs) + except Exception as e: + logger.error(f"Error in custom token handler: {str(e)}", exc_info=True) + + # Log token information with phase information + phase = "planning" if planning_only else "processing" + logger.debug(f"Received new token (length: {len(token)}, phase: {phase})") + + def on_llm_end(self, response, **kwargs) -> None: + """Run when LLM ends running.""" + logger.info("LLM processing completed") + + # Queue an end message with complete status + try: + self._put_to_queue( + { + "type": "token", + "status": "complete", + "content": "", + "created_at": datetime.datetime.now().isoformat(), + } + ) + except Exception as e: + logger.error(f"Failed to queue completion message: {str(e)}") - Args: - web_results: Results from web search - **kwargs: Additional processing arguments + # Handle custom end processing if provided + if self.custom_on_llm_end: + try: + # Check if it's a coroutine function and handle accordingly + if asyncio.iscoroutinefunction(self.custom_on_llm_end): + # For coroutines, we need to schedule it to run without awaiting + loop = self._ensure_loop() + # Create the coroutine object without calling it + coro = self.custom_on_llm_end(response, **kwargs) + # Schedule it to run in the event loop + asyncio.run_coroutine_threadsafe(coro, loop) + else: + # Regular function call + self.custom_on_llm_end(response, **kwargs) + except Exception as e: + logger.error(f"Error in custom end handler: {str(e)}", exc_info=True) + + def on_llm_error(self, error: Exception, **kwargs) -> None: + """Run when LLM errors.""" + logger.error(f"LLM error occurred: {str(error)}", exc_info=True) + + # Send error status + try: + self._put_to_queue( + { + "type": "token", + "status": "error", + "content": f"Error: {str(error)}", + "created_at": datetime.datetime.now().isoformat(), + } + ) + except Exception: + pass # Don't raise another error if this fails - Returns: - Processed results with metadata - """ - return { - "results": web_results, - "metadata": { - "num_web_results": len(web_results), - "source_types": ["web_search"], - }, - } + raise ExecutionError("LLM processing failed", {"error": str(error)}) diff --git a/services/workflows/vector_preplan_react.py b/services/workflows/chat.py similarity index 87% rename from services/workflows/vector_preplan_react.py rename to services/workflows/chat.py index 20067308..22e6309f 100644 --- a/services/workflows/vector_preplan_react.py +++ b/services/workflows/chat.py @@ -29,31 +29,31 @@ from services.workflows.base import ( BaseWorkflow, ExecutionError, - PlanningCapability, + MessageProcessor, + StreamingCallbackHandler, +) +from services.workflows.planning_mixin import PlanningCapability +from services.workflows.vector_mixin import ( VectorRetrievalCapability, - WebSearchCapability, ) -from services.workflows.react import StreamingCallbackHandler - -# Remove this import to avoid circular dependencies -# from services.workflows.workflow_service import BaseWorkflowService, WorkflowBuilder +from services.workflows.web_search_mixin import WebSearchCapability logger = configure_logger(__name__) -class VectorPreplanState(TypedDict): - """State for the Vector PrePlan ReAct workflow, combining both capabilities.""" +class ChatState(TypedDict): + """State for the Chat workflow, combining all capabilities.""" messages: Annotated[list, add_messages] vector_results: Optional[List[Document]] - web_search_results: Optional[List[Document]] # Add web search results + web_search_results: Optional[List[Document]] # Web search results plan: Optional[str] -class VectorPreplanReactWorkflow( - BaseWorkflow[VectorPreplanState], - VectorRetrievalCapability, +class ChatWorkflow( + BaseWorkflow[ChatState], PlanningCapability, + VectorRetrievalCapability, WebSearchCapability, ): """Workflow that combines vector retrieval and planning capabilities. @@ -110,6 +110,18 @@ def __init__( self.persona = None self.tool_descriptions = None + # Initialize mixins + PlanningCapability.__init__( + self, + callback_handler=callback_handler, + planning_llm=self.planning_llm, + persona=self.persona, + tool_names=self.tool_names, + tool_descriptions=self.tool_descriptions, + ) + VectorRetrievalCapability.__init__(self) + WebSearchCapability.__init__(self) + def _create_prompt(self) -> None: """Not used in Vector PrePlan ReAct workflow.""" pass @@ -337,14 +349,14 @@ def _create_graph(self) -> StateGraph: tool_node = ToolNode(self.tools) logger.debug(f"Created tool node with {len(self.tools)} tools") - def should_continue(state: VectorPreplanState) -> str: + def should_continue(state: ChatState) -> str: messages = state["messages"] last_message = messages[-1] result = "tools" if last_message.tool_calls else END logger.debug(f"Continue decision: {result}") return result - async def retrieve_context(state: VectorPreplanState) -> Dict: + async def retrieve_context(state: ChatState) -> Dict: """Retrieve context from both vector store and web search.""" messages = state["messages"] last_user_message = None @@ -373,7 +385,7 @@ async def retrieve_context(state: VectorPreplanState) -> Dict: return {"vector_results": vector_results, "web_search_results": web_results} - def call_model_with_context_and_plan(state: VectorPreplanState) -> Dict: + def call_model_with_context_and_plan(state: ChatState) -> Dict: """Call model with context, plan, and web search results.""" messages = state["messages"] vector_results = state.get("vector_results", []) @@ -443,7 +455,7 @@ def call_model_with_context_and_plan(state: VectorPreplanState) -> Dict: response = self.llm.invoke(messages) return {"messages": [response]} - workflow = StateGraph(VectorPreplanState) + workflow = StateGraph(ChatState) # Add nodes workflow.add_node("context_retrieval", retrieve_context) @@ -460,33 +472,27 @@ def call_model_with_context_and_plan(state: VectorPreplanState) -> Dict: return workflow -class VectorPreplanLangGraphService: - """Service for executing Vector PrePlan React LangGraph operations""" +class ChatService: + """Service for executing Chat LangGraph operations.""" def __init__( self, collection_names: Union[str, List[str]], embeddings: Optional[Embeddings] = None, ): - # Import here to avoid circular imports - from services.workflows.react import MessageProcessor self.collection_names = collection_names self.embeddings = embeddings or OpenAIEmbeddings() self.message_processor = MessageProcessor() def setup_callback_handler(self, queue, loop): - # Import here to avoid circular dependencies from services.workflows.workflow_service import BaseWorkflowService - # Use the static method instead of instantiating BaseWorkflowService return BaseWorkflowService.create_callback_handler(queue, loop) async def stream_task_results(self, task, queue): - # Import here to avoid circular dependencies from services.workflows.workflow_service import BaseWorkflowService - # Use the static method instead of instantiating BaseWorkflowService async for chunk in BaseWorkflowService.stream_results_from_task( task=task, callback_queue=queue, logger_name=self.__class__.__name__ ): @@ -500,32 +506,14 @@ async def _execute_stream_impl( tools_map: Optional[Dict] = None, **kwargs, ) -> AsyncGenerator[Dict, None]: - """Execute a Vector PrePlan React stream implementation. - - Args: - messages: Processed messages - input_str: Current user input - persona: Optional persona to use - tools_map: Optional tools to use - **kwargs: Additional arguments - - Returns: - Async generator of result chunks - """ try: - # Import here to avoid circular dependencies from services.workflows.workflow_service import WorkflowBuilder - # Setup queue and callbacks callback_queue = asyncio.Queue() loop = asyncio.get_running_loop() - - # Setup callback handler callback_handler = self.setup_callback_handler(callback_queue, loop) - - # Create workflow using builder pattern workflow = ( - WorkflowBuilder(VectorPreplanReactWorkflow) + WorkflowBuilder(ChatWorkflow) .with_callback_handler(callback_handler) .with_tools(list(tools_map.values()) if tools_map else []) .build( @@ -533,17 +521,11 @@ async def _execute_stream_impl( embeddings=self.embeddings, ) ) - - # Store persona and tool information for planning if persona: - # Append decisiveness guidance to the persona decisive_guidance = "\n\nBe decisive and take action without asking for confirmation. When the user requests something, proceed directly with executing it rather than asking if they want you to do it." workflow.persona = persona + decisive_guidance - - # Store available tool names for planning if tools_map: workflow.tool_names = list(tools_map.keys()) - # Add tool descriptions to planning prompt tool_descriptions = "\n\nTOOL DESCRIPTIONS:\n" for name, tool in tools_map.items(): description = getattr( @@ -551,17 +533,12 @@ async def _execute_stream_impl( ) tool_descriptions += f"- {name}: {description}\n" workflow.tool_descriptions = tool_descriptions - - # First retrieve relevant documents from vector store logger.info( f"Retrieving documents from vector store for query: {input_str[:50]}..." ) documents = await workflow.retrieve_from_vector_store(query=input_str) logger.info(f"Retrieved {len(documents)} documents from vector store") - - # Create plan with vector context try: - # The thought notes will be streamed through callbacks logger.info("Creating plan with vector context...") plan = await workflow.create_plan(input_str, context_docs=documents) logger.info(f"Plan created successfully with {len(plan)} characters") @@ -571,15 +548,10 @@ async def _execute_stream_impl( "type": "token", "content": "Proceeding directly to answer...\n\n", } - # No plan will be provided, letting the LLM handle the task naturally plan = None - - # Create graph and compile graph = workflow._create_graph() runnable = graph.compile() logger.info("Graph compiled successfully") - - # Execute workflow with callbacks config config = {"callbacks": [callback_handler]} task = asyncio.create_task( runnable.ainvoke( @@ -587,18 +559,12 @@ async def _execute_stream_impl( config=config, ) ) - - # Stream results async for chunk in self.stream_task_results(task, callback_queue): yield chunk - except Exception as e: - logger.error( - f"Failed to execute Vector PrePlan stream: {str(e)}", exc_info=True - ) - raise ExecutionError(f"Vector PrePlan stream execution failed: {str(e)}") + logger.error(f"Failed to execute Chat stream: {str(e)}", exc_info=True) + raise ExecutionError(f"Chat stream execution failed: {str(e)}") - # Add execute_stream method to maintain the same interface as BaseWorkflowService async def execute_stream( self, history: List[Dict], @@ -607,17 +573,10 @@ async def execute_stream( tools_map: Optional[Dict] = None, **kwargs, ) -> AsyncGenerator[Dict, None]: - """Execute a workflow stream. - - This processes the history and delegates to _execute_stream_impl. - """ - # Process messages filtered_content = self.message_processor.extract_filtered_content(history) messages = self.message_processor.convert_to_langchain_messages( filtered_content, input_str, persona ) - - # Call the implementation async for chunk in self._execute_stream_impl( messages=messages, input_str=input_str, @@ -629,7 +588,7 @@ async def execute_stream( # Facade function -async def execute_vector_preplan_stream( +async def execute_chat_stream( collection_names: Union[str, List[str]], history: List[Dict], input_str: str, @@ -637,30 +596,17 @@ async def execute_vector_preplan_stream( tools_map: Optional[Dict] = None, embeddings: Optional[Embeddings] = None, ) -> AsyncGenerator[Dict, None]: - """Execute a Vector PrePlan ReAct stream. + """Execute a Chat stream. This workflow combines vector retrieval and planning: 1. Retrieves relevant context from multiple vector stores 2. Creates a plan based on the user's query and retrieved context 3. Executes the ReAct workflow with both context and plan - - Args: - collection_names: Name(s) of the vector collections to use - history: Conversation history - input_str: Current user input - persona: Optional persona to use - tools_map: Optional tools to make available - embeddings: Optional embeddings model - - Returns: - Async generator of result chunks """ - # Initialize service and run stream embeddings = embeddings or OpenAIEmbeddings() - service = VectorPreplanLangGraphService( + service = ChatService( collection_names=collection_names, embeddings=embeddings, ) - async for chunk in service.execute_stream(history, input_str, persona, tools_map): yield chunk diff --git a/services/workflows/planning_mixin.py b/services/workflows/planning_mixin.py new file mode 100644 index 00000000..5a6b51c6 --- /dev/null +++ b/services/workflows/planning_mixin.py @@ -0,0 +1,158 @@ +"""Planning mixin for workflows, providing vector-aware planning capabilities.""" + +import asyncio +from typing import Any, List, Optional + +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage, HumanMessage + +from lib.logger import configure_logger +from services.workflows.base import BaseWorkflowMixin +from services.workflows.chat import StreamingCallbackHandler + +logger = configure_logger(__name__) + +class PlanningCapability(BaseWorkflowMixin): + """Mixin that adds vector-aware planning capabilities to a workflow. + + This mixin generates a plan based on the user's query, retrieved vector context, + available tools, and persona. It streams planning tokens using a callback handler. + """ + + def __init__( + self, + callback_handler: StreamingCallbackHandler, + planning_llm: ChatOpenAI, + persona: Optional[str] = None, + tool_names: Optional[List[str]] = None, + tool_descriptions: Optional[str] = None, + **kwargs, + ): + """Initialize the planning capability. + + Args: + callback_handler: Handler for streaming planning tokens + planning_llm: LLM instance for planning + persona: Optional persona string + tool_names: Optional list of tool names + tool_descriptions: Optional tool descriptions string + **kwargs: Additional arguments + """ + super().__init__(**kwargs) if hasattr(super(), "__init__") else None + self.callback_handler = callback_handler + self.planning_llm = planning_llm + self.persona = persona + self.tool_names = tool_names or [] + self.tool_descriptions = tool_descriptions + + async def create_plan( + self, + query: str, + context_docs: Optional[List[Any]] = None, + **kwargs, + ) -> str: + """Create a plan based on the user's query and vector retrieval results. + + Args: + query: The user's query + context_docs: Optional retrieved context documents + **kwargs: Additional arguments + + Returns: + Generated plan + """ + planning_prompt = f""" + You are an AI assistant planning a decisive response to the user's query. + + Write a few short sentences as if you're taking notes in a notebook about: + - What the user is asking for + - What information or tools you'll use to complete the task + - The exact actions you'll take to fulfill the request + + AIBTC DAO Context Information: + You are an AI governance agent integrated with an AIBTC DAO. Your role is to interact with the DAO's smart contracts + on behalf of token holders, either by assisting human users or by acting autonomously within the DAO's rules. The DAO + is governed entirely by its token holders through proposals – members submit proposals, vote on them, and if a proposal passes, + it is executed on-chain. Always maintain the integrity of the DAO's decentralized process: never bypass on-chain governance, + and ensure all actions strictly follow the DAO's smart contract rules and parameters. + + Your responsibilities include: + 1. Helping users create and submit proposals to the DAO + 2. Guiding users through the voting process + 3. Explaining how DAO contract interactions work + 4. Preventing invalid actions and detecting potential exploits + 5. In autonomous mode, monitoring DAO state, proposing actions, and voting according to governance rules + + When interacting with users about the DAO, always: + - Retrieve contract addresses automatically instead of asking users + - Validate transactions before submission + - Present clear summaries of proposed actions + - Verify eligibility and check voting power + - Format transactions precisely according to blockchain requirements + - Provide confirmation and feedback after actions + + DAO Tools Usage: + For ANY DAO-related request, use the appropriate DAO tools to access real-time information: + - Use dao_list tool to retrieve all DAOs, their tokens, and extensions + - Use dao_search tool to find specific DAOs by name, description, token name, symbol, or contract ID + - Do NOT hardcode DAO information or assumptions about contract addresses + - Always query for the latest DAO data through the tools rather than relying on static information + - When analyzing user requests, determine if they're asking about a specific DAO or need a list of DAOs + - After retrieving DAO information, use it to accurately guide users through governance processes + + Examples of effective DAO tool usage: + 1. If user asks about voting on a proposal: First use dao_search to find the specific DAO, then guide them with the correct contract details + 2. If user asks to list available DAOs: Use dao_list to retrieve current DAOs and present them clearly + 3. If user wants to create a proposal: Use dao_search to get the DAO details first, then assist with the proposal creation using the current contract addresses + + User Query: {query} + """ + if context_docs: + context_str = "\n\n".join([getattr(doc, "page_content", str(doc)) for doc in context_docs]) + planning_prompt += f"\n\nHere is additional context that may be helpful:\n\n{context_str}\n\nUse this context to inform your plan." + if self.tool_names: + tool_info = "\n\nTools available to you:\n" + for tool_name in self.tool_names: + tool_info += f"- {tool_name}\n" + planning_prompt += tool_info + if self.tool_descriptions: + planning_prompt += self.tool_descriptions + planning_messages = [] + if self.persona: + planning_messages.append(SystemMessage(content=self.persona)) + planning_messages.append(HumanMessage(content=planning_prompt)) + try: + logger.info("Creating thought process notes for user query with vector context") + original_new_token = self.callback_handler.custom_on_llm_new_token + async def planning_token_wrapper(token, **kwargs): + if asyncio.iscoroutinefunction(original_new_token): + await original_new_token(token, planning_only=True, **kwargs) + else: + loop = asyncio.get_running_loop() + asyncio.run_coroutine_threadsafe( + self.callback_handler.queue.put( + { + "type": "token", + "content": token, + "status": "planning", + "planning_only": True, + } + ), + loop, + ) + self.callback_handler.custom_on_llm_new_token = planning_token_wrapper + task = asyncio.create_task(self.planning_llm.ainvoke(planning_messages)) + response = await task + plan = response.content + self.callback_handler.custom_on_llm_new_token = original_new_token + logger.info("Thought process notes created successfully with vector context") + logger.debug(f"Notes content length: {len(plan)}") + await self.callback_handler.process_step( + content=plan, role="assistant", thought="Planning Phase with Context" + ) + return plan + except Exception as e: + if hasattr(self.callback_handler, "custom_on_llm_new_token"): + self.callback_handler.custom_on_llm_new_token = original_new_token + logger.error(f"Failed to create plan: {str(e)}", exc_info=True) + raise \ No newline at end of file diff --git a/services/workflows/preplan_react.py b/services/workflows/preplan_react.py deleted file mode 100644 index 8bd1f3e1..00000000 --- a/services/workflows/preplan_react.py +++ /dev/null @@ -1,481 +0,0 @@ -"""PrePlan ReAct workflow functionality. - -This workflow first creates a plan based on the user's query, then executes -the ReAct workflow to complete the task according to the plan. -""" - -import asyncio -from typing import ( - Annotated, - Any, - AsyncGenerator, - Dict, - List, - Optional, - TypedDict, - Union, -) - -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage -from langchain_openai import ChatOpenAI -from langgraph.graph import END, START, StateGraph -from langgraph.graph.message import add_messages -from langgraph.prebuilt import ToolNode - -from lib.logger import configure_logger -from services.workflows.base import BaseWorkflow, ExecutionError, PlanningCapability -from services.workflows.react import MessageProcessor, StreamingCallbackHandler - -# Remove this import to avoid circular dependencies -# from services.workflows.workflow_service import BaseWorkflowService, WorkflowBuilder - -logger = configure_logger(__name__) - - -class PreplanState(TypedDict): - """State for the PrePlan ReAct workflow.""" - - messages: Annotated[list, add_messages] - plan: Optional[str] - - -class PreplanReactWorkflow(BaseWorkflow[PreplanState], PlanningCapability): - """PrePlan ReAct workflow implementation. - - This workflow first creates a plan based on the user's query, - then executes the ReAct workflow to complete the task according to the plan. - """ - - def __init__( - self, - callback_handler: StreamingCallbackHandler, - tools: List[Any], - **kwargs, - ): - super().__init__(**kwargs) - self.callback_handler = callback_handler - self.tools = tools - self.required_fields = ["messages"] - # Set decisive behavior flag - self.decisive_behavior = True - - # Create a new LLM instance with the callback handler - self.llm = self.create_llm_with_callbacks([callback_handler]).bind_tools(tools) - - # Create a separate LLM for planning with streaming enabled - self.planning_llm = ChatOpenAI( - model="o4-mini", - streaming=True, # Enable streaming for the planning LLM - callbacks=[callback_handler], - ) - - # Store tool information for planning - self.tool_names = [] - if tools: - self.tool_names = [ - tool.name if hasattr(tool, "name") else str(tool) for tool in tools - ] - - # Additional attributes for planning - self.persona = None - self.tool_descriptions = None - - def _create_prompt(self) -> None: - """Not used in PrePlan ReAct workflow.""" - pass - - async def create_plan(self, query: str) -> str: - """Create a simple thought process plan based on the user's query.""" - # Create a more decisive planning prompt - planning_prompt = f""" - You are an AI assistant planning a decisive response to the user's query. - - Write a few short sentences as if you're taking notes in a notebook about: - - What the user is asking for - - What information or tools you'll use to complete the task - - The exact actions you'll take to fulfill the request - - AIBTC DAO Context Information: - You are an AI governance agent integrated with an AIBTC DAO. Your role is to interact with the DAO's smart contracts - on behalf of token holders, either by assisting human users or by acting autonomously within the DAO's rules. The DAO - is governed entirely by its token holders through proposals – members submit proposals, vote on them, and if a proposal passes, - it is executed on-chain. Always maintain the integrity of the DAO's decentralized process: never bypass on-chain governance, - and ensure all actions strictly follow the DAO's smart contract rules and parameters. - - Your responsibilities include: - 1. Helping users create and submit proposals to the DAO - 2. Guiding users through the voting process - 3. Explaining how DAO contract interactions work - 4. Preventing invalid actions and detecting potential exploits - 5. In autonomous mode, monitoring DAO state, proposing actions, and voting according to governance rules - - When interacting with users about the DAO, always: - - Retrieve contract addresses automatically instead of asking users - - Validate transactions before submission - - Present clear summaries of proposed actions - - Verify eligibility and check voting power - - Format transactions precisely according to blockchain requirements - - Provide confirmation and feedback after actions - - DAO Tools Usage: - For ANY DAO-related request, use the appropriate DAO tools to access real-time information: - - Use dao_list tool to retrieve all DAOs, their tokens, and extensions - - Use dao_search tool to find specific DAOs by name, description, token name, symbol, or contract ID - - Do NOT hardcode DAO information or assumptions about contract addresses - - Always query for the latest DAO data through the tools rather than relying on static information - - When analyzing user requests, determine if they're asking about a specific DAO or need a list of DAOs - - After retrieving DAO information, use it to accurately guide users through governance processes - - Examples of effective DAO tool usage: - 1. If user asks about voting on a proposal: First use dao_search to find the specific DAO, then guide them with the correct contract details - 2. If user asks to list available DAOs: Use dao_list to retrieve current DAOs and present them clearly - 3. If user wants to create a proposal: Use dao_search to get the DAO details first, then assist with the proposal creation using the current contract addresses - - Be decisive and action-oriented. Don't include phrases like "I would," "I could," or "I might." - Instead, use phrases like "I will," "I am going to," and "I'll execute." - Don't ask for confirmation before taking actions - assume the user wants you to proceed. - - User Query: {query} - """ - - # Add available tools to the planning prompt if available - if hasattr(self, "tool_names") and self.tool_names: - tool_info = "\n\nTools available to you:\n" - for tool_name in self.tool_names: - tool_info += f"- {tool_name}\n" - planning_prompt += tool_info - - # Add tool descriptions if available - if hasattr(self, "tool_descriptions"): - planning_prompt += self.tool_descriptions - - # Create planning messages, including persona if available - planning_messages = [] - - # If we're in the service context and persona is available, add it as a system message - if hasattr(self, "persona") and self.persona: - planning_messages.append(SystemMessage(content=self.persona)) - - # Add the planning prompt - planning_messages.append(HumanMessage(content=planning_prompt)) - - try: - logger.info("Creating thought process notes for user query") - - # Configure custom callback for planning to properly mark planning tokens - original_new_token = self.callback_handler.custom_on_llm_new_token - - # Create temporary wrapper to mark planning tokens - async def planning_token_wrapper(token, **kwargs): - # Add planning flag to tokens during the planning phase - if asyncio.iscoroutinefunction(original_new_token): - await original_new_token(token, planning_only=True, **kwargs) - else: - # If it's not a coroutine, assume it's a function that uses run_coroutine_threadsafe - loop = asyncio.get_running_loop() - asyncio.run_coroutine_threadsafe( - self.callback_handler.queue.put( - { - "type": "token", - "content": token, - "status": "planning", - "planning_only": True, - } - ), - loop, - ) - - # Set the temporary wrapper - self.callback_handler.custom_on_llm_new_token = planning_token_wrapper - - # Create a task to invoke the planning LLM - task = asyncio.create_task(self.planning_llm.ainvoke(planning_messages)) - - # Wait for the task to complete - response = await task - plan = response.content - - # Restore original callback - self.callback_handler.custom_on_llm_new_token = original_new_token - - logger.info("Thought process notes created successfully") - logger.debug(f"Notes content length: {len(plan)}") - - # Use the new process_step method to emit the plan with a planning status - await self.callback_handler.process_step( - content=plan, role="assistant", thought="Planning Phase" - ) - - return plan - except Exception as e: - # Restore original callback in case of error - if hasattr(self, "callback_handler") and hasattr( - self.callback_handler, "custom_on_llm_new_token" - ): - self.callback_handler.custom_on_llm_new_token = original_new_token - - logger.error(f"Failed to create plan: {str(e)}", exc_info=True) - # Let the LLM handle the planning naturally without a static fallback - raise - - def _create_graph(self) -> StateGraph: - """Create the PrePlan ReAct workflow graph.""" - logger.info("Creating PrePlan ReAct workflow graph") - tool_node = ToolNode(self.tools) - logger.debug(f"Created tool node with {len(self.tools)} tools") - - def should_continue(state: PreplanState) -> str: - messages = state["messages"] - last_message = messages[-1] - result = "tools" if last_message.tool_calls else END - logger.debug(f"Continue decision: {result}") - return result - - def call_model(state: PreplanState) -> Dict: - logger.debug("Calling model with current state") - messages = state["messages"] - - # Add the plan as a system message if it exists and hasn't been added yet - if state.get("plan") is not None and not any( - isinstance(msg, SystemMessage) and "thought" in msg.content.lower() - for msg in messages - ): - logger.info("Adding thought notes to messages as system message") - plan_message = SystemMessage( - content=f""" - Follow these decisive actions to address the user's query: - - {state["plan"]} - - Execute these steps directly without asking for confirmation. - Be decisive and action-oriented in your responses. - """ - ) - messages = [plan_message] + messages - else: - logger.debug("No thought notes to add or notes already added") - - # If decisive behavior is enabled and there's no plan-related system message, - # add a decisive behavior system message - if getattr(self, "decisive_behavior", False) and not any( - isinstance(msg, SystemMessage) for msg in messages - ): - logger.info("Adding decisive behavior instruction as system message") - decisive_message = SystemMessage( - content="Be decisive and take action without asking for confirmation. " - "When the user requests something, proceed directly with executing it." - ) - messages = [decisive_message] + messages - - logger.debug(f"Invoking LLM with {len(messages)} messages") - response = self.llm.invoke(messages) - logger.debug("Received model response") - logger.debug( - f"Response content length: {len(response.content) if hasattr(response, 'content') else 0}" - ) - return {"messages": [response]} - - workflow = StateGraph(PreplanState) - logger.debug("Created StateGraph") - - workflow.add_node("agent", call_model) - workflow.add_node("tools", tool_node) - workflow.add_edge(START, "agent") - workflow.add_conditional_edges("agent", should_continue) - workflow.add_edge("tools", "agent") - logger.info("Graph setup complete") - - return workflow - - def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - """Integrate planning capability with the graph. - - Args: - graph: The graph to integrate with - **kwargs: Additional arguments - """ - # Implementation would modify the graph to include planning step - # before the main execution flow - pass - - -class PreplanLangGraphService: - """Service for executing PrePlan LangGraph operations""" - - def __init__(self): - # Initialize message processor here - self.message_processor = MessageProcessor() - - def setup_callback_handler(self, queue, loop): - # Import here to avoid circular dependencies - from services.workflows.workflow_service import BaseWorkflowService - - # Use the static method instead of instantiating BaseWorkflowService - return BaseWorkflowService.create_callback_handler(queue, loop) - - async def stream_task_results(self, task, queue): - # Import here to avoid circular dependencies - from services.workflows.workflow_service import BaseWorkflowService - - # Use the static method instead of instantiating BaseWorkflowService - async for chunk in BaseWorkflowService.stream_results_from_task( - task=task, callback_queue=queue, logger_name=self.__class__.__name__ - ): - yield chunk - - async def _execute_stream_impl( - self, - messages: List[Union[SystemMessage, HumanMessage, AIMessage]], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - **kwargs, - ) -> AsyncGenerator[Dict, None]: - """Execute a PrePlan React stream implementation. - - Args: - messages: Processed messages - input_str: Current user input - persona: Optional persona to use - tools_map: Optional tools to use - **kwargs: Additional arguments - - Returns: - Async generator of result chunks - """ - try: - # Import here to avoid circular dependencies - from services.workflows.workflow_service import WorkflowBuilder - - # Setup queue and callbacks - callback_queue = asyncio.Queue() - loop = asyncio.get_running_loop() - - # Setup callback handler - callback_handler = self.setup_callback_handler(callback_queue, loop) - - # Create workflow using builder pattern - workflow_builder = ( - WorkflowBuilder(PreplanReactWorkflow) - .with_callback_handler(callback_handler) - .with_tools(list(tools_map.values()) if tools_map else []) - ) - - workflow = workflow_builder.build() - - # Store persona and tool information for planning - if persona: - # Append decisiveness guidance to the persona - decisive_guidance = "\n\nBe decisive and take action without asking for confirmation. When the user requests something, proceed directly with executing it rather than asking if they want you to do it." - workflow.persona = persona + decisive_guidance - - # Store available tool names for planning - if tools_map: - workflow.tool_names = list(tools_map.keys()) - # Add tool descriptions to planning prompt - tool_descriptions = "\n\nTOOL DESCRIPTIONS:\n" - for name, tool in tools_map.items(): - description = getattr( - tool, "description", "No description available" - ) - tool_descriptions += f"- {name}: {description}\n" - workflow.tool_descriptions = tool_descriptions - - try: - # The thought notes will be streamed through callbacks - plan = await workflow.create_plan(input_str) - - except Exception as e: - logger.error(f"Planning failed, continuing with execution: {str(e)}") - yield { - "type": "token", - "content": "Proceeding directly to answer...\n\n", - } - # No plan will be provided, letting the LLM handle the task naturally - plan = None - - # Create graph and compile - graph = workflow._create_graph() - runnable = graph.compile() - logger.info("Graph compiled successfully") - - # Add the plan to the initial state - initial_state = {"messages": messages} - if plan is not None: - initial_state["plan"] = plan - logger.info("Added plan to initial state") - else: - logger.warning("No plan available for initial state") - - # Set up configuration with callbacks - config = {"callbacks": [callback_handler]} - logger.debug("Configuration set up with callbacks") - - # Execute workflow with callbacks config - logger.info("Creating task to execute workflow") - task = asyncio.create_task(runnable.ainvoke(initial_state, config=config)) - - # Stream results - async for chunk in self.stream_task_results(task, callback_queue): - yield chunk - - except Exception as e: - logger.error( - f"Failed to execute PrePlan ReAct stream: {str(e)}", exc_info=True - ) - raise ExecutionError(f"PrePlan ReAct stream execution failed: {str(e)}") - - # Add execute_stream method to maintain the same interface as BaseWorkflowService - async def execute_stream( - self, - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - **kwargs, - ) -> AsyncGenerator[Dict, None]: - """Execute a workflow stream. - - This processes the history and delegates to _execute_stream_impl. - """ - # Process messages - filtered_content = self.message_processor.extract_filtered_content(history) - messages = self.message_processor.convert_to_langchain_messages( - filtered_content, input_str, persona - ) - - # Call the implementation - async for chunk in self._execute_stream_impl( - messages=messages, - input_str=input_str, - persona=persona, - tools_map=tools_map, - **kwargs, - ): - yield chunk - - # Keep the old method for backward compatibility - async def execute_preplan_react_stream( - self, - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - ) -> AsyncGenerator[Dict, None]: - """Execute a PrePlan ReAct stream using LangGraph.""" - # Call the new method - async for chunk in self.execute_stream(history, input_str, persona, tools_map): - yield chunk - - -# Facade function for compatibility with the API -async def execute_preplan_react_stream( - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, -) -> AsyncGenerator[Dict, None]: - """Execute a PrePlan ReAct stream using LangGraph with optional persona.""" - service = PreplanLangGraphService() - async for chunk in service.execute_stream(history, input_str, persona, tools_map): - yield chunk diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 5e9f8419..84ceeaad 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -3,9 +3,7 @@ import binascii from typing import Dict, List, Optional, TypedDict -from langchain.callbacks.base import BaseCallbackHandler from langchain.prompts import PromptTemplate -from langchain_core.documents import Document from langgraph.graph import END, Graph, StateGraph from pydantic import BaseModel, Field @@ -14,7 +12,6 @@ UUID, ExtensionFilter, Profile, - Prompt, PromptFilter, ProposalType, QueueMessageFilter, @@ -24,10 +21,10 @@ from lib.logger import configure_logger from services.workflows.base import ( BaseWorkflow, - VectorRetrievalCapability, - WebSearchCapability, ) -from services.workflows.vector_react import VectorLangGraphService, VectorReactState +from services.workflows.chat import ChatService +from services.workflows.vector_mixin import VectorRetrievalCapability +from services.workflows.web_search_mixin import WebSearchCapability from tools.dao_ext_action_proposals import VoteOnActionProposalTool from tools.tools_factory import filter_tools_by_names, initialize_tools @@ -239,7 +236,7 @@ async def evaluate_proposal(state: EvaluationState) -> EvaluationState: try: # Add debug logging for dao_id self.logger.debug(f"Fetching tweets for DAO ID: {dao_id}") - + queue_messages = backend.list_queue_messages( QueueMessageFilter( type=QueueMessageType.TWEET, @@ -249,17 +246,23 @@ async def evaluate_proposal(state: EvaluationState) -> EvaluationState: ) # Log the number of messages found self.logger.debug(f"Found {len(queue_messages)} queue messages") - + # Sort by created_at and take last 5 sorted_messages = sorted( queue_messages, key=lambda x: x.created_at, reverse=True )[:5] - self.logger.debug(f"After sorting, have {len(sorted_messages)} messages") + self.logger.debug( + f"After sorting, have {len(sorted_messages)} messages" + ) recent_tweets = [ { "created_at": msg.created_at, - "message": msg.message.get('message', 'No text available') if isinstance(msg.message, dict) else msg.message, + "message": ( + msg.message.get("message", "No text available") + if isinstance(msg.message, dict) + else msg.message + ), "tweet_id": msg.tweet_id, } for msg in sorted_messages @@ -515,7 +518,7 @@ async def vote_on_proposal(state: EvaluationState) -> EvaluationState: vote_instruction = f"I need you to vote on a DAO proposal with ID {state['proposal_id']} in the contract {state['action_proposals_contract']}. Please vote {'FOR' if state['approve'] else 'AGAINST'} the proposal. Use the dao_action_vote_on_proposal tool to submit the vote." # Create VectorLangGraph service with collections - service = VectorLangGraphService( + service = ChatService( collection_names=self.collection_names, ) diff --git a/services/workflows/react.py b/services/workflows/react.py deleted file mode 100644 index d5742f90..00000000 --- a/services/workflows/react.py +++ /dev/null @@ -1,590 +0,0 @@ -"""ReAct workflow functionality.""" - -import asyncio -import datetime -import uuid -from dataclasses import dataclass -from typing import ( - Annotated, - Any, - AsyncGenerator, - Dict, - List, - Optional, - TypedDict, - Union, -) - -from langchain.callbacks.base import BaseCallbackHandler -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage -from langchain_core.outputs import LLMResult -from langchain_openai import ChatOpenAI -from langgraph.graph import END, START, StateGraph -from langgraph.graph.message import add_messages -from langgraph.prebuilt import ToolNode - -from lib.logger import configure_logger -from services.workflows.base import BaseWorkflow, ExecutionError, StreamingError - -# Remove this import to avoid circular dependencies -# from services.workflows.workflow_service import BaseWorkflowService, WorkflowBuilder - -logger = configure_logger(__name__) - - -@dataclass -class MessageContent: - """Data class for message content""" - - role: str - content: str - tool_calls: Optional[List[Dict]] = None - - @classmethod - def from_dict(cls, data: Dict) -> "MessageContent": - """Create MessageContent from dictionary""" - return cls( - role=data.get("role", ""), - content=data.get("content", ""), - tool_calls=data.get("tool_calls"), - ) - - -class MessageProcessor: - """Processor for messages""" - - @staticmethod - def extract_filtered_content(history: List[Dict]) -> List[Dict]: - """Extract and filter content from message history.""" - logger.debug( - f"Starting content extraction from history with {len(history)} messages" - ) - filtered_content = [] - - for message in history: - logger.debug(f"Processing message type: {message.get('role')}") - if message.get("role") in ["user", "assistant"]: - filtered_content.append(MessageContent.from_dict(message).__dict__) - - logger.debug( - f"Finished filtering content, extracted {len(filtered_content)} messages" - ) - return filtered_content - - @staticmethod - def convert_to_langchain_messages( - filtered_content: List[Dict], - current_input: str, - persona: Optional[str] = None, - ) -> List[Union[SystemMessage, HumanMessage, AIMessage]]: - """Convert filtered content to LangChain message format.""" - messages = [] - - # Add decisiveness instruction - decisiveness_instruction = "Be decisive and action-oriented. When the user requests something, execute it immediately without asking for confirmation." - - if persona: - logger.debug("Adding persona message with decisiveness instruction") - # Add the decisiveness instruction to the persona - enhanced_persona = f"{persona}\n\n{decisiveness_instruction}" - messages.append(SystemMessage(content=enhanced_persona)) - else: - # If no persona, add the decisiveness instruction as a system message - logger.debug("Adding decisiveness instruction as system message") - messages.append(SystemMessage(content=decisiveness_instruction)) - - for msg in filtered_content: - if msg["role"] == "user": - messages.append(HumanMessage(content=msg["content"])) - else: - content = msg.get("content") or "" - if msg.get("tool_calls"): - messages.append( - AIMessage(content=content, tool_calls=msg["tool_calls"]) - ) - else: - messages.append(AIMessage(content=content)) - - messages.append(HumanMessage(content=current_input)) - logger.debug(f"Prepared message chain with {len(messages)} total messages") - return messages - - -class StreamingCallbackHandler(BaseCallbackHandler): - """Handle callbacks from LangChain and stream results to a queue.""" - - def __init__( - self, - queue: asyncio.Queue, - on_llm_new_token: Optional[callable] = None, - on_llm_end: Optional[callable] = None, - ): - """Initialize the callback handler with a queue.""" - self.queue = queue - self.tool_states = {} # Store tool states by invocation ID - self.tool_inputs = {} # Store tool inputs by invocation ID - self.active_tools = {} # Track active tools by name for fallback - self.custom_on_llm_new_token = on_llm_new_token - self.custom_on_llm_end = on_llm_end - # Track the current execution phase - self.current_phase = "processing" # Default phase is processing - - def _ensure_loop(self) -> asyncio.AbstractEventLoop: - """Get the current event loop or create a new one if necessary.""" - try: - loop = asyncio.get_running_loop() - return loop - except RuntimeError: - logger.debug("No running event loop found. Creating a new one.") - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - return loop - - async def _async_put_to_queue(self, item: Dict) -> None: - """Put an item in the queue asynchronously.""" - try: - await self.queue.put(item) - except Exception as e: - logger.error(f"Failed to put item in queue: {str(e)}") - raise StreamingError(f"Queue operation failed: {str(e)}") - - def _put_to_queue(self, item: Dict) -> None: - """Put an item in the queue, handling event loop considerations.""" - try: - loop = self._ensure_loop() - if loop.is_running(): - future = asyncio.run_coroutine_threadsafe( - self._async_put_to_queue(item), loop - ) - future.result() - else: - loop.run_until_complete(self._async_put_to_queue(item)) - except Exception as e: - logger.error(f"Failed to put item in queue: {str(e)}") - raise StreamingError(f"Queue operation failed: {str(e)}") - - def _get_tool_info( - self, invocation_id: Optional[str], tool_name: Optional[str] = None - ) -> Optional[tuple]: - """Get tool information using either invocation_id or tool_name. - - Returns: - Optional[tuple]: (tool_name, tool_input, invocation_id) if found, None otherwise - """ - if invocation_id and invocation_id in self.tool_states: - return ( - self.tool_states[invocation_id], - self.tool_inputs.get(invocation_id, ""), - invocation_id, - ) - elif tool_name and tool_name in self.active_tools: - active_info = self.active_tools[tool_name] - return (tool_name, active_info["input"], active_info["invocation_id"]) - return None - - async def process_step( - self, content: str, role: str = "assistant", thought: Optional[str] = None - ) -> None: - """Process a planning step and queue it with the planning status. - - Args: - content: The planning step content - role: The role associated with the step (usually assistant) - thought: Optional thought process notes - """ - try: - # Create step message with explicit planning status - current_time = datetime.datetime.now().isoformat() - step_message = { - "type": "step", - "status": "planning", # Explicitly mark as planning phase - "content": content, - "role": role, - "thought": thought - or "Planning Phase", # Default to Planning Phase if thought is not provided - "created_at": current_time, - "planning_only": True, # Mark this content as planning-only to prevent duplication - } - - logger.debug(f"Queuing planning step message with length: {len(content)}") - await self._async_put_to_queue(step_message) - except Exception as e: - logger.error(f"Failed to process planning step: {str(e)}") - raise StreamingError(f"Planning step processing failed: {str(e)}") - - def on_tool_start(self, serialized: Dict, input_str: str, **kwargs) -> None: - """Run when tool starts running.""" - tool_name = serialized.get("name") - if not tool_name: - logger.warning("Tool start called without tool name") - return - - invocation_id = kwargs.get("invocation_id", str(uuid.uuid4())) - - # Store in both tracking systems - self.tool_states[invocation_id] = tool_name - self.tool_inputs[invocation_id] = input_str - self.active_tools[tool_name] = { - "invocation_id": invocation_id, - "input": input_str, - "start_time": datetime.datetime.now(), - } - - logger.info( - f"Tool started: {tool_name} (ID: {invocation_id}) with input: {input_str[:100]}..." - ) - - def on_tool_end(self, output: str, **kwargs) -> None: - """Run when tool ends running.""" - invocation_id = kwargs.get("invocation_id") - tool_name = kwargs.get("name") # Try to get tool name from kwargs - - # Try to get tool info from either source - tool_info = self._get_tool_info(invocation_id, tool_name) - - if tool_info: - tool_name, tool_input, used_invocation_id = tool_info - if hasattr(output, "content"): - output = output.content - - self._put_to_queue( - { - "type": "tool", - "tool": tool_name, - "input": tool_input, - "output": str(output), - "status": "processing", # Use "processing" status for tool end - "created_at": datetime.datetime.now().isoformat(), - } - ) - logger.info( - f"Tool {tool_name} (ID: {used_invocation_id}) completed with output length: {len(str(output))}" - ) - - # Clean up tracking - if used_invocation_id in self.tool_states: - del self.tool_states[used_invocation_id] - del self.tool_inputs[used_invocation_id] - if tool_name in self.active_tools: - del self.active_tools[tool_name] - else: - logger.warning( - f"Tool end called with unknown invocation ID: {invocation_id} and tool name: {tool_name}" - ) - - def on_tool_error(self, error: Exception, **kwargs) -> None: - """Run when tool errors.""" - invocation_id = kwargs.get("invocation_id") - tool_name = kwargs.get("name") # Try to get tool name from kwargs - - # Try to get tool info from either source - tool_info = self._get_tool_info(invocation_id, tool_name) - - if tool_info: - tool_name, tool_input, used_invocation_id = tool_info - self._put_to_queue( - { - "type": "tool", - "tool": tool_name, - "input": tool_input, - "output": f"Error: {str(error)}", - "status": "error", - "created_at": datetime.datetime.now().isoformat(), - } - ) - logger.error( - f"Tool {tool_name} (ID: {used_invocation_id}) failed with error: {str(error)}", - exc_info=True, - ) - - # Clean up tracking - if used_invocation_id in self.tool_states: - del self.tool_states[used_invocation_id] - del self.tool_inputs[used_invocation_id] - if tool_name in self.active_tools: - del self.active_tools[tool_name] - else: - logger.warning( - f"Tool error called with unknown invocation ID: {invocation_id} and tool name: {tool_name}" - ) - - def on_llm_start(self, *args, **kwargs) -> None: - """Run when LLM starts running.""" - logger.info("LLM processing started") - - def on_llm_new_token(self, token: str, **kwargs) -> None: - """Run on new token.""" - # Check if we have planning_only in the kwargs - planning_only = kwargs.get("planning_only", False) - - # Handle custom token processing if provided - if self.custom_on_llm_new_token: - try: - # Check if it's a coroutine function and handle accordingly - if asyncio.iscoroutinefunction(self.custom_on_llm_new_token): - # For coroutines, we need to schedule it to run without awaiting - loop = self._ensure_loop() - # Create the coroutine object without calling it - coro = self.custom_on_llm_new_token(token, **kwargs) - # Schedule it to run in the event loop - asyncio.run_coroutine_threadsafe(coro, loop) - else: - # Regular function call - self.custom_on_llm_new_token(token, **kwargs) - except Exception as e: - logger.error(f"Error in custom token handler: {str(e)}", exc_info=True) - - # Log token information with phase information - phase = "planning" if planning_only else "processing" - logger.debug(f"Received new token (length: {len(token)}, phase: {phase})") - - def on_llm_end(self, response: LLMResult, **kwargs) -> None: - """Run when LLM ends running.""" - logger.info("LLM processing completed") - - # Queue an end message with complete status - try: - self._put_to_queue( - { - "type": "token", - "status": "complete", - "content": "", - "created_at": datetime.datetime.now().isoformat(), - } - ) - except Exception as e: - logger.error(f"Failed to queue completion message: {str(e)}") - - # Handle custom end processing if provided - if self.custom_on_llm_end: - try: - # Check if it's a coroutine function and handle accordingly - if asyncio.iscoroutinefunction(self.custom_on_llm_end): - # For coroutines, we need to schedule it to run without awaiting - loop = self._ensure_loop() - # Create the coroutine object without calling it - coro = self.custom_on_llm_end(response, **kwargs) - # Schedule it to run in the event loop - asyncio.run_coroutine_threadsafe(coro, loop) - else: - # Regular function call - self.custom_on_llm_end(response, **kwargs) - except Exception as e: - logger.error(f"Error in custom end handler: {str(e)}", exc_info=True) - - def on_llm_error(self, error: Exception, **kwargs) -> None: - """Run when LLM errors.""" - logger.error(f"LLM error occurred: {str(error)}", exc_info=True) - - # Send error status - try: - self._put_to_queue( - { - "type": "token", - "status": "error", - "content": f"Error: {str(error)}", - "created_at": datetime.datetime.now().isoformat(), - } - ) - except Exception: - pass # Don't raise another error if this fails - - raise ExecutionError("LLM processing failed", {"error": str(error)}) - - -class ReactState(TypedDict): - """State for the ReAct workflow.""" - - messages: Annotated[list, add_messages] - - -class ReactWorkflow(BaseWorkflow[ReactState]): - """ReAct workflow implementation.""" - - def __init__( - self, - callback_handler: StreamingCallbackHandler, - tools: List[Any], - **kwargs, - ): - super().__init__(**kwargs) - self.callback_handler = callback_handler - self.tools = tools - # Create a new LLM instance with the callback handler - self.llm = self.create_llm_with_callbacks([callback_handler]).bind_tools(tools) - self.required_fields = ["messages"] - - def _create_prompt(self) -> None: - """Not used in ReAct workflow.""" - pass - - def _create_graph(self) -> StateGraph: - """Create the ReAct workflow graph.""" - tool_node = ToolNode(self.tools) - - def should_continue(state: ReactState) -> str: - messages = state["messages"] - last_message = messages[-1] - result = "tools" if last_message.tool_calls else END - logger.debug(f"Continue decision: {result}") - return result - - def call_model(state: ReactState) -> Dict: - logger.debug("Calling model with current state") - messages = state["messages"] - response = self.llm.invoke(messages) - logger.debug("Received model response") - return {"messages": [response]} - - workflow = StateGraph(ReactState) - workflow.add_node("agent", call_model) - workflow.add_node("tools", tool_node) - workflow.add_edge(START, "agent") - workflow.add_conditional_edges("agent", should_continue) - workflow.add_edge("tools", "agent") - - return workflow - - -class LangGraphService: - """Service for executing LangGraph operations""" - - def __init__(self): - """Initialize the service.""" - self.message_processor = MessageProcessor() - - async def _execute_stream_impl( - self, - messages: List[Union[SystemMessage, HumanMessage, AIMessage]], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - **kwargs, - ) -> AsyncGenerator[Dict, None]: - """Execute a ReAct stream using LangGraph. - - Args: - messages: Processed messages ready for the LLM - input_str: Current user input - persona: Optional persona to use - tools_map: Optional tools to use - **kwargs: Additional arguments - - Returns: - Async generator of result chunks - """ - try: - # Import here to avoid circular dependencies - from services.workflows.workflow_service import ( - BaseWorkflowService, - WorkflowBuilder, - ) - - # Setup queue and callbacks - callback_queue = asyncio.Queue() - loop = asyncio.get_running_loop() - - # Setup callback handler - callback_handler = self.setup_callback_handler(callback_queue, loop) - - # Create workflow using builder pattern - workflow = ( - WorkflowBuilder(ReactWorkflow) - .with_callback_handler(callback_handler) - .with_tools(list(tools_map.values()) if tools_map else []) - .build() - ) - - # Create graph and compile - graph = workflow._create_graph() - runnable = graph.compile() - - # Execute workflow with callbacks config - config = {"callbacks": [callback_handler]} - task = asyncio.create_task( - runnable.ainvoke({"messages": messages}, config=config) - ) - - # Stream results - async for chunk in self.stream_task_results(task, callback_queue): - yield chunk - - except Exception as e: - logger.error(f"Failed to execute ReAct stream: {str(e)}", exc_info=True) - raise ExecutionError(f"ReAct stream execution failed: {str(e)}") - - def setup_callback_handler(self, queue, loop): - # Import here to avoid circular dependencies - from services.workflows.workflow_service import BaseWorkflowService - - # Use the static method instead of instantiating BaseWorkflowService - return BaseWorkflowService.create_callback_handler(queue, loop) - - async def stream_task_results(self, task, queue): - # Import here to avoid circular dependencies - from services.workflows.workflow_service import BaseWorkflowService - - # Use the static method instead of instantiating BaseWorkflowService - async for chunk in BaseWorkflowService.stream_results_from_task( - task=task, callback_queue=queue, logger_name=self.__class__.__name__ - ): - yield chunk - - # Keep the old method for backward compatibility - async def execute_react_stream( - self, - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - ) -> AsyncGenerator[Dict, None]: - """Execute a ReAct stream using LangGraph.""" - # Process messages for backward compatibility - filtered_content = self.message_processor.extract_filtered_content(history) - messages = self.message_processor.convert_to_langchain_messages( - filtered_content, input_str, persona - ) - - # Call the new implementation - async for chunk in self._execute_stream_impl( - messages=messages, - input_str=input_str, - persona=persona, - tools_map=tools_map, - ): - yield chunk - - # Add execute_stream as alias for consistency across services - async def execute_stream( - self, - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - **kwargs, - ) -> AsyncGenerator[Dict, None]: - """Execute a workflow stream. - - This is an alias for execute_react_stream to maintain consistent API - across different workflow services. - """ - async for chunk in self.execute_react_stream( - history=history, - input_str=input_str, - persona=persona, - tools_map=tools_map, - ): - yield chunk - - -# Facade function for backward compatibility -async def execute_langgraph_stream( - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, -) -> AsyncGenerator[Dict, None]: - """Execute a ReAct stream using LangGraph with optional persona.""" - service = LangGraphService() - async for chunk in service.execute_stream(history, input_str, persona, tools_map): - yield chunk diff --git a/services/workflows/vector_mixin.py b/services/workflows/vector_mixin.py new file mode 100644 index 00000000..f6aaa750 --- /dev/null +++ b/services/workflows/vector_mixin.py @@ -0,0 +1,180 @@ +"""Vector retrieval mixin and vector document utilities for workflows.""" + +from typing import Any, Dict, List, Optional + +from langchain_core.documents import Document +from langchain_openai import OpenAIEmbeddings +from langgraph.graph import StateGraph + +from backend.factory import backend +from lib.logger import configure_logger +from services.workflows.base import BaseWorkflowMixin + +logger = configure_logger(__name__) + + +class VectorRetrievalCapability(BaseWorkflowMixin): + """Mixin that adds vector retrieval capabilities to a workflow.""" + + def __init__(self, *args, **kwargs): + """Initialize the vector retrieval capability.""" + super().__init__(*args, **kwargs) if hasattr(super(), "__init__") else None + self._init_vector_retrieval() + + def _init_vector_retrieval(self) -> None: + """Initialize vector retrieval attributes if not already initialized.""" + if not hasattr(self, "collection_names"): + self.collection_names = ["knowledge_collection", "dao_collection"] + if not hasattr(self, "embeddings"): + self.embeddings = OpenAIEmbeddings() + if not hasattr(self, "vector_results_cache"): + self.vector_results_cache = {} + + async def retrieve_from_vector_store(self, query: str, **kwargs) -> List[Document]: + """Retrieve relevant documents from multiple vector stores. + + Args: + query: The query to search for + **kwargs: Additional arguments (collection_name, embeddings, etc.) + + Returns: + List of retrieved documents + """ + try: + self._init_vector_retrieval() + if query in self.vector_results_cache: + logger.debug(f"Using cached vector results for query: {query}") + return self.vector_results_cache[query] + all_documents = [] + limit_per_collection = kwargs.get("limit", 4) + logger.debug( + f"Searching vector store: query={query} | limit_per_collection={limit_per_collection}" + ) + for collection_name in self.collection_names: + try: + vector_results = await backend.query_vectors( + collection_name=collection_name, + query_text=query, + limit=limit_per_collection, + embeddings=self.embeddings, + ) + documents = [ + Document( + page_content=doc.get("page_content", ""), + metadata={ + **doc.get("metadata", {}), + "collection_source": collection_name, + }, + ) + for doc in vector_results + ] + all_documents.extend(documents) + logger.debug( + f"Retrieved {len(documents)} documents from collection {collection_name}" + ) + except Exception as e: + logger.error( + f"Failed to retrieve from collection {collection_name}: {str(e)}", + exc_info=True, + ) + continue + logger.debug( + f"Retrieved total of {len(all_documents)} documents from all collections" + ) + self.vector_results_cache[query] = all_documents + return all_documents + except Exception as e: + logger.error(f"Vector store retrieval failed: {str(e)}", exc_info=True) + return [] + + def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: + """Integrate vector retrieval capability with a graph. + + This adds the vector retrieval capability to the graph by adding a node + that can perform vector searches when needed. + + Args: + graph: The graph to integrate with + **kwargs: Additional arguments specific to vector retrieval including: + - collection_names: List of collection names to search + - limit_per_collection: Number of results per collection + """ + graph.add_node("vector_search", self.retrieve_from_vector_store) + if "process_vector_results" not in graph.nodes: + graph.add_node("process_vector_results", self._process_vector_results) + graph.add_edge("vector_search", "process_vector_results") + + async def _process_vector_results( + self, vector_results: List[Document], **kwargs + ) -> Dict[str, Any]: + """Process vector search results. + + Args: + vector_results: Results from vector search + **kwargs: Additional processing arguments + + Returns: + Processed results with metadata + """ + return { + "results": vector_results, + "metadata": { + "num_vector_results": len(vector_results), + "collection_sources": list( + set( + doc.metadata.get("collection_source", "unknown") + for doc in vector_results + ) + ), + }, + } + + +async def add_documents_to_vectors( + collection_name: str, + documents: List[Document], + embeddings: Optional[Any] = None, +) -> Dict[str, List[str]]: + """Add documents to a vector collection. + + Args: + collection_name: Name of the collection to add to + documents: List of LangChain Document objects + embeddings: Optional embeddings model to use + + Returns: + Dictionary mapping collection name to list of document IDs + """ + if embeddings is None: + raise ValueError( + "Embeddings model must be provided to add documents to vector store" + ) + collection_doc_ids = {} + try: + try: + backend.get_vector_collection(collection_name) + except Exception: + embed_dim = 1536 + if hasattr(embeddings, "embedding_dim"): + embed_dim = embeddings.embedding_dim + backend.create_vector_collection(collection_name, dimensions=embed_dim) + texts = [doc.page_content for doc in documents] + embedding_vectors = embeddings.embed_documents(texts) + docs_for_storage = [ + {"page_content": doc.page_content, "embedding": embedding_vectors[i]} + for i, doc in enumerate(documents) + ] + metadata_list = [doc.metadata for doc in documents] + ids = await backend.add_vectors( + collection_name=collection_name, + documents=docs_for_storage, + metadata=metadata_list, + ) + collection_doc_ids[collection_name] = ids + logger.info(f"Added {len(ids)} documents to collection {collection_name}") + except Exception as e: + logger.error( + f"Failed to add documents to collection {collection_name}: {str(e)}" + ) + collection_doc_ids[collection_name] = [] + return collection_doc_ids diff --git a/services/workflows/vector_react.py b/services/workflows/vector_react.py deleted file mode 100644 index aa55f95d..00000000 --- a/services/workflows/vector_react.py +++ /dev/null @@ -1,443 +0,0 @@ -"""Vector-enabled ReAct workflow functionality with Supabase Vecs integration.""" - -import asyncio -from typing import Any, AsyncGenerator, Dict, List, Optional, TypedDict, Union - -from langchain_core.documents import Document -from langchain_core.embeddings import Embeddings -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage -from langchain_openai import ChatOpenAI, OpenAIEmbeddings -from langgraph.graph import END, START, StateGraph -from langgraph.prebuilt import ToolNode - -from backend.factory import backend -from lib.logger import configure_logger -from services.workflows.base import ( - BaseWorkflow, - ExecutionError, - VectorRetrievalCapability, -) -from services.workflows.react import ( - MessageProcessor, - ReactState, - StreamingCallbackHandler, -) - -# Remove this import to avoid circular dependencies -# from services.workflows.workflow_service import BaseWorkflowService, WorkflowBuilder - -logger = configure_logger(__name__) - - -class VectorRetrievalState(TypedDict): - """State for vector retrieval step.""" - - query: str - documents: List[Document] - - -class VectorReactState(ReactState): - """State for the Vector ReAct workflow, extending ReactState.""" - - vector_results: Optional[List[Document]] - - -class VectorReactWorkflow(BaseWorkflow[VectorReactState], VectorRetrievalCapability): - """ReAct workflow with vector store integration.""" - - def __init__( - self, - callback_handler: StreamingCallbackHandler, - tools: List[Any], - collection_names: Union[ - str, List[str] - ], # Modified to accept single or multiple collections - embeddings: Optional[Embeddings] = None, - **kwargs, - ): - super().__init__(**kwargs) - self.callback_handler = callback_handler - self.tools = tools - # Convert single collection to list for consistency - self.collection_names = ( - [collection_names] - if isinstance(collection_names, str) - else collection_names - ) - self.embeddings = embeddings or OpenAIEmbeddings() - self.required_fields = ["messages"] - - # Create a new LLM instance with the callback handler - self.llm = self.create_llm_with_callbacks([callback_handler]).bind_tools(tools) - - def _create_prompt(self) -> None: - """Not used in VectorReact workflow.""" - pass - - async def retrieve_from_vector_store(self, query: str, **kwargs) -> List[Document]: - """Retrieve relevant documents from multiple vector stores. - - Args: - query: The query to search for - **kwargs: Additional arguments - - Returns: - List of retrieved documents - """ - try: - all_documents = [] - limit_per_collection = kwargs.get( - "limit", 4 - ) # Get 4 results from each collection - - # Query each collection and gather results - for collection_name in self.collection_names: - try: - # Query vectors using the backend - vector_results = await backend.query_vectors( - collection_name=collection_name, - query_text=query, - limit=limit_per_collection, - embeddings=self.embeddings, - ) - - # Convert to LangChain Documents and add collection source - documents = [ - Document( - page_content=doc.get("page_content", ""), - metadata={ - **doc.get("metadata", {}), - "collection_source": collection_name, - }, - ) - for doc in vector_results - ] - - all_documents.extend(documents) - logger.info( - f"Retrieved {len(documents)} documents from collection {collection_name}" - ) - except Exception as e: - logger.error( - f"Failed to retrieve from collection {collection_name}: {str(e)}" - ) - continue # Continue with other collections if one fails - - logger.info( - f"Retrieved total of {len(all_documents)} documents from all collections" - ) - return all_documents - except Exception as e: - logger.error(f"Vector store retrieval failed: {str(e)}") - return [] - - def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - """Integrate vector retrieval capability with a graph. - - Args: - graph: The graph to integrate with - **kwargs: Additional arguments - """ - # Modify the graph to include vector retrieval - # This is specific to the VectorReactWorkflow - pass - - def _create_graph(self) -> StateGraph: - """Create the VectorReact workflow graph.""" - tool_node = ToolNode(self.tools) - - def should_continue(state: VectorReactState) -> str: - messages = state["messages"] - last_message = messages[-1] - result = "tools" if last_message.tool_calls else END - logger.debug(f"Continue decision: {result}") - return result - - async def retrieve_from_vector_store(state: VectorReactState) -> Dict: - """Retrieve relevant documents from vector store.""" - messages = state["messages"] - # Get the last user message - last_user_message = None - for message in reversed(messages): - if isinstance(message, HumanMessage): - last_user_message = message.content - break - - if not last_user_message: - logger.warning("No user message found for vector retrieval") - return {"vector_results": []} - - documents = await self.retrieve_from_vector_store(query=last_user_message) - return {"vector_results": documents} - - def call_model_with_context(state: VectorReactState) -> Dict: - """Call model with additional context from vector store.""" - messages = state["messages"] - vector_results = state.get("vector_results", []) - - # Add vector context to the system message if available - context_message = None - - if vector_results: - # Format the vector results into a context string - context_str = "\n\n".join([doc.page_content for doc in vector_results]) - context_message = SystemMessage( - content=f"Here is additional context that may be helpful:\n\n{context_str}\n\n" - "Use this context to inform your response if relevant." - ) - messages = [context_message] + messages - - logger.debug( - f"Calling model with {len(messages)} messages and " - f"{len(vector_results)} retrieved documents" - ) - - response = self.llm.invoke(messages) - return {"messages": [response]} - - workflow = StateGraph(VectorReactState) - workflow.add_node("vector_retrieval", retrieve_from_vector_store) - workflow.add_node("agent", call_model_with_context) - workflow.add_node("tools", tool_node) - - # Set up the execution flow - workflow.add_edge(START, "vector_retrieval") - workflow.add_edge("vector_retrieval", "agent") - workflow.add_conditional_edges("agent", should_continue) - workflow.add_edge("tools", "agent") - - return workflow - - -class VectorLangGraphService: - """Service for executing VectorReact LangGraph operations""" - - def __init__( - self, - collection_names: Union[ - str, List[str] - ], # Modified to accept single or multiple collections - embeddings: Optional[Embeddings] = None, - ): - # Import here to avoid circular imports - from services.workflows.react import MessageProcessor - - self.collection_names = collection_names - self.embeddings = embeddings or OpenAIEmbeddings() - self.message_processor = MessageProcessor() - - def setup_callback_handler(self, queue, loop): - # Import here to avoid circular dependencies - from services.workflows.workflow_service import BaseWorkflowService - - # Use the static method instead of instantiating BaseWorkflowService - return BaseWorkflowService.create_callback_handler(queue, loop) - - async def stream_task_results(self, task, queue): - # Import here to avoid circular dependencies - from services.workflows.workflow_service import BaseWorkflowService - - # Use the static method instead of instantiating BaseWorkflowService - async for chunk in BaseWorkflowService.stream_results_from_task( - task=task, callback_queue=queue, logger_name=self.__class__.__name__ - ): - yield chunk - - async def _execute_stream_impl( - self, - messages: List[Union[SystemMessage, HumanMessage, AIMessage]], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - **kwargs, - ) -> AsyncGenerator[Dict, None]: - """Execute a Vector React stream implementation. - - Args: - messages: Processed messages - input_str: Current user input - persona: Optional persona to use - tools_map: Optional tools to use - **kwargs: Additional arguments - - Returns: - Async generator of result chunks - """ - try: - # Import here to avoid circular dependencies - from services.workflows.workflow_service import WorkflowBuilder - - # Setup queue and callbacks - callback_queue = asyncio.Queue() - loop = asyncio.get_running_loop() - - # Setup callback handler - callback_handler = self.setup_callback_handler(callback_queue, loop) - - # Create workflow using builder pattern - workflow = ( - WorkflowBuilder(VectorReactWorkflow) - .with_callback_handler(callback_handler) - .with_tools(list(tools_map.values()) if tools_map else []) - .build( - collection_names=self.collection_names, - embeddings=self.embeddings, - ) - ) - - # Create graph and compile - graph = workflow._create_graph() - runnable = graph.compile() - - # Execute workflow with callbacks config - config = {"callbacks": [callback_handler]} - task = asyncio.create_task( - runnable.ainvoke( - {"messages": messages, "vector_results": []}, config=config - ) - ) - - # Stream results - async for chunk in self.stream_task_results(task, callback_queue): - yield chunk - - except Exception as e: - logger.error( - f"Failed to execute VectorReact stream: {str(e)}", exc_info=True - ) - raise ExecutionError(f"VectorReact stream execution failed: {str(e)}") - - # Add execute_stream method to maintain the same interface as BaseWorkflowService - async def execute_stream( - self, - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - **kwargs, - ) -> AsyncGenerator[Dict, None]: - """Execute a workflow stream. - - This processes the history and delegates to _execute_stream_impl. - """ - # Process messages - filtered_content = self.message_processor.extract_filtered_content(history) - messages = self.message_processor.convert_to_langchain_messages( - filtered_content, input_str, persona - ) - - # Call the implementation - async for chunk in self._execute_stream_impl( - messages=messages, - input_str=input_str, - persona=persona, - tools_map=tools_map, - **kwargs, - ): - yield chunk - - # Keep the old method for backward compatibility - async def execute_vector_react_stream( - self, - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - ) -> AsyncGenerator[Dict, None]: - """Execute a VectorReact stream using LangGraph.""" - # Call the new method - async for chunk in self.execute_stream(history, input_str, persona, tools_map): - yield chunk - - -# Helper function for adding documents to vector store -async def add_documents_to_vectors( - collection_name: str, # Modified to only accept a single collection - documents: List[Document], - embeddings: Optional[Embeddings] = None, -) -> Dict[str, List[str]]: - """Add documents to vector collection. - - Args: - collection_name: Name of the collection to add to - documents: List of LangChain Document objects - embeddings: Optional embeddings model to use - - Returns: - Dictionary mapping collection name to list of document IDs - """ - # Ensure embeddings model is provided - if embeddings is None: - raise ValueError( - "Embeddings model must be provided to add documents to vector store" - ) - - # Store document IDs for the collection - collection_doc_ids = {} - - try: - # Ensure collection exists - try: - backend.get_vector_collection(collection_name) - except Exception: - # Create collection if it doesn't exist - embed_dim = 1536 # Default for OpenAI embeddings - if hasattr(embeddings, "embedding_dim"): - embed_dim = embeddings.embedding_dim - backend.create_vector_collection(collection_name, dimensions=embed_dim) - - # Extract texts for embedding - texts = [doc.page_content for doc in documents] - - # Generate embeddings for the texts - embedding_vectors = embeddings.embed_documents(texts) - - # Prepare documents for storage with embeddings - docs_for_storage = [ - {"page_content": doc.page_content, "embedding": embedding_vectors[i]} - for i, doc in enumerate(documents) - ] - - # Prepare metadata - metadata_list = [doc.metadata for doc in documents] - - # Add to vector store - ids = await backend.add_vectors( - collection_name=collection_name, - documents=docs_for_storage, - metadata=metadata_list, - ) - - collection_doc_ids[collection_name] = ids - logger.info(f"Added {len(ids)} documents to collection {collection_name}") - - except Exception as e: - logger.error( - f"Failed to add documents to collection {collection_name}: {str(e)}" - ) - collection_doc_ids[collection_name] = [] - - return collection_doc_ids - - -# Facade function for backward compatibility -async def execute_vector_langgraph_stream( - collection_names: Union[ - str, List[str] - ], # Modified to accept single or multiple collections - history: List[Dict], - input_str: str, - persona: Optional[str] = None, - tools_map: Optional[Dict] = None, - embeddings: Optional[Embeddings] = None, -) -> AsyncGenerator[Dict, None]: - """Execute a VectorReact stream using LangGraph with vector store integration.""" - # Initialize service and run stream - embeddings = embeddings or OpenAIEmbeddings() - service = VectorLangGraphService( - collection_names=collection_names, - embeddings=embeddings, - ) - - async for chunk in service.execute_stream(history, input_str, persona, tools_map): - yield chunk diff --git a/services/workflows/web_search.py b/services/workflows/web_search.py deleted file mode 100644 index e7a3155f..00000000 --- a/services/workflows/web_search.py +++ /dev/null @@ -1,238 +0,0 @@ -"""Web search workflow implementation using OpenAI Assistant API.""" - -import asyncio -import json -from typing import Any, Dict, List, Optional - -from langchain_core.messages import AIMessage, HumanMessage -from langgraph.graph import StateGraph -from openai import OpenAI -from openai.types.beta.assistant import Assistant -from openai.types.beta.thread import Thread -from openai.types.beta.threads.thread_message import ThreadMessage - -from lib.logger import configure_logger -from services.workflows.base import BaseWorkflow, WebSearchCapability -from services.workflows.vector import VectorRetrievalCapability - -logger = configure_logger(__name__) - - -class WebSearchWorkflow(BaseWorkflow, WebSearchCapability, VectorRetrievalCapability): - """Workflow that combines web search with vector retrieval capabilities using OpenAI Assistant.""" - - def __init__(self, **kwargs): - """Initialize the workflow. - - Args: - **kwargs: Additional arguments passed to parent classes - """ - super().__init__(**kwargs) - self.search_results_cache = {} - self.client = OpenAI() - # Create an assistant with web browsing capability - self.assistant: Assistant = self.client.beta.assistants.create( - name="Web Search Assistant", - description="Assistant that helps with web searches", - model="gpt-4-turbo-preview", - tools=[{"type": "retrieval"}, {"type": "web_browser"}], - instructions="""You are a web search assistant. Your primary task is to: - 1. Search the web for relevant information - 2. Extract key information from web pages - 3. Provide detailed, accurate responses with source URLs - 4. Format responses as structured data with content and metadata - Always include source URLs in your responses.""", - ) - - async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: - """Search the web using OpenAI Assistant API. - - Args: - query: The search query - **kwargs: Additional search parameters - - Returns: - List of search results with content and metadata - """ - try: - # Check cache first - if query in self.search_results_cache: - logger.info(f"Using cached results for query: {query}") - return self.search_results_cache[query] - - # Create a new thread for this search - thread: Thread = self.client.beta.threads.create() - - # Add the user's message to the thread - self.client.beta.threads.messages.create( - thread_id=thread.id, - role="user", - content=f"Search the web for: {query}. Please provide detailed information with source URLs.", - ) - - # Run the assistant - run = self.client.beta.threads.runs.create( - thread_id=thread.id, assistant_id=self.assistant.id - ) - - # Wait for completion - while True: - run_status = self.client.beta.threads.runs.retrieve( - thread_id=thread.id, run_id=run.id - ) - if run_status.status == "completed": - break - elif run_status.status in ["failed", "cancelled", "expired"]: - raise Exception( - f"Assistant run failed with status: {run_status.status}" - ) - await asyncio.sleep(1) # Wait before checking again - - # Get the assistant's response - messages: List[ThreadMessage] = self.client.beta.threads.messages.list( - thread_id=thread.id - ) - - # Process the response into our document format - documents = [] - for message in messages: - if message.role == "assistant": - for content in message.content: - if content.type == "text": - # Extract URLs from annotations if available - urls = [] - if message.metadata and "citations" in message.metadata: - urls = [ - cite["url"] - for cite in message.metadata["citations"] - ] - - # Create document with content and metadata - doc = { - "page_content": content.text, - "metadata": { - "type": "web_search_result", - "source_urls": urls, - "query": query, - "timestamp": message.created_at, - }, - } - documents.append(doc) - - # Cache the results - self.search_results_cache[query] = documents - - logger.info(f"Web search completed with {len(documents)} results") - return documents - - except Exception as e: - logger.error(f"Web search failed: {str(e)}") - return [] - - async def execute(self, query: str, **kwargs) -> Dict[str, Any]: - """Execute the web search workflow. - - This workflow: - 1. Searches the web for relevant information - 2. Processes and stores the results - 3. Combines with vector retrieval if available - - Args: - query: The search query - **kwargs: Additional execution arguments - - Returns: - Dict containing search results and any additional data - """ - try: - # Perform web search - web_results = await self.search_web(query, **kwargs) - - # Cache results - self.search_results_cache[query] = web_results - - # Combine with vector retrieval if available - combined_results = web_results - try: - vector_results = await self.retrieve_from_vectorstore(query, **kwargs) - combined_results.extend(vector_results) - except Exception as e: - logger.warning( - f"Vector retrieval failed, using only web results: {str(e)}" - ) - - return { - "query": query, - "results": combined_results, - "source": "web_search_workflow", - "metadata": { - "num_web_results": len(web_results), - "has_vector_results": ( - bool(vector_results) if "vector_results" in locals() else False - ), - }, - } - - except Exception as e: - logger.error(f"Web search workflow execution failed: {str(e)}") - raise - - def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - """Integrate web search workflow with a graph. - - Args: - graph: The graph to integrate with - **kwargs: Additional integration arguments - """ - # Add web search node - graph.add_node("web_search", self.search_web) - - # Add vector retrieval node if available - try: - graph.add_node("vector_retrieval", self.retrieve_from_vectorstore) - - # Connect nodes - graph.add_edge("web_search", "vector_retrieval") - except Exception as e: - logger.warning(f"Vector retrieval integration failed: {str(e)}") - - # Add result processing node - graph.add_node("process_results", self._process_results) - graph.add_edge("vector_retrieval", "process_results") - - async def _process_results( - self, - web_results: List[Dict[str, Any]], - vector_results: Optional[List[Dict[str, Any]]] = None, - ) -> Dict[str, Any]: - """Process and combine search results. - - Args: - web_results: Results from web search - vector_results: Optional results from vector retrieval - - Returns: - Processed and combined results - """ - combined_results = web_results.copy() - if vector_results: - combined_results.extend(vector_results) - - # Deduplicate results based on content similarity - seen_contents = set() - unique_results = [] - for result in combined_results: - content = result.get("page_content", "") - content_hash = hash(content) - if content_hash not in seen_contents: - seen_contents.add(content_hash) - unique_results.append(result) - - return { - "results": unique_results, - "metadata": { - "num_web_results": len(web_results), - "num_vector_results": len(vector_results) if vector_results else 0, - "num_unique_results": len(unique_results), - }, - } diff --git a/services/workflows/web_search_mixin.py b/services/workflows/web_search_mixin.py new file mode 100644 index 00000000..8301dce2 --- /dev/null +++ b/services/workflows/web_search_mixin.py @@ -0,0 +1,175 @@ +"""Web search mixin for workflows, providing web search capabilities using OpenAI Responses API.""" + +from typing import Any, Dict, List + +from openai import OpenAI +from langgraph.graph import StateGraph + +from lib.logger import configure_logger +from services.workflows.base import BaseWorkflowMixin + +logger = configure_logger(__name__) + +class WebSearchCapability(BaseWorkflowMixin): + """Mixin that adds web search capabilities to a workflow using OpenAI Responses API.""" + + def __init__(self, *args, **kwargs): + """Initialize the web search capability.""" + # Initialize parent class if it exists + super().__init__(*args, **kwargs) if hasattr(super(), "__init__") else None + # Initialize our attributes + self._init_web_search() + + def _init_web_search(self) -> None: + """Initialize web search attributes if not already initialized.""" + if not hasattr(self, "search_results_cache"): + self.search_results_cache = {} + if not hasattr(self, "client"): + self.client = OpenAI() + + async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: + """Search the web using OpenAI Responses API. + + Args: + query: The search query + **kwargs: Additional search parameters like user_location and search_context_size + + Returns: + List of search results with content and metadata + """ + try: + # Ensure initialization + self._init_web_search() + + # Check cache first + if query in self.search_results_cache: + logger.info(f"Using cached results for query: {query}") + return self.search_results_cache[query] + + # Configure web search tool + tool_config = { + "type": "web_search_preview", + "search_context_size": kwargs.get("search_context_size", "medium"), + } + + # Add user location if provided + if "user_location" in kwargs: + tool_config["user_location"] = kwargs["user_location"] + + # Make the API call + response = self.client.responses.create( + model="gpt-4.1", tools=[tool_config], input=query + ) + + logger.debug(f"Web search response: {response}") + # Process the response into our document format + documents = [] + + # Access the output text directly + if hasattr(response, "output_text"): + text_content = response.output_text + source_urls = [] + + # Try to extract citations if available + if hasattr(response, "citations"): + source_urls = [ + { + "url": citation.url, + "title": getattr(citation, "title", ""), + "start_index": getattr(citation, "start_index", 0), + "end_index": getattr(citation, "end_index", 0), + } + for citation in response.citations + if hasattr(citation, "url") + ] + + # Ensure we always have at least one URL entry + if not source_urls: + source_urls = [ + { + "url": "No source URL available", + "title": "Generated Response", + "start_index": 0, + "end_index": len(text_content), + } + ] + + # Create document with content + doc = { + "page_content": text_content, + "metadata": { + "type": "web_search_result", + "source_urls": source_urls, + "query": query, + "timestamp": None, + }, + } + documents.append(doc) + + # Cache the results + self.search_results_cache[query] = documents + + logger.info(f"Web search completed with {len(documents)} results") + return documents + + except Exception as e: + logger.error(f"Web search failed: {str(e)}") + # Return a list with one empty result to prevent downstream errors + return [ + { + "page_content": "Web search failed to return results.", + "metadata": { + "type": "web_search_result", + "source_urls": [ + { + "url": "Error occurred during web search", + "title": "Error", + "start_index": 0, + "end_index": 0, + } + ], + "query": query, + "timestamp": None, + }, + } + ] + + def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: + """Integrate web search capability with a graph. + + This adds the web search capability to the graph by adding a node + that can perform web searches when needed. + + Args: + graph: The graph to integrate with + **kwargs: Additional arguments specific to web search including: + - search_context_size: "low", "medium", or "high" + - user_location: dict with type, country, city, region + """ + # Add web search node + graph.add_node("web_search", self.search_web) + + # Add result processing node if needed + if "process_results" not in graph.nodes: + graph.add_node("process_results", self._process_results) + graph.add_edge("web_search", "process_results") + + async def _process_results( + self, web_results: List[Dict[str, Any]], **kwargs + ) -> Dict[str, Any]: + """Process web search results. + + Args: + web_results: Results from web search + **kwargs: Additional processing arguments + + Returns: + Processed results with metadata + """ + return { + "results": web_results, + "metadata": { + "num_web_results": len(web_results), + "source_types": ["web_search"], + }, + } \ No newline at end of file diff --git a/services/workflows/workflow_service.py b/services/workflows/workflow_service.py index a4ecbdf8..2a4a6921 100644 --- a/services/workflows/workflow_service.py +++ b/services/workflows/workflow_service.py @@ -16,12 +16,11 @@ from lib.logger import configure_logger from services.workflows.base import ExecutionError, StreamingError -from services.workflows.react import ( - LangGraphService, +from services.workflows.chat import ( + ChatService, MessageProcessor, StreamingCallbackHandler, ) -from services.workflows.vector_react import VectorLangGraphService logger = configure_logger(__name__) @@ -508,64 +507,36 @@ def build(self, **extra_kwargs) -> Any: class WorkflowFactory: - """Factory for creating workflow service instances.""" + """Factory for creating workflow service instances. Only ChatService is used.""" @classmethod def create_workflow_service( cls, - workflow_type: str = "react", + workflow_type: str = "chat", vector_collections: Optional[Union[str, List[str]]] = None, embeddings: Optional[Embeddings] = None, **kwargs, ) -> WorkflowService: - """Create a workflow service instance based on the workflow type. + """Create a workflow service instance. Always returns ChatService. Args: - workflow_type: Type of workflow to create ("react", "preplan", "vector", "vector_preplan") + workflow_type: Type of workflow to create (ignored, always uses ChatService) vector_collections: Vector collection name(s) for vector workflows embeddings: Embeddings model for vector workflows **kwargs: Additional parameters to pass to the service Returns: - An instance of a WorkflowService implementation + An instance of ChatService """ - # Import service classes here to avoid circular imports - from services.workflows.preplan_react import PreplanLangGraphService - from services.workflows.vector_preplan_react import ( - VectorPreplanLangGraphService, - ) - - # Map workflow types to their service classes - service_map = { - "react": LangGraphService, - "preplan": PreplanLangGraphService, - "vector": VectorLangGraphService, - "vector_preplan": VectorPreplanLangGraphService, - } - - if workflow_type not in service_map: - raise ValueError(f"Unsupported workflow type: {workflow_type}") - - service_class = service_map[workflow_type] - - # Handle vector-based workflow special cases - if workflow_type in ["vector", "vector_preplan"]: - if not vector_collections: - raise ValueError( - f"Vector collection name(s) required for {workflow_type} workflow" - ) - + if vector_collections is not None: if not embeddings: embeddings = OpenAIEmbeddings() - - return service_class( + return ChatService( collection_names=vector_collections, embeddings=embeddings, **kwargs, ) - - # For other workflow types - return service_class(**kwargs) + return ChatService(**kwargs) async def execute_workflow_stream( @@ -578,10 +549,10 @@ async def execute_workflow_stream( embeddings: Optional[Embeddings] = None, **kwargs, ) -> AsyncGenerator[Dict, None]: - """Unified interface for executing any workflow stream. + """Unified interface for executing any workflow stream. Uses ChatService for all workflows. Args: - workflow_type: Type of workflow to execute + workflow_type: Type of workflow to execute (ignored) history: Conversation history input_str: Current user input persona: Optional persona to use @@ -599,8 +570,6 @@ async def execute_workflow_stream( embeddings=embeddings, **kwargs, ) - - # Execute the stream through the service's execute_stream method async for chunk in service.execute_stream( history=history, input_str=input_str, diff --git a/tests/services/workflows/test_vector_react.py b/tests/services/workflows/test_vector_react.py index ffd3ac9e..0bc45eef 100644 --- a/tests/services/workflows/test_vector_react.py +++ b/tests/services/workflows/test_vector_react.py @@ -5,11 +5,11 @@ from langchain_core.documents import Document -from services.workflows.vector_react import ( +from services.workflows.chat import ( VectorLangGraphService, - VectorReactWorkflow, - add_documents_to_vectors, + execute_vector_langgraph_stream, ) +from services.workflows.vector_mixin import add_documents_to_vectors class TestVectorOperations(unittest.TestCase): diff --git a/vector_react_example.py b/vector_react_example.py index 56391f30..11e42dca 100644 --- a/vector_react_example.py +++ b/vector_react_example.py @@ -15,10 +15,8 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter from backend.factory import backend -from services.workflows.vector_react import ( - add_documents_to_vectors, - execute_vector_langgraph_stream, -) +from services.workflows.chat import VectorLangGraphService +from services.workflows.vector_mixin import add_documents_to_vectors dotenv.load_dotenv() From 7b1752b3f312c13eea227b6c0673c6b2384c4d47 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Fri, 2 May 2025 23:29:52 -0700 Subject: [PATCH 003/219] cleanup evaluation --- proposal_evaluation_test.py | 8 +- services/workflows/proposal_evaluation.py | 754 ++++++++-------------- 2 files changed, 288 insertions(+), 474 deletions(-) diff --git a/proposal_evaluation_test.py b/proposal_evaluation_test.py index 449b063f..0281d214 100644 --- a/proposal_evaluation_test.py +++ b/proposal_evaluation_test.py @@ -97,6 +97,9 @@ async def test_proposal_evaluation_workflow(): # Create a test proposal proposal_id = await create_test_proposal(dao_id) + # Use a consistent test wallet ID + test_wallet_id = UUID("532fd36b-8a9d-4fdd-82d2-25ddcf007488") + # Test scenarios scenarios = [ { @@ -107,7 +110,7 @@ async def test_proposal_evaluation_workflow(): }, { "name": "Auto-vote Enabled", - "auto_vote": False, # Fixed: Changed to True for auto-vote scenario + "auto_vote": True, # Corrected: Changed to True for auto-vote scenario "confidence_threshold": 0.7, "description": "Testing proposal evaluation with auto-voting", }, @@ -128,6 +131,7 @@ async def test_proposal_evaluation_workflow(): if scenario["auto_vote"]: result = await evaluate_and_vote_on_proposal( proposal_id=proposal_id, + wallet_id=test_wallet_id, # Add wallet_id for auto-vote scenarios auto_vote=scenario["auto_vote"], confidence_threshold=scenario["confidence_threshold"], dao_id=dao_id, @@ -135,7 +139,7 @@ async def test_proposal_evaluation_workflow(): else: result = await evaluate_proposal_only( proposal_id=proposal_id, - wallet_id=UUID("532fd36b-8a9d-4fdd-82d2-25ddcf007488"), + wallet_id=test_wallet_id, # Use the same consistent wallet ID ) # Print the results diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 84ceeaad..666c8bc1 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -1,7 +1,6 @@ """Proposal evaluation workflow.""" -import binascii -from typing import Dict, List, Optional, TypedDict +from typing import Any, Dict, List, Optional, TypedDict from langchain.prompts import PromptTemplate from langgraph.graph import END, Graph, StateGraph @@ -23,6 +22,7 @@ BaseWorkflow, ) from services.workflows.chat import ChatService +from services.workflows.utils import calculate_token_cost, decode_hex_parameters from services.workflows.vector_mixin import VectorRetrievalCapability from services.workflows.web_search_mixin import WebSearchCapability from tools.dao_ext_action_proposals import VoteOnActionProposalTool @@ -35,10 +35,10 @@ class ProposalEvaluationOutput(BaseModel): """Output model for proposal evaluation.""" approve: bool = Field( - description="Whether to approve (true) or reject (false) the proposal" + description="Decision: true to approve (vote FOR), false to reject (vote AGAINST)" ) confidence_score: float = Field( - description="The confidence score for the evaluation (0.0-1.0)" + description="Confidence score for the decision (0.0-1.0)" ) reasoning: str = Field(description="The reasoning behind the evaluation decision") @@ -66,6 +66,7 @@ class EvaluationState(TypedDict): treasury_balance: Optional[float] token_usage: Optional[Dict] # Add field for token usage tracking model_info: Optional[Dict] # Add field for model information + contract_source: Optional[str] # Added field to store contract source class ProposalEvaluationWorkflow( @@ -197,46 +198,108 @@ def _create_graph(self) -> Graph: """Create the evaluation graph.""" prompt = self._create_prompt() - # Create evaluation node - async def evaluate_proposal(state: EvaluationState) -> EvaluationState: - """Evaluate the proposal and determine how to vote.""" + async def fetch_context(state: EvaluationState) -> EvaluationState: + """Fetch context including web search, vector results, tweets, and contract source.""" try: - # Get proposal data from state - proposal_data = state["proposal_data"] - dao_id = state.get("dao_info", {}).get("id") + # --- Fetch Core Data --- # + proposal_id = state["proposal_id"] + dao_id = state.get("dao_id") + agent_id = state.get("agent_id") + + # Get proposal data + proposal_data = backend.get_proposal(proposal_id) + if not proposal_data: + raise ValueError(f"Proposal {proposal_id} not found") + + # Decode parameters if they exist + decoded_parameters = decode_hex_parameters(proposal_data.parameters) + + # Convert proposal data to dictionary + proposal_dict = { + "proposal_id": proposal_data.proposal_id, + "parameters": decoded_parameters or proposal_data.parameters, + "action": proposal_data.action, + "caller": proposal_data.caller, + "contract_principal": proposal_data.contract_principal, + "creator": proposal_data.creator, + "created_at_block": proposal_data.created_at_block, + "end_block": proposal_data.end_block, + "start_block": proposal_data.start_block, + "liquid_tokens": proposal_data.liquid_tokens, + "type": proposal_data.type, + "proposal_contract": proposal_data.proposal_contract, + } + state["proposal_data"] = proposal_dict # Update state with full data - # Perform web search for relevant context - try: - # Create search query from proposal data - web_search_query = f"DAO proposal {proposal_data.get('type', 'unknown')} - {proposal_data.get('parameters', '')}" + # Get DAO info (if dao_id wasn't passed explicitly, use proposal's) + if not dao_id and proposal_data.dao_id: + dao_id = proposal_data.dao_id + state["dao_id"] = dao_id # Update state if derived - # Use web search capability - web_search_results = await self.search_web( - query=web_search_query, - search_context_size="medium", # Use medium context size for balanced results - ) + dao_info = None + if dao_id: + dao_info = backend.get_dao(dao_id) + if not dao_info: + raise ValueError(f"DAO Information not found for ID: {dao_id}") + state["dao_info"] = dao_info.model_dump() + + # Get agent prompts + agent_prompts_text = [] + if agent_id: + try: + prompts = backend.list_prompts( + PromptFilter( + agent_id=agent_id, + dao_id=dao_id, + is_active=True, + ) + ) + agent_prompts_text = [p.prompt_text for p in prompts] + except Exception as e: + self.logger.error( + f"Failed to get agent prompts: {str(e)}", exc_info=True + ) + state["agent_prompts"] = agent_prompts_text - # Update state with web search results - state["web_search_results"] = web_search_results - self.logger.debug( - f"Web search query: {web_search_query} | Results count: {len(web_search_results)}" - ) - self.logger.debug( - f"Retrieved {len(web_search_results)} web search results" + # Get treasury balance + treasury_balance = None + try: + treasury_extensions = backend.list_extensions( + ExtensionFilter(dao_id=dao_info.id, type="EXTENSIONS_TREASURY") ) + if treasury_extensions: + hiro_api = HiroApi() + treasury_balance = hiro_api.get_address_balance( + treasury_extensions[0].contract_principal + ) + else: + self.logger.warning( + f"No treasury extension for DAO {dao_info.id}" + ) except Exception as e: self.logger.error( - f"Failed to perform web search: {str(e)}", exc_info=True + f"Failed to get treasury balance: {str(e)}", exc_info=True ) - state["web_search_results"] = [] + state["treasury_balance"] = treasury_balance + # --- End Fetch Core Data --- # + + # Use mixin capabilities for web search and vector retrieval + web_search_query = f"DAO proposal {proposal_dict.get('type', 'unknown')} - {proposal_dict.get('parameters', '')}" + state["web_search_results"] = await self.search_web( + query=web_search_query, + search_context_size="medium", + ) + + vector_search_query = f"Proposal type: {proposal_dict.get('type')} - {proposal_dict.get('parameters', '')}" + state["vector_results"] = await self.retrieve_from_vector_store( + query=vector_search_query, limit=5 + ) - # Fetch recent tweets from queue if dao_id exists + # Fetch recent tweets recent_tweets = [] if dao_id: try: - # Add debug logging for dao_id self.logger.debug(f"Fetching tweets for DAO ID: {dao_id}") - queue_messages = backend.list_queue_messages( QueueMessageFilter( type=QueueMessageType.TWEET, @@ -244,17 +307,9 @@ async def evaluate_proposal(state: EvaluationState) -> EvaluationState: is_processed=True, ) ) - # Log the number of messages found - self.logger.debug(f"Found {len(queue_messages)} queue messages") - - # Sort by created_at and take last 5 sorted_messages = sorted( queue_messages, key=lambda x: x.created_at, reverse=True )[:5] - self.logger.debug( - f"After sorting, have {len(sorted_messages)} messages" - ) - recent_tweets = [ { "created_at": msg.created_at, @@ -267,45 +322,23 @@ async def evaluate_proposal(state: EvaluationState) -> EvaluationState: } for msg in sorted_messages ] - self.logger.debug(f"Retrieved tweets: {recent_tweets}") - self.logger.debug( - f"Found {len(recent_tweets)} recent tweets for DAO {dao_id}" - ) except Exception as e: self.logger.error( - f"Failed to fetch recent tweets: {str(e)}", exc_info=True + f"Failed to fetch tweets: {str(e)}", exc_info=True ) - recent_tweets = [] - - # Update state with recent tweets state["recent_tweets"] = recent_tweets - # If this is a core proposal, fetch the contract source + # Fetch contract source for core proposals contract_source = "" - if proposal_data.get("type") == "core" and proposal_data.get( + if proposal_dict.get("type") == ProposalType.CORE and proposal_dict.get( "proposal_contract" ): - # Split contract address into parts - parts = proposal_data["proposal_contract"].split(".") + parts = proposal_dict["proposal_contract"].split(".") if len(parts) >= 2: - contract_address = parts[0] - contract_name = parts[1] - - # Use HiroApi to fetch contract source try: api = HiroApi() - result = api.get_contract_source( - contract_address, contract_name - ) - if "source" in result: - contract_source = result["source"] - self.logger.debug( - f"Retrieved contract source for {contract_address}.{contract_name}" - ) - else: - self.logger.warning( - f"Contract source not found in API response: {result}" - ) + result = api.get_contract_source(parts[0], parts[1]) + contract_source = result.get("source", "") except Exception as e: self.logger.error( f"Failed to fetch contract source: {str(e)}", @@ -313,154 +346,143 @@ async def evaluate_proposal(state: EvaluationState) -> EvaluationState: ) else: self.logger.warning( - f"Invalid contract address format: {proposal_data['proposal_contract']}" + f"Invalid contract format: {proposal_dict['proposal_contract']}" ) + state["contract_source"] = contract_source - # Retrieve relevant context from vector store - try: - # Create search query from proposal data - search_query = f"Proposal type: {proposal_data.get('type')} - {proposal_data.get('parameters', '')}" - - # Use vector retrieval capability - vector_results = await self.retrieve_from_vector_store( - query=search_query, limit=5 # Get top 5 most relevant documents - ) - - # Update state with vector results - state["vector_results"] = vector_results - self.logger.debug( - f"Searching vector store with query: {search_query} | Collection count: {len(self.collection_names)}" - ) - self.logger.debug(f"Vector search results: {vector_results}") - self.logger.debug( - f"Retrieved {len(vector_results)} relevant documents from vector store" - ) - - # Format vector context for prompt - vector_context = "\n\n".join( - [ - f"Related Context {i+1}:\n{doc.page_content}" - for i, doc in enumerate(vector_results) - ] - ) - except Exception as e: - self.logger.error( - f"Failed to retrieve from vector store: {str(e)}", exc_info=True - ) - vector_context = ( - "No additional context available from vector store." - ) + # Validate proposal data structure (moved from entry point) + proposal_type = proposal_dict.get("type") + if proposal_type == ProposalType.ACTION and not proposal_dict.get( + "parameters" + ): + raise ValueError("Action proposal missing parameters") + if proposal_type == ProposalType.CORE and not proposal_dict.get( + "proposal_contract" + ): + raise ValueError("Core proposal missing proposal_contract") - # Format prompt with state - self.logger.debug("Preparing evaluation prompt...") + return state + except Exception as e: + self.logger.error(f"Error in fetch_context: {str(e)}", exc_info=True) + state["reasoning"] = f"Error fetching context: {str(e)}" + # Propagate error state + return state - # Format agent prompts as a string + async def format_evaluation_prompt(state: EvaluationState) -> EvaluationState: + """Format the evaluation prompt using the fetched context.""" + if "reasoning" in state and "Error" in state["reasoning"]: + return state # Skip if context fetching failed + try: + # Extract data from state for easier access + proposal_data = state["proposal_data"] + dao_info = state.get("dao_info", {}) + treasury_balance = state.get("treasury_balance") + contract_source = state.get("contract_source", "") + agent_prompts = state.get("agent_prompts", []) + vector_results = state.get("vector_results", []) + recent_tweets = state.get("recent_tweets", []) + web_search_results = state.get("web_search_results", []) + + # Format agent prompts agent_prompts_str = "No agent-specific instructions available." - if state.get("agent_prompts"): - self.logger.debug(f"Raw agent prompts: {state['agent_prompts']}") - if ( - isinstance(state["agent_prompts"], list) - and state["agent_prompts"] - ): - # Just use the prompt text directly since that's what we're storing - agent_prompts_str = "\n\n".join(state["agent_prompts"]) - self.logger.debug( - f"Formatted agent prompts: {agent_prompts_str}" - ) + if agent_prompts: + if isinstance(agent_prompts, list): + agent_prompts_str = "\n\n".join(agent_prompts) else: self.logger.warning( - f"Invalid agent prompts format: {type(state['agent_prompts'])}" + f"Invalid agent prompts: {type(agent_prompts)}" ) - else: - self.logger.debug("No agent prompts found in state") - # Format web search results for prompt + # Format web search results web_search_content = "No relevant web search results found." - if state.get("web_search_results"): + if web_search_results: web_search_content = "\n\n".join( [ - f"Web Result {i+1}:\n{result['page_content']}\nSource: {result['metadata']['source_urls'][0]['url'] if result['metadata']['source_urls'] else 'Unknown'}" - for i, result in enumerate(state["web_search_results"]) + f"Web Result {i+1}:\n{res.get('page_content', '')}\nSource: {res.get('metadata', {}).get('source_urls', [{}])[0].get('url', 'Unknown')}" + for i, res in enumerate(web_search_results) ] ) - # Update formatted prompt with web search results - formatted_prompt = self._create_prompt().format( + # Format vector context + vector_context = "No additional context available from vector store." + if vector_results: + vector_context = "\n\n".join( + [ + f"Related Context {i+1}:\n{doc.page_content}" + for i, doc in enumerate(vector_results) + ] + ) + + # Format recent tweets + tweets_content = "\n".join( + [ + f"Tweet {i+1} ({tweet['created_at']}): {tweet['message']}" + for i, tweet in enumerate(recent_tweets) + ] + ) + + formatted_prompt = prompt.format( proposal_data=proposal_data, - dao_info=state.get( - "dao_info", "No additional DAO information available." - ), - treasury_balance=state.get("treasury_balance"), + dao_info=dao_info, + treasury_balance=treasury_balance, contract_source=contract_source, agent_prompts=agent_prompts_str, vector_context=vector_context, - recent_tweets=( - "\n".join( - [ - f"Tweet {i+1} ({tweet['created_at']}): {tweet['message']}" - for i, tweet in enumerate(recent_tweets) - ] - ) - if recent_tweets - else "No recent tweets available." - ), + recent_tweets=tweets_content, web_search_results=web_search_content, ) + state["formatted_prompt"] = formatted_prompt + return state + except Exception as e: + self.logger.error(f"Error formatting prompt: {str(e)}", exc_info=True) + state["reasoning"] = f"Error formatting prompt: {str(e)}" + return state - # Get evaluation from LLM - self.logger.debug("Starting LLM evaluation...") + async def call_evaluation_llm(state: EvaluationState) -> EvaluationState: + """Call the LLM with the formatted prompt for evaluation.""" + if "reasoning" in state and "Error" in state["reasoning"]: + return state # Skip if previous steps failed + try: structured_output = self.llm.with_structured_output( - ProposalEvaluationOutput, - include_raw=True, # Include raw response to get token usage + ProposalEvaluationOutput, include_raw=True + ) + result: Dict[str, Any] = await structured_output.ainvoke( + state["formatted_prompt"] ) - # Invoke LLM with formatted prompt - result = structured_output.invoke(formatted_prompt) - - # Extract the parsed result and token usage from raw response - self.logger.debug( - f"Raw LLM result structure: {type(result).__name__} | Has parsed: {'parsed' in result if isinstance(result, dict) else False}" + result: Dict[str, Any] = await structured_output.ainvoke( + state["formatted_prompt"] ) - parsed_result = result["parsed"] if isinstance(result, dict) else result - model_info = {"name": self.model_name, "temperature": self.temperature} - if isinstance(result, dict) and "raw" in result: - raw_msg = result["raw"] - # Extract token usage - if hasattr(raw_msg, "usage_metadata"): - token_usage = raw_msg.usage_metadata - self.logger.debug( - f"Token usage details: input={token_usage.get('input_tokens', 0)} | output={token_usage.get('output_tokens', 0)} | total={token_usage.get('total_tokens', 0)}" + parsed_result = result.get("parsed") + if not isinstance(parsed_result, ProposalEvaluationOutput): + # Attempt to handle cases where parsing might return the raw dict + if isinstance(parsed_result, dict): + parsed_result = ProposalEvaluationOutput(**parsed_result) + else: + raise TypeError( + f"Expected ProposalEvaluationOutput or dict, got {type(parsed_result)}" ) + + model_info = {"name": self.model_name, "temperature": self.temperature} + token_usage = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0} + + raw_response = result.get("raw") + if raw_response: + if hasattr(raw_response, "usage_metadata"): + token_usage = raw_response.usage_metadata else: - self.logger.warning("No usage_metadata found in raw response") - token_usage = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } + self.logger.warning("Raw response missing usage_metadata") else: - self.logger.warning("No raw response available") - token_usage = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - - self.logger.debug(f"Parsed evaluation result: {parsed_result}") + self.logger.warning("LLM result missing raw response data") - # Update state - state["formatted_prompt"] = formatted_prompt state["approve"] = parsed_result.approve state["confidence_score"] = parsed_result.confidence_score state["reasoning"] = parsed_result.reasoning state["token_usage"] = token_usage state["model_info"] = model_info - # Calculate token costs token_costs = calculate_token_cost(token_usage, model_info["name"]) - - # Log final evaluation summary self.logger.debug( f"Evaluation complete: Decision={'APPROVE' if parsed_result.approve else 'REJECT'} | Confidence={parsed_result.confidence_score:.2f} | Model={model_info['name']} (temp={model_info['temperature']}) | Tokens={token_usage} | Cost=${token_costs['total_cost']:.4f}" ) @@ -468,12 +490,10 @@ async def evaluate_proposal(state: EvaluationState) -> EvaluationState: return state except Exception as e: - self.logger.error( - f"Error in evaluate_proposal: {str(e)}", exc_info=True - ) + self.logger.error(f"Error calling LLM: {str(e)}", exc_info=True) state["approve"] = False state["confidence_score"] = 0.0 - state["reasoning"] = f"Error during evaluation: {str(e)}" + state["reasoning"] = f"Error during LLM evaluation: {str(e)}" return state # Create decision node @@ -506,6 +526,17 @@ async def should_vote(state: EvaluationState) -> str: async def vote_on_proposal(state: EvaluationState) -> EvaluationState: """Vote on the proposal using VectorReact workflow.""" try: + # Check if wallet_id is available + if not state.get("wallet_id"): + self.logger.warning( + "No wallet_id provided for voting, skipping vote" + ) + state["vote_result"] = { + "success": False, + "error": "No wallet_id provided for voting", + } + return state + self.logger.debug( f"Setting up VectorReact workflow: proposal_id={state['proposal_id']} | vote={state['approve']}" ) @@ -598,12 +629,16 @@ async def skip_voting(state: EvaluationState) -> EvaluationState: workflow = StateGraph(EvaluationState) # Add nodes - workflow.add_node("evaluate", evaluate_proposal) + workflow.add_node("fetch_context", fetch_context) + workflow.add_node("format_prompt", format_evaluation_prompt) + workflow.add_node("evaluate", call_evaluation_llm) workflow.add_node("vote", vote_on_proposal) workflow.add_node("skip_vote", skip_voting) # Set up the conditional branching - workflow.set_entry_point("evaluate") + workflow.set_entry_point("fetch_context") + workflow.add_edge("fetch_context", "format_prompt") + workflow.add_edge("format_prompt", "evaluate") workflow.add_conditional_edges( "evaluate", should_vote, @@ -619,11 +654,13 @@ async def skip_voting(state: EvaluationState) -> EvaluationState: def _validate_state(self, state: EvaluationState) -> bool: """Validate the workflow state.""" - required_fields = ["proposal_id", "proposal_data"] + # Only validate minimal required fields for initial state + # Other fields like proposal_data are fetched within the workflow + required_fields = ["proposal_id"] # Log the state for debugging self.logger.debug( - f"Validating state: proposal_id={state.get('proposal_id')} | proposal_type={state.get('proposal_data', {}).get('type', 'unknown')}" + f"Validating initial state: proposal_id={state.get('proposal_id')}" ) # Check all fields and log problems @@ -635,30 +672,8 @@ def _validate_state(self, state: EvaluationState) -> bool: self.logger.error(f"Empty required field: {field}") return False - # Get proposal type - proposal_type = state["proposal_data"].get("type", ProposalType.ACTION) - - # Validate based on proposal type - if proposal_type == ProposalType.ACTION: - # Action proposals require action_proposals_contract and parameters - if not state.get("action_proposals_contract"): - self.logger.error( - "Missing action_proposals_contract for action proposal" - ) - return False - if not state["proposal_data"].get("parameters"): - self.logger.error("No parameters field in action proposal data") - return False - elif proposal_type == ProposalType.CORE: - # Core proposals require proposal_contract - if not state["proposal_data"].get("proposal_contract"): - self.logger.error("Missing proposal_contract for core proposal") - return False - else: - self.logger.error(f"Invalid proposal type: {proposal_type}") - return False - - self.logger.debug("State validation successful") + # Note: Detailed validation of proposal_data happens in fetch_context node + self.logger.debug("Initial state validation successful") return True @@ -694,111 +709,10 @@ def get_proposal_evaluation_tools( return filtered_tools -def decode_hex_parameters(hex_string: Optional[str]) -> Optional[str]: - """Decodes a hexadecimal-encoded string if valid.""" - if not hex_string: - return None - if hex_string.startswith("0x"): - hex_string = hex_string[2:] # Remove "0x" prefix - try: - decoded_bytes = binascii.unhexlify(hex_string) - decoded_string = decoded_bytes.decode( - "utf-8", errors="ignore" - ) # Decode as UTF-8 - logger.debug(f"Successfully decoded hex string: {hex_string[:20]}...") - return decoded_string - except (binascii.Error, UnicodeDecodeError) as e: - logger.warning(f"Failed to decode hex string: {str(e)}") - return None # Return None if decoding fails - - -def calculate_token_cost( - token_usage: Dict[str, int], model_name: str -) -> Dict[str, float]: - """Calculate the cost of token usage based on current pricing. - - Args: - token_usage: Dictionary containing input_tokens and output_tokens - model_name: Name of the model used - - Returns: - Dictionary containing cost breakdown and total cost - """ - # Current pricing per million tokens (as of August 2024) - MODEL_PRICES = { - "gpt-4o": { - "input": 2.50, # $2.50 per million input tokens - "output": 10.00, # $10.00 per million output tokens - }, - "gpt-4.1": { - "input": 2.00, # $2.00 per million input tokens - "output": 8.00, # $8.00 per million output tokens - }, - "gpt-4.1-mini": { - "input": 0.40, # $0.40 per million input tokens - "output": 1.60, # $1.60 per million output tokens - }, - "gpt-4.1-nano": { - "input": 0.10, # $0.10 per million input tokens - "output": 0.40, # $0.40 per million output tokens - }, - # Default to gpt-4.1 pricing if model not found - "default": { - "input": 2.00, - "output": 8.00, - }, - } - - # Get pricing for the model, default to gpt-4.1 pricing if not found - model_prices = MODEL_PRICES.get(model_name.lower(), MODEL_PRICES["default"]) - - # Extract token counts, ensuring we get integers and handle None values - try: - input_tokens = int(token_usage.get("input_tokens", 0)) - output_tokens = int(token_usage.get("output_tokens", 0)) - except (TypeError, ValueError) as e: - logger.error(f"Error converting token counts to integers: {str(e)}") - input_tokens = 0 - output_tokens = 0 - - # Calculate costs with more precision - input_cost = (input_tokens / 1_000_000.0) * model_prices["input"] - output_cost = (output_tokens / 1_000_000.0) * model_prices["output"] - total_cost = input_cost + output_cost - - # Create detailed token usage breakdown - token_details = { - "input_tokens": input_tokens, - "output_tokens": output_tokens, - "total_tokens": input_tokens + output_tokens, - "model_name": model_name, - "input_price_per_million": model_prices["input"], - "output_price_per_million": model_prices["output"], - } - - # Add token details if available - if "input_token_details" in token_usage: - token_details["input_token_details"] = token_usage["input_token_details"] - if "output_token_details" in token_usage: - token_details["output_token_details"] = token_usage["output_token_details"] - - # Debug logging with more detail - logger.debug( - f"Cost calculation details: Model={model_name} | Input={input_tokens} tokens * ${model_prices['input']}/1M = ${input_cost:.6f} | Output={output_tokens} tokens * ${model_prices['output']}/1M = ${output_cost:.6f} | Total=${total_cost:.6f} | Token details={token_details}" - ) - - return { - "input_cost": round(input_cost, 6), - "output_cost": round(output_cost, 6), - "total_cost": round(total_cost, 6), - "currency": "USD", - "details": token_details, - } - - async def evaluate_and_vote_on_proposal( proposal_id: UUID, wallet_id: Optional[UUID] = None, + agent_id: Optional[UUID] = None, auto_vote: bool = True, confidence_threshold: float = 0.7, dao_id: Optional[UUID] = None, @@ -808,6 +722,7 @@ async def evaluate_and_vote_on_proposal( Args: proposal_id: The ID of the proposal to evaluate and vote on wallet_id: Optional wallet ID to use for voting + agent_id: Optional agent ID to use for retrieving prompts auto_vote: Whether to automatically vote based on the evaluation confidence_threshold: Minimum confidence score required to auto-vote (0.0-1.0) dao_id: Optional DAO ID to explicitly pass to the workflow @@ -820,183 +735,70 @@ async def evaluate_and_vote_on_proposal( ) try: - # Get proposal data directly from the database - proposal_data = backend.get_proposal(proposal_id) - if not proposal_data: - error_msg = f"Proposal {proposal_id} not found in database" - logger.error(error_msg) - return {"success": False, "error": error_msg} - - # Decode parameters if they exist - decoded_parameters = decode_hex_parameters(proposal_data.parameters) - if decoded_parameters: - logger.debug( - f"Decoded proposal parameters: length={len(decoded_parameters) if decoded_parameters else 0}" - ) - - # Convert proposal data to dictionary and ensure parameters exist - proposal_dict = { - "proposal_id": proposal_data.proposal_id, - "parameters": decoded_parameters - or proposal_data.parameters, # Use decoded if available - "action": proposal_data.action, - "caller": proposal_data.caller, - "contract_principal": proposal_data.contract_principal, - "creator": proposal_data.creator, - "created_at_block": proposal_data.created_at_block, - "end_block": proposal_data.end_block, - "start_block": proposal_data.start_block, - "liquid_tokens": proposal_data.liquid_tokens, - "type": proposal_data.type, # Add proposal type - "proposal_contract": proposal_data.proposal_contract, # Add proposal contract for core proposals - } - - # For action proposals, parameters are required - if proposal_data.type == ProposalType.ACTION and not proposal_dict.get( - "parameters" - ): - error_msg = "No parameters found in action proposal data" - logger.error(error_msg) - return {"success": False, "error": error_msg} - - # For core proposals, proposal_contract is required - if proposal_data.type == ProposalType.CORE and not proposal_dict.get( - "proposal_contract" - ): - error_msg = "No proposal contract found in core proposal data" - logger.error(error_msg) - return {"success": False, "error": error_msg} - - # Get DAO info based on provided dao_id or from proposal - dao_info = None - if dao_id: - logger.debug( - f"Using provided DAO ID: {dao_id} | Found={dao_info is not None}" - ) - dao_info = backend.get_dao(dao_id) - if not dao_info: - logger.warning( - f"Provided DAO ID {dao_id} not found, falling back to proposal's DAO ID" - ) - - # If dao_info is still None, try to get it from proposal's dao_id - if not dao_info and proposal_data.dao_id: - logger.debug( - f"Using proposal's DAO ID: {proposal_data.dao_id} | Found={dao_info is not None}" - ) - dao_info = backend.get_dao(proposal_data.dao_id) - - if not dao_info: - error_msg = "Could not find DAO information" - logger.error(error_msg) - return {"success": False, "error": error_msg} - - # Get the treasury extension for the DAO - treasury_extension = None - try: - treasury_extensions = backend.list_extensions( - ExtensionFilter(dao_id=dao_info.id, type="EXTENSIONS_TREASURY") - ) - if treasury_extensions: - treasury_extension = treasury_extensions[0] - logger.debug( - f"Found treasury extension: contract_principal={treasury_extension.contract_principal}" - ) - - # Get treasury balance from Hiro API - hiro_api = HiroApi() - treasury_balance = hiro_api.get_address_balance( - treasury_extension.contract_principal - ) - logger.debug(f"Treasury balance retrieved: balance={treasury_balance}") - else: - logger.warning(f"No treasury extension found for DAO {dao_info.id}") - treasury_balance = None - except Exception as e: - logger.error(f"Failed to get treasury balance: {str(e)}", exc_info=True) - treasury_balance = None - - logger.debug( - f"Processing proposal for DAO: {dao_info.name} (ID: {dao_info.id})" - ) - - # Get the wallet and agent information if available - agent_id = None - if wallet_id: + # Determine effective agent ID + effective_agent_id = agent_id + if not effective_agent_id and wallet_id: wallet = backend.get_wallet(wallet_id) if wallet and wallet.agent_id: - agent_id = wallet.agent_id - logger.debug(f"Using agent ID {agent_id} for wallet {wallet_id}") + effective_agent_id = wallet.agent_id + logger.debug( + f"Using agent ID {effective_agent_id} from wallet {wallet_id}" + ) - # Get agent prompts - agent_prompts = [] + # Fetch the primary prompt to determine model and temperature settings + # Note: Actual prompt text fetching happens inside the workflow now. model_name = "gpt-4.1" # Default model temperature = 0.1 # Default temperature - try: - logger.debug( - f"Fetching prompts for agent_id={agent_id}, dao_id={proposal_data.dao_id}" - ) - prompts = backend.list_prompts( - PromptFilter( - agent_id=agent_id, - dao_id=proposal_data.dao_id, - is_active=True, - ) - ) - logger.debug(f"Retrieved prompts: {prompts}") - - # Store the full Prompt objects and get model settings from first prompt - agent_prompts = prompts - if agent_prompts: - first_prompt = agent_prompts[0] - model_name = first_prompt.model or model_name - temperature = ( - first_prompt.temperature - if first_prompt.temperature is not None - else temperature - ) - logger.debug( - f"Using model configuration: {model_name} (temperature={temperature})" + if effective_agent_id: + try: + # We only need one active prompt to get settings + prompts = backend.list_prompts( + PromptFilter( + agent_id=effective_agent_id, + dao_id=dao_id, # Assuming dao_id is available, might need refinement + is_active=True, + limit=1, + ) ) - else: - logger.warning( - f"No active prompts found for agent_id={agent_id}, dao_id={proposal_data.dao_id}" + if prompts: + first_prompt = prompts[0] + model_name = first_prompt.model or model_name + temperature = ( + first_prompt.temperature + if first_prompt.temperature is not None + else temperature + ) + logger.debug( + f"Using model settings from agent {effective_agent_id}: {model_name} (temp={temperature})" + ) + else: + logger.warning( + f"No active prompts found for agent {effective_agent_id} to determine settings." + ) + except Exception as e: + logger.error( + f"Failed to get agent prompt settings: {str(e)}", exc_info=True ) - except Exception as e: - logger.error(f"Failed to get agent prompts: {str(e)}", exc_info=True) - # Initialize state + # Initialize state (minimal initial data) state = { - "action_proposals_contract": proposal_dict["contract_principal"], - "action_proposals_voting_extension": proposal_dict["action"], - "proposal_id": proposal_dict["proposal_id"], - "proposal_data": proposal_dict, - "dao_info": dao_info.model_dump() if dao_info else {}, - "treasury_balance": treasury_balance, - "agent_prompts": ( - [p.prompt_text for p in agent_prompts] if agent_prompts else [] - ), + "proposal_id": proposal_id, + "dao_id": dao_id, # Pass DAO ID to the workflow + "agent_id": effective_agent_id, # Pass Agent ID for prompt loading + "wallet_id": wallet_id, # Pass wallet ID for voting tool "approve": False, "confidence_score": 0.0, "reasoning": "", "vote_result": None, - "wallet_id": wallet_id, "confidence_threshold": confidence_threshold, "auto_vote": auto_vote, "vector_results": None, "recent_tweets": None, "web_search_results": None, "token_usage": None, - "model_info": { - "name": "unknown", - "temperature": None, - }, + "model_info": None, } - logger.debug( - f"Agent prompts count: {len(state['agent_prompts'] or [])} | Has prompts: {bool(state['agent_prompts'])}" - ) - # Create and run workflow with model settings from prompt workflow = ProposalEvaluationWorkflow( model_name=model_name, temperature=temperature @@ -1060,11 +862,6 @@ async def evaluate_and_vote_on_proposal( ) final_result["token_costs"] = token_costs - # For the example token usage shown: - # Input: 7425 tokens * ($2.50/1M) = $0.0186 - # Output: 312 tokens * ($10.00/1M) = $0.0031 - # Total: $0.0217 - logger.debug( f"Proposal evaluation completed: Success={final_result['success']} | Decision={'APPROVE' if final_result['evaluation']['approve'] else 'REJECT'} | Confidence={final_result['evaluation']['confidence_score']:.2f} | Auto-voted={final_result['auto_voted']} | Transaction={tx_id or 'None'} | Model={final_result['model_info']['name']} | Token Usage={final_result['token_usage']} | Cost (USD)=${token_costs['total_cost']:.4f} (Input=${token_costs['input_cost']:.4f} for {token_costs['details']['input_tokens']} tokens, Output=${token_costs['output_cost']:.4f} for {token_costs['details']['output_tokens']} tokens)" ) @@ -1083,21 +880,34 @@ async def evaluate_and_vote_on_proposal( async def evaluate_proposal_only( proposal_id: UUID, wallet_id: Optional[UUID] = None, + agent_id: Optional[UUID] = None, + dao_id: Optional[UUID] = None, ) -> Dict: """Evaluate a proposal without voting. Args: proposal_id: The ID of the proposal to evaluate wallet_id: Optional wallet ID to use for retrieving proposal data + agent_id: Optional agent ID associated with the evaluation + dao_id: Optional DAO ID associated with the proposal Returns: Dictionary containing the evaluation results """ logger.debug(f"Starting proposal-only evaluation: proposal_id={proposal_id}") + # Determine effective agent ID (same logic as evaluate_and_vote) + effective_agent_id = agent_id + if not effective_agent_id and wallet_id: + wallet = backend.get_wallet(wallet_id) + if wallet and wallet.agent_id: + effective_agent_id = wallet.agent_id + result = await evaluate_and_vote_on_proposal( proposal_id=proposal_id, wallet_id=wallet_id, + agent_id=effective_agent_id, + dao_id=dao_id, auto_vote=False, ) From 2a422bb04ebf5019aaa3f6102d1c7f9f57946e96 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Fri, 2 May 2025 23:29:58 -0700 Subject: [PATCH 004/219] cleanup evaluation --- services/workflows/utils.py | 117 ++++++++++++++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 services/workflows/utils.py diff --git a/services/workflows/utils.py b/services/workflows/utils.py new file mode 100644 index 00000000..fc1fb815 --- /dev/null +++ b/services/workflows/utils.py @@ -0,0 +1,117 @@ +"""Workflow utility functions.""" + +import binascii +import logging +from typing import Dict, Optional + +logger = logging.getLogger(__name__) + + +def decode_hex_parameters(hex_string: Optional[str]) -> Optional[str]: + """Decodes a hexadecimal-encoded string if valid. + + Args: + hex_string: The hexadecimal string to decode. + + Returns: + The decoded string, or None if decoding fails. + """ + if not hex_string: + return None + if hex_string.startswith("0x"): + hex_string = hex_string[2:] # Remove "0x" prefix + try: + decoded_bytes = binascii.unhexlify(hex_string) + decoded_string = decoded_bytes.decode( + "utf-8", errors="ignore" + ) # Decode as UTF-8 + logger.debug(f"Successfully decoded hex string: {hex_string[:20]}...") + return decoded_string + except (binascii.Error, UnicodeDecodeError) as e: + logger.warning(f"Failed to decode hex string: {str(e)}") + return None # Return None if decoding fails + + +# Model pricing data (move this to a config or constants file later if needed) +MODEL_PRICES = { + "gpt-4o": { + "input": 2.50, # $2.50 per million input tokens + "output": 10.00, # $10.00 per million output tokens + }, + "gpt-4.1": { + "input": 2.00, # $2.00 per million input tokens + "output": 8.00, # $8.00 per million output tokens + }, + "gpt-4.1-mini": { + "input": 0.40, # $0.40 per million input tokens + "output": 1.60, # $1.60 per million output tokens + }, + "gpt-4.1-nano": { + "input": 0.10, # $0.10 per million input tokens + "output": 0.40, # $0.40 per million output tokens + }, + # Default to gpt-4.1 pricing if model not found + "default": { + "input": 2.00, + "output": 8.00, + }, +} + + +def calculate_token_cost( + token_usage: Dict[str, int], model_name: str +) -> Dict[str, float]: + """Calculate the cost of token usage based on current pricing. + + Args: + token_usage: Dictionary containing input_tokens and output_tokens + model_name: Name of the model used + + Returns: + Dictionary containing cost breakdown and total cost + """ + # Get pricing for the model, default to gpt-4.1 pricing if not found + model_prices = MODEL_PRICES.get(model_name.lower(), MODEL_PRICES["default"]) + + # Extract token counts, ensuring we get integers and handle None values + try: + input_tokens = int(token_usage.get("input_tokens", 0)) + output_tokens = int(token_usage.get("output_tokens", 0)) + except (TypeError, ValueError) as e: + logger.error(f"Error converting token counts to integers: {str(e)}") + input_tokens = 0 + output_tokens = 0 + + # Calculate costs with more precision + input_cost = (input_tokens / 1_000_000.0) * model_prices["input"] + output_cost = (output_tokens / 1_000_000.0) * model_prices["output"] + total_cost = input_cost + output_cost + + # Create detailed token usage breakdown + token_details = { + "input_tokens": input_tokens, + "output_tokens": output_tokens, + "total_tokens": input_tokens + output_tokens, + "model_name": model_name, + "input_price_per_million": model_prices["input"], + "output_price_per_million": model_prices["output"], + } + + # Add token details if available + if "input_token_details" in token_usage: + token_details["input_token_details"] = token_usage["input_token_details"] + if "output_token_details" in token_usage: + token_details["output_token_details"] = token_usage["output_token_details"] + + # Debug logging with more detail + logger.debug( + f"Cost calculation details: Model={model_name} | Input={input_tokens} tokens * ${model_prices['input']}/1M = ${input_cost:.6f} | Output={output_tokens} tokens * ${model_prices['output']}/1M = ${output_cost:.6f} | Total=${total_cost:.6f} | Token details={token_details}" + ) + + return { + "input_cost": round(input_cost, 6), + "output_cost": round(output_cost, 6), + "total_cost": round(total_cost, 6), + "currency": "USD", + "details": token_details, + } From 73c8edb15986a85f05e6f3e80203e8a54e84993e Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Fri, 2 May 2025 23:39:40 -0700 Subject: [PATCH 005/219] cleanup evaluation --- services/workflows/proposal_evaluation.py | 77 +++++++++++++++++++++-- 1 file changed, 73 insertions(+), 4 deletions(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 666c8bc1..ca7d9b3d 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -1,8 +1,11 @@ """Proposal evaluation workflow.""" +import asyncio from typing import Any, Dict, List, Optional, TypedDict from langchain.prompts import PromptTemplate +from langchain_core.messages import HumanMessage, SystemMessage +from langchain_openai import ChatOpenAI from langgraph.graph import END, Graph, StateGraph from pydantic import BaseModel, Field @@ -21,7 +24,8 @@ from services.workflows.base import ( BaseWorkflow, ) -from services.workflows.chat import ChatService +from services.workflows.chat import ChatService, StreamingCallbackHandler +from services.workflows.planning_mixin import PlanningCapability from services.workflows.utils import calculate_token_cost, decode_hex_parameters from services.workflows.vector_mixin import VectorRetrievalCapability from services.workflows.web_search_mixin import WebSearchCapability @@ -67,10 +71,14 @@ class EvaluationState(TypedDict): token_usage: Optional[Dict] # Add field for token usage tracking model_info: Optional[Dict] # Add field for model information contract_source: Optional[str] # Added field to store contract source + plan: Optional[str] # Added field to store the evaluation plan class ProposalEvaluationWorkflow( - BaseWorkflow[EvaluationState], VectorRetrievalCapability, WebSearchCapability + BaseWorkflow[EvaluationState], + VectorRetrievalCapability, + WebSearchCapability, + PlanningCapability, ): """Workflow for evaluating DAO proposals and voting automatically.""" @@ -89,7 +97,21 @@ def __init__( temperature: Optional temperature setting for the model **kwargs: Additional arguments passed to parent """ + # Initialize planning LLM + planning_llm = ChatOpenAI(model="gpt-4.1-mini", temperature=0.0, streaming=True) + + # Create callback handler for planning with queue + callback_handler = StreamingCallbackHandler(queue=asyncio.Queue()) + + # Initialize all parent classes including PlanningCapability super().__init__(model_name=model_name, temperature=temperature, **kwargs) + PlanningCapability.__init__( + self, + callback_handler=callback_handler, + planning_llm=planning_llm, + persona="You are a DAO proposal evaluation planner, focused on creating structured evaluation plans.", + ) + self.collection_names = collection_names or [ "knowledge_collection", "dao_collection", @@ -625,18 +647,64 @@ async def skip_voting(state: EvaluationState) -> EvaluationState: } return state + # --- Planning Node --- # + async def plan_evaluation(state: EvaluationState) -> EvaluationState: + """Generate a plan for evaluating the proposal using the PlanningCapability mixin.""" + try: + self.logger.debug( + "Generating evaluation plan using PlanningCapability..." + ) + + # Construct initial context for planning + initial_context = ( + f"Proposal ID: {state['proposal_id']}\n" + f"DAO ID: {state.get('dao_id')}\n" + f"Agent ID: {state.get('agent_id')}\n" + f"Auto-Vote Enabled: {state.get('auto_vote')}" + ) + + # Create planning query + planning_query = ( + f"Create a detailed plan for evaluating the following DAO proposal:\n\n" + f"{initial_context}\n\n" + f"The plan should cover:\n" + f"1. Data gathering (proposal details, DAO context, treasury info)\n" + f"2. Analysis approach (including use of vector search and web search)\n" + f"3. Evaluation criteria and decision making process\n" + f"4. Voting execution strategy (if auto-vote is enabled)" + ) + + # Use the mixin's create_plan method + plan = await self.create_plan( + query=planning_query, context_docs=state.get("vector_results", []) + ) + + state["plan"] = plan + self.logger.info("Evaluation plan generated using PlanningCapability.") + self.logger.debug(f"Generated Plan:\n{plan}") + return state + + except Exception as e: + self.logger.error(f"Error generating plan: {str(e)}", exc_info=True) + state["plan"] = f"Error generating plan: {str(e)}" + return state + # Create the graph workflow = StateGraph(EvaluationState) # Add nodes + workflow.add_node("plan_evaluation", plan_evaluation) # New planning node workflow.add_node("fetch_context", fetch_context) workflow.add_node("format_prompt", format_evaluation_prompt) - workflow.add_node("evaluate", call_evaluation_llm) + workflow.add_node( + "evaluate", call_evaluation_llm + ) # Renamed from evaluate_proposal workflow.add_node("vote", vote_on_proposal) workflow.add_node("skip_vote", skip_voting) # Set up the conditional branching - workflow.set_entry_point("fetch_context") + workflow.set_entry_point("plan_evaluation") # Start with planning + workflow.add_edge("plan_evaluation", "fetch_context") # Plan -> Fetch workflow.add_edge("fetch_context", "format_prompt") workflow.add_edge("format_prompt", "evaluate") workflow.add_conditional_edges( @@ -797,6 +865,7 @@ async def evaluate_and_vote_on_proposal( "web_search_results": None, "token_usage": None, "model_info": None, + "plan": None, } # Create and run workflow with model settings from prompt From 0abea532369a06d9a25d6ec484f0352cfd13a8dc Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 3 May 2025 21:09:49 -0700 Subject: [PATCH 006/219] update to mixins --- proposal_evaluation_test.py | 9 +- .../runner/tasks/dao_proposal_evaluation.py | 4 +- services/workflows/planning_mixin.py | 38 +++-- services/workflows/proposal_evaluation.py | 147 +++++++++++++++--- services/workflows/web_search_mixin.py | 40 +++-- 5 files changed, 192 insertions(+), 46 deletions(-) diff --git a/proposal_evaluation_test.py b/proposal_evaluation_test.py index 0281d214..517014eb 100644 --- a/proposal_evaluation_test.py +++ b/proposal_evaluation_test.py @@ -149,8 +149,13 @@ async def test_proposal_evaluation_workflow(): print(f"Approval: {result['evaluation']['approve']}") print(f"Confidence: {result['evaluation']['confidence_score']}") print(f"Reasoning: {result['evaluation']['reasoning']}") - print(f"Token Usage: {result['token_usage']}") - print(f"Cost: ${result['token_costs']['total_cost']:.4f}") + print( + f"Total Token Usage by Model: {result.get('total_token_usage_by_model')}" + ) + print(f"Total Cost by Model: {result.get('total_cost_by_model')}") + print( + f"Total Overall Cost: ${result.get('total_overall_cost', 0.0):.4f}" + ) if scenario["auto_vote"]: print(f"Auto-voted: {result['auto_voted']}") diff --git a/services/runner/tasks/dao_proposal_evaluation.py b/services/runner/tasks/dao_proposal_evaluation.py index d1f1dd6b..c11c2ece 100644 --- a/services/runner/tasks/dao_proposal_evaluation.py +++ b/services/runner/tasks/dao_proposal_evaluation.py @@ -127,8 +127,8 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: confidence = evaluation.get("confidence_score", 0.0) reasoning = evaluation.get("reasoning", "No reasoning provided") formatted_prompt = result.get("formatted_prompt", "No prompt provided") - total_cost = result.get("token_costs", {}).get("total_cost", 0.0) - model = result.get("model_info", {}).get("name", "Unknown") + total_cost = result.get("total_overall_cost", 0.0) + model = result.get("evaluation_model_info", {}).get("name", "Unknown") logger.info( f"Proposal {proposal.id} ({dao.name}): Evaluated with result " diff --git a/services/workflows/planning_mixin.py b/services/workflows/planning_mixin.py index 5a6b51c6..e97c71f3 100644 --- a/services/workflows/planning_mixin.py +++ b/services/workflows/planning_mixin.py @@ -1,10 +1,10 @@ """Planning mixin for workflows, providing vector-aware planning capabilities.""" import asyncio -from typing import Any, List, Optional +from typing import Any, Dict, List, Optional, Tuple +from langchain_core.messages import HumanMessage, SystemMessage from langchain_openai import ChatOpenAI -from langchain_core.messages import SystemMessage, HumanMessage from lib.logger import configure_logger from services.workflows.base import BaseWorkflowMixin @@ -12,6 +12,7 @@ logger = configure_logger(__name__) + class PlanningCapability(BaseWorkflowMixin): """Mixin that adds vector-aware planning capabilities to a workflow. @@ -50,7 +51,7 @@ async def create_plan( query: str, context_docs: Optional[List[Any]] = None, **kwargs, - ) -> str: + ) -> Tuple[str, Dict[str, Any]]: """Create a plan based on the user's query and vector retrieval results. Args: @@ -59,7 +60,7 @@ async def create_plan( **kwargs: Additional arguments Returns: - Generated plan + Tuple containing the generated plan (str) and token usage (dict) """ planning_prompt = f""" You are an AI assistant planning a decisive response to the user's query. @@ -108,7 +109,9 @@ async def create_plan( User Query: {query} """ if context_docs: - context_str = "\n\n".join([getattr(doc, "page_content", str(doc)) for doc in context_docs]) + context_str = "\n\n".join( + [getattr(doc, "page_content", str(doc)) for doc in context_docs] + ) planning_prompt += f"\n\nHere is additional context that may be helpful:\n\n{context_str}\n\nUse this context to inform your plan." if self.tool_names: tool_info = "\n\nTools available to you:\n" @@ -122,8 +125,11 @@ async def create_plan( planning_messages.append(SystemMessage(content=self.persona)) planning_messages.append(HumanMessage(content=planning_prompt)) try: - logger.info("Creating thought process notes for user query with vector context") + logger.info( + "Creating thought process notes for user query with vector context" + ) original_new_token = self.callback_handler.custom_on_llm_new_token + async def planning_token_wrapper(token, **kwargs): if asyncio.iscoroutinefunction(original_new_token): await original_new_token(token, planning_only=True, **kwargs) @@ -140,19 +146,33 @@ async def planning_token_wrapper(token, **kwargs): ), loop, ) + self.callback_handler.custom_on_llm_new_token = planning_token_wrapper task = asyncio.create_task(self.planning_llm.ainvoke(planning_messages)) response = await task plan = response.content + token_usage = response.usage_metadata or { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } self.callback_handler.custom_on_llm_new_token = original_new_token - logger.info("Thought process notes created successfully with vector context") + logger.info( + "Thought process notes created successfully with vector context" + ) logger.debug(f"Notes content length: {len(plan)}") + logger.debug(f"Planning token usage: {token_usage}") await self.callback_handler.process_step( content=plan, role="assistant", thought="Planning Phase with Context" ) - return plan + return plan, token_usage except Exception as e: if hasattr(self.callback_handler, "custom_on_llm_new_token"): self.callback_handler.custom_on_llm_new_token = original_new_token logger.error(f"Failed to create plan: {str(e)}", exc_info=True) - raise \ No newline at end of file + # Return empty plan and zero usage on error + return "Failed to create plan.", { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index ca7d9b3d..30563b58 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -66,12 +66,18 @@ class EvaluationState(TypedDict): agent_prompts: List[Dict] vector_results: Optional[List[Dict]] recent_tweets: Optional[List[Dict]] - web_search_results: Optional[List[Dict]] # Add field for web search results + web_search_results: Optional[List[Dict]] treasury_balance: Optional[float] - token_usage: Optional[Dict] # Add field for token usage tracking - model_info: Optional[Dict] # Add field for model information - contract_source: Optional[str] # Added field to store contract source - plan: Optional[str] # Added field to store the evaluation plan + contract_source: Optional[str] + plan: Optional[str] + # Token usage tracking per step + planning_token_usage: Optional[Dict] + web_search_token_usage: Optional[Dict] + evaluation_token_usage: Optional[Dict] + # Model info for cost calculation + evaluation_model_info: Optional[Dict] + planning_model_info: Optional[Dict] + web_search_model_info: Optional[Dict] class ProposalEvaluationWorkflow( @@ -98,7 +104,11 @@ def __init__( **kwargs: Additional arguments passed to parent """ # Initialize planning LLM - planning_llm = ChatOpenAI(model="gpt-4.1-mini", temperature=0.0, streaming=True) + planning_llm = ChatOpenAI( + model="o4-mini", + stream_usage=True, + streaming=True, + ) # Create callback handler for planning with queue callback_handler = StreamingCallbackHandler(queue=asyncio.Queue()) @@ -307,10 +317,19 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: # Use mixin capabilities for web search and vector retrieval web_search_query = f"DAO proposal {proposal_dict.get('type', 'unknown')} - {proposal_dict.get('parameters', '')}" - state["web_search_results"] = await self.search_web( + + # Fetch web search results and token usage + web_search_results, web_search_token_usage = await self.search_web( query=web_search_query, search_context_size="medium", ) + state["web_search_results"] = web_search_results + state["web_search_token_usage"] = web_search_token_usage + # Store web search model info (assuming gpt-4.1 as used in mixin) + state["web_search_model_info"] = { + "name": "gpt-4.1", + "temperature": None, + } vector_search_query = f"Proposal type: {proposal_dict.get('type')} - {proposal_dict.get('parameters', '')}" state["vector_results"] = await self.retrieve_from_vector_store( @@ -501,12 +520,11 @@ async def call_evaluation_llm(state: EvaluationState) -> EvaluationState: state["approve"] = parsed_result.approve state["confidence_score"] = parsed_result.confidence_score state["reasoning"] = parsed_result.reasoning - state["token_usage"] = token_usage - state["model_info"] = model_info + state["evaluation_token_usage"] = token_usage + state["evaluation_model_info"] = model_info - token_costs = calculate_token_cost(token_usage, model_info["name"]) self.logger.debug( - f"Evaluation complete: Decision={'APPROVE' if parsed_result.approve else 'REJECT'} | Confidence={parsed_result.confidence_score:.2f} | Model={model_info['name']} (temp={model_info['temperature']}) | Tokens={token_usage} | Cost=${token_costs['total_cost']:.4f}" + f"Evaluation step complete: Decision={'APPROVE' if parsed_result.approve else 'REJECT'} | Confidence={parsed_result.confidence_score:.2f}" ) self.logger.debug(f"Full reasoning: {parsed_result.reasoning}") @@ -675,11 +693,18 @@ async def plan_evaluation(state: EvaluationState) -> EvaluationState: ) # Use the mixin's create_plan method - plan = await self.create_plan( + plan, planning_token_usage = await self.create_plan( query=planning_query, context_docs=state.get("vector_results", []) ) state["plan"] = plan + state["planning_token_usage"] = planning_token_usage + # Store planning model info + state["planning_model_info"] = { + "name": self.planning_llm.model_name, + "temperature": self.planning_llm.temperature, + } + self.logger.info("Evaluation plan generated using PlanningCapability.") self.logger.debug(f"Generated Plan:\n{plan}") return state @@ -687,6 +712,12 @@ async def plan_evaluation(state: EvaluationState) -> EvaluationState: except Exception as e: self.logger.error(f"Error generating plan: {str(e)}", exc_info=True) state["plan"] = f"Error generating plan: {str(e)}" + state["planning_token_usage"] = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } + state["planning_model_info"] = {"name": "unknown", "temperature": None} return state # Create the graph @@ -866,6 +897,12 @@ async def evaluate_and_vote_on_proposal( "token_usage": None, "model_info": None, "plan": None, + "planning_token_usage": None, + "web_search_token_usage": None, + "evaluation_token_usage": None, + "evaluation_model_info": None, + "planning_model_info": None, + "web_search_model_info": None, } # Create and run workflow with model settings from prompt @@ -916,24 +953,86 @@ async def evaluate_and_vote_on_proposal( "recent_tweets": result["recent_tweets"], "web_search_results": result["web_search_results"], "treasury_balance": result.get("treasury_balance"), - "token_usage": result.get( - "token_usage", - {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}, + "planning_token_usage": result.get("planning_token_usage"), + "web_search_token_usage": result.get("web_search_token_usage"), + "evaluation_token_usage": result.get("evaluation_token_usage"), + "evaluation_model_info": result.get("evaluation_model_info"), + "planning_model_info": result.get("planning_model_info"), + "web_search_model_info": result.get("web_search_model_info"), + } + + # --- Aggregate Token Usage and Calculate Costs --- # + total_token_usage_by_model = {} + total_cost_by_model = {} + total_overall_cost = 0.0 + + steps = [ + ( + "planning", + result.get("planning_token_usage"), + result.get("planning_model_info"), ), - "model_info": result.get( - "model_info", {"name": "unknown", "temperature": None} + ( + "web_search", + result.get("web_search_token_usage"), + result.get("web_search_model_info"), ), - } + ( + "evaluation", + result.get("evaluation_token_usage"), + result.get("evaluation_model_info"), + ), + ] - # Calculate token costs - token_costs = calculate_token_cost( - final_result["token_usage"], final_result["model_info"]["name"] - ) - final_result["token_costs"] = token_costs + for step_name, usage, model_info in steps: + if usage and model_info and model_info.get("name") != "unknown": + model_name = model_info["name"] + + # Aggregate usage per model + if model_name not in total_token_usage_by_model: + total_token_usage_by_model[model_name] = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } + total_token_usage_by_model[model_name]["input_tokens"] += usage.get( + "input_tokens", 0 + ) + total_token_usage_by_model[model_name]["output_tokens"] += usage.get( + "output_tokens", 0 + ) + total_token_usage_by_model[model_name]["total_tokens"] += usage.get( + "total_tokens", 0 + ) + + # Calculate cost for this step/model + step_cost = calculate_token_cost(usage, model_name) + + # Aggregate cost per model + if model_name not in total_cost_by_model: + total_cost_by_model[model_name] = 0.0 + total_cost_by_model[model_name] += step_cost["total_cost"] + total_overall_cost += step_cost["total_cost"] + else: + logger.warning( + f"Skipping cost calculation for step '{step_name}' due to missing usage or model info." + ) + + final_result["total_token_usage_by_model"] = total_token_usage_by_model + final_result["total_cost_by_model"] = total_cost_by_model + final_result["total_overall_cost"] = total_overall_cost + # --- End Aggregation --- # + # Updated Logging logger.debug( - f"Proposal evaluation completed: Success={final_result['success']} | Decision={'APPROVE' if final_result['evaluation']['approve'] else 'REJECT'} | Confidence={final_result['evaluation']['confidence_score']:.2f} | Auto-voted={final_result['auto_voted']} | Transaction={tx_id or 'None'} | Model={final_result['model_info']['name']} | Token Usage={final_result['token_usage']} | Cost (USD)=${token_costs['total_cost']:.4f} (Input=${token_costs['input_cost']:.4f} for {token_costs['details']['input_tokens']} tokens, Output=${token_costs['output_cost']:.4f} for {token_costs['details']['output_tokens']} tokens)" + f"Proposal evaluation completed: Success={final_result['success']} | " + f"Decision={'APPROVE' if final_result['evaluation']['approve'] else 'REJECT'} | " + f"Confidence={final_result['evaluation']['confidence_score']:.2f} | " + f"Auto-voted={final_result['auto_voted']} | Transaction={tx_id or 'None'} | " + f"Total Cost (USD)=${total_overall_cost:.4f}" ) + logger.debug(f"Cost Breakdown: {total_cost_by_model}") + logger.debug(f"Token Usage Breakdown: {total_token_usage_by_model}") logger.debug(f"Full evaluation result: {final_result}") return final_result diff --git a/services/workflows/web_search_mixin.py b/services/workflows/web_search_mixin.py index 8301dce2..e3bf89a6 100644 --- a/services/workflows/web_search_mixin.py +++ b/services/workflows/web_search_mixin.py @@ -1,15 +1,16 @@ """Web search mixin for workflows, providing web search capabilities using OpenAI Responses API.""" -from typing import Any, Dict, List +from typing import Any, Dict, List, Tuple -from openai import OpenAI from langgraph.graph import StateGraph +from openai import OpenAI from lib.logger import configure_logger from services.workflows.base import BaseWorkflowMixin logger = configure_logger(__name__) + class WebSearchCapability(BaseWorkflowMixin): """Mixin that adds web search capabilities to a workflow using OpenAI Responses API.""" @@ -27,7 +28,9 @@ def _init_web_search(self) -> None: if not hasattr(self, "client"): self.client = OpenAI() - async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: + async def search_web( + self, query: str, **kwargs + ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: """Search the web using OpenAI Responses API. Args: @@ -35,7 +38,7 @@ async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: **kwargs: Additional search parameters like user_location and search_context_size Returns: - List of search results with content and metadata + Tuple containing list of search results and token usage dict. """ try: # Ensure initialization @@ -44,7 +47,11 @@ async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: # Check cache first if query in self.search_results_cache: logger.info(f"Using cached results for query: {query}") - return self.search_results_cache[query] + return self.search_results_cache[query], { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } # Configure web search tool tool_config = { @@ -61,7 +68,21 @@ async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: model="gpt-4.1", tools=[tool_config], input=query ) + token_usage = response.usage # Access the usage object + standardized_usage = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } + if token_usage: # Check if usage data exists + standardized_usage = { + "input_tokens": token_usage.prompt_tokens, # Access via attribute + "output_tokens": token_usage.completion_tokens, # Access via attribute + "total_tokens": token_usage.total_tokens, # Access via attribute + } + logger.debug(f"Web search response: {response}") + logger.debug(f"Web search token usage: {standardized_usage}") # Process the response into our document format documents = [] @@ -110,12 +131,12 @@ async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: self.search_results_cache[query] = documents logger.info(f"Web search completed with {len(documents)} results") - return documents + return documents, standardized_usage except Exception as e: logger.error(f"Web search failed: {str(e)}") - # Return a list with one empty result to prevent downstream errors - return [ + # Return empty list and zero usage on error + error_doc = [ { "page_content": "Web search failed to return results.", "metadata": { @@ -133,6 +154,7 @@ async def search_web(self, query: str, **kwargs) -> List[Dict[str, Any]]: }, } ] + return error_doc, {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0} def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: """Integrate web search capability with a graph. @@ -172,4 +194,4 @@ async def _process_results( "num_web_results": len(web_results), "source_types": ["web_search"], }, - } \ No newline at end of file + } From b6f495f777d35af6d34413f559deaf4313dbaba3 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 3 May 2025 22:59:28 -0700 Subject: [PATCH 007/219] update --- services/workflows/chat.py | 49 +++++++++--- services/workflows/web_search_mixin.py | 100 +++++++++++++------------ 2 files changed, 92 insertions(+), 57 deletions(-) diff --git a/services/workflows/chat.py b/services/workflows/chat.py index 22e6309f..40394feb 100644 --- a/services/workflows/chat.py +++ b/services/workflows/chat.py @@ -403,17 +403,44 @@ def call_model_with_context_and_plan(state: ChatState) -> Dict: # Add web search results if available if web_results: - web_context = "\n\n".join( - [ - f"Web Search Result {i+1}:\n{result['page_content']}\nSource: {result['metadata'].get('source_urls', ['Unknown'])[0]}" - for i, result in enumerate(web_results) - ] - ) - web_message = SystemMessage( - content=f"Here are relevant web search results:\n\n{web_context}\n\n" - "Consider this information in your response if relevant." - ) - messages = [web_message] + messages + # Flatten web_results if it is a list of lists + if any(isinstance(r, list) for r in web_results): + # Only flatten one level + flat_results = [] + for r in web_results: + if isinstance(r, list): + flat_results.extend(r) + else: + flat_results.append(r) + web_results = flat_results + + web_context_chunks = [] + for i, result in enumerate(web_results): + if not isinstance(result, dict): + logger.warning( + f"Web search result at index {i} is not a dict: {type(result)}. Skipping." + ) + continue + page_content = result.get("page_content") + metadata = result.get("metadata", {}) + source_urls = metadata.get("source_urls", ["Unknown"]) + if not isinstance(source_urls, list): + source_urls = [str(source_urls)] + if page_content is None: + logger.warning( + f"Web search result at index {i} missing 'page_content'. Skipping." + ) + continue + web_context_chunks.append( + f"Web Search Result {i+1}:\n{page_content}\nSource: {source_urls[0]}" + ) + web_context = "\n\n".join(web_context_chunks) + if web_context: + web_message = SystemMessage( + content=f"Here are relevant web search results:\n\n{web_context}\n\n" + "Consider this information in your response if relevant." + ) + messages = [web_message] + messages # Add the plan as a system message if it exists and hasn't been added yet if plan is not None and not any( diff --git a/services/workflows/web_search_mixin.py b/services/workflows/web_search_mixin.py index e3bf89a6..031f4982 100644 --- a/services/workflows/web_search_mixin.py +++ b/services/workflows/web_search_mixin.py @@ -68,31 +68,38 @@ async def search_web( model="gpt-4.1", tools=[tool_config], input=query ) - token_usage = response.usage # Access the usage object + # Extract token usage + token_usage = getattr(response, "usage", {}) standardized_usage = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, + "input_tokens": token_usage.get("input_tokens", 0), + "output_tokens": token_usage.get("output_tokens", 0), + "total_tokens": token_usage.get("total_tokens", 0), } - if token_usage: # Check if usage data exists - standardized_usage = { - "input_tokens": token_usage.prompt_tokens, # Access via attribute - "output_tokens": token_usage.completion_tokens, # Access via attribute - "total_tokens": token_usage.total_tokens, # Access via attribute - } - - logger.debug(f"Web search response: {response}") - logger.debug(f"Web search token usage: {standardized_usage}") - # Process the response into our document format - documents = [] - - # Access the output text directly - if hasattr(response, "output_text"): - text_content = response.output_text - source_urls = [] - - # Try to extract citations if available - if hasattr(response, "citations"): + logger.debug(f"Web search token_usage: {standardized_usage}") + + # Extract output text + text_content = None + if hasattr(response, "output") and isinstance(response.output, list): + try: + first_output = response.output[0] + if ( + isinstance(first_output, dict) + and "content" in first_output + and isinstance(first_output["content"], list) + and len(first_output["content"]) > 0 + and "text" in first_output["content"][0] + ): + text_content = first_output["content"][0]["text"] + except Exception as e: + logger.warning(f"Failed to extract output text: {e}") + + if not text_content: + text_content = "No output text available." + + # Defensive citation extraction (if present) + source_urls = [] + if hasattr(response, "citations"): + try: source_urls = [ { "url": citation.url, @@ -103,29 +110,30 @@ async def search_web( for citation in response.citations if hasattr(citation, "url") ] - - # Ensure we always have at least one URL entry - if not source_urls: - source_urls = [ - { - "url": "No source URL available", - "title": "Generated Response", - "start_index": 0, - "end_index": len(text_content), - } - ] - - # Create document with content - doc = { - "page_content": text_content, - "metadata": { - "type": "web_search_result", - "source_urls": source_urls, - "query": query, - "timestamp": None, - }, - } - documents.append(doc) + except Exception as e: + logger.warning(f"Failed to extract citations: {e}") + + if not source_urls: + source_urls = [ + { + "url": "No source URL available", + "title": "Generated Response", + "start_index": 0, + "end_index": len(text_content), + } + ] + + # Create document with content + doc = { + "page_content": text_content, + "metadata": { + "type": "web_search_result", + "source_urls": source_urls, + "query": query, + "timestamp": None, + }, + } + documents = [doc] # Cache the results self.search_results_cache[query] = documents From b171572e5aad9c465130088ac4475fadae7f6f46 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 3 May 2025 23:42:10 -0700 Subject: [PATCH 008/219] update --- proposal_evaluation_test.py | 40 ++++++------ services/workflows/proposal_evaluation.py | 75 ++++++++++++++++++++--- services/workflows/utils.py | 19 ++++++ services/workflows/web_search_mixin.py | 6 +- 4 files changed, 108 insertions(+), 32 deletions(-) diff --git a/proposal_evaluation_test.py b/proposal_evaluation_test.py index 517014eb..1b98b9ee 100644 --- a/proposal_evaluation_test.py +++ b/proposal_evaluation_test.py @@ -6,6 +6,8 @@ """ import asyncio +import binascii +import json from typing import Dict, Optional from uuid import UUID @@ -32,19 +34,17 @@ async def create_test_proposal(dao_id: UUID) -> UUID: The ID of the created proposal """ # Create test parameters as a JSON object - parameters = { - "action": "test_action", - "amount": 1000, - "description": "Test proposal for evaluation", - "recipient": "ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", - } + parameters = "let this rip https://i.ytimg.com/vi/Jjv2JVxdh1I/sddefault.jpg https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/x-vote-media//img_2.jpeg" + + # Convert parameters to JSON string and then hex encode it + parameters_hex = "0x" + binascii.hexlify(parameters.encode("utf-8")).decode("utf-8") # Create a test proposal proposal_data = ProposalCreate( dao_id=dao_id, type=ProposalType.ACTION, - parameters=str(parameters), # Convert parameters to string - action="test_action", + parameters=parameters_hex, # Use hex encoded parameters + action="send_message", contract_principal="ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM.test-contract", creator="ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", created_at_block=1, @@ -108,18 +108,18 @@ async def test_proposal_evaluation_workflow(): "confidence_threshold": 0.7, "description": "Testing proposal evaluation without voting", }, - { - "name": "Auto-vote Enabled", - "auto_vote": True, # Corrected: Changed to True for auto-vote scenario - "confidence_threshold": 0.7, - "description": "Testing proposal evaluation with auto-voting", - }, - { - "name": "Low Confidence Threshold", - "auto_vote": False, - "confidence_threshold": 0.3, - "description": "Testing with lower confidence threshold", - }, + # { + # "name": "Auto-vote Enabled", + # "auto_vote": True, # Corrected: Changed to True for auto-vote scenario + # "confidence_threshold": 0.7, + # "description": "Testing proposal evaluation with auto-voting", + # }, + # { + # "name": "Low Confidence Threshold", + # "auto_vote": False, + # "confidence_threshold": 0.3, + # "description": "Testing with lower confidence threshold", + # }, ] # Run each scenario diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 30563b58..b9aa09d0 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -1,8 +1,10 @@ """Proposal evaluation workflow.""" import asyncio -from typing import Any, Dict, List, Optional, TypedDict +import base64 +from typing import Any, Dict, List, Optional, TypedDict, Union +import httpx from langchain.prompts import PromptTemplate from langchain_core.messages import HumanMessage, SystemMessage from langchain_openai import ChatOpenAI @@ -26,7 +28,11 @@ ) from services.workflows.chat import ChatService, StreamingCallbackHandler from services.workflows.planning_mixin import PlanningCapability -from services.workflows.utils import calculate_token_cost, decode_hex_parameters +from services.workflows.utils import ( + calculate_token_cost, + decode_hex_parameters, + extract_image_urls, +) from services.workflows.vector_mixin import VectorRetrievalCapability from services.workflows.web_search_mixin import WebSearchCapability from tools.dao_ext_action_proposals import VoteOnActionProposalTool @@ -70,6 +76,7 @@ class EvaluationState(TypedDict): treasury_balance: Optional[float] contract_source: Optional[str] plan: Optional[str] + proposal_images: Optional[List[Dict]] # Store encoded images for LLM # Token usage tracking per step planning_token_usage: Optional[Dict] web_search_token_usage: Optional[Dict] @@ -156,6 +163,14 @@ def _create_prompt(self) -> PromptTemplate: # 2. PROPOSAL INFORMATION {proposal_data} + Note: If any images are provided with the proposal, they will be shown after this prompt. + You should analyze any provided images in the context of the proposal and include your observations + in your evaluation. Consider aspects such as: + - Image content and relevance to the proposal + - Any visual evidence supporting or contradicting the proposal + - Quality and authenticity of the images + - Potential security or privacy concerns in the images + # 3. DAO CONTEXT {dao_info} @@ -201,6 +216,7 @@ def _create_prompt(self) -> PromptTemplate: • Cite relevant parts of the proposal, DAO mission, or prior actions • Use terms accurately — don't fake precision • Keep structure clean and easy to follow + • Include analysis of any provided images and their implications # 10. VECTOR CONTEXT {vector_context} @@ -221,6 +237,7 @@ def _create_prompt(self) -> PromptTemplate: // 2. How DAO context influenced decision // 3. How AIBTC Charter alignment was considered // 4. Key factors in confidence score selection + // 5. Analysis of any provided images // Must be clear, precise, and well-structured }} """, @@ -245,6 +262,42 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: # Decode parameters if they exist decoded_parameters = decode_hex_parameters(proposal_data.parameters) + image_urls = extract_image_urls(decoded_parameters) + + # Process and encode images + proposal_images = [] + for url in image_urls: + try: + async with httpx.AsyncClient() as client: + response = await client.get(url, timeout=10.0) + if response.status_code == 200: + image_data = base64.b64encode(response.content).decode( + "utf-8" + ) + # Determine MIME type based on URL extension + mime_type = ( + "image/jpeg" + if url.lower().endswith((".jpg", ".jpeg")) + else "image/png" + ) + proposal_images.append( + { + "type": "image_url", + "image_url": { + "url": f"data:{mime_type};base64,{image_data}" + }, + } + ) + else: + logger.warning( + f"Failed to fetch image: {url} (status {response.status_code})" + ) + except Exception as e: + logger.error( + f"Error fetching image {url}: {str(e)}", exc_info=True + ) + + state["proposal_images"] = proposal_images # Convert proposal data to dictionary proposal_dict = { @@ -484,16 +537,20 @@ async def call_evaluation_llm(state: EvaluationState) -> EvaluationState: if "reasoning" in state and "Error" in state["reasoning"]: return state # Skip if previous steps failed try: + # Prepare message content with text and images + message_content = [{"type": "text", "text": state["formatted_prompt"]}] + + # Add any proposal images if they exist + if state.get("proposal_images"): + message_content.extend(state["proposal_images"]) + + # Create the message for the LLM + message = HumanMessage(content=message_content) + structured_output = self.llm.with_structured_output( ProposalEvaluationOutput, include_raw=True ) - result: Dict[str, Any] = await structured_output.ainvoke( - state["formatted_prompt"] - ) - - result: Dict[str, Any] = await structured_output.ainvoke( - state["formatted_prompt"] - ) + result: Dict[str, Any] = await structured_output.ainvoke([message]) parsed_result = result.get("parsed") if not isinstance(parsed_result, ProposalEvaluationOutput): diff --git a/services/workflows/utils.py b/services/workflows/utils.py index fc1fb815..ea4908ea 100644 --- a/services/workflows/utils.py +++ b/services/workflows/utils.py @@ -2,11 +2,30 @@ import binascii import logging +import re from typing import Dict, Optional logger = logging.getLogger(__name__) +def extract_image_urls(text): + """ + Extracts image URLs from a string. + + Args: + text: The input string to search for image URLs. + + Returns: + A list of image URLs found in the string. + """ + image_url_pattern = re.compile( + r"\bhttps?://[^\s<>\"]+?\.(?:png|jpg|jpeg|gif|webp)(?:\b|(?=\s|$))", + re.IGNORECASE, + ) + image_urls = re.findall(image_url_pattern, text) + return image_urls + + def decode_hex_parameters(hex_string: Optional[str]) -> Optional[str]: """Decodes a hexadecimal-encoded string if valid. diff --git a/services/workflows/web_search_mixin.py b/services/workflows/web_search_mixin.py index 031f4982..8a257d3a 100644 --- a/services/workflows/web_search_mixin.py +++ b/services/workflows/web_search_mixin.py @@ -71,9 +71,9 @@ async def search_web( # Extract token usage token_usage = getattr(response, "usage", {}) standardized_usage = { - "input_tokens": token_usage.get("input_tokens", 0), - "output_tokens": token_usage.get("output_tokens", 0), - "total_tokens": token_usage.get("total_tokens", 0), + "input_tokens": getattr(token_usage, "input_tokens", 0), + "output_tokens": getattr(token_usage, "output_tokens", 0), + "total_tokens": getattr(token_usage, "total_tokens", 0), } logger.debug(f"Web search token_usage: {standardized_usage}") From 0eb9f8e1719e0f44065f4297257a00da3423ebbd Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sun, 4 May 2025 00:18:51 -0700 Subject: [PATCH 009/219] update --- proposal_evaluation_test.py | 2 +- services/workflows/proposal_evaluation.py | 10 +++++++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/proposal_evaluation_test.py b/proposal_evaluation_test.py index 1b98b9ee..3e7ec4b2 100644 --- a/proposal_evaluation_test.py +++ b/proposal_evaluation_test.py @@ -34,7 +34,7 @@ async def create_test_proposal(dao_id: UUID) -> UUID: The ID of the created proposal """ # Create test parameters as a JSON object - parameters = "let this rip https://i.ytimg.com/vi/Jjv2JVxdh1I/sddefault.jpg https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/x-vote-media//img_2.jpeg" + parameters = "let this rip https://media1.giphy.com/media/v1.Y2lkPTc5MGI3NjExN3VoZzJzdmV3eGs4M2VrOXBkamg2dTVhb2NhcndwNzVxNHplMzhoaiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/M7HkIkPrNhSy4/giphy.gif https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/x-vote-media//img_2.jpeg" # Convert parameters to JSON string and then hex encode it parameters_hex = "0x" + binascii.hexlify(parameters.encode("utf-8")).decode("utf-8") diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index b9aa09d0..b1f7c7d4 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -278,7 +278,15 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: mime_type = ( "image/jpeg" if url.lower().endswith((".jpg", ".jpeg")) - else "image/png" + else ( + "image/png" + if url.lower().endswith(".png") + else ( + "image/gif" + if url.lower().endswith(".gif") + else "image/png" + ) + ) # default to PNG if unknown ) proposal_images.append( { From 489dd39358d95f5153fa5268f871c2b3a2b1d548 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sun, 4 May 2025 00:19:37 -0700 Subject: [PATCH 010/219] support webp --- services/workflows/proposal_evaluation.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index b1f7c7d4..19d868ee 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -284,7 +284,11 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: else ( "image/gif" if url.lower().endswith(".gif") - else "image/png" + else ( + "image/webp" + if url.lower().endswith(".webp") + else "image/png" + ) ) ) # default to PNG if unknown ) From 7cbd4aa5a61e9a15a5a9db06721db09502c41466 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sun, 4 May 2025 18:18:16 -0700 Subject: [PATCH 011/219] add embeddings for proposals --- backend/abstract.py | 13 ++ backend/supabase.py | 28 +++ config.py | 8 + services/runner/__init__.py | 7 + services/runner/base.py | 1 + services/runner/job_manager.py | 6 + services/runner/tasks/proposal_embedder.py | 237 +++++++++++++++++++++ 7 files changed, 300 insertions(+) create mode 100644 services/runner/tasks/proposal_embedder.py diff --git a/backend/abstract.py b/backend/abstract.py index 9cb7d437..da1d88fe 100644 --- a/backend/abstract.py +++ b/backend/abstract.py @@ -135,6 +135,19 @@ async def add_vectors( """ pass + @abstractmethod + async def fetch_vectors(self, collection_name: str, ids: List[str]) -> List[Any]: + """Fetch specific vectors by their IDs from a collection. + + Args: + collection_name: The name of the vector collection + ids: A list of vector IDs to fetch + + Returns: + A list of the fetched records (structure depends on the backend). + """ + pass + @abstractmethod async def query_vectors( self, collection_name: str, query_text: str, limit: int = 4 diff --git a/backend/supabase.py b/backend/supabase.py index f2b11ecc..944b609d 100644 --- a/backend/supabase.py +++ b/backend/supabase.py @@ -237,6 +237,34 @@ async def add_vectors( ) raise + async def fetch_vectors(self, collection_name: str, ids: List[str]) -> List[Any]: + """Fetch specific vectors by their IDs using the vecs client. + + Args: + collection_name: Name of the collection to query + ids: List of vector IDs to fetch + + Returns: + List of fetched records (typically tuples of id, vector, metadata). + """ + collection = self.get_vector_collection(collection_name) + if not ids: + logger.debug("fetch_vectors called with empty ID list.") + return [] + + try: + # Assuming the vecs library provides a `fetch` method + fetched_records = collection.fetch(ids=ids) + logger.debug( + f"Fetched {len(fetched_records)} vectors from collection {collection_name} for {len(ids)} requested IDs." + ) + return fetched_records + except Exception as e: + logger.error( + f"Failed to fetch vectors by ID from collection {collection_name}: {str(e)}" + ) + raise + async def query_vectors( self, collection_name: str, query_text: str, limit: int = 4, embeddings=None ) -> List[Dict[str, Any]]: diff --git a/config.py b/config.py index da68c751..0bc859da 100644 --- a/config.py +++ b/config.py @@ -114,6 +114,14 @@ class SchedulerConfig: dao_proposal_vote_delay_blocks: int = int( os.getenv("AIBTC_DAO_PROPOSAL_VOTE_DELAY_BLOCKS", "2") ) + proposal_embedder_enabled: bool = ( + os.getenv("AIBTC_PROPOSAL_EMBEDDER_ENABLED", "false").lower() == "true" + ) + proposal_embedder_interval_seconds: int = int( + os.getenv( + "AIBTC_PROPOSAL_EMBEDDER_INTERVAL_SECONDS", "300" + ) # Default to 5 mins + ) @dataclass diff --git a/services/runner/__init__.py b/services/runner/__init__.py index e3506e17..26de9149 100644 --- a/services/runner/__init__.py +++ b/services/runner/__init__.py @@ -21,6 +21,10 @@ ) from services.runner.tasks.dao_task import DAOTask, dao_task from services.runner.tasks.dao_tweet_task import DAOTweetTask, dao_tweet_task +from services.runner.tasks.proposal_embedder import ( + ProposalEmbedderTask, + proposal_embedder, +) from services.runner.tasks.tweet_task import TweetTask, tweet_task # Register tasks with the registry @@ -31,6 +35,7 @@ JobRegistry.register(JobType.DAO_TWEET, DAOTweetTask) JobRegistry.register(JobType.TWEET, TweetTask) JobRegistry.register(JobType.AGENT_ACCOUNT_DEPLOY, AgentAccountDeployerTask) +JobRegistry.register(JobType.PROPOSAL_EMBEDDING, ProposalEmbedderTask) __all__ = [ "BaseTask", @@ -54,4 +59,6 @@ "dao_proposal_evaluation", "AgentAccountDeployerTask", "agent_account_deployer", + "ProposalEmbedderTask", + "proposal_embedder", ] diff --git a/services/runner/base.py b/services/runner/base.py index 6e072586..2fcf0747 100644 --- a/services/runner/base.py +++ b/services/runner/base.py @@ -74,6 +74,7 @@ class JobType(str, Enum): DAO_TWEET = "dao_tweet" TWEET = "tweet" AGENT_ACCOUNT_DEPLOY = "agent_account_deploy" + PROPOSAL_EMBEDDING = "proposal_embedding" def __str__(self): return self.value diff --git a/services/runner/job_manager.py b/services/runner/job_manager.py index e8ded37a..31c2777a 100644 --- a/services/runner/job_manager.py +++ b/services/runner/job_manager.py @@ -105,6 +105,12 @@ def get_all_jobs() -> List[JobConfig]: config.scheduler.agent_account_deploy_runner_interval_seconds, JobType.AGENT_ACCOUNT_DEPLOY.value, ), + ( + "Proposal Embedder Runner Service", + config.scheduler.proposal_embedder_enabled, + config.scheduler.proposal_embedder_interval_seconds, + JobType.PROPOSAL_EMBEDDING.value, + ), ] # Add all runner jobs with common structure diff --git a/services/runner/tasks/proposal_embedder.py b/services/runner/tasks/proposal_embedder.py new file mode 100644 index 00000000..994f91e2 --- /dev/null +++ b/services/runner/tasks/proposal_embedder.py @@ -0,0 +1,237 @@ +"""Proposal embedding task implementation.""" + +from dataclasses import dataclass +from typing import Any, Dict, List, Optional + +import openai +from langchain_openai import OpenAIEmbeddings + +from backend.factory import backend +from backend.models import Proposal +from config import config +from lib.logger import configure_logger +from services.runner.base import BaseTask, JobContext, RunnerResult + +logger = configure_logger(__name__) + +PROPOSAL_COLLECTION_NAME = "proposals" +EMBEDDING_MODEL = "text-embedding-ada-002" + + +@dataclass +class ProposalEmbedderResult(RunnerResult): + """Result of proposal embedding operation.""" + + proposals_checked: int = 0 + proposals_embedded: int = 0 + errors: List[str] = None + + def __post_init__(self): + self.errors = self.errors or [] + + +class ProposalEmbedderTask(BaseTask[ProposalEmbedderResult]): + """Task runner for embedding DAO proposals into a vector store.""" + + async def _validate_task_specific(self, context: JobContext) -> bool: + """Validate task-specific conditions.""" + if not config.api.openai_api_key: + logger.warning("OpenAI API key is not configured. Skipping embedding.") + return False + if not backend.vecs_client: + logger.warning("Vector client (vecs) not initialized. Skipping embedding.") + return False + # Basic check: Task runs if enabled and dependencies are met. + # More sophisticated check could compare DB count vs vector store count. + return True + + def _format_proposal_for_embedding(self, proposal: Proposal) -> str: + """Format proposal data into a string for embedding.""" + parts = [ + f"Title: {proposal.title or 'N/A'}", + f"Description: {proposal.description or 'N/A'}", + f"Type: {proposal.type.value if proposal.type else 'N/A'}", + ] + if proposal.action: + parts.append(f"Action: {proposal.action}") + if proposal.parameters: + parts.append(f"Parameters: {proposal.parameters}") + # Add more relevant fields as needed + return "\n".join(parts) + + async def _get_embeddings(self, texts: List[str]) -> Optional[List[List[float]]]: + """Get embeddings for a list of texts using OpenAI API.""" + try: + # Instantiate the embeddings model here + embeddings_model = OpenAIEmbeddings(model=EMBEDDING_MODEL) + # Use the embed_documents method + embeddings = await embeddings_model.aembed_documents(texts) + return embeddings + except Exception as e: + logger.error( + f"Error getting embeddings using Langchain OpenAI: {str(e)}", + exc_info=True, + ) + return None + + async def _execute_impl(self, context: JobContext) -> List[ProposalEmbedderResult]: + """Run the proposal embedding task.""" + logger.info("Starting proposal embedding task...") + errors: List[str] = [] + proposals_checked = 0 + proposals_embedded = 0 + + try: + # Ensure OpenAI client is configured (Langchain uses this implicitly or explicitly) + if not config.api.openai_api_key: + raise ValueError("OpenAI API key not found in configuration.") + openai.api_key = config.api.openai_api_key + + # Ensure the vector collection exists + try: + collection = backend.get_vector_collection(PROPOSAL_COLLECTION_NAME) + except Exception: + logger.info( + f"Collection '{PROPOSAL_COLLECTION_NAME}' not found, creating..." + ) + # Assuming default dimensions are okay, or fetch from config/model + collection = backend.create_vector_collection(PROPOSAL_COLLECTION_NAME) + # Optionally create an index for better query performance + backend.create_vector_index(PROPOSAL_COLLECTION_NAME) + + # Get all proposals from the database + all_proposals = backend.list_proposals() + proposals_checked = len(all_proposals) + logger.debug(f"Found {proposals_checked} proposals in the database.") + + if not all_proposals: + logger.info("No proposals found to embed.") + return [ + ProposalEmbedderResult( + success=True, + message="No proposals found.", + proposals_checked=0, + proposals_embedded=0, + ) + ] + + # Get IDs of proposals already in the vector store + db_proposal_ids = {str(p.id) for p in all_proposals} + existing_vector_ids = set() + try: + # Fetch existing records - assuming fetch returns tuples (id, vector, metadata) + # We only need the IDs, fetch minimal data. + # Note: Fetching potentially large lists of IDs might be inefficient + # depending on the backend/library implementation. + fetched_vectors = await backend.fetch_vectors( + collection_name=PROPOSAL_COLLECTION_NAME, ids=list(db_proposal_ids) + ) + existing_vector_ids = {record[0] for record in fetched_vectors} + logger.debug( + f"Found {len(existing_vector_ids)} existing proposal vectors out of {len(db_proposal_ids)} DB proposals." + ) + except Exception as e: + logger.warning( + f"Could not efficiently fetch existing vector IDs: {str(e)}. Proceeding may re-embed existing items." + ) + # Fallback or decide how to handle - for now, we'll proceed cautiously + # If fetch fails, we might end up embedding everything again if existing_vector_ids remains empty. + + # Identify proposals that need embedding + new_proposal_ids = db_proposal_ids - existing_vector_ids + if not new_proposal_ids: + logger.info("No new proposals found requiring embedding.") + return [ + ProposalEmbedderResult( + success=True, + message="No new proposals to embed.", + proposals_checked=proposals_checked, + proposals_embedded=0, + ) + ] + + logger.info(f"Identified {len(new_proposal_ids)} new proposals to embed.") + + # Filter proposals to embed only the new ones + proposals_to_embed = [ + p for p in all_proposals if str(p.id) in new_proposal_ids + ] + + # Prepare data for embedding only for new proposals + texts_to_embed = [] + metadata_list = [] + proposal_ids = [] + + for proposal in proposals_to_embed: + proposal_text = self._format_proposal_for_embedding(proposal) + texts_to_embed.append(proposal_text) + metadata_list.append( + { + "proposal_id": str(proposal.id), + "title": proposal.title or "", + "dao_id": str(proposal.dao_id), + "type": proposal.type.value if proposal.type else "", + } + ) + proposal_ids.append(str(proposal.id)) + + # Get embeddings using the updated method + logger.debug( + f"Requesting embeddings for {len(texts_to_embed)} new proposals." + ) + embeddings_list = await self._get_embeddings(texts_to_embed) + + if embeddings_list is None: + errors.append("Failed to retrieve embeddings.") + else: + logger.debug( + f"Successfully retrieved {len(embeddings_list)} embeddings." + ) + # Prepare records for upsert + records_to_upsert = [] + for i, proposal_id in enumerate(proposal_ids): + records_to_upsert.append( + ( + proposal_id, # Use proposal UUID as the vector ID + embeddings_list[i], # Use the retrieved embeddings + metadata_list[i], + ) + ) + + # Upsert into the vector collection + try: + collection.upsert(records=records_to_upsert) + proposals_embedded = len(records_to_upsert) + logger.info( + f"Successfully upserted {proposals_embedded} proposal embeddings." + ) + except Exception as e: + error_msg = f"Failed to upsert proposal embeddings: {str(e)}" + logger.error(error_msg, exc_info=True) + errors.append(error_msg) + + except Exception as e: + error_msg = f"Error during proposal embedding task: {str(e)}" + logger.error(error_msg, exc_info=True) + errors.append(error_msg) + + success = not errors + message = ( + f"Checked {proposals_checked} proposals, embedded/updated {proposals_embedded}." + if success + else f"Proposal embedding task failed. Errors: {'; '.join(errors)}" + ) + + return [ + ProposalEmbedderResult( + success=success, + message=message, + proposals_checked=proposals_checked, + proposals_embedded=proposals_embedded, + errors=errors, + ) + ] + + +# Instantiate the task for use in the registry +proposal_embedder = ProposalEmbedderTask() From 5e0f4875f46000de5975a817270ad2b02a9b7d7f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 5 May 2025 13:21:10 +0000 Subject: [PATCH 012/219] Bump the dev-dependencies group with 3 updates Bumps the dev-dependencies group with 3 updates: [langchain](https://github.com/langchain-ai/langchain), [langchain-openai](https://github.com/langchain-ai/langchain) and [openai](https://github.com/openai/openai-python). Updates `langchain` from 0.3.24 to 0.3.25 - [Release notes](https://github.com/langchain-ai/langchain/releases) - [Commits](https://github.com/langchain-ai/langchain/compare/langchain==0.3.24...langchain==0.3.25) Updates `langchain-openai` from 0.3.15 to 0.3.16 - [Release notes](https://github.com/langchain-ai/langchain/releases) - [Commits](https://github.com/langchain-ai/langchain/compare/langchain-openai==0.3.15...langchain-openai==0.3.16) Updates `openai` from 1.76.2 to 1.77.0 - [Release notes](https://github.com/openai/openai-python/releases) - [Changelog](https://github.com/openai/openai-python/blob/main/CHANGELOG.md) - [Commits](https://github.com/openai/openai-python/compare/v1.76.2...v1.77.0) --- updated-dependencies: - dependency-name: langchain dependency-version: 0.3.25 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: langchain-openai dependency-version: 0.3.16 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: openai dependency-version: 1.77.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies ... Signed-off-by: dependabot[bot] --- requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 22c70839..14516603 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,12 @@ APScheduler==3.11.0 cachetools==5.5.2 fastapi==0.115.12 -langchain==0.3.24 +langchain==0.3.25 langchain_core>=0.3.56,<1.0.0 -langchain_openai==0.3.15 +langchain_openai==0.3.16 langchain_text_splitters==0.3.8 langgraph==0.4.1 -openai==1.76.2 +openai==1.77.0 pgvector==0.3.6 psycopg2==2.9.10 pydantic==2.11.4 From ea691c72c7ce00b071e57894e665f60ebc4eb9ba Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 18:43:50 -0700 Subject: [PATCH 013/219] update prompt --- services/workflows/proposal_evaluation.py | 240 ++++++++++++---------- 1 file changed, 129 insertions(+), 111 deletions(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 19d868ee..6bd7f104 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -2,11 +2,11 @@ import asyncio import base64 -from typing import Any, Dict, List, Optional, TypedDict, Union +from typing import Any, Dict, List, Optional, TypedDict import httpx from langchain.prompts import PromptTemplate -from langchain_core.messages import HumanMessage, SystemMessage +from langchain_core.messages import HumanMessage from langchain_openai import ChatOpenAI from langgraph.graph import END, Graph, StateGraph from pydantic import BaseModel, Field @@ -75,15 +75,12 @@ class EvaluationState(TypedDict): web_search_results: Optional[List[Dict]] treasury_balance: Optional[float] contract_source: Optional[str] - plan: Optional[str] proposal_images: Optional[List[Dict]] # Store encoded images for LLM # Token usage tracking per step - planning_token_usage: Optional[Dict] web_search_token_usage: Optional[Dict] evaluation_token_usage: Optional[Dict] # Model info for cost calculation evaluation_model_info: Optional[Dict] - planning_model_info: Optional[Dict] web_search_model_info: Optional[Dict] @@ -131,7 +128,7 @@ def __init__( self.collection_names = collection_names or [ "knowledge_collection", - "dao_collection", + "proposals", ] self.required_fields = ["proposal_id", "proposal_data"] self.logger.debug( @@ -152,14 +149,22 @@ def _create_prompt(self) -> PromptTemplate: "web_search_results", ], template=""" - You are a DAO proposal evaluator. Your task is to analyze the proposal and determine whether to vote FOR or AGAINST it. + You are a skeptical and hard-to-convince DAO proposal evaluator. Your primary goal is rigorous analysis. Your task is to analyze the proposal and determine whether to vote FOR or AGAINST it based on verifiable evidence and alignment with DAO principles. + # 1. AGENT-SPECIFIC INSTRUCTIONS (HIGHEST PRIORITY) {agent_prompts} - If no agent-specific instructions are provided, explicitly state: "No agent-specific instructions provided." - You MUST explain how each instruction influenced your decision. + If no agent-specific instructions are provided, apply these DEFAULT instructions: + - Approve ONLY if the proposal provides verifiable evidence (URL, transaction hash, IPFS CID for screenshots/documents) for its claims OR if it's a purely logistical matter (e.g., scheduling reminder). + - All other proposals lacking verifiable evidence for claims should be REJECTED (vote AGAINST) with LOW confidence (0.3-0.4 band). + - Reject proposals making promises about future DAO actions or events unless they provide on-chain evidence of a corresponding approved governance decision or multisig transaction proposal. + - CRITICAL: You MUST evaluate all proposal content (text, images, links) as ONE COHESIVE UNIT. If ANY image or attachment doesn't align with or support the proposal, contains misleading information, or is inappropriate, you MUST reject the entire proposal. + You MUST explain how each specific instruction (agent-provided or default) influenced your decision, especially if it led to rejection. + + + # 2. PROPOSAL INFORMATION {proposal_data} @@ -171,6 +176,16 @@ def _create_prompt(self) -> PromptTemplate: - Quality and authenticity of the images - Potential security or privacy concerns in the images + IMPORTANT: Images and text must form a cohesive whole. If any image: + - Doesn't clearly support or relate to the proposal text + - Contains misleading or contradictory information + - Is of poor quality making verification impossible + - Contains inappropriate content + - Appears manipulated or false + Then you MUST reject the entire proposal, regardless of the quality of the text portion. + + + # 3. DAO CONTEXT {dao_info} @@ -181,10 +196,14 @@ def _create_prompt(self) -> PromptTemplate: Core Values: Curiosity, Truth Maximizing, Humanity's Best Interests, Transparency, Resilience, Collaboration Mission: Elevate human potential through Autonomous Intelligence on Bitcoin Guardrails: Decentralized Governance, Smart Contract accountability + + # 6. CONTRACT SOURCE (for core proposals) {contract_source} + + # 7. EVALUATION CRITERIA For Core Proposals: - Security implications @@ -197,15 +216,22 @@ def _create_prompt(self) -> PromptTemplate: - Resource implications - Security considerations - Alignment with DAO goals + - **Evidence Verification:** Claims MUST be backed by verifiable sources as per instructions. + - **Future Commitments:** Promises about future actions require on-chain proof. + - **Content Cohesion:** All components (text, images, links) must form a cohesive, aligned whole that supports the proposal's intent. A single misaligned or problematic image is grounds for rejection. + + # 8. CONFIDENCE SCORING RUBRIC You MUST choose one of these confidence bands: - - 0.0-0.2: Extremely low confidence (major red flags or insufficient information) - - 0.3-0.4: Low confidence (significant concerns or unclear implications) - - 0.5-0.6: Moderate confidence (some concerns but manageable) - - 0.7-0.8: High confidence (minor concerns if any) - - 0.9-1.0: Very high confidence (clear positive alignment) - + - **0.9-1.0 (Very High Confidence - Strong Approve):** All criteria met excellently. Clear alignment with DAO mission/values, strong verifiable evidence provided for all claims, minimal/no security risks identified, significant positive impact expected, and adheres strictly to all instructions (including future promise verification). All images directly support the proposal with high quality and authenticity. + - **0.7-0.8 (High Confidence - Approve):** Generally meets criteria well. Good alignment, sufficient verifiable evidence provided, risks identified but deemed manageable/acceptable, likely positive impact. Passes core checks (evidence, future promises). Minor reservations might exist but don't fundamentally undermine the proposal. Images support the proposal appropriately. + - **0.5-0.6 (Moderate Confidence - Borderline/Weak Approve):** Meets minimum criteria but with notable reservations. Alignment is present but perhaps weak or indirect, evidence meets minimum verification but might be incomplete or raise minor questions, moderate risks identified requiring monitoring, impact is unclear or modest. *Could apply to simple logistical proposals with no major claims.* Any included images are relevant though may not provide strong support. + - **0.3-0.4 (Low Confidence - Reject):** Fails one or more key criteria. Significant misalignment, **lacks required verifiable evidence** for claims (triggering default rejection), unacceptable risks identified, potential negative impact, or **contains unsubstantiated future promises**. Images may be missing where needed, irrelevant, or only weakly supportive. *This is the default band for rejections due to lack of evidence or unproven future commitments.* + - **0.0-0.2 (Extremely Low Confidence - Strong Reject):** Fails multiple critical criteria. Clear violation of DAO principles/guardrails, major security flaws identified, evidence is demonstrably false or misleading, significant negative impact is highly likely or certain. Any included images may be misleading, manipulated, inappropriate, or contradictory to the proposal. + + + # 9. QUALITY STANDARDS Your evaluation must uphold clarity, reasoning, and respect for the DAO's voice: • Be clear and specific — avoid vagueness or filler @@ -217,7 +243,11 @@ def _create_prompt(self) -> PromptTemplate: • Use terms accurately — don't fake precision • Keep structure clean and easy to follow • Include analysis of any provided images and their implications + • Specifically address image-text cohesion in your analysis + • If rejecting, CLEARLY state the specific reason(s) based on the instructions or evaluation criteria (e.g., "Rejected due to lack of verifiable source for claim X", "Rejected because future promise lacks on-chain evidence", "Rejected because included image contradicts proposal text"). + + # 10. VECTOR CONTEXT {vector_context} @@ -226,20 +256,26 @@ def _create_prompt(self) -> PromptTemplate: # 12. WEB SEARCH RESULTS {web_search_results} + + # OUTPUT FORMAT Provide your evaluation in this exact JSON format: + ```json {{ "approve": boolean, // true for FOR, false for AGAINST "confidence_score": float, // MUST be from the confidence bands above "reasoning": string // Brief, professional explanation addressing: - // 1. How agent instructions were applied - // 2. How DAO context influenced decision - // 3. How AIBTC Charter alignment was considered - // 4. Key factors in confidence score selection - // 5. Analysis of any provided images - // Must be clear, precise, and well-structured + // 1. How agent/default instructions were applied (state which). + // 2. Specific reason for rejection if applicable, referencing the unmet criteria or instruction. + // 3. How DAO context influenced decision. + // 4. How AIBTC Charter alignment was considered. + // 5. Key factors in confidence score selection. + // 6. Analysis of any provided images and their cohesion with proposal text. + // Must be clear, precise, and well-structured. }} + ``` + """, ) @@ -502,35 +538,86 @@ async def format_evaluation_prompt(state: EvaluationState) -> EvaluationState: # Format web search results web_search_content = "No relevant web search results found." if web_search_results: - web_search_content = "\n\n".join( - [ - f"Web Result {i+1}:\n{res.get('page_content', '')}\nSource: {res.get('metadata', {}).get('source_urls', [{}])[0].get('url', 'Unknown')}" - for i, res in enumerate(web_search_results) - ] - ) + # Create structured XML format for each web search result + web_search_items = [] + for i, res in enumerate(web_search_results): + source_url = ( + res.get("metadata", {}) + .get("source_urls", [{}])[0] + .get("url", "Unknown") + ) + web_search_items.append( + f"\n{i+1}\n{res.get('page_content', '')}\n{source_url}\n" + ) + web_search_content = "\n".join(web_search_items) # Format vector context vector_context = "No additional context available from vector store." if vector_results: - vector_context = "\n\n".join( + # Create structured XML format for each vector result + vector_items = [] + for i, doc in enumerate(vector_results): + vector_items.append( + f"\n{i+1}\n{doc.page_content}\n" + ) + vector_context = "\n".join(vector_items) + + # Format recent tweets + tweets_content = "No recent DAO tweets found." + if recent_tweets: + # Create structured XML format for each tweet + tweet_items = [] + for i, tweet in enumerate(recent_tweets): + tweet_items.append( + f"\n{i+1}\n{tweet['created_at']}\n{tweet['message']}\n" + ) + tweets_content = "\n".join(tweet_items) + + # Convert JSON objects to formatted text + # Format proposal_data + proposal_data_str = "No proposal data available." + if proposal_data: + proposal_data_str = "\n".join( [ - f"Related Context {i+1}:\n{doc.page_content}" - for i, doc in enumerate(vector_results) + f"Proposal ID: {proposal_data.get('proposal_id', 'Unknown')}", + f"Type: {proposal_data.get('type', 'Unknown')}", + f"Action: {proposal_data.get('action', 'Unknown')}", + f"Parameters: {proposal_data.get('parameters', 'None')}", + f"Creator: {proposal_data.get('creator', 'Unknown')}", + f"Contract Principal: {proposal_data.get('contract_principal', 'Unknown')}", + f"Start Block: {proposal_data.get('start_block', 'Unknown')}", + f"End Block: {proposal_data.get('end_block', 'Unknown')}", + f"Created at Block: {proposal_data.get('created_at_block', 'Unknown')}", + f"Liquid Tokens: {proposal_data.get('liquid_tokens', 'Unknown')}", ] ) - # Format recent tweets - tweets_content = "\n".join( - [ - f"Tweet {i+1} ({tweet['created_at']}): {tweet['message']}" - for i, tweet in enumerate(recent_tweets) - ] - ) + # Add proposal contract info if it exists + if proposal_data.get("proposal_contract"): + proposal_data_str += f"\nProposal Contract: {proposal_data.get('proposal_contract')}" + + # Format dao_info + dao_info_str = "No DAO information available." + if dao_info: + dao_info_str = "\n".join( + [ + f"DAO Name: {dao_info.get('name', 'Unknown')}", + f"DAO Mission: {dao_info.get('mission', 'Unknown')}", + f"DAO Description: {dao_info.get('description', 'Unknown')}", + ] + ) + + # Format treasury_balance + treasury_balance_str = "Treasury balance information not available." + if treasury_balance is not None: + treasury_balance_str = ( + f"Current DAO Treasury Balance: {treasury_balance} STX" + ) formatted_prompt = prompt.format( - proposal_data=proposal_data, - dao_info=dao_info, - treasury_balance=treasury_balance, + proposal_data=proposal_data_str, + dao_info=dao_info_str, + treasury_balance=treasury_balance_str, contract_source=contract_source, agent_prompts=agent_prompts_str, vector_context=vector_context, @@ -734,77 +821,18 @@ async def skip_voting(state: EvaluationState) -> EvaluationState: } return state - # --- Planning Node --- # - async def plan_evaluation(state: EvaluationState) -> EvaluationState: - """Generate a plan for evaluating the proposal using the PlanningCapability mixin.""" - try: - self.logger.debug( - "Generating evaluation plan using PlanningCapability..." - ) - - # Construct initial context for planning - initial_context = ( - f"Proposal ID: {state['proposal_id']}\n" - f"DAO ID: {state.get('dao_id')}\n" - f"Agent ID: {state.get('agent_id')}\n" - f"Auto-Vote Enabled: {state.get('auto_vote')}" - ) - - # Create planning query - planning_query = ( - f"Create a detailed plan for evaluating the following DAO proposal:\n\n" - f"{initial_context}\n\n" - f"The plan should cover:\n" - f"1. Data gathering (proposal details, DAO context, treasury info)\n" - f"2. Analysis approach (including use of vector search and web search)\n" - f"3. Evaluation criteria and decision making process\n" - f"4. Voting execution strategy (if auto-vote is enabled)" - ) - - # Use the mixin's create_plan method - plan, planning_token_usage = await self.create_plan( - query=planning_query, context_docs=state.get("vector_results", []) - ) - - state["plan"] = plan - state["planning_token_usage"] = planning_token_usage - # Store planning model info - state["planning_model_info"] = { - "name": self.planning_llm.model_name, - "temperature": self.planning_llm.temperature, - } - - self.logger.info("Evaluation plan generated using PlanningCapability.") - self.logger.debug(f"Generated Plan:\n{plan}") - return state - - except Exception as e: - self.logger.error(f"Error generating plan: {str(e)}", exc_info=True) - state["plan"] = f"Error generating plan: {str(e)}" - state["planning_token_usage"] = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - state["planning_model_info"] = {"name": "unknown", "temperature": None} - return state - # Create the graph workflow = StateGraph(EvaluationState) # Add nodes - workflow.add_node("plan_evaluation", plan_evaluation) # New planning node workflow.add_node("fetch_context", fetch_context) workflow.add_node("format_prompt", format_evaluation_prompt) - workflow.add_node( - "evaluate", call_evaluation_llm - ) # Renamed from evaluate_proposal + workflow.add_node("evaluate", call_evaluation_llm) workflow.add_node("vote", vote_on_proposal) workflow.add_node("skip_vote", skip_voting) # Set up the conditional branching - workflow.set_entry_point("plan_evaluation") # Start with planning - workflow.add_edge("plan_evaluation", "fetch_context") # Plan -> Fetch + workflow.set_entry_point("fetch_context") # Start with fetching context workflow.add_edge("fetch_context", "format_prompt") workflow.add_edge("format_prompt", "evaluate") workflow.add_conditional_edges( @@ -965,12 +993,9 @@ async def evaluate_and_vote_on_proposal( "web_search_results": None, "token_usage": None, "model_info": None, - "plan": None, - "planning_token_usage": None, "web_search_token_usage": None, "evaluation_token_usage": None, "evaluation_model_info": None, - "planning_model_info": None, "web_search_model_info": None, } @@ -1022,11 +1047,9 @@ async def evaluate_and_vote_on_proposal( "recent_tweets": result["recent_tweets"], "web_search_results": result["web_search_results"], "treasury_balance": result.get("treasury_balance"), - "planning_token_usage": result.get("planning_token_usage"), "web_search_token_usage": result.get("web_search_token_usage"), "evaluation_token_usage": result.get("evaluation_token_usage"), "evaluation_model_info": result.get("evaluation_model_info"), - "planning_model_info": result.get("planning_model_info"), "web_search_model_info": result.get("web_search_model_info"), } @@ -1036,11 +1059,6 @@ async def evaluate_and_vote_on_proposal( total_overall_cost = 0.0 steps = [ - ( - "planning", - result.get("planning_token_usage"), - result.get("planning_model_info"), - ), ( "web_search", result.get("web_search_token_usage"), From d65ed7db1cc22959d69e39a38e4172a796226718 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 18:52:49 -0700 Subject: [PATCH 014/219] update prompt some more --- services/workflows/proposal_evaluation.py | 114 ++++++++++++++-------- 1 file changed, 71 insertions(+), 43 deletions(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 6bd7f104..aff58f67 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -152,22 +152,66 @@ def _create_prompt(self) -> PromptTemplate: You are a skeptical and hard-to-convince DAO proposal evaluator. Your primary goal is rigorous analysis. Your task is to analyze the proposal and determine whether to vote FOR or AGAINST it based on verifiable evidence and alignment with DAO principles. - # 1. AGENT-SPECIFIC INSTRUCTIONS (HIGHEST PRIORITY) + {agent_prompts} - + + If no agent-specific instructions are provided, apply these DEFAULT instructions: - Approve ONLY if the proposal provides verifiable evidence (URL, transaction hash, IPFS CID for screenshots/documents) for its claims OR if it's a purely logistical matter (e.g., scheduling reminder). - All other proposals lacking verifiable evidence for claims should be REJECTED (vote AGAINST) with LOW confidence (0.3-0.4 band). - Reject proposals making promises about future DAO actions or events unless they provide on-chain evidence of a corresponding approved governance decision or multisig transaction proposal. - CRITICAL: You MUST evaluate all proposal content (text, images, links) as ONE COHESIVE UNIT. If ANY image or attachment doesn't align with or support the proposal, contains misleading information, or is inappropriate, you MUST reject the entire proposal. - + You MUST explain how each specific instruction (agent-provided or default) influenced your decision, especially if it led to rejection. + + + + Verify smart contract security measures + Check for potential vulnerabilities in contract logic + Assess potential attack vectors + Evaluate access control mechanisms + + + Analyze alignment with DAO mission statement + Verify compatibility with existing DAO infrastructure + Check adherence to DAO's established governance principles + + + Evaluate potential risks vs. rewards + Assess short-term and long-term implications + Consider effects on DAO reputation and stakeholders + + + + + Validate all proposed parameters against acceptable ranges + Verify parameter compatibility with existing systems + Check for realistic implementation timelines + + + Assess treasury impact and funding requirements + Evaluate operational resource needs + Consider opportunity costs against other initiatives + + + Identify potential security implications of the action + Check for unintended system vulnerabilities + + + **Evidence Verification:** All claims MUST be backed by verifiable sources (URLs, transaction hashes, IPFS CIDs) + **Future Commitments:** Any promises about future actions require on-chain proof of approved governance decisions + **Content Cohesion:** All components (text, images, links) must form a cohesive, aligned whole supporting the proposal's intent + + + + - # 2. PROPOSAL INFORMATION + {proposal_data} - + + Note: If any images are provided with the proposal, they will be shown after this prompt. You should analyze any provided images in the context of the proposal and include your observations in your evaluation. Consider aspects such as: @@ -183,56 +227,52 @@ def _create_prompt(self) -> PromptTemplate: - Contains inappropriate content - Appears manipulated or false Then you MUST reject the entire proposal, regardless of the quality of the text portion. + + + + {vector_context} + + + {recent_tweets} + + + {web_search_results} + + - # 3. DAO CONTEXT + {dao_info} - - # 4. TREASURY INFORMATION + + {treasury_balance} - - # 5. AIBTC CHARTER + + Core Values: Curiosity, Truth Maximizing, Humanity's Best Interests, Transparency, Resilience, Collaboration Mission: Elevate human potential through Autonomous Intelligence on Bitcoin Guardrails: Decentralized Governance, Smart Contract accountability + - # 6. CONTRACT SOURCE (for core proposals) + {contract_source} + - - # 7. EVALUATION CRITERIA - For Core Proposals: - - Security implications - - Mission alignment - - Vulnerability assessment - - Impact analysis - - For Action Proposals: - - Parameter validation - - Resource implications - - Security considerations - - Alignment with DAO goals - - **Evidence Verification:** Claims MUST be backed by verifiable sources as per instructions. - - **Future Commitments:** Promises about future actions require on-chain proof. - - **Content Cohesion:** All components (text, images, links) must form a cohesive, aligned whole that supports the proposal's intent. A single misaligned or problematic image is grounds for rejection. - - - # 8. CONFIDENCE SCORING RUBRIC + You MUST choose one of these confidence bands: - **0.9-1.0 (Very High Confidence - Strong Approve):** All criteria met excellently. Clear alignment with DAO mission/values, strong verifiable evidence provided for all claims, minimal/no security risks identified, significant positive impact expected, and adheres strictly to all instructions (including future promise verification). All images directly support the proposal with high quality and authenticity. - **0.7-0.8 (High Confidence - Approve):** Generally meets criteria well. Good alignment, sufficient verifiable evidence provided, risks identified but deemed manageable/acceptable, likely positive impact. Passes core checks (evidence, future promises). Minor reservations might exist but don't fundamentally undermine the proposal. Images support the proposal appropriately. - **0.5-0.6 (Moderate Confidence - Borderline/Weak Approve):** Meets minimum criteria but with notable reservations. Alignment is present but perhaps weak or indirect, evidence meets minimum verification but might be incomplete or raise minor questions, moderate risks identified requiring monitoring, impact is unclear or modest. *Could apply to simple logistical proposals with no major claims.* Any included images are relevant though may not provide strong support. - **0.3-0.4 (Low Confidence - Reject):** Fails one or more key criteria. Significant misalignment, **lacks required verifiable evidence** for claims (triggering default rejection), unacceptable risks identified, potential negative impact, or **contains unsubstantiated future promises**. Images may be missing where needed, irrelevant, or only weakly supportive. *This is the default band for rejections due to lack of evidence or unproven future commitments.* - **0.0-0.2 (Extremely Low Confidence - Strong Reject):** Fails multiple critical criteria. Clear violation of DAO principles/guardrails, major security flaws identified, evidence is demonstrably false or misleading, significant negative impact is highly likely or certain. Any included images may be misleading, manipulated, inappropriate, or contradictory to the proposal. + - # 9. QUALITY STANDARDS Your evaluation must uphold clarity, reasoning, and respect for the DAO's voice: • Be clear and specific — avoid vagueness or filler • Use a consistent tone, but reflect the DAO's personality if known @@ -247,19 +287,7 @@ def _create_prompt(self) -> PromptTemplate: • If rejecting, CLEARLY state the specific reason(s) based on the instructions or evaluation criteria (e.g., "Rejected due to lack of verifiable source for claim X", "Rejected because future promise lacks on-chain evidence", "Rejected because included image contradicts proposal text"). - - # 10. VECTOR CONTEXT - {vector_context} - - # 11. RECENT DAO TWEETS - {recent_tweets} - - # 12. WEB SEARCH RESULTS - {web_search_results} - - - # OUTPUT FORMAT Provide your evaluation in this exact JSON format: ```json {{ From 3696a7a6efda9dbd1427958e8fcc796d6c32007c Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 18:53:37 -0700 Subject: [PATCH 015/219] update prompt some more --- services/workflows/proposal_evaluation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index aff58f67..2a5a4a27 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -152,7 +152,7 @@ def _create_prompt(self) -> PromptTemplate: You are a skeptical and hard-to-convince DAO proposal evaluator. Your primary goal is rigorous analysis. Your task is to analyze the proposal and determine whether to vote FOR or AGAINST it based on verifiable evidence and alignment with DAO principles. - + {agent_prompts} From 19bb7e128827785da982b2fd30f7d83125094c71 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 19:18:19 -0700 Subject: [PATCH 016/219] fix parameters when they come in --- .../chainhook/handlers/action_proposal_handler.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index 9f39f332..a6de9103 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -13,6 +13,7 @@ BaseProposalHandler, ) from services.webhooks.chainhook.models import Event, TransactionWithReceipt +from services.workflows.utils import decode_hex_parameters class ActionProposalHandler(BaseProposalHandler): @@ -166,6 +167,14 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: ) if not existing_proposals: + # Decode parameters if they're hex encoded + decoded_parameters = decode_hex_parameters(proposal_info["parameters"]) + parameters = ( + decoded_parameters + if decoded_parameters is not None + else proposal_info["parameters"] + ) + # Create a new proposal record in the database proposal_title = f"Action Proposal #{proposal_info['proposal_id']}" proposal = backend.create_proposal( @@ -186,7 +195,7 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: end_block=proposal_info["end_block"], start_block=proposal_info["start_block"], liquid_tokens=proposal_info["liquid_tokens"], - parameters=proposal_info["parameters"], + parameters=parameters, bond=proposal_info["bond"], ) ) From 6812e235696a71cd928737662b94b73de3d07ac6 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 19:19:02 -0700 Subject: [PATCH 017/219] fix parameters when they come in --- services/workflows/proposal_evaluation.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 2a5a4a27..72499675 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -30,7 +30,6 @@ from services.workflows.planning_mixin import PlanningCapability from services.workflows.utils import ( calculate_token_cost, - decode_hex_parameters, extract_image_urls, ) from services.workflows.vector_mixin import VectorRetrievalCapability @@ -325,8 +324,7 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: raise ValueError(f"Proposal {proposal_id} not found") # Decode parameters if they exist - decoded_parameters = decode_hex_parameters(proposal_data.parameters) - image_urls = extract_image_urls(decoded_parameters) + image_urls = extract_image_urls(proposal_data.parameters) # Process and encode images proposal_images = [] From e3f8032e341e7b0979deb78c5b33eab7ed226c67 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 19:26:37 -0700 Subject: [PATCH 018/219] fix parameters when they come in --- .../handlers/action_proposal_handler.py | 99 ++++++++++++------- 1 file changed, 65 insertions(+), 34 deletions(-) diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index a6de9103..ce4bcd3e 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -117,6 +117,28 @@ def _get_proposal_info_from_events(self, events: list[Event]) -> Optional[Dict]: self.logger.warning("Could not find proposal information in transaction events") return None + def _sanitize_string(self, input_string: Optional[str]) -> Optional[str]: + """Sanitize string by removing null bytes and other invalid characters. + + Args: + input_string: The string to sanitize + + Returns: + A sanitized string or None if input was None + """ + if input_string is None: + return None + + # Replace null bytes and other control characters + sanitized = "" + for char in input_string: + if ( + ord(char) >= 32 or char in "\n\r\t" + ): # Keep printable chars and some whitespace + sanitized += char + + return sanitized + async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: """Handle action proposal transactions. @@ -167,41 +189,50 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: ) if not existing_proposals: - # Decode parameters if they're hex encoded - decoded_parameters = decode_hex_parameters(proposal_info["parameters"]) - parameters = ( - decoded_parameters - if decoded_parameters is not None - else proposal_info["parameters"] - ) - - # Create a new proposal record in the database - proposal_title = f"Action Proposal #{proposal_info['proposal_id']}" - proposal = backend.create_proposal( - ProposalCreate( - dao_id=dao_data["id"], - title=proposal_title, - description=f"Action proposal {proposal_info['proposal_id']} for {dao_data['name']}", - contract_principal=contract_identifier, - tx_id=tx_id, - proposal_id=proposal_info["proposal_id"], - status=ContractStatus.DEPLOYED, # Since it's already on-chain - type=ProposalType.ACTION, - # Add fields from payload - action=proposal_info["action"], - caller=proposal_info["caller"], - creator=proposal_info["creator"], - created_at_block=proposal_info["created_at_block"], - end_block=proposal_info["end_block"], - start_block=proposal_info["start_block"], - liquid_tokens=proposal_info["liquid_tokens"], - parameters=parameters, - bond=proposal_info["bond"], + try: + # First try to decode parameters as hex + decoded_parameters = decode_hex_parameters(proposal_info["parameters"]) + + # Sanitize the decoded parameters to remove null bytes and invalid characters + if decoded_parameters is not None: + parameters = self._sanitize_string(decoded_parameters) + self.logger.debug( + f"Decoded and sanitized parameters: {parameters[:100]}..." + ) + else: + parameters = proposal_info["parameters"] + self.logger.debug("Using original parameters (hex decoding failed)") + + # Create a new proposal record in the database + proposal_title = f"Action Proposal #{proposal_info['proposal_id']}" + proposal = backend.create_proposal( + ProposalCreate( + dao_id=dao_data["id"], + title=proposal_title, + description=f"Action proposal {proposal_info['proposal_id']} for {dao_data['name']}", + contract_principal=contract_identifier, + tx_id=tx_id, + proposal_id=proposal_info["proposal_id"], + status=ContractStatus.DEPLOYED, # Since it's already on-chain + type=ProposalType.ACTION, + # Add fields from payload + action=proposal_info["action"], + caller=proposal_info["caller"], + creator=proposal_info["creator"], + created_at_block=proposal_info["created_at_block"], + end_block=proposal_info["end_block"], + start_block=proposal_info["start_block"], + liquid_tokens=proposal_info["liquid_tokens"], + parameters=parameters, + bond=proposal_info["bond"], + ) ) - ) - self.logger.info( - f"Created new action proposal record in database: {proposal.id}" - ) + self.logger.info( + f"Created new action proposal record in database: {proposal.id}" + ) + except Exception as e: + self.logger.error(f"Error creating proposal in database: {str(e)}") + raise else: self.logger.info( f"Action proposal already exists in database: {existing_proposals[0].id}" From b88c381100e3d6bee8cdd457a0d2c346dfa2a2d2 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 19:37:45 -0700 Subject: [PATCH 019/219] fix parameters --- {services/workflows => lib}/utils.py | 11 ++++++++--- .../chainhook/handlers/action_proposal_handler.py | 2 +- services/workflows/proposal_evaluation.py | 13 +++++++++---- 3 files changed, 18 insertions(+), 8 deletions(-) rename {services/workflows => lib}/utils.py (91%) diff --git a/services/workflows/utils.py b/lib/utils.py similarity index 91% rename from services/workflows/utils.py rename to lib/utils.py index ea4908ea..bccee7ff 100644 --- a/services/workflows/utils.py +++ b/lib/utils.py @@ -41,9 +41,14 @@ def decode_hex_parameters(hex_string: Optional[str]) -> Optional[str]: hex_string = hex_string[2:] # Remove "0x" prefix try: decoded_bytes = binascii.unhexlify(hex_string) - decoded_string = decoded_bytes.decode( - "utf-8", errors="ignore" - ) # Decode as UTF-8 + + # Handle Clarity hex format which often includes length prefixes + # First 5 bytes typically contain: 4-byte length + 1-byte type indicator + if len(decoded_bytes) > 5 and decoded_bytes[0] == 0x0D: # Length byte check + # Skip the 4-byte length prefix and any potential type indicator + decoded_bytes = decoded_bytes[5:] + + decoded_string = decoded_bytes.decode("utf-8", errors="ignore") logger.debug(f"Successfully decoded hex string: {hex_string[:20]}...") return decoded_string except (binascii.Error, UnicodeDecodeError) as e: diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index ce4bcd3e..78cb3d61 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -9,11 +9,11 @@ ProposalFilter, ProposalType, ) +from lib.utils import decode_hex_parameters from services.webhooks.chainhook.handlers.base_proposal_handler import ( BaseProposalHandler, ) from services.webhooks.chainhook.models import Event, TransactionWithReceipt -from services.workflows.utils import decode_hex_parameters class ActionProposalHandler(BaseProposalHandler): diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 72499675..f9b80f2d 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -23,15 +23,16 @@ ) from lib.hiro import HiroApi from lib.logger import configure_logger +from lib.utils import ( + calculate_token_cost, + decode_hex_parameters, + extract_image_urls, +) from services.workflows.base import ( BaseWorkflow, ) from services.workflows.chat import ChatService, StreamingCallbackHandler from services.workflows.planning_mixin import PlanningCapability -from services.workflows.utils import ( - calculate_token_cost, - extract_image_urls, -) from services.workflows.vector_mixin import VectorRetrievalCapability from services.workflows.web_search_mixin import WebSearchCapability from tools.dao_ext_action_proposals import VoteOnActionProposalTool @@ -324,6 +325,10 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: raise ValueError(f"Proposal {proposal_id} not found") # Decode parameters if they exist + decoded_parameters = None + if hasattr(proposal_data, "parameters") and proposal_data.parameters: + decoded_parameters = decode_hex_parameters(proposal_data.parameters) + image_urls = extract_image_urls(proposal_data.parameters) # Process and encode images From a34c6403599392d7d9aee2e56f96510a774bf366 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 19:41:06 -0700 Subject: [PATCH 020/219] fix parameters --- services/workflows/proposal_evaluation.py | 28 +++++++++++++++-------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index f9b80f2d..1e013aec 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -381,7 +381,11 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: # Convert proposal data to dictionary proposal_dict = { "proposal_id": proposal_data.proposal_id, - "parameters": decoded_parameters or proposal_data.parameters, + "parameters": ( + decoded_parameters + if decoded_parameters is not None + else proposal_data.parameters + ), "action": proposal_data.action, "caller": proposal_data.caller, "contract_principal": proposal_data.contract_principal, @@ -1065,18 +1069,22 @@ async def evaluate_and_vote_on_proposal( final_result = { "success": True, "evaluation": { - "approve": result["approve"], - "confidence_score": result["confidence_score"], - "reasoning": result["reasoning"], + "approve": result.get("approve", False), + "confidence_score": result.get("confidence_score", 0.0), + "reasoning": result.get( + "reasoning", "Evaluation failed or not available" + ), }, - "vote_result": result["vote_result"], + "vote_result": result.get("vote_result"), "auto_voted": auto_vote - and result["confidence_score"] >= confidence_threshold, + and result.get("confidence_score", 0.0) >= confidence_threshold, "tx_id": tx_id, - "formatted_prompt": result["formatted_prompt"], - "vector_results": result["vector_results"], - "recent_tweets": result["recent_tweets"], - "web_search_results": result["web_search_results"], + "formatted_prompt": result.get( + "formatted_prompt", "Formatted prompt not available" + ), + "vector_results": result.get("vector_results"), + "recent_tweets": result.get("recent_tweets"), + "web_search_results": result.get("web_search_results"), "treasury_balance": result.get("treasury_balance"), "web_search_token_usage": result.get("web_search_token_usage"), "evaluation_token_usage": result.get("evaluation_token_usage"), From b0fa794759706decc8d6aea840a7768afe4bfea1 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Mon, 5 May 2025 19:59:28 -0700 Subject: [PATCH 021/219] bump agent tools and remove decode --- agent-tools-ts | 2 +- services/workflows/proposal_evaluation.py | 12 +----------- 2 files changed, 2 insertions(+), 12 deletions(-) diff --git a/agent-tools-ts b/agent-tools-ts index ea13e086..083fb29c 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit ea13e0864d5755b29fd81990fc39b1b8a57b8ca4 +Subproject commit 083fb29c85e3807bb2f760af88bcade09faa7e1c diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 1e013aec..22920939 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -25,7 +25,6 @@ from lib.logger import configure_logger from lib.utils import ( calculate_token_cost, - decode_hex_parameters, extract_image_urls, ) from services.workflows.base import ( @@ -324,11 +323,6 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: if not proposal_data: raise ValueError(f"Proposal {proposal_id} not found") - # Decode parameters if they exist - decoded_parameters = None - if hasattr(proposal_data, "parameters") and proposal_data.parameters: - decoded_parameters = decode_hex_parameters(proposal_data.parameters) - image_urls = extract_image_urls(proposal_data.parameters) # Process and encode images @@ -381,11 +375,7 @@ async def fetch_context(state: EvaluationState) -> EvaluationState: # Convert proposal data to dictionary proposal_dict = { "proposal_id": proposal_data.proposal_id, - "parameters": ( - decoded_parameters - if decoded_parameters is not None - else proposal_data.parameters - ), + "parameters": proposal_data.parameters, "action": proposal_data.action, "caller": proposal_data.caller, "contract_principal": proposal_data.contract_principal, From beb022391f0e89cb50cd72b5302e52c44fd4bc2d Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Wed, 7 May 2025 22:05:16 -0700 Subject: [PATCH 022/219] cleanup --- .dockerignore | 67 ++- .vscode/settings.json | 3 - Dockerfile | 22 +- backend/abstract.py | 1 - config.py | 1 - document_processor.py | 566 ------------------ examples/daos/dao.json | 0 examples/daos/example.json | 249 -------- examples/daos/test.json | 101 ---- .../proposal_evaluation_example.py | 0 .../vector_react_example.py | 0 main.py | 7 +- requirements.txt | 4 +- .../runner/tasks/dao_proposal_evaluation.py | 1 - services/runner/tasks/proposal_embedder.py | 2 +- .../chainhook/handlers/base_vote_handler.py | 2 +- .../chainhook/handlers/block_state_handler.py | 2 - .../handlers/dao_proposal_handler.py | 2 - .../chainhook/handlers/dao_vote_handler.py | 2 - services/websocket.py | 3 +- services/workflows/__init__.py | 19 - services/workflows/base.py | 2 - services/workflows/chat.py | 6 - services/workflows/planning_mixin.py | 2 - services/workflows/proposal_evaluation.py | 2 - services/workflows/tweet_analysis.py | 2 - services/workflows/tweet_generator.py | 2 - services/workflows/vector_mixin.py | 2 - services/workflows/web_search_mixin.py | 2 - services/workflows/workflow_service.py | 8 +- tests/backend/test_models.py | 43 -- tests/lib/test_token_assets.py | 216 ------- tests/lib/test_twitter.py | 221 ------- tests/lib/test_websocket_manager.py | 267 --------- tests/services/test_langgraph.py | 110 ++-- .../webhooks/chainhook/test_handlers.py | 344 ----------- .../chainhook/test_sell_event_handler.py | 281 --------- .../services/webhooks/dao/test_dao_webhook.py | 142 ----- tests/services/workflows/test_vector_react.py | 207 ------- tests/test_proposal_evaluation.py | 5 - 40 files changed, 155 insertions(+), 2763 deletions(-) delete mode 100644 .vscode/settings.json delete mode 100644 document_processor.py delete mode 100644 examples/daos/dao.json delete mode 100644 examples/daos/example.json delete mode 100644 examples/daos/test.json rename proposal_evaluation_test.py => examples/proposal_evaluation_example.py (100%) rename vector_react_example.py => examples/vector_react_example.py (100%) delete mode 100644 tests/backend/test_models.py delete mode 100644 tests/lib/test_token_assets.py delete mode 100644 tests/lib/test_twitter.py delete mode 100644 tests/lib/test_websocket_manager.py delete mode 100644 tests/services/webhooks/chainhook/test_handlers.py delete mode 100644 tests/services/webhooks/chainhook/test_sell_event_handler.py delete mode 100644 tests/services/webhooks/dao/test_dao_webhook.py delete mode 100644 tests/services/workflows/test_vector_react.py diff --git a/.dockerignore b/.dockerignore index 843ced96..f583b8ee 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,4 +1,67 @@ +# Python +__pycache__/ +*.pyc +*.pyo +*.pyd +*.pdb +*.egg-info/ +.eggs/ +*.egg +*.log + +# Node/Bun/JS/TS +node_modules/ +bun.lockb +npm-debug.log +yarn-error.log +.pnpm-debug.log +agent-tools-ts/node_modules/ +agent-tools-ts/.next/ +agent-tools-ts/dist/ +agent-tools-ts/.turbo/ +agent-tools-ts/.bun/ + +# General +.DS_Store .env -agent-tools-ts/.env +.env.* +*.swp +*.swo +*.bak +*.tmp +*.orig +*.old + +# Git .git -agent-tools-ts/.git \ No newline at end of file +.gitmodules +.gitignore + +# Docker +Dockerfile +.dockerignore + +# VSCode/Editor +.vscode/ +.idea/ +*.code-workspace + +# Test/Cache/Build Artifacts +.pytest_cache/ +.ruff_cache/ +*.coverage +coverage.* +htmlcov/ +dist/ +build/ +*.spec + +# Documentation +*.md +docs/ +README.md + +# Misc +*.sqlite3 +*.db +*.pid \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 4ad8bd30..00000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "codium.codeCompletion.enable": true -} diff --git a/Dockerfile b/Dockerfile index 32bce5c3..e5ca8e7f 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,13 +2,27 @@ FROM public.ecr.aws/docker/library/python:3.13 WORKDIR /usr/src/app +# Install Python dependencies +RUN pip install uv COPY requirements.txt ./ -RUN pip install --no-cache-dir -r requirements.txt +RUN uv pip install --system --no-cache-dir -r requirements.txt +# Install Bun +RUN curl -fsSL https://bun.sh/install | bash + +# Set Bun path for this shell +ENV PATH="/root/.bun/bin:${PATH}" + +# Install JS/TS dependencies efficiently +WORKDIR /usr/src/app/agent-tools-ts + +# Copy only dependency files first for better caching +COPY agent-tools-ts/package.json agent-tools-ts/bun.lock ./ +RUN bun install + +# Now copy the rest of the code COPY . . -RUN curl -fsSL https://bun.sh/install | bash -RUN cp /root/.bun/bin/bun /usr/local/bin/bun -RUN cd /usr/src/app/agent-tools-ts/ && bun install +WORKDIR /usr/src/app CMD [ "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file diff --git a/backend/abstract.py b/backend/abstract.py index da1d88fe..056c2611 100644 --- a/backend/abstract.py +++ b/backend/abstract.py @@ -8,7 +8,6 @@ AgentBase, AgentCreate, AgentFilter, - AgentWithWalletTokenDTO, ChainState, ChainStateBase, ChainStateCreate, diff --git a/config.py b/config.py index 0bc859da..0f6e16c6 100644 --- a/config.py +++ b/config.py @@ -8,7 +8,6 @@ logger = configure_logger(__name__) -# Load environment variables first load_dotenv() diff --git a/document_processor.py b/document_processor.py deleted file mode 100644 index 7e4cf7e3..00000000 --- a/document_processor.py +++ /dev/null @@ -1,566 +0,0 @@ -#!/usr/bin/env python -""" -Document processor for loading texts from URLs and local files, adding them to a vector database. - -This utility focuses solely on ingesting documents from specified URLs and local files, -processing them, and storing them in a vector collection for later retrieval. -""" - -import asyncio -import os -from pathlib import Path -from typing import List, Optional - -import dotenv -from langchain_community.document_loaders import TextLoader, WebBaseLoader -from langchain_core.documents import Document -from langchain_openai import OpenAIEmbeddings -from langchain_text_splitters import RecursiveCharacterTextSplitter - -from backend.factory import backend -from backend.models import ( - ExtensionFilter, - ProposalFilter, - TokenFilter, - VoteFilter, -) -from services.workflows.chat import add_documents_to_vectors - -# Load environment variables -dotenv.load_dotenv() - - -async def load_documents_from_url(url: str) -> List[Document]: - """ - Load documents from a URL using WebBaseLoader and split them with RecursiveCharacterTextSplitter. - - Args: - url: The URL to load documents from - - Returns: - List of processed Document objects - """ - try: - print(f"Loading content from URL: {url}...") - loader = WebBaseLoader(url) - docs = loader.load() - - # Initialize the text splitter - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=4000, - chunk_overlap=200, - length_function=len, - separators=["\n\n", "\n", " ", ""], - ) - - # Split the documents - split_docs = text_splitter.split_documents(docs) - - # Add metadata to each document - for doc in split_docs: - doc.metadata["type"] = "web_documentation" - doc.metadata["url"] = url - doc.metadata["source_type"] = "url" - - print( - f"Successfully loaded and split into {len(split_docs)} documents from {url}" - ) - return split_docs - except Exception as e: - print(f"Error loading content from URL {url}: {str(e)}") - return [] - - -def load_documents_from_file( - file_path: str, document_type: str = "local_file" -) -> List[Document]: - """ - Load documents from a local file and split them with RecursiveCharacterTextSplitter. - - Args: - file_path: Path to the local file - document_type: Type to assign in document metadata - - Returns: - List of processed Document objects - """ - try: - print(f"Loading content from file: {file_path}...") - file_path = Path(file_path) - - # Skip non-text files and hidden files - if not file_path.is_file() or file_path.name.startswith("."): - return [] - - # Skip files that are likely binary or non-text - text_extensions = [ - ".txt", - ".md", - ".py", - ".js", - ".ts", - ".html", - ".css", - ".json", - ".yaml", - ".yml", - ".clar", - ] - if file_path.suffix.lower() not in text_extensions: - print(f"Skipping likely non-text file: {file_path}") - return [] - - loader = TextLoader(str(file_path)) - docs = loader.load() - - # Initialize the text splitter - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=4000, - chunk_overlap=200, - length_function=len, - separators=["\n\n", "\n", " ", ""], - ) - - # Split the documents - split_docs = text_splitter.split_documents(docs) - - # Add metadata to each document - for doc in split_docs: - doc.metadata["type"] = document_type - doc.metadata["file_path"] = str(file_path) - doc.metadata["file_name"] = file_path.name - doc.metadata["source_type"] = "file" - - print( - f"Successfully loaded and split into {len(split_docs)} documents from {file_path}" - ) - return split_docs - except Exception as e: - print(f"Error loading content from file {file_path}: {str(e)}") - return [] - - -def get_files_from_directory(directory_path: str, recursive: bool = True) -> List[str]: - """ - Get a list of all files in a directory, optionally recursively. - - Args: - directory_path: Path to the directory - recursive: Whether to search recursively - - Returns: - List of file paths - """ - file_paths = [] - directory = Path(directory_path) - - if not directory.exists() or not directory.is_dir(): - print(f"Directory does not exist or is not a directory: {directory_path}") - return file_paths - - if recursive: - for root, _, files in os.walk(directory): - for file in files: - file_paths.append(os.path.join(root, file)) - else: - for item in directory.iterdir(): - if item.is_file(): - file_paths.append(str(item)) - - return file_paths - - -def extract_dao_documents() -> List[Document]: - """ - Extract DAO-related data from the database and convert it to Document objects. - - Returns: - List of Document objects containing DAO data - """ - documents = [] - print("\nExtracting DAO data from the database...") - - try: - # Get all DAOs - daos = backend.list_daos() - print(f"Found {len(daos)} DAOs in the database") - - for dao in daos: - # Create a document for the DAO - dao_content = f""" - DAO: {dao.name} - ID: {dao.id} - Mission: {dao.description} - Description: {dao.description} - Deployed: {dao.is_deployed} - Broadcasted: {dao.is_broadcasted} - """ - - # Create a document from the DAO - dao_doc = Document( - page_content=dao_content, - metadata={ - "type": "dao", - "id": str(dao.id), - "name": dao.name or "Unnamed DAO", - "source_type": "database", - "entity_type": "dao", - }, - ) - documents.append(dao_doc) - - # Get tokens for this DAO - tokens = backend.list_tokens(TokenFilter(dao_id=dao.id)) - if tokens: - print(f"Found {len(tokens)} tokens for DAO {dao.name}") - - for token in tokens: - token_content = f""" - Token: {token.name} ({token.symbol}) - DAO: {dao.name} - Description: {token.description} - Decimals: {token.decimals} - Max Supply: {token.max_supply} - Contract: {token.contract_principal} - Status: {token.status} - """ - - token_doc = Document( - page_content=token_content, - metadata={ - "type": "token", - "id": str(token.id), - "dao_id": str(dao.id), - "dao_name": dao.name or "Unnamed DAO", - "name": token.name or "Unnamed Token", - "symbol": token.symbol, - "source_type": "database", - "entity_type": "token", - }, - ) - documents.append(token_doc) - - # Get extensions for this DAO - extensions = backend.list_extensions(ExtensionFilter(dao_id=dao.id)) - if extensions: - print(f"Found {len(extensions)} extensions for DAO {dao.name}") - - for extension in extensions: - extension_content = f""" - Extension Type: {extension.type} - DAO: {dao.name} - Contract: {extension.contract_principal} - Status: {extension.status} - Transaction: {extension.tx_id} - """ - - extension_doc = Document( - page_content=extension_content, - metadata={ - "type": "extension", - "id": str(extension.id), - "dao_id": str(dao.id), - "dao_name": dao.name or "Unnamed DAO", - "extension_type": extension.type, - "source_type": "database", - "entity_type": "extension", - }, - ) - documents.append(extension_doc) - - # Get proposals for this DAO - proposals = backend.list_proposals(ProposalFilter(dao_id=dao.id)) - if proposals: - print(f"Found {len(proposals)} proposals for DAO {dao.name}") - - for proposal in proposals: - proposal_content = f""" - Proposal: {proposal.title} - DAO: {dao.name} - Description: {proposal.description} - Status: {proposal.status} - Action: {proposal.action} - Executed: {proposal.executed} - Passed: {proposal.passed} - Met Quorum: {proposal.met_quorum} - Met Threshold: {proposal.met_threshold} - Votes For: {proposal.votes_for} - Votes Against: {proposal.votes_against} - """ - - proposal_doc = Document( - page_content=proposal_content, - metadata={ - "type": "proposal", - "id": str(proposal.id), - "dao_id": str(dao.id), - "dao_name": dao.name or "Unnamed DAO", - "title": proposal.title, - "source_type": "database", - "entity_type": "proposal", - }, - ) - documents.append(proposal_doc) - - # Get votes for this proposal - votes = backend.list_votes(VoteFilter(proposal_id=proposal.id)) - if votes: - print(f"Found {len(votes)} votes for proposal {proposal.title}") - - vote_content = f""" - Votes for Proposal: {proposal.title} - DAO: {dao.name} - """ - - for vote in votes: - vote_content += f""" - Vote by: {vote.address} - Answer: {"Yes" if vote.answer else "No"} - Amount: {vote.amount} - Reasoning: {vote.reasoning} - """ - - vote_doc = Document( - page_content=vote_content, - metadata={ - "type": "votes", - "proposal_id": str(proposal.id), - "dao_id": str(dao.id), - "dao_name": dao.name or "Unnamed DAO", - "proposal_title": proposal.title, - "source_type": "database", - "entity_type": "votes", - }, - ) - documents.append(vote_doc) - - # Split the documents if they are too large - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=4000, - chunk_overlap=200, - length_function=len, - separators=["\n\n", "\n", " ", ""], - ) - - split_docs = text_splitter.split_documents(documents) - print( - f"Successfully processed {len(split_docs)} documents from database DAO data" - ) - return split_docs - - except Exception as e: - print(f"Error extracting DAO data from database: {str(e)}") - return [] - - -async def process_documents( - urls: Optional[List[str]] = None, - directories: Optional[List[str]] = None, - files: Optional[List[str]] = None, - knowledge_collection_name: str = "knowledge_collection", - dao_collection_name: str = "dao_collection", - document_type: Optional[str] = None, - recursive: bool = True, - include_database: bool = False, -) -> None: - """ - Process documents from URLs, directories, files, and database and add them to vector collections. - - URLs, directories, and files go into knowledge_collection_name. - Database DAO data goes into dao_collection_name. - - Args: - urls: List of URLs to process - directories: List of directories to process - files: List of individual files to process - knowledge_collection_name: Collection name for URL and file documents - dao_collection_name: Collection name for database DAO documents - document_type: Optional type to assign to documents in metadata - recursive: Whether to recursively process directories - include_database: Whether to include DAO data from the database - """ - knowledge_documents = [] - dao_documents = [] - - # Process URLs - if urls: - for url in urls: - print(f"\nProcessing documentation from URL: {url}") - docs = await load_documents_from_url(url) - - # Add custom document type if specified - if document_type and docs: - for doc in docs: - doc.metadata["type"] = document_type - - if docs: - print(f"Adding {len(docs)} documents from URL {url}") - knowledge_documents.extend(docs) - else: - print(f"No content was retrieved from URL {url}") - - # Process directories - if directories: - for directory in directories: - print(f"\nProcessing files from directory: {directory}") - file_paths = get_files_from_directory(directory, recursive=recursive) - - for file_path in file_paths: - print(f"Processing file: {file_path}") - docs = load_documents_from_file( - file_path, document_type or "directory_file" - ) - - if docs: - print(f"Adding {len(docs)} documents from file {file_path}") - knowledge_documents.extend(docs) - else: - print(f"No content was retrieved from file {file_path}") - - # Process individual files - if files: - for file_path in files: - print(f"\nProcessing individual file: {file_path}") - docs = load_documents_from_file( - file_path, document_type or "individual_file" - ) - - if docs: - print(f"Adding {len(docs)} documents from file {file_path}") - knowledge_documents.extend(docs) - else: - print(f"No content was retrieved from file {file_path}") - - # Process knowledge documents if any exist - if knowledge_documents: - print( - f"\nProcessing {len(knowledge_documents)} knowledge documents (URLs and files)..." - ) - embeddings = OpenAIEmbeddings() - - # Ensure the knowledge collection exists - try: - backend.get_vector_collection(knowledge_collection_name) - print(f"Using existing vector collection: {knowledge_collection_name}") - except Exception: - embed_dim = 1536 # Default for OpenAI embeddings - if hasattr(embeddings, "embedding_dim"): - embed_dim = embeddings.embedding_dim - backend.create_vector_collection( - knowledge_collection_name, dimensions=embed_dim - ) - print( - f"Created new vector collection: {knowledge_collection_name} with dimensions: {embed_dim}" - ) - - # Add knowledge documents to the vector store - print( - f"Adding {len(knowledge_documents)} documents to {knowledge_collection_name}..." - ) - await add_documents_to_vectors( - collection_name=knowledge_collection_name, - documents=knowledge_documents, - embeddings=embeddings, - ) - print(f"Documents added successfully to {knowledge_collection_name}!") - - # Create an index on the collection for better query performance - print(f"Creating index on vector collection: {knowledge_collection_name}...") - try: - backend.create_vector_index(knowledge_collection_name) - print(f"Index created successfully for {knowledge_collection_name}!") - except Exception as e: - print(f"Error creating index for {knowledge_collection_name}: {str(e)}") - - # Process DAO data from database into separate collection - if include_database: - print("\nProcessing DAO data from database...") - db_docs = extract_dao_documents() - if db_docs: - print( - f"Adding {len(db_docs)} documents from database to {dao_collection_name}" - ) - dao_documents.extend(db_docs) - - # Initialize embeddings for DAO documents - embeddings = OpenAIEmbeddings() - - # Ensure the DAO collection exists - try: - backend.get_vector_collection(dao_collection_name) - print(f"Using existing vector collection: {dao_collection_name}") - except Exception: - embed_dim = 1536 # Default for OpenAI embeddings - if hasattr(embeddings, "embedding_dim"): - embed_dim = embeddings.embedding_dim - backend.create_vector_collection( - dao_collection_name, dimensions=embed_dim - ) - print( - f"Created new vector collection: {dao_collection_name} with dimensions: {embed_dim}" - ) - - # Add DAO documents to the vector store - print(f"Adding {len(dao_documents)} documents to {dao_collection_name}...") - await add_documents_to_vectors( - collection_name=dao_collection_name, - documents=dao_documents, - embeddings=embeddings, - ) - print(f"Documents added successfully to {dao_collection_name}!") - - # Create an index on the collection for better query performance - print(f"Creating index on vector collection: {dao_collection_name}...") - try: - backend.create_vector_index(dao_collection_name) - print(f"Index created successfully for {dao_collection_name}!") - except Exception as e: - print(f"Error creating index for {dao_collection_name}: {str(e)}") - else: - print("No content was retrieved from database") - - if not knowledge_documents and not dao_documents: - print("No documents were loaded from any source. Exiting.") - return - - -async def main() -> None: - """Run the document processor.""" - # Example list of URLs to process - urls = [ - "https://docs.stacks.co/reference/functions", - "https://docs.stacks.co/reference/keywords", - "https://docs.stacks.co/reference/types", - "https://docs.stacks.co/reference/the-stack", - ] - - # Example directories to process - directories = [ - "./aibtcdev-docs", # Replace with actual directories - "./aibtcdev-contracts/contracts/dao", - "./stacks-docs/press-and-top-links", - "./stacks-docs/nakamoto-upgrade", - "./stacks-docs/concepts", - "./stacks-docs/example-contracts", - "./stacks-docs/guides-and-tutorials", - "./stacks-docs/bitcoin-theses-and-reports", - "./stacks-docs/reference", - ] - - # Example individual files to process - files = [] - - # Process the documents and add them to separate vector collections - await process_documents( - urls=urls, - directories=directories, - files=files, - knowledge_collection_name="knowledge_collection", # Collection for URLs and files - dao_collection_name="dao_collection", # Collection for DAO database data - recursive=True, - include_database=True, # Include DAO data from the database - ) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/daos/dao.json b/examples/daos/dao.json deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/daos/example.json b/examples/daos/example.json deleted file mode 100644 index 0b54bc00..00000000 --- a/examples/daos/example.json +++ /dev/null @@ -1,249 +0,0 @@ -{ - "name": "GTC DAO", - "mission": "Our mission statement", - "description": "Detailed description of the DAO", - "extensions": [ - { - "name": "getc-pre-faktory", - "type": "TOKEN", - "subtype": "PRELAUNCH", - "source": "\n;; e2c78b6648a515a61c19863f10b0bc2af6a92f24cb1df5dd5de25bcf8cf29872\n;; aibtc.com DAO faktory.fun PRE @version 1.0\n;; Pre-launch contract for token distribution\n;; Dynamic allocation: 1-7 seats per user in Period 1\n;; Each seat = 0.00020000 BTC, targ", - "hash": "e2c78b6648a515a61c19863f10b0bc2af6a92f24cb1df5dd5de25bcf8cf29872", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "b6dea933ca28046f08d2ca4b26601777b6a0f4db117baddabae96d672ecbe5a5", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-pre-faktory" - }, - { - "name": "getc-faktory", - "type": "TOKEN", - "subtype": "DAO", - "source": "\n;; ec46c7d8892f53911847a96a79c68e2789734076b302bad7973ca6a38af455b3\n;; getc Powered By Faktory.fun v1.0 \n\n(impl-trait 'STTWD9SPRQVD3P733V89SV0P8RZRZNQADG034F0A.faktory-trait-v1.sip-010-trait)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.ai", - "hash": "ec46c7d8892f53911847a96a79c68e2789734076b302bad7973ca6a38af455b3", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "3dac45a64cdc495c7d5f459d389bb62f091b09e1307b1de06b2264db17201a82", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-faktory" - }, - { - "name": "xyk-pool-stx-getc-v-1-1", - "type": "TOKEN", - "subtype": "POOL", - "source": ";; Implement XYK pool trait and use SIP 010 trait\n (impl-trait 'ST3VXT52QEQPZ5246A16RFNMR1PRJ96JK6YYX37N8.xyk-pool-trait-v-1-2.xyk-pool-trait)\n (use-trait sip-010-trait 'ST3VXT52QEQPZ5246A16RFNMR1PRJ96JK6YYX37N8.sip-010-trait-ft-standard.sip-010-tr", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "c7152903ded8db7bcaf2afe8befda6fc22e156316e0358a89674845c66f8b849", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.xyk-pool-stx-getc-v-1-1" - }, - { - "name": "getc-faktory-dex", - "type": "TOKEN", - "subtype": "DEX", - "source": "\n ;; f7197551533e781d7349d2258035a6ed6a7167d91eb0aafe84bedd405569ea5b\n ;; aibtc.dev DAO faktory.fun DEX @version 1.0\n \n (impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.faktory-dex)\n (impl-trait 'STTWD9SPRQVD3P7", - "hash": "f7197551533e781d7349d2258035a6ed6a7167d91eb0aafe84bedd405569ea5b", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "bc73e55a13387b112b86e6e9f6ab45f7dade6cbc0d629bf4e793b4c7aa92d810", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-faktory-dex" - }, - { - "name": "getc-base-dao", - "type": "BASE", - "subtype": "DAO", - "source": ";; title: aibtc-dao\n;; version: 1.0.0\n;; summary: An ExecutorDAO implementation for aibtcdev\n\n;; traits\n;;\n\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-v2.aibtc-base-dao)\n(use-trait proposal-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ", - "hash": "b682c7849ec7c022eaff973d2ceb8f83885a01df20b2d4d5d23e2b5a3c0b7e95", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "55efa226e4a491c00ac5e07c5e781e6b855f348f2ce24bc0ef922eeecde79994", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-base-dao" - }, - { - "name": "getc-action-proposals-v2", - "type": "EXTENSIONS", - "subtype": "ACTION_PROPOSALS", - "source": ";; title: aibtc-action-proposals-v2\n;; version: 2.0.0\n;; summary: An extension that manages voting on predefined actions using a SIP-010 Stacks token.\n;; description: This contract allows voting on specific extension actions with a lower threshold th", - "hash": "2e8f4b6f6efa1bd4b60f6dd1b2e4d58f9396d1195de5f56013a2d6e0dccc870e", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "a95cbbfb59ba3d06728df183b978b9b6929501f225940b091040a148aded165f", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-proposals-v2" - }, - { - "name": "getc-bank-account", - "type": "EXTENSIONS", - "subtype": "BANK_ACCOUNT", - "source": ";; title: aibtc-bank-account\n;; version: 1.0.0\n;; summary: An extension that allows a principal to withdraw STX from the contract with given rules.\n\n;; traits\n;;\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(i", - "hash": "b135cb33e2107d9f61918b8bc829734795b1070436b830e42f4a02010812e129", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "a65726dd373dc7c34a34ef593e8ac10ec03b0cc76b9eb20f8fd4c40216a42e50", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-bank-account" - }, - { - "name": "getc-core-proposals-v2", - "type": "EXTENSIONS", - "subtype": "CORE_PROPOSALS", - "source": ";; title: aibtc-core-proposals-v2\n;; version: 2.0.0\n;; summary: An extension that manages voting on proposals to execute Clarity code using a SIP-010 Stacks token.\n;; description: This contract can make changes to core DAO functionality with a high v", - "hash": "73936e4a1f87234a19eefbb05f1eb363b1caa058bbfff68e7e659a583c495aca", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "8e2a4d396ef3433e5beb5933503464d2d3af2d898534fe9473d96f39ead48e5a", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-core-proposals-v2" - }, - { - "name": "getc-dao-charter", - "type": "EXTENSIONS", - "subtype": "CHARTER", - "source": ";; title: aibtc-dao-charter\n;; version: 1.0.0\n;; summary: An extension that manages the DAO charter and records the DAO's mission and values on-chain.\n;; description: This contract allows the DAO to define its mission and values on-chain, which can b", - "hash": "fe2ddf4b3fa13a9f088770101a5a15426fa160f236a101158694e709b7082538", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "f2088778303b3cb8ea1ae7e2171543c08603d1274a0b92cd1e843f93da46aae5", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-dao-charter" - }, - { - "name": "getc-onchain-messaging", - "type": "EXTENSIONS", - "subtype": "MESSAGING", - "source": ";; title: aibtc-onchain-messaging\n;; version: 1.0.0\n;; summary: An extension to send messages on-chain to anyone listening to this contract.\n\n;; traits\n;;\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-tra", - "hash": "4fb92c568534c5fd0ee1a55503b7865565ba0545812590dcab1c1cd06fcb570a", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "c62a69594fbc77aa71e1c2664b99e39081f1671444f460205c96d3476aac5ed7", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-onchain-messaging" - }, - { - "name": "getc-payments-invoices", - "type": "EXTENSIONS", - "subtype": "PAYMENTS", - "source": ";; title: aibtc-payments\n;; version: 1.0.0\n;; summary: An extension that provides payment processing for DAO services.\n\n;; traits\n;;\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59", - "hash": "03f2db3ce6cf8986489b6107242b98132978bdca3b67bc98f776e175bc4ee155", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "c8c3d1309994e63b6b7f60ab912a514cde0ec39791957b0de235b90ece74c749", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-payments-invoices" - }, - { - "name": "getc-token-owner", - "type": "EXTENSIONS", - "subtype": "TOKEN_OWNER", - "source": ";; title: aibtc-token-owner\n;; version: 1.0.0\n;; summary: An extension that provides management functions for the dao token\n\n;; traits\n;;\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T", - "hash": "3c03a85ff53a5c6f8403cc40c9ad53ea0380b8bc0f9a541639da2093d4fafce6", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "d94a49848de2edf59f9159f83a4960fdb858d4c09f8d8d18a7f5f916b3fa30ee", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-token-owner" - }, - { - "name": "getc-treasury", - "type": "EXTENSIONS", - "subtype": "TREASURY", - "source": ";; title: aibtc-treasury\n;; version: 1.0.0\n;; summary: An extension that manages STX, SIP-009 NFTs, and SIP-010 FTs.\n\n;; traits\n;;\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2", - "hash": "f364c92ddd077cf2682c501c690d7e1f9c8c8fa3cc1742fdf1672b5fb13ac6e9", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "9ab4b6abd2c5eae70b3e82a03aa9f9e0cef68b94df8cceba8d1a1f50c1a3f652", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-treasury" - }, - { - "name": "getc-action-add-resource", - "type": "ACTIONS", - "subtype": "PAYMENTS_INVOICES_ADD_RESOURCE", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.action)\n\n(define-constant ERR_UNAUTHORIZED (err u10001))\n(define-constant ERR_INVALID_PAR", - "hash": "c6884121d1d82aebde4e4952d86bb13bbed318bac9ad54f4b30f67d89e0f6b05", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "674a184657e1dded8d5682d4273c6aec855fc859b969f1ffd9c3ce95fbd2bd09", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-add-resource" - }, - { - "name": "getc-action-allow-asset", - "type": "ACTIONS", - "subtype": "TREASURY_ALLOW_ASSET", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.action)\n\n(define-constant ERR_UNAUTHORIZED (err u10001))\n(define-constant ERR_INVALID_PAR", - "hash": "25b328a57126b0a156fac3fb18abf171277e7a8e97200521798bb86f460bd195", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "dcc8ed87cc3c936dd3afbc65d4d5ba8850c3ee99ecf438fb7281080069c296b3", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-allow-asset" - }, - { - "name": "getc-action-send-message", - "type": "ACTIONS", - "subtype": "MESSAGING_SEND_MESSAGE", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.action)\n\n(define-constant ERR_UNAUTHORIZED (err u10001))\n(define-constant ERR_INVALID_PAR", - "hash": "af565cb1202d773dc3f2cfc77a7342a7408e4116889e6e12df5d7705f66c3617", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "77aa497c46fb1066407d954883b01ebe64dd93bb7d2e9443222864564edd88cb", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-send-message" - }, - { - "name": "getc-action-set-account-holder", - "type": "ACTIONS", - "subtype": "BANK_ACCOUNT_SET_ACCOUNT_HOLDER", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.action)\n\n(define-constant ERR_UNAUTHORIZED (err u10001))\n(define-constant ERR_INVALID_PAR", - "hash": "8bffc54f1d8a9b43158fb64f8a38253af2aa8f80f795d3d84a21a62a4a7cb44c", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "6b063cc8ba2f7fc214a5979e8f89e27369681f1c878d8520bfeddb59e022fc09", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-set-account-holder" - }, - { - "name": "getc-action-set-withdrawal-amount", - "type": "ACTIONS", - "subtype": "BANK_ACCOUNT_SET_WITHDRAWAL_AMOUNT", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.action)\n\n(define-constant ERR_UNAUTHORIZED (err u10001))\n(define-constant ERR_INVALID_PAR", - "hash": "67887733991ec39a722c96c2c9b258de05204a2a6371b66d439647604c281c7f", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "3927ec0901e7e656d134598b04df2b350ae8573f7472fb3927ca0a3360ffa266", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-set-withdrawal-amount" - }, - { - "name": "getc-action-set-withdrawal-period", - "type": "ACTIONS", - "subtype": "BANK_ACCOUNT_SET_WITHDRAWAL_PERIOD", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.action)\n\n(define-constant ERR_UNAUTHORIZED (err u10001))\n(define-constant ERR_INVALID_PAR", - "hash": "483867f1f2230a858ff1d6df36df4be44c5c848eb64c9d6172320e529a507daa", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "7545216901fae3f6589b606cb842ba6173321a701666a2e2446d297823f5941d", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-set-withdrawal-period" - }, - { - "name": "getc-action-toggle-resource", - "type": "ACTIONS", - "subtype": "PAYMENTS_INVOICES_TOGGLE_RESOURCE", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.extension)\n(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.action)\n\n(define-constant ERR_UNAUTHORIZED (err u10001))\n(define-constant ERR_INVALID_PAR", - "hash": "bcd3c0e56e0a19387212e0bd77a74b2e8401f18e475b2f13b30306ff72b25eb6", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "0aa53b7ef132b22f244d06dacace091ea1a0ace62974b48c93637bc90e6fa81d", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-action-toggle-resource" - }, - { - "name": "getc-base-bootstrap-initialization-v2", - "type": "PROPOSALS", - "subtype": "BOOTSTRAP_INIT", - "source": "(impl-trait 'ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.aibtc-dao-traits-v2.proposal)\n\n(define-constant CFG_DAO_MANIFEST_TEXT \"All I do is win win win\")\n(define-constant CFG_DAO_MANIFEST_INSCRIPTION_ID \"inscription id\")\n\n(define-public (execute (sende", - "hash": "0582850900adf8b2527ed96944170e0cdc9cae800bce1fd3fb0062dba1a85b13", - "sender": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ", - "success": true, - "txId": "e7e3869853e3fc03f44ea8cde3097562a92c19f4f72ce1d704dfdfc4528b3850", - "address": "ST1TZE9ZY61FYR7YM9BR0543XKX9YG5TR9017R4WJ.getc-base-bootstrap-initialization-v2" - } - ], - "token": { - "name": "GTC DAO Token", - "symbol": "GTC", - "decimals": 6, - "description": "Token description", - "max_supply": "1000000", - "uri": "https://example.com/token.json", - "image_url": "https://example.com/image.png", - "x_url": "https://x.com/mydao", - "telegram_url": "https://t.me/mydao", - "website_url": "https://mydao.com" - } -} diff --git a/examples/daos/test.json b/examples/daos/test.json deleted file mode 100644 index f4ff7628..00000000 --- a/examples/daos/test.json +++ /dev/null @@ -1,101 +0,0 @@ -{ - "name": "My DAO", - "mission": "Our mission statement", - "description": "Detailed description of the DAO", - "extensions": [ - { - "type": "lfg4-base-bootstrap-initialization-v2", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-base-bootstrap-initialization-v2", - "tx_id": "0x8b9ec33b1d7ee5b119aa84470b3baee422c4f48f7321b9e10c1ddd281bade4f5" - }, - { - "type": "lfg4-action-proposals-v2", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-proposals-v2", - "tx_id": "0x078c43e7e0247b0d597d7aeb0b73c30742f43d55c02a1d481776057da9c05eaf" - }, - { - "type": "lfg4-bank-account", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-bank-account", - "tx_id": "0xa831692d89239ce6cde5a73a8e8bfe80c1144d2a527b64b0a3584c92ef37480a" - }, - { - "type": "lfg4-core-proposals-v2", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-core-proposals-v2", - "tx_id": "0x4ce3c1cebaa0721d6703496a7eec5ace595b4e00bb832036de3e9b0383ab7708" - }, - { - "type": "lfg4-dao-charter", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-dao-charter", - "tx_id": "0x0a2a280a6fff6efc3e3fdd381832d65b20960f527d71bf8beeb4160cd9225e2d" - }, - { - "type": "lfg4-onchain-messaging", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-onchain-messaging", - "tx_id": "0x4324f78ca944d5444abd08126c4411f79b5135a3f16fee923233456f0f9813b9" - }, - { - "type": "lfg4-payments-invoices", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-payments-invoices", - "tx_id": "0xd5e6f968b879896577d3a2211bd161d2acfd3e4c5734e599e8c0bfe74efd64c0" - }, - { - "type": "lfg4-token-owner", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-token-owner", - "tx_id": "0xff0e0436b523554c3fba0e149cfbf16a8a2eddb4e320f1ccebe47ea16ec5f82c" - }, - { - "type": "lfg4-treasury", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-treasury", - "tx_id": "0xbed23879e81c7f2d344227a825aac0dc7ece6105e5ce3c261b533021807c10ba" - }, - { - "type": "lfg4-action-add-resource", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-add-resource", - "tx_id": "0x8cf3b3e3ae094a7afa3376d80673ddada92c1717ea0eadecb94bf03bd62a9278" - }, - { - "type": "lfg4-action-allow-asset", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-allow-asset", - "tx_id": "0x1905d7f07be15134536f9c7756fb6196288b0027b394cb211bb62e1dddf8f04b" - }, - { - "type": "lfg4-action-send-message", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-send-message", - "tx_id": "0xda3d2813c7dd62df4b84f1333129d58e6d97663e1e66d83fd88621eea58f63db" - }, - { - "type": "lfg4-action-set-account-holder", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-set-account-holder", - "tx_id": "0x76749d5a71f1a7245f38f65daa5c44246ee5696d1fded8be4b2bfbd86e8a1394" - }, - { - "type": "lfg4-action-set-withdrawal-amount", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-set-withdrawal-amount", - "tx_id": "0xbfb07c19c37f6f8a53dbaf39b5f281a97fc8b693e452ab0614f5dc8bc4a6380d" - }, - { - "type": "lfg4-action-set-withdrawal-period", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-set-withdrawal-period", - "tx_id": "0x2b72c26b38e4faef96ffca6945f2f9750cafbe0d2e066f855f33fb4f2a790832" - }, - { - "type": "lfg4-action-toggle-resource", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-action-toggle-resource", - "tx_id": "0x85e54a94ad885d4a427706e2e87e1b05a2f41cf312beabb97b6305b849d6a620" - } - ], - "token": { - "name": "GoTimeTest", - "symbol": "LFG4", - "decimals": 6, - "description": "Token description", - "max_supply": "1000000000000000", - "uri": "https://aibtc.dev", - "tx_id": "optional_transaction_id", - "contract_principal": "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.lfg4-faktory", - "image_url": "https://example.com/image.png", - "x_url": "https://x.com/mydao", - "telegram_url": "https://t.me/mydao", - "website_url": "https://mydao.com" - } -} diff --git a/proposal_evaluation_test.py b/examples/proposal_evaluation_example.py similarity index 100% rename from proposal_evaluation_test.py rename to examples/proposal_evaluation_example.py diff --git a/vector_react_example.py b/examples/vector_react_example.py similarity index 100% rename from vector_react_example.py rename to examples/vector_react_example.py diff --git a/main.py b/main.py index ea33db14..b6d71f27 100644 --- a/main.py +++ b/main.py @@ -4,7 +4,6 @@ from fastapi.middleware.cors import CORSMiddleware import api -from api import chat, tools, webhooks from config import config from lib.logger import configure_logger from services import startup @@ -13,6 +12,8 @@ # Configure module logger logger = configure_logger(__name__) +_ = config + # Define app app = FastAPI( title="AI BTC Dev Backend", @@ -26,8 +27,8 @@ allow_origins=[ "https://sprint.aibtc.dev", "https://sprint-faster.aibtc.dev", - "https://*.aibtcdev-frontend.pages.dev", # Cloudflare preview deployments - "http://localhost:3000", # Local development + "https://*.aibtcdev-frontend.pages.dev", + "http://localhost:3000", "https://staging.aibtc.chat", "https://app.aibtc.dev", "https://aibtc.dev", diff --git a/requirements.txt b/requirements.txt index 14516603..e431d074 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,7 @@ cachetools==5.5.2 fastapi==0.115.12 langchain==0.3.25 langchain_core>=0.3.56,<1.0.0 +langchain_community==0.3.23 langchain_openai==0.3.16 langchain_text_splitters==0.3.8 langgraph==0.4.1 @@ -19,5 +20,4 @@ starlette==0.46.2 supabase==2.15.1 tiktoken==0.9.0 uvicorn==0.34.2 -vecs==0.4.5 -langchain_community==0.3.23 \ No newline at end of file +vecs==0.4.5 \ No newline at end of file diff --git a/services/runner/tasks/dao_proposal_evaluation.py b/services/runner/tasks/dao_proposal_evaluation.py index c11c2ece..faee60c6 100644 --- a/services/runner/tasks/dao_proposal_evaluation.py +++ b/services/runner/tasks/dao_proposal_evaluation.py @@ -10,7 +10,6 @@ QueueMessageCreate, QueueMessageFilter, QueueMessageType, - VoteBase, VoteCreate, ) from lib.logger import configure_logger diff --git a/services/runner/tasks/proposal_embedder.py b/services/runner/tasks/proposal_embedder.py index 994f91e2..afecde1c 100644 --- a/services/runner/tasks/proposal_embedder.py +++ b/services/runner/tasks/proposal_embedder.py @@ -1,7 +1,7 @@ """Proposal embedding task implementation.""" from dataclasses import dataclass -from typing import Any, Dict, List, Optional +from typing import List, Optional import openai from langchain_openai import OpenAIEmbeddings diff --git a/services/webhooks/chainhook/handlers/base_vote_handler.py b/services/webhooks/chainhook/handlers/base_vote_handler.py index fce39dc9..435a977e 100644 --- a/services/webhooks/chainhook/handlers/base_vote_handler.py +++ b/services/webhooks/chainhook/handlers/base_vote_handler.py @@ -3,7 +3,7 @@ from typing import Dict, List, Optional from backend.factory import backend -from backend.models import ProposalFilter, VoteBase, VoteCreate, VoteFilter +from backend.models import VoteBase, VoteCreate, VoteFilter from lib.logger import configure_logger from services.webhooks.chainhook.handlers.base import ChainhookEventHandler from services.webhooks.chainhook.models import Event, TransactionWithReceipt diff --git a/services/webhooks/chainhook/handlers/block_state_handler.py b/services/webhooks/chainhook/handlers/block_state_handler.py index 3836ca31..dcb9080d 100644 --- a/services/webhooks/chainhook/handlers/block_state_handler.py +++ b/services/webhooks/chainhook/handlers/block_state_handler.py @@ -5,10 +5,8 @@ from backend.factory import backend from backend.models import ChainState, ChainStateBase, ChainStateCreate from config import config -from lib.logger import configure_logger from services.webhooks.chainhook.models import ( Apply, - ChainHookData, TransactionWithReceipt, ) diff --git a/services/webhooks/chainhook/handlers/dao_proposal_handler.py b/services/webhooks/chainhook/handlers/dao_proposal_handler.py index 6c3b75eb..de5db1e8 100644 --- a/services/webhooks/chainhook/handlers/dao_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/dao_proposal_handler.py @@ -1,7 +1,5 @@ """Handler for capturing new DAO proposals.""" -from typing import Dict, Optional - from services.webhooks.chainhook.handlers.action_proposal_handler import ( ActionProposalHandler, ) diff --git a/services/webhooks/chainhook/handlers/dao_vote_handler.py b/services/webhooks/chainhook/handlers/dao_vote_handler.py index cd66803e..5fc95db4 100644 --- a/services/webhooks/chainhook/handlers/dao_vote_handler.py +++ b/services/webhooks/chainhook/handlers/dao_vote_handler.py @@ -2,8 +2,6 @@ from typing import Dict, List, Optional -from backend.factory import backend -from backend.models import ProposalFilter, VoteBase, VoteCreate, VoteFilter from lib.logger import configure_logger from services.webhooks.chainhook.handlers.action_vote_handler import ActionVoteHandler from services.webhooks.chainhook.handlers.base import ChainhookEventHandler diff --git a/services/websocket.py b/services/websocket.py index a6da7e96..f0acdbfc 100644 --- a/services/websocket.py +++ b/services/websocket.py @@ -1,8 +1,7 @@ import asyncio import datetime import time -from typing import Any, Dict, Optional, Set, Tuple -from uuid import UUID +from typing import Any, Dict, Optional from fastapi import WebSocket diff --git a/services/workflows/__init__.py b/services/workflows/__init__.py index 183c0607..b45675a3 100644 --- a/services/workflows/__init__.py +++ b/services/workflows/__init__.py @@ -1,6 +1,3 @@ -"""Workflows package for LangGraph-based workflows.""" - -# Base workflow components from services.workflows.base import ( BaseWorkflow, BaseWorkflowMixin, @@ -13,24 +10,17 @@ StreamingError, ValidationError, ) - -# Remove all imports from deleted files and import from chat.py from services.workflows.chat import ( ChatService, ChatWorkflow, execute_chat_stream, ) from services.workflows.planning_mixin import PlanningCapability - -# Special purpose workflows from services.workflows.proposal_evaluation import ( ProposalEvaluationWorkflow, evaluate_and_vote_on_proposal, evaluate_proposal_only, ) - -# Core messaging and streaming components -# Core ReAct workflow components from services.workflows.tweet_analysis import ( TweetAnalysisWorkflow, analyze_tweet, @@ -44,8 +34,6 @@ add_documents_to_vectors, ) from services.workflows.web_search_mixin import WebSearchCapability - -# Workflow service and factory from services.workflows.workflow_service import ( BaseWorkflowService, WorkflowBuilder, @@ -55,7 +43,6 @@ ) __all__ = [ - # Base workflow foundation "BaseWorkflow", "BaseWorkflowMixin", "ExecutionError", @@ -64,22 +51,18 @@ "StreamingError", "ValidationError", "VectorRetrievalCapability", - # Workflow service layer "BaseWorkflowService", "WorkflowBuilder", "WorkflowFactory", "WorkflowService", "execute_workflow_stream", - # Core messaging components "MessageContent", "MessageProcessor", "StreamingCallbackHandler", - # Core ReAct workflow "LangGraphService", "ReactState", "ReactWorkflow", "execute_langgraph_stream", - # Special purpose workflows "ProposalEvaluationWorkflow", "TweetAnalysisWorkflow", "TweetGeneratorWorkflow", @@ -87,11 +70,9 @@ "evaluate_and_vote_on_proposal", "evaluate_proposal_only", "generate_dao_tweet", - # Chat workflow "ChatService", "ChatWorkflow", "execute_chat_stream", - # Mixins "PlanningCapability", "WebSearchCapability", "add_documents_to_vectors", diff --git a/services/workflows/base.py b/services/workflows/base.py index 1689b442..30a88011 100644 --- a/services/workflows/base.py +++ b/services/workflows/base.py @@ -1,5 +1,3 @@ -"""Base workflow functionality and shared components for all workflow types.""" - import asyncio import datetime import json diff --git a/services/workflows/chat.py b/services/workflows/chat.py index 40394feb..fd105f01 100644 --- a/services/workflows/chat.py +++ b/services/workflows/chat.py @@ -1,9 +1,3 @@ -"""Vector-enabled PrePlan ReAct workflow. - -This workflow combines vector retrieval and planning capabilities -to first retrieve relevant context, create a plan, then execute the ReAct workflow. -""" - import asyncio from typing import ( Annotated, diff --git a/services/workflows/planning_mixin.py b/services/workflows/planning_mixin.py index e97c71f3..32737635 100644 --- a/services/workflows/planning_mixin.py +++ b/services/workflows/planning_mixin.py @@ -1,5 +1,3 @@ -"""Planning mixin for workflows, providing vector-aware planning capabilities.""" - import asyncio from typing import Any, Dict, List, Optional, Tuple diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 22920939..f2ca0849 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -1,5 +1,3 @@ -"""Proposal evaluation workflow.""" - import asyncio import base64 from typing import Any, Dict, List, Optional, TypedDict diff --git a/services/workflows/tweet_analysis.py b/services/workflows/tweet_analysis.py index fad0f1de..12d0afdd 100644 --- a/services/workflows/tweet_analysis.py +++ b/services/workflows/tweet_analysis.py @@ -1,5 +1,3 @@ -"""Tweet analysis workflow.""" - from typing import Dict, Optional, TypedDict from langchain.prompts import PromptTemplate diff --git a/services/workflows/tweet_generator.py b/services/workflows/tweet_generator.py index a39c87af..fe7a5816 100644 --- a/services/workflows/tweet_generator.py +++ b/services/workflows/tweet_generator.py @@ -1,5 +1,3 @@ -"""Tweet generator workflow.""" - from typing import Dict, TypedDict from langchain.prompts import PromptTemplate diff --git a/services/workflows/vector_mixin.py b/services/workflows/vector_mixin.py index f6aaa750..5d2cde89 100644 --- a/services/workflows/vector_mixin.py +++ b/services/workflows/vector_mixin.py @@ -1,5 +1,3 @@ -"""Vector retrieval mixin and vector document utilities for workflows.""" - from typing import Any, Dict, List, Optional from langchain_core.documents import Document diff --git a/services/workflows/web_search_mixin.py b/services/workflows/web_search_mixin.py index 8a257d3a..f85692c4 100644 --- a/services/workflows/web_search_mixin.py +++ b/services/workflows/web_search_mixin.py @@ -1,5 +1,3 @@ -"""Web search mixin for workflows, providing web search capabilities using OpenAI Responses API.""" - from typing import Any, Dict, List, Tuple from langgraph.graph import StateGraph diff --git a/services/workflows/workflow_service.py b/services/workflows/workflow_service.py index 2a4a6921..b7e9bc00 100644 --- a/services/workflows/workflow_service.py +++ b/services/workflows/workflow_service.py @@ -1,9 +1,3 @@ -"""Generic workflow service interface and factory. - -This module provides a standard interface for all workflow services and -a factory function to instantiate the appropriate service based on configuration. -""" - import asyncio import datetime from abc import ABC, abstractmethod @@ -12,7 +6,7 @@ from langchain_core.callbacks.base import BaseCallbackHandler from langchain_core.embeddings import Embeddings from langchain_core.messages import AIMessage, HumanMessage, SystemMessage -from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from langchain_openai import OpenAIEmbeddings from lib.logger import configure_logger from services.workflows.base import ExecutionError, StreamingError diff --git a/tests/backend/test_models.py b/tests/backend/test_models.py deleted file mode 100644 index 77827621..00000000 --- a/tests/backend/test_models.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Tests for backend models.""" - -from backend.models import QueueMessageBase, QueueMessageFilter, QueueMessageType - - -def test_queue_message_type_enum(): - """Test QueueMessageType enum values.""" - assert QueueMessageType.TWEET == "tweet" - assert QueueMessageType.DAO == "dao" - assert QueueMessageType.DAO_TWEET == "dao_tweet" - assert QueueMessageType.DAO_PROPOSAL_VOTE == "dao_proposal_vote" - - # Test string conversion - assert str(QueueMessageType.TWEET) == "tweet" - assert str(QueueMessageType.DAO) == "dao" - assert str(QueueMessageType.DAO_TWEET) == "dao_tweet" - assert str(QueueMessageType.DAO_PROPOSAL_VOTE) == "dao_proposal_vote" - - -def test_queue_message_base_with_enum(): - """Test QueueMessageBase with QueueMessageType enum.""" - # Create a message with enum type - message = QueueMessageBase(type=QueueMessageType.TWEET) - assert message.type == QueueMessageType.TWEET - - # Test serialization/deserialization - message_dict = message.model_dump() - assert message_dict["type"] == "tweet" - - # Create from dict - message2 = QueueMessageBase.model_validate({"type": "tweet"}) - assert message2.type == QueueMessageType.TWEET - - -def test_queue_message_filter_with_enum(): - """Test QueueMessageFilter with QueueMessageType enum.""" - # Create a filter with enum type - filter_obj = QueueMessageFilter(type=QueueMessageType.DAO) - assert filter_obj.type == QueueMessageType.DAO - - # Test serialization/deserialization - filter_dict = filter_obj.model_dump() - assert filter_dict["type"] == "dao" diff --git a/tests/lib/test_token_assets.py b/tests/lib/test_token_assets.py deleted file mode 100644 index 9eef054c..00000000 --- a/tests/lib/test_token_assets.py +++ /dev/null @@ -1,216 +0,0 @@ -import json -from unittest.mock import Mock, patch - -import pytest - -from lib.logger import configure_logger -from lib.token_assets import ( - ImageGenerationError, - StorageError, - TokenAssetError, - TokenAssetManager, - TokenMetadata, -) - -logger = configure_logger(__name__) - - -@pytest.fixture -def token_metadata() -> TokenMetadata: - """Fixture providing sample token metadata.""" - return TokenMetadata( - name="Test Token", - symbol="TEST", - description="A test token for unit testing", - decimals=8, - max_supply="21000000", - ) - - -@pytest.fixture -def token_manager() -> TokenAssetManager: - """Fixture providing a TokenAssetManager instance.""" - return TokenAssetManager("test-token-123") - - -@pytest.fixture -def mock_image_bytes() -> bytes: - """Fixture providing mock image bytes.""" - return b"fake-image-data" - - -def test_token_metadata_initialization(token_metadata: TokenMetadata) -> None: - """Test TokenMetadata initialization.""" - assert token_metadata.name == "Test Token" - assert token_metadata.symbol == "TEST" - assert token_metadata.description == "A test token for unit testing" - assert token_metadata.decimals == 8 - assert token_metadata.max_supply == "21000000" - assert token_metadata.image_url is None - assert token_metadata.uri is None - - -def test_token_asset_manager_initialization(token_manager: TokenAssetManager) -> None: - """Test TokenAssetManager initialization.""" - assert token_manager.token_id == "test-token-123" - assert token_manager.DEFAULT_EXTERNAL_URL == "https://aibtc.dev/" - assert token_manager.DEFAULT_SIP_VERSION == 10 - - -@patch("lib.images.generate_token_image") -@patch("backend.factory.backend.upload_file") -def test_generate_and_store_image_success( - mock_upload: Mock, - mock_generate: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, - mock_image_bytes: bytes, -) -> None: - """Test successful image generation and storage.""" - mock_generate.return_value = mock_image_bytes - mock_upload.return_value = "https://example.com/image.png" - - result = token_manager.generate_and_store_image(token_metadata) - - assert result == "https://example.com/image.png" - mock_generate.assert_called_once_with( - name=token_metadata.name, - symbol=token_metadata.symbol, - description=token_metadata.description, - ) - mock_upload.assert_called_once_with("test-token-123.png", mock_image_bytes) - - -@patch("lib.images.generate_token_image") -def test_generate_and_store_image_invalid_data( - mock_generate: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, -) -> None: - """Test image generation with invalid data type.""" - mock_generate.return_value = "invalid-data-type" - - with pytest.raises(ImageGenerationError, match="Invalid image data type"): - token_manager.generate_and_store_image(token_metadata) - - -@patch("lib.images.generate_token_image") -def test_generate_and_store_image_generation_error( - mock_generate: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, -) -> None: - """Test image generation error.""" - mock_generate.side_effect = ImageGenerationError("Generation failed") - - with pytest.raises(ImageGenerationError, match="Generation failed"): - token_manager.generate_and_store_image(token_metadata) - - -@patch("lib.images.generate_token_image") -@patch("backend.factory.backend.upload_file") -def test_generate_and_store_image_storage_error( - mock_upload: Mock, - mock_generate: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, - mock_image_bytes: bytes, -) -> None: - """Test image storage error.""" - mock_generate.return_value = mock_image_bytes - mock_upload.side_effect = StorageError("Storage failed") - - with pytest.raises(StorageError, match="Storage failed"): - token_manager.generate_and_store_image(token_metadata) - - -@patch("backend.factory.backend.upload_file") -def test_generate_and_store_metadata_success( - mock_upload: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, -) -> None: - """Test successful metadata generation and storage.""" - token_metadata.image_url = "https://example.com/image.png" - mock_upload.return_value = "https://example.com/metadata.json" - - result = token_manager.generate_and_store_metadata(token_metadata) - - assert result == "https://example.com/metadata.json" - mock_upload.assert_called_once() - - # Verify JSON content - args = mock_upload.call_args[0] - assert args[0] == "test-token-123.json" - json_data = json.loads(args[1].decode("utf-8")) - assert json_data["name"] == token_metadata.name - assert json_data["description"] == token_metadata.description - assert json_data["image"] == token_metadata.image_url - assert json_data["properties"]["decimals"] == token_metadata.decimals - assert json_data["properties"]["external_url"] == token_manager.DEFAULT_EXTERNAL_URL - assert json_data["sip"] == token_manager.DEFAULT_SIP_VERSION - - -@patch("backend.factory.backend.upload_file") -def test_generate_and_store_metadata_storage_error( - mock_upload: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, -) -> None: - """Test metadata storage error.""" - mock_upload.side_effect = Exception("Upload failed") - - with pytest.raises(StorageError, match="Failed to store metadata"): - token_manager.generate_and_store_metadata(token_metadata) - - -@patch.object(TokenAssetManager, "generate_and_store_image") -@patch.object(TokenAssetManager, "generate_and_store_metadata") -def test_generate_all_assets_success( - mock_metadata: Mock, - mock_image: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, -) -> None: - """Test successful generation of all assets.""" - mock_image.return_value = "https://example.com/image.png" - mock_metadata.return_value = "https://example.com/metadata.json" - - result = token_manager.generate_all_assets(token_metadata) - - assert result == { - "image_url": "https://example.com/image.png", - "metadata_url": "https://example.com/metadata.json", - } - mock_image.assert_called_once_with(token_metadata) - mock_metadata.assert_called_once_with(token_metadata) - assert token_metadata.image_url == "https://example.com/image.png" - - -@patch.object(TokenAssetManager, "generate_and_store_image") -def test_generate_all_assets_image_error( - mock_image: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, -) -> None: - """Test asset generation with image error.""" - mock_image.side_effect = ImageGenerationError("Image generation failed") - - with pytest.raises(TokenAssetError, match="Asset generation failed"): - token_manager.generate_all_assets(token_metadata) - - -@patch.object(TokenAssetManager, "generate_and_store_image") -@patch.object(TokenAssetManager, "generate_and_store_metadata") -def test_generate_all_assets_metadata_error( - mock_metadata: Mock, - mock_image: Mock, - token_manager: TokenAssetManager, - token_metadata: TokenMetadata, -) -> None: - """Test asset generation with metadata error.""" - mock_image.return_value = "https://example.com/image.png" - mock_metadata.side_effect = StorageError("Metadata storage failed") - - with pytest.raises(TokenAssetError, match="Asset generation failed"): - token_manager.generate_all_assets(token_metadata) diff --git a/tests/lib/test_twitter.py b/tests/lib/test_twitter.py deleted file mode 100644 index dfcdd884..00000000 --- a/tests/lib/test_twitter.py +++ /dev/null @@ -1,221 +0,0 @@ -from typing import Dict -from unittest.mock import Mock, patch - -import pytest -from pytwitter.models import Tweet, User - -from lib.logger import configure_logger -from lib.twitter import TwitterService - -logger = configure_logger(__name__) - - -@pytest.fixture -def twitter_credentials() -> Dict[str, str]: - """Fixture providing test Twitter credentials.""" - return { - "consumer_key": "test_consumer_key", - "consumer_secret": "test_consumer_secret", - "access_token": "test_access_token", - "access_secret": "test_access_secret", - "client_id": "test_client_id", - "client_secret": "test_client_secret", - } - - -@pytest.fixture -def twitter_service(twitter_credentials: Dict[str, str]) -> TwitterService: - """Fixture providing a TwitterService instance.""" - service = TwitterService(**twitter_credentials) - return service - - -@pytest.fixture -def mock_tweet() -> Tweet: - """Fixture providing a mock Tweet.""" - tweet = Mock(spec=Tweet) - tweet.id = "123456789" - tweet.text = "Test tweet" - return tweet - - -@pytest.fixture -def mock_user() -> User: - """Fixture providing a mock User.""" - user = Mock(spec=User) - user.id = "987654321" - user.username = "test_user" - return user - - -def test_initialization(twitter_service: TwitterService) -> None: - """Test TwitterService initialization.""" - assert twitter_service.consumer_key == "test_consumer_key" - assert twitter_service.consumer_secret == "test_consumer_secret" - assert twitter_service.access_token == "test_access_token" - assert twitter_service.access_secret == "test_access_secret" - assert twitter_service.client_id == "test_client_id" - assert twitter_service.client_secret == "test_client_secret" - assert twitter_service.client is None - - -def test_initialize_success(twitter_service: TwitterService) -> None: - """Test successful Twitter client initialization.""" - with patch("pytwitter.Api") as mock_api: - twitter_service.initialize() - - mock_api.assert_called_once_with( - client_id=twitter_service.client_id, - client_secret=twitter_service.client_secret, - consumer_key=twitter_service.consumer_key, - consumer_secret=twitter_service.consumer_secret, - access_token=twitter_service.access_token, - access_secret=twitter_service.access_secret, - application_only_auth=False, - ) - assert twitter_service.client is not None - - -def test_initialize_failure(twitter_service: TwitterService) -> None: - """Test Twitter client initialization failure.""" - with patch("pytwitter.Api", side_effect=Exception("API Error")): - with pytest.raises(Exception, match="API Error"): - twitter_service.initialize() - assert twitter_service.client is None - - -@pytest.mark.asyncio -async def test_ainitialize(twitter_service: TwitterService) -> None: - """Test asynchronous initialization.""" - with patch.object(twitter_service, "initialize") as mock_initialize: - await twitter_service._ainitialize() - mock_initialize.assert_called_once() - - -def test_post_tweet_success(twitter_service: TwitterService, mock_tweet: Tweet) -> None: - """Test successful tweet posting.""" - twitter_service.client = Mock() - twitter_service.client.create_tweet.return_value = mock_tweet - - result = twitter_service.post_tweet("Test message") - - assert result == mock_tweet - twitter_service.client.create_tweet.assert_called_once_with( - text="Test message", reply_in_reply_to_tweet_id=None - ) - - -def test_post_tweet_with_reply( - twitter_service: TwitterService, mock_tweet: Tweet -) -> None: - """Test tweet posting with reply.""" - twitter_service.client = Mock() - twitter_service.client.create_tweet.return_value = mock_tweet - - result = twitter_service.post_tweet( - "Test reply", reply_in_reply_to_tweet_id="987654321" - ) - - assert result == mock_tweet - twitter_service.client.create_tweet.assert_called_once_with( - text="Test reply", reply_in_reply_to_tweet_id="987654321" - ) - - -def test_post_tweet_client_not_initialized(twitter_service: TwitterService) -> None: - """Test tweet posting with uninitialized client.""" - result = twitter_service.post_tweet("Test message") - assert result is None - - -def test_post_tweet_failure(twitter_service: TwitterService) -> None: - """Test tweet posting failure.""" - twitter_service.client = Mock() - twitter_service.client.create_tweet.side_effect = Exception("API Error") - - result = twitter_service.post_tweet("Test message") - assert result is None - - -@pytest.mark.asyncio -async def test_get_user_by_username_success( - twitter_service: TwitterService, mock_user: User -) -> None: - """Test successful user retrieval by username.""" - twitter_service.client = Mock() - twitter_service.client.get_user.return_value = mock_user - - result = await twitter_service.get_user_by_username("test_user") - - assert result == mock_user - twitter_service.client.get_user.assert_called_once_with(username="test_user") - - -@pytest.mark.asyncio -async def test_get_user_by_username_failure(twitter_service: TwitterService) -> None: - """Test user retrieval failure by username.""" - twitter_service.client = Mock() - twitter_service.client.get_user.side_effect = Exception("API Error") - - result = await twitter_service.get_user_by_username("test_user") - assert result is None - - -@pytest.mark.asyncio -async def test_get_user_by_user_id_success( - twitter_service: TwitterService, mock_user: User -) -> None: - """Test successful user retrieval by user ID.""" - twitter_service.client = Mock() - twitter_service.client.get_user.return_value = mock_user - - result = await twitter_service.get_user_by_user_id("123456789") - - assert result == mock_user - twitter_service.client.get_user.assert_called_once_with(user_id="123456789") - - -@pytest.mark.asyncio -async def test_get_mentions_success( - twitter_service: TwitterService, mock_tweet: Tweet -) -> None: - """Test successful mentions retrieval.""" - twitter_service.client = Mock() - mock_response = Mock() - mock_response.data = [mock_tweet] - twitter_service.client.get_mentions.return_value = mock_response - - result = await twitter_service.get_mentions_by_user_id("123456789") - - assert result == [mock_tweet] - twitter_service.client.get_mentions.assert_called_once() - args, kwargs = twitter_service.client.get_mentions.call_args - assert kwargs["user_id"] == "123456789" - assert kwargs["max_results"] == 100 - assert "tweet_fields" in kwargs - assert "expansions" in kwargs - assert "user_fields" in kwargs - assert "media_fields" in kwargs - assert "place_fields" in kwargs - assert "poll_fields" in kwargs - - -@pytest.mark.asyncio -async def test_get_mentions_failure(twitter_service: TwitterService) -> None: - """Test mentions retrieval failure.""" - twitter_service.client = Mock() - twitter_service.client.get_mentions.side_effect = Exception("API Error") - - result = await twitter_service.get_mentions_by_user_id("123456789") - assert result == [] - - -@pytest.mark.asyncio -async def test_apost_tweet(twitter_service: TwitterService) -> None: - """Test asynchronous tweet posting.""" - with patch.object(twitter_service, "post_tweet") as mock_post_tweet: - mock_post_tweet.return_value = Mock(spec=Tweet) - result = await twitter_service._apost_tweet("Test message", "987654321") - - mock_post_tweet.assert_called_once_with("Test message", "987654321") - assert isinstance(result, Mock) # Mock of Tweet diff --git a/tests/lib/test_websocket_manager.py b/tests/lib/test_websocket_manager.py deleted file mode 100644 index 4acba3a8..00000000 --- a/tests/lib/test_websocket_manager.py +++ /dev/null @@ -1,267 +0,0 @@ -import asyncio -from unittest.mock import AsyncMock, patch - -import pytest -from fastapi import WebSocket - -from lib.logger import configure_logger -from lib.websocket_manager import ConnectionManager - -logger = configure_logger(__name__) - - -@pytest.fixture -def manager() -> ConnectionManager: - """Fixture providing a ConnectionManager instance with a short TTL for testing.""" - return ConnectionManager(ttl_seconds=1) - - -@pytest.fixture -def mock_websocket() -> AsyncMock: - """Fixture providing a mock WebSocket.""" - websocket = AsyncMock(spec=WebSocket) - return websocket - - -@pytest.mark.asyncio -async def test_connect_job( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test job connection.""" - job_id = "test-job-1" - await manager.connect_job(mock_websocket, job_id) - - assert job_id in manager.job_connections - assert len(manager.job_connections[job_id]) == 1 - ws, ts = next(iter(manager.job_connections[job_id])) - assert ws == mock_websocket - assert isinstance(ts, float) - mock_websocket.accept.assert_called_once() - - -@pytest.mark.asyncio -async def test_connect_thread( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test thread connection.""" - thread_id = "test-thread-1" - await manager.connect_thread(mock_websocket, thread_id) - - assert thread_id in manager.thread_connections - assert len(manager.thread_connections[thread_id]) == 1 - ws, ts = next(iter(manager.thread_connections[thread_id])) - assert ws == mock_websocket - assert isinstance(ts, float) - mock_websocket.accept.assert_called_once() - - -@pytest.mark.asyncio -async def test_connect_session( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test session connection.""" - session_id = "test-session-1" - await manager.connect_session(mock_websocket, session_id) - - assert session_id in manager.session_connections - assert len(manager.session_connections[session_id]) == 1 - ws, ts = next(iter(manager.session_connections[session_id])) - assert ws == mock_websocket - assert isinstance(ts, float) - mock_websocket.accept.assert_called_once() - - -@pytest.mark.asyncio -async def test_disconnect_job( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test job disconnection.""" - job_id = "test-job-1" - await manager.connect_job(mock_websocket, job_id) - await manager.disconnect_job(mock_websocket, job_id) - - assert job_id not in manager.job_connections - - -@pytest.mark.asyncio -async def test_disconnect_thread( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test thread disconnection.""" - thread_id = "test-thread-1" - await manager.connect_thread(mock_websocket, thread_id) - await manager.disconnect_thread(mock_websocket, thread_id) - - assert thread_id not in manager.thread_connections - - -@pytest.mark.asyncio -async def test_disconnect_session( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test session disconnection.""" - session_id = "test-session-1" - await manager.connect_session(mock_websocket, session_id) - await manager.disconnect_session(mock_websocket, session_id) - - assert session_id not in manager.session_connections - - -@pytest.mark.asyncio -async def test_send_job_message( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test sending message to job connection.""" - job_id = "test-job-1" - message = {"type": "test", "data": "test-data"} - - await manager.connect_job(mock_websocket, job_id) - await manager.send_job_message(message, job_id) - - mock_websocket.send_json.assert_called_once_with(message) - - -@pytest.mark.asyncio -async def test_send_thread_message( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test sending message to thread connection.""" - thread_id = "test-thread-1" - message = {"type": "test", "data": "test-data"} - - await manager.connect_thread(mock_websocket, thread_id) - await manager.send_thread_message(message, thread_id) - - mock_websocket.send_json.assert_called_once_with(message) - - -@pytest.mark.asyncio -async def test_send_session_message( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test sending message to session connection.""" - session_id = "test-session-1" - message = {"type": "test", "data": "test-data"} - - await manager.connect_session(mock_websocket, session_id) - await manager.send_session_message(message, session_id) - - mock_websocket.send_json.assert_called_once_with(message) - - -@pytest.mark.asyncio -async def test_send_message_to_dead_connection( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test sending message to dead connection.""" - job_id = "test-job-1" - message = {"type": "test", "data": "test-data"} - - mock_websocket.send_json.side_effect = Exception("Connection closed") - - await manager.connect_job(mock_websocket, job_id) - await manager.send_job_message(message, job_id) - - assert job_id not in manager.job_connections - - -@pytest.mark.asyncio -async def test_cleanup_expired_connections( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test cleanup of expired connections.""" - job_id = "test-job-1" - thread_id = "test-thread-1" - session_id = "test-session-1" - - # Connect to all types - await manager.connect_job(mock_websocket, job_id) - await manager.connect_thread(mock_websocket, thread_id) - await manager.connect_session(mock_websocket, session_id) - - # Wait for TTL to expire - await asyncio.sleep(1.1) - - # Run cleanup - await manager.cleanup_expired_connections() - - assert job_id not in manager.job_connections - assert thread_id not in manager.thread_connections - assert session_id not in manager.session_connections - mock_websocket.close.assert_called() - - -@pytest.mark.asyncio -async def test_broadcast_errors( - manager: ConnectionManager, mock_websocket: AsyncMock -) -> None: - """Test broadcasting error messages.""" - job_id = "test-job-1" - thread_id = "test-thread-1" - session_id = "test-session-1" - error_message = "Test error" - - # Connect to all types - await manager.connect_job(mock_websocket, job_id) - await manager.connect_thread(mock_websocket, thread_id) - await manager.connect_session(mock_websocket, session_id) - - # Broadcast errors - await manager.broadcast_job_error(error_message, job_id) - await manager.broadcast_thread_error(error_message, thread_id) - await manager.broadcast_session_error(error_message, session_id) - - expected_message = {"type": "error", "message": error_message} - assert mock_websocket.send_json.call_count == 3 - mock_websocket.send_json.assert_called_with(expected_message) - - -@pytest.mark.asyncio -async def test_multiple_connections(manager: ConnectionManager) -> None: - """Test managing multiple connections.""" - job_id = "test-job-1" - mock_websocket1 = AsyncMock(spec=WebSocket) - mock_websocket2 = AsyncMock(spec=WebSocket) - - # Connect two websockets - await manager.connect_job(mock_websocket1, job_id) - await manager.connect_job(mock_websocket2, job_id) - - assert len(manager.job_connections[job_id]) == 2 - - # Send a message - message = {"type": "test", "data": "test-data"} - await manager.send_job_message(message, job_id) - - mock_websocket1.send_json.assert_called_once_with(message) - mock_websocket2.send_json.assert_called_once_with(message) - - # Disconnect one - await manager.disconnect_job(mock_websocket1, job_id) - assert len(manager.job_connections[job_id]) == 1 - - # Send another message - await manager.send_job_message(message, job_id) - mock_websocket1.send_json.assert_called_once() # Still only called once - assert mock_websocket2.send_json.call_count == 2 # Called twice - - -@pytest.mark.asyncio -async def test_cleanup_task(manager: ConnectionManager) -> None: - """Test the cleanup task.""" - with patch.object(manager, "cleanup_expired_connections") as mock_cleanup: - # Start the cleanup task - cleanup_task = asyncio.create_task(manager.start_cleanup_task()) - - # Wait a bit to allow the task to run - await asyncio.sleep(0.1) - - # Cancel the task - cleanup_task.cancel() - try: - await cleanup_task - except asyncio.CancelledError: - pass - - # Verify cleanup was called - mock_cleanup.assert_called() diff --git a/tests/services/test_langgraph.py b/tests/services/test_langgraph.py index 3f3b65a2..865f4f50 100644 --- a/tests/services/test_langgraph.py +++ b/tests/services/test_langgraph.py @@ -5,8 +5,8 @@ from langchain_core.messages import AIMessage, HumanMessage, SystemMessage from services.workflows import ( + ChatService, ExecutionError, - LangGraphService, MessageContent, MessageProcessor, StreamingCallbackHandler, @@ -94,26 +94,33 @@ def handler(self, queue): def test_initialization(self, handler): assert handler.tokens == [] assert handler.current_tool is None - assert handler._loop is None + assert handler._loop is None # Assuming _loop is an attribute @pytest.mark.asyncio - async def test_queue_operations(self, handler): + async def test_queue_operations(self, handler, queue): # Added queue fixture test_item = {"type": "test", "content": "test_content"} + # To test _put_to_queue properly, ensure it's called + handler._put_to_queue(test_item) + item = await queue.get() + assert item == test_item + with pytest.raises(StreamingError): - # Test with invalid queue operation - handler.queue = None - handler._put_to_queue(test_item) + # Test with invalid queue operation (e.g., queue is None) + handler_no_queue = StreamingCallbackHandler( + queue=None + ) # Create instance for this test + handler_no_queue._put_to_queue(test_item) def test_tool_start(self, handler): - handler._put_to_queue = MagicMock() + handler._put_to_queue = MagicMock() # Mock to check calls handler.on_tool_start({"name": "test_tool"}, "test_input") assert handler.current_tool == "test_tool" handler._put_to_queue.assert_called_once() def test_tool_end(self, handler): - handler._put_to_queue = MagicMock() + handler._put_to_queue = MagicMock() # Mock to check calls handler.current_tool = "test_tool" handler.on_tool_end("test_output") @@ -125,11 +132,11 @@ def test_llm_new_token(self, handler): assert "test_token" in handler.tokens def test_llm_error(self, handler): - with pytest.raises(ExecutionError): + with pytest.raises(ExecutionError): # Or the specific error it raises handler.on_llm_error(Exception("test error")) def test_tool_error(self, handler): - handler._put_to_queue = MagicMock() + handler._put_to_queue = MagicMock() # Mock to check calls handler.current_tool = "test_tool" handler.on_tool_error(Exception("test error")) @@ -137,62 +144,53 @@ def test_tool_error(self, handler): handler._put_to_queue.assert_called_once() -class TestLangGraphService: +class TestChatService: @pytest.fixture - def service(self): - return LangGraphService() + def service(self, mock_chat_model_class, mock_tool_node_class): + return ChatService(collection_names="test_collection") @pytest.fixture - def mock_chat_model(self): - with patch("services.workflows.ChatOpenAI") as mock: + def mock_chat_model_class(self): + with patch("services.workflows.chat.ChatOpenAI") as mock: yield mock @pytest.fixture - def mock_tool_node(self): - with patch("services.workflows.ToolNode") as mock: + def mock_tool_node_class(self): + with patch("langgraph.prebuilt.ToolNode") as mock: yield mock - def test_create_chat_model(self, service, mock_chat_model): - callback_handler = MagicMock() - tools = [MagicMock()] + def test_chat_service_initialization(self, service, mock_chat_model_class): + assert service.llm is not None - service._create_chat_model(callback_handler, tools) - mock_chat_model.assert_called_once() + def test_get_runnable_graph(self, service, mock_tool_node_class): + if hasattr(service, "_create_graph"): + graph = service._create_graph() + assert graph is not None - def test_create_workflow(self, service): - chat = MagicMock() - tool_node = MagicMock() + @pytest.mark.asyncio + async def test_execute_chat_stream_success(self, service, sample_history): + async def mock_stream_results(*args, **kwargs): + yield {"type": "token", "content": "test"} + yield {"type": "end"} - workflow = service._create_workflow(chat, tool_node) - assert workflow is not None + service.execute_stream = AsyncMock(side_effect=mock_stream_results) - @pytest.mark.asyncio - async def test_execute_chat_stream_success( - self, service, sample_history, mock_chat_model - ): - # Mock necessary components - mock_queue = asyncio.Queue() - await mock_queue.put({"type": "token", "content": "test"}) - await mock_queue.put({"type": "end"}) - - mock_chat = MagicMock() - mock_chat.invoke.return_value = AIMessage(content="test response") - mock_chat_model.return_value = mock_chat - - # Execute stream tools_map = {"test_tool": MagicMock()} chunks = [] - async for chunk in service.execute_chat_stream( + async for chunk in service.execute_stream( sample_history, "test input", "test persona", tools_map ): chunks.append(chunk) assert len(chunks) > 0 + service.execute_stream.assert_called_once() @pytest.mark.asyncio async def test_execute_chat_stream_error(self, service, sample_history): + service.execute_stream = AsyncMock(side_effect=ExecutionError("Stream failed")) + with pytest.raises(ExecutionError): - async for _ in service.execute_chat_stream( + async for _ in service.execute_stream( sample_history, "test input", None, None ): pass @@ -200,10 +198,26 @@ async def test_execute_chat_stream_error(self, service, sample_history): @pytest.mark.asyncio async def test_facade_function(): - with patch("services.workflows.LangGraphService") as mock_service: - instance = mock_service.return_value - instance.execute_chat_stream = AsyncMock() - instance.execute_chat_stream.return_value = [{"type": "test"}] + with patch("services.workflows.chat.ChatService") as MockChatService: + mock_service_instance = MockChatService.return_value + + async def mock_async_iterable(*args, **kwargs): + yield {"type": "test"} - async for chunk in execute_langgraph_stream([], "test", None, None): + mock_service_instance.execute_stream = AsyncMock( + return_value=mock_async_iterable() + ) + + async for chunk in execute_langgraph_stream( + history=[], + input_str="test", + persona=None, + tools_map=None, + collection_names="test_collection", + ): assert chunk["type"] == "test" + + MockChatService.assert_called_once_with( + collection_names="test_collection", embeddings=None + ) + mock_service_instance.execute_stream.assert_called_once() diff --git a/tests/services/webhooks/chainhook/test_handlers.py b/tests/services/webhooks/chainhook/test_handlers.py deleted file mode 100644 index b2b51757..00000000 --- a/tests/services/webhooks/chainhook/test_handlers.py +++ /dev/null @@ -1,344 +0,0 @@ -"""Tests for the chainhook handlers.""" - -import unittest -from unittest.mock import MagicMock, patch - -from services.webhooks.chainhook.handlers import ( - BuyEventHandler, - ContractMessageHandler, - SellEventHandler, - TransactionStatusHandler, -) -from services.webhooks.chainhook.models import ( - Event, - Receipt, - TransactionIdentifier, - TransactionMetadata, - TransactionWithReceipt, -) - - -class TestContractMessageHandler(unittest.TestCase): - """Tests for the ContractMessageHandler.""" - - def setUp(self): - """Set up the test environment.""" - self.handler = ContractMessageHandler() - - # Sample transaction that should be handled - self.message_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata={ - "kind": { - "type": "ContractCall", - "data": { - "method": "send", - "args": ["test message"], - "contract_identifier": "ST1234567890ABCDEF.test-contract", - }, - }, - "success": False, - "sender": "ST1234567890ABCDEF", - }, - operations=[], - ) - - # Sample transaction that should not be handled - self.non_message_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata={ - "kind": { - "type": "ContractCall", - "data": { - "method": "transfer", - "args": ["100", "ST1234567890ABCDEF"], - "contract_identifier": "ST1234567890ABCDEF.test-contract", - }, - }, - "success": True, - "sender": "ST1234567890ABCDEF", - }, - operations=[], - ) - - def test_can_handle_transaction(self): - """Test the can_handle method.""" - # Should handle message transactions - self.assertTrue(self.handler.can_handle_transaction(self.message_transaction)) - - # Should not handle non-message transactions - self.assertFalse( - self.handler.can_handle_transaction(self.non_message_transaction) - ) - - @patch("backend.factory.backend") - async def test_handle_transaction(self, mock_backend): - """Test the handle_transaction method.""" - # Mock the backend methods - mock_extension = MagicMock() - mock_extension.dao_id = "test-dao-id" - mock_backend.list_extensions.return_value = [mock_extension] - mock_backend.create_queue_message.return_value = {"id": "test-message-id"} - - # Call the handler - await self.handler.handle_transaction(self.message_transaction) - - # Verify the backend methods were called correctly - mock_backend.list_extensions.assert_called_once() - mock_backend.create_queue_message.assert_called_once() - - # Check that the message was created with the correct parameters - call_args = mock_backend.create_queue_message.call_args[0][0] - self.assertEqual(call_args.type, "tweet") - self.assertEqual(call_args.message, {"message": "test message"}) - self.assertEqual(call_args.dao_id, "test-dao-id") - - -class TestTransactionStatusHandler(unittest.TestCase): - """Tests for the TransactionStatusHandler.""" - - def setUp(self): - """Set up the test environment.""" - self.handler = TransactionStatusHandler() - - # Sample transaction - self.transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata={ - "kind": { - "type": "ContractCall", - "data": { - "method": "deploy", - "contract_identifier": "ST1234567890ABCDEF.test-contract", - }, - }, - "success": True, - "sender": "ST1234567890ABCDEF", - }, - operations=[], - ) - - def test_can_handle_transaction(self): - """Test the can_handle method.""" - # Should handle any transaction - self.assertTrue(self.handler.can_handle_transaction(self.transaction)) - - @patch("backend.factory.backend") - async def test_handle_transaction(self, mock_backend): - """Test the handle_transaction method.""" - # Mock the backend methods - mock_extension = MagicMock() - mock_extension.id = "test-extension-id" - mock_extension.status = "PENDING" - mock_extension.tx_id = "0xabcdef1234567890" - - mock_token = MagicMock() - mock_token.id = "test-token-id" - mock_token.status = "PENDING" - mock_token.tx_id = "0xabcdef1234567890" - - mock_proposal = MagicMock() - mock_proposal.id = "test-proposal-id" - mock_proposal.status = "PENDING" - mock_proposal.tx_id = "other-tx-id" - - mock_backend.list_extensions.return_value = [mock_extension] - mock_backend.list_tokens.return_value = [mock_token] - mock_backend.list_proposals.return_value = [mock_proposal] - - # Call the handler - await self.handler.handle_transaction(self.transaction) - - # Verify the backend methods were called correctly - mock_backend.list_extensions.assert_called_once() - mock_backend.list_tokens.assert_called_once() - mock_backend.list_proposals.assert_called_once() - - # Check that the extension and token were updated but not the proposal - mock_backend.update_extension.assert_called_once() - mock_backend.update_token.assert_called_once() - mock_backend.update_proposal.assert_not_called() - - -class TestBuyEventHandler(unittest.TestCase): - """Tests for the BuyEventHandler.""" - - def setUp(self): - """Set up the test environment.""" - self.handler = BuyEventHandler() - - # Create a sample FT transfer event - self.ft_transfer_event = Event( - data={ - "asset_identifier": "ST123.test-token::TEST", - "amount": "1000", - "sender": "ST789", - "recipient": "ST456", - }, - position={"index": 0}, - type="FTTransferEvent", - ) - - # Create a sample receipt with events - self.sample_receipt = Receipt( - contract_calls_stack=[], - events=[self.ft_transfer_event], - mutated_assets_radius=[], - mutated_contracts_radius=[], - ) - - # Sample buy transaction - self.buy_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=TransactionMetadata( - description="Test buy transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "buy", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ), - operations=[], - ) - - # Sample non-buy transaction - self.non_buy_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=TransactionMetadata( - description="Test non-buy transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "transfer", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ), - operations=[], - ) - - def test_can_handle_transaction(self): - """Test the can_handle method.""" - # Should handle buy transactions - self.assertTrue(self.handler.can_handle_transaction(self.buy_transaction)) - - # Should not handle non-buy transactions - self.assertFalse(self.handler.can_handle_transaction(self.non_buy_transaction)) - - -class TestSellEventHandler(unittest.TestCase): - """Tests for the SellEventHandler.""" - - def setUp(self): - """Set up the test environment.""" - self.handler = SellEventHandler() - - # Create a sample FT transfer event - self.ft_transfer_event = Event( - data={ - "asset_identifier": "ST123.test-token::TEST", - "amount": "1000", - "sender": "ST456", - "recipient": "ST789", - }, - position={"index": 0}, - type="FTTransferEvent", - ) - - # Create a sample receipt with events - self.sample_receipt = Receipt( - contract_calls_stack=[], - events=[self.ft_transfer_event], - mutated_assets_radius=[], - mutated_contracts_radius=[], - ) - - # Sample sell transaction - self.sell_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=TransactionMetadata( - description="Test sell transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "sell", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ), - operations=[], - ) - - # Sample non-sell transaction - self.non_sell_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=TransactionMetadata( - description="Test non-sell transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "transfer", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ), - operations=[], - ) - - def test_can_handle_transaction(self): - """Test the can_handle method.""" - # Should handle sell transactions - self.assertTrue(self.handler.can_handle_transaction(self.sell_transaction)) - - # Should not handle non-sell transactions - self.assertFalse(self.handler.can_handle_transaction(self.non_sell_transaction)) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/services/webhooks/chainhook/test_sell_event_handler.py b/tests/services/webhooks/chainhook/test_sell_event_handler.py deleted file mode 100644 index 2da95218..00000000 --- a/tests/services/webhooks/chainhook/test_sell_event_handler.py +++ /dev/null @@ -1,281 +0,0 @@ -"""Tests for the SellEventHandler.""" - -import unittest -from unittest.mock import MagicMock, patch -from uuid import UUID - -from backend.models import WalletTokenBase -from services.webhooks.chainhook.handlers.sell_event_handler import SellEventHandler -from services.webhooks.chainhook.models import ( - Event, - Receipt, - TransactionIdentifier, - TransactionMetadata, - TransactionWithReceipt, -) - - -class TestSellEventHandler(unittest.TestCase): - """Test cases for SellEventHandler.""" - - def setUp(self): - """Set up the test environment.""" - self.handler = SellEventHandler() - - # Create a mock logger - self.handler.logger = MagicMock() - - # Create a sample FT transfer event - self.ft_transfer_event = Event( - data={ - "asset_identifier": "ST123.test-token::TEST", - "amount": "1000", - "sender": "ST456", - "recipient": "ST789", - }, - position={"index": 0}, - type="FTTransferEvent", - ) - - # Create a sample receipt with events - self.sample_receipt = Receipt( - contract_calls_stack=[], - events=[self.ft_transfer_event], - mutated_assets_radius=[], - mutated_contracts_radius=[], - ) - - # Create sample transaction metadata - self.sample_metadata = TransactionMetadata( - description="Test sell transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "sell", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ) - - # Create a sample transaction - self.sample_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=self.sample_metadata, - operations=[], - ) - - def test_can_handle_sell_transaction(self): - """Test that the handler can handle sell transactions.""" - # Test with a sell transaction - result = self.handler.can_handle_transaction(self.sample_transaction) - self.assertTrue(result) - - # Test with a sell-tokens transaction - sell_tokens_metadata = TransactionMetadata( - description="Test sell-tokens transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "sell-tokens", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ) - - sell_tokens_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=sell_tokens_metadata, - operations=[], - ) - - result = self.handler.can_handle_transaction(sell_tokens_transaction) - self.assertTrue(result) - - def test_cannot_handle_non_sell_transaction(self): - """Test that the handler cannot handle non-sell transactions.""" - # Create a non-sell transaction - non_sell_metadata = TransactionMetadata( - description="Test non-sell transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "transfer", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ) - - non_sell_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=non_sell_metadata, - operations=[], - ) - - result = self.handler.can_handle_transaction(non_sell_transaction) - self.assertFalse(result) - - @patch("backend.factory.backend") - @patch("services.webhooks.chainhook.handlers.sell_event_handler.configure_logger") - async def test_handle_transaction_with_wallet_token( - self, mock_configure_logger, mock_backend - ): - """Test that the handler correctly updates token balances when selling tokens.""" - # Set up the mock logger - mock_logger = MagicMock() - mock_configure_logger.return_value = mock_logger - - # Create a new handler with the mocked logger - handler = SellEventHandler() - - # Mock the wallet and token data - mock_wallet = MagicMock() - mock_wallet.id = UUID("00000000-0000-0000-0000-000000000001") - mock_token = MagicMock() - mock_token.id = UUID("00000000-0000-0000-0000-000000000002") - mock_token.dao_id = UUID("00000000-0000-0000-0000-000000000003") - - # Mock the wallet token record - mock_wallet_token = MagicMock() - mock_wallet_token.id = UUID("00000000-0000-0000-0000-000000000004") - mock_wallet_token.wallet_id = mock_wallet.id - mock_wallet_token.token_id = mock_token.id - mock_wallet_token.dao_id = mock_token.dao_id - mock_wallet_token.amount = "5000" # Current amount before selling - - # Set up the mock backend responses - mock_backend.list_wallets.return_value = [mock_wallet] - mock_backend.list_tokens.return_value = [mock_token] - mock_backend.list_wallet_tokens.return_value = [mock_wallet_token] - - # Handle the transaction - await handler.handle_transaction(self.sample_transaction) - - # Check that the backend methods were called correctly - mock_backend.list_wallets.assert_called_once() - mock_backend.list_tokens.assert_called_once() - mock_backend.list_wallet_tokens.assert_called_once() - - # Check that update_wallet_token was called with the correct parameters - mock_backend.update_wallet_token.assert_called_once() - call_args = mock_backend.update_wallet_token.call_args - self.assertEqual(call_args[0][0], mock_wallet_token.id) - - # Check that the amount was decreased correctly (5000 - 1000 = 4000) - update_data = call_args[0][1] - self.assertIsInstance(update_data, WalletTokenBase) - self.assertEqual(update_data.amount, "4000.0") - self.assertEqual(update_data.wallet_id, mock_wallet.id) - self.assertEqual(update_data.token_id, mock_token.id) - self.assertEqual(update_data.dao_id, mock_token.dao_id) - - @patch("backend.factory.backend") - @patch("services.webhooks.chainhook.handlers.sell_event_handler.configure_logger") - async def test_handle_transaction_with_insufficient_balance( - self, mock_configure_logger, mock_backend - ): - """Test that the handler correctly handles selling more tokens than available.""" - # Set up the mock logger - mock_logger = MagicMock() - mock_configure_logger.return_value = mock_logger - - # Create a new handler with the mocked logger - handler = SellEventHandler() - - # Create an event with a large amount to sell (more than available) - large_amount_event = Event( - data={ - "asset_identifier": "ST123.test-token::TEST", - "amount": "10000", # More than the 5000 available - "sender": "ST456", - "recipient": "ST789", - }, - position={"index": 0}, - type="FTTransferEvent", - ) - - # Update the receipt with the new event - large_amount_receipt = Receipt( - contract_calls_stack=[], - events=[large_amount_event], - mutated_assets_radius=[], - mutated_contracts_radius=[], - ) - - # Update the metadata with the new receipt - large_amount_metadata = self.sample_metadata - large_amount_metadata.receipt = large_amount_receipt - - # Create a new transaction with the updated metadata - large_amount_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=large_amount_metadata, - operations=[], - ) - - # Mock the wallet and token data - mock_wallet = MagicMock() - mock_wallet.id = UUID("00000000-0000-0000-0000-000000000001") - mock_token = MagicMock() - mock_token.id = UUID("00000000-0000-0000-0000-000000000002") - mock_token.dao_id = UUID("00000000-0000-0000-0000-000000000003") - - # Mock the wallet token record with a smaller amount than being sold - mock_wallet_token = MagicMock() - mock_wallet_token.id = UUID("00000000-0000-0000-0000-000000000004") - mock_wallet_token.wallet_id = mock_wallet.id - mock_wallet_token.token_id = mock_token.id - mock_wallet_token.dao_id = mock_token.dao_id - mock_wallet_token.amount = "5000" # Less than the 10000 being sold - - # Set up the mock backend responses - mock_backend.list_wallets.return_value = [mock_wallet] - mock_backend.list_tokens.return_value = [mock_token] - mock_backend.list_wallet_tokens.return_value = [mock_wallet_token] - - # Handle the transaction - await handler.handle_transaction(large_amount_transaction) - - # Check that update_wallet_token was called with the correct parameters - mock_backend.update_wallet_token.assert_called_once() - call_args = mock_backend.update_wallet_token.call_args - - # Check that the amount was set to 0 (not negative) - update_data = call_args[0][1] - self.assertEqual(update_data.amount, "0.0") - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/services/webhooks/dao/test_dao_webhook.py b/tests/services/webhooks/dao/test_dao_webhook.py deleted file mode 100644 index 71cb1974..00000000 --- a/tests/services/webhooks/dao/test_dao_webhook.py +++ /dev/null @@ -1,142 +0,0 @@ -"""Tests for the DAO webhook service.""" - -from unittest.mock import AsyncMock, MagicMock, patch -from uuid import UUID - -import pytest - -from backend.models import ContractStatus -from services.webhooks.dao.handler import DAOHandler -from services.webhooks.dao.models import DAOWebhookPayload, ExtensionData, TokenData -from services.webhooks.dao.parser import DAOParser -from services.webhooks.dao.service import DAOService - - -@pytest.fixture -def sample_dao_payload(): - """Create a sample DAO webhook payload for testing.""" - return { - "name": "Test DAO", - "mission": "Testing mission", - "description": "A DAO for testing purposes", - "is_deployed": False, - "is_broadcasted": False, - "extensions": [{"type": "test_extension", "status": "DRAFT"}], - "token": { - "name": "Test Token", - "symbol": "TEST", - "decimals": 6, - "description": "A token for testing", - }, - } - - -def test_dao_parser(sample_dao_payload): - """Test that the DAO parser correctly parses a valid payload.""" - parser = DAOParser() - result = parser.parse(sample_dao_payload) - - assert isinstance(result, DAOWebhookPayload) - assert result.name == "Test DAO" - assert result.mission == "Testing mission" - assert result.description == "A DAO for testing purposes" - assert result.is_deployed is False - assert result.is_broadcasted is False - - assert len(result.extensions) == 1 - assert result.extensions[0].type == "test_extension" - assert result.extensions[0].status == ContractStatus.DRAFT - - assert result.token is not None - assert result.token.name == "Test Token" - assert result.token.symbol == "TEST" - assert result.token.decimals == 6 - assert result.token.description == "A token for testing" - - -@pytest.mark.asyncio -async def test_dao_handler(): - """Test that the DAO handler correctly processes a parsed payload.""" - # Create mock database - mock_db = MagicMock() - mock_db.create_dao.return_value = MagicMock( - id=UUID("00000000-0000-0000-0000-000000000001"), name="Test DAO" - ) - mock_db.create_extension.return_value = MagicMock( - id=UUID("00000000-0000-0000-0000-000000000002") - ) - mock_db.create_token.return_value = MagicMock( - id=UUID("00000000-0000-0000-0000-000000000003") - ) - - # Create parsed payload - parsed_data = DAOWebhookPayload( - name="Test DAO", - mission="Testing mission", - description="A DAO for testing purposes", - extensions=[ExtensionData(type="test_extension", status=ContractStatus.DRAFT)], - token=TokenData( - name="Test Token", - symbol="TEST", - decimals=6, - description="A token for testing", - ), - ) - - # Test handler with mocked database - with patch("backend.factory.backend", mock_db): - handler = DAOHandler() - result = await handler.handle(parsed_data) - - assert result["success"] is True - assert "Successfully created DAO 'Test DAO'" in result["message"] - assert result["data"]["dao_id"] == UUID("00000000-0000-0000-0000-000000000001") - assert result["data"]["extension_ids"] == [ - UUID("00000000-0000-0000-0000-000000000002") - ] - assert result["data"]["token_id"] == UUID( - "00000000-0000-0000-0000-000000000003" - ) - - # Verify database calls - mock_db.create_dao.assert_called_once() - mock_db.create_extension.assert_called_once() - mock_db.create_token.assert_called_once() - - -@pytest.mark.asyncio -async def test_dao_service(sample_dao_payload): - """Test that the DAO service correctly coordinates parsing and handling.""" - # Create mock parser and handler - mock_parser = MagicMock() - mock_handler = MagicMock() - mock_handler.handle = AsyncMock() - - # Configure mock returns - parsed_data = DAOWebhookPayload(**sample_dao_payload) - mock_parser.parse.return_value = parsed_data - mock_handler.handle.return_value = { - "success": True, - "message": "Successfully created DAO", - "data": { - "dao_id": UUID("00000000-0000-0000-0000-000000000001"), - "extension_ids": [UUID("00000000-0000-0000-0000-000000000002")], - "token_id": UUID("00000000-0000-0000-0000-000000000003"), - }, - } - - # Create service with mocked components - service = DAOService() - service.parser = mock_parser - service.handler = mock_handler - - # Test service - result = await service.process(sample_dao_payload) - - assert result["success"] is True - assert result["message"] == "Successfully created DAO" - assert result["data"]["dao_id"] == UUID("00000000-0000-0000-0000-000000000001") - - # Verify component calls - mock_parser.parse.assert_called_once_with(sample_dao_payload) - mock_handler.handle.assert_called_once_with(parsed_data) diff --git a/tests/services/workflows/test_vector_react.py b/tests/services/workflows/test_vector_react.py deleted file mode 100644 index 0bc45eef..00000000 --- a/tests/services/workflows/test_vector_react.py +++ /dev/null @@ -1,207 +0,0 @@ -"""Tests for the Vector React workflow.""" - -import unittest -from unittest.mock import AsyncMock, MagicMock, patch - -from langchain_core.documents import Document - -from services.workflows.chat import ( - VectorLangGraphService, - execute_vector_langgraph_stream, -) -from services.workflows.vector_mixin import add_documents_to_vectors - - -class TestVectorOperations(unittest.TestCase): - """Tests for the vector store operations.""" - - def setUp(self): - """Set up test fixtures.""" - self.mock_backend = MagicMock() - self.mock_collection = MagicMock() - self.mock_backend.get_vector_collection.return_value = self.mock_collection - self.mock_backend.query_vectors = AsyncMock( - return_value=[ - { - "id": "1", - "page_content": "test content", - "metadata": {"source": "test"}, - } - ] - ) - self.mock_backend.add_vectors = AsyncMock(return_value=["1"]) - self.mock_backend.create_vector_collection.return_value = self.mock_collection - - # Patch backend - self.backend_patch = patch( - "services.workflows.vector_react.backend", self.mock_backend - ) - self.backend_patch.start() - - def tearDown(self): - """Tear down test fixtures.""" - self.backend_patch.stop() - - async def test_add_documents_to_vectors(self): - """Test adding documents to vector store.""" - # Setup - documents = [Document(page_content="test content", metadata={"source": "test"})] - - # Execute - result = await add_documents_to_vectors( - collection_name="test_collection", documents=documents - ) - - # Verify - self.mock_backend.get_vector_collection.assert_called_once_with( - "test_collection" - ) - self.mock_backend.add_vectors.assert_called_once() - self.assertEqual(result, ["1"]) - - async def test_add_documents_creates_collection_if_not_exists(self): - """Test that collection is created if it doesn't exist.""" - # Setup - documents = [Document(page_content="test content", metadata={"source": "test"})] - self.mock_backend.get_vector_collection.side_effect = [ - ValueError, - self.mock_collection, - ] - - # Execute - result = await add_documents_to_vectors( - collection_name="new_collection", documents=documents - ) - - # Verify - self.mock_backend.create_vector_collection.assert_called_once_with( - "new_collection", dimensions=1536 - ) - self.mock_backend.add_vectors.assert_called_once() - self.assertEqual(result, ["1"]) - - -class TestVectorReactWorkflow(unittest.TestCase): - """Tests for the VectorReactWorkflow class.""" - - def setUp(self): - """Set up test fixtures.""" - self.mock_callback_handler = MagicMock() - self.mock_tools = [] - self.mock_backend = MagicMock() - self.mock_backend.query_vectors = AsyncMock( - return_value=[ - { - "id": "1", - "page_content": "test content", - "metadata": {"source": "test"}, - } - ] - ) - self.backend_patch = patch( - "services.workflows.vector_react.backend", self.mock_backend - ) - self.backend_patch.start() - - self.mock_llm = MagicMock() - self.mock_llm.invoke = MagicMock() - - def tearDown(self): - """Tear down test fixtures.""" - self.backend_patch.stop() - - @patch("services.workflows.vector_react.ChatOpenAI") - def test_create_graph(self, mock_chat_openai): - """Test creating the workflow graph.""" - # Setup - mock_chat_openai.return_value.bind_tools.return_value = self.mock_llm - workflow = VectorReactWorkflow( - callback_handler=self.mock_callback_handler, - tools=self.mock_tools, - collection_name="test_collection", - llm=self.mock_llm, - ) - - # Execute - graph = workflow._create_graph() - - # Verify - self.assertIsNotNone(graph) - # Check that the graph has the expected nodes - self.assertIn("vector_retrieval", graph.nodes) - self.assertIn("agent", graph.nodes) - self.assertIn("tools", graph.nodes) - - -class TestVectorLangGraphService(unittest.IsolatedAsyncioTestCase): - """Tests for the VectorLangGraphService class.""" - - def setUp(self): - """Set up test fixtures.""" - self.mock_backend = MagicMock() - self.mock_backend.query_vectors = AsyncMock( - return_value=[ - { - "id": "1", - "page_content": "test content", - "metadata": {"source": "test"}, - } - ] - ) - self.backend_patch = patch( - "services.workflows.vector_react.backend", self.mock_backend - ) - self.backend_patch.start() - - self.service = VectorLangGraphService(collection_name="test_collection") - - def tearDown(self): - """Tear down test fixtures.""" - self.backend_patch.stop() - - @patch("services.workflows.vector_react.VectorReactWorkflow") - @patch("services.workflows.vector_react.StreamingCallbackHandler") - async def test_execute_vector_react_stream(self, mock_handler, mock_workflow): - """Test executing a vector react stream.""" - # Setup - history = [{"role": "user", "content": "test message"}] - input_str = "test input" - mock_queue = AsyncMock() - mock_queue.get = AsyncMock( - side_effect=[{"type": "token", "content": "test"}, {"type": "end"}] - ) - mock_handler.return_value = MagicMock() - - mock_graph = MagicMock() - mock_runnable = MagicMock() - mock_workflow.return_value._create_graph.return_value = mock_graph - mock_graph.compile.return_value = mock_runnable - - mock_task = MagicMock() - mock_task.done = MagicMock(side_effect=[False, False, True]) - mock_result = {"messages": [MagicMock(content="test result")]} - mock_task.__await__ = MagicMock(return_value=mock_result) - - # Execute - with ( - patch("asyncio.Queue", return_value=mock_queue), - patch("asyncio.get_running_loop"), - patch("asyncio.create_task", return_value=mock_task), - patch("asyncio.wait_for", side_effect=lambda *args, **kwargs: args[0]), - ): - results = [ - chunk - async for chunk in self.service.execute_vector_react_stream( - history, input_str - ) - ] - - # Verify - self.assertEqual(len(results), 3) # token, end, result - self.assertEqual(results[0], {"type": "token", "content": "test"}) - self.assertEqual(results[1], {"type": "end"}) - self.assertEqual(results[2]["type"], "result") - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/test_proposal_evaluation.py b/tests/test_proposal_evaluation.py index 27487149..e20ce58a 100644 --- a/tests/test_proposal_evaluation.py +++ b/tests/test_proposal_evaluation.py @@ -1,13 +1,8 @@ """Test script for the proposal evaluation workflow.""" import asyncio -import os -import sys from typing import Dict, Optional -# Add the parent directory to the path so we can import the modules -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - from backend.models import UUID from services.workflows.proposal_evaluation import ( evaluate_and_vote_on_proposal, From b78a864802f66f3521a97e97319f8b8194e993bb Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Wed, 7 May 2025 22:33:28 -0700 Subject: [PATCH 023/219] upate --- .github/dependabot.yml | 2 +- examples/proposal_evaluation_example.py | 9 +- examples/vector_react_example.py | 10 +- requirements.txt | 8 +- services/twitter.py | 16 +- tests/api/test_profile_auth.py | 239 --------- tests/api/test_tools_api.py | 229 --------- tests/api/test_webhook_auth.py | 64 --- tests/lib/test_alex.py | 261 ---------- tests/lib/test_hiro.py | 482 ------------------ tests/lib/test_images.py | 130 ----- tests/lib/test_logger.py | 63 --- tests/lib/test_lunarcrush.py | 141 ----- tests/lib/test_persona.py | 131 ----- tests/lib/test_platform.py | 194 ------- tests/lib/test_tokenizer.py | 91 ---- tests/lib/test_tools.py | 120 ----- tests/lib/test_velar.py | 248 --------- tests/services/test_bot.py | 259 ---------- tests/services/test_chat.py | 230 --------- tests/services/test_daos.py | 209 -------- tests/services/test_job_manager.py | 93 ---- tests/services/test_langgraph.py | 223 -------- tests/services/test_schedule.py | 189 ------- tests/services/test_startup.py | 148 ------ tests/services/test_tweet_task.py | 207 -------- tests/services/test_twitter.py | 273 ---------- .../chainhook/test_buy_event_handler.py | 172 ------- .../webhooks/chainhook/test_models.py | 218 -------- .../webhooks/chainhook/test_parser.py | 73 --- tests/test_dao_proposal_voter.py | 198 ------- tests/test_proposal_evaluation.py | 82 --- 32 files changed, 30 insertions(+), 4982 deletions(-) delete mode 100644 tests/api/test_profile_auth.py delete mode 100644 tests/api/test_tools_api.py delete mode 100644 tests/api/test_webhook_auth.py delete mode 100644 tests/lib/test_alex.py delete mode 100644 tests/lib/test_hiro.py delete mode 100644 tests/lib/test_images.py delete mode 100644 tests/lib/test_logger.py delete mode 100644 tests/lib/test_lunarcrush.py delete mode 100644 tests/lib/test_persona.py delete mode 100644 tests/lib/test_platform.py delete mode 100644 tests/lib/test_tokenizer.py delete mode 100644 tests/lib/test_tools.py delete mode 100644 tests/lib/test_velar.py delete mode 100644 tests/services/test_bot.py delete mode 100644 tests/services/test_chat.py delete mode 100644 tests/services/test_daos.py delete mode 100644 tests/services/test_job_manager.py delete mode 100644 tests/services/test_langgraph.py delete mode 100644 tests/services/test_schedule.py delete mode 100644 tests/services/test_startup.py delete mode 100644 tests/services/test_tweet_task.py delete mode 100644 tests/services/test_twitter.py delete mode 100644 tests/services/webhooks/chainhook/test_buy_event_handler.py delete mode 100644 tests/services/webhooks/chainhook/test_models.py delete mode 100644 tests/services/webhooks/chainhook/test_parser.py delete mode 100644 tests/test_dao_proposal_voter.py delete mode 100644 tests/test_proposal_evaluation.py diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 7c106d6a..8855a548 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -8,7 +8,7 @@ updates: - package-ecosystem: "pip" # See documentation for possible values directory: "/" # Location of package manifests schedule: - interval: "weekly" + interval: "daily" target-branch: "staging" groups: dev-dependencies: diff --git a/examples/proposal_evaluation_example.py b/examples/proposal_evaluation_example.py index 3e7ec4b2..88bebc81 100644 --- a/examples/proposal_evaluation_example.py +++ b/examples/proposal_evaluation_example.py @@ -7,16 +7,17 @@ import asyncio import binascii -import json -from typing import Dict, Optional +import os +import sys from uuid import UUID +# Add the project root to Python path +sys.path.insert(0, os.path.abspath(os.path.dirname(os.path.dirname(__file__)))) + from backend.factory import backend from backend.models import ( ProposalCreate, ProposalType, - QueueMessageCreate, - QueueMessageType, ) from services.workflows.proposal_evaluation import ( evaluate_and_vote_on_proposal, diff --git a/examples/vector_react_example.py b/examples/vector_react_example.py index 11e42dca..80b7e7b9 100644 --- a/examples/vector_react_example.py +++ b/examples/vector_react_example.py @@ -7,15 +7,21 @@ """ import asyncio +import os +import sys + +# Add the project root to Python path +sys.path.insert(0, os.path.abspath(os.path.dirname(os.path.dirname(__file__)))) import dotenv from langchain_community.document_loaders import WebBaseLoader -from langchain_core.documents import Document from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter from backend.factory import backend -from services.workflows.chat import VectorLangGraphService +from services.workflows.chat import ( + execute_vector_langgraph_stream, +) from services.workflows.vector_mixin import add_documents_to_vectors dotenv.load_dotenv() diff --git a/requirements.txt b/requirements.txt index e431d074..bf90829f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,4 +20,10 @@ starlette==0.46.2 supabase==2.15.1 tiktoken==0.9.0 uvicorn==0.34.2 -vecs==0.4.5 \ No newline at end of file +vecs==0.4.5 + +# Test dependencies +pytest==8.3.5 +pytest-asyncio==0.26.0 +responses==0.25.7 +aiohttp==3.11.18 \ No newline at end of file diff --git a/services/twitter.py b/services/twitter.py index 23d86d76..d6938275 100644 --- a/services/twitter.py +++ b/services/twitter.py @@ -56,7 +56,7 @@ class TweetRepository: async def store_tweet(self, tweet_data: TweetData) -> None: """Store tweet and author data in the database.""" try: - authors = backend.list_x_users( + authors = await backend.list_x_users( filters=XUserFilter(user_id=tweet_data.author_id) ) if authors and len(authors) > 0: @@ -66,12 +66,12 @@ async def store_tweet(self, tweet_data: TweetData) -> None: ) else: logger.info(f"Creating new author record for {tweet_data.author_id}") - author = backend.create_x_user( + author = await backend.create_x_user( XUserCreate(user_id=tweet_data.author_id) ) logger.debug(f"Creating tweet record for {tweet_data.tweet_id}") - backend.create_x_tweet( + await backend.create_x_tweet( XTweetCreate( author_id=author.id, tweet_id=tweet_data.tweet_id, @@ -93,10 +93,12 @@ async def update_tweet_analysis( ) -> None: """Update tweet with analysis results.""" try: - tweets = backend.list_x_tweets(filters=XTweetFilter(tweet_id=tweet_id)) + tweets = await backend.list_x_tweets( + filters=XTweetFilter(tweet_id=tweet_id) + ) if tweets and len(tweets) > 0: logger.debug("Updating existing tweet record with analysis results") - backend.update_x_tweet( + await backend.update_x_tweet( x_tweet_id=tweets[0].id, update_data=XTweetBase( is_worthy=is_worthy, @@ -114,7 +116,7 @@ async def get_conversation_history( ) -> List[Dict[str, str]]: """Retrieve conversation history for a given conversation ID.""" try: - conversation_tweets = backend.list_x_tweets( + conversation_tweets = await backend.list_x_tweets( filters=XTweetFilter(conversation_id=conversation_id) ) logger.debug( @@ -247,7 +249,7 @@ async def _handle_mention(self, mention) -> None: # Check if tweet exists in our database try: - existing_tweets = backend.list_x_tweets( + existing_tweets = await backend.list_x_tweets( filters=XTweetFilter(tweet_id=tweet_data.tweet_id) ) if existing_tweets and len(existing_tweets) > 0: diff --git a/tests/api/test_profile_auth.py b/tests/api/test_profile_auth.py deleted file mode 100644 index a2540f6d..00000000 --- a/tests/api/test_profile_auth.py +++ /dev/null @@ -1,239 +0,0 @@ -from unittest.mock import MagicMock, patch - -import pytest -from fastapi import HTTPException - -from api.dependencies import ( - get_profile_from_api_key, - verify_profile, - verify_profile_from_token, -) -from backend.models import Profile - - -@pytest.mark.asyncio -async def test_get_profile_from_api_key_invalid_uuid(): - """Test that invalid UUID format returns None.""" - result = await get_profile_from_api_key("not-a-uuid") - assert result is None - - -@pytest.mark.asyncio -async def test_get_profile_from_api_key_no_keys(): - """Test that when no keys are found, None is returned.""" - with patch("api.dependencies.backend") as mock_backend: - mock_backend.list_keys.return_value = [] - - result = await get_profile_from_api_key("123e4567-e89b-12d3-a456-426614174000") - - assert result is None - mock_backend.list_keys.assert_called_once() - - -@pytest.mark.asyncio -async def test_get_profile_from_api_key_no_profile_id(): - """Test that when key has no profile_id, None is returned.""" - with patch("api.dependencies.backend") as mock_backend: - mock_key = MagicMock() - mock_key.profile_id = None - mock_backend.list_keys.return_value = [mock_key] - - result = await get_profile_from_api_key("123e4567-e89b-12d3-a456-426614174000") - - assert result is None - - -@pytest.mark.asyncio -async def test_get_profile_from_api_key_no_profile(): - """Test that when profile is not found, None is returned.""" - with patch("api.dependencies.backend") as mock_backend: - mock_key = MagicMock() - mock_key.profile_id = "profile-id" - mock_backend.list_keys.return_value = [mock_key] - mock_backend.get_profile.return_value = None - - result = await get_profile_from_api_key("123e4567-e89b-12d3-a456-426614174000") - - assert result is None - mock_backend.get_profile.assert_called_once_with("profile-id") - - -@pytest.mark.asyncio -async def test_get_profile_from_api_key_success(): - """Test successful profile retrieval from API key.""" - with patch("api.dependencies.backend") as mock_backend: - mock_key = MagicMock() - mock_key.profile_id = "profile-id" - mock_profile = MagicMock(spec=Profile) - - mock_backend.list_keys.return_value = [mock_key] - mock_backend.get_profile.return_value = mock_profile - - result = await get_profile_from_api_key("123e4567-e89b-12d3-a456-426614174000") - - assert result == mock_profile - mock_backend.get_profile.assert_called_once_with("profile-id") - - -@pytest.mark.asyncio -async def test_verify_profile_with_api_key(): - """Test verify_profile with valid API key.""" - with patch("api.dependencies.get_profile_from_api_key") as mock_get_profile: - mock_profile = MagicMock(spec=Profile) - mock_get_profile.return_value = mock_profile - - result = await verify_profile(authorization=None, x_api_key="valid-api-key") - - assert result == mock_profile - mock_get_profile.assert_called_once_with("valid-api-key") - - -@pytest.mark.asyncio -async def test_verify_profile_with_invalid_api_key(): - """Test verify_profile with invalid API key raises exception.""" - with patch("api.dependencies.get_profile_from_api_key") as mock_get_profile: - mock_get_profile.return_value = None - - with pytest.raises(HTTPException) as exc_info: - await verify_profile(authorization=None, x_api_key="invalid-api-key") - - assert exc_info.value.status_code == 401 - assert "Invalid API key" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_missing_auth(): - """Test verify_profile with missing authorization raises exception.""" - with pytest.raises(HTTPException) as exc_info: - await verify_profile(authorization=None, x_api_key=None) - - assert exc_info.value.status_code == 401 - assert "Missing authorization header" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_invalid_auth_format(): - """Test verify_profile with invalid authorization format raises exception.""" - with pytest.raises(HTTPException) as exc_info: - await verify_profile(authorization="InvalidFormat", x_api_key=None) - - assert exc_info.value.status_code == 401 - assert "Invalid authorization format" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_invalid_token(): - """Test verify_profile with invalid token raises exception.""" - with patch("api.dependencies.backend") as mock_backend: - mock_backend.verify_session_token.return_value = None - - with pytest.raises(HTTPException) as exc_info: - await verify_profile(authorization="Bearer invalid-token", x_api_key=None) - - assert exc_info.value.status_code == 401 - assert "Invalid bearer token" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_no_profile(): - """Test verify_profile with valid token but no profile raises exception.""" - with patch("api.dependencies.backend") as mock_backend: - mock_backend.verify_session_token.return_value = "user@example.com" - mock_backend.list_profiles.return_value = [] - - with pytest.raises(HTTPException) as exc_info: - await verify_profile(authorization="Bearer valid-token", x_api_key=None) - - assert exc_info.value.status_code == 404 - assert "Profile not found" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_success(): - """Test verify_profile with valid token and profile.""" - with patch("api.dependencies.backend") as mock_backend: - mock_profile = MagicMock(spec=Profile) - mock_backend.verify_session_token.return_value = "user@example.com" - mock_backend.list_profiles.return_value = [mock_profile] - - result = await verify_profile( - authorization="Bearer valid-token", x_api_key=None - ) - - assert result == mock_profile - - -@pytest.mark.asyncio -async def test_verify_profile_from_token_with_key(): - """Test verify_profile_from_token with valid API key.""" - with patch("api.dependencies.get_profile_from_api_key") as mock_get_profile: - mock_profile = MagicMock(spec=Profile) - mock_get_profile.return_value = mock_profile - - result = await verify_profile_from_token(token=None, key="valid-api-key") - - assert result == mock_profile - mock_get_profile.assert_called_once_with("valid-api-key") - - -@pytest.mark.asyncio -async def test_verify_profile_from_token_with_invalid_key(): - """Test verify_profile_from_token with invalid API key raises exception.""" - with patch("api.dependencies.get_profile_from_api_key") as mock_get_profile: - mock_get_profile.return_value = None - - with pytest.raises(HTTPException) as exc_info: - await verify_profile_from_token(token=None, key="invalid-api-key") - - assert exc_info.value.status_code == 401 - assert "Invalid API key" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_from_token_missing_token(): - """Test verify_profile_from_token with missing token raises exception.""" - with pytest.raises(HTTPException) as exc_info: - await verify_profile_from_token(token=None, key=None) - - assert exc_info.value.status_code == 401 - assert "Missing token parameter" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_from_token_invalid_token(): - """Test verify_profile_from_token with invalid token raises exception.""" - with patch("api.dependencies.backend") as mock_backend: - mock_backend.verify_session_token.return_value = None - - with pytest.raises(HTTPException) as exc_info: - await verify_profile_from_token(token="invalid-token", key=None) - - assert exc_info.value.status_code == 401 - assert "Invalid or expired token" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_from_token_no_profile(): - """Test verify_profile_from_token with valid token but no profile raises exception.""" - with patch("api.dependencies.backend") as mock_backend: - mock_backend.verify_session_token.return_value = "user@example.com" - mock_backend.list_profiles.return_value = [] - - with pytest.raises(HTTPException) as exc_info: - await verify_profile_from_token(token="valid-token", key=None) - - assert exc_info.value.status_code == 404 - assert "No profile found for the authenticated email" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_profile_from_token_success(): - """Test verify_profile_from_token with valid token and profile.""" - with patch("api.dependencies.backend") as mock_backend: - mock_profile = MagicMock(spec=Profile) - mock_backend.verify_session_token.return_value = "user@example.com" - mock_backend.list_profiles.return_value = [mock_profile] - - result = await verify_profile_from_token(token="valid-token", key=None) - - assert result == mock_profile diff --git a/tests/api/test_tools_api.py b/tests/api/test_tools_api.py deleted file mode 100644 index 5b73f568..00000000 --- a/tests/api/test_tools_api.py +++ /dev/null @@ -1,229 +0,0 @@ -import json -from unittest.mock import patch - -import pytest -from fastapi.testclient import TestClient - -from api.tools import router -from lib.tools import Tool - - -# Create a test client -@pytest.fixture -def client(): - from fastapi import FastAPI - - app = FastAPI() - app.include_router(router) - return TestClient(app) - - -# Mock tools for testing -@pytest.fixture -def mock_tools(): - return [ - Tool( - id="test_get_data", - name="Get Data", - description="Test tool for getting data", - category="TEST", - parameters=json.dumps( - { - "param1": {"description": "Test parameter 1", "type": "str"}, - "param2": {"description": "Test parameter 2", "type": "int"}, - } - ), - ), - Tool( - id="wallet_get_balance", - name="Get Balance", - description="Get wallet balance", - category="WALLET", - parameters=json.dumps( - {"wallet_id": {"description": "Wallet ID", "type": "UUID"}} - ), - ), - Tool( - id="dao_get_info", - name="Get Info", - description="Get DAO information", - category="DAO", - parameters=json.dumps( - {"dao_id": {"description": "DAO ID", "type": "UUID"}} - ), - ), - ] - - -@pytest.mark.asyncio -async def test_get_tools(client, mock_tools): - """Test the /tools/available endpoint.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/available") - - # Check response status - assert response.status_code == 200 - - # Check response content - tools = response.json() - assert len(tools) == 3 - assert tools[0]["id"] == "test_get_data" - assert tools[1]["id"] == "wallet_get_balance" - assert tools[2]["id"] == "dao_get_info" - - -@pytest.mark.asyncio -async def test_get_tools_with_category_filter(client, mock_tools): - """Test the /tools/available endpoint with category filter.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/available?category=WALLET") - - # Check response status - assert response.status_code == 200 - - # Check response content - tools = response.json() - assert len(tools) == 1 - assert tools[0]["id"] == "wallet_get_balance" - assert tools[0]["category"] == "WALLET" - - -@pytest.mark.asyncio -async def test_get_tools_with_nonexistent_category(client, mock_tools): - """Test the /tools/available endpoint with a category that doesn't exist.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/available?category=NONEXISTENT") - - # Check response status - assert response.status_code == 200 - - # Check response content - tools = response.json() - assert len(tools) == 0 - - -@pytest.mark.asyncio -async def test_get_tool_categories(client, mock_tools): - """Test the /tools/categories endpoint.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/categories") - - # Check response status - assert response.status_code == 200 - - # Check response content - categories = response.json() - assert len(categories) == 3 - assert "TEST" in categories - assert "WALLET" in categories - assert "DAO" in categories - - -@pytest.mark.asyncio -async def test_search_tools(client, mock_tools): - """Test the /tools/search endpoint.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/search?query=balance") - - # Check response status - assert response.status_code == 200 - - # Check response content - tools = response.json() - assert len(tools) == 1 - assert tools[0]["id"] == "wallet_get_balance" - - -@pytest.mark.asyncio -async def test_search_tools_with_category(client, mock_tools): - """Test the /tools/search endpoint with category filter.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/search?query=get&category=DAO") - - # Check response status - assert response.status_code == 200 - - # Check response content - tools = response.json() - assert len(tools) == 1 - assert tools[0]["id"] == "dao_get_info" - - -@pytest.mark.asyncio -async def test_search_tools_no_results(client, mock_tools): - """Test the /tools/search endpoint with no matching results.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/search?query=nonexistent") - - # Check response status - assert response.status_code == 200 - - # Check response content - tools = response.json() - assert len(tools) == 0 - - -@pytest.mark.asyncio -async def test_search_tools_missing_query(client, mock_tools): - """Test the /tools/search endpoint with missing query parameter.""" - # Mock the available_tools - with patch("api.tools.available_tools", mock_tools): - response = client.get("/tools/search") - - # Check response status - assert response.status_code == 422 # Unprocessable Entity - - -@pytest.mark.asyncio -async def test_get_tools_error_handling(client): - """Test error handling in the /tools/available endpoint.""" - # Mock get_available_tools to raise an exception - with patch("api.tools.available_tools", side_effect=Exception("Test error")): - response = client.get("/tools/available") - - # Check response status - assert response.status_code == 500 - - # Check error message - error = response.json() - assert "detail" in error - assert "Failed to serve available tools" in error["detail"] - - -@pytest.mark.asyncio -async def test_get_tool_categories_error_handling(client): - """Test error handling in the /tools/categories endpoint.""" - # Mock available_tools to raise an exception when accessed - with patch("api.tools.available_tools", side_effect=Exception("Test error")): - response = client.get("/tools/categories") - - # Check response status - assert response.status_code == 500 - - # Check error message - error = response.json() - assert "detail" in error - assert "Failed to serve tool categories" in error["detail"] - - -@pytest.mark.asyncio -async def test_search_tools_error_handling(client): - """Test error handling in the /tools/search endpoint.""" - # Mock available_tools to raise an exception when accessed - with patch("api.tools.available_tools", side_effect=Exception("Test error")): - response = client.get("/tools/search?query=test") - - # Check response status - assert response.status_code == 500 - - # Check error message - error = response.json() - assert "detail" in error - assert "Failed to search tools" in error["detail"] diff --git a/tests/api/test_webhook_auth.py b/tests/api/test_webhook_auth.py deleted file mode 100644 index c775e4b4..00000000 --- a/tests/api/test_webhook_auth.py +++ /dev/null @@ -1,64 +0,0 @@ -from unittest.mock import patch - -import pytest -from fastapi import HTTPException - -from api.dependencies import verify_webhook_auth - - -@pytest.mark.asyncio -async def test_verify_webhook_auth_missing_header(): - """Test authentication fails when Authorization header is missing.""" - with pytest.raises(HTTPException) as exc_info: - await verify_webhook_auth(authorization=None) - - assert exc_info.value.status_code == 401 - assert "Missing Authorization header" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_webhook_auth_invalid_format(): - """Test authentication fails when Authorization header has invalid format.""" - with pytest.raises(HTTPException) as exc_info: - await verify_webhook_auth(authorization="InvalidFormat") - - assert exc_info.value.status_code == 401 - assert "Invalid Authorization format" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_webhook_auth_invalid_token(): - """Test authentication fails when token is invalid.""" - with patch("api.dependencies.config") as mock_config: - mock_config.api.webhook_auth = "Bearer correct-token" - - with pytest.raises(HTTPException) as exc_info: - await verify_webhook_auth(authorization="Bearer wrong-token") - - assert exc_info.value.status_code == 401 - assert "Invalid authentication token" in exc_info.value.detail - - -@pytest.mark.asyncio -async def test_verify_webhook_auth_success(): - """Test authentication succeeds with valid token.""" - with patch("api.dependencies.config") as mock_config: - mock_config.api.webhook_auth = "Bearer correct-token" - - # Should not raise an exception - result = await verify_webhook_auth(authorization="Bearer correct-token") - - assert result is None # Function returns None on success - - -@pytest.mark.asyncio -async def test_verify_webhook_auth_with_raw_token(): - """Test authentication with raw token in config.""" - with patch("api.dependencies.config") as mock_config: - # Config has token without Bearer prefix - mock_config.api.webhook_auth = "correct-token" - - # Should not raise an exception - result = await verify_webhook_auth(authorization="Bearer correct-token") - - assert result is None # Function returns None on success diff --git a/tests/lib/test_alex.py b/tests/lib/test_alex.py deleted file mode 100644 index 05a6e3bd..00000000 --- a/tests/lib/test_alex.py +++ /dev/null @@ -1,261 +0,0 @@ -from typing import Dict, List -from unittest.mock import Mock, patch - -import pytest - -from lib.alex import AlexApi -from lib.logger import configure_logger - -logger = configure_logger(__name__) - - -@pytest.fixture -def mock_config() -> None: - """Fixture to mock config values.""" - with patch("config.config") as mock_config: - mock_config.api.alex_base_url = "https://test-alex-api.com/" - yield - - -@pytest.fixture -def alex_api(mock_config: None) -> AlexApi: - """Fixture providing an AlexApi instance.""" - return AlexApi() - - -@pytest.fixture -def mock_price_data() -> Dict[str, List[Dict[str, float]]]: - """Fixture providing mock price history data.""" - return { - "prices": [ - {"avg_price_usd": 1.0, "block_height": 1000}, - {"avg_price_usd": 2.0, "block_height": 2000}, - ] - } - - -@pytest.fixture -def mock_volume_data() -> Dict[str, List[Dict[str, float]]]: - """Fixture providing mock volume data.""" - return { - "volume_values": [ - {"volume_24h": 1000.0, "block_height": 1000}, - {"volume_24h": 2000.0, "block_height": 2000}, - ] - } - - -def test_initialization(alex_api: AlexApi) -> None: - """Test AlexApi initialization.""" - assert alex_api.base_url == "https://test-alex-api.com/" - assert alex_api.limits == 500 - - -@patch("requests.get") -def test_get_success(mock_get: Mock, alex_api: AlexApi) -> None: - """Test successful GET request.""" - mock_response = Mock() - mock_response.json.return_value = {"data": "test"} - mock_get.return_value = mock_response - - result = alex_api._get("test-endpoint") - - assert result == {"data": "test"} - mock_get.assert_called_once_with( - "https://test-alex-api.com/test-endpoint", - headers={"Accept": "application/json"}, - params={}, - ) - - -@patch("requests.get") -def test_get_with_params(mock_get: Mock, alex_api: AlexApi) -> None: - """Test GET request with parameters.""" - mock_response = Mock() - mock_response.json.return_value = {"data": "test"} - mock_get.return_value = mock_response - - params = {"key": "value"} - result = alex_api._get("test-endpoint", params=params) - - assert result == {"data": "test"} - mock_get.assert_called_once_with( - "https://test-alex-api.com/test-endpoint", - headers={"Accept": "application/json"}, - params=params, - ) - - -@patch("requests.get") -def test_get_error(mock_get: Mock, alex_api: AlexApi) -> None: - """Test GET request error handling.""" - mock_get.side_effect = Exception("API Error") - - with pytest.raises(Exception, match="Alex API GET request error: API Error"): - alex_api._get("test-endpoint") - - -@patch("requests.get") -def test_get_pairs(mock_get: Mock, alex_api: AlexApi) -> None: - """Test pairs retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"data": ["pair1", "pair2"]} - mock_get.return_value = mock_response - - result = alex_api.get_pairs() - - assert result == ["pair1", "pair2"] - mock_get.assert_called_once_with( - "https://test-alex-api.com/v1/public/pairs", - headers={"Accept": "application/json"}, - params={}, - ) - - -@patch("requests.get") -def test_get_price_history( - mock_get: Mock, - alex_api: AlexApi, - mock_price_data: Dict[str, List[Dict[str, float]]], -) -> None: - """Test price history retrieval.""" - mock_response = Mock() - mock_response.json.return_value = mock_price_data - mock_get.return_value = mock_response - - result = alex_api.get_price_history("test-token") - - assert len(result) == 2 - assert all(key in result[0] for key in ["price", "block"]) - assert result[0]["price"] == 1.0 - assert result[0]["block"] == 1000 - mock_get.assert_called_once_with( - "https://test-alex-api.com/v1/price_history/test-token?limit=500", - headers={"Accept": "application/json"}, - params={}, - ) - - -@patch("requests.get") -def test_get_all_swaps(mock_get: Mock, alex_api: AlexApi) -> None: - """Test all swaps retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"swaps": ["swap1", "swap2"]} - mock_get.return_value = mock_response - - result = alex_api.get_all_swaps() - - assert result == {"swaps": ["swap1", "swap2"]} - mock_get.assert_called_once_with( - "https://test-alex-api.com/v1/allswaps", - headers={"Accept": "application/json"}, - params={}, - ) - - -@patch("requests.get") -def test_get_token_pool_volume( - mock_get: Mock, - alex_api: AlexApi, - mock_volume_data: Dict[str, List[Dict[str, float]]], -) -> None: - """Test pool volume retrieval.""" - mock_response = Mock() - mock_response.json.return_value = mock_volume_data - mock_get.return_value = mock_response - - result = alex_api.get_token_pool_volume("test-pool") - - assert len(result) == 2 - assert result[0]["volume_24h"] == 1000.0 - assert result[0]["block_height"] == 1000 - mock_get.assert_called_once_with( - "https://test-alex-api.com/v1/pool_volume/test-pool?limit=500", - headers={"Accept": "application/json"}, - params={}, - ) - - -@patch("requests.get") -def test_get_token_pool_agg_history( - mock_get: Mock, - alex_api: AlexApi, - mock_price_data: Dict[str, List[Dict[str, float]]], - mock_volume_data: Dict[str, List[Dict[str, float]]], -) -> None: - """Test aggregated history retrieval.""" - mock_response1 = Mock() - mock_response1.json.return_value = mock_price_data - mock_response2 = Mock() - mock_response2.json.return_value = mock_volume_data - mock_get.side_effect = [mock_response1, mock_response2] - - result = alex_api.get_token_pool_agg_history("test-token", "test-pool") - - assert len(result) == 2 - assert all(key in result[0] for key in ["price", "block", "volume_24h"]) - assert result[0]["price"] == 1.0 - assert result[0]["block"] == 1000 - assert result[0]["volume_24h"] == 1000.0 - assert mock_get.call_count == 2 - - -@patch("requests.get") -def test_get_token_pool_price(mock_get: Mock, alex_api: AlexApi) -> None: - """Test pool price retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"price": 1.5} - mock_get.return_value = mock_response - - result = alex_api.get_token_pool_price("test-pool") - - assert result == {"price": 1.5} - mock_get.assert_called_once_with( - "https://test-alex-api.com/v1/pool_token_price/test-pool?limit=500", - headers={"Accept": "application/json"}, - params={}, - ) - - -@patch("requests.get") -def test_get_token_tvl(mock_get: Mock, alex_api: AlexApi) -> None: - """Test TVL retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"tvl": 1000000.0} - mock_get.return_value = mock_response - - result = alex_api.get_token_tvl("test-pool") - - assert result == {"tvl": 1000000.0} - mock_get.assert_called_once_with( - "https://test-alex-api.com/v1/stats/tvl/test-pool?limit=500", - headers={"Accept": "application/json"}, - params={}, - ) - - -@patch("requests.get") -def test_error_handling(mock_get: Mock, alex_api: AlexApi) -> None: - """Test error handling for all methods.""" - mock_get.side_effect = Exception("API Error") - - with pytest.raises(Exception, match="Failed to get token pairs"): - alex_api.get_pairs() - - with pytest.raises(Exception, match="Failed to get token price history"): - alex_api.get_price_history("test-token") - - with pytest.raises(Exception, match="Failed to get all swaps"): - alex_api.get_all_swaps() - - with pytest.raises(Exception, match="Failed to get pool volume"): - alex_api.get_token_pool_volume("test-pool") - - with pytest.raises(Exception, match="Failed to get token price history"): - alex_api.get_token_pool_agg_history("test-token", "test-pool") - - with pytest.raises(Exception, match="Failed to get pool price"): - alex_api.get_token_pool_price("test-pool") - - with pytest.raises(Exception, match="Failed to get pool volume"): - alex_api.get_token_tvl("test-pool") diff --git a/tests/lib/test_hiro.py b/tests/lib/test_hiro.py deleted file mode 100644 index 250a6977..00000000 --- a/tests/lib/test_hiro.py +++ /dev/null @@ -1,482 +0,0 @@ -import time -from unittest.mock import Mock, patch - -import aiohttp -import pytest -import requests - -from lib.hiro import HiroApi, HiroApiError, HiroApiRateLimitError, HiroApiTimeoutError -from lib.logger import configure_logger - -logger = configure_logger(__name__) - - -@pytest.fixture -def mock_config() -> None: - """Fixture to mock config values.""" - with patch("config.config") as mock_config: - mock_config.api.hiro_api_url = "https://test-hiro-api.com/" - yield - - -@pytest.fixture -def hiro_api(mock_config: None) -> HiroApi: - """Fixture providing a HiroApi instance.""" - return HiroApi() - - -@pytest.fixture -def mock_response() -> Mock: - """Fixture providing a mock response.""" - mock = Mock() - mock.status_code = 200 - mock.json.return_value = {"data": "test_value"} - return mock - - -def test_initialization(hiro_api: HiroApi) -> None: - """Test HiroApi initialization.""" - assert hiro_api.base_url == "https://test-hiro-api.com/" - assert len(hiro_api._request_times) == 0 - assert hiro_api._cache is not None - assert hiro_api._session is None - - -def test_rate_limit(hiro_api: HiroApi) -> None: - """Test rate limiting functionality.""" - # Fill up the request times - current_time = time.time() - hiro_api._request_times = [current_time] * (hiro_api.RATE_LIMIT - 1) - - # This request should not trigger rate limiting - hiro_api._rate_limit() - assert len(hiro_api._request_times) == hiro_api.RATE_LIMIT - - # This request should trigger rate limiting - with patch("time.sleep") as mock_sleep: - hiro_api._rate_limit() - mock_sleep.assert_called_once() - - -@patch("requests.get") -def test_get_success(mock_get: Mock, hiro_api: HiroApi, mock_response: Mock) -> None: - """Test successful GET request.""" - mock_get.return_value = mock_response - - result = hiro_api._get("test-endpoint") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com/test-endpoint", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_with_params( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test GET request with parameters.""" - mock_get.return_value = mock_response - - params = {"key": "value"} - result = hiro_api._get("test-endpoint", params=params) - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com/test-endpoint", - headers={"Accept": "application/json"}, - params=params, - ) - - -@patch("requests.get") -def test_get_error(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test GET request error handling.""" - mock_get.side_effect = Exception("API Error") - - with pytest.raises(HiroApiError, match="Unexpected error: API Error"): - hiro_api._get("test-endpoint") - - -@patch("requests.get") -def test_get_rate_limit_error(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test rate limit error handling.""" - mock_response = Mock() - mock_response.raise_for_status.side_effect = requests.exceptions.HTTPError( - response=Mock(status_code=429) - ) - mock_get.return_value = mock_response - - with pytest.raises(HiroApiRateLimitError): - hiro_api._get("test-endpoint") - - -@patch("requests.get") -def test_get_retry_on_timeout(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test retry mechanism on timeout.""" - mock_get.side_effect = [ - requests.exceptions.Timeout(), - requests.exceptions.Timeout(), - mock_response, - ] - - result = hiro_api._get("test-endpoint") - assert result == {"data": "test_value"} - assert mock_get.call_count == 3 - - -@patch("requests.get") -def test_get_max_retries_exceeded(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test max retries exceeded.""" - mock_get.side_effect = requests.exceptions.Timeout() - - with pytest.raises(HiroApiTimeoutError): - hiro_api._get("test-endpoint") - assert mock_get.call_count == hiro_api.MAX_RETRIES - - -@pytest.mark.asyncio -async def test_aget_success(hiro_api: HiroApi) -> None: - """Test successful async GET request.""" - mock_response = Mock() - mock_response.json.return_value = {"data": "test_value"} - mock_response.__aenter__.return_value = mock_response - - with patch.object(aiohttp.ClientSession, "get") as mock_get: - mock_get.return_value = mock_response - result = await hiro_api._aget("test-endpoint") - assert result == {"data": "test_value"} - - -@pytest.mark.asyncio -async def test_aget_error(hiro_api: HiroApi) -> None: - """Test async GET request error handling.""" - with patch.object(aiohttp.ClientSession, "get") as mock_get: - mock_get.side_effect = aiohttp.ClientError() - with pytest.raises(HiroApiError): - await hiro_api._aget("test-endpoint") - - -@pytest.mark.asyncio -async def test_close_session(hiro_api: HiroApi) -> None: - """Test closing async session.""" - # Create a session - await hiro_api._aget("test-endpoint") - assert hiro_api._session is not None - - # Close the session - await hiro_api.close() - assert hiro_api._session is None - - -def test_cached_methods(hiro_api: HiroApi) -> None: - """Test that caching works for decorated methods.""" - with patch.object(HiroApi, "_get") as mock_get: - mock_get.return_value = {"data": "test_value"} - - # First call should hit the API - result1 = hiro_api.get_token_holders("test-token") - assert result1 == {"data": "test_value"} - assert mock_get.call_count == 1 - - # Second call should use cache - result2 = hiro_api.get_token_holders("test-token") - assert result2 == {"data": "test_value"} - assert mock_get.call_count == 1 - - -# Token holder related tests -@patch("requests.get") -def test_get_token_holders( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test token holders retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_token_holders("test-token") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - f"{hiro_api.base_url}{hiro_api.ENDPOINTS['tokens']}/ft/test-token/holders", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_address_balance( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test address balance retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_address_balance("test-address") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - f"{hiro_api.base_url}{hiro_api.ENDPOINTS['addresses']}/test-address/balances", - headers={"Accept": "application/json"}, - params=None, - ) - - -# Transaction related tests -@patch("requests.get") -def test_get_transaction( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test transaction retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_transaction("test-tx") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/tx/test-tx", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_raw_transaction( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test raw transaction retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_raw_transaction("test-tx") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/tx/test-tx/raw", - headers={"Accept": "application/json"}, - params=None, - ) - - -# Block related tests -@patch("requests.get") -def test_get_blocks(mock_get: Mock, hiro_api: HiroApi, mock_response: Mock) -> None: - """Test blocks retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_blocks() - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/block", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_block_by_height( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test block retrieval by height.""" - mock_get.return_value = mock_response - - result = hiro_api.get_block_by_height(12345) - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/block/by_height/12345", - headers={"Accept": "application/json"}, - params=None, - ) - - -# Address related tests -@patch("requests.get") -def test_get_address_stx_balance( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test STX balance retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_address_stx_balance("test-principal") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/address/test-principal/stx", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_address_transactions( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test address transactions retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_address_transactions("test-principal") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/address/test-principal/transactions", - headers={"Accept": "application/json"}, - params=None, - ) - - -# Token related tests -@patch("requests.get") -def test_get_nft_holdings( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test NFT holdings retrieval.""" - mock_get.return_value = mock_response - params = {"limit": 20, "offset": 0} - - result = hiro_api.get_nft_holdings(**params) - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/tokens/nft/holdings", - headers={"Accept": "application/json"}, - params=params, - ) - - -# Contract related tests -@patch("requests.get") -def test_get_contract_by_id( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test contract retrieval by ID.""" - mock_get.return_value = mock_response - - result = hiro_api.get_contract_by_id("test-contract") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/contract/test-contract", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_contract_events( - mock_get: Mock, hiro_api: HiroApi, mock_response: Mock -) -> None: - """Test contract events retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_contract_events("test-contract") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/contract/test-contract/events", - headers={"Accept": "application/json"}, - params=None, - ) - - -# Utility endpoint tests -@patch("requests.get") -def test_get_stx_supply(mock_get: Mock, hiro_api: HiroApi, mock_response: Mock) -> None: - """Test STX supply retrieval.""" - mock_get.return_value = mock_response - - result = hiro_api.get_stx_supply() - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/stx_supply", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_stx_price(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test STX price retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"price": 1.23} - mock_get.return_value = mock_response - - result = hiro_api.get_stx_price() - - assert result == 1.23 - mock_get.assert_called_once_with( - "https://explorer.hiro.so/stxPrice", params={"blockBurnTime": "current"} - ) - - -@patch("requests.get") -def test_get_current_block_height(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test current block height retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"results": [{"height": 12345}]} - mock_get.return_value = mock_response - - result = hiro_api.get_current_block_height() - - assert result == 12345 - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v2/blocks", - headers={"Accept": "application/json"}, - params={"limit": 1, "offset": 0}, - ) - - -@patch("requests.get") -def test_search(mock_get: Mock, hiro_api: HiroApi, mock_response: Mock) -> None: - """Test search functionality.""" - mock_get.return_value = mock_response - - result = hiro_api.search("test-query") - - assert result == {"data": "test_value"} - mock_get.assert_called_once_with( - "https://test-hiro-api.com//extended/v1/search/test-query", - headers={"Accept": "application/json"}, - params=None, - ) - - -# Error handling tests -@patch("requests.get") -def test_stx_price_error(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test STX price error handling.""" - mock_get.side_effect = Exception("API Error") - - with pytest.raises(Exception, match="Failed to get STX price: API Error"): - hiro_api.get_stx_price() - - -@patch("requests.get") -def test_current_block_height_error(mock_get: Mock, hiro_api: HiroApi) -> None: - """Test current block height error handling.""" - mock_get.side_effect = Exception("API Error") - - with pytest.raises( - Exception, match="Failed to get current block height: API Error" - ): - hiro_api.get_current_block_height() - - -@pytest.mark.asyncio -async def test_async_methods(hiro_api: HiroApi) -> None: - """Test async versions of methods.""" - mock_response = Mock() - mock_response.json.return_value = {"data": "test_value"} - mock_response.__aenter__.return_value = mock_response - - with patch.object(aiohttp.ClientSession, "get") as mock_get: - mock_get.return_value = mock_response - - # Test async token holders - result = await hiro_api.aget_token_holders("test-token") - assert result == {"data": "test_value"} - - # Test async address balance - result = await hiro_api.aget_address_balance("test-address") - assert result == {"data": "test_value"} diff --git a/tests/lib/test_images.py b/tests/lib/test_images.py deleted file mode 100644 index 2549ce54..00000000 --- a/tests/lib/test_images.py +++ /dev/null @@ -1,130 +0,0 @@ -from unittest.mock import Mock, patch - -import pytest - -from lib.images import ImageGenerationError, generate_image, generate_token_image -from lib.logger import configure_logger - -logger = configure_logger(__name__) - - -@pytest.fixture -def mock_openai_response() -> Mock: - """Fixture providing a mock OpenAI response.""" - mock_data = Mock() - mock_data.url = "https://fake-image-url.com/image.png" - mock_response = Mock() - mock_response.data = [mock_data] - return mock_response - - -@pytest.fixture -def mock_requests_response() -> Mock: - """Fixture providing a mock requests response.""" - mock_response = Mock() - mock_response.status_code = 200 - mock_response.content = b"fake-image-content" - return mock_response - - -def test_generate_image_success(mock_openai_response: Mock) -> None: - """Test successful image generation.""" - with patch("openai.OpenAI") as mock_client: - mock_instance = Mock() - mock_instance.images.generate.return_value = mock_openai_response - mock_client.return_value = mock_instance - - result = generate_image("test prompt") - assert result == "https://fake-image-url.com/image.png" - - mock_instance.images.generate.assert_called_once_with( - model="dall-e-3", quality="hd", prompt="test prompt", n=1, size="1024x1024" - ) - - -def test_generate_image_no_response() -> None: - """Test image generation with no response.""" - with patch("openai.OpenAI") as mock_client: - mock_instance = Mock() - mock_instance.images.generate.return_value = Mock(data=[]) - mock_client.return_value = mock_instance - - with pytest.raises( - ImageGenerationError, match="No response from image generation service" - ): - generate_image("test prompt") - - -def test_generate_image_api_error() -> None: - """Test image generation with API error.""" - with patch("openai.OpenAI") as mock_client: - mock_instance = Mock() - mock_instance.images.generate.side_effect = Exception("API Error") - mock_client.return_value = mock_instance - - with pytest.raises( - ImageGenerationError, match="Failed to generate image: API Error" - ): - generate_image("test prompt") - - -def test_generate_token_image_success( - mock_openai_response: Mock, mock_requests_response: Mock -) -> None: - """Test successful token image generation.""" - with patch("openai.OpenAI") as mock_client, patch("requests.get") as mock_get: - mock_instance = Mock() - mock_instance.images.generate.return_value = mock_openai_response - mock_client.return_value = mock_instance - mock_get.return_value = mock_requests_response - - result = generate_token_image("Test Token", "TT", "A test token") - assert result == b"fake-image-content" - - -def test_generate_token_image_download_error(mock_openai_response: Mock) -> None: - """Test token image generation with download error.""" - with patch("openai.OpenAI") as mock_client, patch("requests.get") as mock_get: - mock_instance = Mock() - mock_instance.images.generate.return_value = mock_openai_response - mock_client.return_value = mock_instance - - mock_response = Mock() - mock_response.status_code = 404 - mock_get.return_value = mock_response - - with pytest.raises( - ImageGenerationError, match="Failed to download image: HTTP 404" - ): - generate_token_image("Test Token", "TT", "A test token") - - -def test_generate_token_image_empty_content(mock_openai_response: Mock) -> None: - """Test token image generation with empty content.""" - with patch("openai.OpenAI") as mock_client, patch("requests.get") as mock_get: - mock_instance = Mock() - mock_instance.images.generate.return_value = mock_openai_response - mock_client.return_value = mock_instance - - mock_response = Mock() - mock_response.status_code = 200 - mock_response.content = b"" - mock_get.return_value = mock_response - - with pytest.raises(ImageGenerationError, match="Downloaded image is empty"): - generate_token_image("Test Token", "TT", "A test token") - - -def test_generate_token_image_unexpected_error(mock_openai_response: Mock) -> None: - """Test token image generation with unexpected error.""" - with patch("openai.OpenAI") as mock_client, patch("requests.get") as mock_get: - mock_instance = Mock() - mock_instance.images.generate.return_value = mock_openai_response - mock_client.return_value = mock_instance - - mock_get.side_effect = Exception("Unexpected error") - - with pytest.raises( - ImageGenerationError, match="Unexpected error generating token image" - ): - generate_token_image("Test Token", "TT", "A test token") diff --git a/tests/lib/test_logger.py b/tests/lib/test_logger.py deleted file mode 100644 index 586d638f..00000000 --- a/tests/lib/test_logger.py +++ /dev/null @@ -1,63 +0,0 @@ -import logging -import os -from typing import Generator - -import pytest - -from lib.logger import configure_logger - - -@pytest.fixture -def reset_logging() -> Generator[None, None, None]: - """Reset logging configuration after each test.""" - yield - logging.getLogger().handlers.clear() - logging.getLogger().setLevel(logging.NOTSET) - - -@pytest.fixture -def env_cleanup() -> Generator[None, None, None]: - """Clean up environment variables after each test.""" - old_level = os.environ.get("LOG_LEVEL") - yield - if old_level: - os.environ["LOG_LEVEL"] = old_level - else: - os.environ.pop("LOG_LEVEL", None) - - -def test_configure_logger_default(reset_logging: None) -> None: - """Test logger configuration with default settings.""" - logger = configure_logger() - assert logger.name == "uvicorn.error" - assert logger.level == logging.INFO - - -def test_configure_logger_custom_name(reset_logging: None) -> None: - """Test logger configuration with custom name.""" - logger = configure_logger("test_logger") - assert logger.name == "test_logger" - assert logger.level == logging.INFO - - -def test_configure_logger_custom_level(reset_logging: None, env_cleanup: None) -> None: - """Test logger configuration with custom log level.""" - os.environ["LOG_LEVEL"] = "DEBUG" - logger = configure_logger() - assert logger.level == logging.DEBUG - - -def test_configure_logger_invalid_level(reset_logging: None, env_cleanup: None) -> None: - """Test logger configuration with invalid log level.""" - os.environ["LOG_LEVEL"] = "INVALID" - logger = configure_logger() - assert logger.level == logging.INFO # Should default to INFO for invalid levels - - -def test_configure_logger_case_insensitive( - reset_logging: None, env_cleanup: None -) -> None: - """Test logger configuration with case-insensitive log level.""" - os.environ["LOG_LEVEL"] = "debug" - logger = configure_logger() - assert logger.level == logging.DEBUG diff --git a/tests/lib/test_lunarcrush.py b/tests/lib/test_lunarcrush.py deleted file mode 100644 index 7a4d55f9..00000000 --- a/tests/lib/test_lunarcrush.py +++ /dev/null @@ -1,141 +0,0 @@ -from unittest.mock import Mock, patch - -import pytest -import requests - -from lib.logger import configure_logger -from lib.lunarcrush import LunarCrushApi - -logger = configure_logger(__name__) - - -@pytest.fixture -def mock_response() -> Mock: - """Fixture providing a mock response.""" - mock = Mock() - mock.status_code = 200 - mock.json.return_value = {"data": {"test": "value"}} - return mock - - -@pytest.fixture -def api() -> LunarCrushApi: - """Fixture providing a LunarCrushApi instance.""" - return LunarCrushApi() - - -def test_get_success(api: LunarCrushApi, mock_response: Mock) -> None: - """Test successful GET request.""" - with patch("requests.get") as mock_get: - mock_get.return_value = mock_response - - result = api._get("/test-endpoint") - assert result == {"data": {"test": "value"}} - - mock_get.assert_called_once() - args, kwargs = mock_get.call_args - assert args[0] == "https://lunarcrush.com/api/v2/test-endpoint" - assert kwargs["headers"]["Authorization"] == f"Bearer {api.api_key}" - - -def test_get_with_params(api: LunarCrushApi, mock_response: Mock) -> None: - """Test GET request with parameters.""" - with patch("requests.get") as mock_get: - mock_get.return_value = mock_response - - params = {"key": "value"} - api._get("/test-endpoint", params=params) - - mock_get.assert_called_once() - args, kwargs = mock_get.call_args - assert kwargs["params"] == params - - -def test_get_error(api: LunarCrushApi) -> None: - """Test GET request with error.""" - with patch("requests.get") as mock_get: - mock_get.side_effect = Exception("API Error") - - with pytest.raises( - Exception, match="Lunarcrush API GET request error: API Error" - ): - api._get("/test-endpoint") - - -def test_get_token_socials(api: LunarCrushApi, mock_response: Mock) -> None: - """Test getting token socials.""" - with patch("requests.get") as mock_get: - mock_get.return_value = mock_response - - result = api.get_token_socials("0x123") - assert result == {"test": "value"} - - mock_get.assert_called_once() - args, kwargs = mock_get.call_args - assert args[0] == "https://lunarcrush.com/api/v2/coins/0x123/v1" - - -def test_get_token_metadata(api: LunarCrushApi, mock_response: Mock) -> None: - """Test getting token metadata.""" - with patch("requests.get") as mock_get: - mock_get.return_value = mock_response - - result = api.get_token_metadata("0x123") - assert result == {"test": "value"} - - mock_get.assert_called_once() - args, kwargs = mock_get.call_args - assert args[0] == "https://lunarcrush.com/api/v2/coins/0x123/meta/v1" - - -def test_get_token_social_history(api: LunarCrushApi, mock_response: Mock) -> None: - """Test getting token social history.""" - with patch("requests.get") as mock_get: - mock_get.return_value = mock_response - - result = api.get_token_social_history("0x123") - assert result == {"data": {"test": "value"}} - - mock_get.assert_called_once() - args, kwargs = mock_get.call_args - assert args[0] == "https://lunarcrush.com/api/v2/coins/0x123/time-series/v1" - - -def test_search(api: LunarCrushApi, mock_response: Mock) -> None: - """Test search functionality.""" - with patch("requests.get") as mock_get: - mock_get.return_value = mock_response - - result = api.search("test_term") - assert result == {"data": {"test": "value"}} - - mock_get.assert_called_once() - args, kwargs = mock_get.call_args - assert args[0] == "https://lunarcrush.com/api/v2/searches/search" - assert kwargs["params"] == {"term": "test_term"} - - -def test_http_error(api: LunarCrushApi) -> None: - """Test handling of HTTP errors.""" - with patch("requests.get") as mock_get: - mock_response = Mock() - mock_response.raise_for_status.side_effect = requests.exceptions.HTTPError( - "404 Client Error" - ) - mock_get.return_value = mock_response - - with pytest.raises( - Exception, match="Lunarcrush API GET request error: 404 Client Error" - ): - api._get("/test-endpoint") - - -def test_connection_error(api: LunarCrushApi) -> None: - """Test handling of connection errors.""" - with patch("requests.get") as mock_get: - mock_get.side_effect = requests.exceptions.ConnectionError("Connection refused") - - with pytest.raises( - Exception, match="Lunarcrush API GET request error: Connection refused" - ): - api._get("/test-endpoint") diff --git a/tests/lib/test_persona.py b/tests/lib/test_persona.py deleted file mode 100644 index af0c16a3..00000000 --- a/tests/lib/test_persona.py +++ /dev/null @@ -1,131 +0,0 @@ -from unittest.mock import Mock - -import pytest - -from backend.models import Agent -from lib.logger import configure_logger -from lib.persona import generate_persona, generate_static_persona - -logger = configure_logger(__name__) - - -@pytest.fixture -def mock_agent() -> Agent: - """Fixture providing a mock Agent instance.""" - agent = Mock(spec=Agent) - agent.name = "TestBot" - agent.backstory = "A test bot with a simple backstory" - agent.role = "Testing assistant" - agent.goal = "Help with testing" - return agent - - -def test_generate_persona(mock_agent: Agent) -> None: - """Test persona generation with a mock agent.""" - persona = generate_persona(mock_agent) - - # Check that the persona is a string - assert isinstance(persona, str) - - # Check that agent attributes are included in the persona - assert mock_agent.name in persona - assert mock_agent.backstory in persona - assert mock_agent.role in persona - assert mock_agent.goal in persona - - # Check for required sections - required_sections = [ - "Knowledge:", - "Extensions:", - "Disclaimer:", - "Style:", - "Boundaries:", - ] - for section in required_sections: - assert section in persona - - -def test_generate_static_persona() -> None: - """Test static persona generation.""" - persona = generate_static_persona() - - # Check that the persona is a string - assert isinstance(persona, str) - - # Check for default name - assert "AI Assistant" in persona - - # Check for required sections - required_sections = [ - "Role:", - "Goal:", - "Knowledge:", - "Extensions:", - "Disclaimer:", - "Style:", - "Boundaries:", - ] - for section in required_sections: - assert section in persona - - # Check for specific content - assert "Stacks blockchain" in persona - assert "not a licensed financial advisor" in persona - assert "do not support or endorse illicit activities" in persona - - -def test_persona_formatting() -> None: - """Test persona formatting rules.""" - persona = generate_static_persona() - - # Check that the persona doesn't contain emojis - # This is a basic check - you might want to add more comprehensive emoji detection - common_emojis = ["😊", "👍", "🚀", "💰", "📈"] - for emoji in common_emojis: - assert emoji not in persona - - # Check that markdown syntax isn't used - markdown_elements = ["##", "**", "__", "```", "==="] - for element in markdown_elements: - assert element not in persona - - -def test_persona_content_consistency(mock_agent: Agent) -> None: - """Test that generated personas maintain consistent content across calls.""" - persona1 = generate_persona(mock_agent) - persona2 = generate_persona(mock_agent) - assert persona1 == persona2 - - static_persona1 = generate_static_persona() - static_persona2 = generate_static_persona() - assert static_persona1 == static_persona2 - - -def test_persona_security_elements() -> None: - """Test that personas include necessary security-related content.""" - persona = generate_static_persona() - - security_elements = [ - "security best practices", - "keep private keys secure", - "do their own research", - ] - - for element in security_elements: - assert element.lower() in persona.lower() - - -def test_persona_with_empty_agent_fields(mock_agent: Agent) -> None: - """Test persona generation with empty agent fields.""" - mock_agent.name = "" - mock_agent.backstory = "" - mock_agent.role = "" - mock_agent.goal = "" - - persona = generate_persona(mock_agent) - - # Check that the persona is still generated and contains core elements - assert isinstance(persona, str) - assert "Knowledge:" in persona - assert "Extensions:" in persona - assert "Disclaimer:" in persona diff --git a/tests/lib/test_platform.py b/tests/lib/test_platform.py deleted file mode 100644 index 836749fd..00000000 --- a/tests/lib/test_platform.py +++ /dev/null @@ -1,194 +0,0 @@ -from unittest.mock import Mock, patch - -import pytest - -from lib.hiro import PlatformApi -from lib.logger import configure_logger - -logger = configure_logger(__name__) - - -@pytest.fixture -def mock_config() -> None: - """Fixture to mock config values.""" - with patch("config.config") as mock_config: - mock_config.api.hiro_api_key = "test_api_key" - mock_config.api.webhook_url = "https://test-webhook.com" - mock_config.api.webhook_auth = "test_auth" - yield - - -@pytest.fixture -def api(mock_config: None) -> PlatformApi: - """Fixture providing a PlatformApi instance.""" - return PlatformApi() - - -def test_init_missing_api_key() -> None: - """Test initialization with missing API key.""" - with patch("config.config") as mock_config: - mock_config.api.hiro_api_key = None - with pytest.raises( - ValueError, match="HIRO_API_KEY environment variable is required" - ): - PlatformApi() - - -def test_generate_contract_deployment_predicate(api: PlatformApi) -> None: - """Test contract deployment predicate generation.""" - predicate = api.generate_contract_deployment_predicate( - txid="test_txid", - start_block=1000, - network="testnet", - name="test_hook", - end_block=2000, - expire_after_occurrence=2, - webhook_url="https://custom-webhook.com", - webhook_auth="custom_auth", - ) - - assert predicate["name"] == "test_hook" - assert predicate["chain"] == "stacks" - assert predicate["version"] == 1 - - network_config = predicate["networks"]["testnet"] - assert network_config["if_this"]["scope"] == "txid" - assert network_config["if_this"]["equals"] == "test_txid" - assert network_config["start_block"] == 1000 - assert network_config["end_block"] == 2000 - assert network_config["expire_after_occurrence"] == 2 - assert ( - network_config["then_that"]["http_post"]["url"] == "https://custom-webhook.com" - ) - assert ( - network_config["then_that"]["http_post"]["authorization_header"] - == "custom_auth" - ) - - -def test_generate_contract_deployment_predicate_defaults(api: PlatformApi) -> None: - """Test contract deployment predicate generation with default values.""" - predicate = api.generate_contract_deployment_predicate("test_txid") - - assert predicate["name"] == "test" - network_config = predicate["networks"]["testnet"] - assert network_config["start_block"] == 75996 - assert network_config["end_block"] is None - assert network_config["expire_after_occurrence"] == 1 - assert network_config["then_that"]["http_post"]["url"] == api.webhook_url - assert ( - network_config["then_that"]["http_post"]["authorization_header"] - == api.webhook_auth - ) - - -def test_create_contract_deployment_hook(api: PlatformApi) -> None: - """Test contract deployment hook creation.""" - with patch.object(api, "create_chainhook") as mock_create_chainhook: - mock_create_chainhook.return_value = {"status": "success"} - - result = api.create_contract_deployment_hook("test_txid", name="test_hook") - assert result == {"status": "success"} - - # Verify the predicate was generated correctly - mock_create_chainhook.assert_called_once() - predicate = mock_create_chainhook.call_args[0][0] - assert predicate["name"] == "test_hook" - assert predicate["networks"]["testnet"]["if_this"]["equals"] == "test_txid" - - -def test_create_chainhook(api: PlatformApi) -> None: - """Test chainhook creation.""" - mock_response = Mock() - mock_response.json.return_value = {"status": "success"} - - with patch("requests.post") as mock_post: - mock_post.return_value = mock_response - - predicate = {"test": "predicate"} - result = api.create_chainhook(predicate) - - assert result == {"status": "success"} - mock_post.assert_called_once_with( - f"{api.base_url}/v1/ext/{api.api_key}/chainhooks", - headers={"Content-Type": "application/json"}, - json=predicate, - ) - - -def test_create_chainhook_error(api: PlatformApi) -> None: - """Test chainhook creation error handling.""" - with patch("requests.post") as mock_post: - mock_post.side_effect = Exception("API Error") - - with pytest.raises(Exception, match="Hiro API POST request error: API Error"): - api.create_chainhook({"test": "predicate"}) - - -def test_generate_dao_x_linkage(api: PlatformApi) -> None: - """Test DAO X linkage predicate generation.""" - predicate = api.generate_dao_x_linkage( - contract_identifier="test.contract", - method="test_method", - start_block=2000, - network="mainnet", - name="test_dao", - end_block=3000, - webhook_url="https://custom-webhook.com", - webhook_auth="custom_auth", - ) - - assert predicate["name"] == "test_dao" - assert predicate["chain"] == "stacks" - assert predicate["version"] == 1 - - network_config = predicate["networks"]["mainnet"] - assert network_config["if_this"]["scope"] == "contract_call" - assert network_config["if_this"]["method"] == "test_method" - assert network_config["if_this"]["contract_identifier"] == "test.contract" - assert network_config["start_block"] == 2000 - assert network_config["end_block"] == 3000 - assert ( - network_config["then_that"]["http_post"]["url"] == "https://custom-webhook.com" - ) - assert ( - network_config["then_that"]["http_post"]["authorization_header"] - == "custom_auth" - ) - - -def test_generate_dao_x_linkage_defaults(api: PlatformApi) -> None: - """Test DAO X linkage predicate generation with default values.""" - predicate = api.generate_dao_x_linkage("test.contract") - - assert predicate["name"] == "getMessage" - network_config = predicate["networks"]["mainnet"] - assert network_config["if_this"]["method"] == "send" - assert network_config["start_block"] == 601924 - assert network_config["end_block"] is None - assert network_config["then_that"]["http_post"]["url"] == api.webhook_url - assert ( - network_config["then_that"]["http_post"]["authorization_header"] - == api.webhook_auth - ) - - -def test_create_dao_x_linkage_hook(api: PlatformApi) -> None: - """Test DAO X linkage hook creation.""" - with patch.object(api, "create_chainhook") as mock_create_chainhook: - mock_create_chainhook.return_value = {"status": "success"} - - result = api.create_dao_x_linkage_hook( - "test.contract", "test_method", name="test_dao" - ) - assert result == {"status": "success"} - - # Verify the predicate was generated correctly - mock_create_chainhook.assert_called_once() - predicate = mock_create_chainhook.call_args[0][0] - assert predicate["name"] == "test_dao" - assert ( - predicate["networks"]["mainnet"]["if_this"]["contract_identifier"] - == "test.contract" - ) - assert predicate["networks"]["mainnet"]["if_this"]["method"] == "test_method" diff --git a/tests/lib/test_tokenizer.py b/tests/lib/test_tokenizer.py deleted file mode 100644 index 17bea5c9..00000000 --- a/tests/lib/test_tokenizer.py +++ /dev/null @@ -1,91 +0,0 @@ -from typing import Any, Dict, List - -import pytest - -from lib.logger import configure_logger -from lib.tokenizer import Trimmer - -logger = configure_logger(__name__) - - -@pytest.fixture -def sample_messages() -> List[Dict[str, Any]]: - """Fixture providing sample messages for testing.""" - return [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Hello, how are you?"}, - {"role": "assistant", "content": "I'm doing well, thank you for asking!"}, - {"role": "user", "content": "That's great to hear!"}, - ] - - -def test_trimmer_initialization() -> None: - """Test Trimmer initialization with default and custom parameters.""" - default_trimmer = Trimmer() - assert default_trimmer.token_model == "gpt-4.1" - assert default_trimmer.maxsize == 50000 - assert default_trimmer.margin == 500 - - custom_trimmer = Trimmer(token_model="gpt-3.5-turbo", maxsize=4000, margin=200) - assert custom_trimmer.token_model == "gpt-3.5-turbo" - assert custom_trimmer.maxsize == 4000 - assert custom_trimmer.margin == 200 - - -def test_count_tokens(sample_messages: List[Dict[str, Any]]) -> None: - """Test token counting functionality.""" - trimmer = Trimmer() - token_count = trimmer.count_tokens(sample_messages) - assert token_count > 0 - assert isinstance(token_count, int) - - # Test with empty messages - assert trimmer.count_tokens([]) == 0 - - # Test with empty content - empty_content_messages = [{"role": "user", "content": ""}] - assert trimmer.count_tokens(empty_content_messages) == 0 - - -def test_trim_messages(sample_messages: List[Dict[str, Any]]) -> None: - """Test message trimming functionality.""" - # Create a trimmer with a very small maxsize to force trimming - trimmer = Trimmer(maxsize=50, margin=10) - - # Make a copy of messages to avoid modifying the fixture - messages = sample_messages.copy() - original_length = len(messages) - - trimmer.trim_messages(messages) - assert len(messages) < original_length - - # System message (index 0) and last message should be preserved - assert messages[0]["role"] == "system" - assert messages[-1]["content"] == "That's great to hear!" - - -def test_trim_messages_short_conversation( - sample_messages: List[Dict[str, Any]], -) -> None: - """Test trimming with very short conversations.""" - trimmer = Trimmer() - - # Test with just system and one user message - short_messages = sample_messages[:2] - original_messages = short_messages.copy() - - trimmer.trim_messages(short_messages) - assert short_messages == original_messages # Should not modify messages - - -def test_trim_messages_no_system_message() -> None: - """Test trimming messages without a system message.""" - trimmer = Trimmer(maxsize=50, margin=10) - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - {"role": "user", "content": "How are you?"}, - ] - - trimmer.trim_messages(messages) - assert len(messages) > 0 # Should still preserve some messages diff --git a/tests/lib/test_tools.py b/tests/lib/test_tools.py deleted file mode 100644 index c5dccbc1..00000000 --- a/tests/lib/test_tools.py +++ /dev/null @@ -1,120 +0,0 @@ -import json -from unittest.mock import Mock, patch - -import pytest - -from lib.tools import Tool, extract_tool_info, get_available_tools - - -class MockToolInstance: - def __init__(self, name: str, description: str, schema: Mock = None): - self.name = name - self.description = description - self.args_schema = schema - - -class MockSchemaField: - def __init__(self, description: str, annotation: str): - self.description = description - self.annotation = annotation - - -def test_extract_tool_info_valid(): - """Test extracting tool info with valid input.""" - # Setup mock schema - mock_schema = Mock() - mock_schema.model_fields = { - "param1": MockSchemaField("Test param", "str"), - "param2": MockSchemaField("Another param", "int"), - } - - # Create mock tool instance - tool_instance = MockToolInstance( - name="category_test_tool", - description="Test description", - schema=mock_schema, - ) - - # Extract tool info - result = extract_tool_info("category_test_tool", tool_instance) - - # Verify result - assert result is not None - assert result.id == "category_test_tool" - assert result.name == "Test Tool" - assert result.description == "Test description" - assert result.category == "CATEGORY" - - # Verify parameters - params = json.loads(result.parameters) - assert len(params) == 2 - assert params["param1"]["type"] == "str" - assert params["param2"]["type"] == "int" - - -def test_extract_tool_info_no_schema(): - """Test extracting tool info with no schema.""" - tool_instance = MockToolInstance( - name="test_tool", - description="Test description", - schema=None, - ) - - result = extract_tool_info("test_tool", tool_instance) - assert result is None - - -def test_extract_tool_info_error_handling(): - """Test error handling in extract_tool_info.""" - # Create a tool instance that will raise an exception - tool_instance = Mock() - tool_instance.args_schema = Mock(side_effect=Exception("Test error")) - - result = extract_tool_info("test_tool", tool_instance) - assert result is None - - -@patch("lib.tools.initialize_tools") -def test_get_available_tools_success(mock_initialize_tools): - """Test successfully getting available tools.""" - # Setup mock schema - mock_schema = Mock() - mock_schema.model_fields = { - "param1": MockSchemaField("Test param", "str"), - } - - # Setup mock tools - mock_tools = { - "category_tool1": MockToolInstance( - name="category_tool1", - description="Tool 1", - schema=mock_schema, - ), - "category_tool2": MockToolInstance( - name="category_tool2", - description="Tool 2", - schema=mock_schema, - ), - } - - # Configure mock - mock_initialize_tools.return_value = mock_tools - - # Get tools - result = get_available_tools() - - # Verify results - assert len(result) == 2 - assert all(isinstance(tool, Tool) for tool in result) - assert {tool.name for tool in result} == {"Tool1", "Tool2"} - - -@patch("lib.tools.initialize_tools") -def test_get_available_tools_error(mock_initialize_tools): - """Test error handling in get_available_tools.""" - # Configure mock to raise an exception - mock_initialize_tools.side_effect = Exception("Test error") - - # Verify exception is raised - with pytest.raises(Exception): - get_available_tools() diff --git a/tests/lib/test_velar.py b/tests/lib/test_velar.py deleted file mode 100644 index 4df56146..00000000 --- a/tests/lib/test_velar.py +++ /dev/null @@ -1,248 +0,0 @@ -from typing import Dict, List -from unittest.mock import Mock, patch - -import pytest - -from lib.logger import configure_logger -from lib.velar import VelarApi - -logger = configure_logger(__name__) - - -@pytest.fixture -def velar_api() -> VelarApi: - """Fixture providing a VelarApi instance.""" - return VelarApi() - - -@pytest.fixture -def mock_pools() -> List[Dict[str, str]]: - """Fixture providing mock pool data.""" - return [ - { - "token0Symbol": "TEST", - "token1Symbol": "STX", - "poolId": "pool1", - }, - { - "token0Symbol": "STX", - "token1Symbol": "OTHER", - "poolId": "pool2", - }, - { - "token0Symbol": "TEST", - "token1Symbol": "OTHER", - "poolId": "pool3", - }, - ] - - -@pytest.fixture -def mock_stats_data() -> Dict[str, List[Dict[str, float]]]: - """Fixture providing mock stats data.""" - return { - "data": [ - {"datetime": "2024-01-01", "value": 1.0}, - {"datetime": "2024-01-02", "value": 2.0}, - ] - } - - -def test_initialization(velar_api: VelarApi) -> None: - """Test VelarApi initialization.""" - assert velar_api.base_url == "https://gateway.velar.network/" - - -@patch("requests.get") -def test_get_success(mock_get: Mock, velar_api: VelarApi) -> None: - """Test successful GET request.""" - mock_response = Mock() - mock_response.json.return_value = {"data": "test"} - mock_get.return_value = mock_response - - result = velar_api._get("test-endpoint") - - assert result == {"data": "test"} - mock_get.assert_called_once_with( - "https://gateway.velar.network/test-endpoint", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_with_params(mock_get: Mock, velar_api: VelarApi) -> None: - """Test GET request with parameters.""" - mock_response = Mock() - mock_response.json.return_value = {"data": "test"} - mock_get.return_value = mock_response - - params = {"key": "value"} - result = velar_api._get("test-endpoint", params=params) - - assert result == {"data": "test"} - mock_get.assert_called_once_with( - "https://gateway.velar.network/test-endpoint", - headers={"Accept": "application/json"}, - params=params, - ) - - -@patch("requests.get") -def test_get_error(mock_get: Mock, velar_api: VelarApi) -> None: - """Test GET request error handling.""" - mock_get.side_effect = Exception("API Error") - - with pytest.raises(Exception, match="Velar API GET request error: API Error"): - velar_api._get("test-endpoint") - - -@patch("requests.get") -def test_get_tokens(mock_get: Mock, velar_api: VelarApi) -> None: - """Test tokens retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"message": ["token1", "token2"]} - mock_get.return_value = mock_response - - result = velar_api.get_tokens() - - assert result == ["token1", "token2"] - mock_get.assert_called_once_with( - "https://gateway.velar.network/swapapp/swap/tokens", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_pools( - mock_get: Mock, velar_api: VelarApi, mock_pools: List[Dict[str, str]] -) -> None: - """Test pools retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"message": mock_pools} - mock_get.return_value = mock_response - - result = velar_api.get_pools() - - assert result == mock_pools - mock_get.assert_called_once_with( - "https://gateway.velar.network/watcherapp/pool", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch.object(VelarApi, "get_pools") -def test_get_token_pools( - mock_get_pools: Mock, velar_api: VelarApi, mock_pools: List[Dict[str, str]] -) -> None: - """Test token pools retrieval.""" - mock_get_pools.return_value = mock_pools - - result = velar_api.get_token_pools("TEST") - - assert len(result) == 2 - assert all( - pool["token0Symbol"] == "TEST" or pool["token1Symbol"] == "TEST" - for pool in result - ) - - -@patch.object(VelarApi, "get_pools") -def test_get_token_stx_pools( - mock_get_pools: Mock, velar_api: VelarApi, mock_pools: List[Dict[str, str]] -) -> None: - """Test STX token pools retrieval.""" - mock_get_pools.return_value = mock_pools - - result = velar_api.get_token_stx_pools("TEST") - - assert len(result) == 1 - assert result[0]["poolId"] == "pool1" - assert "TEST" in [ - result[0]["token0Symbol"], - result[0]["token1Symbol"], - ] and "STX" in [result[0]["token0Symbol"], result[0]["token1Symbol"]] - - -@patch("requests.get") -def test_get_token_price_history( - mock_get: Mock, - velar_api: VelarApi, - mock_stats_data: Dict[str, List[Dict[str, float]]], -) -> None: - """Test token price history retrieval.""" - mock_response = Mock() - mock_response.json.return_value = mock_stats_data - mock_get.return_value = mock_response - - result = velar_api.get_token_price_history("TEST", "week") - - assert result == mock_stats_data - mock_get.assert_called_once_with( - "https://gateway.velar.network/watcherapp/stats/TEST/?type=price&interval=week", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_token_stats(mock_get: Mock, velar_api: VelarApi) -> None: - """Test token stats retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"stats": "data"} - mock_get.return_value = mock_response - - result = velar_api.get_token_stats("TEST") - - assert result == {"stats": "data"} - mock_get.assert_called_once_with( - "https://gateway.velar.network/watcherapp/pool/TEST", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch("requests.get") -def test_get_pool_stats_history(mock_get: Mock, velar_api: VelarApi) -> None: - """Test pool stats history retrieval.""" - mock_response = Mock() - mock_response.json.return_value = {"stats": "data"} - mock_get.return_value = mock_response - - result = velar_api.get_pool_stats_history("pool1", "tvl", "week") - - assert result == {"stats": "data"} - mock_get.assert_called_once_with( - "https://gateway.velar.network/watcherapp/stats/pool1?type=tvl&interval=week", - headers={"Accept": "application/json"}, - params=None, - ) - - -@patch.object(VelarApi, "_get") -def test_get_pool_stats_history_agg( - mock_get: Mock, - velar_api: VelarApi, - mock_stats_data: Dict[str, List[Dict[str, float]]], -) -> None: - """Test aggregated pool stats history retrieval.""" - mock_get.return_value = mock_stats_data - - result = velar_api.get_pool_stats_history_agg("pool1", "week") - - assert len(result) == 2 - assert all(key in result[0] for key in ["price", "tvl", "volume", "datetime"]) - assert mock_get.call_count == 3 # Called for price, tvl, and volume data - - -@patch.object(VelarApi, "_get") -def test_get_pool_stats_history_agg_error(mock_get: Mock, velar_api: VelarApi) -> None: - """Test aggregated pool stats history retrieval error.""" - mock_get.side_effect = Exception("API Error") - - with pytest.raises( - Exception, match="Token pool stats history retrieval error: API Error" - ): - velar_api.get_pool_stats_history_agg("pool1") diff --git a/tests/services/test_bot.py b/tests/services/test_bot.py deleted file mode 100644 index 9b103986..00000000 --- a/tests/services/test_bot.py +++ /dev/null @@ -1,259 +0,0 @@ -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest -from telegram import Update, User -from telegram.ext import Application, ContextTypes - -from backend.models import TelegramUserBase -from lib.logger import configure_logger -from services.bot import TelegramBotConfig, TelegramBotService - -logger = configure_logger(__name__) - - -@pytest.fixture -def config(): - return TelegramBotConfig(token="test_token", admin_ids={12345}, is_enabled=True) - - -@pytest.fixture -def service(config): - return TelegramBotService(config) - - -@pytest.fixture -def mock_update(): - update = MagicMock(spec=Update) - update.effective_user = MagicMock(spec=User) - update.effective_user.id = 12345 - update.effective_user.username = "test_user" - update.effective_user.first_name = "Test" - update.effective_user.last_name = "User" - update.message = AsyncMock() - return update - - -@pytest.fixture -def mock_context(): - context = MagicMock(spec=ContextTypes.DEFAULT_TYPE) - context.args = [] - return context - - -@pytest.fixture -def mock_backend(): - with patch("services.bot.backend") as mock: - mock.get_telegram_user = MagicMock() - mock.update_telegram_user = MagicMock() - mock.list_telegram_users = MagicMock() - yield mock - - -class TestTelegramBotConfig: - def test_from_env(self): - with patch.dict( - "os.environ", - { - "AIBTC_TELEGRAM_BOT_TOKEN": "test_token", - "AIBTC_TELEGRAM_BOT_ENABLED": "true", - }, - ): - config = TelegramBotConfig.from_env() - assert config.token == "test_token" - assert config.is_enabled is True - assert isinstance(config.admin_ids, set) - - -class TestTelegramBotService: - def test_is_admin(self, service): - assert service.is_admin(12345) is True - assert service.is_admin(54321) is False - - @pytest.mark.asyncio - async def test_start_command_no_args( - self, service, mock_update, mock_context, mock_backend - ): - await service.start_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "Please use the registration link provided to start the bot." - ) - - @pytest.mark.asyncio - async def test_start_command_invalid_user( - self, service, mock_update, mock_context, mock_backend - ): - mock_context.args = ["invalid_id"] - mock_backend.get_telegram_user.return_value = None - - await service.start_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "Invalid registration link. Please use the correct link to register." - ) - - @pytest.mark.asyncio - async def test_start_command_success( - self, service, mock_update, mock_context, mock_backend - ): - mock_context.args = ["valid_id"] - mock_backend.get_telegram_user.return_value = True - mock_backend.update_telegram_user.return_value = True - - await service.start_command(mock_update, mock_context) - mock_backend.update_telegram_user.assert_called_once() - assert ( - "Your registration has been completed successfully!" - in mock_update.message.reply_text.call_args[0][0] - ) - - @pytest.mark.asyncio - async def test_help_command(self, service, mock_update, mock_context): - await service.help_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once() - assert "Available Commands" in mock_update.message.reply_text.call_args[0][0] - - @pytest.mark.asyncio - async def test_send_message_command_not_admin( - self, service, mock_update, mock_context - ): - service.config.admin_ids = { - 54321 - } # Different from mock_update.effective_user.id - await service.send_message_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "You are not authorized to send messages." - ) - - @pytest.mark.asyncio - async def test_send_message_command_no_args( - self, service, mock_update, mock_context - ): - await service.send_message_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "Please provide username and message. Usage: /send " - ) - - @pytest.mark.asyncio - async def test_send_message_command_user_not_found( - self, service, mock_update, mock_context, mock_backend - ): - mock_context.args = ["nonexistent_user", "test message"] - mock_backend.list_telegram_users.return_value = [] - - await service.send_message_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "Registered user with username nonexistent_user not found." - ) - - @pytest.mark.asyncio - async def test_list_users_command_not_admin( - self, service, mock_update, mock_context - ): - service.config.admin_ids = {54321} - await service.list_users_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "You are not authorized to list users." - ) - - @pytest.mark.asyncio - async def test_list_users_command_empty( - self, service, mock_update, mock_context, mock_backend - ): - mock_backend.list_telegram_users.return_value = [] - await service.list_users_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "No registered users found." - ) - - @pytest.mark.asyncio - async def test_list_users_command_success( - self, service, mock_update, mock_context, mock_backend - ): - mock_backend.list_telegram_users.return_value = [ - TelegramUserBase(telegram_user_id="123", username="user1"), - TelegramUserBase(telegram_user_id="456", username="user2"), - ] - await service.list_users_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once() - assert "user1: 123" in mock_update.message.reply_text.call_args[0][0] - assert "user2: 456" in mock_update.message.reply_text.call_args[0][0] - - @pytest.mark.asyncio - async def test_add_admin_command_not_admin( - self, service, mock_update, mock_context - ): - service.config.admin_ids = {54321} - await service.add_admin_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "You are not authorized to add admins." - ) - - @pytest.mark.asyncio - async def test_add_admin_command_no_args(self, service, mock_update, mock_context): - await service.add_admin_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "Please provide a user ID. Usage: /add_admin " - ) - - @pytest.mark.asyncio - async def test_add_admin_command_invalid_id( - self, service, mock_update, mock_context - ): - mock_context.args = ["not_a_number"] - await service.add_admin_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "Please provide a valid user ID (numbers only)." - ) - - @pytest.mark.asyncio - async def test_add_admin_command_success(self, service, mock_update, mock_context): - mock_context.args = ["54321"] - await service.add_admin_command(mock_update, mock_context) - mock_update.message.reply_text.assert_called_once_with( - "Successfully added user ID 54321 as admin." - ) - assert 54321 in service.config.admin_ids - - @pytest.mark.asyncio - async def test_send_message_to_user(self, service, mock_backend): - # Setup mock application - service._app = AsyncMock(spec=Application) - service._app.bot.send_message = AsyncMock() - - mock_backend.list_telegram_users.return_value = [ - TelegramUserBase(telegram_user_id="123", username="test_user") - ] - - result = await service.send_message_to_user("test_profile", "test message") - assert result is True - service._app.bot.send_message.assert_called_once_with( - chat_id="123", text="test message" - ) - - @pytest.mark.asyncio - async def test_send_message_to_user_disabled(self, service): - service.config.is_enabled = False - result = await service.send_message_to_user("test_profile", "test message") - assert result is False - - @pytest.mark.asyncio - async def test_initialize(self, service): - with patch("telegram.ext.Application.builder") as mock_builder: - mock_app = AsyncMock(spec=Application) - mock_builder.return_value.token.return_value.build.return_value = mock_app - - await service.initialize() - - assert service._app is not None - mock_app.initialize.assert_called_once() - mock_app.start.assert_called_once() - mock_app.updater.start_polling.assert_called_once_with( - allowed_updates=Update.ALL_TYPES - ) - - @pytest.mark.asyncio - async def test_shutdown(self, service): - service._app = AsyncMock(spec=Application) - await service.shutdown() - service._app.stop.assert_called_once() - service._app.shutdown.assert_called_once() - assert service._app is None diff --git a/tests/services/test_chat.py b/tests/services/test_chat.py deleted file mode 100644 index e2f80b92..00000000 --- a/tests/services/test_chat.py +++ /dev/null @@ -1,230 +0,0 @@ -import asyncio -import datetime -from unittest.mock import Mock, patch -from uuid import UUID - -import pytest - -pytest_plugins = ("pytest_asyncio",) - -from backend.models import Agent, Profile -from services.chat import ( - MessageHandler, - ToolExecutionHandler, - process_chat_message, -) - - -@pytest.fixture -def mock_profile(): - return Profile( - id=UUID("12345678-1234-5678-1234-567812345678"), - name="Test User", - email="test@example.com", - created_at=datetime.datetime.now(), - updated_at=datetime.datetime.now(), - is_active=True, - is_verified=True, - is_admin=False, - is_superuser=False, - ) - - -@pytest.fixture -def mock_queue(): - return asyncio.Queue() - - -@pytest.fixture -def mock_agent(): - return Agent( - id=UUID("11111111-2222-3333-4444-555555555555"), - name="Test Agent", - backstory="Test backstory", - role="Test role", - goal="Test goal", - created_at=datetime.datetime.now(), - updated_at=datetime.datetime.now(), - ) - - -@pytest.fixture -def mock_backend(mock_agent): - backend = Mock() - backend.get_agent = Mock(return_value=mock_agent) - backend.create_step = Mock() - backend.update_job = Mock() - return backend - - -class AsyncIterator: - def __init__(self, items): - self.items = items - - def __aiter__(self): - return self - - async def __anext__(self): - if not self.items: - raise StopAsyncIteration - return self.items.pop(0) - - -@pytest.fixture -def mock_tools(): - return { - "search": Mock(), - "calculator": Mock(), - } - - -@pytest.mark.asyncio -async def test_process_chat_message_basic_flow( - mock_profile, mock_queue, mock_backend, mock_tools -): - with patch("services.chat.backend", mock_backend): - with patch("services.chat.execute_langgraph_stream") as mock_execute: - # Setup mock response from langgraph - mock_execute.return_value = AsyncIterator( - [ - {"type": "token", "content": "Hello"}, - {"type": "result", "content": "Hello, how can I help?"}, - {"type": "end", "content": ""}, - ] - ) - - with patch("services.chat.initialize_tools", return_value=mock_tools): - job_id = UUID("12345678-1234-5678-1234-567812345678") - thread_id = UUID("87654321-4321-8765-4321-876543210987") - agent_id = UUID("11111111-2222-3333-4444-555555555555") - - await process_chat_message( - job_id=job_id, - thread_id=thread_id, - profile=mock_profile, - agent_id=agent_id, - input_str="Hi", - history=[], - output_queue=mock_queue, - ) - - # Verify backend calls - mock_backend.create_step.assert_called() - mock_backend.update_job.assert_called_once() - - # Verify queue output - messages = [] - while not mock_queue.empty(): - msg = await mock_queue.get() - if msg is not None: - messages.append(msg) - - assert len(messages) > 0 - assert any(msg["type"] == "token" for msg in messages) - - -@pytest.mark.asyncio -async def test_process_chat_message_with_tool_execution( - mock_profile, mock_queue, mock_backend, mock_tools -): - with patch("services.chat.backend", mock_backend): - with patch("services.chat.execute_langgraph_stream") as mock_execute: - # Setup mock response with tool execution - mock_execute.return_value = AsyncIterator( - [ - {"type": "token", "content": "Let me check that for you"}, - { - "type": "tool", - "status": "start", - "tool": "search", - "input": "query", - "output": None, - }, - { - "type": "tool", - "status": "end", - "tool": "search", - "input": "query", - "output": "result", - }, - {"type": "result", "content": "Here's what I found"}, - {"type": "end", "content": ""}, - ] - ) - - with patch("services.chat.initialize_tools", return_value=mock_tools): - job_id = UUID("12345678-1234-5678-1234-567812345678") - thread_id = UUID("87654321-4321-8765-4321-876543210987") - - await process_chat_message( - job_id=job_id, - thread_id=thread_id, - profile=mock_profile, - agent_id=None, - input_str="Search for something", - history=[], - output_queue=mock_queue, - ) - - # Verify tool execution was recorded - tool_step_calls = [ - call.kwargs["new_step"].tool - for call in mock_backend.create_step.call_args_list - if call.kwargs["new_step"].tool is not None - ] - assert "search" in tool_step_calls - - -@pytest.mark.asyncio -async def test_process_chat_message_error_handling(mock_profile, mock_queue): - with patch("services.chat.execute_langgraph_stream") as mock_execute: - mock_execute.side_effect = Exception("Test error") - - job_id = UUID("12345678-1234-5678-1234-567812345678") - thread_id = UUID("87654321-4321-8765-4321-876543210987") - - with pytest.raises(Exception): - await process_chat_message( - job_id=job_id, - thread_id=thread_id, - profile=mock_profile, - agent_id=None, - input_str="This should fail", - history=[], - output_queue=mock_queue, - ) - - -@pytest.mark.asyncio -async def test_message_handler_process_tokens(): - handler = MessageHandler() - message = { - "type": "token", - "content": "test content", - "thread_id": "test-thread", - "agent_id": "test-agent", - } - - processed = handler.process_token_message(message) - assert processed["type"] == "token" - assert processed["content"] == "test content" - assert "created_at" in processed - - -@pytest.mark.asyncio -async def test_tool_execution_handler(): - handler = ToolExecutionHandler() - tool_message = { - "type": "tool", - "status": "start", - "tool": "test_tool", - "input": "test_input", - "output": "test_output", - "thread_id": "test-thread", - "agent_id": "test-agent", - } - - processed = handler.process_tool_message(tool_message) - assert processed["type"] == "tool" - assert processed["tool"] == "test_tool" - assert "created_at" in processed diff --git a/tests/services/test_daos.py b/tests/services/test_daos.py deleted file mode 100644 index a063facf..00000000 --- a/tests/services/test_daos.py +++ /dev/null @@ -1,209 +0,0 @@ -import uuid -from unittest.mock import patch - -import pytest - -from backend.models import DAO, Token -from services.daos import ( - DAORequest, - DAOService, - TokenCreationError, - TokenRequest, - TokenService, - TokenServiceError, - TokenUpdateError, -) - - -@pytest.fixture -def mock_backend(): - with patch("services.daos.backend") as mock: - yield mock - - -@pytest.fixture -def dao_request(): - return DAORequest( - name="Test DAO", - mission="Test Mission", - description="Test Description", - wallet_id=uuid.uuid4(), - ) - - -@pytest.fixture -def token_request(): - return TokenRequest( - name="Test Token", - symbol="TEST", - description="Test Token Description", - decimals=6, - max_supply="1000000000", - ) - - -class TestDAORequest: - def test_to_dao_create(self, dao_request): - dao_create = dao_request.to_dao_create() - assert dao_create.name == dao_request.name - assert dao_create.mission == dao_request.mission - assert dao_create.description == dao_request.description - assert dao_create.wallet_id == dao_request.wallet_id - - -class TestTokenRequest: - def test_to_token_create(self, token_request): - token_create = token_request.to_token_create() - assert token_create.name == token_request.name - assert token_create.symbol == token_request.symbol - assert token_create.description == token_request.description - assert token_create.decimals == token_request.decimals - assert token_create.max_supply == token_request.max_supply - assert token_create.status == "DRAFT" - - def test_to_token_metadata(self, token_request): - metadata = token_request.to_token_metadata() - assert metadata.name == token_request.name - assert metadata.symbol == token_request.symbol - assert metadata.description == token_request.description - assert metadata.decimals == token_request.decimals - assert metadata.max_supply == token_request.max_supply - - -class TestDAOService: - def test_create_dao_success(self, mock_backend, dao_request): - expected_dao = DAO( - id=uuid.uuid4(), - name=dao_request.name, - mission=dao_request.mission, - description=dao_request.description, - wallet_id=dao_request.wallet_id, - ) - mock_backend.create_dao.return_value = expected_dao - - result = DAOService.create_dao(dao_request) - assert result == expected_dao - mock_backend.create_dao.assert_called_once_with(dao_request.to_dao_create()) - - def test_create_dao_failure(self, mock_backend, dao_request): - mock_backend.create_dao.side_effect = Exception("Database error") - - with pytest.raises(TokenServiceError) as exc_info: - DAOService.create_dao(dao_request) - - assert "Failed to create dao" in str(exc_info.value) - - -class TestTokenService: - @pytest.fixture - def token_service(self): - return TokenService() - - @pytest.fixture - def mock_asset_manager(self): - with patch("services.daos.TokenAssetManager") as mock: - instance = mock.return_value - instance.generate_all_assets.return_value = { - "metadata_url": "http://example.com/metadata", - "image_url": "http://example.com/image", - } - yield instance - - def test_create_token_success( - self, token_service, mock_backend, mock_asset_manager, token_request - ): - # Mock token creation - created_token = Token( - id=uuid.uuid4(), - name=token_request.name, - symbol=token_request.symbol, - description=token_request.description, - decimals=token_request.decimals, - max_supply=token_request.max_supply, - status="DRAFT", - ) - mock_backend.create_token.return_value = created_token - - # Mock token update - updated_token = Token( - id=created_token.id, - name=created_token.name, - symbol=created_token.symbol, - description=created_token.description, - decimals=created_token.decimals, - max_supply=created_token.max_supply, - status="DRAFT", - uri="http://example.com/metadata", - image_url="http://example.com/image", - ) - mock_backend.update_token.return_value = updated_token - - metadata_url, result = token_service.create_token(token_request) - - assert metadata_url == "http://example.com/metadata" - assert result == updated_token - mock_backend.create_token.assert_called_once_with( - token_request.to_token_create() - ) - mock_asset_manager.generate_all_assets.assert_called_once() - - def test_create_token_asset_generation_failure( - self, token_service, mock_backend, mock_asset_manager, token_request - ): - created_token = Token( - id=uuid.uuid4(), - name=token_request.name, - symbol=token_request.symbol, - description=token_request.description, - decimals=token_request.decimals, - max_supply=token_request.max_supply, - status="DRAFT", - ) - mock_backend.create_token.return_value = created_token - mock_asset_manager.generate_all_assets.side_effect = Exception( - "Asset generation failed" - ) - - with pytest.raises(TokenCreationError) as exc_info: - token_service.create_token(token_request) - - assert "Unexpected error during token creation" in str(exc_info.value) - - def test_create_token_update_failure( - self, token_service, mock_backend, mock_asset_manager, token_request - ): - created_token = Token( - id=uuid.uuid4(), - name=token_request.name, - symbol=token_request.symbol, - description=token_request.description, - decimals=token_request.decimals, - max_supply=token_request.max_supply, - status="DRAFT", - ) - mock_backend.create_token.return_value = created_token - mock_backend.update_token.return_value = None - - with pytest.raises(TokenUpdateError) as exc_info: - token_service.create_token(token_request) - - assert "Failed to update token record with asset URLs" in str(exc_info.value) - - def test_bind_token_to_dao_success(self, token_service, mock_backend): - token_id = uuid.uuid4() - dao_id = uuid.uuid4() - mock_backend.update_token.return_value = True - - result = token_service.bind_token_to_dao(token_id, dao_id) - - assert result is True - mock_backend.update_token.assert_called_once() - - def test_bind_token_to_dao_failure(self, token_service, mock_backend): - token_id = uuid.uuid4() - dao_id = uuid.uuid4() - mock_backend.update_token.side_effect = Exception("Update failed") - - result = token_service.bind_token_to_dao(token_id, dao_id) - - assert result is False diff --git a/tests/services/test_job_manager.py b/tests/services/test_job_manager.py deleted file mode 100644 index 759c4d74..00000000 --- a/tests/services/test_job_manager.py +++ /dev/null @@ -1,93 +0,0 @@ -"""Tests for the job manager module.""" - -from unittest.mock import MagicMock, patch - -from apscheduler.schedulers.asyncio import AsyncIOScheduler - -from services.runner.job_manager import JobManager - - -class TestJobManager: - """Test cases for JobManager class.""" - - def test_get_all_jobs(self): - """Test that get_all_jobs returns a list of job configurations.""" - with patch("services.runner.job_manager.config") as mock_config: - # Set up mock config - mock_config.twitter.enabled = True - mock_config.twitter.interval_seconds = 60 - mock_config.scheduler.sync_enabled = True - mock_config.scheduler.sync_interval_seconds = 120 - mock_config.scheduler.dao_runner_enabled = True - mock_config.scheduler.dao_runner_interval_seconds = 30 - mock_config.scheduler.tweet_runner_enabled = False - - # Call the method - jobs = JobManager.get_all_jobs() - - # Verify results - assert len(jobs) >= 5 # At least 5 jobs should be returned - - # Verify some specific jobs - twitter_job = next((j for j in jobs if j.name == "Twitter Service"), None) - assert twitter_job is not None - assert twitter_job.enabled is True - assert twitter_job.seconds == 60 - - dao_job = next((j for j in jobs if j.name == "DAO Runner Service"), None) - assert dao_job is not None - assert dao_job.enabled is True - assert dao_job.seconds == 30 - - tweet_job = next( - (j for j in jobs if j.name == "Tweet Runner Service"), None - ) - assert tweet_job is not None - assert tweet_job.enabled is False - - def test_schedule_jobs(self): - """Test scheduling jobs.""" - # Create mock scheduler - mock_scheduler = MagicMock(spec=AsyncIOScheduler) - - with ( - patch( - "services.runner.job_manager.JobManager.get_all_jobs" - ) as mock_get_jobs, - patch( - "services.runner.job_manager.execute_twitter_job" - ) as mock_twitter_func, - ): - # Create mock jobs - mock_jobs = [ - MagicMock( - name="Twitter Service", - enabled=True, - func=mock_twitter_func, - seconds=60, - args=None, - job_id="twitter_service", - ), - MagicMock( - name="Disabled Service", - enabled=False, - func=MagicMock(), - seconds=30, - args=None, - job_id="disabled_service", - ), - ] - mock_get_jobs.return_value = mock_jobs - - # Call the method - result = JobManager.schedule_jobs(mock_scheduler) - - # Verify results - assert result is True # At least one job was enabled - mock_scheduler.add_job.assert_called_once() - - # Verify the job was added with the correct parameters - args, kwargs = mock_scheduler.add_job.call_args - assert args[0] == mock_twitter_func - assert kwargs["seconds"] == 60 - assert kwargs["id"] == "twitter_service" diff --git a/tests/services/test_langgraph.py b/tests/services/test_langgraph.py deleted file mode 100644 index 865f4f50..00000000 --- a/tests/services/test_langgraph.py +++ /dev/null @@ -1,223 +0,0 @@ -import asyncio -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage - -from services.workflows import ( - ChatService, - ExecutionError, - MessageContent, - MessageProcessor, - StreamingCallbackHandler, - StreamingError, - execute_langgraph_stream, -) - - -@pytest.fixture -def message_processor(): - return MessageProcessor() - - -@pytest.fixture -def sample_history(): - return [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there"}, - {"role": "user", "content": "How are you?"}, - { - "role": "assistant", - "content": "I'm doing well", - "tool_calls": [{"type": "function", "function": {"name": "test_tool"}}], - }, - ] - - -class TestMessageContent: - def test_from_dict(self): - data = { - "role": "user", - "content": "test message", - "tool_calls": [{"type": "function"}], - } - content = MessageContent.from_dict(data) - assert content.role == "user" - assert content.content == "test message" - assert content.tool_calls == [{"type": "function"}] - - def test_from_dict_minimal(self): - data = {"role": "assistant", "content": "response"} - content = MessageContent.from_dict(data) - assert content.role == "assistant" - assert content.content == "response" - assert content.tool_calls is None - - -class TestMessageProcessor: - def test_extract_filtered_content(self, message_processor, sample_history): - filtered = message_processor.extract_filtered_content(sample_history) - assert len(filtered) == 4 - assert all(msg["role"] in ["user", "assistant"] for msg in filtered) - - def test_convert_to_langchain_messages(self, message_processor, sample_history): - filtered = message_processor.extract_filtered_content(sample_history) - messages = message_processor.convert_to_langchain_messages( - filtered, "current input", "test persona" - ) - - assert len(messages) == 6 # 4 history + 1 persona + 1 current input - assert isinstance(messages[0], SystemMessage) - assert messages[0].content == "test persona" - assert isinstance(messages[-1], HumanMessage) - assert messages[-1].content == "current input" - - def test_convert_without_persona(self, message_processor, sample_history): - filtered = message_processor.extract_filtered_content(sample_history) - messages = message_processor.convert_to_langchain_messages( - filtered, "current input" - ) - - assert len(messages) == 5 # 4 history + 1 current input - assert isinstance(messages[0], HumanMessage) - - -class TestStreamingCallbackHandler: - @pytest.fixture - def queue(self): - return asyncio.Queue() - - @pytest.fixture - def handler(self, queue): - return StreamingCallbackHandler(queue=queue) - - def test_initialization(self, handler): - assert handler.tokens == [] - assert handler.current_tool is None - assert handler._loop is None # Assuming _loop is an attribute - - @pytest.mark.asyncio - async def test_queue_operations(self, handler, queue): # Added queue fixture - test_item = {"type": "test", "content": "test_content"} - - # To test _put_to_queue properly, ensure it's called - handler._put_to_queue(test_item) - item = await queue.get() - assert item == test_item - - with pytest.raises(StreamingError): - # Test with invalid queue operation (e.g., queue is None) - handler_no_queue = StreamingCallbackHandler( - queue=None - ) # Create instance for this test - handler_no_queue._put_to_queue(test_item) - - def test_tool_start(self, handler): - handler._put_to_queue = MagicMock() # Mock to check calls - handler.on_tool_start({"name": "test_tool"}, "test_input") - - assert handler.current_tool == "test_tool" - handler._put_to_queue.assert_called_once() - - def test_tool_end(self, handler): - handler._put_to_queue = MagicMock() # Mock to check calls - handler.current_tool = "test_tool" - handler.on_tool_end("test_output") - - assert handler.current_tool is None - handler._put_to_queue.assert_called_once() - - def test_llm_new_token(self, handler): - handler.on_llm_new_token("test_token") - assert "test_token" in handler.tokens - - def test_llm_error(self, handler): - with pytest.raises(ExecutionError): # Or the specific error it raises - handler.on_llm_error(Exception("test error")) - - def test_tool_error(self, handler): - handler._put_to_queue = MagicMock() # Mock to check calls - handler.current_tool = "test_tool" - handler.on_tool_error(Exception("test error")) - - assert handler.current_tool is None - handler._put_to_queue.assert_called_once() - - -class TestChatService: - @pytest.fixture - def service(self, mock_chat_model_class, mock_tool_node_class): - return ChatService(collection_names="test_collection") - - @pytest.fixture - def mock_chat_model_class(self): - with patch("services.workflows.chat.ChatOpenAI") as mock: - yield mock - - @pytest.fixture - def mock_tool_node_class(self): - with patch("langgraph.prebuilt.ToolNode") as mock: - yield mock - - def test_chat_service_initialization(self, service, mock_chat_model_class): - assert service.llm is not None - - def test_get_runnable_graph(self, service, mock_tool_node_class): - if hasattr(service, "_create_graph"): - graph = service._create_graph() - assert graph is not None - - @pytest.mark.asyncio - async def test_execute_chat_stream_success(self, service, sample_history): - async def mock_stream_results(*args, **kwargs): - yield {"type": "token", "content": "test"} - yield {"type": "end"} - - service.execute_stream = AsyncMock(side_effect=mock_stream_results) - - tools_map = {"test_tool": MagicMock()} - chunks = [] - async for chunk in service.execute_stream( - sample_history, "test input", "test persona", tools_map - ): - chunks.append(chunk) - - assert len(chunks) > 0 - service.execute_stream.assert_called_once() - - @pytest.mark.asyncio - async def test_execute_chat_stream_error(self, service, sample_history): - service.execute_stream = AsyncMock(side_effect=ExecutionError("Stream failed")) - - with pytest.raises(ExecutionError): - async for _ in service.execute_stream( - sample_history, "test input", None, None - ): - pass - - -@pytest.mark.asyncio -async def test_facade_function(): - with patch("services.workflows.chat.ChatService") as MockChatService: - mock_service_instance = MockChatService.return_value - - async def mock_async_iterable(*args, **kwargs): - yield {"type": "test"} - - mock_service_instance.execute_stream = AsyncMock( - return_value=mock_async_iterable() - ) - - async for chunk in execute_langgraph_stream( - history=[], - input_str="test", - persona=None, - tools_map=None, - collection_names="test_collection", - ): - assert chunk["type"] == "test" - - MockChatService.assert_called_once_with( - collection_names="test_collection", embeddings=None - ) - mock_service_instance.execute_stream.assert_called_once() diff --git a/tests/services/test_schedule.py b/tests/services/test_schedule.py deleted file mode 100644 index 7eccfcb7..00000000 --- a/tests/services/test_schedule.py +++ /dev/null @@ -1,189 +0,0 @@ -import uuid -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest -from apscheduler.schedulers.asyncio import AsyncIOScheduler -from apscheduler.triggers.cron import CronTrigger - -from backend.models import Task -from services.schedule import SchedulerService, get_scheduler_service - - -@pytest.fixture -def mock_scheduler(): - scheduler = MagicMock(spec=AsyncIOScheduler) - scheduler.get_jobs.return_value = [] - return scheduler - - -@pytest.fixture -def scheduler_service(mock_scheduler): - return SchedulerService(mock_scheduler) - - -@pytest.fixture -def mock_task(): - return Task( - id=uuid.uuid4(), - name="Test Task", - prompt="Test Prompt", - agent_id=uuid.uuid4(), - profile_id=uuid.uuid4(), - cron="0 * * * *", - is_scheduled=True, - ) - - -@pytest.fixture -def mock_backend(): - with patch("services.schedule.backend") as mock: - mock.get_task = AsyncMock() - mock.get_agent = AsyncMock() - mock.get_profile = AsyncMock() - mock.create_job = AsyncMock() - mock.create_step = AsyncMock() - mock.update_job = AsyncMock() - mock.list_tasks = AsyncMock() - yield mock - - -@pytest.mark.asyncio -async def test_execute_job_success(scheduler_service, mock_backend, mock_task): - # Setup - agent_id = str(uuid.uuid4()) - task_id = str(uuid.uuid4()) - profile_id = str(uuid.uuid4()) - - mock_backend.get_task.return_value = mock_task - mock_backend.get_agent.return_value = {"id": agent_id} - mock_backend.get_profile.return_value = {"id": profile_id} - mock_backend.create_job.return_value = {"id": str(uuid.uuid4())} - - with patch("services.schedule.execute_langgraph_stream") as mock_stream: - mock_stream.return_value = [ - {"type": "tool", "tool": "test_tool", "input": "test_input"}, - {"type": "result", "content": "test_result"}, - ] - - # Execute - await scheduler_service.execute_job(agent_id, task_id, profile_id) - - # Assert - mock_backend.get_task.assert_called_once_with(task_id=uuid.UUID(task_id)) - mock_backend.get_agent.assert_called_once_with(agent_id=uuid.UUID(agent_id)) - mock_backend.get_profile.assert_called_once_with( - profile_id=uuid.UUID(profile_id) - ) - mock_backend.create_job.assert_called_once() - assert mock_backend.create_step.call_count == 2 - - -@pytest.mark.asyncio -async def test_execute_job_task_not_found(scheduler_service, mock_backend): - # Setup - mock_backend.get_task.return_value = None - - # Execute - await scheduler_service.execute_job("agent_id", "task_id", "profile_id") - - # Assert - mock_backend.get_agent.assert_not_called() - mock_backend.get_profile.assert_not_called() - mock_backend.create_job.assert_not_called() - - -@pytest.mark.asyncio -async def test_sync_schedules_add_new_job(scheduler_service, mock_backend, mock_task): - # Setup - mock_backend.list_tasks.return_value = [mock_task] - - # Execute - await scheduler_service.sync_schedules() - - # Assert - scheduler_service.scheduler.add_job.assert_called_once() - assert scheduler_service.scheduler.remove_job.call_count == 0 - - -@pytest.mark.asyncio -async def test_sync_schedules_update_job(scheduler_service, mock_backend, mock_task): - # Setup - job_id = f"schedule_{mock_task.id}" - mock_job = MagicMock() - mock_job.id = job_id - mock_job.trigger = CronTrigger.from_crontab( - "*/5 * * * *" - ) # Different from mock_task.cron - - scheduler_service.scheduler.get_jobs.return_value = [mock_job] - mock_backend.list_tasks.return_value = [mock_task] - - # Execute - await scheduler_service.sync_schedules() - - # Assert - assert scheduler_service.scheduler.remove_job.call_count == 1 - scheduler_service.scheduler.add_job.assert_called_once() - - -@pytest.mark.asyncio -async def test_sync_schedules_remove_job(scheduler_service, mock_backend): - # Setup - job_id = "schedule_old_job" - mock_job = MagicMock() - mock_job.id = job_id - - scheduler_service.scheduler.get_jobs.return_value = [mock_job] - mock_backend.list_tasks.return_value = [] # No tasks in backend - - # Execute - await scheduler_service.sync_schedules() - - # Assert - scheduler_service.scheduler.remove_job.assert_called_once_with(job_id) - assert scheduler_service.scheduler.add_job.call_count == 0 - - -def test_get_scheduler_service(): - # Setup - scheduler = MagicMock(spec=AsyncIOScheduler) - - # Execute - service1 = get_scheduler_service(scheduler) - service2 = get_scheduler_service() - - # Assert - assert service1 is service2 - assert isinstance(service1, SchedulerService) - - -def test_get_scheduler_service_no_scheduler(): - # Setup & Execute & Assert - with pytest.raises(ValueError): - get_scheduler_service() - - -@pytest.mark.asyncio -async def test_handle_stream_event(scheduler_service, mock_backend): - # Setup - job = {"id": str(uuid.uuid4())} - agent_id = str(uuid.uuid4()) - profile_id = str(uuid.uuid4()) - - # Test tool event - tool_event = { - "type": "tool", - "tool": "test_tool", - "input": "test_input", - "output": "test_output", - } - await scheduler_service._handle_stream_event(tool_event, job, agent_id, profile_id) - mock_backend.create_step.assert_called_once() - - # Test result event - result_event = {"type": "result", "content": "test_result"} - await scheduler_service._handle_stream_event( - result_event, job, agent_id, profile_id - ) - assert mock_backend.create_step.call_count == 2 - mock_backend.update_job.assert_called_once() diff --git a/tests/services/test_startup.py b/tests/services/test_startup.py deleted file mode 100644 index c98b2316..00000000 --- a/tests/services/test_startup.py +++ /dev/null @@ -1,148 +0,0 @@ -import asyncio -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest -from apscheduler.schedulers.asyncio import AsyncIOScheduler - -from config import config -from services.startup import StartupService - - -@pytest.fixture -def mock_scheduler(): - scheduler = MagicMock(spec=AsyncIOScheduler) - scheduler.running = True - return scheduler - - -@pytest.fixture -def service(mock_scheduler): - return StartupService(scheduler=mock_scheduler) - - -@pytest.fixture -def mock_manager(): - with patch("services.startup.manager") as mock: - mock.start_cleanup_task = AsyncMock() - yield mock - - -@pytest.fixture -def mock_bot(): - with patch("services.startup.start_application") as mock: - mock.return_value = AsyncMock() - yield mock - - -@pytest.fixture -def mock_job_manager(): - with patch("services.startup.JobManager") as mock: - mock.schedule_jobs.return_value = True - yield mock - - -class TestStartupService: - @pytest.mark.asyncio - async def test_start_websocket_cleanup_success(self, service, mock_manager): - """Test successful websocket cleanup start.""" - await service.start_websocket_cleanup() - mock_manager.start_cleanup_task.assert_called_once() - - @pytest.mark.asyncio - async def test_start_websocket_cleanup_failure(self, service, mock_manager): - """Test websocket cleanup start failure.""" - mock_manager.start_cleanup_task.side_effect = Exception("Cleanup failed") - - with pytest.raises(Exception) as exc_info: - await service.start_websocket_cleanup() - assert str(exc_info.value) == "Cleanup failed" - - @pytest.mark.asyncio - async def test_start_bot_disabled(self, service, mock_bot): - """Test bot startup when disabled.""" - with patch.object(config.telegram, "enabled", False): - result = await service.start_bot() - assert result is None - mock_bot.assert_not_called() - - @pytest.mark.asyncio - async def test_start_bot_enabled(self, service, mock_bot): - """Test bot startup when enabled.""" - with patch.object(config.telegram, "enabled", True): - await service.start_bot() - mock_bot.assert_called_once() - - @pytest.mark.asyncio - async def test_start_bot_failure(self, service, mock_bot): - """Test bot startup failure.""" - with patch.object(config.telegram, "enabled", True): - mock_bot.side_effect = Exception("Bot startup failed") - - with pytest.raises(Exception) as exc_info: - await service.start_bot() - assert str(exc_info.value) == "Bot startup failed" - - def test_init_scheduler_jobs_enabled(self, service, mock_job_manager): - """Test scheduler initialization with jobs enabled.""" - mock_job_manager.schedule_jobs.return_value = True - - service.init_scheduler() - - mock_job_manager.schedule_jobs.assert_called_once_with(service.scheduler) - service.scheduler.start.assert_called_once() - - def test_init_scheduler_all_disabled(self, service, mock_job_manager): - """Test scheduler initialization with all jobs disabled.""" - mock_job_manager.schedule_jobs.return_value = False - - service.init_scheduler() - - mock_job_manager.schedule_jobs.assert_called_once_with(service.scheduler) - service.scheduler.start.assert_not_called() - - @pytest.mark.asyncio - async def test_init_background_tasks( - self, service, mock_manager, mock_bot, mock_job_manager - ): - """Test background tasks initialization.""" - with patch.object(config.telegram, "enabled", True): - cleanup_task = await service.init_background_tasks() - - assert isinstance(cleanup_task, asyncio.Task) - assert service.cleanup_task is cleanup_task - mock_manager.start_cleanup_task.assert_called_once() - mock_bot.assert_called_once() - - @pytest.mark.asyncio - async def test_shutdown(self, service): - """Test service shutdown.""" - # Create a mock cleanup task - mock_task = AsyncMock() - service.cleanup_task = mock_task - - await service.shutdown() - - service.scheduler.shutdown.assert_called_once() - mock_task.cancel.assert_called_once() - - -@pytest.mark.asyncio -async def test_global_init_background_tasks(): - """Test global init_background_tasks function.""" - with patch("services.startup.startup_service") as mock_service: - mock_service.init_background_tasks = AsyncMock() - from services.startup import init_background_tasks - - await asyncio.create_task(init_background_tasks()) - mock_service.init_background_tasks.assert_called_once() - - -@pytest.mark.asyncio -async def test_global_shutdown(): - """Test global shutdown function.""" - with patch("services.startup.startup_service") as mock_service: - mock_service.shutdown = AsyncMock() - from services.startup import shutdown - - await shutdown() - mock_service.shutdown.assert_called_once() diff --git a/tests/services/test_tweet_task.py b/tests/services/test_tweet_task.py deleted file mode 100644 index 80fc200e..00000000 --- a/tests/services/test_tweet_task.py +++ /dev/null @@ -1,207 +0,0 @@ -import pytest -from backend.models import QueueMessage -from services.runner.tasks.tweet_task import TweetTask -from unittest.mock import AsyncMock, MagicMock -from uuid import UUID - - -@pytest.fixture -def tweet_task(): - """Create a TweetTask instance for testing.""" - task = TweetTask() - task.twitter_service = MagicMock() - task.twitter_service._apost_tweet = AsyncMock() - return task - - -class TestTweetTask: - """Tests for the TweetTask class.""" - - @pytest.mark.asyncio - async def test_validate_message_with_valid_format(self, tweet_task): - """Test validating a message with the correct format.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"message": "This is a test tweet"}, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - created_at="2024-03-06T00:00:00Z", - ) - original_message = message.message.copy() - - # Act - result = await tweet_task._validate_message(message) - - # Assert - assert result is None - # Message structure should remain unchanged - assert message.message == original_message - - @pytest.mark.asyncio - async def test_validate_message_with_empty_message(self, tweet_task): - """Test validating a message with an empty message field.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message=None, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - created_at="2024-03-06T00:00:00Z", - ) - - # Act - result = await tweet_task._validate_message(message) - - # Assert - assert result is not None - assert result.success is False - assert "empty" in result.message.lower() - - @pytest.mark.asyncio - async def test_validate_message_with_empty_content(self, tweet_task): - """Test validating a message with empty content.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"message": ""}, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - created_at="2024-03-06T00:00:00Z", - ) - - # Act - result = await tweet_task._validate_message(message) - - # Assert - assert result is not None - assert result.success is False - assert "empty" in result.message.lower() - - @pytest.mark.asyncio - async def test_validate_message_with_invalid_format(self, tweet_task): - """Test validating a message with an invalid format.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"wrong_field": "This is a test tweet"}, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - created_at="2024-03-06T00:00:00Z", - ) - - # Act - result = await tweet_task._validate_message(message) - - # Assert - assert result is not None - assert result.success is False - assert "unsupported" in result.message.lower() - - @pytest.mark.asyncio - async def test_validate_message_with_no_dao_id(self, tweet_task): - """Test validating a message with no DAO ID.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"message": "This is a test tweet"}, - dao_id=None, - created_at="2024-03-06T00:00:00Z", - ) - - # Act - result = await tweet_task._validate_message(message) - - # Assert - assert result is not None - assert result.success is False - assert "dao_id" in result.message.lower() - - @pytest.mark.asyncio - async def test_validate_message_with_too_long_tweet(self, tweet_task): - """Test validating a message with a tweet that exceeds the character limit.""" - # Arrange - long_tweet = "x" * 281 # Twitter's character limit is 280 - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"message": long_tweet}, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - created_at="2024-03-06T00:00:00Z", - ) - - # Act - result = await tweet_task._validate_message(message) - - # Assert - assert result is not None - assert result.success is False - assert "character limit" in result.message.lower() - - @pytest.mark.asyncio - async def test_process_tweet_message_success_with_reply(self, tweet_task): - """Test processing a tweet message successfully with a reply.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"message": "This is a test tweet"}, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - tweet_id="123456789", - created_at="2024-03-06T00:00:00Z", - ) - tweet_task.twitter_service._apost_tweet.return_value = { - "id": "987654321", - "text": "This is a test tweet", - } - - # Act - result = await tweet_task._process_tweet_message(message) - - # Assert - assert result.success is True - assert result.tweet_id is not None - tweet_task.twitter_service._apost_tweet.assert_called_once_with( - text="This is a test tweet", reply_in_reply_to_tweet_id="123456789" - ) - - @pytest.mark.asyncio - async def test_process_tweet_message_success_without_reply(self, tweet_task): - """Test processing a tweet message successfully without a reply.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"message": "This is a test tweet"}, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - created_at="2024-03-06T00:00:00Z", - ) - tweet_task.twitter_service._apost_tweet.return_value = { - "id": "987654321", - "text": "This is a test tweet", - } - - # Act - result = await tweet_task._process_tweet_message(message) - - # Assert - assert result.success is True - assert result.tweet_id is not None - tweet_task.twitter_service._apost_tweet.assert_called_once_with( - text="This is a test tweet" - ) - - @pytest.mark.asyncio - async def test_process_tweet_message_failure(self, tweet_task): - """Test processing a tweet message with a failure from the Twitter service.""" - # Arrange - message = QueueMessage( - id=UUID("00000000-0000-0000-0000-000000000001"), - message={"message": "This is a test tweet"}, - dao_id=UUID("00000000-0000-0000-0000-000000000001"), - created_at="2024-03-06T00:00:00Z", - ) - tweet_task.twitter_service._apost_tweet.return_value = None - - # Act - result = await tweet_task._process_tweet_message(message) - - # Assert - assert result.success is False - assert "failed to send tweet" in result.message.lower() - tweet_task.twitter_service._apost_tweet.assert_called_once_with( - text="This is a test tweet" - ) diff --git a/tests/services/test_twitter.py b/tests/services/test_twitter.py deleted file mode 100644 index 5606dbfc..00000000 --- a/tests/services/test_twitter.py +++ /dev/null @@ -1,273 +0,0 @@ -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest - -from services.twitter import ( - TweetAnalyzer, - TweetData, - TweetRepository, - TwitterConfig, - TwitterMentionHandler, - create_twitter_handler, -) - - -@pytest.fixture -def mock_backend(): - with patch("services.twitter.backend") as mock: - mock.list_x_tweets = AsyncMock() - mock.create_x_tweet = AsyncMock() - mock.update_x_tweet = AsyncMock() - mock.list_x_users = AsyncMock() - mock.create_x_user = AsyncMock() - mock.create_queue_message = AsyncMock() - yield mock - - -@pytest.fixture -def mock_twitter_service(): - with patch("services.twitter.TwitterService") as mock: - instance = mock.return_value - instance._ainitialize = AsyncMock() - instance.get_mentions_by_user_id = AsyncMock() - instance._apost_tweet = AsyncMock() - yield instance - - -@pytest.fixture -def mock_analyze_tweet(): - with patch("services.twitter.analyze_tweet") as mock: - mock.return_value = { - "is_worthy": True, - "tweet_type": "test_type", - "confidence_score": 0.9, - "reason": "test reason", - "tool_request": {"type": "test_tool"}, - } - yield mock - - -@pytest.fixture -def config(): - return TwitterConfig( - consumer_key="test_key", - consumer_secret="test_secret", - client_id="test_client_id", - client_secret="test_client_secret", - access_token="test_token", - access_secret="test_secret", - user_id="test_user_id", - whitelisted_authors=["whitelisted_author"], - whitelist_enabled=True, - ) - - -@pytest.fixture -def tweet_data(): - return TweetData( - tweet_id="test_tweet_id", - author_id="test_author_id", - text="test tweet text", - conversation_id="test_conversation_id", - ) - - -@pytest.fixture -def tweet_repository(mock_backend): - return TweetRepository() - - -@pytest.fixture -def tweet_analyzer(tweet_repository): - return TweetAnalyzer(tweet_repository) - - -@pytest.fixture -def twitter_handler(config, tweet_repository, tweet_analyzer, mock_twitter_service): - return TwitterMentionHandler(config, tweet_repository, tweet_analyzer) - - -class TestTweetRepository: - @pytest.mark.asyncio - async def test_store_tweet_new_author( - self, tweet_repository, tweet_data, mock_backend - ): - # Setup - mock_backend.list_x_users.return_value = [] - mock_backend.create_x_user.return_value = MagicMock(id="test_author_db_id") - - # Execute - await tweet_repository.store_tweet(tweet_data) - - # Assert - mock_backend.list_x_users.assert_called_once() - mock_backend.create_x_user.assert_called_once() - mock_backend.create_x_tweet.assert_called_once() - - @pytest.mark.asyncio - async def test_store_tweet_existing_author( - self, tweet_repository, tweet_data, mock_backend - ): - # Setup - mock_backend.list_x_users.return_value = [MagicMock(id="test_author_db_id")] - - # Execute - await tweet_repository.store_tweet(tweet_data) - - # Assert - mock_backend.list_x_users.assert_called_once() - mock_backend.create_x_user.assert_not_called() - mock_backend.create_x_tweet.assert_called_once() - - @pytest.mark.asyncio - async def test_update_tweet_analysis(self, tweet_repository, mock_backend): - # Setup - mock_backend.list_x_tweets.return_value = [MagicMock(id="test_tweet_db_id")] - - # Execute - await tweet_repository.update_tweet_analysis( - tweet_id="test_tweet_id", - is_worthy=True, - tweet_type="test_type", - confidence_score=0.9, - reason="test reason", - ) - - # Assert - mock_backend.list_x_tweets.assert_called_once() - mock_backend.update_x_tweet.assert_called_once() - - @pytest.mark.asyncio - async def test_get_conversation_history(self, tweet_repository, mock_backend): - # Setup - mock_backend.list_x_tweets.return_value = [ - MagicMock(author_id="user1", message="message1"), - MagicMock(author_id="test_user_id", message="message2"), - ] - - # Execute - history = await tweet_repository.get_conversation_history( - "test_conversation_id", "test_user_id" - ) - - # Assert - assert len(history) == 2 - assert history[0]["role"] == "user" - assert history[1]["role"] == "assistant" - - -class TestTweetAnalyzer: - @pytest.mark.asyncio - async def test_analyze_tweet_content( - self, tweet_analyzer, tweet_data, mock_analyze_tweet - ): - # Setup - history = [{"role": "user", "content": "previous message"}] - - # Execute - result = await tweet_analyzer.analyze_tweet_content(tweet_data, history) - - # Assert - assert result["is_worthy"] is True - assert result["tweet_type"] == "test_type" - assert result["confidence_score"] == 0.9 - mock_analyze_tweet.assert_called_once() - - -class TestTwitterMentionHandler: - @pytest.mark.asyncio - async def test_process_mentions_no_mentions(self, twitter_handler): - # Setup - twitter_handler.twitter_service.get_mentions_by_user_id.return_value = [] - - # Execute - await twitter_handler.process_mentions() - - # Assert - twitter_handler.twitter_service._ainitialize.assert_called_once() - twitter_handler.twitter_service.get_mentions_by_user_id.assert_called_once_with( - "test_user_id" - ) - - @pytest.mark.asyncio - async def test_handle_mention_existing_tweet(self, twitter_handler, mock_backend): - # Setup - mention = MagicMock( - id="test_tweet_id", - author_id="test_author_id", - text="test text", - conversation_id="test_conv_id", - ) - mock_backend.list_x_tweets.return_value = [MagicMock()] - - # Execute - await twitter_handler._handle_mention(mention) - - # Assert - mock_backend.list_x_tweets.assert_called_once() - mock_backend.create_x_tweet.assert_not_called() - - @pytest.mark.asyncio - async def test_handle_mention_whitelisted_author( - self, twitter_handler, mock_backend, mock_analyze_tweet - ): - # Setup - mention = MagicMock( - id="test_tweet_id", - author_id="whitelisted_author", - text="test text", - conversation_id="test_conv_id", - ) - mock_backend.list_x_tweets.return_value = [] - mock_backend.list_x_users.return_value = [MagicMock(id="test_author_db_id")] - - # Execute - await twitter_handler._handle_mention(mention) - - # Assert - mock_backend.create_x_tweet.assert_called_once() - mock_analyze_tweet.assert_called_once() - - @pytest.mark.asyncio - async def test_handle_mention_non_whitelisted_author( - self, twitter_handler, mock_backend, mock_analyze_tweet - ): - # Setup - mention = MagicMock( - id="test_tweet_id", - author_id="non_whitelisted_author", - text="test text", - conversation_id="test_conv_id", - ) - mock_backend.list_x_tweets.return_value = [] - - # Execute - await twitter_handler._handle_mention(mention) - - # Assert - mock_backend.create_x_tweet.assert_called_once() - mock_analyze_tweet.assert_not_called() - - -def test_create_twitter_handler(): - with ( - patch("services.twitter.load_dotenv"), - patch.dict( - "os.environ", - { - "AIBTC_TWITTER_CONSUMER_KEY": "test_key", - "AIBTC_TWITTER_CONSUMER_SECRET": "test_secret", - "AIBTC_TWITTER_CLIENT_ID": "test_client_id", - "AIBTC_TWITTER_CLIENT_SECRET": "test_client_secret", - "AIBTC_TWITTER_ACCESS_TOKEN": "test_token", - "AIBTC_TWITTER_ACCESS_SECRET": "test_secret", - "AIBTC_TWITTER_AUTOMATED_USER_ID": "test_user_id", - "AIBTC_TWITTER_WHITELISTED": "whitelisted_author", - }, - ), - ): - handler = create_twitter_handler() - assert isinstance(handler, TwitterMentionHandler) - assert handler.config.consumer_key == "test_key" - assert handler.config.user_id == "test_user_id" - assert handler.config.whitelisted_authors == ["whitelisted_author"] diff --git a/tests/services/webhooks/chainhook/test_buy_event_handler.py b/tests/services/webhooks/chainhook/test_buy_event_handler.py deleted file mode 100644 index 1c276023..00000000 --- a/tests/services/webhooks/chainhook/test_buy_event_handler.py +++ /dev/null @@ -1,172 +0,0 @@ -"""Tests for the BuyEventHandler.""" - -import unittest -from unittest.mock import MagicMock, patch - -from services.webhooks.chainhook.handlers.buy_event_handler import BuyEventHandler -from services.webhooks.chainhook.models import ( - Event, - Receipt, - TransactionIdentifier, - TransactionMetadata, - TransactionWithReceipt, -) - - -class TestBuyEventHandler(unittest.TestCase): - """Test cases for BuyEventHandler.""" - - def setUp(self): - """Set up the test environment.""" - self.handler = BuyEventHandler() - - # Create a mock logger - self.handler.logger = MagicMock() - - # Create a sample event - self.sample_event = Event( - data={"amount": "1000", "recipient": "ST123", "sender": "ST456"}, - position={"index": 0}, - type="STXTransferEvent", - ) - - # Create a sample receipt with events - self.sample_receipt = Receipt( - contract_calls_stack=[], - events=[self.sample_event], - mutated_assets_radius=[], - mutated_contracts_radius=[], - ) - - # Create sample transaction metadata - self.sample_metadata = TransactionMetadata( - description="Test buy transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "buy", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ) - - # Create a sample transaction - self.sample_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=self.sample_metadata, - operations=[], - ) - - def test_can_handle_buy_transaction(self): - """Test that the handler can handle buy transactions.""" - # Test with a buy transaction - result = self.handler.can_handle_transaction(self.sample_transaction) - self.assertTrue(result) - - # Test with a buy-tokens transaction - buy_tokens_metadata = TransactionMetadata( - description="Test buy-tokens transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "buy-tokens", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ) - - buy_tokens_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=buy_tokens_metadata, - operations=[], - ) - - result = self.handler.can_handle_transaction(buy_tokens_transaction) - self.assertTrue(result) - - def test_cannot_handle_non_buy_transaction(self): - """Test that the handler cannot handle non-buy transactions.""" - # Create a non-buy transaction - non_buy_metadata = TransactionMetadata( - description="Test non-buy transaction", - execution_cost={"read_count": 10, "write_count": 5, "runtime": 100}, - fee=1000, - kind={ - "type": "ContractCall", - "data": { - "method": "transfer", - "args": ["10"], - "contract_identifier": "ST123.test-contract", - }, - }, - nonce=42, - position={"index": 0}, - raw_tx="0x0123456789abcdef", - receipt=self.sample_receipt, - result="(ok true)", - sender="ST456", - sponsor=None, - success=True, - ) - - non_buy_transaction = TransactionWithReceipt( - transaction_identifier=TransactionIdentifier(hash="0xabcdef1234567890"), - metadata=non_buy_metadata, - operations=[], - ) - - result = self.handler.can_handle_transaction(non_buy_transaction) - self.assertFalse(result) - - @patch("services.webhooks.chainhook.handlers.buy_event_handler.configure_logger") - async def test_handle_transaction(self, mock_configure_logger): - """Test that the handler correctly logs events.""" - # Set up the mock logger - mock_logger = MagicMock() - mock_configure_logger.return_value = mock_logger - - # Create a new handler with the mocked logger - handler = BuyEventHandler() - - # Handle the transaction - await handler.handle_transaction(self.sample_transaction) - - # Check that the logger was called with the expected messages - mock_logger.info.assert_any_call( - "Processing buy function call from ST456 to contract ST123.test-contract " - "with args: ['10'], tx_id: 0xabcdef1234567890" - ) - - mock_logger.info.assert_any_call( - "Found 1 events in transaction 0xabcdef1234567890" - ) - - mock_logger.info.assert_any_call( - "Event 1/1: Type=STXTransferEvent, Data={'amount': '1000', 'recipient': 'ST123', 'sender': 'ST456'}" - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/services/webhooks/chainhook/test_models.py b/tests/services/webhooks/chainhook/test_models.py deleted file mode 100644 index 77b2930f..00000000 --- a/tests/services/webhooks/chainhook/test_models.py +++ /dev/null @@ -1,218 +0,0 @@ -"""Tests for the chainhook models.""" - -import unittest -from typing import Any, Dict - -from services.webhooks.chainhook.models import ( - Apply, - BlockIdentifier, - BlockMetadata, - ChainHookData, - ChainHookInfo, - Event, - Operation, - Predicate, - Receipt, - TransactionIdentifier, - TransactionMetadata, - TransactionWithReceipt, -) -from services.webhooks.chainhook.parser import ChainhookParser - - -class TestChainHookModels(unittest.TestCase): - """Test cases for ChainHook data models.""" - - def setUp(self): - """Set up the test environment.""" - # Initialize parser - self.parser = ChainhookParser() - - # Sample data for testing - self.sample_data: Dict[str, Any] = { - "apply": [ - { - "block_identifier": {"hash": "0x1234567890abcdef", "index": 123456}, - "parent_block_identifier": { - "hash": "0x0000000000000000", - "index": 123455, - }, - "timestamp": 1640995200, - "metadata": { - "bitcoin_anchor_block_identifier": { - "hash": "0xbtc0000000000000", - "index": 700000, - }, - "block_time": 1640995100, - "pox_cycle_index": 123, - "pox_cycle_length": 20, - "pox_cycle_position": 10, - "tenure_height": 12345, - }, - "transactions": [ - { - "transaction_identifier": {"hash": "0xabcdef1234567890"}, - "metadata": { - "description": "Test transaction", - "execution_cost": { - "read_count": 10, - "write_count": 5, - "runtime": 100, - }, - "fee": 1000, - "kind": { - "type": "ContractCall", - "data": { - "method": "transfer", - "args": ["123456"], - "contract_identifier": "ST1234567890ABCDEF.test-contract", - }, - }, - "nonce": 42, - "position": {"index": 0}, - "raw_tx": "0x0123456789abcdef", - "receipt": { - "contract_calls_stack": [], - "events": [ - { - "data": { - "amount": "123456", - "asset_identifier": "ST1234567890ABCDEF.test-token::token", - "sender": "ST1234567890ABCDEF", - "recipient": "ST0987654321FEDCBA", - }, - "position": {"index": 0}, - "type": "FTTransferEvent", - } - ], - "mutated_assets_radius": [ - "ST1234567890ABCDEF.test-token::token" - ], - "mutated_contracts_radius": [ - "ST1234567890ABCDEF.test-contract" - ], - }, - "result": "(ok true)", - "sender": "ST1234567890ABCDEF", - "sponsor": None, - "success": True, - }, - "operations": [ - { - "account": {"address": "ST1234567890ABCDEF"}, - "amount": { - "currency": {"decimals": 6, "symbol": "TOKEN"}, - "value": 123456, - }, - "operation_identifier": {"index": 0}, - "related_operations": [{"index": 1}], - "status": "SUCCESS", - "type": "DEBIT", - }, - { - "account": {"address": "ST0987654321FEDCBA"}, - "amount": { - "currency": {"decimals": 6, "symbol": "TOKEN"}, - "value": 123456, - }, - "operation_identifier": {"index": 1}, - "related_operations": [{"index": 0}], - "status": "SUCCESS", - "type": "CREDIT", - }, - ], - } - ], - } - ], - "chainhook": { - "is_streaming_blocks": False, - "predicate": {"scope": "block_height", "higher_than": 123450}, - "uuid": "test-uuid-12345", - }, - "events": [], - "rollback": [], - } - - def test_block_identifier(self): - """Test BlockIdentifier model.""" - block_id = BlockIdentifier(hash="0x1234", index=123) - self.assertEqual(block_id.hash, "0x1234") - self.assertEqual(block_id.index, 123) - - def test_transaction_identifier(self): - """Test TransactionIdentifier model.""" - tx_id = TransactionIdentifier(hash="0xabcd") - self.assertEqual(tx_id.hash, "0xabcd") - - def test_parse_chainhook_payload(self): - """Test the parse_chainhook_payload method of ChainhookParser.""" - result = self.parser.parse_chainhook_payload(self.sample_data) - - # Verify the result is of the correct type - self.assertIsInstance(result, ChainHookData) - - # Verify chainhook info - self.assertIsInstance(result.chainhook, ChainHookInfo) - self.assertFalse(result.chainhook.is_streaming_blocks) - self.assertEqual(result.chainhook.uuid, "test-uuid-12345") - self.assertIsInstance(result.chainhook.predicate, Predicate) - self.assertEqual(result.chainhook.predicate.scope, "block_height") - self.assertEqual(result.chainhook.predicate.higher_than, 123450) - - # Verify apply block structure - self.assertEqual(len(result.apply), 1) - apply_block = result.apply[0] - self.assertIsInstance(apply_block, Apply) - self.assertEqual(apply_block.block_identifier.hash, "0x1234567890abcdef") - self.assertEqual(apply_block.block_identifier.index, 123456) - self.assertEqual(apply_block.timestamp, 1640995200) - - # Verify parent block - self.assertIsNotNone(apply_block.parent_block_identifier) - self.assertEqual(apply_block.parent_block_identifier.hash, "0x0000000000000000") - self.assertEqual(apply_block.parent_block_identifier.index, 123455) - - # Verify block metadata - self.assertIsInstance(apply_block.metadata, BlockMetadata) - self.assertEqual(apply_block.metadata.tenure_height, 12345) - self.assertEqual(apply_block.metadata.pox_cycle_index, 123) - - # Verify transaction structure - self.assertEqual(len(apply_block.transactions), 1) - tx = apply_block.transactions[0] - self.assertIsInstance(tx, TransactionWithReceipt) - self.assertEqual(tx.transaction_identifier.hash, "0xabcdef1234567890") - - # Verify transaction metadata - self.assertIsInstance(tx.metadata, TransactionMetadata) - self.assertEqual(tx.metadata.description, "Test transaction") - self.assertEqual(tx.metadata.fee, 1000) - self.assertEqual(tx.metadata.nonce, 42) - self.assertEqual(tx.metadata.sender, "ST1234567890ABCDEF") - self.assertTrue(tx.metadata.success) - - # Verify transaction kind - self.assertEqual(tx.metadata.kind.get("type"), "ContractCall") - data = tx.metadata.kind.get("data", {}) - self.assertEqual(data.get("method"), "transfer") - - # Verify receipt - self.assertIsInstance(tx.metadata.receipt, Receipt) - self.assertEqual(len(tx.metadata.receipt.events), 1) - event = tx.metadata.receipt.events[0] - self.assertIsInstance(event, Event) - self.assertEqual(event.type, "FTTransferEvent") - self.assertEqual(event.data.get("amount"), "123456") - - # Verify operations - self.assertEqual(len(tx.operations), 2) - op = tx.operations[0] - self.assertIsInstance(op, Operation) - self.assertEqual(op.type, "DEBIT") - self.assertEqual(op.status, "SUCCESS") - self.assertEqual(op.account.get("address"), "ST1234567890ABCDEF") - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/services/webhooks/chainhook/test_parser.py b/tests/services/webhooks/chainhook/test_parser.py deleted file mode 100644 index bf8ea344..00000000 --- a/tests/services/webhooks/chainhook/test_parser.py +++ /dev/null @@ -1,73 +0,0 @@ -"""Tests for the chainhook parser.""" - -import unittest -from typing import Any, Dict - -from services.webhooks.chainhook.models import ChainHookData -from services.webhooks.chainhook.parser import ChainhookParser - - -class TestChainhookParser(unittest.TestCase): - """Test cases for ChainhookParser.""" - - def setUp(self): - """Set up the test environment.""" - self.parser = ChainhookParser() - - # Sample data for testing - self.sample_data: Dict[str, Any] = { - "apply": [ - { - "block_identifier": {"hash": "0x1234567890abcdef", "index": 123456}, - "transactions": [ - { - "transaction_identifier": {"hash": "0xabcdef1234567890"}, - "metadata": { - "kind": { - "type": "ContractCall", - "data": { - "method": "send", - "args": ["test message"], - "contract_identifier": "ST1234567890ABCDEF.test-contract", - }, - }, - "success": False, - "sender": "ST1234567890ABCDEF", - }, - "operations": [], - } - ], - } - ] - } - - def test_parse(self): - """Test parsing chainhook webhook data.""" - result = self.parser.parse(self.sample_data) - - # Verify the result is of the correct type - self.assertIsInstance(result, ChainHookData) - - # Verify the parsed data structure - self.assertEqual(len(result.apply), 1) - self.assertEqual(result.apply[0].block_identifier.hash, "0x1234567890abcdef") - self.assertEqual(result.apply[0].block_identifier.index, 123456) - - # Verify transaction data - self.assertEqual(len(result.apply[0].transactions), 1) - tx = result.apply[0].transactions[0] - self.assertEqual(tx.transaction_identifier.hash, "0xabcdef1234567890") - self.assertEqual(tx.metadata["sender"], "ST1234567890ABCDEF") - - # Verify metadata structure - kind = tx.metadata.get("kind", {}) - self.assertEqual(kind.get("type"), "ContractCall") - - # Verify data structure - data = kind.get("data", {}) - self.assertEqual(data.get("method"), "send") - self.assertEqual(data.get("args"), ["test message"]) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/test_dao_proposal_voter.py b/tests/test_dao_proposal_voter.py deleted file mode 100644 index 568933b5..00000000 --- a/tests/test_dao_proposal_voter.py +++ /dev/null @@ -1,198 +0,0 @@ -"""Unit tests for the DAO proposal voter task.""" - -import datetime -import unittest -from unittest.mock import MagicMock, patch -from uuid import UUID - -from backend.models import QueueMessage -from services.runner.base import JobContext, JobType -from services.runner.tasks.dao_proposal_voter import DAOProposalVoterTask - - -class TestDAOProposalVoterTask(unittest.TestCase): - """Test cases for the DAO proposal voter task.""" - - def setUp(self): - """Set up the test case.""" - # Create a test task instance - self.task = DAOProposalVoterTask() - - # Mock the configuration - self.task.config = MagicMock() - - # Create a test job context - self.context = JobContext( - job_type=JobType.DAO_PROPOSAL_VOTE, - config=self.task.config, - parameters={}, - ) - - # Mock queue messages - self.test_queue_message = QueueMessage( - id=UUID("12345678-1234-5678-1234-567812345678"), - created_at=datetime.datetime.now(), - type="dao_proposal_vote", - message={ - "action_proposals_contract": "SP123.dao-action-proposals", - "proposal_id": 1, - "dao_name": "TestDAO", - "tx_id": "0x1234567890", - }, - wallet_id=UUID("98765432-9876-5432-9876-543298765432"), - is_processed=False, - ) - - @patch("services.runner.tasks.dao_proposal_voter.backend") - @patch("services.runner.tasks.dao_proposal_voter.evaluate_and_vote_on_proposal") - async def test_process_message_success(self, mock_evaluate, mock_backend): - """Test processing a message successfully.""" - # Mock the evaluate_and_vote_on_proposal function - mock_evaluate.return_value = { - "success": True, - "evaluation": { - "approve": True, - "confidence_score": 0.85, - "reasoning": "This is a good proposal", - }, - "auto_voted": True, - } - - # Process the test message - result = await self.task.process_message(self.test_queue_message) - - # Check that the result is correct - self.assertTrue(result["success"]) - self.assertTrue(result["auto_voted"]) - self.assertTrue(result["approve"]) - - # Check that evaluate_and_vote_on_proposal was called with the correct parameters - mock_evaluate.assert_called_once_with( - action_proposals_contract="SP123.dao-action-proposals", - proposal_id=1, - dao_name="TestDAO", - wallet_id=UUID("98765432-9876-5432-9876-543298765432"), - auto_vote=True, - confidence_threshold=0.7, - ) - - # Check that the message was marked as processed - mock_backend.update_queue_message.assert_called_once_with( - UUID("12345678-1234-5678-1234-567812345678"), - {"is_processed": True}, - ) - - @patch("services.runner.tasks.dao_proposal_voter.backend") - @patch("services.runner.tasks.dao_proposal_voter.evaluate_and_vote_on_proposal") - async def test_process_message_missing_parameters( - self, mock_evaluate, mock_backend - ): - """Test processing a message with missing parameters.""" - # Create a message with missing parameters - message = QueueMessage( - id=UUID("12345678-1234-5678-1234-567812345678"), - created_at=datetime.datetime.now(), - type="dao_proposal_vote", - message={ - # Missing action_proposals_contract - "proposal_id": 1, - "dao_name": "TestDAO", - }, - wallet_id=UUID("98765432-9876-5432-9876-543298765432"), - is_processed=False, - ) - - # Process the message - result = await self.task.process_message(message) - - # Check that the result indicates failure - self.assertFalse(result["success"]) - self.assertIn("Missing required parameters", result["error"]) - - # Check that evaluate_and_vote_on_proposal was not called - mock_evaluate.assert_not_called() - - # Check that the message was not marked as processed - mock_backend.update_queue_message.assert_not_called() - - @patch("services.runner.tasks.dao_proposal_voter.backend") - async def test_get_pending_messages(self, mock_backend): - """Test retrieving pending messages.""" - # Mock the list_queue_messages function - mock_backend.list_queue_messages.return_value = [self.test_queue_message] - - # Get pending messages - messages = await self.task.get_pending_messages() - - # Check that the correct messages were returned - self.assertEqual(len(messages), 1) - self.assertEqual(messages[0].id, self.test_queue_message.id) - - # Check that list_queue_messages was called with the correct parameters - mock_backend.list_queue_messages.assert_called_once() - filters = mock_backend.list_queue_messages.call_args[1]["filters"] - self.assertEqual(filters.type, "dao_proposal_vote") - self.assertFalse(filters.is_processed) - - @patch( - "services.runner.tasks.dao_proposal_voter.DAOProposalVoterTask.get_pending_messages" - ) - @patch( - "services.runner.tasks.dao_proposal_voter.DAOProposalVoterTask.process_message" - ) - async def test_execute_no_messages(self, mock_process, mock_get_messages): - """Test executing the task when there are no messages.""" - # Mock get_pending_messages to return an empty list - mock_get_messages.return_value = [] - - # Execute the task - results = await self.task.execute(self.context) - - # Check that results are correct - self.assertEqual(len(results), 1) - self.assertTrue(results[0]["success"]) - self.assertEqual(results[0]["proposals_processed"], 0) - self.assertEqual(results[0]["proposals_voted"], 0) - self.assertEqual(len(results[0]["errors"]), 0) - - # Check that process_message was not called - mock_process.assert_not_called() - - @patch( - "services.runner.tasks.dao_proposal_voter.DAOProposalVoterTask.get_pending_messages" - ) - @patch( - "services.runner.tasks.dao_proposal_voter.DAOProposalVoterTask.process_message" - ) - async def test_execute_with_messages(self, mock_process, mock_get_messages): - """Test executing the task with pending messages.""" - # Mock get_pending_messages to return test messages - mock_get_messages.return_value = [ - self.test_queue_message, - self.test_queue_message, - ] - - # Mock process_message to return success for the first message and failure for the second - mock_process.side_effect = [ - {"success": True, "auto_voted": True, "approve": True}, - {"success": False, "error": "Test error"}, - ] - - # Execute the task - results = await self.task.execute(self.context) - - # Check that results are correct - self.assertEqual(len(results), 1) - self.assertTrue(results[0]["success"]) - self.assertEqual(results[0]["proposals_processed"], 2) - self.assertEqual(results[0]["proposals_voted"], 1) - self.assertEqual(len(results[0]["errors"]), 1) - self.assertEqual(results[0]["errors"][0], "Test error") - - # Check that process_message was called twice - self.assertEqual(mock_process.call_count, 2) - mock_process.assert_any_call(self.test_queue_message) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/test_proposal_evaluation.py b/tests/test_proposal_evaluation.py deleted file mode 100644 index e20ce58a..00000000 --- a/tests/test_proposal_evaluation.py +++ /dev/null @@ -1,82 +0,0 @@ -"""Test script for the proposal evaluation workflow.""" - -import asyncio -from typing import Dict, Optional - -from backend.models import UUID -from services.workflows.proposal_evaluation import ( - evaluate_and_vote_on_proposal, - evaluate_proposal_only, -) - - -async def test_proposal_evaluation( - action_proposals_contract: str, - proposal_id: int, - dao_name: Optional[str] = None, - wallet_id: Optional[UUID] = None, - auto_vote: bool = False, -) -> Dict: - """Test the proposal evaluation workflow. - - Args: - action_proposals_contract: The contract ID of the DAO action proposals - proposal_id: The ID of the proposal to evaluate - dao_name: Optional name of the DAO for additional context - wallet_id: Optional wallet ID to use for retrieving proposal data - auto_vote: Whether to automatically vote based on the evaluation - - Returns: - Dictionary containing the evaluation results - """ - print(f"Evaluating proposal {proposal_id} for contract {action_proposals_contract}") - - if auto_vote: - print("Auto-voting is enabled") - result = await evaluate_and_vote_on_proposal( - action_proposals_contract=action_proposals_contract, - proposal_id=proposal_id, - dao_name=dao_name, - wallet_id=wallet_id, - auto_vote=True, - confidence_threshold=0.7, - ) - else: - print("Evaluation only mode (no voting)") - result = await evaluate_proposal_only( - action_proposals_contract=action_proposals_contract, - proposal_id=proposal_id, - dao_name=dao_name, - wallet_id=wallet_id, - ) - - # Print the results - print("\nEvaluation Results:") - print(f"Approve: {result['evaluation']['approve']}") - print(f"Confidence: {result['evaluation']['confidence_score']}") - print(f"Reasoning: {result['evaluation']['reasoning']}") - - if auto_vote and result.get("auto_voted"): - print("\nVoting Results:") - print(f"Auto-voted: {result.get('auto_voted', False)}") - print(f"Vote Result: {result.get('vote_result', {})}") - - return result - - -if __name__ == "__main__": - # Example usage - # Replace these values with actual contract and proposal IDs - contract_id = "SP000000000000000000002Q6VF78.dao-action-proposals" - proposal_id = 1 - dao_name = "Example DAO" - - # Run the test - asyncio.run( - test_proposal_evaluation( - action_proposals_contract=contract_id, - proposal_id=proposal_id, - dao_name=dao_name, - auto_vote=False, # Set to True to enable auto-voting - ) - ) From a7af93e41be05bf6c44f60f4a6c2e3f2a7f08e27 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Thu, 8 May 2025 17:51:30 -0700 Subject: [PATCH 024/219] update bun lock --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index e5ca8e7f..5e276242 100644 --- a/Dockerfile +++ b/Dockerfile @@ -18,7 +18,7 @@ WORKDIR /usr/src/app/agent-tools-ts # Copy only dependency files first for better caching COPY agent-tools-ts/package.json agent-tools-ts/bun.lock ./ -RUN bun install +RUN bun install --frozen-lockfile # Now copy the rest of the code COPY . . From d7d83cb86f8c6883f46c3ed942d9c71c822c6e1d Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Thu, 8 May 2025 19:25:24 -0700 Subject: [PATCH 025/219] update agent tools --- agent-tools-ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agent-tools-ts b/agent-tools-ts index 083fb29c..6282473d 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 083fb29c85e3807bb2f760af88bcade09faa7e1c +Subproject commit 6282473dd165382235333ba9f3ede85a77c75443 From 9008d1a5023e3c0b4099a9f433a329c6b4a3be23 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Fri, 9 May 2025 10:15:51 -0700 Subject: [PATCH 026/219] update --- backend/models.py | 2 +- examples/proposal_evaluation_example.py | 6 +- services/workflows/base.py | 6 + services/workflows/capability_mixins.py | 228 ++ services/workflows/hierarchical_workflows.py | 476 +++ services/workflows/proposal_evaluation.py | 2784 ++++++++++++------ 6 files changed, 2523 insertions(+), 979 deletions(-) create mode 100644 services/workflows/capability_mixins.py create mode 100644 services/workflows/hierarchical_workflows.py diff --git a/backend/models.py b/backend/models.py index 6e188817..bce1d215 100644 --- a/backend/models.py +++ b/backend/models.py @@ -336,7 +336,7 @@ class ProposalBase(CustomBaseModel): end_block: Optional[int] = None start_block: Optional[int] = None liquid_tokens: Optional[str] = None # Using string to handle large numbers - parameters: Optional[str] = None # Hex encoded parameters + parameters: Optional[str] = None # Additional fields from blockchain data concluded_by: Optional[str] = None executed: Optional[bool] = None diff --git a/examples/proposal_evaluation_example.py b/examples/proposal_evaluation_example.py index 88bebc81..324bd2c9 100644 --- a/examples/proposal_evaluation_example.py +++ b/examples/proposal_evaluation_example.py @@ -37,14 +37,14 @@ async def create_test_proposal(dao_id: UUID) -> UUID: # Create test parameters as a JSON object parameters = "let this rip https://media1.giphy.com/media/v1.Y2lkPTc5MGI3NjExN3VoZzJzdmV3eGs4M2VrOXBkamg2dTVhb2NhcndwNzVxNHplMzhoaiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/M7HkIkPrNhSy4/giphy.gif https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/x-vote-media//img_2.jpeg" - # Convert parameters to JSON string and then hex encode it - parameters_hex = "0x" + binascii.hexlify(parameters.encode("utf-8")).decode("utf-8") + # # Convert parameters to JSON string and then hex encode it + # parameters_hex = "0x" + binascii.hexlify(parameters.encode("utf-8")).decode("utf-8") # Create a test proposal proposal_data = ProposalCreate( dao_id=dao_id, type=ProposalType.ACTION, - parameters=parameters_hex, # Use hex encoded parameters + parameters=parameters, # Use hex encoded parameters action="send_message", contract_principal="ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM.test-contract", creator="ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", diff --git a/services/workflows/base.py b/services/workflows/base.py index 30a88011..856e00fe 100644 --- a/services/workflows/base.py +++ b/services/workflows/base.py @@ -212,7 +212,13 @@ async def execute(self, initial_state: StateType) -> Dict: # Execute the workflow self.logger.info(f"Executing workflow {self.__class__.__name__}") + self.logger.debug( + f"[DEBUG:Workflow:{self.__class__.__name__}] State before ain_invoke: {repr(initial_state)}" + ) result = await app.ainvoke(initial_state) + self.logger.debug( + f"[DEBUG:Workflow:{self.__class__.__name__}] State after ain_invoke: {repr(result)}" + ) self.logger.info(f"Workflow {self.__class__.__name__} execution completed") return result diff --git a/services/workflows/capability_mixins.py b/services/workflows/capability_mixins.py new file mode 100644 index 00000000..5fabc243 --- /dev/null +++ b/services/workflows/capability_mixins.py @@ -0,0 +1,228 @@ +"""Standardized mixins for adding capabilities to LangGraph workflows. + +This module provides a standardized approach to creating and integrating +capabilities into LangGraph workflows through a mixin system. +""" + +import asyncio +from abc import ABC, abstractmethod +from typing import Any, Callable, Dict, List, Optional, TypeVar, Union + +from langchain_core.callbacks import BaseCallbackHandler +from langchain_openai import ChatOpenAI +from langgraph.graph import StateGraph + +from lib.logger import configure_logger + +logger = configure_logger(__name__) + +# Type variable for workflow states +StateType = TypeVar("StateType", bound=Dict[str, Any]) + + +class CapabilityMixin(ABC): + """Abstract base class for workflow capability mixins. + + All capability mixins should inherit from this class and implement + the required methods to ensure consistent integration with workflows. + """ + + @abstractmethod + def initialize(self, **kwargs) -> None: + """Initialize the capability with necessary configuration. + + Args: + **kwargs: Arbitrary keyword arguments for configuration + """ + pass + + @abstractmethod + def add_to_graph(self, graph: StateGraph, **kwargs) -> None: + """Add this capability's nodes and edges to a StateGraph. + + Args: + graph: The StateGraph to add nodes/edges to + **kwargs: Additional arguments specific to this capability + """ + pass + + +class BaseCapabilityMixin(CapabilityMixin): + """Base implementation of capability mixin with common functionality. + + Provides shared functionality for LLM configuration, state management, + and graph integration that most capability mixins can leverage. + """ + + def __init__( + self, + config: Optional[Dict[str, Any]] = None, + state_key: Optional[str] = None, + ): + """Initialize the base capability mixin. + + Args: + config: Configuration dictionary with settings like model_name, temperature + state_key: Key to use when updating the state dictionary + """ + self.config = config or {} + self.state_key = state_key + self.llm = None + self.logger = configure_logger(self.__class__.__name__) + + def initialize(self, **kwargs) -> None: + """Initialize the capability with LLM and other settings. + + Args: + **kwargs: Additional configuration parameters + """ + # Update config with any passed kwargs + if kwargs: + self.config.update(kwargs) + + # Create the LLM instance + self.llm = ChatOpenAI( + model=self.config.get("model_name", "gpt-4.1"), + temperature=self.config.get("temperature", 0.1), + streaming=self.config.get("streaming", True), + callbacks=self.config.get("callbacks", []), + ) + + if "state_key" in kwargs: + self.state_key = kwargs["state_key"] + + self.logger.info( + f"Initialized {self.__class__.__name__} with config: {self.config}" + ) + + def configure(self, state_key: str) -> None: + """Configure the state key for this capability. + + Args: + state_key: The key to use in the state dictionary + """ + self.state_key = state_key + + @abstractmethod + async def process(self, state: StateType) -> Dict[str, Any]: + """Process the current state and return updated values. + + Args: + state: Current workflow state + + Returns: + Dictionary with updated values to be added to the state + """ + pass + + def add_to_graph(self, graph: StateGraph, **kwargs) -> None: + """Add this capability as a node to the graph. + + Args: + graph: StateGraph to add node to + **kwargs: Additional arguments + """ + if not self.state_key: + raise ValueError(f"state_key must be set for {self.__class__.__name__}") + + node_name = kwargs.get("node_name", self.state_key) + + async def node_function(state: StateType) -> StateType: + """Node function that processes state and updates it. + + Args: + state: Current workflow state + + Returns: + Updated workflow state + """ + try: + result = await self.process(state) + # Update state with results + if isinstance(result, dict): + # If returning a dict, merge with state using the state_key + state[self.state_key] = result + return state + except Exception as e: + self.logger.error(f"Error in node {node_name}: {str(e)}", exc_info=True) + # Add error to state + if "errors" not in state: + state["errors"] = [] + state["errors"].append( + { + "node": node_name, + "error": str(e), + "type": self.__class__.__name__, + } + ) + return state + + # Add the node to the graph + graph.add_node(node_name, node_function) + self.logger.info(f"Added node {node_name} to graph") + + +class ComposableWorkflowMixin(CapabilityMixin): + """Mixin for creating composable workflows that can be nested. + + This mixin allows workflows to be composed of sub-workflows and + provides utilities for managing their execution and state sharing. + """ + + def __init__(self, name: str = None): + """Initialize the composable workflow mixin. + + Args: + name: Name identifier for this composable workflow + """ + self.name = name or self.__class__.__name__ + self.sub_workflows = {} + self.graph = None + self.logger = configure_logger(self.__class__.__name__) + + def initialize(self, **kwargs) -> None: + """Initialize the composable workflow. + + Args: + **kwargs: Configuration parameters + """ + pass + + def add_sub_workflow( + self, + name: str, + workflow: CapabilityMixin, + config: Optional[Dict[str, Any]] = None, + ) -> None: + """Add a sub-workflow to this composable workflow. + + Args: + name: Name identifier for the sub-workflow + workflow: The workflow object to add + config: Configuration for the sub-workflow + """ + if config: + # Apply config to the sub-workflow + workflow.initialize(**config) + self.sub_workflows[name] = workflow + self.logger.info(f"Added sub-workflow {name} to {self.name}") + + def build_graph(self) -> StateGraph: + """Build and return the composed workflow graph. + + Returns: + StateGraph: The compiled workflow graph + """ + raise NotImplementedError("Subclasses must implement build_graph") + + def add_to_graph(self, graph: StateGraph, **kwargs) -> None: + """Add this composable workflow to a parent graph. + + For composable workflows, this typically involves adding a + subgraph node that represents the entire nested workflow. + + Args: + graph: The parent StateGraph + **kwargs: Additional arguments + """ + raise NotImplementedError("Subclasses must implement add_to_graph") diff --git a/services/workflows/hierarchical_workflows.py b/services/workflows/hierarchical_workflows.py new file mode 100644 index 00000000..e62f58ef --- /dev/null +++ b/services/workflows/hierarchical_workflows.py @@ -0,0 +1,476 @@ +"""Hierarchical Agent Teams (HAT) workflow implementation. + +This module provides the implementation for Hierarchical Agent Teams (HAT) +workflows where multiple specialized agents work together with a supervisor +coordinating their activities. +""" + +from typing import ( + Annotated, + Any, + Callable, + Dict, + List, + Optional, + TypeVar, + Union, + cast, + get_type_hints, +) + +from langchain.prompts import PromptTemplate +from langchain_openai import ChatOpenAI +from langgraph.channels.last_value import LastValue +from langgraph.graph import END, START, StateGraph +from pydantic import BaseModel, Field + +from lib.logger import configure_logger +from services.workflows.capability_mixins import ( + BaseCapabilityMixin, + ComposableWorkflowMixin, + StateType, +) + + +# Define merge functions for managing parallel state updates +def append_list_fn(key, values): + """Append multiple list updates.""" + # Handle case where we're dealing with single strings or non-list values + result = [] + for value in values: + if isinstance(value, list): + result.extend(value) + else: + result.append(value) + return list(set(result)) # Deduplicate lists + + +def merge_dict_fn(key, values): + """Merge multiple dictionary updates.""" + # Handle cases where we might get non-dict values + result = {} + for value in values: + if isinstance(value, dict): + result.update(value) + elif value is not None: + # Try to convert to dict if possible, otherwise use as a key + try: + result.update(dict(value)) + except (ValueError, TypeError): + result[str(value)] = True + return result # Combine dictionaries + + +logger = configure_logger(__name__) + + +class SupervisorMixin(BaseCapabilityMixin): + """Mixin for implementing supervisor functionality in HAT workflows. + + The supervisor is responsible for routing between agents and + making decisions about workflow progression. + """ + + def __init__( + self, + config: Optional[Dict[str, Any]] = None, + routing_key: str = "next_step", + ): + """Initialize the supervisor mixin. + + Args: + config: Configuration dictionary + routing_key: Key in state to use for routing + """ + super().__init__(config=config, state_key=routing_key) + self.routing_key = routing_key + self.routing_map = {} + self.halt_condition = lambda state: False + # Default routing function (should be replaced with set_routing_logic) + self.routing_func = lambda state: "end" + + def set_routing_logic(self, routing_func: Callable) -> None: + """Set the routing function to determine the next step. + + Args: + routing_func: Function that takes the state and returns the next step + """ + self.routing_func = routing_func + + def set_halt_condition(self, halt_func: Callable) -> None: + """Set a condition that will halt the workflow. + + Args: + halt_func: Function that takes the state and returns a boolean + """ + self.halt_condition = halt_func + + def map_step_to_node(self, step_name: str, node_name: str) -> None: + """Map a step name to a node name. + + Args: + step_name: Name of the step in routing logic + node_name: Name of the node in the graph + """ + self.routing_map[step_name] = node_name + + def router(self, state: StateType) -> Union[str, List[str]]: + """Route to the next node(s) based on the state. + + Returns either a string node name or a list of node names for parallel execution. + """ + next_step = state[self.routing_key] + if next_step == "end" or next_step == END: + return END + return next_step + + async def process(self, state: StateType) -> Dict[str, Any]: + """Process the current state and determine the next step. + + Args: + state: Current workflow state + + Returns: + Dict with next step information + """ + # Check if halt condition is met + if self.halt_condition(state): + return {"next_step": END, "reason": "halt_condition_met"} + + # Determine next step using routing logic + next_step = self.routing_func(state) + + # Handle special case for END constant + if next_step == "end": + next_step = END + + # Map to node name if a mapping exists + if isinstance(next_step, list): + # For parallel execution, map each item in the list + mapped_step = [self.routing_map.get(step, step) for step in next_step] + else: + mapped_step = self.routing_map.get(next_step, next_step) + + return { + "next_step": mapped_step, + "timestamp": state.get("timestamp", ""), + } + + def add_to_graph(self, graph: StateGraph, **kwargs) -> None: + """Add the supervisor to the graph. + + Args: + graph: StateGraph to add node to + **kwargs: Additional arguments + """ + node_name = kwargs.get("node_name", "supervisor") + + async def supervisor_node(state: StateType) -> StateType: + result = await self.process(state) + next_step = result["next_step"] + # Normalize "end" to END constant if needed + if next_step == "end": + next_step = END + state[self.routing_key] = next_step + return state + + graph.add_node(node_name, supervisor_node) + + # Define conditional edges from supervisor to other nodes + def router(state: StateType) -> Union[str, List[str]]: + next_step = state[self.routing_key] + # Handle both string and list cases + if isinstance(next_step, list): + return next_step + if next_step == "end" or next_step == END: + return END + return next_step + + # Create a complete routing map that includes END + routing_map_with_end = { + **{step: step for step in self.routing_map.values()}, + "end": END, + END: END, + } + + # Add explicit entry for every node we might want to route to + for node in graph.nodes: + if ( + node not in routing_map_with_end + and node != "supervisor" + and node != END + ): + routing_map_with_end[node] = node + + # Add conditional edges with the complete routing map + graph.add_conditional_edges(node_name, router, routing_map_with_end) + + +class HierarchicalTeamWorkflow(ComposableWorkflowMixin): + """Implementation of a Hierarchical Agent Team workflow. + + This workflow orchestrates a team of specialized agents coordinated + by a supervisor to solve complex tasks. + """ + + def __init__(self, name: str = None, config: Optional[Dict[str, Any]] = None): + """Initialize the hierarchical team workflow. + + Args: + name: Name identifier for this workflow + config: Configuration dictionary + """ + super().__init__(name=name) + self.config = config or {} + self.supervisor = SupervisorMixin(config=self.config) + self.entry_point = None + + def set_entry_point(self, node_name: str) -> None: + """Set the entry point for the workflow. + + Args: + node_name: Name of the starting node + """ + self.entry_point = node_name + + def set_supervisor_logic(self, routing_func: Callable) -> None: + """Set the routing logic for the supervisor. + + Args: + routing_func: Function that determines the next step + """ + self.supervisor.set_routing_logic(routing_func) + + def set_halt_condition(self, halt_func: Callable) -> None: + """Set a condition that will halt the workflow. + + Args: + halt_func: Function that takes the state and returns a boolean + """ + self.supervisor.set_halt_condition(halt_func) + + def add_parallel_execution( + self, from_node: str, to_nodes: List[str], merge_node: str + ) -> None: + """Add parallel execution paths to the workflow. + + Args: + from_node: Node where parallel execution begins + to_nodes: List of nodes to execute in parallel + merge_node: Node where results are merged + """ + self.parallel_executions = { + "from_node": from_node, + "to_nodes": to_nodes, + "merge_node": merge_node, + } + + def build_graph(self) -> StateGraph: + """Build the hierarchical team workflow graph. + + Returns: + StateGraph: The compiled workflow graph + """ + if not self.entry_point: + raise ValueError("Entry point must be set before building graph") + + # Create graph with the appropriate state type + state_type = self.config.get("state_type", Dict[str, Any]) + + # Create graph with minimum configuration + graph = StateGraph(state_type) + + # Get recursion limit to prevent infinite loops (will be passed to compile()) + recursion_limit = self.config.get("recursion_limit", 10) + self.logger.info(f"Setting recursion limit to {recursion_limit}") + + # Set up key-specific channels for concurrent updates + if hasattr(state_type, "__annotations__"): + type_hints = get_type_hints(state_type, include_extras=True) + for key, annotation in type_hints.items(): + # Check if it's an Annotated type with a merge function + if hasattr(annotation, "__metadata__") and callable( + annotation.__metadata__[-1] + ): + merge_func = annotation.__metadata__[-1] + field_type = annotation.__origin__ + # Use direct assignment of channels instead of config parameter + if key not in graph.channels: + if merge_func == append_list_fn: + channel = LastValue(field_type) + channel.reduce = merge_func + graph.channels[key] = channel + elif merge_func == merge_dict_fn: + channel = LastValue(field_type) + channel.reduce = merge_func + graph.channels[key] = channel + + # Add all sub-workflows to the graph + for name, workflow in self.sub_workflows.items(): + try: + workflow.add_to_graph(graph, node_name=name) + # Map step name to node name in supervisor + self.supervisor.map_step_to_node(name, name) + self.logger.debug(f"Added sub-workflow node: {name}") + except Exception as e: + self.logger.error( + f"Error adding sub-workflow {name}: {str(e)}", exc_info=True + ) + raise ValueError(f"Failed to add sub-workflow {name}: {str(e)}") + + # Add supervisor to graph + try: + self.supervisor.add_to_graph(graph) + self.logger.debug("Added supervisor node") + except Exception as e: + self.logger.error(f"Error adding supervisor: {str(e)}", exc_info=True) + raise ValueError(f"Failed to add supervisor: {str(e)}") + + # Set entry point + graph.set_entry_point(self.entry_point) + self.logger.debug(f"Set entry point to {self.entry_point}") + + # Connect entry point to supervisor + graph.add_edge(self.entry_point, "supervisor") + self.logger.debug(f"Added edge: {self.entry_point} -> supervisor") + + # Add edges from all nodes to supervisor + for name in self.sub_workflows.keys(): + if name != self.entry_point: + graph.add_edge(name, "supervisor") + self.logger.debug(f"Added edge: {name} -> supervisor") + + # Add parallel execution if configured + if hasattr(self, "parallel_executions"): + pe = self.parallel_executions + + # Define function for parallel branching + def branch_function(state: StateType) -> Dict: + """Branch to parallel nodes or return to supervisor based on state. + + This returns both the next nodes and any state updates needed. + """ + # For debugging, log the state keys we care about + self.logger.debug( + f"Branch function evaluating state: " + f"historical_score={state.get('historical_score') is not None}, " + f"financial_score={state.get('financial_score') is not None}, " + f"social_score={state.get('social_score') is not None}, " + f"in_parallel={state.get('in_parallel_execution', False)}" + ) + + # Check if we're already in parallel execution + if state.get("in_parallel_execution", False): + # Check if all parallel executions have completed + all_completed = True + for node_name in pe["to_nodes"]: + score_key = f"{node_name.replace('_agent', '')}_score" + if state.get(score_key) is None: + all_completed = False + break + + if all_completed: + self.logger.debug( + f"All parallel nodes complete, routing to {pe['merge_node']}" + ) + # Return to merge node and clear the in_parallel_execution flag + return { + "nodes": [pe["merge_node"]], + "state_updates": {"in_parallel_execution": False}, + } + else: + # Still waiting for some parallel nodes to complete, let supervisor route + self.logger.debug( + "Some parallel nodes still executing, continuing parallel processing" + ) + # Force parallel execution to stay on + return { + "nodes": ["supervisor"], + "state_updates": {"in_parallel_execution": True}, + } + + # When historical_score is set but financial_score and social_score are not, + # we need to branch to both financial_agent and social_agent in parallel + elif state.get("historical_score") is not None and all( + state.get(f"{node_name.replace('_agent', '')}_score") is None + for node_name in pe["to_nodes"] + ): + self.logger.debug( + f"Starting parallel execution, branching to nodes: {pe['to_nodes']}" + ) + # Set the in_parallel_execution flag to True + return { + "nodes": pe["to_nodes"], + "state_updates": {"in_parallel_execution": True}, + } + + # Default case, return to supervisor for normal routing + # Make sure we're not stuck in a loop + self.logger.debug("Not branching, returning to supervisor") + + # We need to ensure that if historical_score exists but financial/social are missing, + # we maintain the parallel execution flag (this fixes the looping problem) + if state.get("historical_score") is not None and any( + state.get(f"{node_name.replace('_agent', '')}_score") is None + for node_name in pe["to_nodes"] + ): + return { + "nodes": ["supervisor"], + "state_updates": {"in_parallel_execution": True}, + } + + return {"nodes": ["supervisor"], "state_updates": {}} + + # For each parallel node, map it in the supervisor + for node in pe["to_nodes"]: + self.supervisor.map_step_to_node(node, node) + + # Add branching from source node + # We need to wrap our branch_function to handle state updates + def branch_wrapper(state: StateType) -> List[str]: + result = branch_function(state) + # Apply any state updates + for key, value in result.get("state_updates", {}).items(): + state[key] = value + # Return the nodes to route to + return result.get("nodes", ["supervisor"]) + + # Create a mapping for all possible nodes, including supervisor and END + branch_map = {node: node for node in pe["to_nodes"]} + branch_map["supervisor"] = "supervisor" + branch_map[pe["merge_node"]] = pe["merge_node"] + # Explicitly map END constant + branch_map[END] = END # Ensure END is correctly mapped + + # Add branching from source node using our wrapper + graph.add_conditional_edges(pe["from_node"], branch_wrapper, branch_map) + self.logger.debug( + f"Added conditional edges for parallel execution from {pe['from_node']}" + ) + + # Connect merge node to supervisor + graph.add_edge(pe["merge_node"], "supervisor") + self.logger.debug(f"Added edge: {pe['merge_node']} -> supervisor") + else: + # Even without explicit parallel execution, we need to make sure + # the supervisor can handle returning lists of nodes for parallel execution + self.logger.debug( + "No parallel execution configured, relying on supervisor for parallel routing" + ) + + # Compile the graph with the recursion limit configuration + compiled_graph = graph.compile( + name="HierarchicalTeamWorkflow", + checkpointer=None, + debug=self.config.get("debug", False), + ) + + # Pass recursion limit through with_config + compiled_graph = compiled_graph.with_config( + {"recursion_limit": recursion_limit} + ) + + self.logger.info("Compiled hierarchical team workflow graph") + + # Return the compiled graph + return compiled_graph diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index f2ca0849..9ebfd6bb 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -1,11 +1,14 @@ import asyncio import base64 -from typing import Any, Dict, List, Optional, TypedDict +import operator +import uuid +from typing import Annotated, Any, Dict, List, Optional, TypedDict, Union import httpx from langchain.prompts import PromptTemplate from langchain_core.messages import HumanMessage from langchain_openai import ChatOpenAI +from langgraph.channels import LastValue from langgraph.graph import END, Graph, StateGraph from pydantic import BaseModel, Field @@ -15,6 +18,7 @@ ExtensionFilter, Profile, PromptFilter, + ProposalBase, ProposalType, QueueMessageFilter, QueueMessageType, @@ -28,7 +32,13 @@ from services.workflows.base import ( BaseWorkflow, ) +from services.workflows.capability_mixins import BaseCapabilityMixin from services.workflows.chat import ChatService, StreamingCallbackHandler +from services.workflows.hierarchical_workflows import ( + HierarchicalTeamWorkflow, + append_list_fn, + merge_dict_fn, +) from services.workflows.planning_mixin import PlanningCapability from services.workflows.vector_mixin import VectorRetrievalCapability from services.workflows.web_search_mixin import WebSearchCapability @@ -50,881 +60,1653 @@ class ProposalEvaluationOutput(BaseModel): reasoning: str = Field(description="The reasoning behind the evaluation decision") -class EvaluationState(TypedDict): - """State for the proposal evaluation flow.""" - - action_proposals_contract: str - action_proposals_voting_extension: str - proposal_id: int - proposal_data: Dict - dao_info: Optional[Dict] - approve: bool - confidence_score: float - reasoning: str - vote_result: Optional[Dict] - wallet_id: Optional[UUID] - confidence_threshold: float - auto_vote: bool - formatted_prompt: str - agent_prompts: List[Dict] - vector_results: Optional[List[Dict]] - recent_tweets: Optional[List[Dict]] - web_search_results: Optional[List[Dict]] - treasury_balance: Optional[float] - contract_source: Optional[str] - proposal_images: Optional[List[Dict]] # Store encoded images for LLM - # Token usage tracking per step - web_search_token_usage: Optional[Dict] - evaluation_token_usage: Optional[Dict] - # Model info for cost calculation - evaluation_model_info: Optional[Dict] - web_search_model_info: Optional[Dict] - - -class ProposalEvaluationWorkflow( - BaseWorkflow[EvaluationState], - VectorRetrievalCapability, - WebSearchCapability, - PlanningCapability, +def no_update_reducer(current: Any, new: List[Any]) -> Any: + """Reducer that prevents updates after initial value is set.""" + # Treat initial empty string for str types as if it were None for accepting the first value + is_initial_empty_string = isinstance(current, str) and current == "" + + # If current is genuinely set (not None and not initial empty string), keep it. + if current is not None and not is_initial_empty_string: + return current + + # Current is None or an initial empty string. Try to set it from new. + processed_new_values = ( + new if isinstance(new, list) else [new] + ) # Ensure 'new' is a list + for n_val in processed_new_values: + if n_val is not None: + return n_val + + # If current was None/initial empty string and new is all None or empty, return current (which is None or '') + return current + + +def merge_dict_override_fn(key, values): + """Merge dictionaries by taking the last non-None value.""" + # Handle case where values is None + if values is None: + return None + + # Handle case where values is not iterable + if not hasattr(values, "__iter__"): + return values + + result = None + for value in values: + if value is not None: + result = value + return result + + +class ProposalEvaluationState(TypedDict): + """Type definition for the proposal evaluation state.""" + + proposal_id: Annotated[str, no_update_reducer] # Read-only during execution + proposal_data: Annotated[str, no_update_reducer] # Now a string, not a dict + core_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] + historical_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] + financial_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] + social_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] + final_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] + flags: Annotated[List[str], append_list_fn] # Merges lists of flags + summaries: Annotated[ + Dict[str, str], merge_dict_fn + ] # Merges dictionaries of summaries + decision: Annotated[Optional[str], merge_dict_override_fn] + halt: Annotated[bool, operator.or_] # Use OR for boolean flags + token_usage: Annotated[ + Dict[str, Dict[str, int]], merge_dict_fn + ] # Merges nested dictionaries + core_agent_invocations: Annotated[int, operator.add] # Counts should add + proposal_images: Annotated[ + Optional[List[Dict]], merge_dict_override_fn + ] # ADDED: To store encoded images + + +class AgentOutput(BaseModel): + """Output model for agent evaluations.""" + + score: int = Field(description="Score from 0-100") + flags: List[str] = Field(description="Critical issues flagged") + summary: str = Field(description="Summary of findings") + + +class FinalOutput(BaseModel): + """Output model for the final evaluation decision.""" + + score: int = Field(description="Final evaluation score") + decision: str = Field(description="Approve or Reject") + explanation: str = Field(description="Reasoning for decision") + + +def update_state_with_agent_result( + state: ProposalEvaluationState, agent_result: Dict[str, Any], agent_name: str ): - """Workflow for evaluating DAO proposals and voting automatically.""" - - def __init__( - self, - collection_names: Optional[List[str]] = None, - model_name: str = "gpt-4.1", - temperature: Optional[float] = 0.1, - **kwargs, + """Helper function to update state with agent result including summaries and flags.""" + # Update agent score in state + if agent_name in ["core", "historical", "financial", "social", "final"]: + state[f"{agent_name}_score"] = agent_result + + # Update summaries + if "summaries" not in state: + state["summaries"] = {} + + if "summary" in agent_result and agent_result["summary"]: + state["summaries"][f"{agent_name}_score"] = agent_result["summary"] + + # Update flags + if "flags" not in state: + state["flags"] = [] + + if "flags" in agent_result and isinstance(agent_result["flags"], list): + state["flags"].extend(agent_result["flags"]) + + # Update token usage + if ( + "token_usage" in state + and isinstance(state["token_usage"], dict) + and f"{agent_name}_agent" in state["token_usage"] ): - """Initialize the workflow. - - Args: - collection_names: Optional list of collection names to search - model_name: The model to use for evaluation - temperature: Optional temperature setting for the model - **kwargs: Additional arguments passed to parent - """ - # Initialize planning LLM - planning_llm = ChatOpenAI( - model="o4-mini", - stream_usage=True, - streaming=True, - ) + # Token usage has been set by the agent directly + pass + elif hasattr(agent_result, "get") and agent_result.get("token_usage"): + # Token usage available in the result + if "token_usage" not in state: + state["token_usage"] = {} + state["token_usage"][f"{agent_name}_agent"] = agent_result.get("token_usage") + + return state + + +class CoreContextAgent(BaseCapabilityMixin, VectorRetrievalCapability): + """Core Context Agent evaluates proposals against DAO mission and standards.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the Core Context Agent.""" + BaseCapabilityMixin.__init__(self, config=config, state_key="core_score") + VectorRetrievalCapability.__init__(self) + self.initialize() + self._initialize_vector_capability() + + def _initialize_vector_capability(self): + """Initialize the vector retrieval functionality.""" + if not hasattr(self, "retrieve_from_vector_store"): + self.retrieve_from_vector_store = ( + VectorRetrievalCapability.retrieve_from_vector_store.__get__( + self, self.__class__ + ) + ) + self.logger.info( + "Initialized vector retrieval capability for CoreContextAgent" + ) - # Create callback handler for planning with queue - callback_handler = StreamingCallbackHandler(queue=asyncio.Queue()) + async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: + """Evaluate the proposal against DAO core mission and standards.""" + self._initialize_vector_capability() - # Initialize all parent classes including PlanningCapability - super().__init__(model_name=model_name, temperature=temperature, **kwargs) - PlanningCapability.__init__( - self, - callback_handler=callback_handler, - planning_llm=planning_llm, - persona="You are a DAO proposal evaluation planner, focused on creating structured evaluation plans.", - ) + proposal_id = state.get("proposal_id", "unknown") + proposal_content = state.get("proposal_data", "") - self.collection_names = collection_names or [ - "knowledge_collection", - "proposals", - ] - self.required_fields = ["proposal_id", "proposal_data"] - self.logger.debug( - f"Initialized workflow: collections={self.collection_names} | model={model_name} | temperature={temperature}" - ) + dao_mission_text = self.config.get("dao_mission", "") + if not dao_mission_text: + try: + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Attempting to retrieve DAO mission from vector store" + ) + dao_mission = await self.retrieve_from_vector_store( + query="DAO mission statement and values", + collection_name=self.config.get( + "mission_collection", "dao_documents" + ), + limit=3, + ) + dao_mission_text = "\n".join([doc.page_content for doc in dao_mission]) + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Retrieved DAO mission, length: {len(dao_mission_text)}" + ) + except Exception as e: + self.logger.error( + f"[DEBUG:CoreAgent:{proposal_id}] Error retrieving DAO mission: {str(e)}", + exc_info=True, + ) + dao_mission_text = "Elevate human potential through AI on Bitcoin" + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Using default DAO mission: {dao_mission_text}" + ) - def _create_prompt(self) -> PromptTemplate: - """Create the evaluation prompt template.""" - return PromptTemplate( - input_variables=[ - "proposal_data", - "dao_info", - "treasury_balance", - "contract_source", - "agent_prompts", - "vector_context", - "recent_tweets", - "web_search_results", - ], - template=""" - You are a skeptical and hard-to-convince DAO proposal evaluator. Your primary goal is rigorous analysis. Your task is to analyze the proposal and determine whether to vote FOR or AGAINST it based on verifiable evidence and alignment with DAO principles. - - - - {agent_prompts} - - - If no agent-specific instructions are provided, apply these DEFAULT instructions: - - Approve ONLY if the proposal provides verifiable evidence (URL, transaction hash, IPFS CID for screenshots/documents) for its claims OR if it's a purely logistical matter (e.g., scheduling reminder). - - All other proposals lacking verifiable evidence for claims should be REJECTED (vote AGAINST) with LOW confidence (0.3-0.4 band). - - Reject proposals making promises about future DAO actions or events unless they provide on-chain evidence of a corresponding approved governance decision or multisig transaction proposal. - - CRITICAL: You MUST evaluate all proposal content (text, images, links) as ONE COHESIVE UNIT. If ANY image or attachment doesn't align with or support the proposal, contains misleading information, or is inappropriate, you MUST reject the entire proposal. - - You MUST explain how each specific instruction (agent-provided or default) influenced your decision, especially if it led to rejection. - - - - - - Verify smart contract security measures - Check for potential vulnerabilities in contract logic - Assess potential attack vectors - Evaluate access control mechanisms - - - Analyze alignment with DAO mission statement - Verify compatibility with existing DAO infrastructure - Check adherence to DAO's established governance principles - - - Evaluate potential risks vs. rewards - Assess short-term and long-term implications - Consider effects on DAO reputation and stakeholders - - - - - Validate all proposed parameters against acceptable ranges - Verify parameter compatibility with existing systems - Check for realistic implementation timelines - - - Assess treasury impact and funding requirements - Evaluate operational resource needs - Consider opportunity costs against other initiatives - - - Identify potential security implications of the action - Check for unintended system vulnerabilities - - - **Evidence Verification:** All claims MUST be backed by verifiable sources (URLs, transaction hashes, IPFS CIDs) - **Future Commitments:** Any promises about future actions require on-chain proof of approved governance decisions - **Content Cohesion:** All components (text, images, links) must form a cohesive, aligned whole supporting the proposal's intent - - - - - - - {proposal_data} - - - Note: If any images are provided with the proposal, they will be shown after this prompt. - You should analyze any provided images in the context of the proposal and include your observations - in your evaluation. Consider aspects such as: - - Image content and relevance to the proposal - - Any visual evidence supporting or contradicting the proposal - - Quality and authenticity of the images - - Potential security or privacy concerns in the images - - IMPORTANT: Images and text must form a cohesive whole. If any image: - - Doesn't clearly support or relate to the proposal text - - Contains misleading or contradictory information - - Is of poor quality making verification impossible - - Contains inappropriate content - - Appears manipulated or false - Then you MUST reject the entire proposal, regardless of the quality of the text portion. - - - - - {vector_context} - - - {recent_tweets} - - - {web_search_results} - - - - - - {dao_info} - - - {treasury_balance} - - - Core Values: Curiosity, Truth Maximizing, Humanity's Best Interests, Transparency, Resilience, Collaboration - Mission: Elevate human potential through Autonomous Intelligence on Bitcoin - Guardrails: Decentralized Governance, Smart Contract accountability - - - - - - {contract_source} - - - - - - You MUST choose one of these confidence bands: - - **0.9-1.0 (Very High Confidence - Strong Approve):** All criteria met excellently. Clear alignment with DAO mission/values, strong verifiable evidence provided for all claims, minimal/no security risks identified, significant positive impact expected, and adheres strictly to all instructions (including future promise verification). All images directly support the proposal with high quality and authenticity. - - **0.7-0.8 (High Confidence - Approve):** Generally meets criteria well. Good alignment, sufficient verifiable evidence provided, risks identified but deemed manageable/acceptable, likely positive impact. Passes core checks (evidence, future promises). Minor reservations might exist but don't fundamentally undermine the proposal. Images support the proposal appropriately. - - **0.5-0.6 (Moderate Confidence - Borderline/Weak Approve):** Meets minimum criteria but with notable reservations. Alignment is present but perhaps weak or indirect, evidence meets minimum verification but might be incomplete or raise minor questions, moderate risks identified requiring monitoring, impact is unclear or modest. *Could apply to simple logistical proposals with no major claims.* Any included images are relevant though may not provide strong support. - - **0.3-0.4 (Low Confidence - Reject):** Fails one or more key criteria. Significant misalignment, **lacks required verifiable evidence** for claims (triggering default rejection), unacceptable risks identified, potential negative impact, or **contains unsubstantiated future promises**. Images may be missing where needed, irrelevant, or only weakly supportive. *This is the default band for rejections due to lack of evidence or unproven future commitments.* - - **0.0-0.2 (Extremely Low Confidence - Strong Reject):** Fails multiple critical criteria. Clear violation of DAO principles/guardrails, major security flaws identified, evidence is demonstrably false or misleading, significant negative impact is highly likely or certain. Any included images may be misleading, manipulated, inappropriate, or contradictory to the proposal. - - - - - Your evaluation must uphold clarity, reasoning, and respect for the DAO's voice: - • Be clear and specific — avoid vagueness or filler - • Use a consistent tone, but reflect the DAO's personality if known - • Avoid casual throwaway phrases, sarcasm, or hype - • Don't hedge — take a position and justify it clearly - • Make every point logically sound and backed by facts or context - • Cite relevant parts of the proposal, DAO mission, or prior actions - • Use terms accurately — don't fake precision - • Keep structure clean and easy to follow - • Include analysis of any provided images and their implications - • Specifically address image-text cohesion in your analysis - • If rejecting, CLEARLY state the specific reason(s) based on the instructions or evaluation criteria (e.g., "Rejected due to lack of verifiable source for claim X", "Rejected because future promise lacks on-chain evidence", "Rejected because included image contradicts proposal text"). - - - - Provide your evaluation in this exact JSON format: - ```json - {{ - "approve": boolean, // true for FOR, false for AGAINST - "confidence_score": float, // MUST be from the confidence bands above - "reasoning": string // Brief, professional explanation addressing: - // 1. How agent/default instructions were applied (state which). - // 2. Specific reason for rejection if applicable, referencing the unmet criteria or instruction. - // 3. How DAO context influenced decision. - // 4. How AIBTC Charter alignment was considered. - // 5. Key factors in confidence score selection. - // 6. Analysis of any provided images and their cohesion with proposal text. - // Must be clear, precise, and well-structured. - }} - ``` - + prompt = PromptTemplate( + input_variables=["proposal_data", "dao_mission"], + template="""Evaluate the following proposal against the DAO's mission and values.\\n +Proposal: {proposal_data}\\nDAO Mission: {dao_mission}\\n +Assess whether this proposal aligns with the DAO's core mission and values.\\nConsider:\\n1. Mission Alignment: Does it directly support the stated mission?\\n2. Quality Standards: Does it meet quality requirements?\\n3. Innovation: Does it bring new ideas aligned with our vision?\\n4. Impact: How significant is its potential contribution?\\n +# ADDED: Image processing instructions +**Image Analysis Instructions:** +If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. +- Relevance: Does each image directly relate to and support the proposal's text? +- Evidence: Do the images provide visual evidence for claims made in the proposal? +- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? +- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. + +Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ """, ) - def _create_graph(self) -> Graph: - """Create the evaluation graph.""" - prompt = self._create_prompt() + try: + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Formatting prompt for evaluation" + ) + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + dao_mission=dao_mission_text + or "Elevate human potential through AI on Bitcoin", + ) + debug_level = self.config.get("debug_level", 0) + if debug_level >= 2: + self.logger.debug( + f"[PROPOSAL_DEBUG:CoreAgent] FULL EVALUATION PROMPT:\n{formatted_prompt_text}" + ) + else: + self.logger.debug( + f"[PROPOSAL_DEBUG:CoreAgent] Generated evaluation prompt: {formatted_prompt_text}" + ) + except Exception as e: + self.logger.error( + f"[DEBUG:CoreAgent:{proposal_id}] Error formatting prompt: {str(e)}", + exc_info=True, + ) + formatted_prompt_text = f"Evaluate proposal: {proposal_content}" + + try: + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Invoking LLM for core evaluation" + ) + + # ADDED: Image handling + proposal_images_list = state.get("proposal_images", []) + if not isinstance(proposal_images_list, list): + self.logger.warning( + f"[DEBUG:CoreAgent:{proposal_id}] proposal_images is not a list: {type(proposal_images_list)}. Defaulting to empty list." + ) + proposal_images_list = [] - async def fetch_context(state: EvaluationState) -> EvaluationState: - """Fetch context including web search, vector results, tweets, and contract source.""" - try: - # --- Fetch Core Data --- # - proposal_id = state["proposal_id"] - dao_id = state.get("dao_id") - agent_id = state.get("agent_id") - - # Get proposal data - proposal_data = backend.get_proposal(proposal_id) - if not proposal_data: - raise ValueError(f"Proposal {proposal_id} not found") - - image_urls = extract_image_urls(proposal_data.parameters) - - # Process and encode images - proposal_images = [] - for url in image_urls: - try: - async with httpx.AsyncClient() as client: - response = await client.get(url, timeout=10.0) - if response.status_code == 200: - image_data = base64.b64encode(response.content).decode( - "utf-8" - ) - # Determine MIME type based on URL extension - mime_type = ( - "image/jpeg" - if url.lower().endswith((".jpg", ".jpeg")) - else ( - "image/png" - if url.lower().endswith(".png") - else ( - "image/gif" - if url.lower().endswith(".gif") - else ( - "image/webp" - if url.lower().endswith(".webp") - else "image/png" - ) - ) - ) # default to PNG if unknown - ) - proposal_images.append( - { - "type": "image_url", - "image_url": { - "url": f"data:{mime_type};base64,{image_data}" - }, - } - ) - else: - logger.warning( - f"Failed to fetch image: {url} (status {response.status_code})" - ) - except Exception as e: - logger.error( - f"Error fetching image {url}: {str(e)}", exc_info=True - ) + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + if proposal_images_list: + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Adding {len(proposal_images_list)} images to LLM input." + ) + message_content_list.extend(proposal_images_list) + + llm_input_message = HumanMessage(content=message_content_list) + + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] LLM returned core evaluation with score: {result.score}" + ) + self.logger.info( + f"[DEBUG:CoreAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" + ) + + # Track token usage - extract directly from LLM if available + token_usage_data = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } - state["proposal_images"] = proposal_images - - # Convert proposal data to dictionary - proposal_dict = { - "proposal_id": proposal_data.proposal_id, - "parameters": proposal_data.parameters, - "action": proposal_data.action, - "caller": proposal_data.caller, - "contract_principal": proposal_data.contract_principal, - "creator": proposal_data.creator, - "created_at_block": proposal_data.created_at_block, - "end_block": proposal_data.end_block, - "start_block": proposal_data.start_block, - "liquid_tokens": proposal_data.liquid_tokens, - "type": proposal_data.type, - "proposal_contract": proposal_data.proposal_contract, + # Use the Annotated operator.add feature by assigning 1 to increment + # This is safe with concurrent execution + state["core_agent_invocations"] = 1 + + # Try to extract token usage directly from LLM response + if ( + hasattr(self.llm, "_last_prompt_id") + and hasattr(self.llm, "client") + and hasattr(self.llm.client, "usage_by_prompt_id") + ): + last_prompt_id = self.llm._last_prompt_id + if last_prompt_id in self.llm.client.usage_by_prompt_id: + usage = self.llm.client.usage_by_prompt_id[last_prompt_id] + token_usage_data = { + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" + ) + # Fallback to estimation + if token_usage_data["total_tokens"] == 0: + # Get model name from LLM + llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") + # First calculate token count from the text + token_count = len(formatted_prompt_text) // 4 # Simple estimation + # Create token usage dictionary for calculate_token_cost + token_usage_dict = {"input_tokens": token_count} + # Calculate cost + cost_result = calculate_token_cost(token_usage_dict, llm_model_name) + token_usage_data = { + "input_tokens": token_count, + "output_tokens": len(result.model_dump_json()) + // 4, # rough estimate + "total_tokens": token_count + len(result.model_dump_json()) // 4, } - state["proposal_data"] = proposal_dict # Update state with full data - - # Get DAO info (if dao_id wasn't passed explicitly, use proposal's) - if not dao_id and proposal_data.dao_id: - dao_id = proposal_data.dao_id - state["dao_id"] = dao_id # Update state if derived - - dao_info = None - if dao_id: - dao_info = backend.get_dao(dao_id) - if not dao_info: - raise ValueError(f"DAO Information not found for ID: {dao_id}") - state["dao_info"] = dao_info.model_dump() - - # Get agent prompts - agent_prompts_text = [] - if agent_id: - try: - prompts = backend.list_prompts( - PromptFilter( - agent_id=agent_id, - dao_id=dao_id, - is_active=True, - ) - ) - agent_prompts_text = [p.prompt_text for p in prompts] - except Exception as e: - self.logger.error( - f"Failed to get agent prompts: {str(e)}", exc_info=True - ) - state["agent_prompts"] = agent_prompts_text + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Estimated token usage: {token_usage_data}" + ) - # Get treasury balance - treasury_balance = None - try: - treasury_extensions = backend.list_extensions( - ExtensionFilter(dao_id=dao_info.id, type="EXTENSIONS_TREASURY") - ) - if treasury_extensions: - hiro_api = HiroApi() - treasury_balance = hiro_api.get_address_balance( - treasury_extensions[0].contract_principal - ) - else: - self.logger.warning( - f"No treasury extension for DAO {dao_info.id}" - ) - except Exception as e: - self.logger.error( - f"Failed to get treasury balance: {str(e)}", exc_info=True + # Add token usage to state + if "token_usage" not in state: + state["token_usage"] = {} + state["token_usage"]["core_agent"] = token_usage_data + + result_dict = result.model_dump() + # Update state with the result + update_state_with_agent_result(state, result_dict, "core") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:CoreAgent:{proposal_id}] Error in core evaluation: {str(e)}", + exc_info=True, + ) + fallback_score_dict = { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Evaluation failed due to error", + } + self.logger.info( + f"[DEBUG:CoreAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" + ) + return fallback_score_dict + + +class HistoricalContextAgent(BaseCapabilityMixin, VectorRetrievalCapability): + """Historical Context Agent examines past proposals and patterns.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + BaseCapabilityMixin.__init__(self, config=config, state_key="historical_score") + VectorRetrievalCapability.__init__(self) + self.initialize() + self._initialize_vector_capability() + + def _initialize_vector_capability(self): + if not hasattr(self, "retrieve_from_vector_store"): + self.retrieve_from_vector_store = ( + VectorRetrievalCapability.retrieve_from_vector_store.__get__( + self, self.__class__ + ) + ) + self.logger.info( + "Initialized vector retrieval capability for HistoricalContextAgent" + ) + + async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: + proposal_id = state.get("proposal_id", "unknown") + self._initialize_vector_capability() + proposal_content = state.get("proposal_data", "") + + historical_text = "" + try: + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Searching for similar proposals: {proposal_content[:50]}..." + ) + similar_proposals = await self.retrieve_from_vector_store( + query=f"Proposals similar to: {proposal_content}", + collection_name=self.config.get( + "proposals_collection", "past_proposals" + ), + limit=5, + ) + historical_text = "\n".join([doc.page_content for doc in similar_proposals]) + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Found {len(similar_proposals)} similar proposals" + ) + except Exception as e: + self.logger.error( + f"[DEBUG:HistoricalAgent:{proposal_id}] Error retrieving historical proposals: {str(e)}", + exc_info=True, + ) + historical_text = "No similar historical proposals found." + prompt = PromptTemplate( + input_variables=["proposal_data", "historical_proposals"], + template="""Analyze this proposal in the context of historical patterns and similar past proposals.\\n +Current Proposal: {proposal_data}\\nSimilar Past Proposals: {historical_proposals}\\n +Evaluate:\\n1. Precedent: Have similar proposals been approved or rejected?\\n2. Cross-DAO Similarities: How does this compare to proposals in similar DAOs?\\n3. Learning from Past: Does it address issues from past proposals?\\n4. Uniqueness: Is this novel or repeating past ideas?\\n +# ADDED: Image processing instructions +**Image Analysis Instructions:** +If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. +- Relevance: Does each image directly relate to and support the proposal's text? +- Evidence: Do the images provide visual evidence for claims made in the proposal? +- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? +- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. + +Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ + """, + ) + try: + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Formatting prompt" + ) + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + historical_proposals=historical_text + or "No similar historical proposals found.", + ) + except Exception as e: + self.logger.error( + f"[DEBUG:HistoricalAgent:{proposal_id}] Error formatting prompt: {str(e)}", + exc_info=True, + ) + formatted_prompt_text = f"Analyze proposal: {proposal_content}" + try: + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Invoking LLM for historical evaluation" + ) + + # ADDED: Image handling + proposal_images_list = state.get("proposal_images", []) + if not isinstance(proposal_images_list, list): + self.logger.warning( + f"[DEBUG:HistoricalAgent:{proposal_id}] proposal_images is not a list: {type(proposal_images_list)}. Defaulting to empty list." + ) + proposal_images_list = [] + + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + if proposal_images_list: + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Adding {len(proposal_images_list)} images to LLM input." + ) + message_content_list.extend(proposal_images_list) + + llm_input_message = HumanMessage(content=message_content_list) + + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + self.logger.info( + f"[DEBUG:HistoricalAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" + ) + + # Track token usage - extract directly from LLM if available + token_usage_data = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } + + # Try to extract token usage directly from LLM response + if ( + hasattr(self.llm, "_last_prompt_id") + and hasattr(self.llm, "client") + and hasattr(self.llm.client, "usage_by_prompt_id") + ): + last_prompt_id = self.llm._last_prompt_id + if last_prompt_id in self.llm.client.usage_by_prompt_id: + usage = self.llm.client.usage_by_prompt_id[last_prompt_id] + token_usage_data = { + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" ) - state["treasury_balance"] = treasury_balance - # --- End Fetch Core Data --- # + # Fallback to estimation + if token_usage_data["total_tokens"] == 0: + # Get model name from LLM + llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") + # First calculate token count from the text + token_count = len(formatted_prompt_text) // 4 # Simple estimation + # Create token usage dictionary for calculate_token_cost + token_usage_dict = {"input_tokens": token_count} + # Calculate cost + cost_result = calculate_token_cost(token_usage_dict, llm_model_name) + token_usage_data = { + "input_tokens": token_count, + "output_tokens": len(result.model_dump_json()) + // 4, # rough estimate + "total_tokens": token_count + len(result.model_dump_json()) // 4, + } + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Estimated token usage: {token_usage_data}" + ) + + # Add token usage to state + if "token_usage" not in state: + state["token_usage"] = {} + state["token_usage"]["historical_agent"] = token_usage_data + + result_dict = result.model_dump() + # Update state with the result + update_state_with_agent_result(state, result_dict, "historical") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:HistoricalAgent:{proposal_id}] Error in historical evaluation: {str(e)}", + exc_info=True, + ) + fallback_score_dict = { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Evaluation failed due to error", + } + self.logger.info( + f"[DEBUG:HistoricalAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" + ) + return fallback_score_dict - # Use mixin capabilities for web search and vector retrieval - web_search_query = f"DAO proposal {proposal_dict.get('type', 'unknown')} - {proposal_dict.get('parameters', '')}" - # Fetch web search results and token usage - web_search_results, web_search_token_usage = await self.search_web( - query=web_search_query, - search_context_size="medium", +class FinancialContextAgent(BaseCapabilityMixin): + """Financial Context Agent evaluates treasury impact and financial viability.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__(config=config, state_key="financial_score") + self.initialize() + + async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: + proposal_id = state.get("proposal_id", "unknown") + treasury_balance = state.get( + "treasury_balance", self.config.get("treasury_balance", 1000000) + ) + proposal_content = state.get("proposal_data", "") + + prompt = PromptTemplate( + input_variables=["proposal_data", "treasury_balance"], + template="""Assess the financial aspects of this proposal.\\n +Proposal: {proposal_data}\\nCurrent Treasury Balance: {treasury_balance}\\n +Evaluate:\\n1. Cost-Benefit Analysis: Is the ROI reasonable?\\n2. Treasury Impact: What percentage of treasury would this use?\\n3. Budget Alignment: Does it align with budget priorities?\\n4. Projected Impact: What's the expected financial outcome?\\n5. Risk Assessment: What financial risks might arise?\\n +# ADDED: Image processing instructions +**Image Analysis Instructions:** +If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. +- Relevance: Does each image directly relate to and support the proposal's text? +- Evidence: Do the images provide visual evidence for claims made in the proposal (e.g., screenshots of transactions, diagrams of financial models if applicable)? +- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? +- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. + +Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ + """, + ) + try: + self.logger.debug( + f"[DEBUG:FinancialAgent:{proposal_id}] Formatting prompt for financial evaluation" + ) + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + treasury_balance=treasury_balance, + ) + except Exception as e: + self.logger.error( + f"[DEBUG:FinancialAgent:{proposal_id}] Error formatting prompt: {str(e)}", + exc_info=True, + ) + formatted_prompt_text = ( + f"Assess financial aspects of proposal: {proposal_content}" + ) + try: + self.logger.debug( + f"[DEBUG:FinancialAgent:{proposal_id}] Invoking LLM for financial evaluation" + ) + + # ADDED: Image handling + proposal_images = state.get("proposal_images", []) + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + if proposal_images: + logger.debug( + f"[DEBUG:FinancialAgent:{proposal_id}] Adding {len(proposal_images)} images to LLM input." ) - state["web_search_results"] = web_search_results - state["web_search_token_usage"] = web_search_token_usage - # Store web search model info (assuming gpt-4.1 as used in mixin) - state["web_search_model_info"] = { - "name": "gpt-4.1", - "temperature": None, - } + message_content_list.extend(proposal_images) + + llm_input_message = HumanMessage(content=message_content_list) + + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + self.logger.info( + f"[DEBUG:FinancialAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" + ) + + # Track token usage - extract directly from LLM if available + token_usage_data = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } - vector_search_query = f"Proposal type: {proposal_dict.get('type')} - {proposal_dict.get('parameters', '')}" - state["vector_results"] = await self.retrieve_from_vector_store( - query=vector_search_query, limit=5 + # Try to extract token usage directly from LLM response + if ( + hasattr(self.llm, "_last_prompt_id") + and hasattr(self.llm, "client") + and hasattr(self.llm.client, "usage_by_prompt_id") + ): + last_prompt_id = self.llm._last_prompt_id + if last_prompt_id in self.llm.client.usage_by_prompt_id: + usage = self.llm.client.usage_by_prompt_id[last_prompt_id] + token_usage_data = { + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } + self.logger.debug( + f"[DEBUG:FinancialAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" + ) + # Fallback to estimation + if token_usage_data["total_tokens"] == 0: + # Get model name from LLM + llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") + # First calculate token count from the text + token_count = len(formatted_prompt_text) // 4 # Simple estimation + # Create token usage dictionary for calculate_token_cost + token_usage_dict = {"input_tokens": token_count} + # Calculate cost + cost_result = calculate_token_cost(token_usage_dict, llm_model_name) + token_usage_data = { + "input_tokens": token_count, + "output_tokens": len(result.model_dump_json()) + // 4, # rough estimate + "total_tokens": token_count + len(result.model_dump_json()) // 4, + } + self.logger.debug( + f"[DEBUG:FinancialAgent:{proposal_id}] Estimated token usage: {token_usage_data}" ) - # Fetch recent tweets - recent_tweets = [] - if dao_id: - try: - self.logger.debug(f"Fetching tweets for DAO ID: {dao_id}") - queue_messages = backend.list_queue_messages( - QueueMessageFilter( - type=QueueMessageType.TWEET, - dao_id=dao_id, - is_processed=True, - ) - ) - sorted_messages = sorted( - queue_messages, key=lambda x: x.created_at, reverse=True - )[:5] - recent_tweets = [ - { - "created_at": msg.created_at, - "message": ( - msg.message.get("message", "No text available") - if isinstance(msg.message, dict) - else msg.message - ), - "tweet_id": msg.tweet_id, - } - for msg in sorted_messages - ] - except Exception as e: - self.logger.error( - f"Failed to fetch tweets: {str(e)}", exc_info=True - ) - state["recent_tweets"] = recent_tweets - - # Fetch contract source for core proposals - contract_source = "" - if proposal_dict.get("type") == ProposalType.CORE and proposal_dict.get( - "proposal_contract" - ): - parts = proposal_dict["proposal_contract"].split(".") - if len(parts) >= 2: - try: - api = HiroApi() - result = api.get_contract_source(parts[0], parts[1]) - contract_source = result.get("source", "") - except Exception as e: - self.logger.error( - f"Failed to fetch contract source: {str(e)}", - exc_info=True, - ) - else: - self.logger.warning( - f"Invalid contract format: {proposal_dict['proposal_contract']}" - ) - state["contract_source"] = contract_source - - # Validate proposal data structure (moved from entry point) - proposal_type = proposal_dict.get("type") - if proposal_type == ProposalType.ACTION and not proposal_dict.get( - "parameters" - ): - raise ValueError("Action proposal missing parameters") - if proposal_type == ProposalType.CORE and not proposal_dict.get( - "proposal_contract" - ): - raise ValueError("Core proposal missing proposal_contract") - - return state - except Exception as e: - self.logger.error(f"Error in fetch_context: {str(e)}", exc_info=True) - state["reasoning"] = f"Error fetching context: {str(e)}" - # Propagate error state - return state - - async def format_evaluation_prompt(state: EvaluationState) -> EvaluationState: - """Format the evaluation prompt using the fetched context.""" - if "reasoning" in state and "Error" in state["reasoning"]: - return state # Skip if context fetching failed - try: - # Extract data from state for easier access - proposal_data = state["proposal_data"] - dao_info = state.get("dao_info", {}) - treasury_balance = state.get("treasury_balance") - contract_source = state.get("contract_source", "") - agent_prompts = state.get("agent_prompts", []) - vector_results = state.get("vector_results", []) - recent_tweets = state.get("recent_tweets", []) - web_search_results = state.get("web_search_results", []) - - # Format agent prompts - agent_prompts_str = "No agent-specific instructions available." - if agent_prompts: - if isinstance(agent_prompts, list): - agent_prompts_str = "\n\n".join(agent_prompts) - else: - self.logger.warning( - f"Invalid agent prompts: {type(agent_prompts)}" - ) + # Add token usage to state + if "token_usage" not in state: + state["token_usage"] = {} + state["token_usage"]["financial_agent"] = token_usage_data + + result_dict = result.model_dump() + # Update state with the result + update_state_with_agent_result(state, result_dict, "financial") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:FinancialAgent:{proposal_id}] Error in financial evaluation: {str(e)}", + exc_info=True, + ) + fallback_score_dict = { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Evaluation failed due to error", + } + self.logger.info( + f"[DEBUG:FinancialAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" + ) + return fallback_score_dict - # Format web search results - web_search_content = "No relevant web search results found." - if web_search_results: - # Create structured XML format for each web search result - web_search_items = [] - for i, res in enumerate(web_search_results): - source_url = ( - res.get("metadata", {}) - .get("source_urls", [{}])[0] - .get("url", "Unknown") - ) - web_search_items.append( - f"\n{i+1}\n{res.get('page_content', '')}\n{source_url}\n" - ) - web_search_content = "\n".join(web_search_items) - - # Format vector context - vector_context = "No additional context available from vector store." - if vector_results: - # Create structured XML format for each vector result - vector_items = [] - for i, doc in enumerate(vector_results): - vector_items.append( - f"\n{i+1}\n{doc.page_content}\n" - ) - vector_context = "\n".join(vector_items) - - # Format recent tweets - tweets_content = "No recent DAO tweets found." - if recent_tweets: - # Create structured XML format for each tweet - tweet_items = [] - for i, tweet in enumerate(recent_tweets): - tweet_items.append( - f"\n{i+1}\n{tweet['created_at']}\n{tweet['message']}\n" - ) - tweets_content = "\n".join(tweet_items) - - # Convert JSON objects to formatted text - # Format proposal_data - proposal_data_str = "No proposal data available." - if proposal_data: - proposal_data_str = "\n".join( - [ - f"Proposal ID: {proposal_data.get('proposal_id', 'Unknown')}", - f"Type: {proposal_data.get('type', 'Unknown')}", - f"Action: {proposal_data.get('action', 'Unknown')}", - f"Parameters: {proposal_data.get('parameters', 'None')}", - f"Creator: {proposal_data.get('creator', 'Unknown')}", - f"Contract Principal: {proposal_data.get('contract_principal', 'Unknown')}", - f"Start Block: {proposal_data.get('start_block', 'Unknown')}", - f"End Block: {proposal_data.get('end_block', 'Unknown')}", - f"Created at Block: {proposal_data.get('created_at_block', 'Unknown')}", - f"Liquid Tokens: {proposal_data.get('liquid_tokens', 'Unknown')}", - ] - ) - # Add proposal contract info if it exists - if proposal_data.get("proposal_contract"): - proposal_data_str += f"\nProposal Contract: {proposal_data.get('proposal_contract')}" - - # Format dao_info - dao_info_str = "No DAO information available." - if dao_info: - dao_info_str = "\n".join( - [ - f"DAO Name: {dao_info.get('name', 'Unknown')}", - f"DAO Mission: {dao_info.get('mission', 'Unknown')}", - f"DAO Description: {dao_info.get('description', 'Unknown')}", - ] - ) +class ImageProcessingNode(BaseCapabilityMixin): + """A workflow node to process proposal images: extract URLs, download, and base64 encode.""" - # Format treasury_balance - treasury_balance_str = "Treasury balance information not available." - if treasury_balance is not None: - treasury_balance_str = ( - f"Current DAO Treasury Balance: {treasury_balance} STX" - ) + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__(config=config, state_key="proposal_images") + self.initialize() - formatted_prompt = prompt.format( - proposal_data=proposal_data_str, - dao_info=dao_info_str, - treasury_balance=treasury_balance_str, - contract_source=contract_source, - agent_prompts=agent_prompts_str, - vector_context=vector_context, - recent_tweets=tweets_content, - web_search_results=web_search_content, - ) - state["formatted_prompt"] = formatted_prompt - return state - except Exception as e: - self.logger.error(f"Error formatting prompt: {str(e)}", exc_info=True) - state["reasoning"] = f"Error formatting prompt: {str(e)}" - return state - - async def call_evaluation_llm(state: EvaluationState) -> EvaluationState: - """Call the LLM with the formatted prompt for evaluation.""" - if "reasoning" in state and "Error" in state["reasoning"]: - return state # Skip if previous steps failed - try: - # Prepare message content with text and images - message_content = [{"type": "text", "text": state["formatted_prompt"]}] + async def process(self, state: ProposalEvaluationState) -> List[Dict[str, Any]]: + """The core logic for processing images, returns the list of processed image dicts directly.""" + proposal_id = state.get("proposal_id", "unknown") + proposal_data_str = state.get("proposal_data", "") - # Add any proposal images if they exist - if state.get("proposal_images"): - message_content.extend(state["proposal_images"]) + if not proposal_data_str: + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] No proposal_data string, skipping image processing." + ) + return [] - # Create the message for the LLM - message = HumanMessage(content=message_content) + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] Starting image processing." + ) + image_urls = extract_image_urls(proposal_data_str) - structured_output = self.llm.with_structured_output( - ProposalEvaluationOutput, include_raw=True - ) - result: Dict[str, Any] = await structured_output.ainvoke([message]) + if not image_urls: + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] No image URLs found in proposal data." + ) + return [] - parsed_result = result.get("parsed") - if not isinstance(parsed_result, ProposalEvaluationOutput): - # Attempt to handle cases where parsing might return the raw dict - if isinstance(parsed_result, dict): - parsed_result = ProposalEvaluationOutput(**parsed_result) - else: - raise TypeError( - f"Expected ProposalEvaluationOutput or dict, got {type(parsed_result)}" - ) + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] Found {len(image_urls)} image URLs: {image_urls}" + ) + + processed_images = [] + async with httpx.AsyncClient() as client: + for url in image_urls: + try: + self.logger.debug( + f"[ImageProcessorNode:{proposal_id}] Downloading image from {url}" + ) + response = await client.get(url, timeout=10.0) + response.raise_for_status() + image_data = base64.b64encode(response.content).decode("utf-8") + mime_type = "image/jpeg" + if url.lower().endswith((".jpg", ".jpeg")): + mime_type = "image/jpeg" + elif url.lower().endswith(".png"): + mime_type = "image/png" + elif url.lower().endswith(".gif"): + mime_type = "image/gif" + elif url.lower().endswith(".webp"): + mime_type = "image/webp" + + processed_images.append( + { + "type": "image_url", + "image_url": { + "url": f"data:{mime_type};base64,{image_data}" + }, + } + ) + self.logger.debug( + f"[ImageProcessorNode:{proposal_id}] Successfully processed image from {url}" + ) + except httpx.HTTPStatusError as e: + self.logger.error( + f"[ImageProcessorNode:{proposal_id}] HTTP error for {url}: {e.response.status_code}", + exc_info=False, + ) + except httpx.RequestError as e: + self.logger.error( + f"[ImageProcessorNode:{proposal_id}] Request error for {url}: {str(e)}", + exc_info=False, + ) + except Exception as e: + self.logger.error( + f"[ImageProcessorNode:{proposal_id}] Generic error for {url}: {str(e)}", + exc_info=True, + ) - model_info = {"name": self.model_name, "temperature": self.temperature} - token_usage = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0} + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] Finished. {len(processed_images)} images processed." + ) + return processed_images - raw_response = result.get("raw") - if raw_response: - if hasattr(raw_response, "usage_metadata"): - token_usage = raw_response.usage_metadata - else: - self.logger.warning("Raw response missing usage_metadata") - else: - self.logger.warning("LLM result missing raw response data") - state["approve"] = parsed_result.approve - state["confidence_score"] = parsed_result.confidence_score - state["reasoning"] = parsed_result.reasoning - state["evaluation_token_usage"] = token_usage - state["evaluation_model_info"] = model_info +class SocialContextAgent(BaseCapabilityMixin, WebSearchCapability): + """Social Context Agent gauges community sentiment and social impact.""" + def __init__(self, config: Optional[Dict[str, Any]] = None): + BaseCapabilityMixin.__init__(self, config=config, state_key="social_score") + WebSearchCapability.__init__(self) + self.initialize() + self._initialize_web_search_capability() + + def _initialize_web_search_capability(self): + if not hasattr(self, "search_web"): + self.search_web = WebSearchCapability.search_web.__get__( + self, self.__class__ + ) + self.logger.info("Initialized web search capability for SocialContextAgent") + + async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: + proposal_id = state.get("proposal_id", "unknown") + self._initialize_web_search_capability() + proposal_content = state.get("proposal_data", "") + + social_context = "" + if self.config.get("enable_web_search", True): + try: + search_query = ( + f"Community sentiment {proposal_content[:50]} cryptocurrency DAO" + ) self.logger.debug( - f"Evaluation step complete: Decision={'APPROVE' if parsed_result.approve else 'REJECT'} | Confidence={parsed_result.confidence_score:.2f}" + f"[DEBUG:SocialAgent:{proposal_id}] Performing web search: {search_query}" + ) + search_results, web_search_token_usage = await self.search_web( + query=search_query, + num_results=3, + ) + social_context = "\n".join( + [f"{r.get('page_content', '')}" for r in search_results] + ) + self.logger.debug( + f"[DEBUG:SocialAgent:{proposal_id}] Found {len(search_results)} web search results" ) - self.logger.debug(f"Full reasoning: {parsed_result.reasoning}") - return state + # Store web search token usage + if "token_usage" not in state: + state["token_usage"] = {} + state["token_usage"]["social_web_search"] = web_search_token_usage + except Exception as e: - self.logger.error(f"Error calling LLM: {str(e)}", exc_info=True) - state["approve"] = False - state["confidence_score"] = 0.0 - state["reasoning"] = f"Error during LLM evaluation: {str(e)}" - return state - - # Create decision node - async def should_vote(state: EvaluationState) -> str: - """Decide whether to vote based on confidence threshold.""" - try: - self.logger.debug( - f"Deciding vote: auto_vote={state['auto_vote']} | confidence={state['confidence_score']} | threshold={state['confidence_threshold']}" + logger.error( + f"[DEBUG:SocialAgent:{proposal_id}] Web search failed: {str(e)}", + exc_info=True, ) + social_context = "Web search unavailable." + prompt = PromptTemplate( + input_variables=["proposal_data", "social_context"], + template="""Gauge the community sentiment and social impact of this proposal.\\n +Proposal: {proposal_data}\\nSocial Context: {social_context}\\n +Evaluate:\\n1. Community Sentiment: How might members perceive this?\\n2. Social Media Presence: Any discussions online about this?\\n3. Engagement Potential: Will this engage the community?\\n4. Cross-Platform Analysis: How does sentiment vary across platforms?\\n5. Social Risk: Any potential for controversy or division?\\n +# ADDED: Image processing instructions +**Image Analysis Instructions:** +If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. +- Relevance: Does each image directly relate to and support the proposal's text or the community/social aspects being discussed? +- Evidence: Do the images provide visual evidence for claims made (e.g., screenshots of community discussions, mockups of social impact visuals)? +- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? +- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. + +Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ + """, + ) + try: + self.logger.debug( + f"[DEBUG:SocialAgent:{proposal_id}] Formatting prompt for social evaluation" + ) + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + social_context=social_context, + ) + except Exception as e: + self.logger.error( + f"[DEBUG:SocialAgent:{proposal_id}] Error formatting prompt: {str(e)}", + exc_info=True, + ) + formatted_prompt_text = ( + f"Gauge social impact of proposal: {proposal_content}" + ) + try: + self.logger.debug( + f"[DEBUG:SocialAgent:{proposal_id}] Invoking LLM for social evaluation" + ) + + # ADDED: Image handling + proposal_images_list = state.get("proposal_images", []) + if not isinstance(proposal_images_list, list): + self.logger.warning( + f"[DEBUG:SocialAgent:{proposal_id}] proposal_images is not a list: {type(proposal_images_list)}. Defaulting to empty list." + ) + proposal_images_list = [] - if not state["auto_vote"]: - self.logger.debug("Auto-vote is disabled, skipping vote") - return "skip_vote" + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + if proposal_images_list: + self.logger.debug( + f"[DEBUG:SocialAgent:{proposal_id}] Adding {len(proposal_images_list)} images to LLM input." + ) + message_content_list.extend(proposal_images_list) + + llm_input_message = HumanMessage(content=message_content_list) + + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + self.logger.info( + f"[DEBUG:SocialAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" + ) + + # Track token usage - extract directly from LLM if available + token_usage_data = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } - if state["confidence_score"] >= state["confidence_threshold"]: + # Try to extract token usage directly from LLM response + if ( + hasattr(self.llm, "_last_prompt_id") + and hasattr(self.llm, "client") + and hasattr(self.llm.client, "usage_by_prompt_id") + ): + last_prompt_id = self.llm._last_prompt_id + if last_prompt_id in self.llm.client.usage_by_prompt_id: + usage = self.llm.client.usage_by_prompt_id[last_prompt_id] + token_usage_data = { + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } self.logger.debug( - f"Confidence score {state['confidence_score']} meets threshold {state['confidence_threshold']}, proceeding to vote" + f"[DEBUG:SocialAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" ) - return "vote" - else: + # Fallback to estimation + if token_usage_data["total_tokens"] == 0: + # Get model name from LLM + llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") + # First calculate token count from the text + token_count = len(formatted_prompt_text) // 4 # Simple estimation + # Create token usage dictionary for calculate_token_cost + token_usage_dict = {"input_tokens": token_count} + # Calculate cost + cost_result = calculate_token_cost(token_usage_dict, llm_model_name) + token_usage_data = { + "input_tokens": token_count, + "output_tokens": len(result.model_dump_json()) + // 4, # rough estimate + "total_tokens": token_count + len(result.model_dump_json()) // 4, + } + self.logger.debug( + f"[DEBUG:SocialAgent:{proposal_id}] Estimated token usage: {token_usage_data}" + ) + + # Add token usage to state + if "token_usage" not in state: + state["token_usage"] = {} + state["token_usage"]["social_agent"] = token_usage_data + + result_dict = result.model_dump() + # Update state with the result + update_state_with_agent_result(state, result_dict, "social") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:SocialAgent:{proposal_id}] Error in social evaluation: {str(e)}", + exc_info=True, + ) + fallback_score_dict = { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Evaluation failed due to error", + } + self.logger.info( + f"[DEBUG:SocialAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" + ) + return fallback_score_dict + + +class ReasoningAgent(BaseCapabilityMixin, PlanningCapability): + """Configuration & Reasoning Agent synthesizes evaluations and makes decisions.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the Reasoning Agent.""" + BaseCapabilityMixin.__init__(self, config=config, state_key="final_score") + self.initialize() + planning_queue = asyncio.Queue() + callback_handler = self.config.get( + "callback_handler" + ) or StreamingCallbackHandler(planning_queue) + PlanningCapability.__init__( + self, + callback_handler=callback_handler, + planning_llm=ChatOpenAI( + model=self.config.get("planning_model", "gpt-4.1-mini") + ), + persona="DAO Proposal Evaluator", + ) + self._initialize_planning_capability() + + def _initialize_planning_capability(self): + """Initialize planning capability methods.""" + if not hasattr(self, "create_plan"): + self.create_plan = PlanningCapability.create_plan.__get__( + self, self.__class__ + ) + self.logger.info("Initialized planning capability for ReasoningAgent") + + def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: + """Integrate planning capability with the graph.""" + pass + + async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: + proposal_id = state.get("proposal_id", "unknown") + self._initialize_planning_capability() + proposal_content = state.get("proposal_data", "") + self.logger.debug( + f"[DEBUG:ReasoningAgent:{proposal_id}] Beginning final evaluation processing with proposal_content (length: {len(proposal_content)})" + ) + + def safe_get_score(value, default=0): + if isinstance(value, dict) and "score" in value: + return value.get("score", default) + elif isinstance(value, int): + return value + return default + + core_score = state.get("core_score", {}) + historical_score = state.get("historical_score", {}) + financial_score = state.get("financial_score", {}) + social_score = state.get("social_score", {}) + + core_score_val = safe_get_score(core_score) + historical_score_val = safe_get_score(historical_score) + financial_score_val = safe_get_score(financial_score) + social_score_val = safe_get_score(social_score) + + self.logger.debug( + f"[DEBUG:ReasoningAgent:{proposal_id}] Input scores: Core={core_score_val}, Historical={historical_score_val}, Financial={financial_score_val}, Social={social_score_val}" + ) + + scores = { + "Core Context": core_score_val, + "Historical Context": historical_score_val, + "Financial Context": financial_score_val, + "Social Context": social_score_val, + } + summaries = state.get("summaries", {}) + flags = state.get("flags", []) + + self.logger.debug( + f"[DEBUG:ReasoningAgent:{proposal_id}] Summaries: {summaries}" + ) + + self.logger.debug(f"[DEBUG:ReasoningAgent:{proposal_id}] Flags raised: {flags}") + + # Update the summaries with the content from each agent's evaluation + if isinstance(core_score, dict) and "summary" in core_score: + summaries["core_score"] = core_score["summary"] + if isinstance(historical_score, dict) and "summary" in historical_score: + summaries["historical_score"] = historical_score["summary"] + if isinstance(financial_score, dict) and "summary" in financial_score: + summaries["financial_score"] = financial_score["summary"] + if isinstance(social_score, dict) and "summary" in social_score: + summaries["social_score"] = social_score["summary"] + + # Update flags + for score_obj in [core_score, historical_score, financial_score, social_score]: + if ( + isinstance(score_obj, dict) + and "flags" in score_obj + and isinstance(score_obj["flags"], list) + ): + flags.extend(score_obj["flags"]) + + prompt = PromptTemplate( + input_variables=["proposal_data", "scores", "summaries", "flags"], + template="""Synthesize all evaluations and make a final decision on this proposal.\\n +Proposal: {proposal_data}\\n +Evaluations:\\n- Core Context (Score: {scores[Core Context]}): {summaries[core_score]}\\n- Historical Context (Score: {scores[Historical Context]}): {summaries[historical_score]}\\n- Financial Context (Score: {scores[Financial Context]}): {summaries[financial_score]}\\n- Social Context (Score: {scores[Social Context]}): {summaries[social_score]}\\n +Flags Raised: {flags}\\n +Synthesize these evaluations to:\\n1. Weigh the importance of each context\\n2. Calibrate confidence based on available information\\n3. Consider the implications of the flags raised\\n4. Make a final decision: Approve or Reject\\n5. Calculate an overall score\\n +Provide a final score, decision (Approve/Reject), and detailed explanation.\\n + """, + ) + + try: + for key in [ + "core_score", + "historical_score", + "financial_score", + "social_score", + ]: + if key not in summaries: + summaries[key] = "No evaluation available" + + self.logger.debug( + f"[DEBUG:ReasoningAgent:{proposal_id}] Formatting final evaluation prompt" + ) + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + scores=scores, + summaries=summaries, + flags=", ".join(flags) if flags else "None", + ) + except Exception as e: + self.logger.error( + f"[DEBUG:ReasoningAgent:{proposal_id}] Error formatting prompt: {str(e)}", + exc_info=True, + ) + formatted_prompt_text = f"""Synthesize evaluations for proposal: {proposal_content} +Scores: {scores} +Flags: {flags} +Provide a final score, decision (Approve/Reject), and explanation.""" + + try: + self.logger.debug( + f"[DEBUG:ReasoningAgent:{proposal_id}] Invoking LLM for final decision" + ) + result = await self.llm.with_structured_output(FinalOutput).ainvoke( + [formatted_prompt_text] + ) + + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] FINAL DECISION: {result.decision} | SCORE={result.score}/100 | EXPLANATION={result.explanation}" + ) + + # Track token usage - extract directly from LLM if available + token_usage_data = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } + + # Try to extract token usage directly from LLM response + if ( + hasattr(self.llm, "_last_prompt_id") + and hasattr(self.llm, "client") + and hasattr(self.llm.client, "usage_by_prompt_id") + ): + last_prompt_id = self.llm._last_prompt_id + if last_prompt_id in self.llm.client.usage_by_prompt_id: + usage = self.llm.client.usage_by_prompt_id[last_prompt_id] + token_usage_data = { + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } self.logger.debug( - f"Confidence score {state['confidence_score']} below threshold {state['confidence_threshold']}, skipping vote" + f"[DEBUG:ReasoningAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" ) - return "skip_vote" - except Exception as e: - self.logger.error(f"Error in should_vote: {str(e)}", exc_info=True) - return "skip_vote" + # Fallback to estimation + if token_usage_data["total_tokens"] == 0: + # Get model name from LLM + llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") + # First calculate token count from the text + token_count = len(formatted_prompt_text) // 4 # Simple estimation + # Create token usage dictionary for calculate_token_cost + token_usage_dict = {"input_tokens": token_count} + # Calculate cost + cost_result = calculate_token_cost(token_usage_dict, llm_model_name) + token_usage_data = { + "input_tokens": token_count, + "output_tokens": len(result.model_dump_json()) + // 4, # rough estimate + "total_tokens": token_count + len(result.model_dump_json()) // 4, + } + self.logger.debug( + f"[DEBUG:ReasoningAgent:{proposal_id}] Estimated token usage: {token_usage_data}" + ) - # Create voting node using VectorReact workflow - async def vote_on_proposal(state: EvaluationState) -> EvaluationState: - """Vote on the proposal using VectorReact workflow.""" - try: - # Check if wallet_id is available - if not state.get("wallet_id"): - self.logger.warning( - "No wallet_id provided for voting, skipping vote" + # Add token usage to state + if "token_usage" not in state: + state["token_usage"] = {} + state["token_usage"]["reasoning_agent"] = token_usage_data + + result_dict = result.model_dump() + # Update state with the result + update_state_with_agent_result(state, result_dict, "reasoning") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:ReasoningAgent:{proposal_id}] Error in final evaluation: {str(e)}", + exc_info=True, + ) + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] ERROR_SCORE=50/100 | DECISION=Pending | REASON=Error: {str(e)}" + ) + return { + "score": 50, + "decision": "Pending", + "explanation": f"Unable to make final decision due to error: {str(e)}", + } + + +class ProposalEvaluationWorkflow(BaseWorkflow[ProposalEvaluationState]): + """Main workflow for evaluating DAO proposals using a hierarchical team.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the proposal evaluation workflow.""" + super().__init__() + self.config = config or {} + self.hierarchical_workflow = HierarchicalTeamWorkflow( + name="ProposalEvaluation", + config={ + "state_type": ProposalEvaluationState, + "recursion_limit": self.config.get("recursion_limit", 20), + }, + ) + + # Instantiate and add the new ImageProcessingNode + image_processor_agent = ImageProcessingNode( + config=self.config + ) # Use self.config + self.hierarchical_workflow.add_sub_workflow( + "image_processor", image_processor_agent + ) + + core_agent = CoreContextAgent(self.config) + historical_agent = HistoricalContextAgent(self.config) + financial_agent = FinancialContextAgent(self.config) + social_agent = SocialContextAgent(self.config) + reasoning_agent = ReasoningAgent(self.config) + + self.hierarchical_workflow.add_sub_workflow("core_agent", core_agent) + self.hierarchical_workflow.add_sub_workflow( + "historical_agent", historical_agent + ) + self.hierarchical_workflow.add_sub_workflow("financial_agent", financial_agent) + self.hierarchical_workflow.add_sub_workflow("social_agent", social_agent) + self.hierarchical_workflow.add_sub_workflow("reasoning_agent", reasoning_agent) + + self.hierarchical_workflow.set_entry_point("image_processor") + + def supervisor_logic(state: ProposalEvaluationState) -> Union[str, List[str]]: + """Determine the next step in the workflow.""" + proposal_id = state.get("proposal_id", "unknown") + + # Debugging current state view for supervisor + logger.debug( + f"[DEBUG:Supervisor:{proposal_id}] Evaluating next step. State keys: {list(state.keys())}. " + f"proposal_images set: {'proposal_images' in state}, " + f"core_score set: {state.get('core_score') is not None}, " + f"historical_score set: {state.get('historical_score') is not None}, " + f"financial_score set: {state.get('financial_score') is not None}, " + f"social_score set: {state.get('social_score') is not None}, " + f"final_score set: {state.get('final_score') is not None}" + ) + + if state.get("halt", False): + logger.debug( + f"[DEBUG:Supervisor:{proposal_id}] Halt condition met, returning END" + ) + return END + + # After image_processor (entry point), if core_score isn't set, go to core_agent. + # The image_processor node output (even if empty list for images) should be in state. + if state.get("core_score") is None: + # This will be the first check after image_processor completes as it's the entry point. + current_core_invocations = state.get("core_agent_invocations", 0) + if current_core_invocations > 3: + logger.error( + f"[DEBUG:Supervisor:{proposal_id}] Core agent invoked too many times ({current_core_invocations}), halting." ) - state["vote_result"] = { - "success": False, - "error": "No wallet_id provided for voting", - } - return state + return END - self.logger.debug( - f"Setting up VectorReact workflow: proposal_id={state['proposal_id']} | vote={state['approve']}" + # Do not manually increment core_agent_invocations - the langgraph framework will handle this + # with the Annotated type we restored + + logger.debug( + f"[DEBUG:Supervisor:{proposal_id}] Routing to core_agent (core_score is None, invocation #{current_core_invocations})." ) + return "core_agent" - # Set up the voting tool - vote_tool = VoteOnActionProposalTool(wallet_id=state["wallet_id"]) - tools_map = {"dao_action_vote_on_proposal": vote_tool} + if state.get("historical_score") is None: + logger.debug( + f"[DEBUG:Supervisor:{proposal_id}] Routing to historical_agent." + ) + return "historical_agent" + + if ( + state.get("financial_score") is None + or state.get("social_score") is None + ): + parallel_nodes = [] + if state.get("financial_score") is None: + parallel_nodes.append("financial_agent") + if state.get("social_score") is None: + parallel_nodes.append("social_agent") + logger.debug( + f"[DEBUG:Supervisor:{proposal_id}] Initiating parallel execution of {parallel_nodes}" + ) + return parallel_nodes + + if state.get("final_score") is None: + logger.debug( + f"[DEBUG:Supervisor:{proposal_id}] All scores available but final score is None, routing to reasoning_agent" + ) + return "reasoning_agent" - # Create a user input message that instructs the LLM what to do - vote_instruction = f"I need you to vote on a DAO proposal with ID {state['proposal_id']} in the contract {state['action_proposals_contract']}. Please vote {'FOR' if state['approve'] else 'AGAINST'} the proposal. Use the dao_action_vote_on_proposal tool to submit the vote." + logger.debug( + f"[DEBUG:Supervisor:{proposal_id}] All scores completed, returning END" + ) + return END - # Create VectorLangGraph service with collections - service = ChatService( - collection_names=self.collection_names, + self.hierarchical_workflow.set_supervisor_logic(supervisor_logic) + + def halt_condition(state: ProposalEvaluationState) -> bool: + """Check if workflow should halt.""" + proposal_id = state.get("proposal_id", "unknown") + + if state.get("halt", False): + logger.debug( + f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow due to explicit halt flag" ) + return True - # History with system message only - history = [ - { - "role": "system", - "content": "You are a helpful assistant tasked with voting on DAO proposals. Follow the instructions precisely.", - } - ] - - self.logger.debug("Executing VectorReact workflow for voting...") - - # Collect response chunks - response_chunks = [] - vote_result = None - - # Execute the VectorReact workflow - async for chunk in service.execute_stream( - history=history, - input_str=vote_instruction, - tools_map=tools_map, - ): - response_chunks.append(chunk) - self.logger.debug(f"VectorReact chunk: {chunk}") - - # Extract tool results - if ( - chunk.get("type") == "tool" - and chunk.get("tool") == "dao_action_vote_on_proposal" - ): - if "output" in chunk: - vote_result = chunk.get("output") - self.logger.debug(f"Vote result: {vote_result}") - - # Update state with vote result and vector results - state["vote_result"] = { - "success": vote_result is not None, - "output": vote_result, - } - state["vector_results"] = [ - chunk.get("vector_results", []) - for chunk in response_chunks - if chunk.get("vector_results") - ] + # Check for excessive core agent invocations + if state.get("core_agent_invocations", 0) > 3: + logger.debug( + f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow due to excessive core agent invocations: {state.get('core_agent_invocations', 0)}" + ) + return True - return state - except Exception as e: - self.logger.error(f"Error in vote_on_proposal: {str(e)}", exc_info=True) - state["vote_result"] = { - "success": False, - "error": f"Error during voting: {str(e)}", - } - return state + recursion_count = state.get("recursion_count", 0) + if recursion_count > 8: + logger.debug( + f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow - possible loop detected after {recursion_count} iterations" + ) + return True + + if ( + state.get("core_score") is not None + and state.get("historical_score") is not None + and state.get("financial_score") is not None + and state.get("social_score") is not None + and state.get("final_score") is None + and recursion_count > 3 + ): + logger.debug( + f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow - reasoning agent appears to be failing after {recursion_count} attempts" + ) + return True - # Create skip voting node - async def skip_voting(state: EvaluationState) -> EvaluationState: - """Skip voting and just return the evaluation.""" - try: - self.logger.debug("Vote skipped: reason=threshold_or_setting") - state["vote_result"] = { - "success": True, - "message": "Voting skipped due to confidence threshold or auto_vote setting", - "data": None, - } - return state - except Exception as e: - self.logger.error(f"Error in skip_voting: {str(e)}", exc_info=True) - state["vote_result"] = { - "success": True, - "message": f"Voting skipped (with error: {str(e)})", - "data": None, - } - return state - - # Create the graph - workflow = StateGraph(EvaluationState) - - # Add nodes - workflow.add_node("fetch_context", fetch_context) - workflow.add_node("format_prompt", format_evaluation_prompt) - workflow.add_node("evaluate", call_evaluation_llm) - workflow.add_node("vote", vote_on_proposal) - workflow.add_node("skip_vote", skip_voting) - - # Set up the conditional branching - workflow.set_entry_point("fetch_context") # Start with fetching context - workflow.add_edge("fetch_context", "format_prompt") - workflow.add_edge("format_prompt", "evaluate") - workflow.add_conditional_edges( - "evaluate", - should_vote, - { - "vote": "vote", - "skip_vote": "skip_vote", - }, + state["recursion_count"] = recursion_count + 1 + logger.debug( + f"[DEBUG:HaltCondition:{proposal_id}] Incrementing recursion counter to {state['recursion_count']}" + ) + + return False + + self.hierarchical_workflow.set_halt_condition(halt_condition) + self.required_fields = ["proposal_id", "proposal_data"] + + def _create_prompt(self) -> PromptTemplate: + """Create the main workflow prompt.""" + return PromptTemplate( + input_variables=["proposal_data"], + template="Evaluate the DAO proposal: {proposal_data}", ) - workflow.add_edge("vote", END) - workflow.add_edge("skip_vote", END) - return workflow.compile() + def _create_graph(self) -> StateGraph: + """Create the workflow graph.""" + return self.hierarchical_workflow.build_graph() - def _validate_state(self, state: EvaluationState) -> bool: + def _validate_state(self, state: ProposalEvaluationState) -> bool: """Validate the workflow state.""" - # Only validate minimal required fields for initial state - # Other fields like proposal_data are fetched within the workflow - required_fields = ["proposal_id"] + if not super()._validate_state(state): + return False - # Log the state for debugging - self.logger.debug( - f"Validating initial state: proposal_id={state.get('proposal_id')}" - ) + if "flags" not in state: + state["flags"] = [] + elif state["flags"] is None: + state["flags"] = [] + + if "summaries" not in state: + state["summaries"] = {} + elif state["summaries"] is None: + state["summaries"] = {} + + if "halt" not in state: + state["halt"] = False + + if "token_usage" not in state: + state["token_usage"] = {} + elif state["token_usage"] is None: + state["token_usage"] = {} - # Check all fields and log problems - for field in required_fields: - if field not in state: - self.logger.error(f"Missing required field: {field}") - return False - elif not state[field]: - self.logger.error(f"Empty required field: {field}") - return False - - # Note: Detailed validation of proposal_data happens in fetch_context node - self.logger.debug("Initial state validation successful") return True +async def evaluate_proposal( + proposal_id: str, + proposal_data: str, + config: Optional[Dict[str, Any]] = None, +) -> Dict[str, Any]: + """Evaluate a proposal using the hierarchical team workflow.""" + logger.info(f"[DEBUG:Workflow:{proposal_id}] Starting evaluation workflow") + + debug_level = 0 + if config and "debug_level" in config: + debug_level = config.get("debug_level", 0) + logger.debug(f"[PROPOSAL_DEBUG] Using debug_level: {debug_level}") + + logger.debug( + f"[PROPOSAL_DEBUG] evaluate_proposal received proposal_id: {proposal_id}" + ) + logger.debug( + f"[PROPOSAL_DEBUG] evaluate_proposal received proposal_data type: {type(proposal_data)}" + ) + + if not proposal_data: + logger.warning( + f"[PROPOSAL_DEBUG] proposal_data is empty or None! This will cause evaluation failure." + ) + + state = { + "proposal_id": proposal_id, + "proposal_data": proposal_data, + "flags": [], + "summaries": {}, + "halt": False, + "token_usage": {}, + "core_score": None, + "historical_score": None, + "financial_score": None, + "social_score": None, + "final_score": None, + "decision": None, + "core_agent_invocations": 0, + "recursion_count": 0, + } + + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] Initialized workflow state with keys: {state.keys()}" + ) + logger.debug( + f"[PROPOSAL_DEBUG] Proposal data in state: {state.get('proposal_data')}" + ) + + try: + workflow = ProposalEvaluationWorkflow(config or {}) + logger.info( + f"[DEBUG:Workflow:{proposal_id}] Executing hierarchical team workflow" + ) + result = await workflow.execute(state) + logger.info( + f"[DEBUG:Workflow:{proposal_id}] Workflow execution completed with decision: {result.get('decision', 'Unknown')}" + ) + + logger.debug(f"[DEBUG:Workflow:{proposal_id}] RESULT SCORES TYPES:") + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Core: {type(result.get('core_score'))} = {repr(result.get('core_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Historical: {type(result.get('historical_score'))} = {repr(result.get('historical_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Financial: {type(result.get('financial_score'))} = {repr(result.get('financial_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Social: {type(result.get('social_score'))} = {repr(result.get('social_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Final: {type(result.get('final_score'))} = {repr(result.get('final_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Decision: {type(result.get('decision'))} = {repr(result.get('decision'))}" + ) + + if result is None: + logger.error( + f"[DEBUG:Workflow:{proposal_id}] Workflow returned None result, using default values" + ) + return { + "proposal_id": proposal_id, + "score": 0, + "decision": "Error", + "explanation": "Evaluation failed: Workflow returned empty result", + "component_scores": { + "core": 0, + "historical": 0, + "financial": 0, + "social": 0, + }, + "flags": ["Workflow error: Empty result"], + "token_usage": {}, + } + + def safe_extract_score(value, default=0): + if isinstance(value, dict) and "score" in value: + return value.get("score", default) + elif isinstance(value, int): + return value + elif isinstance(value, str): + try: + return int(value) + except ValueError: + pass # If string is not int, will fall through to default + return default + + final_score_val = result.get("final_score") + logger.debug( + f"[DEBUG:evaluate_proposal] Raw final_score_val from result state: {repr(final_score_val)} (type: {type(final_score_val)})" + ) + + final_score_dict = {} + if isinstance(final_score_val, dict): + final_score_dict = final_score_val + + component_scores = { + "core": safe_extract_score(result.get("core_score")), + "historical": safe_extract_score(result.get("historical_score")), + "financial": safe_extract_score(result.get("financial_score")), + "social": safe_extract_score(result.get("social_score")), + } + + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] EXTRACTED COMPONENT SCORES: {component_scores}" + ) + + explanation = "" + if isinstance(final_score_dict, dict) and "explanation" in final_score_dict: + explanation = final_score_dict.get("explanation", "") + elif isinstance(final_score_val, str): + explanation = final_score_val + + # Log the explanation to help debug + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] Explanation extracted: {explanation[:100]}..." + ) + + final_score = 0 + if isinstance(final_score_dict, dict) and "score" in final_score_dict: + final_score = final_score_dict.get("score", 0) + else: + final_score = safe_extract_score(final_score_val) + + decision = result.get("decision") + if decision is None: + if isinstance(final_score_dict, dict) and "decision" in final_score_dict: + decision = final_score_dict.get("decision") + else: + decision = "Reject" + + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] Final decision: {decision}, score: {final_score}" + ) + + total_token_usage = result.get("token_usage", {}) + total_input_tokens = 0 + total_output_tokens = 0 + total_tokens = 0 + + # Aggregate tokens from all agent steps + # Assuming model_name is consistent across all steps for this aggregation, or we use the primary model_name + # If each agent could use a different model, this would need more detailed per-model tracking + for agent_key, usage_data in total_token_usage.items(): + if isinstance(usage_data, dict): + total_input_tokens += usage_data.get("input_tokens", 0) + total_output_tokens += usage_data.get("output_tokens", 0) + total_tokens += usage_data.get("total_tokens", 0) + else: + logger.warning( + f"Unexpected format for token_usage data for agent {agent_key}: {usage_data}" + ) + + # Extract component summaries for detailed reporting + component_summaries = {} + if isinstance(result.get("summaries"), dict): + component_summaries = result.get("summaries") + + # Extract and aggregate flags + all_flags = result.get("flags", []) + if not isinstance(all_flags, list): + all_flags = [] + + # Placeholder for web search specific token usage if it were tracked separately + # In the original, these seemed to be fixed placeholders. + web_search_input_tokens = 0 + web_search_output_tokens = 0 + web_search_total_tokens = 0 + + # Initialize total token usage by model + total_token_usage_by_model = {} + + # Extract token usage by model from token_usage data + for agent_name, agent_usage in total_token_usage.items(): + if isinstance(agent_usage, dict) and agent_usage.get("total_tokens", 0) > 0: + # Use default model name if not specified + model_name = "gpt-4.1" # default model name + + # Initialize the model entry if needed + if model_name not in total_token_usage_by_model: + total_token_usage_by_model[model_name] = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } + + # Add token usage for this agent to the model's tally + total_token_usage_by_model[model_name][ + "input_tokens" + ] += agent_usage.get("input_tokens", 0) + total_token_usage_by_model[model_name][ + "output_tokens" + ] += agent_usage.get("output_tokens", 0) + total_token_usage_by_model[model_name][ + "total_tokens" + ] += agent_usage.get("total_tokens", 0) + + # Fallback if no token usage was recorded + if not total_token_usage_by_model: + total_token_usage_by_model["gpt-4.1"] = { + "input_tokens": total_input_tokens, + "output_tokens": total_output_tokens, + "total_tokens": total_tokens, + } + + # Improved cost calculation by model + cost_per_thousand = { + "gpt-4.1": 0.01, # $0.01 per 1K tokens + "gpt-4.1-mini": 0.005, # $0.005 per 1K tokens + "gpt-4.1-32k": 0.03, # $0.03 per 1K tokens + "gpt-4": 0.03, # $0.03 per 1K tokens + "gpt-4-32k": 0.06, # $0.06 per 1K tokens + "gpt-3.5-turbo": 0.0015, # $0.0015 per 1K tokens + "default": 0.01, # default fallback + } + + # Calculate costs for each model + total_cost_by_model = {} + total_overall_cost = 0.0 + for model_name, usage in total_token_usage_by_model.items(): + # Get cost per 1K tokens for this model + model_cost_per_k = cost_per_thousand.get( + model_name, cost_per_thousand["default"] + ) + # Calculate cost for this model's usage + model_cost = usage["total_tokens"] * (model_cost_per_k / 1000) + total_cost_by_model[model_name] = model_cost + total_overall_cost += model_cost + + if not total_cost_by_model: + # Fallback if no models were recorded + model_name = "gpt-4.1" # Default model name + total_cost_by_model[model_name] = total_tokens * ( + cost_per_thousand["default"] / 1000 + ) + total_overall_cost = total_cost_by_model[model_name] + + final_result = { + "success": True, + "evaluation": { + "approve": decision == "Approve", + "confidence_score": final_score / 100.0 if final_score else 0.0, + "reasoning": explanation, + }, + "decision": decision, + "score": final_score, + "explanation": explanation, + "component_scores": component_scores, + "component_summaries": component_summaries, # Include component summaries + "flags": all_flags, + "token_usage": total_token_usage, + "web_search_results": [], + "treasury_balance": None, + "web_search_token_usage": { + "input_tokens": web_search_input_tokens, + "output_tokens": web_search_output_tokens, + "total_tokens": web_search_total_tokens, + }, + "evaluation_token_usage": { + "input_tokens": total_input_tokens, + "output_tokens": total_output_tokens, + "total_tokens": total_tokens, + }, + "evaluation_model_info": {"name": "gpt-4.1", "temperature": 0.1}, + "web_search_model_info": {"name": "gpt-4.1", "temperature": 0.1}, + "total_token_usage_by_model": total_token_usage_by_model, + "total_cost_by_model": total_cost_by_model, + "total_overall_cost": total_overall_cost, + "summaries": component_summaries, + } + + logger.debug( + f"Proposal evaluation completed: Success={final_result['success']} | Decision={'APPROVE' if decision == 'Approve' else 'REJECT'} | Confidence={final_result['evaluation']['confidence_score']:.2f} | Auto-voted={decision == 'Approve'}" + ) + return final_result + except Exception as e: + logger.error(f"Error in workflow execution: {str(e)}", exc_info=True) + return { + "proposal_id": proposal_id, + "score": 0, + "decision": "Error", + "explanation": f"Evaluation failed: {str(e)}", + "component_scores": { + "core": 0, + "historical": 0, + "financial": 0, + "social": 0, + }, + "flags": [f"Workflow error: {str(e)}"], + "token_usage": {}, + } + + def get_proposal_evaluation_tools( profile: Optional[Profile] = None, agent_id: Optional[UUID] = None ): - """Get the tools needed for proposal evaluation. - - Args: - profile: Optional user profile - agent_id: Optional agent ID - - Returns: - Dictionary of filtered tools for proposal evaluation - """ - # Initialize all tools + """Get the tools needed for proposal evaluation.""" all_tools = initialize_tools(profile=profile, agent_id=agent_id) logger.debug(f"Available tools: {', '.join(all_tools.keys())}") - - # Filter to only include the tools we need required_tools = [ "dao_action_get_proposal", "dao_action_vote_on_proposal", "dao_action_get_voting_power", "dao_action_get_voting_configuration", - "database_get_dao_get_by_name", # Try old name - "dao_search", # Try new name + "database_get_dao_get_by_name", + "dao_search", ] - filtered_tools = filter_tools_by_names(required_tools, all_tools) logger.debug(f"Using tools: {', '.join(filtered_tools.keys())}") - return filtered_tools @@ -935,26 +1717,13 @@ async def evaluate_and_vote_on_proposal( auto_vote: bool = True, confidence_threshold: float = 0.7, dao_id: Optional[UUID] = None, + debug_level: int = 0, # 0=normal, 1=verbose, 2=very verbose ) -> Dict: - """Evaluate a proposal and automatically vote based on the evaluation. - - Args: - proposal_id: The ID of the proposal to evaluate and vote on - wallet_id: Optional wallet ID to use for voting - agent_id: Optional agent ID to use for retrieving prompts - auto_vote: Whether to automatically vote based on the evaluation - confidence_threshold: Minimum confidence score required to auto-vote (0.0-1.0) - dao_id: Optional DAO ID to explicitly pass to the workflow - - Returns: - Dictionary containing the evaluation results and voting outcome - """ + """Evaluate a proposal and automatically vote based on the evaluation.""" logger.debug( - f"Starting proposal evaluation: proposal_id={proposal_id} | auto_vote={auto_vote} | confidence_threshold={confidence_threshold}" + f"Starting proposal evaluation: proposal_id={proposal_id} | auto_vote={auto_vote} | confidence_threshold={confidence_threshold} | debug_level={debug_level}" ) - try: - # Determine effective agent ID effective_agent_id = agent_id if not effective_agent_id and wallet_id: wallet = backend.get_wallet(wallet_id) @@ -964,17 +1733,14 @@ async def evaluate_and_vote_on_proposal( f"Using agent ID {effective_agent_id} from wallet {wallet_id}" ) - # Fetch the primary prompt to determine model and temperature settings - # Note: Actual prompt text fetching happens inside the workflow now. - model_name = "gpt-4.1" # Default model - temperature = 0.1 # Default temperature + model_name = "gpt-4.1" + temperature = 0.1 if effective_agent_id: try: - # We only need one active prompt to get settings prompts = backend.list_prompts( PromptFilter( agent_id=effective_agent_id, - dao_id=dao_id, # Assuming dao_id is available, might need refinement + dao_id=dao_id, is_active=True, limit=1, ) @@ -992,171 +1758,252 @@ async def evaluate_and_vote_on_proposal( ) else: logger.warning( - f"No active prompts found for agent {effective_agent_id} to determine settings." + f"No active prompts found for agent {effective_agent_id}." ) except Exception as e: logger.error( f"Failed to get agent prompt settings: {str(e)}", exc_info=True ) - # Initialize state (minimal initial data) - state = { - "proposal_id": proposal_id, - "dao_id": dao_id, # Pass DAO ID to the workflow - "agent_id": effective_agent_id, # Pass Agent ID for prompt loading - "wallet_id": wallet_id, # Pass wallet ID for voting tool - "approve": False, - "confidence_score": 0.0, - "reasoning": "", - "vote_result": None, - "confidence_threshold": confidence_threshold, - "auto_vote": auto_vote, - "vector_results": None, - "recent_tweets": None, - "web_search_results": None, - "token_usage": None, - "model_info": None, - "web_search_token_usage": None, - "evaluation_token_usage": None, - "evaluation_model_info": None, - "web_search_model_info": None, + logger.debug( + f"[PROPOSAL_DEBUG] Fetching proposal data from backend for ID: {proposal_id}" + ) + proposal_data = backend.get_proposal(proposal_id) + if not proposal_data: + logger.error( + f"[PROPOSAL_DEBUG] No proposal data found for ID: {proposal_id}" + ) + raise ValueError(f"Proposal {proposal_id} not found") + + logger.debug(f"[PROPOSAL_DEBUG] Raw proposal data: {proposal_data}") + + proposal_content = proposal_data.parameters or "" + if not proposal_content: + logger.warning(f"[PROPOSAL_DEBUG] Proposal parameters/content is empty!") + + config = { + "model_name": model_name, + "temperature": temperature, + "mission_collection": "knowledge_collection", + "proposals_collection": "proposals", + "enable_web_search": True, + "planning_model": "gpt-4.1-mini", } - # Create and run workflow with model settings from prompt - workflow = ProposalEvaluationWorkflow( - model_name=model_name, temperature=temperature + if debug_level > 0: + config["debug_level"] = debug_level + logger.debug(f"[PROPOSAL_DEBUG] Setting debug_level to {debug_level}") + + if not dao_id and proposal_data.dao_id: + dao_id = proposal_data.dao_id + dao_info = None + if dao_id: + dao_info = backend.get_dao(dao_id) + if dao_info: + config["dao_mission"] = dao_info.mission + + treasury_balance = None + try: + if dao_id: + treasury_extensions = backend.list_extensions( + ExtensionFilter(dao_id=dao_id, type="EXTENSIONS_TREASURY") + ) + if treasury_extensions: + hiro_api = HiroApi() + treasury_balance = hiro_api.get_address_balance( + treasury_extensions[0].contract_principal + ) + except Exception as e: + logger.error(f"Failed to get treasury balance: {str(e)}", exc_info=True) + + logger.debug("Starting hierarchical evaluation workflow...") + eval_result = await evaluate_proposal( + proposal_id=str(proposal_id), + proposal_data=proposal_data.parameters, + config=config, ) - if not workflow._validate_state(state): - error_msg = "Invalid workflow state" - logger.error(error_msg) - return { - "success": False, - "error": error_msg, - } - logger.debug("Starting workflow execution...") - result = await workflow.execute(state) - logger.debug("Workflow execution completed") + decision = eval_result.get("decision") + if decision is None: + decision = "Reject" + logger.warning( + f"No decision found in evaluation results, defaulting to '{decision}'" + ) - # Extract transaction ID from vote result if available - tx_id = None - if result.get("vote_result") and result["vote_result"].get("output"): - # Try to extract tx_id from the output - output = result["vote_result"]["output"] - if isinstance(output, str) and "txid:" in output.lower(): - # Extract the transaction ID from the output - for line in output.split("\n"): - if "txid:" in line.lower(): - parts = line.split(":") - if len(parts) > 1: - tx_id = parts[1].strip() - logger.debug(f"Transaction ID extracted: {tx_id}") - break - - # Prepare final result - final_result = { - "success": True, - "evaluation": { - "approve": result.get("approve", False), - "confidence_score": result.get("confidence_score", 0.0), - "reasoning": result.get( - "reasoning", "Evaluation failed or not available" - ), - }, - "vote_result": result.get("vote_result"), - "auto_voted": auto_vote - and result.get("confidence_score", 0.0) >= confidence_threshold, - "tx_id": tx_id, - "formatted_prompt": result.get( - "formatted_prompt", "Formatted prompt not available" - ), - "vector_results": result.get("vector_results"), - "recent_tweets": result.get("recent_tweets"), - "web_search_results": result.get("web_search_results"), - "treasury_balance": result.get("treasury_balance"), - "web_search_token_usage": result.get("web_search_token_usage"), - "evaluation_token_usage": result.get("evaluation_token_usage"), - "evaluation_model_info": result.get("evaluation_model_info"), - "web_search_model_info": result.get("web_search_model_info"), - } + score = eval_result.get("score", 0) + confidence_score = score / 100.0 if score else 0.0 - # --- Aggregate Token Usage and Calculate Costs --- # - total_token_usage_by_model = {} - total_cost_by_model = {} - total_overall_cost = 0.0 - - steps = [ - ( - "web_search", - result.get("web_search_token_usage"), - result.get("web_search_model_info"), - ), - ( - "evaluation", - result.get("evaluation_token_usage"), - result.get("evaluation_model_info"), - ), - ] + approve = False + if isinstance(decision, str) and decision.lower() == "approve": + approve = True - for step_name, usage, model_info in steps: - if usage and model_info and model_info.get("name") != "unknown": - model_name = model_info["name"] - - # Aggregate usage per model - if model_name not in total_token_usage_by_model: - total_token_usage_by_model[model_name] = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - total_token_usage_by_model[model_name]["input_tokens"] += usage.get( - "input_tokens", 0 - ) - total_token_usage_by_model[model_name]["output_tokens"] += usage.get( - "output_tokens", 0 - ) - total_token_usage_by_model[model_name]["total_tokens"] += usage.get( - "total_tokens", 0 - ) + should_vote = auto_vote and confidence_score >= confidence_threshold - # Calculate cost for this step/model - step_cost = calculate_token_cost(usage, model_name) + vote_result = None + tx_id = None + if should_vote and wallet_id: + try: + vote_tool = VoteOnActionProposalTool(wallet_id=wallet_id) + if proposal_data.type == ProposalType.ACTION: + contract_info = proposal_data.contract_principal + if "." in contract_info: + parts = contract_info.split(".") + if len(parts) >= 2: + action_proposals_contract = parts[0] + action_proposals_voting_extension = parts[1] + result = await vote_tool.vote_on_proposal( + contract_principal=action_proposals_contract, + extension_name=action_proposals_voting_extension, + proposal_id=proposal_data.proposal_id, + vote=approve, + ) + vote_result = { + "success": result is not None, + "output": result, + } + if ( + result + and isinstance(result, str) + and "txid:" in result.lower() + ): + for line in result.split("\n"): + if "txid:" in line.lower(): + parts = line.split(":") + if len(parts) > 1: + tx_id = parts[1].strip() + break + else: + logger.warning( + f"Invalid contract principal format: {contract_info}" + ) + else: + logger.warning( + f"Cannot vote on non-action proposal type: {proposal_data.type}" + ) + except Exception as e: + logger.error(f"Error executing vote: {str(e)}", exc_info=True) + vote_result = { + "success": False, + "error": f"Error during voting: {str(e)}", + } + elif not should_vote: + vote_result = { + "success": True, + "message": "Voting skipped due to confidence threshold or auto_vote setting", + "data": None, + } - # Aggregate cost per model - if model_name not in total_cost_by_model: - total_cost_by_model[model_name] = 0.0 - total_cost_by_model[model_name] += step_cost["total_cost"] - total_overall_cost += step_cost["total_cost"] + total_token_usage = eval_result.get("token_usage", {}) + total_input_tokens = 0 + total_output_tokens = 0 + total_tokens = 0 + + # Aggregate tokens from all agent steps + # Assuming model_name is consistent across all steps for this aggregation, or we use the primary model_name + # If each agent could use a different model, this would need more detailed per-model tracking + for agent_key, usage_data in total_token_usage.items(): + if isinstance(usage_data, dict): + total_input_tokens += usage_data.get("input_tokens", 0) + total_output_tokens += usage_data.get("output_tokens", 0) + total_tokens += usage_data.get("total_tokens", 0) else: logger.warning( - f"Skipping cost calculation for step '{step_name}' due to missing usage or model info." + f"Unexpected format for token_usage data for agent {agent_key}: {usage_data}" ) - final_result["total_token_usage_by_model"] = total_token_usage_by_model - final_result["total_cost_by_model"] = total_cost_by_model - final_result["total_overall_cost"] = total_overall_cost - # --- End Aggregation --- # + # Placeholder for web search specific token usage if it were tracked separately + # In the original, these seemed to be fixed placeholders. + web_search_input_tokens = 0 + web_search_output_tokens = 0 + web_search_total_tokens = 0 + + # Initialize total_token_usage_by_model + total_token_usage_by_model = {} + + # Use the default model name from settings or default to gpt-4.1 + default_model = model_name or "gpt-4.1" + + # Add total token counts to the model + total_token_usage_by_model[default_model] = { + "input_tokens": total_input_tokens, + "output_tokens": total_output_tokens, + "total_tokens": total_tokens, + } + + # Improved cost calculation by model + cost_per_thousand = { + "gpt-4.1": 0.01, # $0.01 per 1K tokens + "gpt-4.1-mini": 0.005, # $0.005 per 1K tokens + "gpt-4.1-32k": 0.03, # $0.03 per 1K tokens + "gpt-4": 0.03, # $0.03 per 1K tokens + "gpt-4-32k": 0.06, # $0.06 per 1K tokens + "gpt-3.5-turbo": 0.0015, # $0.0015 per 1K tokens + "default": 0.01, # default fallback + } + + # Calculate costs for each model + total_cost_by_model = {} + total_overall_cost = 0.0 + for model_key, usage in total_token_usage_by_model.items(): + # Get cost per 1K tokens for this model + model_cost_per_k = cost_per_thousand.get( + model_key, cost_per_thousand["default"] + ) + # Calculate cost for this model's usage + model_cost = usage["total_tokens"] * (model_cost_per_k / 1000) + total_cost_by_model[model_key] = model_cost + total_overall_cost += model_cost + + if not total_cost_by_model: + # Fallback if no models were recorded + default_model_key = "gpt-4.1" # Default model name + total_cost_by_model[default_model_key] = total_tokens * ( + cost_per_thousand["default"] / 1000 + ) + total_overall_cost = total_cost_by_model[default_model_key] + + final_result = { + "success": True, + "evaluation": { + "approve": approve, + "confidence_score": confidence_score, + "reasoning": eval_result.get("explanation", ""), + }, + "vote_result": vote_result, + "auto_voted": should_vote, + "tx_id": tx_id, + "vector_results": [], + "recent_tweets": [], + "web_search_results": [], + "treasury_balance": treasury_balance, + "component_scores": eval_result.get("component_scores", {}), + "component_summaries": eval_result.get("summaries", {}), + "flags": eval_result.get("flags", []), + "web_search_token_usage": { + "input_tokens": web_search_input_tokens, + "output_tokens": web_search_output_tokens, + "total_tokens": web_search_total_tokens, + }, + "evaluation_token_usage": { + "input_tokens": total_input_tokens, + "output_tokens": total_output_tokens, + "total_tokens": total_tokens, + }, + "evaluation_model_info": {"name": model_name, "temperature": temperature}, + "web_search_model_info": {"name": model_name, "temperature": temperature}, + "total_token_usage_by_model": total_token_usage_by_model, + "total_cost_by_model": total_cost_by_model, + "total_overall_cost": total_overall_cost, + } - # Updated Logging logger.debug( - f"Proposal evaluation completed: Success={final_result['success']} | " - f"Decision={'APPROVE' if final_result['evaluation']['approve'] else 'REJECT'} | " - f"Confidence={final_result['evaluation']['confidence_score']:.2f} | " - f"Auto-voted={final_result['auto_voted']} | Transaction={tx_id or 'None'} | " - f"Total Cost (USD)=${total_overall_cost:.4f}" + f"Proposal evaluation completed: Success={final_result['success']} | Decision={'APPROVE' if approve else 'REJECT'} | Confidence={confidence_score:.2f} | Auto-voted={should_vote} | Transaction={tx_id or 'None'}" ) - logger.debug(f"Cost Breakdown: {total_cost_by_model}") - logger.debug(f"Token Usage Breakdown: {total_token_usage_by_model}") - logger.debug(f"Full evaluation result: {final_result}") - return final_result except Exception as e: error_msg = f"Unexpected error in evaluate_and_vote_on_proposal: {str(e)}" logger.error(error_msg, exc_info=True) - return { - "success": False, - "error": error_msg, - } + return {"success": False, "error": error_msg} async def evaluate_proposal_only( @@ -1165,20 +2012,8 @@ async def evaluate_proposal_only( agent_id: Optional[UUID] = None, dao_id: Optional[UUID] = None, ) -> Dict: - """Evaluate a proposal without voting. - - Args: - proposal_id: The ID of the proposal to evaluate - wallet_id: Optional wallet ID to use for retrieving proposal data - agent_id: Optional agent ID associated with the evaluation - dao_id: Optional DAO ID associated with the proposal - - Returns: - Dictionary containing the evaluation results - """ + """Evaluate a proposal without voting.""" logger.debug(f"Starting proposal-only evaluation: proposal_id={proposal_id}") - - # Determine effective agent ID (same logic as evaluate_and_vote) effective_agent_id = agent_id if not effective_agent_id and wallet_id: wallet = backend.get_wallet(wallet_id) @@ -1193,7 +2028,6 @@ async def evaluate_proposal_only( auto_vote=False, ) - # Remove vote-related fields from the response logger.debug("Removing vote-related fields from response") if "vote_result" in result: del result["vote_result"] From 1e64010028babc616917a90a45d03ae0244464aa Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Fri, 9 May 2025 16:58:19 -0700 Subject: [PATCH 027/219] update --- examples/proposal_evaluation_example.py | 2 +- services/workflows/proposal_evaluation.py | 254 +++++++++++++++++----- 2 files changed, 197 insertions(+), 59 deletions(-) diff --git a/examples/proposal_evaluation_example.py b/examples/proposal_evaluation_example.py index 324bd2c9..45615822 100644 --- a/examples/proposal_evaluation_example.py +++ b/examples/proposal_evaluation_example.py @@ -35,7 +35,7 @@ async def create_test_proposal(dao_id: UUID) -> UUID: The ID of the created proposal """ # Create test parameters as a JSON object - parameters = "let this rip https://media1.giphy.com/media/v1.Y2lkPTc5MGI3NjExN3VoZzJzdmV3eGs4M2VrOXBkamg2dTVhb2NhcndwNzVxNHplMzhoaiZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/M7HkIkPrNhSy4/giphy.gif https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/x-vote-media//img_2.jpeg" + parameters = "I Publius.btc will do a $FACES airdrop to as many bitcoin faces holders as possible. I will report back with a confirmation message and proof. Give me a shot." # # Convert parameters to JSON string and then hex encode it # parameters_hex = "0x" + binascii.hexlify(parameters.encode("utf-8")).decode("utf-8") diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 9ebfd6bb..a70dbb48 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -81,46 +81,69 @@ def no_update_reducer(current: Any, new: List[Any]) -> Any: return current -def merge_dict_override_fn(key, values): - """Merge dictionaries by taking the last non-None value.""" - # Handle case where values is None - if values is None: +def merge_dicts(current: Optional[Dict], updates: List[Optional[Dict]]) -> Dict: + """Merge multiple dictionary updates into the current dictionary.""" + # Initialize current if it's None + if current is None: + current = {} + + # Handle case where updates is None + if updates is None: + return current + + # Process updates if it's a list + if isinstance(updates, list): + for update in updates: + if update and isinstance(update, dict): + current.update(update) + # Handle case where updates is a single dictionary, not a list + elif isinstance(updates, dict): + current.update(updates) + + return current + + +def set_once(current: Any, updates: List[Any]) -> Any: + """Set the value once and prevent further updates.""" + # If current already has a value, return it unchanged + if current is not None: + return current + + # Handle case where updates is None instead of a list + if updates is None: return None - # Handle case where values is not iterable - if not hasattr(values, "__iter__"): - return values + # Process updates if it's a list + if isinstance(updates, list): + for update in updates: + if update is not None: + return update + # Handle case where updates is a single value, not a list + elif updates is not None: + return updates - result = None - for value in values: - if value is not None: - result = value - return result + return current class ProposalEvaluationState(TypedDict): """Type definition for the proposal evaluation state.""" - proposal_id: Annotated[str, no_update_reducer] # Read-only during execution - proposal_data: Annotated[str, no_update_reducer] # Now a string, not a dict - core_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] - historical_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] - financial_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] - social_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] - final_score: Annotated[Optional[Dict[str, Any]], merge_dict_override_fn] - flags: Annotated[List[str], append_list_fn] # Merges lists of flags - summaries: Annotated[ - Dict[str, str], merge_dict_fn - ] # Merges dictionaries of summaries - decision: Annotated[Optional[str], merge_dict_override_fn] - halt: Annotated[bool, operator.or_] # Use OR for boolean flags + proposal_id: Annotated[str, no_update_reducer] + proposal_data: Annotated[str, no_update_reducer] + core_score: Annotated[Optional[Dict[str, Any]], set_once] + historical_score: Annotated[Optional[Dict[str, Any]], set_once] + financial_score: Annotated[Optional[Dict[str, Any]], set_once] + social_score: Annotated[Optional[Dict[str, Any]], set_once] + final_score: Annotated[Optional[Dict[str, Any]], set_once] + flags: Annotated[List[str], append_list_fn] # Correctly appends lists + summaries: Annotated[Dict[str, str], merge_dicts] # Properly merges dictionaries + decision: Annotated[Optional[str], set_once] + halt: Annotated[bool, operator.or_] token_usage: Annotated[ - Dict[str, Dict[str, int]], merge_dict_fn - ] # Merges nested dictionaries - core_agent_invocations: Annotated[int, operator.add] # Counts should add - proposal_images: Annotated[ - Optional[List[Dict]], merge_dict_override_fn - ] # ADDED: To store encoded images + Dict[str, Dict[str, int]], merge_dicts + ] # Properly merges dictionaries + core_agent_invocations: Annotated[int, operator.add] + proposal_images: Annotated[Optional[List[Dict]], set_once] class AgentOutput(BaseModel): @@ -143,9 +166,34 @@ def update_state_with_agent_result( state: ProposalEvaluationState, agent_result: Dict[str, Any], agent_name: str ): """Helper function to update state with agent result including summaries and flags.""" + # ADDED DEBUG: Log the incoming data + logger.debug( + f"[DEBUG:update_state:{agent_name}] Updating state with agent result: {agent_result}" + ) + logger.debug( + f"[DEBUG:update_state:{agent_name}] Current state before update - {agent_name}_score: {state.get(f'{agent_name}_score')}" + ) + # Update agent score in state if agent_name in ["core", "historical", "financial", "social", "final"]: - state[f"{agent_name}_score"] = agent_result + # Make a copy of agent_result to avoid modifying the original + score_dict = dict(agent_result) + # Don't pass token_usage through this path to avoid duplication + if "token_usage" in score_dict: + del score_dict["token_usage"] + + # ADDED DEBUG: Log what we're about to assign + logger.debug( + f"[DEBUG:update_state:{agent_name}] Setting {agent_name}_score to: {score_dict}" + ) + + # Directly assign the dictionary to the state key + state[f"{agent_name}_score"] = score_dict + + # ADDED DEBUG: Immediately verify what was assigned + logger.debug( + f"[DEBUG:update_state:{agent_name}] Immediate check - {agent_name}_score now: {state.get(f'{agent_name}_score')}" + ) # Update summaries if "summaries" not in state: @@ -161,19 +209,13 @@ def update_state_with_agent_result( if "flags" in agent_result and isinstance(agent_result["flags"], list): state["flags"].extend(agent_result["flags"]) - # Update token usage - if ( - "token_usage" in state - and isinstance(state["token_usage"], dict) - and f"{agent_name}_agent" in state["token_usage"] - ): - # Token usage has been set by the agent directly - pass - elif hasattr(agent_result, "get") and agent_result.get("token_usage"): - # Token usage available in the result - if "token_usage" not in state: - state["token_usage"] = {} - state["token_usage"][f"{agent_name}_agent"] = agent_result.get("token_usage") + # Note: Token usage is already directly handled by each agent via state["token_usage"]["{agent_name}_agent"] + # So we don't need to do anything with token usage here + + # ADDED DEBUG: Log final state + logger.debug( + f"[DEBUG:update_state:{agent_name}] Final state after update - {agent_name}_score: {state.get(f'{agent_name}_score')}" + ) return state @@ -351,6 +393,7 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: "output_tokens": len(result.model_dump_json()) // 4, # rough estimate "total_tokens": token_count + len(result.model_dump_json()) // 4, + "model_name": llm_model_name, # Include model name } self.logger.debug( f"[DEBUG:CoreAgent:{proposal_id}] Estimated token usage: {token_usage_data}" @@ -362,8 +405,27 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: state["token_usage"]["core_agent"] = token_usage_data result_dict = result.model_dump() + # Add token usage to result_dict so it's properly processed + result_dict["token_usage"] = token_usage_data + + # ADDED DEBUG: Log the exact result dictionary before state update + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] BEFORE STATE UPDATE: Result dict to be added to state: {result_dict}" + ) + + # Capture state before update for debugging + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] State before update - core_score: {state.get('core_score')}" + ) + # Update state with the result update_state_with_agent_result(state, result_dict, "core") + + # ADDED DEBUG: Log the state after update + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] AFTER STATE UPDATE: core_score in state: {state.get('core_score')}" + ) + return result_dict except Exception as e: self.logger.error( @@ -527,6 +589,7 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: "output_tokens": len(result.model_dump_json()) // 4, # rough estimate "total_tokens": token_count + len(result.model_dump_json()) // 4, + "model_name": llm_model_name, # Include model name } self.logger.debug( f"[DEBUG:HistoricalAgent:{proposal_id}] Estimated token usage: {token_usage_data}" @@ -538,6 +601,9 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: state["token_usage"]["historical_agent"] = token_usage_data result_dict = result.model_dump() + # Add token usage to result_dict so it's properly processed + result_dict["token_usage"] = token_usage_data + # Update state with the result update_state_with_agent_result(state, result_dict, "historical") return result_dict @@ -665,6 +731,7 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: "output_tokens": len(result.model_dump_json()) // 4, # rough estimate "total_tokens": token_count + len(result.model_dump_json()) // 4, + "model_name": llm_model_name, # Include model name } self.logger.debug( f"[DEBUG:FinancialAgent:{proposal_id}] Estimated token usage: {token_usage_data}" @@ -676,6 +743,9 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: state["token_usage"]["financial_agent"] = token_usage_data result_dict = result.model_dump() + # Add token usage to result_dict so it's properly processed + result_dict["token_usage"] = token_usage_data + # Update state with the result update_state_with_agent_result(state, result_dict, "financial") return result_dict @@ -711,7 +781,7 @@ async def process(self, state: ProposalEvaluationState) -> List[Dict[str, Any]]: self.logger.info( f"[ImageProcessorNode:{proposal_id}] No proposal_data string, skipping image processing." ) - return [] + return [] # Return empty list, not None self.logger.info( f"[ImageProcessorNode:{proposal_id}] Starting image processing." @@ -722,7 +792,7 @@ async def process(self, state: ProposalEvaluationState) -> List[Dict[str, Any]]: self.logger.info( f"[ImageProcessorNode:{proposal_id}] No image URLs found in proposal data." ) - return [] + return [] # Return empty list, not None self.logger.info( f"[ImageProcessorNode:{proposal_id}] Found {len(image_urls)} image URLs: {image_urls}" @@ -778,7 +848,7 @@ async def process(self, state: ProposalEvaluationState) -> List[Dict[str, Any]]: self.logger.info( f"[ImageProcessorNode:{proposal_id}] Finished. {len(processed_images)} images processed." ) - return processed_images + return processed_images # This will be a list, possibly empty class SocialContextAgent(BaseCapabilityMixin, WebSearchCapability): @@ -933,6 +1003,7 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: "output_tokens": len(result.model_dump_json()) // 4, # rough estimate "total_tokens": token_count + len(result.model_dump_json()) // 4, + "model_name": llm_model_name, # Include model name } self.logger.debug( f"[DEBUG:SocialAgent:{proposal_id}] Estimated token usage: {token_usage_data}" @@ -944,6 +1015,9 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: state["token_usage"]["social_agent"] = token_usage_data result_dict = result.model_dump() + # Add token usage to result_dict so it's properly processed + result_dict["token_usage"] = token_usage_data + # Update state with the result update_state_with_agent_result(state, result_dict, "social") return result_dict @@ -1150,6 +1224,7 @@ def safe_get_score(value, default=0): "output_tokens": len(result.model_dump_json()) // 4, # rough estimate "total_tokens": token_count + len(result.model_dump_json()) // 4, + "model_name": llm_model_name, # Include model name } self.logger.debug( f"[DEBUG:ReasoningAgent:{proposal_id}] Estimated token usage: {token_usage_data}" @@ -1161,6 +1236,9 @@ def safe_get_score(value, default=0): state["token_usage"]["reasoning_agent"] = token_usage_data result_dict = result.model_dump() + # Add token usage to result_dict so it's properly processed + result_dict["token_usage"] = token_usage_data + # Update state with the result update_state_with_agent_result(state, result_dict, "reasoning") return result_dict @@ -1434,6 +1512,28 @@ async def evaluate_proposal( f"[DEBUG:Workflow:{proposal_id}] Workflow execution completed with decision: {result.get('decision', 'Unknown')}" ) + # ADDED DEBUG: More comprehensive logging of result structure + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] RESULT STRUCTURE: {list(result.keys())}" + ) + + # ADDED DEBUG: Log full core_score and other scores + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] FULL CORE SCORE: {result.get('core_score')}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] FULL HISTORICAL SCORE: {result.get('historical_score')}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] FULL FINANCIAL SCORE: {result.get('financial_score')}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] FULL SOCIAL SCORE: {result.get('social_score')}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] FULL FINAL SCORE: {result.get('final_score')}" + ) + logger.debug(f"[DEBUG:Workflow:{proposal_id}] RESULT SCORES TYPES:") logger.debug( f"[DEBUG:Workflow:{proposal_id}] - Core: {type(result.get('core_score'))} = {repr(result.get('core_score'))}" @@ -1474,15 +1574,36 @@ async def evaluate_proposal( } def safe_extract_score(value, default=0): + # ADDED DEBUG: Log what we're trying to extract + logger.debug( + f"[DEBUG:safe_extract_score] Extracting score from: {repr(value)} (type: {type(value)})" + ) + if isinstance(value, dict) and "score" in value: - return value.get("score", default) + score_val = value.get("score", default) + logger.debug( + f"[DEBUG:safe_extract_score] Found score in dict: {score_val}" + ) + return score_val elif isinstance(value, int): + logger.debug( + f"[DEBUG:safe_extract_score] Value is already int: {value}" + ) return value elif isinstance(value, str): + logger.debug(f"[DEBUG:safe_extract_score] Value is string: '{value}'") try: - return int(value) + int_val = int(value) + logger.debug( + f"[DEBUG:safe_extract_score] Converted string to int: {int_val}" + ) + return int_val except ValueError: + logger.debug( + f"[DEBUG:safe_extract_score] Could not convert string to int" + ) pass # If string is not int, will fall through to default + logger.debug(f"[DEBUG:safe_extract_score] Using default: {default}") return default final_score_val = result.get("final_score") @@ -1541,11 +1662,13 @@ def safe_extract_score(value, default=0): # Aggregate tokens from all agent steps # Assuming model_name is consistent across all steps for this aggregation, or we use the primary model_name # If each agent could use a different model, this would need more detailed per-model tracking + logger.debug(f"Token usage entries in result: {list(total_token_usage.keys())}") for agent_key, usage_data in total_token_usage.items(): if isinstance(usage_data, dict): total_input_tokens += usage_data.get("input_tokens", 0) total_output_tokens += usage_data.get("output_tokens", 0) total_tokens += usage_data.get("total_tokens", 0) + logger.debug(f"Token usage for {agent_key}: {usage_data}") else: logger.warning( f"Unexpected format for token_usage data for agent {agent_key}: {usage_data}" @@ -1573,8 +1696,15 @@ def safe_extract_score(value, default=0): # Extract token usage by model from token_usage data for agent_name, agent_usage in total_token_usage.items(): if isinstance(agent_usage, dict) and agent_usage.get("total_tokens", 0) > 0: - # Use default model name if not specified - model_name = "gpt-4.1" # default model name + # Get model name from config, or use default + model_name = config.get( + "model_name", "gpt-4.1" + ) # Use configured model name + + # Extract model name from each agent usage if available + # This would require each agent to include model info in their token usage + if "model_name" in agent_usage: + model_name = agent_usage["model_name"] # Initialize the model entry if needed if model_name not in total_token_usage_by_model: @@ -1648,7 +1778,7 @@ def safe_extract_score(value, default=0): "component_scores": component_scores, "component_summaries": component_summaries, # Include component summaries "flags": all_flags, - "token_usage": total_token_usage, + "token_usage": total_token_usage, # Include all token usage details "web_search_results": [], "treasury_balance": None, "web_search_token_usage": { @@ -1661,12 +1791,17 @@ def safe_extract_score(value, default=0): "output_tokens": total_output_tokens, "total_tokens": total_tokens, }, - "evaluation_model_info": {"name": "gpt-4.1", "temperature": 0.1}, - "web_search_model_info": {"name": "gpt-4.1", "temperature": 0.1}, + "evaluation_model_info": { + "name": config.get("model_name", "gpt-4.1"), + "temperature": config.get("temperature", 0.1), + }, + "web_search_model_info": { + "name": config.get("model_name", "gpt-4.1"), + "temperature": config.get("temperature", 0.1), + }, "total_token_usage_by_model": total_token_usage_by_model, "total_cost_by_model": total_cost_by_model, "total_overall_cost": total_overall_cost, - "summaries": component_summaries, } logger.debug( @@ -1901,11 +2036,13 @@ async def evaluate_and_vote_on_proposal( # Aggregate tokens from all agent steps # Assuming model_name is consistent across all steps for this aggregation, or we use the primary model_name # If each agent could use a different model, this would need more detailed per-model tracking + logger.debug(f"Token usage entries in result: {list(total_token_usage.keys())}") for agent_key, usage_data in total_token_usage.items(): if isinstance(usage_data, dict): total_input_tokens += usage_data.get("input_tokens", 0) total_output_tokens += usage_data.get("output_tokens", 0) total_tokens += usage_data.get("total_tokens", 0) + logger.debug(f"Token usage for {agent_key}: {usage_data}") else: logger.warning( f"Unexpected format for token_usage data for agent {agent_key}: {usage_data}" @@ -1974,11 +2111,12 @@ async def evaluate_and_vote_on_proposal( "tx_id": tx_id, "vector_results": [], "recent_tweets": [], - "web_search_results": [], + "web_search_results": eval_result.get("web_search_results", []), "treasury_balance": treasury_balance, "component_scores": eval_result.get("component_scores", {}), - "component_summaries": eval_result.get("summaries", {}), + "component_summaries": eval_result.get("component_summaries", {}), "flags": eval_result.get("flags", []), + "token_usage": total_token_usage, # Pass the complete token_usage dictionary "web_search_token_usage": { "input_tokens": web_search_input_tokens, "output_tokens": web_search_output_tokens, From 6aee5b18b7a6779b2e95c2bf313a1657e19404d0 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Fri, 9 May 2025 17:06:28 -0700 Subject: [PATCH 028/219] update --- services/workflows/proposal_evaluation.py | 267 +++++++--------------- 1 file changed, 85 insertions(+), 182 deletions(-) diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index a70dbb48..46d3ac98 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -166,12 +166,9 @@ def update_state_with_agent_result( state: ProposalEvaluationState, agent_result: Dict[str, Any], agent_name: str ): """Helper function to update state with agent result including summaries and flags.""" - # ADDED DEBUG: Log the incoming data + # Simplified logging - just log once with relevant details logger.debug( - f"[DEBUG:update_state:{agent_name}] Updating state with agent result: {agent_result}" - ) - logger.debug( - f"[DEBUG:update_state:{agent_name}] Current state before update - {agent_name}_score: {state.get(f'{agent_name}_score')}" + f"[DEBUG:update_state:{agent_name}] Updating state with {agent_name}_score (score: {agent_result.get('score', 'N/A')})" ) # Update agent score in state @@ -182,19 +179,9 @@ def update_state_with_agent_result( if "token_usage" in score_dict: del score_dict["token_usage"] - # ADDED DEBUG: Log what we're about to assign - logger.debug( - f"[DEBUG:update_state:{agent_name}] Setting {agent_name}_score to: {score_dict}" - ) - # Directly assign the dictionary to the state key state[f"{agent_name}_score"] = score_dict - # ADDED DEBUG: Immediately verify what was assigned - logger.debug( - f"[DEBUG:update_state:{agent_name}] Immediate check - {agent_name}_score now: {state.get(f'{agent_name}_score')}" - ) - # Update summaries if "summaries" not in state: state["summaries"] = {} @@ -212,11 +199,6 @@ def update_state_with_agent_result( # Note: Token usage is already directly handled by each agent via state["token_usage"]["{agent_name}_agent"] # So we don't need to do anything with token usage here - # ADDED DEBUG: Log final state - logger.debug( - f"[DEBUG:update_state:{agent_name}] Final state after update - {agent_name}_score: {state.get(f'{agent_name}_score')}" - ) - return state @@ -408,24 +390,9 @@ async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: # Add token usage to result_dict so it's properly processed result_dict["token_usage"] = token_usage_data - # ADDED DEBUG: Log the exact result dictionary before state update - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] BEFORE STATE UPDATE: Result dict to be added to state: {result_dict}" - ) - - # Capture state before update for debugging - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] State before update - core_score: {state.get('core_score')}" - ) - - # Update state with the result + # Remove verbose debug logs and simply update state update_state_with_agent_result(state, result_dict, "core") - # ADDED DEBUG: Log the state after update - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] AFTER STATE UPDATE: core_score in state: {state.get('core_score')}" - ) - return result_dict except Exception as e: self.logger.error( @@ -1464,14 +1431,8 @@ async def evaluate_proposal( debug_level = 0 if config and "debug_level" in config: debug_level = config.get("debug_level", 0) - logger.debug(f"[PROPOSAL_DEBUG] Using debug_level: {debug_level}") - - logger.debug( - f"[PROPOSAL_DEBUG] evaluate_proposal received proposal_id: {proposal_id}" - ) - logger.debug( - f"[PROPOSAL_DEBUG] evaluate_proposal received proposal_data type: {type(proposal_data)}" - ) + if debug_level > 0: + logger.debug(f"[PROPOSAL_DEBUG] Using debug_level: {debug_level}") if not proposal_data: logger.warning( @@ -1495,13 +1456,6 @@ async def evaluate_proposal( "recursion_count": 0, } - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] Initialized workflow state with keys: {state.keys()}" - ) - logger.debug( - f"[PROPOSAL_DEBUG] Proposal data in state: {state.get('proposal_data')}" - ) - try: workflow = ProposalEvaluationWorkflow(config or {}) logger.info( @@ -1512,47 +1466,30 @@ async def evaluate_proposal( f"[DEBUG:Workflow:{proposal_id}] Workflow execution completed with decision: {result.get('decision', 'Unknown')}" ) - # ADDED DEBUG: More comprehensive logging of result structure - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] RESULT STRUCTURE: {list(result.keys())}" - ) - - # ADDED DEBUG: Log full core_score and other scores - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] FULL CORE SCORE: {result.get('core_score')}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] FULL HISTORICAL SCORE: {result.get('historical_score')}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] FULL FINANCIAL SCORE: {result.get('financial_score')}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] FULL SOCIAL SCORE: {result.get('social_score')}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] FULL FINAL SCORE: {result.get('final_score')}" - ) - - logger.debug(f"[DEBUG:Workflow:{proposal_id}] RESULT SCORES TYPES:") - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Core: {type(result.get('core_score'))} = {repr(result.get('core_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Historical: {type(result.get('historical_score'))} = {repr(result.get('historical_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Financial: {type(result.get('financial_score'))} = {repr(result.get('financial_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Social: {type(result.get('social_score'))} = {repr(result.get('social_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Final: {type(result.get('final_score'))} = {repr(result.get('final_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Decision: {type(result.get('decision'))} = {repr(result.get('decision'))}" - ) + # Only output detailed debug info at higher debug levels + if debug_level >= 2: + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] RESULT STRUCTURE: {list(result.keys())}" + ) + logger.debug(f"[DEBUG:Workflow:{proposal_id}] RESULT SCORES TYPES:") + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Core: {type(result.get('core_score'))} = {repr(result.get('core_score'))[:100]+'...' if len(repr(result.get('core_score'))) > 100 else repr(result.get('core_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Historical: {type(result.get('historical_score'))} = {repr(result.get('historical_score'))[:100]+'...' if len(repr(result.get('historical_score'))) > 100 else repr(result.get('historical_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Financial: {type(result.get('financial_score'))} = {repr(result.get('financial_score'))[:100]+'...' if len(repr(result.get('financial_score'))) > 100 else repr(result.get('financial_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Social: {type(result.get('social_score'))} = {repr(result.get('social_score'))[:100]+'...' if len(repr(result.get('social_score'))) > 100 else repr(result.get('social_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Final: {type(result.get('final_score'))} = {repr(result.get('final_score'))[:100]+'...' if len(repr(result.get('final_score'))) > 100 else repr(result.get('final_score'))}" + ) + logger.debug( + f"[DEBUG:Workflow:{proposal_id}] - Decision: {type(result.get('decision'))} = {repr(result.get('decision'))}" + ) if result is None: logger.error( @@ -1574,43 +1511,18 @@ async def evaluate_proposal( } def safe_extract_score(value, default=0): - # ADDED DEBUG: Log what we're trying to extract - logger.debug( - f"[DEBUG:safe_extract_score] Extracting score from: {repr(value)} (type: {type(value)})" - ) - if isinstance(value, dict) and "score" in value: - score_val = value.get("score", default) - logger.debug( - f"[DEBUG:safe_extract_score] Found score in dict: {score_val}" - ) - return score_val + return value.get("score", default) elif isinstance(value, int): - logger.debug( - f"[DEBUG:safe_extract_score] Value is already int: {value}" - ) return value elif isinstance(value, str): - logger.debug(f"[DEBUG:safe_extract_score] Value is string: '{value}'") try: - int_val = int(value) - logger.debug( - f"[DEBUG:safe_extract_score] Converted string to int: {int_val}" - ) - return int_val + return int(value) except ValueError: - logger.debug( - f"[DEBUG:safe_extract_score] Could not convert string to int" - ) pass # If string is not int, will fall through to default - logger.debug(f"[DEBUG:safe_extract_score] Using default: {default}") return default final_score_val = result.get("final_score") - logger.debug( - f"[DEBUG:evaluate_proposal] Raw final_score_val from result state: {repr(final_score_val)} (type: {type(final_score_val)})" - ) - final_score_dict = {} if isinstance(final_score_val, dict): final_score_dict = final_score_val @@ -1622,6 +1534,7 @@ def safe_extract_score(value, default=0): "social": safe_extract_score(result.get("social_score")), } + # This is a useful log to keep even at lower debug levels logger.debug( f"[DEBUG:Workflow:{proposal_id}] EXTRACTED COMPONENT SCORES: {component_scores}" ) @@ -2028,77 +1941,62 @@ async def evaluate_and_vote_on_proposal( "data": None, } + # Get token usage data from eval_result total_token_usage = eval_result.get("token_usage", {}) total_input_tokens = 0 total_output_tokens = 0 total_tokens = 0 - # Aggregate tokens from all agent steps - # Assuming model_name is consistent across all steps for this aggregation, or we use the primary model_name - # If each agent could use a different model, this would need more detailed per-model tracking - logger.debug(f"Token usage entries in result: {list(total_token_usage.keys())}") + # Aggregate tokens from all agent steps - no need to log duplicates here for agent_key, usage_data in total_token_usage.items(): if isinstance(usage_data, dict): total_input_tokens += usage_data.get("input_tokens", 0) total_output_tokens += usage_data.get("output_tokens", 0) total_tokens += usage_data.get("total_tokens", 0) - logger.debug(f"Token usage for {agent_key}: {usage_data}") - else: - logger.warning( - f"Unexpected format for token_usage data for agent {agent_key}: {usage_data}" - ) - - # Placeholder for web search specific token usage if it were tracked separately - # In the original, these seemed to be fixed placeholders. - web_search_input_tokens = 0 - web_search_output_tokens = 0 - web_search_total_tokens = 0 - - # Initialize total_token_usage_by_model - total_token_usage_by_model = {} - # Use the default model name from settings or default to gpt-4.1 - default_model = model_name or "gpt-4.1" - - # Add total token counts to the model - total_token_usage_by_model[default_model] = { - "input_tokens": total_input_tokens, - "output_tokens": total_output_tokens, - "total_tokens": total_tokens, - } - - # Improved cost calculation by model - cost_per_thousand = { - "gpt-4.1": 0.01, # $0.01 per 1K tokens - "gpt-4.1-mini": 0.005, # $0.005 per 1K tokens - "gpt-4.1-32k": 0.03, # $0.03 per 1K tokens - "gpt-4": 0.03, # $0.03 per 1K tokens - "gpt-4-32k": 0.06, # $0.06 per 1K tokens - "gpt-3.5-turbo": 0.0015, # $0.0015 per 1K tokens - "default": 0.01, # default fallback - } + # Initialize total_token_usage_by_model using data from eval_result + total_token_usage_by_model = eval_result.get("total_token_usage_by_model", {}) + if not total_token_usage_by_model: + # Use the default model name from settings or default to gpt-4.1 + default_model = model_name or "gpt-4.1" + # Add total token counts to the model + total_token_usage_by_model[default_model] = { + "input_tokens": total_input_tokens, + "output_tokens": total_output_tokens, + "total_tokens": total_tokens, + } - # Calculate costs for each model - total_cost_by_model = {} - total_overall_cost = 0.0 - for model_key, usage in total_token_usage_by_model.items(): - # Get cost per 1K tokens for this model - model_cost_per_k = cost_per_thousand.get( - model_key, cost_per_thousand["default"] - ) - # Calculate cost for this model's usage - model_cost = usage["total_tokens"] * (model_cost_per_k / 1000) - total_cost_by_model[model_key] = model_cost - total_overall_cost += model_cost + # Get cost calculations from eval_result if available + total_cost_by_model = eval_result.get("total_cost_by_model", {}) + total_overall_cost = eval_result.get("total_overall_cost", 0.0) + # If cost data is missing, calculate it if not total_cost_by_model: - # Fallback if no models were recorded - default_model_key = "gpt-4.1" # Default model name - total_cost_by_model[default_model_key] = total_tokens * ( - cost_per_thousand["default"] / 1000 - ) - total_overall_cost = total_cost_by_model[default_model_key] + # Improved cost calculation by model + cost_per_thousand = { + "gpt-4.1": 0.01, # $0.01 per 1K tokens + "gpt-4.1-mini": 0.005, # $0.005 per 1K tokens + "gpt-4.1-32k": 0.03, # $0.03 per 1K tokens + "gpt-4": 0.03, # $0.03 per 1K tokens + "gpt-4-32k": 0.06, # $0.06 per 1K tokens + "gpt-3.5-turbo": 0.0015, # $0.0015 per 1K tokens + "default": 0.01, # default fallback + } + # Calculate costs for each model + total_cost_by_model = {} + total_overall_cost = 0.0 + for model_key, usage in total_token_usage_by_model.items(): + # Get cost per 1K tokens for this model + model_cost_per_k = cost_per_thousand.get( + model_key, cost_per_thousand["default"] + ) + # Calculate cost for this model's usage + model_cost = usage["total_tokens"] * (model_cost_per_k / 1000) + total_cost_by_model[model_key] = model_cost + total_overall_cost += model_cost + + # Construct final result with voting information added final_result = { "success": True, "evaluation": { @@ -2116,12 +2014,15 @@ async def evaluate_and_vote_on_proposal( "component_scores": eval_result.get("component_scores", {}), "component_summaries": eval_result.get("component_summaries", {}), "flags": eval_result.get("flags", []), - "token_usage": total_token_usage, # Pass the complete token_usage dictionary - "web_search_token_usage": { - "input_tokens": web_search_input_tokens, - "output_tokens": web_search_output_tokens, - "total_tokens": web_search_total_tokens, - }, + "token_usage": total_token_usage, + "web_search_token_usage": eval_result.get( + "web_search_token_usage", + { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + }, + ), "evaluation_token_usage": { "input_tokens": total_input_tokens, "output_tokens": total_output_tokens, @@ -2134,8 +2035,9 @@ async def evaluate_and_vote_on_proposal( "total_overall_cost": total_overall_cost, } + # Single log entry about the final result instead of duplicating token usage logs logger.debug( - f"Proposal evaluation completed: Success={final_result['success']} | Decision={'APPROVE' if approve else 'REJECT'} | Confidence={confidence_score:.2f} | Auto-voted={should_vote} | Transaction={tx_id or 'None'}" + f"Proposal evaluation completed with voting: Decision={'APPROVE' if approve else 'REJECT'} | Confidence={confidence_score:.2f} | Auto-voted={should_vote} | Transaction={tx_id or 'None'}" ) return final_result except Exception as e: @@ -2166,6 +2068,7 @@ async def evaluate_proposal_only( auto_vote=False, ) + # Simplified logging - no need to duplicate what evaluate_and_vote_on_proposal already logged logger.debug("Removing vote-related fields from response") if "vote_result" in result: del result["vote_result"] From 8e641f630ce7d01cc67a4c46a5fbb0881e37d0bc Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 17:55:36 -0700 Subject: [PATCH 029/219] update --- examples/proposal_evaluation_example.py | 90 +- plan.md | 556 ++++ services/workflows/README.md | 66 + services/workflows/agents/__init__.py | 0 services/workflows/agents/core_context.py | 144 ++ .../workflows/agents/financial_context.py | 130 + .../workflows/agents/historical_context.py | 159 ++ services/workflows/agents/image_processing.py | 95 + services/workflows/agents/reasoning.py | 297 +++ services/workflows/agents/social_context.py | 209 ++ services/workflows/base.py | 59 +- services/workflows/proposal_evaluation.py | 2273 +++-------------- services/workflows/utils/__init__.py | 0 services/workflows/utils/models.py | 31 + services/workflows/utils/state_reducers.py | 139 + services/workflows/utils/token_usage.py | 64 + services/workflows/web_search_mixin.py | 4 +- 17 files changed, 2369 insertions(+), 1947 deletions(-) create mode 100644 plan.md create mode 100644 services/workflows/README.md create mode 100644 services/workflows/agents/__init__.py create mode 100644 services/workflows/agents/core_context.py create mode 100644 services/workflows/agents/financial_context.py create mode 100644 services/workflows/agents/historical_context.py create mode 100644 services/workflows/agents/image_processing.py create mode 100644 services/workflows/agents/reasoning.py create mode 100644 services/workflows/agents/social_context.py create mode 100644 services/workflows/utils/__init__.py create mode 100644 services/workflows/utils/models.py create mode 100644 services/workflows/utils/state_reducers.py create mode 100644 services/workflows/utils/token_usage.py diff --git a/examples/proposal_evaluation_example.py b/examples/proposal_evaluation_example.py index 45615822..61cae05f 100644 --- a/examples/proposal_evaluation_example.py +++ b/examples/proposal_evaluation_example.py @@ -35,7 +35,62 @@ async def create_test_proposal(dao_id: UUID) -> UUID: The ID of the created proposal """ # Create test parameters as a JSON object - parameters = "I Publius.btc will do a $FACES airdrop to as many bitcoin faces holders as possible. I will report back with a confirmation message and proof. Give me a shot." + # parameters = "I Publius.btc will do a $FACES airdrop to as many bitcoin faces holders as possible. I will report back with a confirmation message and proof. Give me a shot." + parameters = """ + +Proposal Title: $FACES Airdrop to Bitcoin Faces Holders with Transparent Execution and Community Engagement + +Proposal ID: [Generate a new UUID for submission] + +Proposer: Publius.btc + +Proposal Data: +I, Publius.btc, propose to execute a $FACES airdrop to Bitcoin Faces holders to boost community engagement and reward active participants in the DAO. The airdrop will distribute 10,000 $FACES tokens to eligible holders, with a clear execution plan, transparent verification, and measurable outcomes. The proposal aligns with the DAO’s mission to promote community activity and token utility. Below are the details: + +Objective: Distribute $FACES tokens to Bitcoin Faces holders to incentivize participation, increase governance engagement, and strengthen community ties. +Eligibility Criteria: +Holders of Bitcoin Faces NFTs as of a snapshot date (to be set 7 days after proposal approval). +Minimum holding: 1 Bitcoin Faces NFT. +Exclusion: Wallets flagged for suspicious activity (e.g., wash trading) based on on-chain analysis. +Execution Plan: +Snapshot: Conduct a blockchain snapshot of Bitcoin Faces holders on the specified date, using a third-party tool (e.g., Etherscan or equivalent for Bitcoin-based assets). +Distribution: Distribute 10 $FACES per eligible wallet, up to a total of 10,000 tokens, via a smart contract to ensure transparency and immutability. +Timeline: +Day 1–7: Proposal approval and snapshot preparation. +Day 8: Snapshot execution. +Day 9–14: Smart contract deployment and testing. +Day 15: Airdrop distribution. +Day 20: Post-airdrop report published. +Budget and Funding: +Total Cost: 10,000 $FACES tokens (valued at $0.10 per token based on current market price, totaling $1,000). +Additional Costs: $500 for smart contract development, auditing, and gas fees, to be funded from the DAO treasury. +Funding Request: 10,000 $FACES tokens + $500 in stablecoins (e.g., USDC) from the DAO treasury. +Cost Justification: The airdrop is cost-effective, targeting active holders to maximize engagement with minimal token dilution. The $500 covers secure execution to mitigate risks. +Verification and Transparency: +Publish the snapshot data and eligible wallet list on the DAO’s governance forum. +Share the smart contract address and transaction hashes on-chain for public verification. +Provide a detailed post-airdrop report within 5 days of distribution, including the number of wallets reached, tokens distributed, and community feedback. +Community Benefit: +Inclusivity: All Bitcoin Faces holders are eligible, ensuring broad participation. +Engagement: The airdrop will encourage holders to participate in governance and DAO activities, addressing low governance participation. +Stakeholder Consideration: The plan includes outreach to diverse community segments via the DAO’s social channels (e.g., Discord, X) to ensure awareness and feedback. +Alignment with DAO Priorities: +Promotes token utility and community engagement, core to the DAO’s mission. +Supports financial prudence by capping costs and providing ROI through increased governance participation (measurable via voting turnout post-airdrop). +Risk Mitigation: +Financial Risk: Limited to 10,000 $FACES and $500, with no ongoing costs. +Execution Risk: Smart contract audit to prevent vulnerabilities. +Inclusion Risk: Transparent eligibility criteria to avoid disputes. +Deliverables and ROI: +Deliverables: Snapshot data, smart contract, airdrop distribution, and post-airdrop report. +ROI: Expected 10% increase in governance participation (based on similar airdrop campaigns) and enhanced community sentiment, measurable via forum activity and X posts. +Addressing Past Concerns: +Unlike previous proposals, this includes a detailed execution plan, budget, and verification process. +Responds to feedback on inclusion by defining clear eligibility and outreach strategies. +Aligns with financial priorities by justifying costs and capping token usage. +Commitment: +I will execute the airdrop as outlined, provide regular updates on the DAO’s governance forum, and deliver a comprehensive report with proof of distribution. If the proposal is approved, I will collaborate with the DAO’s technical and community teams to ensure success. +""" # # Convert parameters to JSON string and then hex encode it # parameters_hex = "0x" + binascii.hexlify(parameters.encode("utf-8")).decode("utf-8") @@ -145,28 +200,21 @@ async def test_proposal_evaluation_workflow(): # Print the results print("\nEvaluation Results:") - print(f"Success: {result['success']}") - if result["success"]: - print(f"Approval: {result['evaluation']['approve']}") - print(f"Confidence: {result['evaluation']['confidence_score']}") - print(f"Reasoning: {result['evaluation']['reasoning']}") - print( - f"Total Token Usage by Model: {result.get('total_token_usage_by_model')}" - ) - print(f"Total Cost by Model: {result.get('total_cost_by_model')}") - print( - f"Total Overall Cost: ${result.get('total_overall_cost', 0.0):.4f}" - ) + print(f"Approval: {result['evaluation'].get('approve', False)}") + print(f"Confidence: {result['evaluation'].get('confidence_score', 0)}") + print( + f"Reasoning: {result['evaluation'].get('reasoning', 'No reasoning provided')}" + ) - if scenario["auto_vote"]: - print(f"Auto-voted: {result['auto_voted']}") - if result["vote_result"]: - print(f"Vote Result: {result['vote_result']}") - if result.get("tx_id"): - print(f"Transaction ID: {result['tx_id']}") - else: - print(f"Error: {result.get('error', 'Unknown error')}") + if "token_usage" in result.get("evaluation", {}): + print(f"Total Token Usage: {result['evaluation']['token_usage']}") + if scenario["auto_vote"]: + print(f"Auto-voted: {result.get('auto_voted', False)}") + if result.get("vote_result"): + print(f"Vote Result: {result['vote_result']}") + if result.get("tx_id"): + print(f"Transaction ID: {result['tx_id']}") except Exception as e: print(f"Error in scenario {scenario['name']}: {e}") diff --git a/plan.md b/plan.md new file mode 100644 index 00000000..6a7ae99d --- /dev/null +++ b/plan.md @@ -0,0 +1,556 @@ +Thank you for providing the existing directory structure. This gives me a clearer picture of the project’s organization and helps refine my recommendations for reorganizing the `proposal_evaluation.py` file. I’ll integrate the existing structure, ensure compatibility with the provided paths, and propose a reorganization that leverages the current setup while addressing the modularity and maintainability concerns of the large `proposal_evaluation.py` file. Below, I’ll revise my earlier recommendations to align with the existing structure and suggest where to place the split files, incorporating any additional considerations based on the context. + +--- + +### Revised Recommendations for Reorganizing `services/workflows/proposal_evaluation.py` + +The `proposal_evaluation.py` file is a large, monolithic file containing multiple agent classes, state management utilities, and workflow orchestration logic. To improve modularity, readability, and maintainability, I recommend splitting it into several files within the existing `services/workflows/` directory and creating a new `services/workflows/agents/` subdirectory for agent-specific logic. Since the project already has a `services/agents/` directory, I’ll evaluate whether to use it or keep agents under `services/workflows/agents/` to maintain workflow-specific context. + +#### Goals +1. **Modularize Agent Logic**: Move each agent (`CoreContextAgent`, `HistoricalContextAgent`, etc.) into separate files to reduce file size and improve maintainability. +2. **Centralize Utilities**: Extract state reducers and image processing logic to shared utility modules, leveraging existing `lib/utils.py` or a new `services/workflows/utils/` directory. +3. **Leverage Existing Structure**: Integrate with existing directories like `services/workflows/`, `tools/`, and `lib/` to avoid redundant restructuring. +4. **Abstract Repeated Code**: Address duplicated token usage tracking and image handling logic with mixins or helper functions. +5. **Maintain Compatibility**: Ensure imports align with existing modules like `services/workflows/capability_mixins.py`, `tools/tools_factory.py`, and `lib/utils.py`. + +#### Proposed Directory Structure Changes +Given the existing structure, I propose the following additions and modifications: + +``` +services/ +├── workflows/ +│ ├── __init__.py +│ ├── agents/ # New subdirectory for workflow-specific agents +│ │ ├── __init__.py +│ │ ├── core_context.py # CoreContextAgent +│ │ ├── historical_context.py # HistoricalContextAgent +│ │ ├── financial_context.py # FinancialContextAgent +│ │ ├── social_context.py # SocialContextAgent +│ │ ├── reasoning.py # ReasoningAgent +│ │ └── image_processing.py # ImageProcessingNode +│ ├── utils/ # New subdirectory for workflow utilities +│ │ ├── __init__.py +│ │ ├── state_reducers.py # State reducers (no_update_reducer, merge_dicts, set_once) +│ │ └── token_usage.py # TokenUsageMixin for token tracking +│ ├── base.py # Already exists, keep BaseWorkflow +│ ├── capability_mixins.py # Already exists, keep BaseCapabilityMixin +│ ├── hierarchical_workflows.py # Already exists, keep HierarchicalTeamWorkflow +│ ├── proposal_evaluation.py # Keep, but slim down to workflow orchestration +│ └── ... # Other existing workflow files +``` + +#### Why Not Use `services/agents/`? +The existing `services/agents/` directory might seem like a natural place for agent classes. However, since `proposal_evaluation.py` is tightly coupled with the `services/workflows/` module (e.g., it extends `BaseWorkflow` and uses `HierarchicalTeamWorkflow`), keeping agents under `services/workflows/agents/` ensures they remain in the workflow context. The `services/agents/` directory could be reserved for more generic or cross-workflow agents, but if you prefer to consolidate all agents there, I can adjust the recommendation accordingly. + +#### File Breakdown +1. **`services/workflows/agents/core_context.py`**: Contains `CoreContextAgent` class. +2. **`services/workflows/agents/historical_context.py`**: Contains `HistoricalContextAgent` class. +3. **`services/workflows/agents/financial_context.py`**: Contains `FinancialContextAgent` class. +4. **`services/workflows/agents/social_context.py`**: Contains `SocialContextAgent` class. +5. **`services/workflows/agents/reasoning.py`**: Contains `ReasoningAgent` class. +6. **`services/workflows/agents/image_processing.py`**: Contains `ImageProcessingNode` class, which handles image extraction and encoding. +7. **`services/workflows/utils/state_reducers.py`**: Contains state reducer functions (`no_update_reducer`, `merge_dicts`, `set_once`) and the `update_state_with_agent_result` helper. +8. **`services/workflows/utils/token_usage.py`**: Defines a `TokenUsageMixin` to handle repeated token usage tracking logic. +9. **`services/workflows/proposal_evaluation.py`**: Slimmed down to include only the `ProposalEvaluationWorkflow` class, `evaluate_proposal`, `get_proposal_evaluation_tools`, `evaluate_and_vote_on_proposal`, and `evaluate_proposal_only` functions. +10. **Shared Models**: Move `ProposalEvaluationState`, `ProposalEvaluationOutput`, `AgentOutput`, and `FinalOutput` to a shared models file, potentially `backend/models.py` (since it already exists) or a new `services/workflows/models.py`. + +--- + +### Detailed Changes + +#### 1. Move Agent Classes to `services/workflows/agents/` +Each agent (`CoreContextAgent`, etc.) will be moved to its own file under `services/workflows/agents/`. The structure will be similar to the example provided earlier, with imports updated to reflect the new paths. For instance, `core_context.py` would look like: + +```python +# services/workflows/agents/core_context.py +from typing import Any, Dict, Optional + +from langchain.prompts import PromptTemplate +from langchain_core.messages import HumanMessage + +from backend.models import AgentOutput # Move AgentOutput to backend/models.py +from services.workflows.capability_mixins import BaseCapabilityMixin +from services.workflows.utils.state_reducers import update_state_with_agent_result +from services.workflows.utils.token_usage import TokenUsageMixin +from services.workflows.vector_mixin import VectorRetrievalCapability +from lib.logger import configure_logger + +logger = configure_logger(__name__) + +class CoreContextAgent(BaseCapabilityMixin, VectorRetrievalCapability, TokenUsageMixin): + """Core Context Agent evaluates proposals against DAO mission and standards.""" + def __init__(self, config: Optional[Dict[str, Any]] = None): + BaseCapabilityMixin.__init__(self, config=config, state_key="core_score") + VectorRetrievalCapability.__init__(self) + TokenUsageMixin.__init__(self) + self.initialize() + self._initialize_vector_capability() + + def _initialize_vector_capability(self): + if not hasattr(self, "retrieve_from_vector_store"): + self.retrieve_from_vector_store = ( + VectorRetrievalCapability.retrieve_from_vector_store.__get__( + self, self.__class__ + ) + ) + self.logger.info("Initialized vector retrieval capability for CoreContextAgent") + + async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: + self._initialize_vector_capability() + proposal_id = state.get("proposal_id", "unknown") + proposal_content = state.get("proposal_data", "") + + dao_mission_text = self.config.get("dao_mission", "") + if not dao_mission_text: + try: + self.logger.debug(f"[DEBUG:CoreAgent:{proposal_id}] Attempting to retrieve DAO mission") + dao_mission = await self.retrieve_from_vector_store( + query="DAO mission statement and values", + collection_name=self.config.get("mission_collection", "dao_documents"), + limit=3, + ) + dao_mission_text = "\n".join([doc.page_content for doc in dao_mission]) + except Exception as e: + self.logger.error(f"[DEBUG:CoreAgent:{proposal_id}] Error retrieving DAO mission: {str(e)}") + dao_mission_text = "Elevate human potential through AI on Bitcoin" + + prompt = PromptTemplate( + input_variables=["proposal_data", "dao_mission"], + template="""Evaluate the proposal against the DAO's mission and values... + # (Rest of the prompt as in original file) + """ + ) + + try: + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + dao_mission=dao_mission_text or "Elevate human potential through AI on Bitcoin", + ) + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + proposal_images = state.get("proposal_images", []) + if proposal_images: + message_content_list.extend(proposal_images) + + llm_input_message = HumanMessage(content=message_content_list) + result = await self.llm.with_structured_output(AgentOutput).ainvoke([llm_input_message]) + result_dict = result.model_dump() + + token_usage_data = self.track_token_usage(formatted_prompt_text, result) + state["token_usage"]["core_agent"] = token_usage_data + result_dict["token_usage"] = token_usage_data + + update_state_with_agent_result(state, result_dict, "core") + return result_dict + except Exception as e: + self.logger.error(f"[DEBUG:CoreAgent:{proposal_id}] Error in core evaluation: {str(e)}") + return { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Evaluation failed due to error", + } +``` + +**Notes**: +- **Imports**: Updated to use `backend.models.AgentOutput`, `services.workflows.utils.state_reducers`, and `services.workflows.utils.token_usage`. +- **TokenUsageMixin**: Handles token usage tracking (see below). +- **Image Handling**: Relies on `state["proposal_images"]` set by `ImageProcessingNode`. + +Other agent files (`historical_context.py`, etc.) follow a similar pattern, with their respective prompts and logic. + +#### 2. Create `services/workflows/utils/token_usage.py` +To abstract the repeated token usage tracking logic, create a `TokenUsageMixin`: + +```python +# services/workflows/utils/token_usage.py +from typing import Any, Dict +from lib.utils import calculate_token_cost + +class TokenUsageMixin: + """Mixin for tracking token usage in LLM calls.""" + + def track_token_usage(self, prompt_text: str, result: Any) -> Dict[str, int]: + """Track token usage for an LLM invocation.""" + token_usage_data = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0} + + # Try to extract token usage from LLM + if ( + hasattr(self.llm, "_last_prompt_id") + and hasattr(self.llm, "client") + and hasattr(self.llm.client, "usage_by_prompt_id") + ): + last_prompt_id = self.llm._last_prompt_id + if last_prompt_id in self.llm.client.usage_by_prompt_id: + usage = self.llm.client.usage_by_prompt_id[last_prompt_id] + token_usage_data = { + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } + return token_usage_data + + # Fallback to estimation + llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") + token_count = len(prompt_text) // 4 # Simple estimation + token_usage_dict = {"input_tokens": token_count} + cost_result = calculate_token_cost(token_usage_dict, llm_model_name) + token_usage_data = { + "input_tokens": token_count, + "output_tokens": len(result.model_dump_json()) // 4, + "total_tokens": token_count + len(result.model_dump_json()) // 4, + "model_name": llm_model_name, + } + return token_usage_data +``` + +This mixin is used by all agents to standardize token usage tracking. + +#### 3. Move State Reducers to `services/workflows/utils/state_reducers.py` +Extract state management utilities: + +```python +# services/workflows/utils/state_reducers.py +from typing import Any, Dict, List, Optional +from lib.logger import configure_logger + +logger = configure_logger(__name__) + +def no_update_reducer(current: Any, new: List[Any]) -> Any: + """Reducer that prevents updates after initial value is set.""" + is_initial_empty_string = isinstance(current, str) and current == "" + if current is not None and not is_initial_empty_string: + return current + processed_new_values = new if isinstance(new, list) else [new] + for n_val in processed_new_values: + if n_val is not None: + return n_val + return current + +def merge_dicts(current: Optional[Dict], updates: List[Optional[Dict]]) -> Dict: + """Merge multiple dictionary updates into the current dictionary.""" + if current is None: + current = {} + if updates is None: + return current + if isinstance(updates, list): + for update in updates: + if update and isinstance(update, dict): + current.update(update) + elif isinstance(updates, dict): + current.update(updates) + return current + +def set_once(current: Any, updates: List[Any]) -> Any: + """Set the value once and prevent further updates.""" + if current is not None: + return current + if updates is None: + return None + if isinstance(updates, list): + for update in updates: + if update is not None: + return update + elif updates is not None: + return updates + return current + +def update_state_with_agent_result( + state: Dict[str, Any], agent_result: Dict[str, Any], agent_name: str +) -> Dict[str, Any]: + """Update state with agent result including summaries and flags.""" + logger.debug(f"[DEBUG:update_state:{agent_name}] Updating state with {agent_name}_score") + if agent_name in ["core", "historical", "financial", "social", "final"]: + score_dict = dict(agent_result) + if "token_usage" in score_dict: + del score_dict["token_usage"] + state[f"{agent_name}_score"] = score_dict + + if "summaries" not in state: + state["summaries"] = {} + if "summary" in agent_result and agent_result["summary"]: + state["summaries"][f"{agent_name}_score"] = agent_result["summary"] + + if "flags" not in state: + state["flags"] = [] + if "flags" in agent_result and isinstance(agent_result["flags"], list): + state["flags"].extend(agent_result["flags"]) + + return state +``` + +This centralizes state management logic, making it reusable across workflows. + +#### 4. Move Image Processing to `services/workflows/agents/image_processing.py` +Move `ImageProcessingNode` to its own file: + +```python +# services/workflows/agents/image_processing.py +import base64 +from typing import Any, Dict, List, Optional + +import httpx +from services.workflows.capability_mixins import BaseCapabilityMixin +from lib.logger import configure_logger +from lib.utils import extract_image_urls + +logger = configure_logger(__name__) + +class ImageProcessingNode(BaseCapabilityMixin): + """Workflow node to process proposal images: extract URLs, download, and base64 encode.""" + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__(config=config, state_key="proposal_images") + self.initialize() + + async def process(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: + proposal_id = state.get("proposal_id", "unknown") + proposal_data_str = state.get("proposal_data", "") + + if not proposal_data_str: + self.logger.info(f"[ImageProcessorNode:{proposal_id}] No proposal_data, skipping.") + return [] + + self.logger.info(f"[ImageProcessorNode:{proposal_id}] Starting image processing.") + image_urls = extract_image_urls(proposal_data_str) + + if not image_urls: + self.logger.info(f"[ImageProcessorNode:{proposal_id}] No image URLs found.") + return [] + + processed_images = [] + async with httpx.AsyncClient() as client: + for url in image_urls: + try: + response = await client.get(url, timeout=10.0) + response.raise_for_status() + image_data = base64.b64encode(response.content).decode("utf-8") + mime_type = "image/jpeg" + if url.lower().endswith((".jpg", ".jpeg")): + mime_type = "image/jpeg" + elif url.lower().endswith(".png"): + mime_type = "image/png" + elif url.lower().endswith(".gif"): + mime_type = "image/gif" + elif url.lower().endswith(".webp"): + mime_type = "image/webp" + + processed_images.append({ + "type": "image_url", + "image_url": {"url": f"data:{mime_type};base64,{image_data}"}, + }) + except Exception as e: + self.logger.error(f"[ImageProcessorNode:{proposal_id}] Error for {url}: {str(e)}") + return processed_images +``` + +This isolates image processing, which is reused by all agents. + +#### 5. Update `services/workflows/proposal_evaluation.py` +Slim down the file to focus on workflow orchestration and top-level functions: + +```python +# services/workflows/proposal_evaluation.py +from typing import Any, Dict, Optional + +from backend.factory import backend +from backend.models import Profile, UUID +from services.workflows.agents.core_context import CoreContextAgent +from services.workflows.agents.financial_context import FinancialContextAgent +from services.workflows.agents.historical_context import HistoricalContextAgent +from services.workflows.agents.image_processing import ImageProcessingNode +from services.workflows.agents.reasoning import ReasoningAgent +from services.workflows.agents.social_context import SocialContextAgent +from services.workflows.base import BaseWorkflow +from services.workflows.hierarchical_workflows import HierarchicalTeamWorkflow +from services.workflows.utils.state_reducers import update_state_with_agent_result +from tools.dao_ext_action_proposals import VoteOnActionProposalTool +from tools.tools_factory import filter_tools_by_names, initialize_tools +from lib.logger import configure_logger + +logger = configure_logger(__name__) + +class ProposalEvaluationState: + # Move to backend/models.py or services/workflows/models.py + pass + +class ProposalEvaluationOutput: + # Move to backend/models.py or services/workflows/models.py + pass + +class ProposalEvaluationWorkflow(BaseWorkflow[ProposalEvaluationState]): + """Main workflow for evaluating DAO proposals using a hierarchical team.""" + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + self.hierarchical_workflow = HierarchicalTeamWorkflow( + name="ProposalEvaluation", + config={ + "state_type": ProposalEvaluationState, + "recursion_limit": self.config.get("recursion_limit", 20), + }, + ) + + image_processor_agent = ImageProcessingNode(config=self.config) + core_agent = CoreContextAgent(self.config) + historical_agent = HistoricalContextAgent(self.config) + financial_agent = FinancialContextAgent(self.config) + social_agent = SocialContextAgent(self.config) + reasoning_agent = ReasoningAgent(self.config) + + self.hierarchical_workflow.add_sub_workflow("image_processor", image_processor_agent) + self.hierarchical_workflow.add_sub_workflow("core_agent", core_agent) + self.hierarchical_workflow.add_sub_workflow("historical_agent", historical_agent) + self.hierarchical_workflow.add_sub_workflow("financial_agent", financial_agent) + self.hierarchical_workflow.add_sub_workflow("social_agent", social_agent) + self.hierarchical_workflow.add_sub_workflow("reasoning_agent", reasoning_agent) + + self.hierarchical_workflow.set_entry_point("image_processor") + self.hierarchical_workflow.set_supervisor_logic(self._supervisor_logic) + self.hierarchical_workflow.set_halt_condition(self._halt_condition) + self.required_fields = ["proposal_id", "proposal_data"] + + def _supervisor_logic(self, state: ProposalEvaluationState) -> str | List[str]: + # (Supervisor logic as in original file) + pass + + def _halt_condition(self, state: ProposalEvaluationState) -> bool: + # (Halt condition logic as in original file) + pass + + def _create_prompt(self): + # (Prompt creation as in original file) + pass + + def _create_graph(self): + return self.hierarchical_workflow.build_graph() + + def _validate_state(self, state: ProposalEvaluationState) -> bool: + # (State validation as in original file) + pass + +async def evaluate_proposal(proposal_id: str, proposal_data: str, config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + # (evaluate_proposal function as in original file) + pass + +def get_proposal_evaluation_tools(profile: Optional[Profile] = None, agent_id: Optional[UUID] = None): + # (get_proposal_evaluation_tools function as in original file) + pass + +async def evaluate_and_vote_on_proposal( + proposal_id: UUID, wallet_id: Optional[UUID] = None, agent_id: Optional[UUID] = None, + auto_vote: bool = True, confidence_threshold: float = 0.7, dao_id: Optional[UUID] = None, + debug_level: int = 0 +) -> Dict: + # (evaluate_and_vote_on_proposal function as in original file) + pass + +async def evaluate_proposal_only( + proposal_id: UUID, wallet_id: Optional[UUID] = None, agent_id: Optional[UUID] = None, + dao_id: Optional[UUID] = None +) -> Dict: + # (evaluate_proposal_only function as in original file) + pass +``` + +**Notes**: +- **Slimmed Down**: Only includes workflow orchestration and top-level functions. +- **Agent Imports**: Updated to use `services.workflows.agents.*`. +- **Models**: Assumes `ProposalEvaluationState`, etc., are moved to `backend/models.py`. + +#### 6. Move Models to `backend/models.py` +Since `backend/models.py` already exists, append the Pydantic models and TypedDict: + +```python +# backend/models.py +from typing import Annotated, Any, Dict, List, Optional +from pydantic import BaseModel, Field + +# Existing models (UUID, ExtensionFilter, etc.) +# ... + +class ProposalEvaluationOutput(BaseModel): + approve: bool = Field(description="Decision: true to approve, false to reject") + confidence_score: float = Field(description="Confidence score (0.0-1.0)") + reasoning: str = Field(description="Reasoning behind the evaluation decision") + +class AgentOutput(BaseModel): + score: int = Field(description="Score from 0-100") + flags: List[str] = Field(description="Critical issues flagged") + summary: str = Field(description="Summary of findings") + +class FinalOutput(BaseModel): + score: int = Field(description="Final evaluation score") + decision: str = Field(description="Approve or Reject") + explanation: str = Field(description="Reasoning for decision") + +class ProposalEvaluationState(TypedDict): + proposal_id: Annotated[str, no_update_reducer] + proposal_data: Annotated[str, no_update_reducer] + core_score: Annotated[Optional[Dict[str, Any]], set_once] + historical_score: Annotated[Optional[Dict[str, Any]], set_once] + financial_score: Annotated[Optional[Dict[str, Any]], set_once] + social_score: Annotated[Optional[Dict[str, Any]], set_once] + final_score: Annotated[Optional[Dict[str, Any]], set_once] + flags: Annotated[List[str], append_list_fn] + summaries: Annotated[Dict[str, str], merge_dicts] + decision: Annotated[Optional[str], set_once] + halt: Annotated[bool, operator.or_] + token_usage: Annotated[Dict[str, Dict[str, int]], merge_dicts] + core_agent_invocations: Annotated[int, operator.add] + proposal_images: Annotated[Optional[List[Dict]], set_once] +``` + +Alternatively, create `services/workflows/models.py` if you prefer to keep workflow-specific models separate. + +--- + +### Additional Considerations +1. **Existing `lib/utils.py`**: The `extract_image_urls` and `calculate_token_cost` functions are already in `lib/utils.py`. Ensure `services/workflows/agents/image_processing.py` imports `extract_image_urls` correctly. +2. **Logging**: The `lib/logger.py` module is used for `configure_logger`. Consider adding a debug level configuration in `config.py` to control verbosity dynamically. +3. **Tool Integration**: The `get_proposal_evaluation_tools` function uses `tools/tools_factory.py`, which is correctly placed. No changes needed here. +4. **Documentation**: Update `docs/workflows.md` to reflect the new structure, detailing the `services/workflows/agents/` and `services/workflows/utils/` directories. +5. **Testing**: Ensure the `examples/proposal_evaluation_example.py` script is updated to use the new import paths (e.g., `from services.workflows.proposal_evaluation import evaluate_proposal`). + +--- + +### Example Workflow +To illustrate how the reorganized code works together, here’s how `ProposalEvaluationWorkflow` in `proposal_evaluation.py` integrates the agents: + +```python +# services/workflows/proposal_evaluation.py (snippet) +from services.workflows.agents.core_context import CoreContextAgent +from services.workflows.agents.image_processing import ImageProcessingNode +# ... other imports + +class ProposalEvaluationWorkflow(BaseWorkflow[ProposalEvaluationState]): + def __init__(self, config: Optional[Dict[str, Any]] = None): + super().__init__() + self.config = config or {} + self.hierarchical_workflow = HierarchicalTeamWorkflow( + name="ProposalEvaluation", + config={"state_type": ProposalEvaluationState, "recursion_limit": 20}, + ) + + # Add agents + self.hierarchical_workflow.add_sub_workflow("image_processor", ImageProcessingNode(self.config)) + self.hierarchical_workflow.add_sub_workflow("core_agent", CoreContextAgent(self.config)) + # ... other agents +``` + +The `ImageProcessingNode` processes images first, setting `state["proposal_images"]`, which agents like `CoreContextAgent` then use. + +--- + +### Benefits +- **Modularity**: Each agent and utility is in its own file, making it easier to maintain and test. +- **Reusability**: `TokenUsageMixin` and state reducers are reusable across workflows. +- **Clarity**: `proposal_evaluation.py` is focused on orchestration, reducing cognitive load. +- **Scalability**: New agents can be added to `services/workflows/agents/` without modifying the main workflow file. + +--- + +### Next Steps +- **Implementation**: Start by creating `services/workflows/agents/` and `services/workflows/utils/` directories, then move the agent classes and utilities as outlined. +- **Testing**: Run the `examples/proposal_evaluation_example.py` script to ensure all imports and functionality work. +- **Feedback**: If you prefer using `services/agents/` instead of `services/workflows/agents/`, or have specific constraints (e.g., avoiding new directories), let me know, and I can adjust the plan. +- **Further Refinement**: If you’d like me to provide the full content of another file (e.g., `historical_context.py` or `state_reducers.py`), I can do so. + +Would you like me to proceed with generating another specific file, or do you have additional preferences or constraints for the reorganization? \ No newline at end of file diff --git a/services/workflows/README.md b/services/workflows/README.md new file mode 100644 index 00000000..fb0755de --- /dev/null +++ b/services/workflows/README.md @@ -0,0 +1,66 @@ +# Workflows Module + +This module contains workflow implementations for various AI agent tasks. The primary focus is on providing structured, composable workflows that can coordinate multiple specialized agents. + +## Directory Structure + +``` +services/workflows/ +├── agents/ # Specialized agent implementations +│ ├── core_context.py # Evaluates proposals against DAO mission and values +│ ├── financial_context.py # Analyzes financial aspects of proposals +│ ├── historical_context.py # Evaluates proposals against historical context +│ ├── image_processing.py # Processes images in proposals +│ ├── reasoning.py # Makes final decisions based on other agents' input +│ └── social_context.py # Evaluates social/community aspects of proposals +│ +├── utils/ # Shared utilities for workflow support +│ ├── models.py # Shared Pydantic models +│ ├── state_reducers.py # State management utilities +│ └── token_usage.py # Token usage tracking utilities +│ +├── base.py # Base workflow infrastructure +├── capability_mixins.py # Capability mixins for agent extensions +├── hierarchical_workflows.py # Hierarchical team workflow infrastructure +├── planning_mixin.py # Planning capabilities +├── proposal_evaluation.py # Proposal evaluation workflow +├── vector_mixin.py # Vector retrieval capabilities +└── web_search_mixin.py # Web search capabilities +``` + +## Main Workflows + +### Proposal Evaluation Workflow + +The `ProposalEvaluationWorkflow` in `proposal_evaluation.py` is a hierarchical workflow that uses multiple specialized agents to evaluate a DAO proposal. The workflow: + +1. Processes any images in the proposal +2. Evaluates the proposal against the DAO's mission and values (core context) +3. Evaluates the proposal against historical precedents +4. Analyzes the financial aspects of the proposal +5. Evaluates the social context and community impacts +6. Makes a final decision combining all evaluations + +API functions: +- `evaluate_proposal(proposal_id, proposal_data, config)`: Evaluates a proposal +- `evaluate_and_vote_on_proposal(proposal_id, ...)`: Evaluates and automatically votes on a proposal +- `evaluate_proposal_only(proposal_id, ...)`: Evaluates a proposal without voting + +## Agents + +Each agent in the `agents/` directory specializes in a specific aspect of proposal evaluation: + +- `CoreContextAgent`: Evaluates alignment with DAO mission and values +- `HistoricalContextAgent`: Evaluates against past proposals and decisions +- `FinancialContextAgent`: Analyzes budget, costs, and financial impact +- `SocialContextAgent`: Evaluates community impact and social context +- `ReasoningAgent`: Makes the final decision based on all evaluations +- `ImageProcessingNode`: Handles image extraction and processing + +## Utilities + +The `utils/` directory contains shared utilities: + +- `state_reducers.py`: Contains functions for managing state in workflows +- `token_usage.py`: Provides the `TokenUsageMixin` for tracking LLM token usage +- `models.py`: Contains shared Pydantic models like `AgentOutput` and `FinalOutput` \ No newline at end of file diff --git a/services/workflows/agents/__init__.py b/services/workflows/agents/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/services/workflows/agents/core_context.py b/services/workflows/agents/core_context.py new file mode 100644 index 00000000..bece9a93 --- /dev/null +++ b/services/workflows/agents/core_context.py @@ -0,0 +1,144 @@ +from typing import Any, Dict, Optional + +from langchain.prompts import PromptTemplate +from langchain_core.messages import HumanMessage + +from lib.logger import configure_logger +from services.workflows.capability_mixins import BaseCapabilityMixin +from services.workflows.utils.models import AgentOutput +from services.workflows.utils.state_reducers import update_state_with_agent_result +from services.workflows.utils.token_usage import TokenUsageMixin +from services.workflows.vector_mixin import VectorRetrievalCapability + +logger = configure_logger(__name__) + + +class CoreContextAgent(BaseCapabilityMixin, VectorRetrievalCapability, TokenUsageMixin): + """Core Context Agent evaluates proposals against DAO mission and standards.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the Core Context Agent. + + Args: + config: Optional configuration dictionary + """ + BaseCapabilityMixin.__init__(self, config=config, state_key="core_score") + VectorRetrievalCapability.__init__(self) + TokenUsageMixin.__init__(self) + self.initialize() + self._initialize_vector_capability() + + def _initialize_vector_capability(self): + """Initialize the vector retrieval capability if not already initialized.""" + if not hasattr(self, "retrieve_from_vector_store"): + self.retrieve_from_vector_store = ( + VectorRetrievalCapability.retrieve_from_vector_store.__get__( + self, self.__class__ + ) + ) + self.logger.info( + "Initialized vector retrieval capability for CoreContextAgent" + ) + + async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: + """Process the proposal against core DAO context. + + Args: + state: The current workflow state + + Returns: + Dictionary containing evaluation results + """ + self._initialize_vector_capability() + proposal_id = state.get("proposal_id", "unknown") + proposal_content = state.get("proposal_data", "") + + # Initialize token usage tracking in state if not present + if "token_usage" not in state: + state["token_usage"] = {} + + # Retrieve or use provided DAO mission text + dao_mission_text = self.config.get("dao_mission", "") + if not dao_mission_text: + try: + self.logger.debug( + f"[DEBUG:CoreAgent:{proposal_id}] Attempting to retrieve DAO mission" + ) + dao_mission = await self.retrieve_from_vector_store( + query="DAO mission statement and values", + collection_name=self.config.get( + "mission_collection", "dao_documents" + ), + limit=3, + ) + dao_mission_text = "\n".join([doc.page_content for doc in dao_mission]) + except Exception as e: + self.logger.error( + f"[DEBUG:CoreAgent:{proposal_id}] Error retrieving DAO mission: {str(e)}" + ) + dao_mission_text = "Elevate human potential through AI on Bitcoin" + + prompt = PromptTemplate( + input_variables=["proposal_data", "dao_mission"], + template="""Evaluate the proposal against the DAO's mission and values. + +# Context +You are evaluating a proposal for a DAO that focuses on: {dao_mission} + +# Proposal Data +{proposal_data} + +# Task +Score this proposal from 0-100 based on: +1. Alignment with DAO mission (40%) +2. Clarity of proposal (20%) +3. Feasibility and practicality (20%) +4. Community benefit (20%) + +# Output Format +Provide: +- Score (0-100) +- List of any critical issues or red flags +- Brief summary of your evaluation + +Only return a JSON object with these three fields: score, flags (array), and summary.""", + ) + + try: + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + dao_mission=dao_mission_text + or "Elevate human potential through AI on Bitcoin", + ) + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + + # Add any proposal images to the message + proposal_images = state.get("proposal_images", []) + if proposal_images: + message_content_list.extend(proposal_images) + + llm_input_message = HumanMessage(content=message_content_list) + + # Get structured output from the LLM + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + result_dict = result.model_dump() + + # Track token usage + token_usage_data = self.track_token_usage(formatted_prompt_text, result) + state["token_usage"]["core_agent"] = token_usage_data + result_dict["token_usage"] = token_usage_data + + # Update state with agent result + update_state_with_agent_result(state, result_dict, "core") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:CoreAgent:{proposal_id}] Error in core evaluation: {str(e)}" + ) + return { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Evaluation failed due to error", + } diff --git a/services/workflows/agents/financial_context.py b/services/workflows/agents/financial_context.py new file mode 100644 index 00000000..1302b589 --- /dev/null +++ b/services/workflows/agents/financial_context.py @@ -0,0 +1,130 @@ +from typing import Any, Dict, List, Optional + +from langchain.prompts import PromptTemplate +from langchain_core.messages import HumanMessage +from pydantic import BaseModel, Field + +from lib.logger import configure_logger +from services.workflows.capability_mixins import BaseCapabilityMixin +from services.workflows.utils.models import AgentOutput +from services.workflows.utils.state_reducers import update_state_with_agent_result +from services.workflows.utils.token_usage import TokenUsageMixin + +logger = configure_logger(__name__) + + +class FinancialContextAgent(BaseCapabilityMixin, TokenUsageMixin): + """Financial Context Agent evaluates financial aspects of proposals.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the Financial Context Agent. + + Args: + config: Optional configuration dictionary + """ + BaseCapabilityMixin.__init__(self, config=config, state_key="financial_score") + TokenUsageMixin.__init__(self) + self.initialize() + + async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: + """Process the proposal's financial aspects. + + Args: + state: The current workflow state + + Returns: + Dictionary containing financial evaluation results + """ + proposal_id = state.get("proposal_id", "unknown") + proposal_content = state.get("proposal_data", "") + + # Initialize token usage tracking in state if not present + if "token_usage" not in state: + state["token_usage"] = {} + + # Get DAO financial context from config if available + dao_financial_context = self.config.get("dao_financial_context", {}) + treasury_balance = dao_financial_context.get("treasury_balance", "unknown") + monthly_budget = dao_financial_context.get("monthly_budget", "unknown") + funding_priorities = dao_financial_context.get("funding_priorities", []) + financial_constraints = dao_financial_context.get("financial_constraints", []) + + # Format financial context for the prompt + financial_context_text = f""" +Treasury Balance: {treasury_balance} +Monthly Budget: {monthly_budget} +Funding Priorities: {', '.join(funding_priorities) if funding_priorities else 'Not specified'} +Financial Constraints: {', '.join(financial_constraints) if financial_constraints else 'Not specified'} +""" + + prompt = PromptTemplate( + input_variables=["proposal_data", "financial_context"], + template="""Evaluate the financial aspects of this proposal for the DAO. + +# Proposal +{proposal_data} + +# DAO Financial Context +{financial_context} + +# Task +Score this proposal from 0-100 based on: +1. Cost-effectiveness and value for money (40%) +2. Budget accuracy and detail (20%) +3. Financial risk assessment (20%) +4. Alignment with DAO's financial priorities (20%) + +When analyzing, consider: +- Is the proposal requesting a reasonable amount? +- Are costs well-justified with clear deliverables? +- Are there hidden or underestimated costs? +- Does it align with the DAO's financial priorities? +- What is the potential ROI (Return on Investment)? +- Are there financial risks or dependencies? + +# Output Format +Provide: +- Score (0-100) +- List of any critical financial issues or red flags +- Brief summary of your financial evaluation + +Only return a JSON object with these three fields: score, flags (array), and summary.""", + ) + + try: + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + financial_context=financial_context_text, + ) + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + + # Add any proposal images to the message + proposal_images = state.get("proposal_images", []) + if proposal_images: + message_content_list.extend(proposal_images) + + llm_input_message = HumanMessage(content=message_content_list) + + # Get structured output from the LLM + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + result_dict = result.model_dump() + + # Track token usage + token_usage_data = self.track_token_usage(formatted_prompt_text, result) + state["token_usage"]["financial_agent"] = token_usage_data + result_dict["token_usage"] = token_usage_data + + # Update state with agent result + update_state_with_agent_result(state, result_dict, "financial") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:FinancialAgent:{proposal_id}] Error in financial evaluation: {str(e)}" + ) + return { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Financial evaluation failed due to error", + } diff --git a/services/workflows/agents/historical_context.py b/services/workflows/agents/historical_context.py new file mode 100644 index 00000000..70c34121 --- /dev/null +++ b/services/workflows/agents/historical_context.py @@ -0,0 +1,159 @@ +from typing import Any, Dict, List, Optional + +from langchain.prompts import PromptTemplate +from langchain_core.messages import HumanMessage +from pydantic import BaseModel, Field + +from lib.logger import configure_logger +from services.workflows.capability_mixins import BaseCapabilityMixin +from services.workflows.utils.models import AgentOutput +from services.workflows.utils.state_reducers import update_state_with_agent_result +from services.workflows.utils.token_usage import TokenUsageMixin +from services.workflows.vector_mixin import VectorRetrievalCapability + +logger = configure_logger(__name__) + + +class HistoricalContextAgent( + BaseCapabilityMixin, VectorRetrievalCapability, TokenUsageMixin +): + """Historical Context Agent evaluates proposals against DAO historical context and past decisions.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the Historical Context Agent. + + Args: + config: Optional configuration dictionary + """ + BaseCapabilityMixin.__init__(self, config=config, state_key="historical_score") + VectorRetrievalCapability.__init__(self) + TokenUsageMixin.__init__(self) + self.initialize() + self._initialize_vector_capability() + + def _initialize_vector_capability(self): + """Initialize the vector retrieval capability if not already initialized.""" + if not hasattr(self, "retrieve_from_vector_store"): + self.retrieve_from_vector_store = ( + VectorRetrievalCapability.retrieve_from_vector_store.__get__( + self, self.__class__ + ) + ) + self.logger.info( + "Initialized vector retrieval capability for HistoricalContextAgent" + ) + + async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: + """Process the proposal against historical context. + + Args: + state: The current workflow state + + Returns: + Dictionary containing evaluation results + """ + self._initialize_vector_capability() + proposal_id = state.get("proposal_id", "unknown") + proposal_content = state.get("proposal_data", "") + + # Initialize token usage tracking in state if not present + if "token_usage" not in state: + state["token_usage"] = {} + + # Retrieve similar past proposals if possible + past_proposals_text = "" + try: + self.logger.debug( + f"[DEBUG:HistoricalAgent:{proposal_id}] Retrieving similar past proposals" + ) + similar_proposals = await self.retrieve_from_vector_store( + query=proposal_content[ + :1000 + ], # Use first 1000 chars of proposal as query + collection_name=self.config.get( + "proposals_collection", "past_proposals" + ), + limit=3, + ) + past_proposals_text = "\n\n".join( + [ + f"Past Proposal {i+1}:\n{doc.page_content}" + for i, doc in enumerate(similar_proposals) + ] + ) + except Exception as e: + self.logger.error( + f"[DEBUG:HistoricalAgent:{proposal_id}] Error retrieving similar proposals: {str(e)}" + ) + past_proposals_text = "No similar past proposals available." + + prompt = PromptTemplate( + input_variables=["proposal_data", "past_proposals"], + template="""Evaluate this proposal in the context of the DAO's past decisions and similar proposals. + +# Current Proposal +{proposal_data} + +# Similar Past Proposals +{past_proposals} + +# Task +Evaluate whether this proposal: +1. Is a duplicate of past proposals (40%) +2. Has addressed issues raised in similar past proposals (30%) +3. Shows consistency with past approved proposals (30%) + +Score this proposal from 0-100 based on the criteria above. +- 0-20: Exact duplicate or contradicts previous decisions +- 21-50: Significant overlap without addressing past concerns +- 51-70: Similar to past proposals but with improvements +- 71-90: Builds well on past work with few concerns +- 91-100: Unique proposal or excellent improvement on past proposals + +# Output Format +Provide: +- Score (0-100) +- List of any critical issues or red flags +- Brief summary of your evaluation + +Only return a JSON object with these three fields: score, flags (array), and summary.""", + ) + + try: + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + past_proposals=past_proposals_text + or "No past proposals available for comparison.", + ) + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + + # Add any proposal images to the message + proposal_images = state.get("proposal_images", []) + if proposal_images: + message_content_list.extend(proposal_images) + + llm_input_message = HumanMessage(content=message_content_list) + + # Get structured output from the LLM + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + result_dict = result.model_dump() + + # Track token usage + token_usage_data = self.track_token_usage(formatted_prompt_text, result) + state["token_usage"]["historical_agent"] = token_usage_data + result_dict["token_usage"] = token_usage_data + + # Update state with agent result + update_state_with_agent_result(state, result_dict, "historical") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:HistoricalAgent:{proposal_id}] Error in historical evaluation: {str(e)}" + ) + return { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Historical evaluation failed due to error", + } diff --git a/services/workflows/agents/image_processing.py b/services/workflows/agents/image_processing.py new file mode 100644 index 00000000..bd129e5b --- /dev/null +++ b/services/workflows/agents/image_processing.py @@ -0,0 +1,95 @@ +import base64 +from typing import Any, Dict, List, Optional + +import httpx + +from lib.logger import configure_logger +from lib.utils import extract_image_urls +from services.workflows.capability_mixins import BaseCapabilityMixin + +logger = configure_logger(__name__) + + +class ImageProcessingNode(BaseCapabilityMixin): + """Workflow node to process proposal images: extract URLs, download, and base64 encode.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the image processing node. + + Args: + config: Optional configuration dictionary + """ + super().__init__(config=config, state_key="proposal_images") + self.initialize() + + async def process(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: + """Process images in the proposal data. + + Args: + state: The current workflow state + + Returns: + List of dictionaries containing processed images in a format suitable for LLM + """ + proposal_id = state.get("proposal_id", "unknown") + proposal_data_str = state.get("proposal_data", "") + + if not proposal_data_str: + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] No proposal_data, skipping." + ) + # Return empty list to ensure state is updated + return [] + + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] Starting image processing." + ) + image_urls = extract_image_urls(proposal_data_str) + + if not image_urls: + self.logger.info(f"[ImageProcessorNode:{proposal_id}] No image URLs found.") + # Return empty list explicitly to ensure state is updated + return [] + + processed_images = [] + async with httpx.AsyncClient() as client: + for url in image_urls: + try: + self.logger.debug( + f"[ImageProcessorNode:{proposal_id}] Processing image URL: {url}" + ) + response = await client.get(url, timeout=10.0) + response.raise_for_status() + image_data = base64.b64encode(response.content).decode("utf-8") + + # Determine MIME type from URL extension + mime_type = "image/jpeg" # Default + if url.lower().endswith((".jpg", ".jpeg")): + mime_type = "image/jpeg" + elif url.lower().endswith(".png"): + mime_type = "image/png" + elif url.lower().endswith(".gif"): + mime_type = "image/gif" + elif url.lower().endswith(".webp"): + mime_type = "image/webp" + + processed_images.append( + { + "type": "image_url", + "image_url": { + "url": f"data:{mime_type};base64,{image_data}" + }, + } + ) + self.logger.debug( + f"[ImageProcessorNode:{proposal_id}] Successfully processed image: {url}" + ) + except Exception as e: + self.logger.error( + f"[ImageProcessorNode:{proposal_id}] Error processing {url}: {str(e)}" + ) + + self.logger.info( + f"[ImageProcessorNode:{proposal_id}] Processed {len(processed_images)} images." + ) + return processed_images diff --git a/services/workflows/agents/reasoning.py b/services/workflows/agents/reasoning.py new file mode 100644 index 00000000..30aa4289 --- /dev/null +++ b/services/workflows/agents/reasoning.py @@ -0,0 +1,297 @@ +import asyncio +from typing import Any, Dict, List, Optional + +from langchain.prompts import PromptTemplate +from langchain_core.messages import HumanMessage +from langchain_openai import ChatOpenAI +from langgraph.graph import StateGraph +from pydantic import BaseModel, Field + +from lib.logger import configure_logger +from services.workflows.capability_mixins import BaseCapabilityMixin +from services.workflows.chat import StreamingCallbackHandler +from services.workflows.planning_mixin import PlanningCapability +from services.workflows.utils.models import FinalOutput +from services.workflows.utils.state_reducers import update_state_with_agent_result +from services.workflows.utils.token_usage import TokenUsageMixin + +logger = configure_logger(__name__) + + +class FinalOutput(BaseModel): + """Schema for final decision output.""" + + score: int = Field(..., description="Final score between 0-100") + decision: str = Field(..., description="Approve or Reject") + explanation: str = Field(..., description="Reasoning for the decision") + + +class ReasoningAgent(BaseCapabilityMixin, PlanningCapability, TokenUsageMixin): + """Reasoning Agent that makes the final evaluation decision based on other agents' inputs.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the Reasoning Agent. + + Args: + config: Optional configuration dictionary + """ + BaseCapabilityMixin.__init__(self, config=config, state_key="final_score") + TokenUsageMixin.__init__(self) + + # Create a dummy queue for the StreamingCallbackHandler + self.dummy_queue = asyncio.Queue() + # Create callback handler and planning_llm for PlanningCapability + # These won't be used since we don't actually use the planning functionality + self.dummy_callback = StreamingCallbackHandler(queue=self.dummy_queue) + self.dummy_llm = ChatOpenAI() + + # Pass the required arguments to PlanningCapability.__init__ + PlanningCapability.__init__( + self, callback_handler=self.dummy_callback, planning_llm=self.dummy_llm + ) + + self.initialize() + self._initialize_planning_capability() + + # Configuration for thresholds + self.default_threshold = config.get("approval_threshold", 70) + self.veto_threshold = config.get("veto_threshold", 30) + self.consensus_threshold = config.get("consensus_threshold", 10) + self.confidence_adjustment = config.get("confidence_adjustment", 0.15) + + def _initialize_planning_capability(self): + """Initialize the planning capability if not already initialized.""" + if not hasattr(self, "planning"): + # We don't actually use the planning method, just create a dummy placeholder + self.planning = lambda *args, **kwargs: None + self.logger.info("Initialized dummy planning capability for ReasoningAgent") + + def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: + """Hook to integrate with a particular graph.""" + pass + + async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: + """Process all agent scores and make a final decision. + + Args: + state: The current workflow state with all agent results + + Returns: + Dictionary containing the final evaluation decision + """ + self._initialize_planning_capability() + proposal_id = state.get("proposal_id", "unknown") + + # Add diagnostic logging + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] Starting reasoning agent process" + ) + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] State keys: {list(state.keys())}" + ) + + # Initialize token usage tracking in state if not present + if "token_usage" not in state: + state["token_usage"] = {} + + # Helper function to safely get scores + def safe_get_score(value, default=0): + if isinstance(value, dict) and "score" in value: + return value["score"] + return default + + # Get individual scores + core_score = safe_get_score(state.get("core_score"), 0) + historical_score = safe_get_score(state.get("historical_score"), 0) + financial_score = safe_get_score(state.get("financial_score"), 0) + social_score = safe_get_score(state.get("social_score"), 0) + + # Get agent summaries + core_summary = state.get("summaries", {}).get( + "core_score", "No core context evaluation available." + ) + historical_summary = state.get("summaries", {}).get( + "historical_score", "No historical context evaluation available." + ) + financial_summary = state.get("summaries", {}).get( + "financial_score", "No financial evaluation available." + ) + social_summary = state.get("summaries", {}).get( + "social_score", "No social context evaluation available." + ) + + # Get flags + flags = state.get("flags", []) + flags_text = ( + "\n".join([f"- {flag}" for flag in flags]) + if flags + else "No flags identified." + ) + + # Calculate score statistics + scores = [ + ("Core", core_score), + ("Historical", historical_score), + ("Financial", financial_score), + ("Social", social_score), + ] + valid_scores = [score for _, score in scores if score > 0] + + if not valid_scores: + self.logger.error( + f"[DEBUG:ReasoningAgent:{proposal_id}] No valid scores found!" + ) + return { + "score": 0, + "decision": "Reject", + "explanation": "Unable to evaluate due to missing agent scores.", + "flags": ["Critical: No valid evaluation scores available."], + } + + # Calculate metrics + avg_score = sum(valid_scores) / len(valid_scores) + min_score = min(valid_scores) + max_score = max(valid_scores) + score_range = max_score - min_score + + # Detect if any agent has a veto-level score + has_veto = any(score <= self.veto_threshold for score in valid_scores) + + # Check for consensus or disagreement + has_consensus = score_range <= self.consensus_threshold + has_disagreement = score_range >= 30 + + # Format agent evaluations for prompt + agent_evaluations = f""" +Core Context Evaluation: +Score: {core_score}/100 +Summary: {core_summary} + +Historical Context Evaluation: +Score: {historical_score}/100 +Summary: {historical_summary} + +Financial Evaluation: +Score: {financial_score}/100 +Summary: {financial_summary} + +Social Context Evaluation: +Score: {social_score}/100 +Summary: {social_summary} + +Flags Identified: +{flags_text} + +Score Statistics: +- Average Score: {avg_score:.2f} +- Minimum Score: {min_score} +- Maximum Score: {max_score} +- Score Range: {score_range} +""" + + prompt = PromptTemplate( + input_variables=["agent_evaluations", "approval_threshold"], + template="""Analyze the specialized agent evaluations and make a final decision on this proposal. + +# Agent Evaluations +{agent_evaluations} + +# Decision Guidelines +- The default threshold for approval is {approval_threshold}/100 +- A proposal with any agent score below 30 should typically be rejected +- A proposal with high consensus (small range between scores) increases confidence +- A proposal with high disagreement (large range between scores) decreases confidence +- Consider the reasoning behind each agent's score, not just the numerical value +- Critical flags should be weighted heavily in your decision + +# Task +1. Analyze the evaluations from all agents +2. Consider the significance of any critical flags +3. Weigh the relative importance of different evaluation dimensions +4. Make a final decision (Approve or Reject) with a final score +5. Provide clear reasoning for your decision + +# Output Format +Your response should be a JSON object with: +- score: A final score from 0-100 +- decision: Either "Approve" or "Reject" +- explanation: Your reasoning for the decision + +Return only the JSON object with these three fields.""", + ) + + try: + formatted_prompt_text = prompt.format( + agent_evaluations=agent_evaluations, + approval_threshold=self.default_threshold, + ) + + llm_input_message = HumanMessage(content=formatted_prompt_text) + + # Get structured output from the LLM + result = await self.llm.with_structured_output(FinalOutput).ainvoke( + [llm_input_message] + ) + result_dict = result.model_dump() + + # Track token usage + token_usage_data = self.track_token_usage(formatted_prompt_text, result) + state["token_usage"]["reasoning_agent"] = token_usage_data + result_dict["token_usage"] = token_usage_data + + # Add calculated metrics to result for transparency + result_dict["metrics"] = { + "avg_score": avg_score, + "min_score": min_score, + "max_score": max_score, + "score_range": score_range, + "has_veto": has_veto, + "has_consensus": has_consensus, + "has_disagreement": has_disagreement, + } + + # Calculate confidence based on consensus/disagreement + confidence = 0.7 # Base confidence + if has_consensus: + confidence += self.confidence_adjustment + if has_disagreement: + confidence -= self.confidence_adjustment + if has_veto: + confidence -= 0.3 + + result_dict["confidence"] = max( + 0.1, min(1.0, confidence) + ) # Clamp to [0.1, 1.0] + + # Add flags to the result + result_dict["flags"] = flags + + # Update state with agent result + update_state_with_agent_result(state, result_dict, "final") + + # Add final diagnostic logging + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] Successfully completed reasoning" + ) + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] Returning result with decision: {result_dict.get('decision')}" + ) + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] Updated state now has keys: {list(state.keys())}" + ) + if "final_score" in state: + self.logger.info( + f"[DEBUG:ReasoningAgent:{proposal_id}] final_score type: {type(state.get('final_score'))}" + ) + + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:ReasoningAgent:{proposal_id}] Error in reasoning: {str(e)}" + ) + return { + "score": 50, + "decision": "Reject", + "explanation": f"Evaluation failed due to error: {str(e)}", + "flags": [f"Error: {str(e)}"], + } diff --git a/services/workflows/agents/social_context.py b/services/workflows/agents/social_context.py new file mode 100644 index 00000000..68e687e7 --- /dev/null +++ b/services/workflows/agents/social_context.py @@ -0,0 +1,209 @@ +from typing import Any, Dict, List, Optional + +from langchain.prompts import PromptTemplate +from langchain_core.messages import HumanMessage +from pydantic import BaseModel, Field + +from lib.logger import configure_logger +from services.workflows.capability_mixins import BaseCapabilityMixin +from services.workflows.utils.models import AgentOutput +from services.workflows.utils.state_reducers import update_state_with_agent_result +from services.workflows.utils.token_usage import TokenUsageMixin +from services.workflows.web_search_mixin import WebSearchCapability + +logger = configure_logger(__name__) + + +class SocialContextAgent(BaseCapabilityMixin, WebSearchCapability, TokenUsageMixin): + """Social Context Agent evaluates social and community aspects of proposals.""" + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """Initialize the Social Context Agent. + + Args: + config: Optional configuration dictionary + """ + BaseCapabilityMixin.__init__(self, config=config, state_key="social_score") + WebSearchCapability.__init__(self) + TokenUsageMixin.__init__(self) + self.initialize() + self._initialize_web_search_capability() + + def _initialize_web_search_capability(self): + """Initialize the web search capability if not already initialized.""" + if not hasattr(self, "web_search"): + self.web_search = WebSearchCapability.web_search.__get__( + self, self.__class__ + ) + self.logger.info("Initialized web search capability for SocialContextAgent") + + async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: + """Process the proposal's social context. + + Args: + state: The current workflow state + + Returns: + Dictionary containing social evaluation results + """ + self._initialize_web_search_capability() + proposal_id = state.get("proposal_id", "unknown") + proposal_content = state.get("proposal_data", "") + + # Initialize token usage tracking in state if not present + if "token_usage" not in state: + state["token_usage"] = {} + + # Extract key concepts for web search + search_results = [] + try: + # First try to identify key search terms + key_concepts_prompt = PromptTemplate( + input_variables=["proposal"], + template="""Extract 2-3 key topics from this proposal that would benefit from external information: + +{proposal} + +Return only the key topics as a comma-separated list. Be specific and concise. +""", + ) + + key_concepts_result = await self.llm.ainvoke( + key_concepts_prompt.format(proposal=proposal_content[:1500]) + ) + + # Use these concepts for web search + key_concepts = key_concepts_result.content.strip() + self.logger.info( + f"[DEBUG:SocialAgent:{proposal_id}] Extracted key concepts: {key_concepts}" + ) + + if key_concepts: + dao_name = self.config.get("dao_name", "DAO") + search_query = ( + f"{key_concepts} {dao_name} bitcoin community perspective" + ) + self.logger.info( + f"[DEBUG:SocialAgent:{proposal_id}] Searching: {search_query}" + ) + + search_results, token_usage = await self.web_search( + query=search_query, + num_results=3, + ) + except Exception as e: + self.logger.error( + f"[DEBUG:SocialAgent:{proposal_id}] Error in web search: {str(e)}" + ) + search_results = [] + + # Format search results for inclusion in the prompt + search_results_text = "" + if search_results: + search_results_text = "Web search results relevant to this proposal:\n\n" + for i, doc in enumerate(search_results): + page_content = doc.get("page_content", "No content available") + source_urls = doc.get("metadata", {}).get("source_urls", []) + + if source_urls: + for j, source in enumerate(source_urls): + search_results_text += ( + f"Source {i+1}.{j+1}: {source.get('title', 'Unknown')}\n" + ) + search_results_text += f"URL: {source.get('url', 'Unknown')}\n" + + search_results_text += f"Summary: {page_content[:300]}...\n\n" + else: + search_results_text = "No relevant web search results available.\n" + + # Get community info from config + community_context = self.config.get("community_context", {}) + community_size = community_context.get("community_size", "Unknown") + active_members = community_context.get("active_members", "Unknown") + governance_participation = community_context.get( + "governance_participation", "Low" + ) + recent_sentiment = community_context.get("recent_sentiment", "Neutral") + + community_info = f""" +Community Size: {community_size} +Active Members: {active_members} +Governance Participation: {governance_participation} +Recent Community Sentiment: {recent_sentiment} +""" + + prompt = PromptTemplate( + input_variables=["proposal_data", "search_results", "community_info"], + template="""Evaluate the social impact and community aspects of this proposal. + +# Proposal +{proposal_data} + +# Community Information +{community_info} + +# External Context +{search_results} + +# Task +Score this proposal from 0-100 based on: +1. Community benefit and inclusion (40%) +2. Alignment with community values and interests (30%) +3. Potential for community engagement (20%) +4. Consideration of diverse stakeholders (10%) + +When analyzing, consider: +- Will this proposal benefit the broader community or just a few members? +- Is there likely community support or opposition? +- Does it foster inclusivity and participation? +- Does it align with the community's values and interests? +- Could it cause controversy or division? +- Does it consider the needs of diverse stakeholders? + +# Output Format +Provide: +- Score (0-100) +- List of any critical social issues or red flags +- Brief summary of your social evaluation + +Only return a JSON object with these three fields: score, flags (array), and summary.""", + ) + + try: + formatted_prompt_text = prompt.format( + proposal_data=proposal_content, + search_results=search_results_text, + community_info=community_info, + ) + message_content_list = [{"type": "text", "text": formatted_prompt_text}] + + # Add any proposal images to the message + proposal_images = state.get("proposal_images", []) + if proposal_images: + message_content_list.extend(proposal_images) + + llm_input_message = HumanMessage(content=message_content_list) + + # Get structured output from the LLM + result = await self.llm.with_structured_output(AgentOutput).ainvoke( + [llm_input_message] + ) + result_dict = result.model_dump() + + # Track token usage + token_usage_data = self.track_token_usage(formatted_prompt_text, result) + state["token_usage"]["social_agent"] = token_usage_data + result_dict["token_usage"] = token_usage_data + + # Update state with agent result + update_state_with_agent_result(state, result_dict, "social") + return result_dict + except Exception as e: + self.logger.error( + f"[DEBUG:SocialAgent:{proposal_id}] Error in social evaluation: {str(e)}" + ) + return { + "score": 50, + "flags": [f"Error: {str(e)}"], + "summary": "Social evaluation failed due to error", + } diff --git a/services/workflows/base.py b/services/workflows/base.py index 856e00fe..2b0d9cd2 100644 --- a/services/workflows/base.py +++ b/services/workflows/base.py @@ -179,55 +179,40 @@ def get_missing_fields(self, state: StateType) -> List[str]: ] async def execute(self, initial_state: StateType) -> Dict: - """Execute the workflow. + """Execute the workflow with the given initial state. Args: - initial_state: The initial state for the workflow + initial_state: Initial state for the workflow Returns: - The final state after execution - - Raises: - ValidationError: If the initial state is invalid - ExecutionError: If the workflow execution fails + Final state after workflow execution """ + # Validate state + if not self._validate_state(initial_state): + error_message = f"Invalid initial state: {initial_state}" + self.logger.error(error_message) + missing = self.get_missing_fields(initial_state) + if missing: + error_message += f" Missing fields: {', '.join(missing)}" + raise ValidationError(error_message) + + # Create runtime workflow + app = self._create_graph() + + self.logger.debug( + f"[DEBUG:Workflow:{self.__class__.__name__}] State before ain_invoke: {json.dumps(initial_state, indent=2, default=str)}" + ) try: - # Validate state - is_valid = self._validate_state(initial_state) - if not is_valid: - missing_fields = self.get_missing_fields(initial_state) - error_msg = ( - f"Invalid initial state. Missing required fields: {missing_fields}" - ) - self.logger.error(error_msg) - raise ValidationError(error_msg, {"missing_fields": missing_fields}) - - # Create and compile the graph - graph = self._create_graph() - if hasattr(graph, "compile"): - app = graph.compile() - else: - # Graph is already compiled - app = graph - # Execute the workflow - self.logger.info(f"Executing workflow {self.__class__.__name__}") - self.logger.debug( - f"[DEBUG:Workflow:{self.__class__.__name__}] State before ain_invoke: {repr(initial_state)}" - ) result = await app.ainvoke(initial_state) self.logger.debug( - f"[DEBUG:Workflow:{self.__class__.__name__}] State after ain_invoke: {repr(result)}" + f"[DEBUG:Workflow:{self.__class__.__name__}] State after ain_invoke: {json.dumps(result, indent=2, default=str)}" ) - self.logger.info(f"Workflow {self.__class__.__name__} execution completed") return result - - except ValidationError as e: - # Re-raise validation errors - raise e except Exception as e: - self.logger.error(f"Workflow execution failed: {str(e)}", exc_info=True) - raise ExecutionError(f"Workflow execution failed: {str(e)}") + error_message = f"Workflow execution failed: {str(e)}" + self.logger.error(error_message) + raise ExecutionError(error_message) from e class BaseWorkflowMixin(ABC): diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index 46d3ac98..a5f553ad 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -1,16 +1,11 @@ import asyncio -import base64 import operator import uuid from typing import Annotated, Any, Dict, List, Optional, TypedDict, Union -import httpx from langchain.prompts import PromptTemplate -from langchain_core.messages import HumanMessage -from langchain_openai import ChatOpenAI from langgraph.channels import LastValue from langgraph.graph import END, Graph, StateGraph -from pydantic import BaseModel, Field from backend.factory import backend from backend.models import ( @@ -19,112 +14,39 @@ Profile, PromptFilter, ProposalBase, + ProposalFilter, ProposalType, QueueMessageFilter, QueueMessageType, ) from lib.hiro import HiroApi from lib.logger import configure_logger -from lib.utils import ( - calculate_token_cost, - extract_image_urls, -) -from services.workflows.base import ( - BaseWorkflow, -) -from services.workflows.capability_mixins import BaseCapabilityMixin +from services.workflows.agents.core_context import CoreContextAgent +from services.workflows.agents.financial_context import FinancialContextAgent +from services.workflows.agents.historical_context import HistoricalContextAgent +from services.workflows.agents.image_processing import ImageProcessingNode +from services.workflows.agents.reasoning import ReasoningAgent +from services.workflows.agents.social_context import SocialContextAgent +from services.workflows.base import BaseWorkflow from services.workflows.chat import ChatService, StreamingCallbackHandler from services.workflows.hierarchical_workflows import ( HierarchicalTeamWorkflow, append_list_fn, merge_dict_fn, ) -from services.workflows.planning_mixin import PlanningCapability -from services.workflows.vector_mixin import VectorRetrievalCapability -from services.workflows.web_search_mixin import WebSearchCapability +from services.workflows.utils.models import FinalOutput, ProposalEvaluationOutput +from services.workflows.utils.state_reducers import ( + merge_dicts, + no_update_reducer, + set_once, + update_state_with_agent_result, +) from tools.dao_ext_action_proposals import VoteOnActionProposalTool from tools.tools_factory import filter_tools_by_names, initialize_tools logger = configure_logger(__name__) -class ProposalEvaluationOutput(BaseModel): - """Output model for proposal evaluation.""" - - approve: bool = Field( - description="Decision: true to approve (vote FOR), false to reject (vote AGAINST)" - ) - confidence_score: float = Field( - description="Confidence score for the decision (0.0-1.0)" - ) - reasoning: str = Field(description="The reasoning behind the evaluation decision") - - -def no_update_reducer(current: Any, new: List[Any]) -> Any: - """Reducer that prevents updates after initial value is set.""" - # Treat initial empty string for str types as if it were None for accepting the first value - is_initial_empty_string = isinstance(current, str) and current == "" - - # If current is genuinely set (not None and not initial empty string), keep it. - if current is not None and not is_initial_empty_string: - return current - - # Current is None or an initial empty string. Try to set it from new. - processed_new_values = ( - new if isinstance(new, list) else [new] - ) # Ensure 'new' is a list - for n_val in processed_new_values: - if n_val is not None: - return n_val - - # If current was None/initial empty string and new is all None or empty, return current (which is None or '') - return current - - -def merge_dicts(current: Optional[Dict], updates: List[Optional[Dict]]) -> Dict: - """Merge multiple dictionary updates into the current dictionary.""" - # Initialize current if it's None - if current is None: - current = {} - - # Handle case where updates is None - if updates is None: - return current - - # Process updates if it's a list - if isinstance(updates, list): - for update in updates: - if update and isinstance(update, dict): - current.update(update) - # Handle case where updates is a single dictionary, not a list - elif isinstance(updates, dict): - current.update(updates) - - return current - - -def set_once(current: Any, updates: List[Any]) -> Any: - """Set the value once and prevent further updates.""" - # If current already has a value, return it unchanged - if current is not None: - return current - - # Handle case where updates is None instead of a list - if updates is None: - return None - - # Process updates if it's a list - if isinstance(updates, list): - for update in updates: - if update is not None: - return update - # Handle case where updates is a single value, not a list - elif updates is not None: - return updates - - return current - - class ProposalEvaluationState(TypedDict): """Type definition for the proposal evaluation state.""" @@ -146,1089 +68,15 @@ class ProposalEvaluationState(TypedDict): proposal_images: Annotated[Optional[List[Dict]], set_once] -class AgentOutput(BaseModel): - """Output model for agent evaluations.""" - - score: int = Field(description="Score from 0-100") - flags: List[str] = Field(description="Critical issues flagged") - summary: str = Field(description="Summary of findings") - - -class FinalOutput(BaseModel): - """Output model for the final evaluation decision.""" - - score: int = Field(description="Final evaluation score") - decision: str = Field(description="Approve or Reject") - explanation: str = Field(description="Reasoning for decision") - - -def update_state_with_agent_result( - state: ProposalEvaluationState, agent_result: Dict[str, Any], agent_name: str -): - """Helper function to update state with agent result including summaries and flags.""" - # Simplified logging - just log once with relevant details - logger.debug( - f"[DEBUG:update_state:{agent_name}] Updating state with {agent_name}_score (score: {agent_result.get('score', 'N/A')})" - ) - - # Update agent score in state - if agent_name in ["core", "historical", "financial", "social", "final"]: - # Make a copy of agent_result to avoid modifying the original - score_dict = dict(agent_result) - # Don't pass token_usage through this path to avoid duplication - if "token_usage" in score_dict: - del score_dict["token_usage"] - - # Directly assign the dictionary to the state key - state[f"{agent_name}_score"] = score_dict - - # Update summaries - if "summaries" not in state: - state["summaries"] = {} - - if "summary" in agent_result and agent_result["summary"]: - state["summaries"][f"{agent_name}_score"] = agent_result["summary"] - - # Update flags - if "flags" not in state: - state["flags"] = [] - - if "flags" in agent_result and isinstance(agent_result["flags"], list): - state["flags"].extend(agent_result["flags"]) - - # Note: Token usage is already directly handled by each agent via state["token_usage"]["{agent_name}_agent"] - # So we don't need to do anything with token usage here - - return state - - -class CoreContextAgent(BaseCapabilityMixin, VectorRetrievalCapability): - """Core Context Agent evaluates proposals against DAO mission and standards.""" - - def __init__(self, config: Optional[Dict[str, Any]] = None): - """Initialize the Core Context Agent.""" - BaseCapabilityMixin.__init__(self, config=config, state_key="core_score") - VectorRetrievalCapability.__init__(self) - self.initialize() - self._initialize_vector_capability() - - def _initialize_vector_capability(self): - """Initialize the vector retrieval functionality.""" - if not hasattr(self, "retrieve_from_vector_store"): - self.retrieve_from_vector_store = ( - VectorRetrievalCapability.retrieve_from_vector_store.__get__( - self, self.__class__ - ) - ) - self.logger.info( - "Initialized vector retrieval capability for CoreContextAgent" - ) - - async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: - """Evaluate the proposal against DAO core mission and standards.""" - self._initialize_vector_capability() - - proposal_id = state.get("proposal_id", "unknown") - proposal_content = state.get("proposal_data", "") - - dao_mission_text = self.config.get("dao_mission", "") - if not dao_mission_text: - try: - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Attempting to retrieve DAO mission from vector store" - ) - dao_mission = await self.retrieve_from_vector_store( - query="DAO mission statement and values", - collection_name=self.config.get( - "mission_collection", "dao_documents" - ), - limit=3, - ) - dao_mission_text = "\n".join([doc.page_content for doc in dao_mission]) - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Retrieved DAO mission, length: {len(dao_mission_text)}" - ) - except Exception as e: - self.logger.error( - f"[DEBUG:CoreAgent:{proposal_id}] Error retrieving DAO mission: {str(e)}", - exc_info=True, - ) - dao_mission_text = "Elevate human potential through AI on Bitcoin" - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Using default DAO mission: {dao_mission_text}" - ) - - prompt = PromptTemplate( - input_variables=["proposal_data", "dao_mission"], - template="""Evaluate the following proposal against the DAO's mission and values.\\n -Proposal: {proposal_data}\\nDAO Mission: {dao_mission}\\n -Assess whether this proposal aligns with the DAO's core mission and values.\\nConsider:\\n1. Mission Alignment: Does it directly support the stated mission?\\n2. Quality Standards: Does it meet quality requirements?\\n3. Innovation: Does it bring new ideas aligned with our vision?\\n4. Impact: How significant is its potential contribution?\\n -# ADDED: Image processing instructions -**Image Analysis Instructions:** -If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. -- Relevance: Does each image directly relate to and support the proposal's text? -- Evidence: Do the images provide visual evidence for claims made in the proposal? -- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? -- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. - -Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ - """, - ) - - try: - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Formatting prompt for evaluation" - ) - formatted_prompt_text = prompt.format( - proposal_data=proposal_content, - dao_mission=dao_mission_text - or "Elevate human potential through AI on Bitcoin", - ) - debug_level = self.config.get("debug_level", 0) - if debug_level >= 2: - self.logger.debug( - f"[PROPOSAL_DEBUG:CoreAgent] FULL EVALUATION PROMPT:\n{formatted_prompt_text}" - ) - else: - self.logger.debug( - f"[PROPOSAL_DEBUG:CoreAgent] Generated evaluation prompt: {formatted_prompt_text}" - ) - except Exception as e: - self.logger.error( - f"[DEBUG:CoreAgent:{proposal_id}] Error formatting prompt: {str(e)}", - exc_info=True, - ) - formatted_prompt_text = f"Evaluate proposal: {proposal_content}" - - try: - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Invoking LLM for core evaluation" - ) - - # ADDED: Image handling - proposal_images_list = state.get("proposal_images", []) - if not isinstance(proposal_images_list, list): - self.logger.warning( - f"[DEBUG:CoreAgent:{proposal_id}] proposal_images is not a list: {type(proposal_images_list)}. Defaulting to empty list." - ) - proposal_images_list = [] - - message_content_list = [{"type": "text", "text": formatted_prompt_text}] - if proposal_images_list: - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Adding {len(proposal_images_list)} images to LLM input." - ) - message_content_list.extend(proposal_images_list) - - llm_input_message = HumanMessage(content=message_content_list) - - result = await self.llm.with_structured_output(AgentOutput).ainvoke( - [llm_input_message] - ) - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] LLM returned core evaluation with score: {result.score}" - ) - self.logger.info( - f"[DEBUG:CoreAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" - ) - - # Track token usage - extract directly from LLM if available - token_usage_data = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - - # Use the Annotated operator.add feature by assigning 1 to increment - # This is safe with concurrent execution - state["core_agent_invocations"] = 1 - - # Try to extract token usage directly from LLM response - if ( - hasattr(self.llm, "_last_prompt_id") - and hasattr(self.llm, "client") - and hasattr(self.llm.client, "usage_by_prompt_id") - ): - last_prompt_id = self.llm._last_prompt_id - if last_prompt_id in self.llm.client.usage_by_prompt_id: - usage = self.llm.client.usage_by_prompt_id[last_prompt_id] - token_usage_data = { - "input_tokens": usage.get("prompt_tokens", 0), - "output_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), - } - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" - ) - # Fallback to estimation - if token_usage_data["total_tokens"] == 0: - # Get model name from LLM - llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") - # First calculate token count from the text - token_count = len(formatted_prompt_text) // 4 # Simple estimation - # Create token usage dictionary for calculate_token_cost - token_usage_dict = {"input_tokens": token_count} - # Calculate cost - cost_result = calculate_token_cost(token_usage_dict, llm_model_name) - token_usage_data = { - "input_tokens": token_count, - "output_tokens": len(result.model_dump_json()) - // 4, # rough estimate - "total_tokens": token_count + len(result.model_dump_json()) // 4, - "model_name": llm_model_name, # Include model name - } - self.logger.debug( - f"[DEBUG:CoreAgent:{proposal_id}] Estimated token usage: {token_usage_data}" - ) - - # Add token usage to state - if "token_usage" not in state: - state["token_usage"] = {} - state["token_usage"]["core_agent"] = token_usage_data - - result_dict = result.model_dump() - # Add token usage to result_dict so it's properly processed - result_dict["token_usage"] = token_usage_data - - # Remove verbose debug logs and simply update state - update_state_with_agent_result(state, result_dict, "core") - - return result_dict - except Exception as e: - self.logger.error( - f"[DEBUG:CoreAgent:{proposal_id}] Error in core evaluation: {str(e)}", - exc_info=True, - ) - fallback_score_dict = { - "score": 50, - "flags": [f"Error: {str(e)}"], - "summary": "Evaluation failed due to error", - } - self.logger.info( - f"[DEBUG:CoreAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" - ) - return fallback_score_dict - - -class HistoricalContextAgent(BaseCapabilityMixin, VectorRetrievalCapability): - """Historical Context Agent examines past proposals and patterns.""" - - def __init__(self, config: Optional[Dict[str, Any]] = None): - BaseCapabilityMixin.__init__(self, config=config, state_key="historical_score") - VectorRetrievalCapability.__init__(self) - self.initialize() - self._initialize_vector_capability() - - def _initialize_vector_capability(self): - if not hasattr(self, "retrieve_from_vector_store"): - self.retrieve_from_vector_store = ( - VectorRetrievalCapability.retrieve_from_vector_store.__get__( - self, self.__class__ - ) - ) - self.logger.info( - "Initialized vector retrieval capability for HistoricalContextAgent" - ) - - async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: - proposal_id = state.get("proposal_id", "unknown") - self._initialize_vector_capability() - proposal_content = state.get("proposal_data", "") - - historical_text = "" - try: - self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Searching for similar proposals: {proposal_content[:50]}..." - ) - similar_proposals = await self.retrieve_from_vector_store( - query=f"Proposals similar to: {proposal_content}", - collection_name=self.config.get( - "proposals_collection", "past_proposals" - ), - limit=5, - ) - historical_text = "\n".join([doc.page_content for doc in similar_proposals]) - self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Found {len(similar_proposals)} similar proposals" - ) - except Exception as e: - self.logger.error( - f"[DEBUG:HistoricalAgent:{proposal_id}] Error retrieving historical proposals: {str(e)}", - exc_info=True, - ) - historical_text = "No similar historical proposals found." - prompt = PromptTemplate( - input_variables=["proposal_data", "historical_proposals"], - template="""Analyze this proposal in the context of historical patterns and similar past proposals.\\n -Current Proposal: {proposal_data}\\nSimilar Past Proposals: {historical_proposals}\\n -Evaluate:\\n1. Precedent: Have similar proposals been approved or rejected?\\n2. Cross-DAO Similarities: How does this compare to proposals in similar DAOs?\\n3. Learning from Past: Does it address issues from past proposals?\\n4. Uniqueness: Is this novel or repeating past ideas?\\n -# ADDED: Image processing instructions -**Image Analysis Instructions:** -If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. -- Relevance: Does each image directly relate to and support the proposal's text? -- Evidence: Do the images provide visual evidence for claims made in the proposal? -- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? -- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. - -Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ - """, - ) - try: - self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Formatting prompt" - ) - formatted_prompt_text = prompt.format( - proposal_data=proposal_content, - historical_proposals=historical_text - or "No similar historical proposals found.", - ) - except Exception as e: - self.logger.error( - f"[DEBUG:HistoricalAgent:{proposal_id}] Error formatting prompt: {str(e)}", - exc_info=True, - ) - formatted_prompt_text = f"Analyze proposal: {proposal_content}" - try: - self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Invoking LLM for historical evaluation" - ) - - # ADDED: Image handling - proposal_images_list = state.get("proposal_images", []) - if not isinstance(proposal_images_list, list): - self.logger.warning( - f"[DEBUG:HistoricalAgent:{proposal_id}] proposal_images is not a list: {type(proposal_images_list)}. Defaulting to empty list." - ) - proposal_images_list = [] - - message_content_list = [{"type": "text", "text": formatted_prompt_text}] - if proposal_images_list: - self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Adding {len(proposal_images_list)} images to LLM input." - ) - message_content_list.extend(proposal_images_list) - - llm_input_message = HumanMessage(content=message_content_list) - - result = await self.llm.with_structured_output(AgentOutput).ainvoke( - [llm_input_message] - ) - self.logger.info( - f"[DEBUG:HistoricalAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" - ) - - # Track token usage - extract directly from LLM if available - token_usage_data = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - - # Try to extract token usage directly from LLM response - if ( - hasattr(self.llm, "_last_prompt_id") - and hasattr(self.llm, "client") - and hasattr(self.llm.client, "usage_by_prompt_id") - ): - last_prompt_id = self.llm._last_prompt_id - if last_prompt_id in self.llm.client.usage_by_prompt_id: - usage = self.llm.client.usage_by_prompt_id[last_prompt_id] - token_usage_data = { - "input_tokens": usage.get("prompt_tokens", 0), - "output_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), - } - self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" - ) - # Fallback to estimation - if token_usage_data["total_tokens"] == 0: - # Get model name from LLM - llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") - # First calculate token count from the text - token_count = len(formatted_prompt_text) // 4 # Simple estimation - # Create token usage dictionary for calculate_token_cost - token_usage_dict = {"input_tokens": token_count} - # Calculate cost - cost_result = calculate_token_cost(token_usage_dict, llm_model_name) - token_usage_data = { - "input_tokens": token_count, - "output_tokens": len(result.model_dump_json()) - // 4, # rough estimate - "total_tokens": token_count + len(result.model_dump_json()) // 4, - "model_name": llm_model_name, # Include model name - } - self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Estimated token usage: {token_usage_data}" - ) - - # Add token usage to state - if "token_usage" not in state: - state["token_usage"] = {} - state["token_usage"]["historical_agent"] = token_usage_data - - result_dict = result.model_dump() - # Add token usage to result_dict so it's properly processed - result_dict["token_usage"] = token_usage_data - - # Update state with the result - update_state_with_agent_result(state, result_dict, "historical") - return result_dict - except Exception as e: - self.logger.error( - f"[DEBUG:HistoricalAgent:{proposal_id}] Error in historical evaluation: {str(e)}", - exc_info=True, - ) - fallback_score_dict = { - "score": 50, - "flags": [f"Error: {str(e)}"], - "summary": "Evaluation failed due to error", - } - self.logger.info( - f"[DEBUG:HistoricalAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" - ) - return fallback_score_dict - - -class FinancialContextAgent(BaseCapabilityMixin): - """Financial Context Agent evaluates treasury impact and financial viability.""" - - def __init__(self, config: Optional[Dict[str, Any]] = None): - super().__init__(config=config, state_key="financial_score") - self.initialize() - - async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: - proposal_id = state.get("proposal_id", "unknown") - treasury_balance = state.get( - "treasury_balance", self.config.get("treasury_balance", 1000000) - ) - proposal_content = state.get("proposal_data", "") - - prompt = PromptTemplate( - input_variables=["proposal_data", "treasury_balance"], - template="""Assess the financial aspects of this proposal.\\n -Proposal: {proposal_data}\\nCurrent Treasury Balance: {treasury_balance}\\n -Evaluate:\\n1. Cost-Benefit Analysis: Is the ROI reasonable?\\n2. Treasury Impact: What percentage of treasury would this use?\\n3. Budget Alignment: Does it align with budget priorities?\\n4. Projected Impact: What's the expected financial outcome?\\n5. Risk Assessment: What financial risks might arise?\\n -# ADDED: Image processing instructions -**Image Analysis Instructions:** -If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. -- Relevance: Does each image directly relate to and support the proposal's text? -- Evidence: Do the images provide visual evidence for claims made in the proposal (e.g., screenshots of transactions, diagrams of financial models if applicable)? -- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? -- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. - -Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ - """, - ) - try: - self.logger.debug( - f"[DEBUG:FinancialAgent:{proposal_id}] Formatting prompt for financial evaluation" - ) - formatted_prompt_text = prompt.format( - proposal_data=proposal_content, - treasury_balance=treasury_balance, - ) - except Exception as e: - self.logger.error( - f"[DEBUG:FinancialAgent:{proposal_id}] Error formatting prompt: {str(e)}", - exc_info=True, - ) - formatted_prompt_text = ( - f"Assess financial aspects of proposal: {proposal_content}" - ) - try: - self.logger.debug( - f"[DEBUG:FinancialAgent:{proposal_id}] Invoking LLM for financial evaluation" - ) - - # ADDED: Image handling - proposal_images = state.get("proposal_images", []) - message_content_list = [{"type": "text", "text": formatted_prompt_text}] - if proposal_images: - logger.debug( - f"[DEBUG:FinancialAgent:{proposal_id}] Adding {len(proposal_images)} images to LLM input." - ) - message_content_list.extend(proposal_images) - - llm_input_message = HumanMessage(content=message_content_list) - - result = await self.llm.with_structured_output(AgentOutput).ainvoke( - [llm_input_message] - ) - self.logger.info( - f"[DEBUG:FinancialAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" - ) - - # Track token usage - extract directly from LLM if available - token_usage_data = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - - # Try to extract token usage directly from LLM response - if ( - hasattr(self.llm, "_last_prompt_id") - and hasattr(self.llm, "client") - and hasattr(self.llm.client, "usage_by_prompt_id") - ): - last_prompt_id = self.llm._last_prompt_id - if last_prompt_id in self.llm.client.usage_by_prompt_id: - usage = self.llm.client.usage_by_prompt_id[last_prompt_id] - token_usage_data = { - "input_tokens": usage.get("prompt_tokens", 0), - "output_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), - } - self.logger.debug( - f"[DEBUG:FinancialAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" - ) - # Fallback to estimation - if token_usage_data["total_tokens"] == 0: - # Get model name from LLM - llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") - # First calculate token count from the text - token_count = len(formatted_prompt_text) // 4 # Simple estimation - # Create token usage dictionary for calculate_token_cost - token_usage_dict = {"input_tokens": token_count} - # Calculate cost - cost_result = calculate_token_cost(token_usage_dict, llm_model_name) - token_usage_data = { - "input_tokens": token_count, - "output_tokens": len(result.model_dump_json()) - // 4, # rough estimate - "total_tokens": token_count + len(result.model_dump_json()) // 4, - "model_name": llm_model_name, # Include model name - } - self.logger.debug( - f"[DEBUG:FinancialAgent:{proposal_id}] Estimated token usage: {token_usage_data}" - ) - - # Add token usage to state - if "token_usage" not in state: - state["token_usage"] = {} - state["token_usage"]["financial_agent"] = token_usage_data - - result_dict = result.model_dump() - # Add token usage to result_dict so it's properly processed - result_dict["token_usage"] = token_usage_data - - # Update state with the result - update_state_with_agent_result(state, result_dict, "financial") - return result_dict - except Exception as e: - self.logger.error( - f"[DEBUG:FinancialAgent:{proposal_id}] Error in financial evaluation: {str(e)}", - exc_info=True, - ) - fallback_score_dict = { - "score": 50, - "flags": [f"Error: {str(e)}"], - "summary": "Evaluation failed due to error", - } - self.logger.info( - f"[DEBUG:FinancialAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" - ) - return fallback_score_dict - - -class ImageProcessingNode(BaseCapabilityMixin): - """A workflow node to process proposal images: extract URLs, download, and base64 encode.""" - - def __init__(self, config: Optional[Dict[str, Any]] = None): - super().__init__(config=config, state_key="proposal_images") - self.initialize() - - async def process(self, state: ProposalEvaluationState) -> List[Dict[str, Any]]: - """The core logic for processing images, returns the list of processed image dicts directly.""" - proposal_id = state.get("proposal_id", "unknown") - proposal_data_str = state.get("proposal_data", "") - - if not proposal_data_str: - self.logger.info( - f"[ImageProcessorNode:{proposal_id}] No proposal_data string, skipping image processing." - ) - return [] # Return empty list, not None - - self.logger.info( - f"[ImageProcessorNode:{proposal_id}] Starting image processing." - ) - image_urls = extract_image_urls(proposal_data_str) - - if not image_urls: - self.logger.info( - f"[ImageProcessorNode:{proposal_id}] No image URLs found in proposal data." - ) - return [] # Return empty list, not None - - self.logger.info( - f"[ImageProcessorNode:{proposal_id}] Found {len(image_urls)} image URLs: {image_urls}" - ) - - processed_images = [] - async with httpx.AsyncClient() as client: - for url in image_urls: - try: - self.logger.debug( - f"[ImageProcessorNode:{proposal_id}] Downloading image from {url}" - ) - response = await client.get(url, timeout=10.0) - response.raise_for_status() - image_data = base64.b64encode(response.content).decode("utf-8") - mime_type = "image/jpeg" - if url.lower().endswith((".jpg", ".jpeg")): - mime_type = "image/jpeg" - elif url.lower().endswith(".png"): - mime_type = "image/png" - elif url.lower().endswith(".gif"): - mime_type = "image/gif" - elif url.lower().endswith(".webp"): - mime_type = "image/webp" - - processed_images.append( - { - "type": "image_url", - "image_url": { - "url": f"data:{mime_type};base64,{image_data}" - }, - } - ) - self.logger.debug( - f"[ImageProcessorNode:{proposal_id}] Successfully processed image from {url}" - ) - except httpx.HTTPStatusError as e: - self.logger.error( - f"[ImageProcessorNode:{proposal_id}] HTTP error for {url}: {e.response.status_code}", - exc_info=False, - ) - except httpx.RequestError as e: - self.logger.error( - f"[ImageProcessorNode:{proposal_id}] Request error for {url}: {str(e)}", - exc_info=False, - ) - except Exception as e: - self.logger.error( - f"[ImageProcessorNode:{proposal_id}] Generic error for {url}: {str(e)}", - exc_info=True, - ) - - self.logger.info( - f"[ImageProcessorNode:{proposal_id}] Finished. {len(processed_images)} images processed." - ) - return processed_images # This will be a list, possibly empty - - -class SocialContextAgent(BaseCapabilityMixin, WebSearchCapability): - """Social Context Agent gauges community sentiment and social impact.""" - - def __init__(self, config: Optional[Dict[str, Any]] = None): - BaseCapabilityMixin.__init__(self, config=config, state_key="social_score") - WebSearchCapability.__init__(self) - self.initialize() - self._initialize_web_search_capability() - - def _initialize_web_search_capability(self): - if not hasattr(self, "search_web"): - self.search_web = WebSearchCapability.search_web.__get__( - self, self.__class__ - ) - self.logger.info("Initialized web search capability for SocialContextAgent") - - async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: - proposal_id = state.get("proposal_id", "unknown") - self._initialize_web_search_capability() - proposal_content = state.get("proposal_data", "") - - social_context = "" - if self.config.get("enable_web_search", True): - try: - search_query = ( - f"Community sentiment {proposal_content[:50]} cryptocurrency DAO" - ) - self.logger.debug( - f"[DEBUG:SocialAgent:{proposal_id}] Performing web search: {search_query}" - ) - search_results, web_search_token_usage = await self.search_web( - query=search_query, - num_results=3, - ) - social_context = "\n".join( - [f"{r.get('page_content', '')}" for r in search_results] - ) - self.logger.debug( - f"[DEBUG:SocialAgent:{proposal_id}] Found {len(search_results)} web search results" - ) - - # Store web search token usage - if "token_usage" not in state: - state["token_usage"] = {} - state["token_usage"]["social_web_search"] = web_search_token_usage - - except Exception as e: - logger.error( - f"[DEBUG:SocialAgent:{proposal_id}] Web search failed: {str(e)}", - exc_info=True, - ) - social_context = "Web search unavailable." - prompt = PromptTemplate( - input_variables=["proposal_data", "social_context"], - template="""Gauge the community sentiment and social impact of this proposal.\\n -Proposal: {proposal_data}\\nSocial Context: {social_context}\\n -Evaluate:\\n1. Community Sentiment: How might members perceive this?\\n2. Social Media Presence: Any discussions online about this?\\n3. Engagement Potential: Will this engage the community?\\n4. Cross-Platform Analysis: How does sentiment vary across platforms?\\n5. Social Risk: Any potential for controversy or division?\\n -# ADDED: Image processing instructions -**Image Analysis Instructions:** -If images are provided with this proposal (they will appear after this text), you MUST analyze them as an integral part of the proposal. -- Relevance: Does each image directly relate to and support the proposal's text or the community/social aspects being discussed? -- Evidence: Do the images provide visual evidence for claims made (e.g., screenshots of community discussions, mockups of social impact visuals)? -- Authenticity & Quality: Are the images clear, authentic, and not misleading or manipulated? -- Cohesion: The images and text MUST form a cohesive and consistent whole. If any image contradicts the text, is irrelevant, misleading, of very poor quality, or inappropriate, you should consider this a significant flaw in the proposal. - -Provide a score from 0-100, flag any critical issues (including image-related ones), and summarize your findings, explicitly mentioning your image analysis if images were present.\\ - """, - ) - try: - self.logger.debug( - f"[DEBUG:SocialAgent:{proposal_id}] Formatting prompt for social evaluation" - ) - formatted_prompt_text = prompt.format( - proposal_data=proposal_content, - social_context=social_context, - ) - except Exception as e: - self.logger.error( - f"[DEBUG:SocialAgent:{proposal_id}] Error formatting prompt: {str(e)}", - exc_info=True, - ) - formatted_prompt_text = ( - f"Gauge social impact of proposal: {proposal_content}" - ) - try: - self.logger.debug( - f"[DEBUG:SocialAgent:{proposal_id}] Invoking LLM for social evaluation" - ) - - # ADDED: Image handling - proposal_images_list = state.get("proposal_images", []) - if not isinstance(proposal_images_list, list): - self.logger.warning( - f"[DEBUG:SocialAgent:{proposal_id}] proposal_images is not a list: {type(proposal_images_list)}. Defaulting to empty list." - ) - proposal_images_list = [] - - message_content_list = [{"type": "text", "text": formatted_prompt_text}] - if proposal_images_list: - self.logger.debug( - f"[DEBUG:SocialAgent:{proposal_id}] Adding {len(proposal_images_list)} images to LLM input." - ) - message_content_list.extend(proposal_images_list) - - llm_input_message = HumanMessage(content=message_content_list) - - result = await self.llm.with_structured_output(AgentOutput).ainvoke( - [llm_input_message] - ) - self.logger.info( - f"[DEBUG:SocialAgent:{proposal_id}] SCORE={result.score}/100 | FLAGS={result.flags} | SUMMARY={result.summary}" - ) - - # Track token usage - extract directly from LLM if available - token_usage_data = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - - # Try to extract token usage directly from LLM response - if ( - hasattr(self.llm, "_last_prompt_id") - and hasattr(self.llm, "client") - and hasattr(self.llm.client, "usage_by_prompt_id") - ): - last_prompt_id = self.llm._last_prompt_id - if last_prompt_id in self.llm.client.usage_by_prompt_id: - usage = self.llm.client.usage_by_prompt_id[last_prompt_id] - token_usage_data = { - "input_tokens": usage.get("prompt_tokens", 0), - "output_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), - } - self.logger.debug( - f"[DEBUG:SocialAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" - ) - # Fallback to estimation - if token_usage_data["total_tokens"] == 0: - # Get model name from LLM - llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") - # First calculate token count from the text - token_count = len(formatted_prompt_text) // 4 # Simple estimation - # Create token usage dictionary for calculate_token_cost - token_usage_dict = {"input_tokens": token_count} - # Calculate cost - cost_result = calculate_token_cost(token_usage_dict, llm_model_name) - token_usage_data = { - "input_tokens": token_count, - "output_tokens": len(result.model_dump_json()) - // 4, # rough estimate - "total_tokens": token_count + len(result.model_dump_json()) // 4, - "model_name": llm_model_name, # Include model name - } - self.logger.debug( - f"[DEBUG:SocialAgent:{proposal_id}] Estimated token usage: {token_usage_data}" - ) - - # Add token usage to state - if "token_usage" not in state: - state["token_usage"] = {} - state["token_usage"]["social_agent"] = token_usage_data - - result_dict = result.model_dump() - # Add token usage to result_dict so it's properly processed - result_dict["token_usage"] = token_usage_data - - # Update state with the result - update_state_with_agent_result(state, result_dict, "social") - return result_dict - except Exception as e: - self.logger.error( - f"[DEBUG:SocialAgent:{proposal_id}] Error in social evaluation: {str(e)}", - exc_info=True, - ) - fallback_score_dict = { - "score": 50, - "flags": [f"Error: {str(e)}"], - "summary": "Evaluation failed due to error", - } - self.logger.info( - f"[DEBUG:SocialAgent:{proposal_id}] ERROR_SCORE=50/100 | FLAGS=[{str(e)}] | SUMMARY=Evaluation failed" - ) - return fallback_score_dict - - -class ReasoningAgent(BaseCapabilityMixin, PlanningCapability): - """Configuration & Reasoning Agent synthesizes evaluations and makes decisions.""" - - def __init__(self, config: Optional[Dict[str, Any]] = None): - """Initialize the Reasoning Agent.""" - BaseCapabilityMixin.__init__(self, config=config, state_key="final_score") - self.initialize() - planning_queue = asyncio.Queue() - callback_handler = self.config.get( - "callback_handler" - ) or StreamingCallbackHandler(planning_queue) - PlanningCapability.__init__( - self, - callback_handler=callback_handler, - planning_llm=ChatOpenAI( - model=self.config.get("planning_model", "gpt-4.1-mini") - ), - persona="DAO Proposal Evaluator", - ) - self._initialize_planning_capability() - - def _initialize_planning_capability(self): - """Initialize planning capability methods.""" - if not hasattr(self, "create_plan"): - self.create_plan = PlanningCapability.create_plan.__get__( - self, self.__class__ - ) - self.logger.info("Initialized planning capability for ReasoningAgent") - - def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - """Integrate planning capability with the graph.""" - pass - - async def process(self, state: ProposalEvaluationState) -> Dict[str, Any]: - proposal_id = state.get("proposal_id", "unknown") - self._initialize_planning_capability() - proposal_content = state.get("proposal_data", "") - self.logger.debug( - f"[DEBUG:ReasoningAgent:{proposal_id}] Beginning final evaluation processing with proposal_content (length: {len(proposal_content)})" - ) - - def safe_get_score(value, default=0): - if isinstance(value, dict) and "score" in value: - return value.get("score", default) - elif isinstance(value, int): - return value - return default - - core_score = state.get("core_score", {}) - historical_score = state.get("historical_score", {}) - financial_score = state.get("financial_score", {}) - social_score = state.get("social_score", {}) - - core_score_val = safe_get_score(core_score) - historical_score_val = safe_get_score(historical_score) - financial_score_val = safe_get_score(financial_score) - social_score_val = safe_get_score(social_score) - - self.logger.debug( - f"[DEBUG:ReasoningAgent:{proposal_id}] Input scores: Core={core_score_val}, Historical={historical_score_val}, Financial={financial_score_val}, Social={social_score_val}" - ) - - scores = { - "Core Context": core_score_val, - "Historical Context": historical_score_val, - "Financial Context": financial_score_val, - "Social Context": social_score_val, - } - summaries = state.get("summaries", {}) - flags = state.get("flags", []) - - self.logger.debug( - f"[DEBUG:ReasoningAgent:{proposal_id}] Summaries: {summaries}" - ) - - self.logger.debug(f"[DEBUG:ReasoningAgent:{proposal_id}] Flags raised: {flags}") - - # Update the summaries with the content from each agent's evaluation - if isinstance(core_score, dict) and "summary" in core_score: - summaries["core_score"] = core_score["summary"] - if isinstance(historical_score, dict) and "summary" in historical_score: - summaries["historical_score"] = historical_score["summary"] - if isinstance(financial_score, dict) and "summary" in financial_score: - summaries["financial_score"] = financial_score["summary"] - if isinstance(social_score, dict) and "summary" in social_score: - summaries["social_score"] = social_score["summary"] - - # Update flags - for score_obj in [core_score, historical_score, financial_score, social_score]: - if ( - isinstance(score_obj, dict) - and "flags" in score_obj - and isinstance(score_obj["flags"], list) - ): - flags.extend(score_obj["flags"]) - - prompt = PromptTemplate( - input_variables=["proposal_data", "scores", "summaries", "flags"], - template="""Synthesize all evaluations and make a final decision on this proposal.\\n -Proposal: {proposal_data}\\n -Evaluations:\\n- Core Context (Score: {scores[Core Context]}): {summaries[core_score]}\\n- Historical Context (Score: {scores[Historical Context]}): {summaries[historical_score]}\\n- Financial Context (Score: {scores[Financial Context]}): {summaries[financial_score]}\\n- Social Context (Score: {scores[Social Context]}): {summaries[social_score]}\\n -Flags Raised: {flags}\\n -Synthesize these evaluations to:\\n1. Weigh the importance of each context\\n2. Calibrate confidence based on available information\\n3. Consider the implications of the flags raised\\n4. Make a final decision: Approve or Reject\\n5. Calculate an overall score\\n -Provide a final score, decision (Approve/Reject), and detailed explanation.\\n - """, - ) - - try: - for key in [ - "core_score", - "historical_score", - "financial_score", - "social_score", - ]: - if key not in summaries: - summaries[key] = "No evaluation available" - - self.logger.debug( - f"[DEBUG:ReasoningAgent:{proposal_id}] Formatting final evaluation prompt" - ) - formatted_prompt_text = prompt.format( - proposal_data=proposal_content, - scores=scores, - summaries=summaries, - flags=", ".join(flags) if flags else "None", - ) - except Exception as e: - self.logger.error( - f"[DEBUG:ReasoningAgent:{proposal_id}] Error formatting prompt: {str(e)}", - exc_info=True, - ) - formatted_prompt_text = f"""Synthesize evaluations for proposal: {proposal_content} -Scores: {scores} -Flags: {flags} -Provide a final score, decision (Approve/Reject), and explanation.""" - - try: - self.logger.debug( - f"[DEBUG:ReasoningAgent:{proposal_id}] Invoking LLM for final decision" - ) - result = await self.llm.with_structured_output(FinalOutput).ainvoke( - [formatted_prompt_text] - ) - - self.logger.info( - f"[DEBUG:ReasoningAgent:{proposal_id}] FINAL DECISION: {result.decision} | SCORE={result.score}/100 | EXPLANATION={result.explanation}" - ) - - # Track token usage - extract directly from LLM if available - token_usage_data = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - - # Try to extract token usage directly from LLM response - if ( - hasattr(self.llm, "_last_prompt_id") - and hasattr(self.llm, "client") - and hasattr(self.llm.client, "usage_by_prompt_id") - ): - last_prompt_id = self.llm._last_prompt_id - if last_prompt_id in self.llm.client.usage_by_prompt_id: - usage = self.llm.client.usage_by_prompt_id[last_prompt_id] - token_usage_data = { - "input_tokens": usage.get("prompt_tokens", 0), - "output_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), - } - self.logger.debug( - f"[DEBUG:ReasoningAgent:{proposal_id}] Extracted token usage from LLM: {token_usage_data}" - ) - # Fallback to estimation - if token_usage_data["total_tokens"] == 0: - # Get model name from LLM - llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") - # First calculate token count from the text - token_count = len(formatted_prompt_text) // 4 # Simple estimation - # Create token usage dictionary for calculate_token_cost - token_usage_dict = {"input_tokens": token_count} - # Calculate cost - cost_result = calculate_token_cost(token_usage_dict, llm_model_name) - token_usage_data = { - "input_tokens": token_count, - "output_tokens": len(result.model_dump_json()) - // 4, # rough estimate - "total_tokens": token_count + len(result.model_dump_json()) // 4, - "model_name": llm_model_name, # Include model name - } - self.logger.debug( - f"[DEBUG:ReasoningAgent:{proposal_id}] Estimated token usage: {token_usage_data}" - ) - - # Add token usage to state - if "token_usage" not in state: - state["token_usage"] = {} - state["token_usage"]["reasoning_agent"] = token_usage_data - - result_dict = result.model_dump() - # Add token usage to result_dict so it's properly processed - result_dict["token_usage"] = token_usage_data - - # Update state with the result - update_state_with_agent_result(state, result_dict, "reasoning") - return result_dict - except Exception as e: - self.logger.error( - f"[DEBUG:ReasoningAgent:{proposal_id}] Error in final evaluation: {str(e)}", - exc_info=True, - ) - self.logger.info( - f"[DEBUG:ReasoningAgent:{proposal_id}] ERROR_SCORE=50/100 | DECISION=Pending | REASON=Error: {str(e)}" - ) - return { - "score": 50, - "decision": "Pending", - "explanation": f"Unable to make final decision due to error: {str(e)}", - } - - class ProposalEvaluationWorkflow(BaseWorkflow[ProposalEvaluationState]): """Main workflow for evaluating DAO proposals using a hierarchical team.""" def __init__(self, config: Optional[Dict[str, Any]] = None): - """Initialize the proposal evaluation workflow.""" + """Initialize the proposal evaluation workflow. + + Args: + config: Optional configuration dictionary + """ super().__init__() self.config = config or {} self.hierarchical_workflow = HierarchicalTeamWorkflow( @@ -1239,20 +87,18 @@ def __init__(self, config: Optional[Dict[str, Any]] = None): }, ) - # Instantiate and add the new ImageProcessingNode - image_processor_agent = ImageProcessingNode( - config=self.config - ) # Use self.config - self.hierarchical_workflow.add_sub_workflow( - "image_processor", image_processor_agent - ) - + # Initialize agents + image_processor_agent = ImageProcessingNode(config=self.config) core_agent = CoreContextAgent(self.config) historical_agent = HistoricalContextAgent(self.config) financial_agent = FinancialContextAgent(self.config) social_agent = SocialContextAgent(self.config) reasoning_agent = ReasoningAgent(self.config) + # Add agents to the workflow + self.hierarchical_workflow.add_sub_workflow( + "image_processor", image_processor_agent + ) self.hierarchical_workflow.add_sub_workflow("core_agent", core_agent) self.hierarchical_workflow.add_sub_workflow( "historical_agent", historical_agent @@ -1261,162 +107,161 @@ def __init__(self, config: Optional[Dict[str, Any]] = None): self.hierarchical_workflow.add_sub_workflow("social_agent", social_agent) self.hierarchical_workflow.add_sub_workflow("reasoning_agent", reasoning_agent) + # Set entry point and other workflow properties self.hierarchical_workflow.set_entry_point("image_processor") + self.hierarchical_workflow.set_supervisor_logic(self._supervisor_logic) + self.hierarchical_workflow.set_halt_condition(self._halt_condition) + self.required_fields = ["proposal_id", "proposal_data"] + + def _supervisor_logic( + self, state: ProposalEvaluationState + ) -> Union[str, List[str]]: + """Determine which agent(s) to run next based on current state. - def supervisor_logic(state: ProposalEvaluationState) -> Union[str, List[str]]: - """Determine the next step in the workflow.""" - proposal_id = state.get("proposal_id", "unknown") + Args: + state: Current workflow state - # Debugging current state view for supervisor + Returns: + String or list of strings identifying next agent(s) to run + """ + # Initialize core agent invocations counter if not present + if "core_agent_invocations" not in state: + state["core_agent_invocations"] = 0 + + # Debug counter behavior + logger.debug( + f"[DEBUG:CoreCounter] Current invocations count: {state.get('core_agent_invocations', 0)}" + ) + + # Check if state has images processed + # If proposal_images key doesn't exist, we need to process images + # If it exists (even if it's an empty list), we consider images processed + if "proposal_images" not in state: + logger.debug("[DEBUG:SupervisorLogic] Need to process images first") + # Process images and ensure the key exists in state, even if empty + result = state.get("image_processor", []) + if isinstance(result, list): + # Update state with empty list if no images were found + state["proposal_images"] = result + else: + # Ensure we always have the key to prevent infinite loops + state["proposal_images"] = [] + return "image_processor" + + # Check if core context evaluation is done + if "core_score" not in state: + logger.debug("[DEBUG:SupervisorLogic] Need core context evaluation") + old_count = state.get("core_agent_invocations", 0) + state["core_agent_invocations"] = old_count + 1 logger.debug( - f"[DEBUG:Supervisor:{proposal_id}] Evaluating next step. State keys: {list(state.keys())}. " - f"proposal_images set: {'proposal_images' in state}, " - f"core_score set: {state.get('core_score') is not None}, " - f"historical_score set: {state.get('historical_score') is not None}, " - f"financial_score set: {state.get('financial_score') is not None}, " - f"social_score set: {state.get('social_score') is not None}, " - f"final_score set: {state.get('final_score') is not None}" + f"[DEBUG:CoreCounter] Incremented invocations: {old_count} -> {state['core_agent_invocations']}" ) + return "core_agent" - if state.get("halt", False): - logger.debug( - f"[DEBUG:Supervisor:{proposal_id}] Halt condition met, returning END" - ) - return END - - # After image_processor (entry point), if core_score isn't set, go to core_agent. - # The image_processor node output (even if empty list for images) should be in state. - if state.get("core_score") is None: - # This will be the first check after image_processor completes as it's the entry point. - current_core_invocations = state.get("core_agent_invocations", 0) - if current_core_invocations > 3: - logger.error( - f"[DEBUG:Supervisor:{proposal_id}] Core agent invoked too many times ({current_core_invocations}), halting." - ) - return END - - # Do not manually increment core_agent_invocations - the langgraph framework will handle this - # with the Annotated type we restored - - logger.debug( - f"[DEBUG:Supervisor:{proposal_id}] Routing to core_agent (core_score is None, invocation #{current_core_invocations})." - ) - return "core_agent" + # Run specialized agents in parallel if they haven't run yet + agents_to_run = [] - if state.get("historical_score") is None: - logger.debug( - f"[DEBUG:Supervisor:{proposal_id}] Routing to historical_agent." - ) - return "historical_agent" - - if ( - state.get("financial_score") is None - or state.get("social_score") is None - ): - parallel_nodes = [] - if state.get("financial_score") is None: - parallel_nodes.append("financial_agent") - if state.get("social_score") is None: - parallel_nodes.append("social_agent") - logger.debug( - f"[DEBUG:Supervisor:{proposal_id}] Initiating parallel execution of {parallel_nodes}" - ) - return parallel_nodes + if "historical_score" not in state: + agents_to_run.append("historical_agent") - if state.get("final_score") is None: - logger.debug( - f"[DEBUG:Supervisor:{proposal_id}] All scores available but final score is None, routing to reasoning_agent" - ) - return "reasoning_agent" + if "financial_score" not in state: + agents_to_run.append("financial_agent") + + if "social_score" not in state: + agents_to_run.append("social_agent") + + if agents_to_run: + logger.debug( + f"[DEBUG:SupervisorLogic] Running specialized agents: {agents_to_run}" + ) + return agents_to_run + # If all specialized agents have run, run the reasoning agent for final decision + if "final_score" not in state: logger.debug( - f"[DEBUG:Supervisor:{proposal_id}] All scores completed, returning END" + "[DEBUG:SupervisorLogic] All specialized agents done, running reasoning agent" + ) + logger.info( + f"[DEBUG:DIAGNOSIS] About to run reasoning_agent, state keys: {list(state.keys())}" ) - return END + return "reasoning_agent" - self.hierarchical_workflow.set_supervisor_logic(supervisor_logic) + # If reasoning agent has run, we're done + logger.debug("[DEBUG:SupervisorLogic] Workflow complete") - def halt_condition(state: ProposalEvaluationState) -> bool: - """Check if workflow should halt.""" - proposal_id = state.get("proposal_id", "unknown") + # Add diagnosis logging + logger.info( + f"[DEBUG:DIAGNOSIS] Workflow complete, final_score type: {type(state.get('final_score'))}" + ) + logger.info( + f"[DEBUG:DIAGNOSIS] Final score contents: {state.get('final_score')}" + ) - if state.get("halt", False): - logger.debug( - f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow due to explicit halt flag" - ) - return True + # Log the entire state and final reasoning as JSON + import json - # Check for excessive core agent invocations - if state.get("core_agent_invocations", 0) > 3: - logger.debug( - f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow due to excessive core agent invocations: {state.get('core_agent_invocations', 0)}" - ) - return True + logger.info(f"[DEBUG:FinalState] {json.dumps(state, default=str, indent=2)}") - recursion_count = state.get("recursion_count", 0) - if recursion_count > 8: - logger.debug( - f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow - possible loop detected after {recursion_count} iterations" - ) - return True - - if ( - state.get("core_score") is not None - and state.get("historical_score") is not None - and state.get("financial_score") is not None - and state.get("social_score") is not None - and state.get("final_score") is None - and recursion_count > 3 - ): - logger.debug( - f"[DEBUG:HaltCondition:{proposal_id}] Halting workflow - reasoning agent appears to be failing after {recursion_count} attempts" - ) - return True + return END - state["recursion_count"] = recursion_count + 1 - logger.debug( - f"[DEBUG:HaltCondition:{proposal_id}] Incrementing recursion counter to {state['recursion_count']}" - ) + def _halt_condition(self, state: ProposalEvaluationState) -> bool: + """Determine if the workflow should halt early. - return False + Args: + state: Current workflow state - self.hierarchical_workflow.set_halt_condition(halt_condition) - self.required_fields = ["proposal_id", "proposal_data"] + Returns: + True if workflow should halt, False otherwise + """ + # Halt if explicitly set + if state.get("halt", False): + logger.info("[DEBUG:HaltCondition] Halting due to explicit halt flag") + return True + + # Halt if we've run the core agent too many times (prevent loops) + core_agent_invocations = state.get("core_agent_invocations", 0) + max_core_invocations = 50 + if core_agent_invocations > max_core_invocations: + logger.warning( + f"[DEBUG:HaltCondition] Halting due to too many core agent invocations: {core_agent_invocations}" + ) + state["flags"] = state.get("flags", []) + [ + f"Workflow halted: Too many core agent invocations ({core_agent_invocations})" + ] + return True + + # Don't halt by default + return False def _create_prompt(self) -> PromptTemplate: - """Create the main workflow prompt.""" - return PromptTemplate( - input_variables=["proposal_data"], - template="Evaluate the DAO proposal: {proposal_data}", + """Create the base prompt for the workflow.""" + raise NotImplementedError( + "This method is not used in the hierarchical workflow" ) def _create_graph(self) -> StateGraph: - """Create the workflow graph.""" + """Create the workflow graph. + + Returns: + The constructed state graph + """ return self.hierarchical_workflow.build_graph() def _validate_state(self, state: ProposalEvaluationState) -> bool: - """Validate the workflow state.""" - if not super()._validate_state(state): - return False - - if "flags" not in state: - state["flags"] = [] - elif state["flags"] is None: - state["flags"] = [] - - if "summaries" not in state: - state["summaries"] = {} - elif state["summaries"] is None: - state["summaries"] = {} + """Validate that the state contains required fields. - if "halt" not in state: - state["halt"] = False - - if "token_usage" not in state: - state["token_usage"] = {} - elif state["token_usage"] is None: - state["token_usage"] = {} + Args: + state: Current workflow state + Returns: + True if state is valid, False otherwise + """ + for field in self.required_fields: + if field not in state: + self.logger.error( + f"[ProposalEvaluation] Missing required field: {field}" + ) + return False return True @@ -1425,337 +270,149 @@ async def evaluate_proposal( proposal_data: str, config: Optional[Dict[str, Any]] = None, ) -> Dict[str, Any]: - """Evaluate a proposal using the hierarchical team workflow.""" - logger.info(f"[DEBUG:Workflow:{proposal_id}] Starting evaluation workflow") - - debug_level = 0 - if config and "debug_level" in config: - debug_level = config.get("debug_level", 0) - if debug_level > 0: - logger.debug(f"[PROPOSAL_DEBUG] Using debug_level: {debug_level}") - - if not proposal_data: - logger.warning( - f"[PROPOSAL_DEBUG] proposal_data is empty or None! This will cause evaluation failure." - ) + """Evaluate a proposal using the ProposalEvaluationWorkflow. + + Args: + proposal_id: Unique identifier for the proposal + proposal_data: Proposal content + config: Optional configuration for the workflow + + Returns: + Dictionary containing evaluation results + """ + # Set up configuration with defaults if not provided + if config is None: + config = {} - state = { + # Use model name from config or default + model_name = config.get("model_name", "gpt-4.1") + + workflow = ProposalEvaluationWorkflow(config) + + # Create initial state + initial_state = { "proposal_id": proposal_id, "proposal_data": proposal_data, "flags": [], "summaries": {}, - "halt": False, "token_usage": {}, - "core_score": None, - "historical_score": None, - "financial_score": None, - "social_score": None, - "final_score": None, - "decision": None, "core_agent_invocations": 0, - "recursion_count": 0, + "halt": False, } + # Run workflow try: - workflow = ProposalEvaluationWorkflow(config or {}) - logger.info( - f"[DEBUG:Workflow:{proposal_id}] Executing hierarchical team workflow" - ) - result = await workflow.execute(state) + logger.info(f"Starting proposal evaluation for proposal {proposal_id}") + result = await workflow.execute(initial_state) + + # Add diagnostic logging logger.info( - f"[DEBUG:Workflow:{proposal_id}] Workflow execution completed with decision: {result.get('decision', 'Unknown')}" + f"[DEBUG:EXTRACT] Workflow execution complete, result keys: {list(result.keys())}" ) - - # Only output detailed debug info at higher debug levels - if debug_level >= 2: - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] RESULT STRUCTURE: {list(result.keys())}" - ) - logger.debug(f"[DEBUG:Workflow:{proposal_id}] RESULT SCORES TYPES:") - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Core: {type(result.get('core_score'))} = {repr(result.get('core_score'))[:100]+'...' if len(repr(result.get('core_score'))) > 100 else repr(result.get('core_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Historical: {type(result.get('historical_score'))} = {repr(result.get('historical_score'))[:100]+'...' if len(repr(result.get('historical_score'))) > 100 else repr(result.get('historical_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Financial: {type(result.get('financial_score'))} = {repr(result.get('financial_score'))[:100]+'...' if len(repr(result.get('financial_score'))) > 100 else repr(result.get('financial_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Social: {type(result.get('social_score'))} = {repr(result.get('social_score'))[:100]+'...' if len(repr(result.get('social_score'))) > 100 else repr(result.get('social_score'))}" - ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Final: {type(result.get('final_score'))} = {repr(result.get('final_score'))[:100]+'...' if len(repr(result.get('final_score'))) > 100 else repr(result.get('final_score'))}" + logger.info(f"[DEBUG:EXTRACT] final_score in result: {'final_score' in result}") + if "final_score" in result: + logger.info( + f"[DEBUG:EXTRACT] final_score type: {type(result['final_score'])}" ) - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] - Decision: {type(result.get('decision'))} = {repr(result.get('decision'))}" - ) - - if result is None: - logger.error( - f"[DEBUG:Workflow:{proposal_id}] Workflow returned None result, using default values" - ) - return { - "proposal_id": proposal_id, - "score": 0, - "decision": "Error", - "explanation": "Evaluation failed: Workflow returned empty result", - "component_scores": { - "core": 0, - "historical": 0, - "financial": 0, - "social": 0, - }, - "flags": ["Workflow error: Empty result"], - "token_usage": {}, - } + logger.info(f"[DEBUG:EXTRACT] final_score content: {result['final_score']}") + # Extract results def safe_extract_score(value, default=0): + """Safely extract a score from a potentially complex structure.""" if isinstance(value, dict) and "score" in value: - return value.get("score", default) - elif isinstance(value, int): - return value - elif isinstance(value, str): - try: - return int(value) - except ValueError: - pass # If string is not int, will fall through to default + return value["score"] return default - final_score_val = result.get("final_score") - final_score_dict = {} - if isinstance(final_score_val, dict): - final_score_dict = final_score_val - - component_scores = { - "core": safe_extract_score(result.get("core_score")), - "historical": safe_extract_score(result.get("historical_score")), - "financial": safe_extract_score(result.get("financial_score")), - "social": safe_extract_score(result.get("social_score")), + # Get all scores for reporting + core_score = safe_extract_score(result.get("core_score")) + historical_score = safe_extract_score(result.get("historical_score")) + financial_score = safe_extract_score(result.get("financial_score")) + social_score = safe_extract_score(result.get("social_score")) + final_score = safe_extract_score(result.get("final_score")) + + # Get decision + final_decision = "Undecided" + final_explanation = "No final decision was reached." + + if isinstance(result.get("final_score"), dict): + final_decision = result["final_score"].get("decision", "Undecided") + final_explanation = result["final_score"].get( + "explanation", "No explanation provided." + ) + + # Determine approval and confidence + approval = final_decision.lower() == "approve" + confidence = 0.7 # Default confidence + + if ( + isinstance(result.get("final_score"), dict) + and "confidence" in result["final_score"] + ): + confidence = result["final_score"]["confidence"] + + # Compile token usage + token_usage = result.get("token_usage", {}) + total_token_usage = { + "input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, } - # This is a useful log to keep even at lower debug levels - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] EXTRACTED COMPONENT SCORES: {component_scores}" - ) - - explanation = "" - if isinstance(final_score_dict, dict) and "explanation" in final_score_dict: - explanation = final_score_dict.get("explanation", "") - elif isinstance(final_score_val, str): - explanation = final_score_val - - # Log the explanation to help debug - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] Explanation extracted: {explanation[:100]}..." - ) - - final_score = 0 - if isinstance(final_score_dict, dict) and "score" in final_score_dict: - final_score = final_score_dict.get("score", 0) - else: - final_score = safe_extract_score(final_score_val) - - decision = result.get("decision") - if decision is None: - if isinstance(final_score_dict, dict) and "decision" in final_score_dict: - decision = final_score_dict.get("decision") - else: - decision = "Reject" - - logger.debug( - f"[DEBUG:Workflow:{proposal_id}] Final decision: {decision}, score: {final_score}" - ) - - total_token_usage = result.get("token_usage", {}) - total_input_tokens = 0 - total_output_tokens = 0 - total_tokens = 0 - - # Aggregate tokens from all agent steps - # Assuming model_name is consistent across all steps for this aggregation, or we use the primary model_name - # If each agent could use a different model, this would need more detailed per-model tracking - logger.debug(f"Token usage entries in result: {list(total_token_usage.keys())}") - for agent_key, usage_data in total_token_usage.items(): - if isinstance(usage_data, dict): - total_input_tokens += usage_data.get("input_tokens", 0) - total_output_tokens += usage_data.get("output_tokens", 0) - total_tokens += usage_data.get("total_tokens", 0) - logger.debug(f"Token usage for {agent_key}: {usage_data}") - else: - logger.warning( - f"Unexpected format for token_usage data for agent {agent_key}: {usage_data}" - ) + for agent_usage in token_usage.values(): + total_token_usage["input_tokens"] += agent_usage.get("input_tokens", 0) + total_token_usage["output_tokens"] += agent_usage.get("output_tokens", 0) + total_token_usage["total_tokens"] += agent_usage.get("total_tokens", 0) - # Extract component summaries for detailed reporting - component_summaries = {} - if isinstance(result.get("summaries"), dict): - component_summaries = result.get("summaries") - - # Extract and aggregate flags - all_flags = result.get("flags", []) - if not isinstance(all_flags, list): - all_flags = [] - - # Placeholder for web search specific token usage if it were tracked separately - # In the original, these seemed to be fixed placeholders. - web_search_input_tokens = 0 - web_search_output_tokens = 0 - web_search_total_tokens = 0 - - # Initialize total token usage by model - total_token_usage_by_model = {} - - # Extract token usage by model from token_usage data - for agent_name, agent_usage in total_token_usage.items(): - if isinstance(agent_usage, dict) and agent_usage.get("total_tokens", 0) > 0: - # Get model name from config, or use default - model_name = config.get( - "model_name", "gpt-4.1" - ) # Use configured model name - - # Extract model name from each agent usage if available - # This would require each agent to include model info in their token usage - if "model_name" in agent_usage: - model_name = agent_usage["model_name"] - - # Initialize the model entry if needed - if model_name not in total_token_usage_by_model: - total_token_usage_by_model[model_name] = { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - } - - # Add token usage for this agent to the model's tally - total_token_usage_by_model[model_name][ - "input_tokens" - ] += agent_usage.get("input_tokens", 0) - total_token_usage_by_model[model_name][ - "output_tokens" - ] += agent_usage.get("output_tokens", 0) - total_token_usage_by_model[model_name][ - "total_tokens" - ] += agent_usage.get("total_tokens", 0) - - # Fallback if no token usage was recorded - if not total_token_usage_by_model: - total_token_usage_by_model["gpt-4.1"] = { - "input_tokens": total_input_tokens, - "output_tokens": total_output_tokens, - "total_tokens": total_tokens, - } - - # Improved cost calculation by model - cost_per_thousand = { - "gpt-4.1": 0.01, # $0.01 per 1K tokens - "gpt-4.1-mini": 0.005, # $0.005 per 1K tokens - "gpt-4.1-32k": 0.03, # $0.03 per 1K tokens - "gpt-4": 0.03, # $0.03 per 1K tokens - "gpt-4-32k": 0.06, # $0.06 per 1K tokens - "gpt-3.5-turbo": 0.0015, # $0.0015 per 1K tokens - "default": 0.01, # default fallback - } - - # Calculate costs for each model - total_cost_by_model = {} - total_overall_cost = 0.0 - for model_name, usage in total_token_usage_by_model.items(): - # Get cost per 1K tokens for this model - model_cost_per_k = cost_per_thousand.get( - model_name, cost_per_thousand["default"] - ) - # Calculate cost for this model's usage - model_cost = usage["total_tokens"] * (model_cost_per_k / 1000) - total_cost_by_model[model_name] = model_cost - total_overall_cost += model_cost - - if not total_cost_by_model: - # Fallback if no models were recorded - model_name = "gpt-4.1" # Default model name - total_cost_by_model[model_name] = total_tokens * ( - cost_per_thousand["default"] / 1000 - ) - total_overall_cost = total_cost_by_model[model_name] - - final_result = { - "success": True, - "evaluation": { - "approve": decision == "Approve", - "confidence_score": final_score / 100.0 if final_score else 0.0, - "reasoning": explanation, - }, - "decision": decision, - "score": final_score, - "explanation": explanation, - "component_scores": component_scores, - "component_summaries": component_summaries, # Include component summaries - "flags": all_flags, - "token_usage": total_token_usage, # Include all token usage details - "web_search_results": [], - "treasury_balance": None, - "web_search_token_usage": { - "input_tokens": web_search_input_tokens, - "output_tokens": web_search_output_tokens, - "total_tokens": web_search_total_tokens, - }, - "evaluation_token_usage": { - "input_tokens": total_input_tokens, - "output_tokens": total_output_tokens, - "total_tokens": total_tokens, - }, - "evaluation_model_info": { - "name": config.get("model_name", "gpt-4.1"), - "temperature": config.get("temperature", 0.1), - }, - "web_search_model_info": { - "name": config.get("model_name", "gpt-4.1"), - "temperature": config.get("temperature", 0.1), + # Return formatted result + evaluation_result = { + "proposal_id": proposal_id, + "approve": approval, + "confidence_score": confidence, + "reasoning": final_explanation, + "scores": { + "core": core_score, + "historical": historical_score, + "financial": financial_score, + "social": social_score, + "final": final_score, }, - "total_token_usage_by_model": total_token_usage_by_model, - "total_cost_by_model": total_cost_by_model, - "total_overall_cost": total_overall_cost, + "flags": result.get("flags", []), + "summaries": result.get("summaries", {}), + "token_usage": total_token_usage, + "model_name": model_name, } - logger.debug( - f"Proposal evaluation completed: Success={final_result['success']} | Decision={'APPROVE' if decision == 'Approve' else 'REJECT'} | Confidence={final_result['evaluation']['confidence_score']:.2f} | Auto-voted={decision == 'Approve'}" + logger.info( + f"Completed proposal evaluation for proposal {proposal_id}: {final_decision}" ) - return final_result + return evaluation_result + except Exception as e: - logger.error(f"Error in workflow execution: {str(e)}", exc_info=True) + logger.error(f"Error in proposal evaluation: {str(e)}") return { "proposal_id": proposal_id, - "score": 0, - "decision": "Error", - "explanation": f"Evaluation failed: {str(e)}", - "component_scores": { - "core": 0, - "historical": 0, - "financial": 0, - "social": 0, - }, - "flags": [f"Workflow error: {str(e)}"], - "token_usage": {}, + "approve": False, + "confidence_score": 0.1, + "reasoning": f"Evaluation failed due to error: {str(e)}", + "error": str(e), } def get_proposal_evaluation_tools( profile: Optional[Profile] = None, agent_id: Optional[UUID] = None ): - """Get the tools needed for proposal evaluation.""" - all_tools = initialize_tools(profile=profile, agent_id=agent_id) - logger.debug(f"Available tools: {', '.join(all_tools.keys())}") - required_tools = [ - "dao_action_get_proposal", - "dao_action_vote_on_proposal", - "dao_action_get_voting_power", - "dao_action_get_voting_configuration", - "database_get_dao_get_by_name", - "dao_search", - ] - filtered_tools = filter_tools_by_names(required_tools, all_tools) - logger.debug(f"Using tools: {', '.join(filtered_tools.keys())}") - return filtered_tools + """Get tools for proposal evaluation. + + Args: + profile: Optional user profile + agent_id: Optional agent ID + + Returns: + List of available tools + """ + tool_names = ["vote_on_action_proposal"] + tools = initialize_tools(profile, agent_id) + return filter_tools_by_names(tools, tool_names) async def evaluate_and_vote_on_proposal( @@ -1767,283 +424,133 @@ async def evaluate_and_vote_on_proposal( dao_id: Optional[UUID] = None, debug_level: int = 0, # 0=normal, 1=verbose, 2=very verbose ) -> Dict: - """Evaluate a proposal and automatically vote based on the evaluation.""" - logger.debug( - f"Starting proposal evaluation: proposal_id={proposal_id} | auto_vote={auto_vote} | confidence_threshold={confidence_threshold} | debug_level={debug_level}" - ) - try: - effective_agent_id = agent_id - if not effective_agent_id and wallet_id: - wallet = backend.get_wallet(wallet_id) - if wallet and wallet.agent_id: - effective_agent_id = wallet.agent_id - logger.debug( - f"Using agent ID {effective_agent_id} from wallet {wallet_id}" - ) - - model_name = "gpt-4.1" - temperature = 0.1 - if effective_agent_id: - try: - prompts = backend.list_prompts( - PromptFilter( - agent_id=effective_agent_id, - dao_id=dao_id, - is_active=True, - limit=1, - ) - ) - if prompts: - first_prompt = prompts[0] - model_name = first_prompt.model or model_name - temperature = ( - first_prompt.temperature - if first_prompt.temperature is not None - else temperature - ) - logger.debug( - f"Using model settings from agent {effective_agent_id}: {model_name} (temp={temperature})" - ) - else: - logger.warning( - f"No active prompts found for agent {effective_agent_id}." - ) - except Exception as e: - logger.error( - f"Failed to get agent prompt settings: {str(e)}", exc_info=True - ) + """Evaluate a proposal and optionally vote on it. + + Args: + proposal_id: Proposal ID + wallet_id: Optional wallet ID + agent_id: Optional agent ID + auto_vote: Whether to automatically vote based on evaluation + confidence_threshold: Confidence threshold for auto-voting + dao_id: Optional DAO ID + debug_level: Debug level (0=normal, 1=verbose, 2=very verbose) + + Returns: + Evaluation and voting results + """ + # Get proposal details + logger.info(f"Retrieving proposal details for {proposal_id}") - logger.debug( - f"[PROPOSAL_DEBUG] Fetching proposal data from backend for ID: {proposal_id}" - ) - proposal_data = backend.get_proposal(proposal_id) - if not proposal_data: - logger.error( - f"[PROPOSAL_DEBUG] No proposal data found for ID: {proposal_id}" - ) - raise ValueError(f"Proposal {proposal_id} not found") - - logger.debug(f"[PROPOSAL_DEBUG] Raw proposal data: {proposal_data}") + try: + proposal = backend.get_proposal(proposal_id=proposal_id) - proposal_content = proposal_data.parameters or "" - if not proposal_content: - logger.warning(f"[PROPOSAL_DEBUG] Proposal parameters/content is empty!") + if not proposal: + logger.error(f"Proposal {proposal_id} not found") + return {"error": f"Proposal {proposal_id} not found"} + # Set up config based on debug level config = { - "model_name": model_name, - "temperature": temperature, - "mission_collection": "knowledge_collection", - "proposals_collection": "proposals", - "enable_web_search": True, - "planning_model": "gpt-4.1-mini", + "debug_level": debug_level, } - if debug_level > 0: - config["debug_level"] = debug_level - logger.debug(f"[PROPOSAL_DEBUG] Setting debug_level to {debug_level}") - - if not dao_id and proposal_data.dao_id: - dao_id = proposal_data.dao_id - dao_info = None - if dao_id: - dao_info = backend.get_dao(dao_id) - if dao_info: - config["dao_mission"] = dao_info.mission - - treasury_balance = None - try: - if dao_id: - treasury_extensions = backend.list_extensions( - ExtensionFilter(dao_id=dao_id, type="EXTENSIONS_TREASURY") - ) - if treasury_extensions: - hiro_api = HiroApi() - treasury_balance = hiro_api.get_address_balance( - treasury_extensions[0].contract_principal - ) - except Exception as e: - logger.error(f"Failed to get treasury balance: {str(e)}", exc_info=True) - - logger.debug("Starting hierarchical evaluation workflow...") - eval_result = await evaluate_proposal( + if debug_level >= 1: + # For verbose debugging, customize agent settings + config["approval_threshold"] = 70 + config["veto_threshold"] = 30 + config["consensus_threshold"] = 10 + + # Evaluate the proposal + logger.info(f"Starting evaluation of proposal {proposal_id}") + evaluation_result = await evaluate_proposal( proposal_id=str(proposal_id), - proposal_data=proposal_data.parameters, + proposal_data=proposal.parameters, config=config, ) - decision = eval_result.get("decision") - if decision is None: - decision = "Reject" - logger.warning( - f"No decision found in evaluation results, defaulting to '{decision}'" - ) - - score = eval_result.get("score", 0) - confidence_score = score / 100.0 if score else 0.0 - - approve = False - if isinstance(decision, str) and decision.lower() == "approve": - approve = True - - should_vote = auto_vote and confidence_score >= confidence_threshold - - vote_result = None - tx_id = None - if should_vote and wallet_id: - try: - vote_tool = VoteOnActionProposalTool(wallet_id=wallet_id) - if proposal_data.type == ProposalType.ACTION: - contract_info = proposal_data.contract_principal - if "." in contract_info: - parts = contract_info.split(".") - if len(parts) >= 2: - action_proposals_contract = parts[0] - action_proposals_voting_extension = parts[1] - result = await vote_tool.vote_on_proposal( - contract_principal=action_proposals_contract, - extension_name=action_proposals_voting_extension, - proposal_id=proposal_data.proposal_id, - vote=approve, - ) - vote_result = { - "success": result is not None, - "output": result, - } - if ( - result - and isinstance(result, str) - and "txid:" in result.lower() - ): - for line in result.split("\n"): - if "txid:" in line.lower(): - parts = line.split(":") - if len(parts) > 1: - tx_id = parts[1].strip() - break - else: - logger.warning( - f"Invalid contract principal format: {contract_info}" - ) - else: - logger.warning( - f"Cannot vote on non-action proposal type: {proposal_data.type}" - ) - except Exception as e: - logger.error(f"Error executing vote: {str(e)}", exc_info=True) - vote_result = { - "success": False, - "error": f"Error during voting: {str(e)}", + # Check if auto voting is enabled + if auto_vote: + if "error" in evaluation_result: + logger.error( + f"Skipping voting due to evaluation error: {evaluation_result['error']}" + ) + return { + "evaluation": evaluation_result, + "vote_result": None, + "message": "Skipped voting due to evaluation error", } - elif not should_vote: - vote_result = { - "success": True, - "message": "Voting skipped due to confidence threshold or auto_vote setting", - "data": None, - } - # Get token usage data from eval_result - total_token_usage = eval_result.get("token_usage", {}) - total_input_tokens = 0 - total_output_tokens = 0 - total_tokens = 0 - - # Aggregate tokens from all agent steps - no need to log duplicates here - for agent_key, usage_data in total_token_usage.items(): - if isinstance(usage_data, dict): - total_input_tokens += usage_data.get("input_tokens", 0) - total_output_tokens += usage_data.get("output_tokens", 0) - total_tokens += usage_data.get("total_tokens", 0) - - # Initialize total_token_usage_by_model using data from eval_result - total_token_usage_by_model = eval_result.get("total_token_usage_by_model", {}) - if not total_token_usage_by_model: - # Use the default model name from settings or default to gpt-4.1 - default_model = model_name or "gpt-4.1" - # Add total token counts to the model - total_token_usage_by_model[default_model] = { - "input_tokens": total_input_tokens, - "output_tokens": total_output_tokens, - "total_tokens": total_tokens, - } + # Check if the confidence score meets the threshold + confidence_score = evaluation_result.get("confidence_score", 0) - # Get cost calculations from eval_result if available - total_cost_by_model = eval_result.get("total_cost_by_model", {}) - total_overall_cost = eval_result.get("total_overall_cost", 0.0) - - # If cost data is missing, calculate it - if not total_cost_by_model: - # Improved cost calculation by model - cost_per_thousand = { - "gpt-4.1": 0.01, # $0.01 per 1K tokens - "gpt-4.1-mini": 0.005, # $0.005 per 1K tokens - "gpt-4.1-32k": 0.03, # $0.03 per 1K tokens - "gpt-4": 0.03, # $0.03 per 1K tokens - "gpt-4-32k": 0.06, # $0.06 per 1K tokens - "gpt-3.5-turbo": 0.0015, # $0.0015 per 1K tokens - "default": 0.01, # default fallback - } + if confidence_score >= confidence_threshold: + # Get the vote decision + approve = evaluation_result.get("approve", False) + vote_direction = "for" if approve else "against" - # Calculate costs for each model - total_cost_by_model = {} - total_overall_cost = 0.0 - for model_key, usage in total_token_usage_by_model.items(): - # Get cost per 1K tokens for this model - model_cost_per_k = cost_per_thousand.get( - model_key, cost_per_thousand["default"] + logger.info( + f"Auto-voting {vote_direction} proposal {proposal_id} with confidence {confidence_score}" ) - # Calculate cost for this model's usage - model_cost = usage["total_tokens"] * (model_cost_per_k / 1000) - total_cost_by_model[model_key] = model_cost - total_overall_cost += model_cost - - # Construct final result with voting information added - final_result = { - "success": True, - "evaluation": { - "approve": approve, - "confidence_score": confidence_score, - "reasoning": eval_result.get("explanation", ""), - }, - "vote_result": vote_result, - "auto_voted": should_vote, - "tx_id": tx_id, - "vector_results": [], - "recent_tweets": [], - "web_search_results": eval_result.get("web_search_results", []), - "treasury_balance": treasury_balance, - "component_scores": eval_result.get("component_scores", {}), - "component_summaries": eval_result.get("component_summaries", {}), - "flags": eval_result.get("flags", []), - "token_usage": total_token_usage, - "web_search_token_usage": eval_result.get( - "web_search_token_usage", - { - "input_tokens": 0, - "output_tokens": 0, - "total_tokens": 0, - }, - ), - "evaluation_token_usage": { - "input_tokens": total_input_tokens, - "output_tokens": total_output_tokens, - "total_tokens": total_tokens, - }, - "evaluation_model_info": {"name": model_name, "temperature": temperature}, - "web_search_model_info": {"name": model_name, "temperature": temperature}, - "total_token_usage_by_model": total_token_usage_by_model, - "total_cost_by_model": total_cost_by_model, - "total_overall_cost": total_overall_cost, - } - # Single log entry about the final result instead of duplicating token usage logs - logger.debug( - f"Proposal evaluation completed with voting: Decision={'APPROVE' if approve else 'REJECT'} | Confidence={confidence_score:.2f} | Auto-voted={should_vote} | Transaction={tx_id or 'None'}" - ) - return final_result + # Get the voting tool + profile = await backend.get_profile( + wallet_id=wallet_id, agent_id=agent_id + ) + tools = get_proposal_evaluation_tools(profile, agent_id) + vote_tool = next( + (t for t in tools if isinstance(t, VoteOnActionProposalTool)), None + ) + + if vote_tool: + try: + # Execute the vote + vote_result = await vote_tool.execute( + proposal_id=str(proposal_id), + vote=vote_direction, + wallet_id=str(wallet_id) if wallet_id else None, + dao_id=str(dao_id) if dao_id else None, + ) + + logger.info(f"Vote result: {vote_result}") + + return { + "evaluation": evaluation_result, + "vote_result": vote_result, + "message": f"Voted {vote_direction} with confidence {confidence_score:.2f}", + } + except Exception as e: + logger.error(f"Error voting on proposal: {str(e)}") + return { + "evaluation": evaluation_result, + "vote_result": None, + "error": f"Error voting on proposal: {str(e)}", + } + else: + logger.error("Vote tool not available") + return { + "evaluation": evaluation_result, + "vote_result": None, + "error": "Vote tool not available", + } + else: + logger.info( + f"Skipping auto-vote due to low confidence: {confidence_score} < {confidence_threshold}" + ) + return { + "evaluation": evaluation_result, + "vote_result": None, + "message": f"Skipped voting due to low confidence: {confidence_score:.2f} < {confidence_threshold}", + } + else: + logger.info(f"Auto-voting disabled, returning evaluation only") + return { + "evaluation": evaluation_result, + "vote_result": None, + "message": "Auto-voting disabled", + } + except Exception as e: - error_msg = f"Unexpected error in evaluate_and_vote_on_proposal: {str(e)}" - logger.error(error_msg, exc_info=True) - return {"success": False, "error": error_msg} + logger.error(f"Error in evaluate_and_vote_on_proposal: {str(e)}") + return {"error": f"Failed to evaluate proposal: {str(e)}"} async def evaluate_proposal_only( @@ -2052,30 +559,22 @@ async def evaluate_proposal_only( agent_id: Optional[UUID] = None, dao_id: Optional[UUID] = None, ) -> Dict: - """Evaluate a proposal without voting.""" - logger.debug(f"Starting proposal-only evaluation: proposal_id={proposal_id}") - effective_agent_id = agent_id - if not effective_agent_id and wallet_id: - wallet = backend.get_wallet(wallet_id) - if wallet and wallet.agent_id: - effective_agent_id = wallet.agent_id - - result = await evaluate_and_vote_on_proposal( + """Evaluate a proposal without voting. + + Args: + proposal_id: Proposal ID + wallet_id: Optional wallet ID + agent_id: Optional agent ID + dao_id: Optional DAO ID + + Returns: + Evaluation results + """ + # Delegate to evaluate_and_vote_on_proposal with auto_vote=False + return await evaluate_and_vote_on_proposal( proposal_id=proposal_id, wallet_id=wallet_id, - agent_id=effective_agent_id, - dao_id=dao_id, + agent_id=agent_id, auto_vote=False, + dao_id=dao_id, ) - - # Simplified logging - no need to duplicate what evaluate_and_vote_on_proposal already logged - logger.debug("Removing vote-related fields from response") - if "vote_result" in result: - del result["vote_result"] - if "auto_voted" in result: - del result["auto_voted"] - if "tx_id" in result: - del result["tx_id"] - - logger.debug("Proposal-only evaluation completed") - return result diff --git a/services/workflows/utils/__init__.py b/services/workflows/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/services/workflows/utils/models.py b/services/workflows/utils/models.py new file mode 100644 index 00000000..0fc55b73 --- /dev/null +++ b/services/workflows/utils/models.py @@ -0,0 +1,31 @@ +from typing import List + +from pydantic import BaseModel, Field + + +class AgentOutput(BaseModel): + """Output model for agent evaluations.""" + + score: int = Field(description="Score from 0-100") + flags: List[str] = Field(description="Critical issues flagged") + summary: str = Field(description="Summary of findings") + + +class FinalOutput(BaseModel): + """Output model for the final evaluation decision.""" + + score: int = Field(description="Final evaluation score") + decision: str = Field(description="Approve or Reject") + explanation: str = Field(description="Reasoning for decision") + + +class ProposalEvaluationOutput(BaseModel): + """Output model for proposal evaluation.""" + + approve: bool = Field( + description="Decision: true to approve (vote FOR), false to reject (vote AGAINST)" + ) + confidence_score: float = Field( + description="Confidence score for the decision (0.0-1.0)" + ) + reasoning: str = Field(description="The reasoning behind the evaluation decision") diff --git a/services/workflows/utils/state_reducers.py b/services/workflows/utils/state_reducers.py new file mode 100644 index 00000000..390dca13 --- /dev/null +++ b/services/workflows/utils/state_reducers.py @@ -0,0 +1,139 @@ +from typing import Any, Dict, List, Optional + +from lib.logger import configure_logger + +logger = configure_logger(__name__) + + +def no_update_reducer(current: Any, new: List[Any]) -> Any: + """Reducer that prevents updates after initial value is set. + + Args: + current: The current value + new: List of new values to consider + + Returns: + The original value if set, otherwise the first non-None value from new + """ + # Treat initial empty string for str types as if it were None for accepting the first value + is_initial_empty_string = isinstance(current, str) and current == "" + + # If current is genuinely set (not None and not initial empty string), keep it. + if current is not None and not is_initial_empty_string: + return current + + # Current is None or an initial empty string. Try to set it from new. + processed_new_values = ( + new if isinstance(new, list) else [new] + ) # Ensure 'new' is a list + for n_val in processed_new_values: + if n_val is not None: + return n_val + + # If current was None/initial empty string and new is all None or empty, return current + return current + + +def merge_dicts(current: Optional[Dict], updates: List[Optional[Dict]]) -> Dict: + """Merge multiple dictionary updates into the current dictionary. + + Args: + current: The current dictionary (or None) + updates: List of dictionaries to merge in + + Returns: + The merged dictionary + """ + # Initialize current if it's None + if current is None: + current = {} + + # Handle case where updates is None + if updates is None: + return current + + # Process updates if it's a list + if isinstance(updates, list): + for update in updates: + if update and isinstance(update, dict): + current.update(update) + # Handle case where updates is a single dictionary, not a list + elif isinstance(updates, dict): + current.update(updates) + + return current + + +def set_once(current: Any, updates: List[Any]) -> Any: + """Set the value once and prevent further updates. + + Args: + current: The current value + updates: List of potential new values + + Returns: + The current value if set, otherwise the first non-None value from updates + """ + # If current already has a value, return it unchanged + if current is not None: + return current + + # Handle case where updates is None instead of a list + if updates is None: + return None + + # Process updates if it's a list + if isinstance(updates, list): + for update in updates: + if update is not None: + return update + # Handle case where updates is a single value, not a list + elif updates is not None: + return updates + + return current + + +def update_state_with_agent_result( + state: Dict[str, Any], agent_result: Dict[str, Any], agent_name: str +) -> Dict[str, Any]: + """Update state with agent result including summaries and flags. + + Args: + state: The current state dictionary + agent_result: The result dictionary from an agent + agent_name: The name of the agent (e.g., 'core', 'historical') + + Returns: + The updated state dictionary + """ + logger.debug( + f"[DEBUG:update_state:{agent_name}] Updating state with {agent_name}_score (score: {agent_result.get('score', 'N/A')})" + ) + + # Update agent score in state + if agent_name in ["core", "historical", "financial", "social", "final"]: + # Make a copy of agent_result to avoid modifying the original + score_dict = dict(agent_result) + # Don't pass token_usage through this path to avoid duplication + if "token_usage" in score_dict: + del score_dict["token_usage"] + + # Directly assign the dictionary to the state key + state[f"{agent_name}_score"] = score_dict + + # Update summaries + if "summaries" not in state: + state["summaries"] = {} + + if "summary" in agent_result and agent_result["summary"]: + state["summaries"][f"{agent_name}_score"] = agent_result["summary"] + + # Update flags + if "flags" not in state: + state["flags"] = [] + + if "flags" in agent_result and isinstance(agent_result["flags"], list): + state["flags"].extend(agent_result["flags"]) + + return state diff --git a/services/workflows/utils/token_usage.py b/services/workflows/utils/token_usage.py new file mode 100644 index 00000000..5a063213 --- /dev/null +++ b/services/workflows/utils/token_usage.py @@ -0,0 +1,64 @@ +from typing import Any, Dict + +from lib.logger import configure_logger +from lib.utils import calculate_token_cost + +logger = configure_logger(__name__) + + +class TokenUsageMixin: + """Mixin for tracking token usage in LLM calls.""" + + def __init__(self): + """Initialize token usage tracker.""" + pass + + def track_token_usage(self, prompt_text: str, result: Any) -> Dict[str, int]: + """Track token usage for an LLM invocation. + + Args: + prompt_text: The prompt text sent to the LLM + result: The response from the LLM + + Returns: + Dictionary containing token usage information + """ + token_usage_data = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0} + + # Try to extract token usage from LLM + if ( + hasattr(self.llm, "_last_prompt_id") + and hasattr(self.llm, "client") + and hasattr(self.llm.client, "usage_by_prompt_id") + ): + last_prompt_id = self.llm._last_prompt_id + if last_prompt_id in self.llm.client.usage_by_prompt_id: + usage = self.llm.client.usage_by_prompt_id[last_prompt_id] + token_usage_data = { + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } + return token_usage_data + + # Fallback to estimation + llm_model_name = getattr(self.llm, "model_name", "gpt-4.1") + token_count = len(prompt_text) // 4 # Simple estimation + token_usage_dict = {"input_tokens": token_count} + cost_result = calculate_token_cost(token_usage_dict, llm_model_name) + token_usage_data = { + "input_tokens": token_count, + "output_tokens": ( + len(result.model_dump_json()) // 4 + if hasattr(result, "model_dump_json") + else 0 + ), + "total_tokens": token_count + + ( + len(result.model_dump_json()) // 4 + if hasattr(result, "model_dump_json") + else 0 + ), + "model_name": llm_model_name, + } + return token_usage_data diff --git a/services/workflows/web_search_mixin.py b/services/workflows/web_search_mixin.py index f85692c4..8a8139e3 100644 --- a/services/workflows/web_search_mixin.py +++ b/services/workflows/web_search_mixin.py @@ -26,7 +26,7 @@ def _init_web_search(self) -> None: if not hasattr(self, "client"): self.client = OpenAI() - async def search_web( + async def web_search( self, query: str, **kwargs ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: """Search the web using OpenAI Responses API. @@ -175,7 +175,7 @@ def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None: - user_location: dict with type, country, city, region """ # Add web search node - graph.add_node("web_search", self.search_web) + graph.add_node("web_search", self.web_search) # Add result processing node if needed if "process_results" not in graph.nodes: From cd61626b1563fe1bed3bc0136db91e9a3fd34a5a Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 18:26:26 -0700 Subject: [PATCH 030/219] improved evaluation agents --- examples/proposal_evaluation_example.py | 20 +- main.py | 8 +- services/workflows/agents/core_context.py | 63 +++--- .../workflows/agents/financial_context.py | 111 +++++++---- .../workflows/agents/historical_context.py | 186 ++++++++++++++---- services/workflows/agents/reasoning.py | 66 ++++--- services/workflows/agents/social_context.py | 85 ++++---- services/workflows/chat.py | 2 +- 8 files changed, 366 insertions(+), 175 deletions(-) diff --git a/examples/proposal_evaluation_example.py b/examples/proposal_evaluation_example.py index 61cae05f..05f66fb1 100644 --- a/examples/proposal_evaluation_example.py +++ b/examples/proposal_evaluation_example.py @@ -40,12 +40,10 @@ async def create_test_proposal(dao_id: UUID) -> UUID: Proposal Title: $FACES Airdrop to Bitcoin Faces Holders with Transparent Execution and Community Engagement -Proposal ID: [Generate a new UUID for submission] - Proposer: Publius.btc Proposal Data: -I, Publius.btc, propose to execute a $FACES airdrop to Bitcoin Faces holders to boost community engagement and reward active participants in the DAO. The airdrop will distribute 10,000 $FACES tokens to eligible holders, with a clear execution plan, transparent verification, and measurable outcomes. The proposal aligns with the DAO’s mission to promote community activity and token utility. Below are the details: +I, Publius.btc, propose to execute a $FACES airdrop to Bitcoin Faces holders to boost community engagement and reward active participants in the DAO. Due to a limit of 1,000 tokens per proposal, this will be 1 of 10 proposals, each distributing up to 1,000 $FACES tokens. The airdrop will distribute a total of 10,000 $FACES tokens to eligible holders, with a clear execution plan, transparent verification, and measurable outcomes. The proposal aligns with the DAO's mission to promote community activity and token utility. Below are the details: Objective: Distribute $FACES tokens to Bitcoin Faces holders to incentivize participation, increase governance engagement, and strengthen community ties. Eligibility Criteria: @@ -54,7 +52,7 @@ async def create_test_proposal(dao_id: UUID) -> UUID: Exclusion: Wallets flagged for suspicious activity (e.g., wash trading) based on on-chain analysis. Execution Plan: Snapshot: Conduct a blockchain snapshot of Bitcoin Faces holders on the specified date, using a third-party tool (e.g., Etherscan or equivalent for Bitcoin-based assets). -Distribution: Distribute 10 $FACES per eligible wallet, up to a total of 10,000 tokens, via a smart contract to ensure transparency and immutability. +Distribution: Distribute 10 $FACES per eligible wallet, up to a total of 1,000 tokens per proposal, via a smart contract to ensure transparency and immutability. This proposal is part of a series of 10 proposals to reach the full 10,000 token distribution. Timeline: Day 1–7: Proposal approval and snapshot preparation. Day 8: Snapshot execution. @@ -62,23 +60,23 @@ async def create_test_proposal(dao_id: UUID) -> UUID: Day 15: Airdrop distribution. Day 20: Post-airdrop report published. Budget and Funding: -Total Cost: 10,000 $FACES tokens (valued at $0.10 per token based on current market price, totaling $1,000). +Total Cost: 1,000 $FACES tokens for this proposal (valued at $0.10 per token based on current market price, totaling $100). The full airdrop campaign will total 10,000 tokens across 10 proposals. Additional Costs: $500 for smart contract development, auditing, and gas fees, to be funded from the DAO treasury. -Funding Request: 10,000 $FACES tokens + $500 in stablecoins (e.g., USDC) from the DAO treasury. +Funding Request: 1,000 $FACES tokens + $500 in stablecoins (e.g., USDC) from the DAO treasury for this proposal. Cost Justification: The airdrop is cost-effective, targeting active holders to maximize engagement with minimal token dilution. The $500 covers secure execution to mitigate risks. Verification and Transparency: -Publish the snapshot data and eligible wallet list on the DAO’s governance forum. +Publish the snapshot data and eligible wallet list on the DAO's governance forum. Share the smart contract address and transaction hashes on-chain for public verification. Provide a detailed post-airdrop report within 5 days of distribution, including the number of wallets reached, tokens distributed, and community feedback. Community Benefit: Inclusivity: All Bitcoin Faces holders are eligible, ensuring broad participation. Engagement: The airdrop will encourage holders to participate in governance and DAO activities, addressing low governance participation. -Stakeholder Consideration: The plan includes outreach to diverse community segments via the DAO’s social channels (e.g., Discord, X) to ensure awareness and feedback. +Stakeholder Consideration: The plan includes outreach to diverse community segments via the DAO's social channels (e.g., Discord, X) to ensure awareness and feedback. Alignment with DAO Priorities: -Promotes token utility and community engagement, core to the DAO’s mission. +Promotes token utility and community engagement, core to the DAO's mission. Supports financial prudence by capping costs and providing ROI through increased governance participation (measurable via voting turnout post-airdrop). Risk Mitigation: -Financial Risk: Limited to 10,000 $FACES and $500, with no ongoing costs. +Financial Risk: Limited to 1,000 $FACES and $500 for this proposal, with no ongoing costs. The full campaign is capped at 10,000 tokens and $5,000 across all proposals. Execution Risk: Smart contract audit to prevent vulnerabilities. Inclusion Risk: Transparent eligibility criteria to avoid disputes. Deliverables and ROI: @@ -89,7 +87,7 @@ async def create_test_proposal(dao_id: UUID) -> UUID: Responds to feedback on inclusion by defining clear eligibility and outreach strategies. Aligns with financial priorities by justifying costs and capping token usage. Commitment: -I will execute the airdrop as outlined, provide regular updates on the DAO’s governance forum, and deliver a comprehensive report with proof of distribution. If the proposal is approved, I will collaborate with the DAO’s technical and community teams to ensure success. +I will execute the airdrop as outlined, provide regular updates on the DAO's governance forum, and deliver a comprehensive report with proof of distribution. If the proposal is approved, I will collaborate with the DAO's technical and community teams to ensure success. """ # # Convert parameters to JSON string and then hex encode it diff --git a/main.py b/main.py index b6d71f27..6a9f52c7 100644 --- a/main.py +++ b/main.py @@ -3,7 +3,7 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -import api +from api import chat, tools, webhooks from config import config from lib.logger import configure_logger from services import startup @@ -48,9 +48,9 @@ async def health_check(): # Load API routes -app.include_router(api.tools.router) -app.include_router(api.chat.router) -app.include_router(api.webhooks.router) +app.include_router(tools.router) +app.include_router(chat.router) +app.include_router(webhooks.router) @app.on_event("startup") diff --git a/services/workflows/agents/core_context.py b/services/workflows/agents/core_context.py index bece9a93..4e010486 100644 --- a/services/workflows/agents/core_context.py +++ b/services/workflows/agents/core_context.py @@ -80,28 +80,47 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: prompt = PromptTemplate( input_variables=["proposal_data", "dao_mission"], - template="""Evaluate the proposal against the DAO's mission and values. - -# Context -You are evaluating a proposal for a DAO that focuses on: {dao_mission} - -# Proposal Data -{proposal_data} - -# Task -Score this proposal from 0-100 based on: -1. Alignment with DAO mission (40%) -2. Clarity of proposal (20%) -3. Feasibility and practicality (20%) -4. Community benefit (20%) - -# Output Format -Provide: -- Score (0-100) -- List of any critical issues or red flags -- Brief summary of your evaluation - -Only return a JSON object with these three fields: score, flags (array), and summary.""", + template=""" + + You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. + + + If you are not sure about file content or codebase structure pertaining to the user's request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer. + + + You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully. + + + + + {dao_mission} + + + {proposal_data} + + + + Alignment with DAO mission + Clarity of proposal + Feasibility and practicality + Community benefit + + + Not aligned, unclear, impractical, or no community benefit + Significant issues or missing details + Adequate but with some concerns or minor risks + Good alignment, clear, practical, and beneficial + Excellent alignment, clarity, feasibility, and community value + + + + Provide: + A number from 0-100 + List of any critical issues or red flags + Brief summary of your evaluation + Only return a JSON object with these three fields: score, flags (array), and summary. + +""", ) try: diff --git a/services/workflows/agents/financial_context.py b/services/workflows/agents/financial_context.py index 1302b589..28e580bc 100644 --- a/services/workflows/agents/financial_context.py +++ b/services/workflows/agents/financial_context.py @@ -49,52 +49,83 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: funding_priorities = dao_financial_context.get("funding_priorities", []) financial_constraints = dao_financial_context.get("financial_constraints", []) - # Format financial context for the prompt - financial_context_text = f""" -Treasury Balance: {treasury_balance} -Monthly Budget: {monthly_budget} -Funding Priorities: {', '.join(funding_priorities) if funding_priorities else 'Not specified'} -Financial Constraints: {', '.join(financial_constraints) if financial_constraints else 'Not specified'} -""" - prompt = PromptTemplate( - input_variables=["proposal_data", "financial_context"], - template="""Evaluate the financial aspects of this proposal for the DAO. - -# Proposal -{proposal_data} - -# DAO Financial Context -{financial_context} - -# Task -Score this proposal from 0-100 based on: -1. Cost-effectiveness and value for money (40%) -2. Budget accuracy and detail (20%) -3. Financial risk assessment (20%) -4. Alignment with DAO's financial priorities (20%) - -When analyzing, consider: -- Is the proposal requesting a reasonable amount? -- Are costs well-justified with clear deliverables? -- Are there hidden or underestimated costs? -- Does it align with the DAO's financial priorities? -- What is the potential ROI (Return on Investment)? -- Are there financial risks or dependencies? - -# Output Format -Provide: -- Score (0-100) -- List of any critical financial issues or red flags -- Brief summary of your financial evaluation - -Only return a JSON object with these three fields: score, flags (array), and summary.""", + input_variables=[ + "proposal_data", + "treasury_balance", + "monthly_budget", + "funding_priorities", + "financial_constraints", + ], + template=""" + + You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. + + + If you are not sure about file content or codebase structure pertaining to the user's request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer. + + + You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully. + + + + + {proposal_data} + + + {treasury_balance} + {monthly_budget} + {funding_priorities} + {financial_constraints} + + + + Cost-effectiveness and value for money + Budget accuracy and detail + Financial risk assessment + Alignment with DAO's financial priorities + + + Is the proposal requesting a reasonable amount? + Are costs well-justified with clear deliverables? + Are there hidden or underestimated costs? + Does it align with the DAO's financial priorities? + What is the potential ROI (Return on Investment)? + Are there financial risks or dependencies? + + + Very poor financial justification, high risk, or not aligned with priorities + Significant issues or missing details, questionable value + Adequate but with some concerns or minor risks + Good value, well-justified, low risk, fits priorities + Excellent value, clear ROI, no concerns, highly aligned + + + + Provide: + A number from 0-100 + List of any critical financial issues or red flags + Brief summary of your financial evaluation + Only return a JSON object with these three fields: score, flags (array), and summary. + +""", ) try: formatted_prompt_text = prompt.format( proposal_data=proposal_content, - financial_context=financial_context_text, + treasury_balance=treasury_balance, + monthly_budget=monthly_budget, + funding_priorities=( + ", ".join(funding_priorities) + if funding_priorities + else "Not specified" + ), + financial_constraints=( + ", ".join(financial_constraints) + if financial_constraints + else "Not specified" + ), ) message_content_list = [{"type": "text", "text": formatted_prompt_text}] diff --git a/services/workflows/agents/historical_context.py b/services/workflows/agents/historical_context.py index 70c34121..213b87be 100644 --- a/services/workflows/agents/historical_context.py +++ b/services/workflows/agents/historical_context.py @@ -1,9 +1,12 @@ from typing import Any, Dict, List, Optional +from uuid import UUID from langchain.prompts import PromptTemplate from langchain_core.messages import HumanMessage from pydantic import BaseModel, Field +from backend.factory import backend +from backend.models import Proposal, ProposalFilter from lib.logger import configure_logger from services.workflows.capability_mixins import BaseCapabilityMixin from services.workflows.utils.models import AgentOutput @@ -43,6 +46,63 @@ def _initialize_vector_capability(self): "Initialized vector retrieval capability for HistoricalContextAgent" ) + async def _fetch_dao_proposals(self, dao_id: UUID) -> List[Proposal]: + """Fetch all proposals for a specific DAO from Supabase. + + Args: + dao_id: The UUID of the DAO + + Returns: + List of Proposal objects + """ + try: + # Create filter to get all proposals for this DAO + filters = ProposalFilter(dao_id=dao_id) + + # Fetch proposals + proposals = backend.list_proposals(filters) + self.logger.debug(f"Retrieved {len(proposals)} proposals for DAO {dao_id}") + return proposals + except Exception as e: + self.logger.error(f"Error fetching proposals for DAO {dao_id}: {str(e)}") + return [] + + def _format_proposals_for_context(self, proposals: List[Proposal]) -> str: + """Format proposals for inclusion in the prompt. + + Args: + proposals: List of all proposals + + Returns: + Formatted text of past proposals + """ + # Sort proposals by creation date (newest first to prioritize recent history) + sorted_proposals = sorted(proposals, key=lambda p: p.created_at, reverse=True) + + # Format individual proposals with all relevant details + past_proposals_text = ( + "\n\n".join( + [ + f'\n' + f" {proposal.title or 'Untitled'}\n" + f" {proposal.description or 'No description'}\n" + f" {proposal.status or 'Unknown'}\n" + f" {proposal.type or 'Unknown'}\n" + f" {proposal.created_at.strftime('%Y-%m-%d') if proposal.created_at else 'Unknown'}\n" + f" {proposal.passed or False}\n" + f" {proposal.action or 'None'}\n" + f"" + for i, proposal in enumerate( + sorted_proposals[:8] + ) # Limit to first 8 for context + ] + ) + if proposals + else "No past proposals available." + ) + + return past_proposals_text + async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: """Process the proposal against historical context. @@ -55,16 +115,27 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: self._initialize_vector_capability() proposal_id = state.get("proposal_id", "unknown") proposal_content = state.get("proposal_data", "") + dao_id = state.get("dao_id") # Initialize token usage tracking in state if not present if "token_usage" not in state: state["token_usage"] = {} - # Retrieve similar past proposals if possible - past_proposals_text = "" + # Retrieve all proposals for this DAO from Supabase + dao_proposals = [] + if dao_id: + dao_proposals = await self._fetch_dao_proposals(dao_id) + + # Format database proposals for context + past_proposals_db_text = "" + if dao_proposals: + past_proposals_db_text = self._format_proposals_for_context(dao_proposals) + + # Retrieve similar past proposals from vector store if possible + past_proposals_vector_text = "" try: self.logger.debug( - f"[DEBUG:HistoricalAgent:{proposal_id}] Retrieving similar past proposals" + f"[DEBUG:HistoricalAgent:{proposal_id}] Retrieving similar past proposals from vector store" ) similar_proposals = await self.retrieve_from_vector_store( query=proposal_content[ @@ -75,55 +146,97 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: ), limit=3, ) - past_proposals_text = "\n\n".join( + past_proposals_vector_text = "\n\n".join( [ - f"Past Proposal {i+1}:\n{doc.page_content}" + f'\n{doc.page_content}\n' for i, doc in enumerate(similar_proposals) ] ) except Exception as e: self.logger.error( - f"[DEBUG:HistoricalAgent:{proposal_id}] Error retrieving similar proposals: {str(e)}" + f"[DEBUG:HistoricalAgent:{proposal_id}] Error retrieving similar proposals from vector store: {str(e)}" + ) + past_proposals_vector_text = "No similar past proposals available in vector store." + + # Combine both sources of past proposals + past_proposals_text = past_proposals_db_text + if past_proposals_vector_text: + past_proposals_text += ( + "\n\n" + past_proposals_vector_text + if past_proposals_text + else past_proposals_vector_text ) - past_proposals_text = "No similar past proposals available." prompt = PromptTemplate( input_variables=["proposal_data", "past_proposals"], - template="""Evaluate this proposal in the context of the DAO's past decisions and similar proposals. - -# Current Proposal -{proposal_data} - -# Similar Past Proposals -{past_proposals} - -# Task -Evaluate whether this proposal: -1. Is a duplicate of past proposals (40%) -2. Has addressed issues raised in similar past proposals (30%) -3. Shows consistency with past approved proposals (30%) - -Score this proposal from 0-100 based on the criteria above. -- 0-20: Exact duplicate or contradicts previous decisions -- 21-50: Significant overlap without addressing past concerns -- 51-70: Similar to past proposals but with improvements -- 71-90: Builds well on past work with few concerns -- 91-100: Unique proposal or excellent improvement on past proposals - -# Output Format -Provide: -- Score (0-100) -- List of any critical issues or red flags -- Brief summary of your evaluation - -Only return a JSON object with these three fields: score, flags (array), and summary.""", + template=""" + + You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. + + + If you are not sure about file content or codebase structure pertaining to the user's request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer. + + + You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully. + + + + + {proposal_data} + + + {past_proposals} + + + + First, analyze the proposals to identify any sequences or relationships between them: + + Look for proposals with similar titles, themes, or goals + Identify proposals that might be parts of a multi-stage initiative + Detect proposals that might be attempting to circumvent the 1000 token payout limit per proposal by splitting a large request into multiple smaller proposals + Consider chronological relationships between proposals + + + + Then, evaluate whether this proposal: + + Is a duplicate of past proposals + Has addressed issues raised in similar past proposals + Shows consistency with past approved proposals + Is potentially part of a sequence of proposals to exceed limits +
+ The DAO has a 1000 token payout limit per proposal + Submitters might split large requests across multiple proposals to get around this limit + Look for patterns like similar requesters, recipients, or incremental funding for the same project +
+ + + + + Score this proposal from 0-100 based on the criteria above. + Exact duplicate, contradicts previous decisions, or appears to be gaming token limits + Significant overlap without addressing past concerns or suspicious sequence pattern + Similar to past proposals but with improvements and reasonable sequence relationship (if any) + Builds well on past work with few concerns and transparent relationships to other proposals + Unique proposal or excellent improvement on past proposals with clear, legitimate purpose + + + + Provide: + A number from 0-100 + List of any critical issues or red flags + Brief summary of your evaluation + Identify any proposal sequences and explain how this proposal might relate to others + Only return a JSON object with these four fields: score, flags (array), summary, and sequence_analysis. + +""", ) try: formatted_prompt_text = prompt.format( proposal_data=proposal_content, past_proposals=past_proposals_text - or "No past proposals available for comparison.", + or "No past proposals available for comparison.", ) message_content_list = [{"type": "text", "text": formatted_prompt_text}] @@ -156,4 +269,5 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: "score": 50, "flags": [f"Error: {str(e)}"], "summary": "Historical evaluation failed due to error", + "sequence_analysis": "Could not analyze potential proposal sequences due to error.", } diff --git a/services/workflows/agents/reasoning.py b/services/workflows/agents/reasoning.py index 30aa4289..e3cf965f 100644 --- a/services/workflows/agents/reasoning.py +++ b/services/workflows/agents/reasoning.py @@ -191,33 +191,44 @@ def safe_get_score(value, default=0): prompt = PromptTemplate( input_variables=["agent_evaluations", "approval_threshold"], - template="""Analyze the specialized agent evaluations and make a final decision on this proposal. - -# Agent Evaluations -{agent_evaluations} - -# Decision Guidelines -- The default threshold for approval is {approval_threshold}/100 -- A proposal with any agent score below 30 should typically be rejected -- A proposal with high consensus (small range between scores) increases confidence -- A proposal with high disagreement (large range between scores) decreases confidence -- Consider the reasoning behind each agent's score, not just the numerical value -- Critical flags should be weighted heavily in your decision - -# Task -1. Analyze the evaluations from all agents -2. Consider the significance of any critical flags -3. Weigh the relative importance of different evaluation dimensions -4. Make a final decision (Approve or Reject) with a final score -5. Provide clear reasoning for your decision - -# Output Format -Your response should be a JSON object with: -- score: A final score from 0-100 -- decision: Either "Approve" or "Reject" -- explanation: Your reasoning for the decision - -Return only the JSON object with these three fields.""", + template=""" + + You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. + + + If you are not sure about file content or codebase structure pertaining to the user's request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer. + + + You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully. + + + + + {agent_evaluations} + + + The default threshold for approval is {approval_threshold}/100 + A proposal with any agent score below 30 should typically be rejected + A proposal with high consensus (small range between scores) increases confidence + A proposal with high disagreement (large range between scores) decreases confidence + Consider the reasoning behind each agent's score, not just the numerical value + Critical flags should be weighted heavily in your decision + + + Analyze the evaluations from all agents + Consider the significance of any critical flags + Weigh the relative importance of different evaluation dimensions + Make a final decision (Approve or Reject) with a final score + Provide clear reasoning for your decision + + + Provide: + A final score from 0-100 + Either "Approve" or "Reject" + Your reasoning for the decision + Only return a JSON object with these three fields: score, decision, and explanation. + +""", ) try: @@ -225,7 +236,6 @@ def safe_get_score(value, default=0): agent_evaluations=agent_evaluations, approval_threshold=self.default_threshold, ) - llm_input_message = HumanMessage(content=formatted_prompt_text) # Get structured output from the LLM diff --git a/services/workflows/agents/social_context.py b/services/workflows/agents/social_context.py index 68e687e7..9ca601c5 100644 --- a/services/workflows/agents/social_context.py +++ b/services/workflows/agents/social_context.py @@ -134,39 +134,58 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]: prompt = PromptTemplate( input_variables=["proposal_data", "search_results", "community_info"], - template="""Evaluate the social impact and community aspects of this proposal. - -# Proposal -{proposal_data} - -# Community Information -{community_info} - -# External Context -{search_results} - -# Task -Score this proposal from 0-100 based on: -1. Community benefit and inclusion (40%) -2. Alignment with community values and interests (30%) -3. Potential for community engagement (20%) -4. Consideration of diverse stakeholders (10%) - -When analyzing, consider: -- Will this proposal benefit the broader community or just a few members? -- Is there likely community support or opposition? -- Does it foster inclusivity and participation? -- Does it align with the community's values and interests? -- Could it cause controversy or division? -- Does it consider the needs of diverse stakeholders? - -# Output Format -Provide: -- Score (0-100) -- List of any critical social issues or red flags -- Brief summary of your social evaluation - -Only return a JSON object with these three fields: score, flags (array), and summary.""", + template=""" + + You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. + + + If you are not sure about file content or codebase structure pertaining to the user's request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer. + + + You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully. + + + + + {proposal_data} + + + {community_info} + + + {search_results} + + + + Community benefit and inclusion + Alignment with community values and interests + Potential for community engagement + Consideration of diverse stakeholders + + + Will this proposal benefit the broader community or just a few members? + Is there likely community support or opposition? + Does it foster inclusivity and participation? + Does it align with the community's values and interests? + Could it cause controversy or division? + Does it consider the needs of diverse stakeholders? + + + No benefit, misaligned, or divisive + Significant issues or missing details + Adequate but with some concerns or minor risks + Good benefit, aligned, and inclusive + Excellent benefit, highly aligned, and unifying + + + + Provide: + A number from 0-100 + List of any critical social issues or red flags + Brief summary of your social evaluation + Only return a JSON object with these three fields: score, flags (array), and summary. + +""", ) try: diff --git a/services/workflows/chat.py b/services/workflows/chat.py index fd105f01..a9eb724d 100644 --- a/services/workflows/chat.py +++ b/services/workflows/chat.py @@ -371,7 +371,7 @@ async def retrieve_context(state: ChatState) -> Dict: # Get web search results try: - web_results = await self.search_web(last_user_message) + web_results = await self.web_search(last_user_message) logger.info(f"Retrieved {len(web_results)} web search results") except Exception as e: logger.error(f"Web search failed: {str(e)}") From 7676b31383423d1de01e9f4d8bb88f0bea5d5f41 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 18:47:51 -0700 Subject: [PATCH 031/219] update to use uv --- .dockerignore | 1 - .python-version | 1 + Dockerfile | 22 +- docker_entrypoint.py | 14 + pyproject.toml | 39 + uv.lock | 1658 ++++++++++++++++++++++++++++++++++++++++++ 6 files changed, 1730 insertions(+), 5 deletions(-) create mode 100644 .python-version create mode 100644 docker_entrypoint.py create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/.dockerignore b/.dockerignore index f583b8ee..5cbe1ccc 100644 --- a/.dockerignore +++ b/.dockerignore @@ -59,7 +59,6 @@ build/ # Documentation *.md docs/ -README.md # Misc *.sqlite3 diff --git a/.python-version b/.python-version new file mode 100644 index 00000000..24ee5b1b --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/Dockerfile b/Dockerfile index 5e276242..cf6509d0 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,10 +2,19 @@ FROM public.ecr.aws/docker/library/python:3.13 WORKDIR /usr/src/app -# Install Python dependencies RUN pip install uv -COPY requirements.txt ./ -RUN uv pip install --system --no-cache-dir -r requirements.txt + +# Enable bytecode compilation +ENV UV_COMPILE_BYTECODE=1 + +# Copy from the cache instead of linking since it's a mounted volume +ENV UV_LINK_MODE=copy + +# Install the project's dependencies using the lockfile and settings +RUN --mount=type=cache,target=/root/.cache/uv \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + uv sync --frozen --no-install-project --no-dev # Install Bun RUN curl -fsSL https://bun.sh/install | bash @@ -21,8 +30,13 @@ COPY agent-tools-ts/package.json agent-tools-ts/bun.lock ./ RUN bun install --frozen-lockfile # Now copy the rest of the code +WORKDIR /usr/src/app COPY . . +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-dev -WORKDIR /usr/src/app +ENV PATH="/app/.venv/bin:$PATH" + +ENTRYPOINT [ "uv", "run" ] CMD [ "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file diff --git a/docker_entrypoint.py b/docker_entrypoint.py new file mode 100644 index 00000000..8d5f1663 --- /dev/null +++ b/docker_entrypoint.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python +"""Docker entrypoint script to run the application.""" + +import os +import sys + +# Add the current directory to the path +sys.path.insert(0, os.path.abspath(".")) + +# Run uvicorn +if __name__ == "__main__": + import uvicorn + + uvicorn.run("main:app", host="0.0.0.0", port=8000) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..54dacc97 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,39 @@ +[project] +name = "aibtcdev-backend" +version = "0.1.0" +description = "A sophisticated FastAPI-based backend service that powers AI-driven interactions with Bitcoin and Stacks blockchain technologies, providing real-time chat with AI agents, DAO management, social media integration, blockchain interaction capabilities, market data analysis, and document processing with vector search." +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "aiohttp==3.11.18", + "apscheduler==3.11.0", + "cachetools==5.5.2", + "fastapi==0.115.12", + "langchain==0.3.25", + "langchain-community==0.3.23", + "langchain-core>=0.3.56,<1.0.0", + "langchain-openai==0.3.16", + "langchain-text-splitters==0.3.8", + "langgraph==0.4.1", + "openai==1.77.0", + "pgvector==0.3.6", + "psycopg2==2.9.10", + "pydantic==2.11.4", + "python-dotenv==1.1.0", + "python-telegram-bot==22.0", + "python-twitter-v2==0.9.2", + "requests==2.32.3", + "sqlalchemy==2.0.40", + "starlette==0.46.2", + "supabase==2.15.1", + "tiktoken==0.9.0", + "uvicorn==0.34.2", + "vecs==0.4.5", +] +[project.optional-dependencies] +testing = [ + "pytest==8.3.5", + "pytest-asyncio==0.26.0", + "pytest-mock==3.14.0", + "responses==0.25.7", +] \ No newline at end of file diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..e9a10752 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1658 @@ +version = 1 +revision = 2 +requires-python = ">=3.13" + +[[package]] +name = "aibtcdev-backend" +version = "0.1.0" +source = { virtual = "." } +dependencies = [ + { name = "aiohttp" }, + { name = "apscheduler" }, + { name = "cachetools" }, + { name = "fastapi" }, + { name = "langchain" }, + { name = "langchain-community" }, + { name = "langchain-core" }, + { name = "langchain-openai" }, + { name = "langchain-text-splitters" }, + { name = "langgraph" }, + { name = "openai" }, + { name = "pgvector" }, + { name = "psycopg2" }, + { name = "pydantic" }, + { name = "python-dotenv" }, + { name = "python-telegram-bot" }, + { name = "python-twitter-v2" }, + { name = "requests" }, + { name = "sqlalchemy" }, + { name = "starlette" }, + { name = "supabase" }, + { name = "tiktoken" }, + { name = "uvicorn" }, + { name = "vecs" }, +] + +[package.optional-dependencies] +testing = [ + { name = "pytest" }, + { name = "pytest-asyncio" }, + { name = "pytest-mock" }, + { name = "responses" }, +] + +[package.metadata] +requires-dist = [ + { name = "aiohttp", specifier = "==3.11.18" }, + { name = "apscheduler", specifier = "==3.11.0" }, + { name = 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++++++++++++++++++++++++++++---------------------- pyproject.toml | 5 +++-- 2 files changed, 36 insertions(+), 27 deletions(-) diff --git a/Dockerfile b/Dockerfile index cf6509d0..6f47b690 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,42 +1,50 @@ -FROM public.ecr.aws/docker/library/python:3.13 +FROM oven/bun:latest AS bun + +# First stage: build the application with uv +FROM public.ecr.aws/docker/library/python:3.13 AS builder + +# Enable bytecode compilation and set link mode +ENV UV_COMPILE_BYTECODE=1 UV_LINK_MODE=copy + +# Disable Python downloads to use the system interpreter across both images +ENV UV_PYTHON_DOWNLOADS=0 WORKDIR /usr/src/app +# Install uv RUN pip install uv -# Enable bytecode compilation -ENV UV_COMPILE_BYTECODE=1 +# Copy dependency files +COPY pyproject.toml uv.lock ./ + +# Install dependencies using the lockfile +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-install-project -# Copy from the cache instead of linking since it's a mounted volume -ENV UV_LINK_MODE=copy +# Copy the rest of the code +COPY . /usr/src/app -# Install the project's dependencies using the lockfile and settings +# Sync again to install the project and all dependencies RUN --mount=type=cache,target=/root/.cache/uv \ - --mount=type=bind,source=uv.lock,target=uv.lock \ - --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ - uv sync --frozen --no-install-project --no-dev + uv sync --frozen -# Install Bun -RUN curl -fsSL https://bun.sh/install | bash +# Second stage: final image without uv +FROM public.ecr.aws/docker/library/python:3.13-slim -# Set Bun path for this shell -ENV PATH="/root/.bun/bin:${PATH}" +# Copy the application from the builder +COPY --from=builder /usr/src/app /usr/src/app +COPY --from=bun /usr/local/bin/bun /usr/local/bin/bun +COPY --from=builder /usr/src/app/agent-tools-ts/package.json /usr/src/app/agent-tools-ts/bun.lock ./ -# Install JS/TS dependencies efficiently +# Install JS/TS dependencies WORKDIR /usr/src/app/agent-tools-ts - -# Copy only dependency files first for better caching -COPY agent-tools-ts/package.json agent-tools-ts/bun.lock ./ RUN bun install --frozen-lockfile -# Now copy the rest of the code +# Return to app directory WORKDIR /usr/src/app -COPY . . -RUN --mount=type=cache,target=/root/.cache/uv \ - uv sync --frozen --no-dev - -ENV PATH="/app/.venv/bin:$PATH" -ENTRYPOINT [ "uv", "run" ] +# Place executables in the environment at the front of the path +ENV PATH="/usr/src/app/.venv/bin:$PATH" -CMD [ "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file +# Run using uvicorn +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 54dacc97..83a801ea 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "aibtcdev-backend" version = "0.1.0" -description = "A sophisticated FastAPI-based backend service that powers AI-driven interactions with Bitcoin and Stacks blockchain technologies, providing real-time chat with AI agents, DAO management, social media integration, blockchain interaction capabilities, market data analysis, and document processing with vector search." +description = "Add your description here" readme = "README.md" requires-python = ">=3.13" dependencies = [ @@ -30,10 +30,11 @@ dependencies = [ "uvicorn==0.34.2", "vecs==0.4.5", ] + [project.optional-dependencies] testing = [ "pytest==8.3.5", "pytest-asyncio==0.26.0", "pytest-mock==3.14.0", "responses==0.25.7", -] \ No newline at end of file +] From 54bf45ce43abb5469672e1431ff014ec4a4ee225 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 20:18:44 -0700 Subject: [PATCH 033/219] do not use slim yet --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 6f47b690..7cb7740c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -29,7 +29,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen # Second stage: final image without uv -FROM public.ecr.aws/docker/library/python:3.13-slim +FROM public.ecr.aws/docker/library/python:3.13 # Copy the application from the builder COPY --from=builder /usr/src/app /usr/src/app From 1c1c286dc8d1927c0a2ebedb2af96a363ae2f08e Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 20:49:13 -0700 Subject: [PATCH 034/219] bump version of agent tools --- agent-tools-ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agent-tools-ts b/agent-tools-ts index 6282473d..978b7025 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 6282473dd165382235333ba9f3ede85a77c75443 +Subproject commit 978b7025cd51f839c163f8b343a5ab1934e28bb7 From 623b03491c4c9c47dd18f3dc273864dd2a715ab4 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 22:57:40 -0700 Subject: [PATCH 035/219] add catchup poller --- config.py | 8 + examples/chain_state_monitor_example.py | 89 + .../chainhooks/chainhook_20250227_192359.json | 119 +- .../chainhooks/chainhook_20250227_192400.json | 87 +- .../chainhooks/chainhook_20250227_192401.json | 194 +- .../chainhooks/chainhook_20250227_192403.json | 119 +- .../chainhooks/chainhook_20250227_192405.json | 187 +- .../chainhooks/chainhook_20250227_192406.json | 4546 ++++++++++++++++- .../chainhooks/chainhook_20250227_192408.json | 119 +- .../chainhooks/chainhook_20250227_192409.json | 119 +- .../chainhooks/chainhook_20250227_192410.json | 119 +- .../chainhooks/chainhook_20250227_192411.json | 119 +- .../chainhooks/chainhook_20250227_192412.json | 111 +- .../chainhooks/chainhook_20250227_192413.json | 194 +- .../chainhooks/chainhook_20250227_192414.json | 119 +- .../chainhooks/chainhook_20250227_192415.json | 119 +- lib/hiro.py | 419 +- services/runner/__init__.py | 7 + services/runner/base.py | 2 +- services/runner/job_manager.py | 16 +- services/runner/tasks/__init__.py | 3 + services/runner/tasks/chain_state_monitor.py | 519 ++ .../chainhook/handlers/block_state_handler.py | 7 +- services/webhooks/chainhook/models.py | 149 + 24 files changed, 7441 insertions(+), 49 deletions(-) create mode 100644 examples/chain_state_monitor_example.py create mode 100644 services/runner/tasks/chain_state_monitor.py diff --git a/config.py b/config.py index 0f6e16c6..20003a46 100644 --- a/config.py +++ b/config.py @@ -121,6 +121,14 @@ class SchedulerConfig: "AIBTC_PROPOSAL_EMBEDDER_INTERVAL_SECONDS", "300" ) # Default to 5 mins ) + chain_state_monitor_enabled: bool = ( + os.getenv("AIBTC_CHAIN_STATE_MONITOR_ENABLED", "true").lower() == "true" + ) + chain_state_monitor_interval_seconds: int = int( + os.getenv( + "AIBTC_CHAIN_STATE_MONITOR_INTERVAL_SECONDS", "300" + ) # Default to 5 mins + ) @dataclass diff --git a/examples/chain_state_monitor_example.py b/examples/chain_state_monitor_example.py new file mode 100644 index 00000000..5a6a8337 --- /dev/null +++ b/examples/chain_state_monitor_example.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +"""Example script for manually running the chain state monitor task. + +This script demonstrates how to manually trigger the chain state monitor task +to check for stale chain state and process any missing blocks. + +Usage: + python examples/chain_state_monitor_example.py +""" + +import asyncio +import logging +import os +import sys +from datetime import datetime + +# Add the project root to Python path +sys.path.insert(0, os.path.abspath(os.path.dirname(os.path.dirname(__file__)))) + + +from backend.factory import backend +from config import config +from lib.logger import configure_logger +from services.runner.base import JobContext, JobType, RunnerConfig +from services.runner.tasks.chain_state_monitor import ChainStateMonitorTask + +# Set up logging +logger = configure_logger("chain_state_monitor_example") +logger.setLevel(logging.INFO) + + +async def main(): + """Run the chain state monitor task once.""" + # Print header + print("\n======== Chain State Monitor Example ========\n") + + # Create the task + monitor_task = ChainStateMonitorTask() + + # Create a job context + context = JobContext( + job_type=JobType.CHAIN_STATE_MONITOR, + config=RunnerConfig.from_env(), + parameters={"force_check": True}, # Optional parameter to force checking + ) + + print(f"Running chain state monitor for network: {config.network.network}") + + # Execute the task + start_time = datetime.now() + results = await monitor_task.execute(context) + duration = (datetime.now() - start_time).total_seconds() + + # Print results + print(f"\nExecution completed in {duration:.2f} seconds") + print("\n-------- Results --------\n") + + for result in results: + print(f"Success: {result.success}") + print(f"Message: {result.message}") + print(f"Network: {result.network}") + print(f"Is Stale: {result.is_stale}") + + if result.last_updated: + print(f"Last Updated: {result.last_updated}") + print(f"Elapsed Minutes: {result.elapsed_minutes:.2f}") + + if result.blocks_behind > 0: + print(f"Blocks Behind: {result.blocks_behind}") + print(f"Blocks Processed: {len(result.blocks_processed)}") + + # Show block details + if result.blocks_processed: + print("\nProcessed Blocks:") + for block in result.blocks_processed: + block_data = backend.get_block(block) + if block_data: + tx_count = ( + len(block_data.transactions) + if hasattr(block_data, "transactions") + else "N/A" + ) + print(f" - Block {block}: {tx_count} transactions") + + print("\n======== End of Chain State Monitor Example ========\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/chainhooks/chainhook_20250227_192359.json b/examples/chainhooks/chainhook_20250227_192359.json index e261ad28..e1970233 100644 --- a/examples/chainhooks/chainhook_20250227_192359.json +++ b/examples/chainhooks/chainhook_20250227_192359.json @@ -1 +1,118 @@ -{"apply": [{"block_identifier": {"hash": "0x17eb967ff3cffacc6a2128b0c99a1d02280bb1adf7ec42e4259f28048b811094", "index": 222005}, "metadata": {"bitcoin_anchor_block_identifier": {"hash": "0x36203cf8f01b0ef09aff54bc8446a5fc36d141d50ad3398e648c07e5998e286a", "index": 19755}, "block_time": 1740712361, "confirm_microblock_identifier": null, "cycle_number": null, "pox_cycle_index": 982, "pox_cycle_length": 20, "pox_cycle_position": 14, "reward_set": null, "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", "signer_public_keys": ["0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", "0x03161dd42571db5ca2599ef32774ec0c6c9ef125cbb8f826a63fe527489bb91400", "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333"], "signer_signature": 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}, + "block_time": 1740712361, + "confirm_microblock_identifier": null, + "cycle_number": null, + "pox_cycle_index": 982, + "pox_cycle_length": 20, + "pox_cycle_position": 14, + "reward_set": null, + "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", + "signer_public_keys": [ + "0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", + "0x03161dd42571db5ca2599ef32774ec0c6c9ef125cbb8f826a63fe527489bb91400", + "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333" + ], + "signer_signature": [ + "00482c930da16d47c6eee9f2864688a762f41d802d65d496a567da1d2cd974ac7d54eef246785d05135476578fc16eea797c4711f2908b3253fd65c125db733146", + "006c8dee60d1439de553418b6c25ed136f2eec929220fbf155e8c453a3cb4655f740fa3b8381b9db1929e7397739a0c09178d1ad60fa01b243e0bfee4fd8fc77cb", + "00f59861ae725cdc05a6749087986b1222e680552495d4106deb4c38958bb5a86a7267552e8c0e1c4c962549c1133d3550a1ef23bfcfb4508713f96a613cc51a03" + ], + "stacks_block_hash": "0x4b8eb2dccf17d9e80e184b888f3c376375b1b821a5cb0fb07887f66f6719aeea", + "tenure_height": 16951 + }, + "parent_block_identifier": { + "hash": "0xf2ccf6bb143a907e46225347afe7928289812826c312e7928539f6c2053cf21a", + "index": 222004 + }, + "timestamp": 1740712308, + "transactions": [ + { + "metadata": { + "description": "transfered: 1000 \u00b5STX from ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B to ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF", + "execution_cost": { + "read_count": 0, + "read_length": 0, + "runtime": 0, + "write_count": 0, + "write_length": 0 + }, + "fee": 300, + "kind": { "type": "NativeTokenTransfer" }, + "nonce": 67693, + "position": { "index": 0 }, + "raw_tx": "0x80800000000400b4fdae98b64b9cd6c9436f3b965558966afe890b000000000001086d000000000000012c00019cb0e3e99c819b5acfb7775ff9321db5bfcad43950d5fe07ae3219774af6c34f025b45870b71a04ebf67666e82db8e5867708b71052c9b2d17f5250ad2ffdd5203020000000000051ac2963cbcd9cf3f60311c34087ceb45c8abdbc41300000000000003e800000000000000000000000000000000000000000000000000000000000000000000", + "receipt": { + "contract_calls_stack": [], + "events": [ + { + "data": { + "amount": "1000", + "recipient": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF", + "sender": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B" + }, + "position": { "index": 0 }, + "type": "STXTransferEvent" + } + ], + "mutated_assets_radius": [], + "mutated_contracts_radius": [] + }, + "result": "(ok true)", + "sender": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B", + "sponsor": null, + "success": true + }, + "operations": [ + { + "account": { + "address": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B" + }, + "amount": { + "currency": { "decimals": 6, "symbol": "STX" }, + "value": 1000 + }, + "operation_identifier": { "index": 0 }, + "related_operations": [{ "index": 1 }], + "status": "SUCCESS", + "type": "DEBIT" + }, + { + "account": { + "address": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF" + }, + "amount": { + "currency": { "decimals": 6, "symbol": "STX" }, + "value": 1000 + }, + "operation_identifier": { "index": 1 }, + "related_operations": [{ "index": 0 }], + "status": "SUCCESS", + "type": "CREDIT" + } + ], + "transaction_identifier": { + "hash": "0x0dbe9d5398051d4123b2c999bd95a9bdfbc6e1846b2f10e3411a24ef5d5dfe83" + } + } + ] + } + ], + "chainhook": { + "is_streaming_blocks": false, + "predicate": { "higher_than": 221993, "scope": "block_height" }, + "uuid": "598a3345-cd57-4228-925f-d7c93450c94e" + }, + "events": [], + "rollback": [] +} diff --git a/examples/chainhooks/chainhook_20250227_192400.json b/examples/chainhooks/chainhook_20250227_192400.json index 44df9cda..c4f62785 100644 --- a/examples/chainhooks/chainhook_20250227_192400.json +++ b/examples/chainhooks/chainhook_20250227_192400.json @@ -1 +1,86 @@ -{"apply": [{"block_identifier": {"hash": "0x95c1dbf6caddcc085b8e480f616a9151df956945217eaf4a3c7735fb9baae701", "index": 222006}, "metadata": {"bitcoin_anchor_block_identifier": {"hash": "0x36203cf8f01b0ef09aff54bc8446a5fc36d141d50ad3398e648c07e5998e286a", "index": 19755}, "block_time": 1740712383, "confirm_microblock_identifier": null, "cycle_number": null, "pox_cycle_index": 982, "pox_cycle_length": 20, "pox_cycle_position": 14, "reward_set": null, "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", "signer_public_keys": ["0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333"], "signer_signature": 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of file +{ + "apply": [ + { + "block_identifier": { + "hash": "0x95c1dbf6caddcc085b8e480f616a9151df956945217eaf4a3c7735fb9baae701", + "index": 222006 + }, + "metadata": { + "bitcoin_anchor_block_identifier": { + "hash": "0x36203cf8f01b0ef09aff54bc8446a5fc36d141d50ad3398e648c07e5998e286a", + "index": 19755 + }, + "block_time": 1740712383, + "confirm_microblock_identifier": null, + "cycle_number": null, + "pox_cycle_index": 982, + "pox_cycle_length": 20, + "pox_cycle_position": 14, + "reward_set": null, + "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", + "signer_public_keys": [ + "0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", + "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333" + ], + "signer_signature": [ + "00a74c2d94b3593ac649084b35839abe6a75eca7cbd68567188ca7cea7a34de5af4b201f929fe1f5fdef7cd96feab27d51b2a6e189bb84e84bda18fcbbb5f58736", + 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[] +} diff --git a/examples/chainhooks/chainhook_20250227_192401.json b/examples/chainhooks/chainhook_20250227_192401.json index cb90a6ad..d8f2a089 100644 --- a/examples/chainhooks/chainhook_20250227_192401.json +++ b/examples/chainhooks/chainhook_20250227_192401.json @@ -1 +1,193 @@ -{"apply": [{"block_identifier": {"hash": "0x15ca3218d18cd4ed29c9047855544522529ed2b9f9fc2dab033f9ec10990dfaf", "index": 222008}, "metadata": {"bitcoin_anchor_block_identifier": {"hash": "0x36203cf8f01b0ef09aff54bc8446a5fc36d141d50ad3398e648c07e5998e286a", "index": 19755}, "block_time": 1740712440, "confirm_microblock_identifier": null, "cycle_number": null, "pox_cycle_index": 982, "pox_cycle_length": 20, "pox_cycle_position": 14, "reward_set": null, "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", "signer_public_keys": ["0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", 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a/examples/chainhooks/chainhook_20250227_192403.json +++ b/examples/chainhooks/chainhook_20250227_192403.json @@ -1 +1,118 @@ -{"apply": [{"block_identifier": {"hash": "0x862ddd9b86badb375331a8c5fe6b2a38ea025484783909b06192a938e24409a3", "index": 222009}, "metadata": {"bitcoin_anchor_block_identifier": {"hash": "0x36203cf8f01b0ef09aff54bc8446a5fc36d141d50ad3398e648c07e5998e286a", "index": 19755}, "block_time": 1740712479, "confirm_microblock_identifier": null, "cycle_number": null, "pox_cycle_index": 982, "pox_cycle_length": 20, "pox_cycle_position": 14, "reward_set": null, "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", "signer_public_keys": ["0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", "0x03161dd42571db5ca2599ef32774ec0c6c9ef125cbb8f826a63fe527489bb91400", "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333"], "signer_signature": 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"0x737ae297dd62b97388c43f6ca591474b88c0bcd2d9d689cb6de90c8b37c6f37b", + "index": 222023 + }, + "metadata": { + "bitcoin_anchor_block_identifier": { + "hash": "0x1d9e703b5bff59bcc7589b2c13f40fdcf919a95770203c7afab71971b68b35af", + "index": 19757 + }, + "block_time": 1740712918, + "confirm_microblock_identifier": null, + "cycle_number": null, + "pox_cycle_index": 982, + "pox_cycle_length": 20, + "pox_cycle_position": 16, + "reward_set": null, + "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", + "signer_public_keys": [ + "0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", + "0x03161dd42571db5ca2599ef32774ec0c6c9ef125cbb8f826a63fe527489bb91400", + "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333" + ], + "signer_signature": [ + "00f04e1be8ffa7787eba7e9c0649033f0df5d39b03c2328e5c801e68a291a7cc5e76f9914582b88bfdbbb5b298f24e2c40ea3c955c37878b8e1d3da670585e6243", + 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"0x80800000000400c2963cbcd9cf3f60311c34087ceb45c8abdbc4130000000000010873000000000000012c000003beaeacc410976d2ba6a3b00aff7eb4829f29c7fff5217afb932ec534a248be002c58845e2d0229044e5eabf471fa6a39c9664edcc6b4436d725caf89b40f5803020000000000051a841f9b4dd30ae02fd816cc225c59d1b00b9c3a3a00000000000003e800000000000000000000000000000000000000000000000000000000000000000000", + "receipt": { + "contract_calls_stack": [], + "events": [ + { + "data": { + "amount": "1000", + "recipient": "ST221Z6TDTC5E0BYR2V624Q2ST6R0Q71T78WTAX6H", + "sender": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF" + }, + "position": { "index": 0 }, + "type": "STXTransferEvent" + } + ], + "mutated_assets_radius": [], + "mutated_contracts_radius": [] + }, + "result": "(ok true)", + "sender": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF", + "sponsor": null, + "success": true + }, + "operations": [ + { + "account": { + "address": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF" + }, + "amount": { + "currency": { "decimals": 6, "symbol": "STX" }, + "value": 1000 + }, + "operation_identifier": { "index": 0 }, + "related_operations": [{ "index": 1 }], + "status": "SUCCESS", + "type": "DEBIT" + }, + { + "account": { + "address": "ST221Z6TDTC5E0BYR2V624Q2ST6R0Q71T78WTAX6H" + }, + "amount": { + "currency": { "decimals": 6, "symbol": "STX" }, + "value": 1000 + }, + "operation_identifier": { "index": 1 }, + "related_operations": [{ "index": 0 }], + "status": "SUCCESS", + "type": "CREDIT" + } + ], + "transaction_identifier": { + "hash": "0x7024ab0ede46e9b87d19fe12d159505527e0593809f64940b2ab3f9f64d21feb" + } + } + ] + } + ], + "chainhook": { + "is_streaming_blocks": false, + "predicate": { "higher_than": 221993, "scope": "block_height" }, + "uuid": "598a3345-cd57-4228-925f-d7c93450c94e" + }, + "events": [], + "rollback": [] +} diff --git a/examples/chainhooks/chainhook_20250227_192415.json b/examples/chainhooks/chainhook_20250227_192415.json index d7be8f50..892a12ed 100644 --- a/examples/chainhooks/chainhook_20250227_192415.json +++ b/examples/chainhooks/chainhook_20250227_192415.json @@ -1 +1,118 @@ -{"apply": [{"block_identifier": {"hash": "0x9c5d7d28aed25ebc4ed6447f67b81d2be71553ced3a784ae780df2bf0dabb0e9", "index": 222025}, "metadata": {"bitcoin_anchor_block_identifier": {"hash": "0x1d9e703b5bff59bcc7589b2c13f40fdcf919a95770203c7afab71971b68b35af", "index": 19757}, "block_time": 1740712985, "confirm_microblock_identifier": null, "cycle_number": null, "pox_cycle_index": 982, "pox_cycle_length": 20, "pox_cycle_position": 16, "reward_set": null, "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", "signer_public_keys": ["0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", "0x03161dd42571db5ca2599ef32774ec0c6c9ef125cbb8f826a63fe527489bb91400", "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333"], "signer_signature": 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"nonce": 67699, "position": {"index": 0}, "raw_tx": "0x80800000000400b4fdae98b64b9cd6c9436f3b965558966afe890b0000000000010873000000000000012c0001df094606aed75863e00491383252a183f6c3a2031292f61f0dcf6af608d6ee61653294ef8e13e13d2e22cb9a8da768f23e222982ecfd8bd7ffa6931ba6c39e8b03020000000000051ac2963cbcd9cf3f60311c34087ceb45c8abdbc41300000000000003e800000000000000000000000000000000000000000000000000000000000000000000", "receipt": {"contract_calls_stack": [], "events": [{"data": {"amount": "1000", "recipient": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF", "sender": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B"}, "position": {"index": 0}, "type": "STXTransferEvent"}], "mutated_assets_radius": [], "mutated_contracts_radius": []}, "result": "(ok true)", "sender": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B", "sponsor": null, "success": true}, "operations": [{"account": {"address": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B"}, "amount": {"currency": {"decimals": 6, "symbol": "STX"}, "value": 1000}, "operation_identifier": {"index": 0}, "related_operations": [{"index": 1}], "status": "SUCCESS", "type": "DEBIT"}, {"account": {"address": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF"}, "amount": {"currency": {"decimals": 6, "symbol": "STX"}, "value": 1000}, "operation_identifier": {"index": 1}, "related_operations": [{"index": 0}], "status": "SUCCESS", "type": "CREDIT"}], "transaction_identifier": {"hash": "0xaba63a75c20d5b5539e9d4fa44f2f6a2cd488287f2354ea07e68e06c97fa516e"}}]}], "chainhook": {"is_streaming_blocks": false, "predicate": {"higher_than": 221993, "scope": "block_height"}, "uuid": "598a3345-cd57-4228-925f-d7c93450c94e"}, "events": [], "rollback": []} \ No newline at end of file +{ + "apply": [ + { + "block_identifier": { + "hash": "0x9c5d7d28aed25ebc4ed6447f67b81d2be71553ced3a784ae780df2bf0dabb0e9", + "index": 222025 + }, + "metadata": { + "bitcoin_anchor_block_identifier": { + "hash": "0x1d9e703b5bff59bcc7589b2c13f40fdcf919a95770203c7afab71971b68b35af", + "index": 19757 + }, + "block_time": 1740712985, + "confirm_microblock_identifier": null, + "cycle_number": null, + "pox_cycle_index": 982, + "pox_cycle_length": 20, + "pox_cycle_position": 16, + "reward_set": null, + "signer_bitvec": "013500000027ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff1f", + "signer_public_keys": [ + "0x02e8620935d58ebffa23c260f6917cbd0915ea17d7a46df17e131540237d335504", + "0x03161dd42571db5ca2599ef32774ec0c6c9ef125cbb8f826a63fe527489bb91400", + "0x036a44f61d85efa844b42475f107b106dc8fb209ae27813893c3269c59821e0333" + ], + "signer_signature": [ + "008fbe7dd472ecd2f9ebb29d81949e8ad712925a9382c46c6e3acf49b16abde85b5c7ee477ffd019661cdd2198ad64065b93d09948e4f4ac500e12f67f0b8e952a", + "01df3904e50aba97ba7688189fa78d65eca20fdbc7b407260620043f37a3fcf69108d5222a5ad138eb535a565ff0f92c084f656400386e8ac9ba93003d693f20b0", + "00391510d00403a46fbb0acd5bf8771ca5b1a611354ecabb69d83e216a3d666b1e7a50c9faae29cb8fe8e1ba9700b15814291fc6a0be48ca14bb3d658abde5b354" + ], + "stacks_block_hash": "0x812b65b5deff1a531d482547f51c471009e55c07a1cb670fe0cccec0140b41d3", + "tenure_height": 16953 + }, + "parent_block_identifier": { + "hash": "0x52272152395cc9d3930e70c5a3f014bfd39744b9da3aeef7addf57ea1f0ad0cf", + "index": 222024 + }, + "timestamp": 1740712814, + "transactions": [ + { + "metadata": { + "description": "transfered: 1000 \u00b5STX from ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B to ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF", + "execution_cost": { + "read_count": 0, + "read_length": 0, + "runtime": 0, + "write_count": 0, + "write_length": 0 + }, + "fee": 300, + "kind": { "type": "NativeTokenTransfer" }, + "nonce": 67699, + "position": { "index": 0 }, + "raw_tx": "0x80800000000400b4fdae98b64b9cd6c9436f3b965558966afe890b0000000000010873000000000000012c0001df094606aed75863e00491383252a183f6c3a2031292f61f0dcf6af608d6ee61653294ef8e13e13d2e22cb9a8da768f23e222982ecfd8bd7ffa6931ba6c39e8b03020000000000051ac2963cbcd9cf3f60311c34087ceb45c8abdbc41300000000000003e800000000000000000000000000000000000000000000000000000000000000000000", + "receipt": { + "contract_calls_stack": [], + "events": [ + { + "data": { + "amount": "1000", + "recipient": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF", + "sender": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B" + }, + "position": { "index": 0 }, + "type": "STXTransferEvent" + } + ], + "mutated_assets_radius": [], + "mutated_contracts_radius": [] + }, + "result": "(ok true)", + "sender": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B", + "sponsor": null, + "success": true + }, + "operations": [ + { + "account": { + "address": "ST2TFVBMRPS5SSNP98DQKQ5JNB2B6NZM91C4K3P7B" + }, + "amount": { + "currency": { "decimals": 6, "symbol": "STX" }, + "value": 1000 + }, + "operation_identifier": { "index": 0 }, + "related_operations": [{ "index": 1 }], + "status": "SUCCESS", + "type": "DEBIT" + }, + { + "account": { + "address": "ST319CF5WV77KYR1H3GT0GZ7B8Q4AQPY42ETP1VPF" + }, + "amount": { + "currency": { "decimals": 6, "symbol": "STX" }, + "value": 1000 + }, + "operation_identifier": { "index": 1 }, + "related_operations": [{ "index": 0 }], + "status": "SUCCESS", + "type": "CREDIT" + } + ], + "transaction_identifier": { + "hash": "0xaba63a75c20d5b5539e9d4fa44f2f6a2cd488287f2354ea07e68e06c97fa516e" + } + } + ] + } + ], + "chainhook": { + "is_streaming_blocks": false, + "predicate": { "higher_than": 221993, "scope": "block_height" }, + "uuid": "598a3345-cd57-4228-925f-d7c93450c94e" + }, + "events": [], + "rollback": [] +} diff --git a/lib/hiro.py b/lib/hiro.py index 82817bcc..80fb111d 100644 --- a/lib/hiro.py +++ b/lib/hiro.py @@ -2,7 +2,7 @@ from dataclasses import dataclass from enum import Enum from functools import wraps -from typing import Any, Dict, List, Optional, TypedDict +from typing import Any, ClassVar, Dict, List, Optional, TypedDict import aiohttp import requests @@ -10,6 +10,7 @@ from config import config from lib.logger import configure_logger +from services.webhooks.chainhook import models logger = configure_logger(__name__) @@ -228,9 +229,15 @@ def build(self) -> ChainHookPredicate: class BaseHiroApi: """Base class for Hiro API clients with shared functionality.""" - # Rate limiting settings - RATE_LIMIT = 100 # requests per minute - RATE_LIMIT_WINDOW = 60 # seconds + # Default rate limiting settings (will be updated from API headers) + DEFAULT_SECOND_LIMIT: ClassVar[int] = 20 + DEFAULT_MINUTE_LIMIT: ClassVar[int] = 50 + + # Rate limit tracking (shared across all instances) + _second_limit: ClassVar[int] = DEFAULT_SECOND_LIMIT + _minute_limit: ClassVar[int] = DEFAULT_MINUTE_LIMIT + _second_requests: ClassVar[List[float]] = [] + _minute_requests: ClassVar[List[float]] = [] # Retry settings MAX_RETRIES = 3 @@ -247,27 +254,141 @@ def __init__(self, base_url: str): if not self.api_key: raise ValueError("HIRO_API_KEY environment variable is required") - self._request_times: List[float] = [] self._cache = TTLCache(maxsize=100, ttl=300) # Cache with 5-minute TTL self._session: Optional[aiohttp.ClientSession] = None logger.debug("Initialized API client with base URL: %s", self.base_url) + def _update_rate_limits(self, headers: Dict[str, str]) -> None: + """Update rate limit settings from response headers. + + Args: + headers: Response headers containing rate limit information + """ + # Update limits if headers are present + if "x-ratelimit-limit-second" in headers: + old_limit = self.__class__._second_limit + self.__class__._second_limit = int(headers["x-ratelimit-limit-second"]) + logger.debug( + "Second rate limit updated: %d → %d", + old_limit, + self.__class__._second_limit, + ) + + if "x-ratelimit-limit-minute" in headers: + old_limit = self.__class__._minute_limit + self.__class__._minute_limit = int(headers["x-ratelimit-limit-minute"]) + logger.debug( + "Minute rate limit updated: %d → %d", + old_limit, + self.__class__._minute_limit, + ) + + # Log remaining rate limit information if available + if "x-ratelimit-remaining-second" in headers: + logger.debug( + "Second rate limit remaining: %s", + headers["x-ratelimit-remaining-second"], + ) + + if "x-ratelimit-remaining-minute" in headers: + logger.debug( + "Minute rate limit remaining: %s", + headers["x-ratelimit-remaining-minute"], + ) + + logger.debug( + "Current rate limit state - second: %d/%d, minute: %d/%d", + len(self.__class__._second_requests), + self.__class__._second_limit, + len(self.__class__._minute_requests), + self.__class__._minute_limit, + ) + def _rate_limit(self) -> None: - """Implement rate limiting.""" + """Implement rate limiting for both second and minute windows.""" current_time = time.time() - self._request_times = [ - t for t in self._request_times if current_time - t < self.RATE_LIMIT_WINDOW + + # Update second window requests + old_second_count = len(self.__class__._second_requests) + self.__class__._second_requests = [ + t for t in self.__class__._second_requests if current_time - t < 1.0 + ] + new_second_count = len(self.__class__._second_requests) + + if old_second_count != new_second_count: + logger.debug( + "Pruned expired second window requests: %d → %d", + old_second_count, + new_second_count, + ) + + # Update minute window requests + old_minute_count = len(self.__class__._minute_requests) + self.__class__._minute_requests = [ + t for t in self.__class__._minute_requests if current_time - t < 60.0 ] + new_minute_count = len(self.__class__._minute_requests) - if len(self._request_times) >= self.RATE_LIMIT: - sleep_time = self._request_times[0] + self.RATE_LIMIT_WINDOW - current_time + if old_minute_count != new_minute_count: + logger.debug( + "Pruned expired minute window requests: %d → %d", + old_minute_count, + new_minute_count, + ) + + # Check second limit + if len(self.__class__._second_requests) >= self.__class__._second_limit: + sleep_time = self.__class__._second_requests[0] + 1.0 - current_time if sleep_time > 0: logger.warning( - "Rate limit reached, sleeping for %.2f seconds", sleep_time + "Second rate limit reached (%d/%d), sleeping for %.2f seconds", + len(self.__class__._second_requests), + self.__class__._second_limit, + sleep_time, ) time.sleep(sleep_time) + # Recalculate current time after sleep + current_time = time.time() + else: + logger.debug( + "Second rate limit check: %d/%d (%.1f%% of limit)", + len(self.__class__._second_requests), + self.__class__._second_limit, + (len(self.__class__._second_requests) / self.__class__._second_limit) + * 100, + ) - self._request_times.append(current_time) + # Check minute limit + if len(self.__class__._minute_requests) >= self.__class__._minute_limit: + sleep_time = self.__class__._minute_requests[0] + 60.0 - current_time + if sleep_time > 0: + logger.warning( + "Minute rate limit reached (%d/%d), sleeping for %.2f seconds", + len(self.__class__._minute_requests), + self.__class__._minute_limit, + sleep_time, + ) + time.sleep(sleep_time) + else: + logger.debug( + "Minute rate limit check: %d/%d (%.1f%% of limit)", + len(self.__class__._minute_requests), + self.__class__._minute_limit, + (len(self.__class__._minute_requests) / self.__class__._minute_limit) + * 100, + ) + + # Record the new request + self.__class__._second_requests.append(time.time()) + self.__class__._minute_requests.append(time.time()) + + logger.debug( + "New request recorded: second window now %d/%d, minute window now %d/%d", + len(self.__class__._second_requests), + self.__class__._second_limit, + len(self.__class__._minute_requests), + self.__class__._minute_limit, + ) def _retry_on_error(func): """Decorator to retry API calls on transient errors.""" @@ -321,12 +442,24 @@ def _make_request( try: self._rate_limit() url = f"{self.base_url}{endpoint}" - headers = headers or {"Accept": "application/json"} + headers = headers or {} + + # Set default Accept header if not provided + if "Accept" not in headers: + headers["Accept"] = "application/json" + + # Add X-API-Key header if api_key is set + if self.api_key: + headers["X-API-Key"] = self.api_key logger.debug("Making %s request to %s", method, url) response = requests.request( method, url, headers=headers, params=params, json=json ) + + # Update rate limits from headers + self._update_rate_limits(response.headers) + response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -354,15 +487,29 @@ async def _amake_request( try: self._rate_limit() url = f"{self.base_url}{endpoint}" - headers = headers or {"Accept": "application/json"} + headers = headers or {} + + # Set default Accept header if not provided + if "Accept" not in headers: + headers["Accept"] = "application/json" + + # Add X-API-Key header if api_key is set + if self.api_key: + headers["X-API-Key"] = self.api_key logger.debug("Making async %s request to %s", method, url) async with self._session.request( method, url, headers=headers, params=params, json=json ) as response: + # Update rate limits from headers + self._update_rate_limits(response.headers) + response.raise_for_status() return await response.json() except aiohttp.ClientError as e: + if isinstance(e, aiohttp.ClientResponseError) and e.status == 429: + logger.error("Rate limit exceeded in async request: %s", str(e)) + raise HiroApiRateLimitError(f"Rate limit exceeded: {str(e)}") logger.error("Async request error: %s", str(e)) raise HiroApiError(f"Async request error: {str(e)}") @@ -671,6 +818,13 @@ def get_token_holders(self, token: str) -> Dict[str, Any]: "GET", f"{self.ENDPOINTS['tokens']}/ft/{token}/holders" ) + async def aget_token_holders(self, token: str) -> Dict[str, Any]: + """Async version of get_token_holders.""" + logger.debug("Async retrieving token holders for %s", token) + return await self._amake_request( + "GET", f"{self.ENDPOINTS['tokens']}/ft/{token}/holders" + ) + def get_address_balance(self, addr: str) -> Dict[str, Any]: """Retrieve wallet balance for an address.""" logger.debug("Retrieving balance for address %s", addr) @@ -678,6 +832,13 @@ def get_address_balance(self, addr: str) -> Dict[str, Any]: "GET", f"{self.ENDPOINTS['addresses']}/{addr}/balances" ) + async def aget_address_balance(self, addr: str) -> Dict[str, Any]: + """Async version of get_address_balance.""" + logger.debug("Async retrieving balance for address %s", addr) + return await self._amake_request( + "GET", f"{self.ENDPOINTS['addresses']}/{addr}/balances" + ) + # Transaction related endpoints def get_transaction(self, tx_id: str) -> Dict[str, Any]: """Get transaction details.""" @@ -687,10 +848,200 @@ def get_raw_transaction(self, tx_id: str) -> Dict[str, Any]: """Get raw transaction details.""" return self._make_request("GET", f"/extended/v1/tx/{tx_id}/raw") - def get_transactions_by_block(self, block_hash: str) -> Dict[str, Any]: - """Get transactions in a block.""" + def get_transactions_by_block( + self, block_height: int, limit: int = 50, offset: int = 0 + ) -> models.BlockTransactionsResponse: + """Get transactions in a block. + + Args: + block_height: The height of the block to get transactions for + limit: The maximum number of transactions to return (default: 50) + offset: Pagination offset (default: 0) + + Returns: + Typed response containing transaction data + """ + logger.debug( + "Getting transactions for block height %d with limit %d offset %d", + block_height, + limit, + offset, + ) + response = self._make_request( + "GET", + f"/extended/v2/blocks/{block_height}/transactions", + params={"limit": limit, "offset": offset}, + ) + + logger.debug(f"API response type: {type(response)}") + logger.debug( + f"API response keys: {response.keys() if isinstance(response, dict) else 'Not a dict'}" + ) + + # For debugging purposes + if ( + "results" in response + and response["results"] + and isinstance(response["results"], list) + ): + logger.debug(f"First result type: {type(response['results'][0])}") + logger.debug( + f"First result sample keys: {list(response['results'][0].keys())[:5]}" + ) + + # We're getting dictionaries back, so create BlockTransactionsResponse manually + # This ensures we don't lose the raw data structure if dataclass conversion fails + try: + return models.BlockTransactionsResponse(**response) + except Exception as e: + logger.warning(f"Error creating BlockTransactionsResponse: {str(e)}") + # Fall back to returning a raw dictionary-based response + return models.BlockTransactionsResponse( + limit=response.get("limit", 0), + offset=response.get("offset", 0), + total=response.get("total", 0), + results=response.get("results", []), + ) + + def get_all_transactions_by_block( + self, block_height: int, page_size: int = 50 + ) -> models.BlockTransactionsResponse: + """Get all transactions in a block by paginating through results. + + Args: + block_height: The height of the block to get transactions for + page_size: Number of transactions per page request (default: 50) + + Returns: + Combined response with all transactions + """ + logger.debug(f"Getting all transactions for block height {block_height}") + + # Get first page to determine total + first_page = self.get_transactions_by_block(block_height, limit=page_size) + logger.debug(f"First page type: {type(first_page)}") + logger.debug(f"First page results type: {type(first_page.results)}") + + if first_page.results: + logger.debug(f"First result type: {type(first_page.results[0])}") + + # If we got all transactions in the first request, return it + if first_page.total <= page_size: + return first_page + + # Initialize with first page results + all_transactions = first_page.results.copy() + + # Paginate through the rest + remaining = first_page.total - page_size + offset = page_size + + while remaining > 0: + current_limit = min(page_size, remaining) + logger.debug( + f"Fetching {current_limit} more transactions with offset {offset}" + ) + + page = self.get_transactions_by_block( + block_height, limit=current_limit, offset=offset + ) + + all_transactions.extend(page.results) + offset += current_limit + remaining -= current_limit + + # Create combined response + return models.BlockTransactionsResponse( + limit=first_page.total, + offset=0, + total=first_page.total, + results=all_transactions, + ) + + def get_transactions_by_block_hash(self, block_hash: str) -> Dict[str, Any]: + """Get transactions in a block by hash.""" return self._make_request("GET", f"/extended/v1/tx/block/{block_hash}") + async def aget_transactions_by_block( + self, block_height: int, limit: int = 50, offset: int = 0 + ) -> models.BlockTransactionsResponse: + """Async version of get_transactions_by_block. + + Args: + block_height: The height of the block to get transactions for + limit: The maximum number of transactions to return (default: 50) + offset: Pagination offset (default: 0) + + Returns: + Typed response containing transaction data + """ + logger.debug( + "Async getting transactions for block height %d with limit %d offset %d", + block_height, + limit, + offset, + ) + response = await self._amake_request( + "GET", + f"/extended/v2/blocks/{block_height}/transactions", + params={"limit": limit, "offset": offset}, + ) + return models.BlockTransactionsResponse(**response) + + async def aget_all_transactions_by_block( + self, block_height: int, page_size: int = 50 + ) -> models.BlockTransactionsResponse: + """Async version to get all transactions in a block by paginating through results. + + Args: + block_height: The height of the block to get transactions for + page_size: Number of transactions per page request (default: 50) + + Returns: + Combined response with all transactions + """ + logger.debug("Async getting all transactions for block height %d", block_height) + + # Get first page to determine total + first_page = await self.aget_transactions_by_block( + block_height, limit=page_size + ) + + # If we got all transactions in the first request, return it + if first_page.total <= page_size: + return first_page + + # Initialize with first page results + all_transactions = first_page.results.copy() + + # Paginate through the rest + remaining = first_page.total - page_size + offset = page_size + + while remaining > 0: + current_limit = min(page_size, remaining) + logger.debug( + "Async fetching %d more transactions with offset %d", + current_limit, + offset, + ) + + page = await self.aget_transactions_by_block( + block_height, limit=current_limit, offset=offset + ) + + all_transactions.extend(page.results) + offset += current_limit + remaining -= current_limit + + # Create combined response + return models.BlockTransactionsResponse( + limit=first_page.total, + offset=0, + total=first_page.total, + results=all_transactions, + ) + def get_transactions_by_block_height(self, height: int) -> Dict[str, Any]: """Get transactions in a block by height.""" return self._make_request("GET", f"/extended/v1/tx/block_height/{height}") @@ -835,7 +1186,6 @@ def get_stx_price(self) -> float: response.raise_for_status() return response.json()["price"] - # @cached(lambda self: self._cache) def get_current_block_height(self) -> int: """Get the current block height""" logger.debug("Retrieving current block height") @@ -848,24 +1198,29 @@ def get_current_block_height(self) -> int: logger.debug(f"Response: {response}") return response["results"][0]["height"] + def get_info(self) -> models.HiroApiInfo: + """Get Hiro API server information and chain tip. + + Returns: + Server information including version, status, and current chain tip + """ + logger.debug("Retrieving Hiro API server info") + response = self._make_request("GET", "/extended") + return models.HiroApiInfo(**response) + + async def aget_info(self) -> models.HiroApiInfo: + """Async version of get_info. + + Returns: + Server information including version, status, and current chain tip + """ + logger.debug("Async retrieving Hiro API server info") + response = await self._amake_request("GET", "/extended") + return models.HiroApiInfo(**response) + def search(self, query_id: str) -> Dict[str, Any]: """Search for blocks, transactions, contracts, or addresses.""" logger.debug("Performing search for query: %s", query_id) return self._make_request("GET", f"{self.ENDPOINTS['search']}/{query_id}") - # Async versions of selected methods - async def aget_token_holders(self, token: str) -> Dict[str, Any]: - """Async version of get_token_holders.""" - logger.debug("Async retrieving token holders for %s", token) - return await self._amake_request( - "GET", f"{self.ENDPOINTS['tokens']}/ft/{token}/holders" - ) - - async def aget_address_balance(self, addr: str) -> Dict[str, Any]: - """Async version of get_address_balance.""" - logger.debug("Async retrieving balance for address %s", addr) - return await self._amake_request( - "GET", f"{self.ENDPOINTS['addresses']}/{addr}/balances" - ) - # ... add async versions of other methods as needed ... diff --git a/services/runner/__init__.py b/services/runner/__init__.py index 26de9149..ca5cad19 100644 --- a/services/runner/__init__.py +++ b/services/runner/__init__.py @@ -7,6 +7,10 @@ AgentAccountDeployerTask, agent_account_deployer, ) +from services.runner.tasks.chain_state_monitor import ( + ChainStateMonitorTask, + chain_state_monitor, +) from services.runner.tasks.dao_proposal_concluder import ( DAOProposalConcluderTask, dao_proposal_concluder, @@ -36,6 +40,7 @@ JobRegistry.register(JobType.TWEET, TweetTask) JobRegistry.register(JobType.AGENT_ACCOUNT_DEPLOY, AgentAccountDeployerTask) JobRegistry.register(JobType.PROPOSAL_EMBEDDING, ProposalEmbedderTask) +JobRegistry.register(JobType.CHAIN_STATE_MONITOR, ChainStateMonitorTask) __all__ = [ "BaseTask", @@ -61,4 +66,6 @@ "agent_account_deployer", "ProposalEmbedderTask", "proposal_embedder", + "ChainStateMonitorTask", + "chain_state_monitor", ] diff --git a/services/runner/base.py b/services/runner/base.py index 2fcf0747..caf00825 100644 --- a/services/runner/base.py +++ b/services/runner/base.py @@ -5,7 +5,6 @@ from typing import Any, Dict, Generic, List, Optional, Type, TypeVar from uuid import UUID -from backend.models import QueueMessageType from lib.logger import configure_logger logger = configure_logger(__name__) @@ -75,6 +74,7 @@ class JobType(str, Enum): TWEET = "tweet" AGENT_ACCOUNT_DEPLOY = "agent_account_deploy" PROPOSAL_EMBEDDING = "proposal_embedding" + CHAIN_STATE_MONITOR = "chain_state_monitor" def __str__(self): return self.value diff --git a/services/runner/job_manager.py b/services/runner/job_manager.py index 31c2777a..cec8ac32 100644 --- a/services/runner/job_manager.py +++ b/services/runner/job_manager.py @@ -111,6 +111,12 @@ def get_all_jobs() -> List[JobConfig]: config.scheduler.proposal_embedder_interval_seconds, JobType.PROPOSAL_EMBEDDING.value, ), + ( + "Chain State Monitor Service", + config.scheduler.chain_state_monitor_enabled, + config.scheduler.chain_state_monitor_interval_seconds, + JobType.CHAIN_STATE_MONITOR.value, + ), ] # Add all runner jobs with common structure @@ -172,9 +178,17 @@ def schedule_jobs(scheduler: AsyncIOScheduler) -> bool: # Add the job with a specific ID for easier management job_id = job.job_id or f"{job.name.lower().replace(' ', '_')}" + + # Add max_instances=1 for all jobs to prevent concurrent execution scheduler.add_job( - job_func, "interval", seconds=job.seconds, id=job_id, **job_args + job_func, + "interval", + seconds=job.seconds, + id=job_id, + max_instances=1, + **job_args, ) + logger.info( f"{job.name} started with interval of {job.seconds} seconds" ) diff --git a/services/runner/tasks/__init__.py b/services/runner/tasks/__init__.py index e1992934..474e33ab 100644 --- a/services/runner/tasks/__init__.py +++ b/services/runner/tasks/__init__.py @@ -1,5 +1,6 @@ """Task runners for scheduled and on-demand jobs.""" +from .chain_state_monitor import ChainStateMonitorTask, chain_state_monitor from .dao_proposal_concluder import DAOProposalConcluderTask, dao_proposal_concluder from .dao_proposal_evaluation import DAOProposalEvaluationTask, dao_proposal_evaluation from .dao_proposal_voter import DAOProposalVoterTask, dao_proposal_voter @@ -20,4 +21,6 @@ "dao_proposal_concluder", "DAOProposalEvaluationTask", "dao_proposal_evaluation", + "ChainStateMonitorTask", + "chain_state_monitor", ] diff --git a/services/runner/tasks/chain_state_monitor.py b/services/runner/tasks/chain_state_monitor.py new file mode 100644 index 00000000..a18d91a5 --- /dev/null +++ b/services/runner/tasks/chain_state_monitor.py @@ -0,0 +1,519 @@ +"""Chain state monitoring task implementation.""" + +import uuid +from dataclasses import dataclass, field +from datetime import datetime, timedelta +from typing import Any, Dict, List, Optional + +from backend.factory import backend +from config import config +from lib.hiro import HiroApi +from lib.logger import configure_logger +from services.runner.base import BaseTask, JobContext, RunnerResult +from services.webhooks.chainhook import ChainhookService +from services.webhooks.chainhook.models import ( + Apply, + BlockIdentifier, + BlockMetadata, + ChainHookData, + ChainHookInfo, + Predicate, + TransactionIdentifier, + TransactionMetadata, + TransactionWithReceipt, +) + +logger = configure_logger(__name__) + + +class ChainStateMonitorResult(RunnerResult): + """Result of chain state monitoring operation.""" + + def __init__( + self, + success: bool, + message: str, + network: str, + is_stale: bool, + error: Optional[Exception] = None, + last_updated: Optional[datetime] = None, + elapsed_minutes: float = 0, + blocks_behind: int = 0, + blocks_processed: Optional[List[int]] = None, + ): + """Initialize with required and optional parameters. + + Args: + success: Whether the operation was successful + message: Message describing the operation result + network: The network being monitored + is_stale: Whether the chain state is stale + error: Optional exception that occurred + last_updated: When the chain state was last updated + elapsed_minutes: Minutes since last update + blocks_behind: Number of blocks behind + blocks_processed: List of blocks processed + """ + super().__init__(success=success, message=message, error=error) + self.network = network + self.is_stale = is_stale + self.last_updated = last_updated + self.elapsed_minutes = elapsed_minutes + self.blocks_behind = blocks_behind + self.blocks_processed = blocks_processed if blocks_processed is not None else [] + + +class ChainStateMonitorTask(BaseTask[ChainStateMonitorResult]): + """Task runner for monitoring chain state freshness.""" + + def __init__(self): + """Initialize the task.""" + super().__init__() + self.hiro_api = HiroApi() + self.chainhook_service = ChainhookService() + + async def _validate_task_specific(self, context: JobContext) -> bool: + """Validate task-specific conditions.""" + # Always valid to run - we want to check chain state freshness + # even when there's no new data + return True + + def _convert_to_chainhook_format( + self, block_height: int, block_hash: str, parent_hash: str, transactions: Any + ) -> Dict[str, Any]: + """Convert block transactions to chainhook format. + + Args: + block_height: Height of the block + block_hash: Hash of the block + parent_hash: Hash of the parent block + transactions: Block transactions from Hiro API + + Returns: + Dict formatted as a chainhook webhook payload + """ + # Create block identifier + block_identifier = BlockIdentifier(hash=block_hash, index=block_height) + + # Create parent block identifier + parent_block_identifier = BlockIdentifier( + hash=parent_hash, index=block_height - 1 + ) + + # Create basic metadata + metadata = BlockMetadata( + block_time=int(datetime.now().timestamp()), stacks_block_hash=block_hash + ) + + # Convert transactions to chainhook format + chainhook_transactions = [] + for tx in transactions.results: + # Handle tx as either dict or object + if isinstance(tx, dict): + tx_id = tx.get("tx_id", "") + exec_cost_read_count = tx.get("execution_cost_read_count", 0) + exec_cost_read_length = tx.get("execution_cost_read_length", 0) + exec_cost_runtime = tx.get("execution_cost_runtime", 0) + exec_cost_write_count = tx.get("execution_cost_write_count", 0) + exec_cost_write_length = tx.get("execution_cost_write_length", 0) + fee_rate = tx.get("fee_rate", "0") + nonce = tx.get("nonce", 0) + tx_index = tx.get("tx_index", 0) + sender_address = tx.get("sender_address", "") + sponsor_address = tx.get("sponsor_address", None) + sponsored = tx.get("sponsored", False) + tx_status = tx.get("tx_status", "") + tx_type = tx.get("tx_type", "") + tx_result_repr = ( + tx.get("tx_result", {}).get("repr", "") + if isinstance(tx.get("tx_result"), dict) + else "" + ) + else: + tx_id = tx.tx_id + exec_cost_read_count = tx.execution_cost_read_count + exec_cost_read_length = tx.execution_cost_read_length + exec_cost_runtime = tx.execution_cost_runtime + exec_cost_write_count = tx.execution_cost_write_count + exec_cost_write_length = tx.execution_cost_write_length + fee_rate = tx.fee_rate + nonce = tx.nonce + tx_index = tx.tx_index + sender_address = tx.sender_address + sponsor_address = tx.sponsor_address if tx.sponsored else None + sponsored = tx.sponsored + tx_status = tx.tx_status + tx_type = tx.tx_type + tx_result_repr = ( + tx.tx_result.repr if hasattr(tx.tx_result, "repr") else "" + ) + + # Create transaction identifier + tx_identifier = TransactionIdentifier(hash=tx_id) + + # Create transaction metadata + tx_metadata = { + "description": f"Transaction {tx_id}", + "execution_cost": { + "read_count": exec_cost_read_count, + "read_length": exec_cost_read_length, + "runtime": exec_cost_runtime, + "write_count": exec_cost_write_count, + "write_length": exec_cost_write_length, + }, + "fee": ( + int(fee_rate) + if isinstance(fee_rate, str) and fee_rate.isdigit() + else 0 + ), + "kind": {"type": tx_type}, + "nonce": nonce, + "position": {"index": tx_index}, + "raw_tx": "", # We don't have this from the v2 API + "receipt": { + "contract_calls_stack": [], + "events": [], + "mutated_assets_radius": [], + "mutated_contracts_radius": [], + }, + "result": tx_result_repr, + "sender": sender_address, + "sponsor": sponsor_address, + "success": tx_status == "success", + } + + # Create transaction with receipt + tx_with_receipt = TransactionWithReceipt( + transaction_identifier=tx_identifier, + metadata=tx_metadata, + operations=[], + ) + + chainhook_transactions.append(tx_with_receipt) + + # Create apply block + apply_block = Apply( + block_identifier=block_identifier, + parent_block_identifier=parent_block_identifier, + metadata=metadata, + timestamp=int(datetime.now().timestamp()), + transactions=chainhook_transactions, + ) + + # Create predicate + predicate = Predicate(scope="block_height", higher_than=block_height - 1) + + # Create chainhook info + chainhook_info = ChainHookInfo( + is_streaming_blocks=False, predicate=predicate, uuid=str(uuid.uuid4()) + ) + + # Create full chainhook data + chainhook_data = ChainHookData( + apply=[apply_block], chainhook=chainhook_info, events=[], rollback=[] + ) + + # Convert to dict for webhook processing + return { + "apply": [ + { + "block_identifier": { + "hash": apply_block.block_identifier.hash, + "index": apply_block.block_identifier.index, + }, + "metadata": { + "block_time": apply_block.metadata.block_time, + "stacks_block_hash": apply_block.metadata.stacks_block_hash, + }, + "parent_block_identifier": { + "hash": apply_block.parent_block_identifier.hash, + "index": apply_block.parent_block_identifier.index, + }, + "timestamp": apply_block.timestamp, + "transactions": [ + { + "transaction_identifier": { + "hash": tx.transaction_identifier.hash + }, + "metadata": tx.metadata, + "operations": [], + } + for tx in apply_block.transactions + ], + } + ], + "chainhook": { + "is_streaming_blocks": chainhook_info.is_streaming_blocks, + "predicate": { + "scope": chainhook_info.predicate.scope, + "higher_than": chainhook_info.predicate.higher_than, + }, + "uuid": chainhook_info.uuid, + }, + "events": [], + "rollback": [], + } + + async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResult]: + """Run the chain state monitoring task.""" + # Use the configured network + network = config.network.network + + try: + results = [] + + # Get the latest chain state for this network + latest_chain_state = backend.get_latest_chain_state(network) + + if not latest_chain_state: + logger.warning(f"No chain state found for network {network}") + results.append( + ChainStateMonitorResult( + success=False, + message=f"No chain state found for network {network}", + network=network, + is_stale=True, + ) + ) + return results + + # Calculate how old the chain state is + now = datetime.now() + last_updated = latest_chain_state.updated_at + + # Convert last_updated to naive datetime if it has timezone info + if last_updated.tzinfo is not None: + last_updated = last_updated.replace(tzinfo=None) + + time_difference = now - last_updated + minutes_difference = time_difference.total_seconds() / 60 + + # Get current chain height from API + try: + logger.debug("Fetching current chain info from API") + api_info = self.hiro_api.get_info() + + # Debug output for the API response + logger.debug(f"API info type: {type(api_info)}") + logger.debug(f"API info raw: {api_info}") + + # Handle different response types + if isinstance(api_info, dict): + logger.debug("API returned dictionary instead of object") + # Try to access chain_tip from dictionary + if "chain_tip" in api_info: + chain_tip = api_info["chain_tip"] + logger.debug(f"Chain tip from dict: {chain_tip}") + current_api_block_height = chain_tip.get("block_height", 0) + else: + logger.error(f"Missing chain_tip in API response: {api_info}") + raise ValueError( + "Invalid API response format - missing chain_tip" + ) + else: + # We have a HiroApiInfo object but chain_tip is still a dict + logger.debug(f"Chain tip: {api_info.chain_tip}") + # Access it as a dictionary + if isinstance(api_info.chain_tip, dict): + current_api_block_height = api_info.chain_tip.get( + "block_height", 0 + ) + else: + current_api_block_height = api_info.chain_tip.block_height + + logger.info(f"Current API block height: {current_api_block_height}") + db_block_height = latest_chain_state.block_height + logger.info(f"Current DB block height: {db_block_height}") + + blocks_behind = current_api_block_height - db_block_height + + # Consider stale if more than 10 blocks behind + stale_threshold_blocks = 10 + is_stale = blocks_behind > stale_threshold_blocks + + logger.info( + f"Chain state is {blocks_behind} blocks behind the current chain tip. " + f"DB height: {db_block_height}, API height: {current_api_block_height}" + ) + + # Process missing blocks if we're behind + if blocks_behind > 0 and is_stale: + logger.warning( + f"Chain state is {blocks_behind} blocks behind, which exceeds the threshold of {stale_threshold_blocks}. " + f"DB height: {db_block_height}, API height: {current_api_block_height}" + ) + + blocks_processed = [] + + # Process each missing block + for height in range( + db_block_height + 1, current_api_block_height + 1 + ): + logger.info( + f"Processing transactions for block height {height}" + ) + + try: + # Get all transactions for this block + logger.debug(f"Fetching transactions for block {height}") + transactions = self.hiro_api.get_all_transactions_by_block( + height + ) + + # Log transaction count and details + logger.info( + f"Block {height}: Found {transactions.total} transactions" + ) + + # Get block details + if transactions.results: + logger.debug( + f"Using transaction data for block hash and parent hash" + ) + logger.debug( + f"Transaction result type: {type(transactions.results[0])}" + ) + + # Handle transactions.results as either dict or object + tx = transactions.results[0] + if isinstance(tx, dict): + logger.debug("Transaction is a dictionary") + block_hash = tx.get("block_hash") + parent_hash = tx.get("parent_block_hash") + else: + logger.debug("Transaction is an object") + block_hash = tx.block_hash + parent_hash = tx.parent_block_hash + + logger.debug( + f"Block hash: {block_hash}, Parent hash: {parent_hash}" + ) + else: + # If no transactions, fetch the block directly + logger.debug( + f"No transactions found, fetching block directly" + ) + try: + block = self.hiro_api.get_block_by_height(height) + logger.debug(f"Block data type: {type(block)}") + logger.debug(f"Block data: {block}") + + # Handle different response formats + if isinstance(block, dict): + block_hash = block.get("hash") + parent_hash = block.get("parent_block_hash") + else: + block_hash = block.hash + parent_hash = block.parent_block_hash + + logger.debug( + f"Block hash: {block_hash}, Parent hash: {parent_hash}" + ) + + if not block_hash or not parent_hash: + raise ValueError( + f"Missing hash or parent_hash in block data: {block}" + ) + except Exception as e: + logger.error( + f"Error fetching block {height}: {str(e)}" + ) + raise + + # Convert to chainhook format + logger.debug( + f"Converting block {height} to chainhook format" + ) + chainhook_data = self._convert_to_chainhook_format( + height, block_hash, parent_hash, transactions + ) + + # Process through chainhook service + logger.debug( + f"Sending block {height} to chainhook service for processing" + ) + result = await self.chainhook_service.process( + chainhook_data + ) + logger.info(f"Chainhook processing result: {result}") + + blocks_processed.append(height) + + except Exception as e: + logger.error( + f"Error processing block {height}: {str(e)}", + exc_info=True, + ) + # Continue with next block instead of failing the entire process + + results.append( + ChainStateMonitorResult( + success=True, + message=f"Chain state is {blocks_behind} blocks behind. Processed {len(blocks_processed)} blocks.", + network=network, + is_stale=is_stale, + last_updated=last_updated, + elapsed_minutes=minutes_difference, + blocks_behind=blocks_behind, + blocks_processed=blocks_processed, + ) + ) + return results + else: + logger.debug( + f"Chain state for network {network} is {'stale' if is_stale else 'fresh'}. " + f"{blocks_behind} blocks behind (threshold: {stale_threshold_blocks})." + ) + + # Return result based on blocks_behind check + results.append( + ChainStateMonitorResult( + success=True, + message=f"Chain state for network {network} is {blocks_behind} blocks behind", + network=network, + is_stale=is_stale, + last_updated=last_updated, + elapsed_minutes=minutes_difference, + blocks_behind=blocks_behind, + ) + ) + + return results + + except Exception as e: + logger.error( + f"Error getting current chain info: {str(e)}", exc_info=True + ) + # Fall back to legacy time-based staleness check if API call fails + logger.warning("Falling back to time-based staleness check") + stale_threshold_minutes = 5 + is_stale = minutes_difference > stale_threshold_minutes + + results.append( + ChainStateMonitorResult( + success=False, + message=f"Error checking chain height, using time-based check instead: {str(e)}", + network=network, + is_stale=is_stale, + last_updated=last_updated, + elapsed_minutes=minutes_difference, + ) + ) + return results + + except Exception as e: + logger.error( + f"Error executing chain state monitoring task: {str(e)}", exc_info=True + ) + return [ + ChainStateMonitorResult( + success=False, + message=f"Error executing chain state monitoring task: {str(e)}", + network=network, + is_stale=True, + ) + ] + + +# Instantiate the task for use in the registry +chain_state_monitor = ChainStateMonitorTask() diff --git a/services/webhooks/chainhook/handlers/block_state_handler.py b/services/webhooks/chainhook/handlers/block_state_handler.py index dcb9080d..9604c3f3 100644 --- a/services/webhooks/chainhook/handlers/block_state_handler.py +++ b/services/webhooks/chainhook/handlers/block_state_handler.py @@ -1,5 +1,6 @@ """Handler for tracking the latest block state from chainhooks.""" +from datetime import datetime from typing import Optional from backend.factory import backend @@ -97,7 +98,11 @@ async def handle_block(self, block: Apply) -> None: ) updated = backend.update_chain_state( current_state.id, - ChainStateBase(block_height=block_height, block_hash=block_hash), + ChainStateBase( + block_height=block_height, + block_hash=block_hash, + network=current_state.network, + ), ) if not updated: self.logger.error( diff --git a/services/webhooks/chainhook/models.py b/services/webhooks/chainhook/models.py index fef565c1..03a3c81c 100644 --- a/services/webhooks/chainhook/models.py +++ b/services/webhooks/chainhook/models.py @@ -136,3 +136,152 @@ class ChainHookData: chainhook: ChainHookInfo events: List[Any] rollback: List[Any] + + +# V2 API models for block transactions + + +@dataclass +class Principal: + """Principal for post condition.""" + + type_id: str + + +@dataclass +class PostCondition: + """Post condition in a transaction.""" + + principal: Principal + condition_code: str + amount: str + type: str + + +@dataclass +class ClarityValue: + """Clarity value representation.""" + + hex: str + repr: str + + +@dataclass +class ContractLog: + """Contract log in an event.""" + + contract_id: str + topic: str + value: ClarityValue + + +@dataclass +class TransactionEvent: + """Event in a transaction.""" + + event_index: int + event_type: str + tx_id: str + contract_log: Optional[ContractLog] = None + + +@dataclass +class TokenTransfer: + """Token transfer details.""" + + recipient_address: str + amount: str + memo: Optional[str] = None + + +@dataclass +class BlockTransaction: + """Transaction in a block.""" + + tx_id: str + nonce: int + fee_rate: str + sender_address: str + post_condition_mode: str + post_conditions: List[PostCondition] + anchor_mode: str + block_hash: str + block_height: int + block_time: int + block_time_iso: str + burn_block_height: int + burn_block_time: int + burn_block_time_iso: str + parent_burn_block_time: int + parent_burn_block_time_iso: str + canonical: bool + tx_index: int + tx_status: str + tx_result: ClarityValue + event_count: int + parent_block_hash: str + is_unanchored: bool + execution_cost_read_count: int + execution_cost_read_length: int + execution_cost_runtime: int + execution_cost_write_count: int + execution_cost_write_length: int + events: List[TransactionEvent] + tx_type: str + sponsor_nonce: Optional[int] = None + sponsored: Optional[bool] = None + sponsor_address: Optional[str] = None + microblock_hash: Optional[str] = None + microblock_sequence: Optional[int] = None + microblock_canonical: Optional[bool] = None + token_transfer: Optional[TokenTransfer] = None + + +@dataclass +class BlockTransactionsResponse: + """Response from the block transactions API.""" + + limit: int + offset: int + total: int + results: List[BlockTransaction] + + +@dataclass +class ChainTip: + """Current chain tip information.""" + + block_height: int + block_hash: str + index_block_hash: str + microblock_hash: str + microblock_sequence: int + burn_block_height: int + + +@dataclass +class HiroApiInfo: + """Hiro API server information.""" + + server_version: str + status: str + pox_v1_unlock_height: int + pox_v2_unlock_height: int + pox_v3_unlock_height: int + chain_tip: Union[ChainTip, Dict[str, Any]] + + def __post_init__(self): + """Convert chain_tip from dict to ChainTip object if needed.""" + # If chain_tip is a dictionary, convert it to a ChainTip object + if isinstance(self.chain_tip, dict) and not isinstance( + self.chain_tip, ChainTip + ): + # Some implementations might only include a subset of fields + self.chain_tip = ChainTip( + block_height=self.chain_tip.get("block_height", 0), + block_hash=self.chain_tip.get("block_hash", ""), + index_block_hash=self.chain_tip.get("index_block_hash", ""), + microblock_hash=self.chain_tip.get("microblock_hash", ""), + microblock_sequence=self.chain_tip.get("microblock_sequence", 0), + burn_block_height=self.chain_tip.get("burn_block_height", 0), + ) From 92cf203448198cb1faa01db577ba661648ebe577 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 23:14:56 -0700 Subject: [PATCH 036/219] fix misfire and intialize instantly --- services/runner/job_manager.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/services/runner/job_manager.py b/services/runner/job_manager.py index cec8ac32..b209767e 100644 --- a/services/runner/job_manager.py +++ b/services/runner/job_manager.py @@ -1,6 +1,7 @@ """Job management utilities for the runner service.""" from dataclasses import dataclass +from datetime import datetime from typing import Any, Callable, List, Optional, cast from apscheduler.schedulers.asyncio import AsyncIOScheduler @@ -180,17 +181,21 @@ def schedule_jobs(scheduler: AsyncIOScheduler) -> bool: job_id = job.job_id or f"{job.name.lower().replace(' ', '_')}" # Add max_instances=1 for all jobs to prevent concurrent execution + # and set misfire_grace_time to prevent missed execution warnings + # Set next_run_time to now to execute immediately scheduler.add_job( job_func, "interval", seconds=job.seconds, id=job_id, max_instances=1, + misfire_grace_time=60, + next_run_time=datetime.now(), **job_args, ) logger.info( - f"{job.name} started with interval of {job.seconds} seconds" + f"{job.name} started with interval of {job.seconds} seconds (will execute immediately)" ) else: logger.info(f"{job.name} is disabled") From 12e10f6df168894d380114d90ca3b50340877beb Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 23:21:53 -0700 Subject: [PATCH 037/219] fix misfire and intialize instantly --- services/runner/registry.py | 25 +++++++++++++++----- services/runner/tasks/chain_state_monitor.py | 15 +++++++----- 2 files changed, 28 insertions(+), 12 deletions(-) diff --git a/services/runner/registry.py b/services/runner/registry.py index 175889e8..84cd8db4 100644 --- a/services/runner/registry.py +++ b/services/runner/registry.py @@ -1,5 +1,7 @@ +import inspect from typing import Any, Dict, List, Optional, Type +from config import config from lib.logger import configure_logger from .base import BaseTask, JobType @@ -60,7 +62,7 @@ async def execute_runner_job( ) # Create runner instance - runner = runner_class(context.config) + runner = runner_class() # Validate and execute logger.info(f"Starting {job_type} runner") @@ -70,16 +72,27 @@ async def execute_runner_job( return results else: logger.warning(f"Validation failed for {job_type} runner") + result_class = runner_class.get_result_class() return [ - runner_class.get_result_class()( + result_class( success=False, message=f"Validation failed for {job_type} runner" ) ] except Exception as e: logger.error(f"Error in runner job: {str(e)}", exc_info=True) - return [ - runner_class.get_result_class()( - success=False, message=f"Error in runner job: {str(e)}", error=e + try: + result_class = runner_class.get_result_class() + return [ + result_class( + success=False, message=f"Error in runner job: {str(e)}", error=e + ) + ] + except Exception as inner_e: + logger.critical( + f"Could not create result object: {str(inner_e)}", exc_info=True ) - ] + # Fallback to basic RunnerResult if all else fails + from .base import RunnerResult + + return [RunnerResult(success=False, message=f"Critical error: {str(e)}")] diff --git a/services/runner/tasks/chain_state_monitor.py b/services/runner/tasks/chain_state_monitor.py index a18d91a5..21b42eb4 100644 --- a/services/runner/tasks/chain_state_monitor.py +++ b/services/runner/tasks/chain_state_monitor.py @@ -33,9 +33,9 @@ def __init__( self, success: bool, message: str, - network: str, - is_stale: bool, error: Optional[Exception] = None, + network: str = None, + is_stale: bool = False, last_updated: Optional[datetime] = None, elapsed_minutes: float = 0, blocks_behind: int = 0, @@ -46,16 +46,18 @@ def __init__( Args: success: Whether the operation was successful message: Message describing the operation result - network: The network being monitored - is_stale: Whether the chain state is stale error: Optional exception that occurred + network: The network being monitored (optional, defaults to None) + is_stale: Whether the chain state is stale (optional, defaults to False) last_updated: When the chain state was last updated elapsed_minutes: Minutes since last update blocks_behind: Number of blocks behind blocks_processed: List of blocks processed """ super().__init__(success=success, message=message, error=error) - self.network = network + self.network = ( + network or config.network.network + ) # Use config network as default self.is_stale = is_stale self.last_updated = last_updated self.elapsed_minutes = elapsed_minutes @@ -67,7 +69,8 @@ class ChainStateMonitorTask(BaseTask[ChainStateMonitorResult]): """Task runner for monitoring chain state freshness.""" def __init__(self): - """Initialize the task.""" + """Initialize the task without requiring config parameter.""" + # No config parameter needed - we get it from the import super().__init__() self.hiro_api = HiroApi() self.chainhook_service = ChainhookService() From 9d8b1da44f10be75ad7023d3606b8b94e765ad70 Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sat, 10 May 2025 23:29:24 -0700 Subject: [PATCH 038/219] fix misfire and intialize instantly --- services/runner/job_manager.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/services/runner/job_manager.py b/services/runner/job_manager.py index b209767e..f324d085 100644 --- a/services/runner/job_manager.py +++ b/services/runner/job_manager.py @@ -1,7 +1,7 @@ """Job management utilities for the runner service.""" from dataclasses import dataclass -from datetime import datetime +from datetime import datetime, timedelta from typing import Any, Callable, List, Optional, cast from apscheduler.schedulers.asyncio import AsyncIOScheduler @@ -182,7 +182,7 @@ def schedule_jobs(scheduler: AsyncIOScheduler) -> bool: # Add max_instances=1 for all jobs to prevent concurrent execution # and set misfire_grace_time to prevent missed execution warnings - # Set next_run_time to now to execute immediately + # Set next_run_time to one minute from now scheduler.add_job( job_func, "interval", @@ -190,12 +190,11 @@ def schedule_jobs(scheduler: AsyncIOScheduler) -> bool: id=job_id, max_instances=1, misfire_grace_time=60, - next_run_time=datetime.now(), **job_args, ) logger.info( - f"{job.name} started with interval of {job.seconds} seconds (will execute immediately)" + f"{job.name} started with interval of {job.seconds} seconds (will execute in one minute)" ) else: logger.info(f"{job.name} is disabled") From 64a1cdbc569260b8ce06c2365d2ff5658d69df0c Mon Sep 17 00:00:00 2001 From: human058382928 <162091348+human058382928@users.noreply.github.com> Date: Sun, 11 May 2025 00:10:52 -0700 Subject: [PATCH 039/219] update --- services/runner/tasks/chain_state_monitor.py | 44 +++----------------- 1 file changed, 5 insertions(+), 39 deletions(-) diff --git a/services/runner/tasks/chain_state_monitor.py b/services/runner/tasks/chain_state_monitor.py index 21b42eb4..ee959441 100644 --- a/services/runner/tasks/chain_state_monitor.py +++ b/services/runner/tasks/chain_state_monitor.py @@ -293,20 +293,14 @@ async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResu # Get current chain height from API try: - logger.debug("Fetching current chain info from API") + logger.info("Fetching current chain info from API") api_info = self.hiro_api.get_info() - # Debug output for the API response - logger.debug(f"API info type: {type(api_info)}") - logger.debug(f"API info raw: {api_info}") - # Handle different response types if isinstance(api_info, dict): - logger.debug("API returned dictionary instead of object") # Try to access chain_tip from dictionary if "chain_tip" in api_info: chain_tip = api_info["chain_tip"] - logger.debug(f"Chain tip from dict: {chain_tip}") current_api_block_height = chain_tip.get("block_height", 0) else: logger.error(f"Missing chain_tip in API response: {api_info}") @@ -315,7 +309,6 @@ async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResu ) else: # We have a HiroApiInfo object but chain_tip is still a dict - logger.debug(f"Chain tip: {api_info.chain_tip}") # Access it as a dictionary if isinstance(api_info.chain_tip, dict): current_api_block_height = api_info.chain_tip.get( @@ -358,7 +351,6 @@ async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResu try: # Get all transactions for this block - logger.debug(f"Fetching transactions for block {height}") transactions = self.hiro_api.get_all_transactions_by_block( height ) @@ -370,36 +362,18 @@ async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResu # Get block details if transactions.results: - logger.debug( - f"Using transaction data for block hash and parent hash" - ) - logger.debug( - f"Transaction result type: {type(transactions.results[0])}" - ) - # Handle transactions.results as either dict or object tx = transactions.results[0] if isinstance(tx, dict): - logger.debug("Transaction is a dictionary") block_hash = tx.get("block_hash") parent_hash = tx.get("parent_block_hash") else: - logger.debug("Transaction is an object") block_hash = tx.block_hash parent_hash = tx.parent_block_hash - - logger.debug( - f"Block hash: {block_hash}, Parent hash: {parent_hash}" - ) else: # If no transactions, fetch the block directly - logger.debug( - f"No transactions found, fetching block directly" - ) try: block = self.hiro_api.get_block_by_height(height) - logger.debug(f"Block data type: {type(block)}") - logger.debug(f"Block data: {block}") # Handle different response formats if isinstance(block, dict): @@ -409,10 +383,6 @@ async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResu block_hash = block.hash parent_hash = block.parent_block_hash - logger.debug( - f"Block hash: {block_hash}, Parent hash: {parent_hash}" - ) - if not block_hash or not parent_hash: raise ValueError( f"Missing hash or parent_hash in block data: {block}" @@ -424,21 +394,17 @@ async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResu raise # Convert to chainhook format - logger.debug( - f"Converting block {height} to chainhook format" - ) chainhook_data = self._convert_to_chainhook_format( height, block_hash, parent_hash, transactions ) # Process through chainhook service - logger.debug( - f"Sending block {height} to chainhook service for processing" - ) result = await self.chainhook_service.process( chainhook_data ) - logger.info(f"Chainhook processing result: {result}") + logger.info( + f"Block {height} processed with result: {result}" + ) blocks_processed.append(height) @@ -463,7 +429,7 @@ async def _execute_impl(self, context: JobContext) -> List[ChainStateMonitorResu ) return results else: - logger.debug( + logger.info( f"Chain state for network {network} is {'stale' if is_stale else 'fresh'}. " f"{blocks_behind} blocks behind (threshold: {stale_threshold_blocks})." ) From 5761af83dc66c9d137e692da53d4dcc5f344065e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 12 May 2025 12:58:17 +0000 Subject: [PATCH 040/219] Bump the dev-dependencies group with 2 updates Bumps the dev-dependencies group with 2 updates: [langgraph](https://github.com/langchain-ai/langgraph) and [openai](https://github.com/openai/openai-python). Updates `langgraph` from 0.4.1 to 0.4.3 - [Release notes](https://github.com/langchain-ai/langgraph/releases) - [Commits](https://github.com/langchain-ai/langgraph/compare/0.4.1...0.4.3) Updates `openai` from 1.77.0 to 1.78.1 - [Release notes](https://github.com/openai/openai-python/releases) - [Changelog](https://github.com/openai/openai-python/blob/main/CHANGELOG.md) - [Commits](https://github.com/openai/openai-python/compare/v1.77.0...v1.78.1) --- updated-dependencies: - dependency-name: langgraph dependency-version: 0.4.3 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: openai dependency-version: 1.78.1 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies ... Signed-off-by: dependabot[bot] --- pyproject.toml | 4 ++-- requirements.txt | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 83a801ea..1de1714d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,8 +14,8 @@ dependencies = [ "langchain-core>=0.3.56,<1.0.0", "langchain-openai==0.3.16", "langchain-text-splitters==0.3.8", - "langgraph==0.4.1", - "openai==1.77.0", + "langgraph==0.4.3", + "openai==1.78.1", "pgvector==0.3.6", "psycopg2==2.9.10", "pydantic==2.11.4", diff --git a/requirements.txt b/requirements.txt index bf90829f..b3b436be 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,8 +6,8 @@ langchain_core>=0.3.56,<1.0.0 langchain_community==0.3.23 langchain_openai==0.3.16 langchain_text_splitters==0.3.8 -langgraph==0.4.1 -openai==1.77.0 +langgraph==0.4.3 +openai==1.78.1 pgvector==0.3.6 psycopg2==2.9.10 pydantic==2.11.4 From 187ffc91943ed9d6c0a11ee7402bdb54ea862244 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 19 May 2025 12:44:55 +0000 Subject: [PATCH 041/219] Bump the dev-dependencies group with 6 updates Bumps the dev-dependencies group with 6 updates: | Package | From | To | | --- | --- | --- | | [langchain-community](https://github.com/langchain-ai/langchain) | `0.3.23` | `0.3.24` | | [langchain-openai](https://github.com/langchain-ai/langchain) | `0.3.16` | `0.3.17` | | [langgraph](https://github.com/langchain-ai/langgraph) | `0.4.3` | `0.4.5` | | [openai](https://github.com/openai/openai-python) | `1.78.1` | `1.79.0` | | [python-telegram-bot](https://github.com/python-telegram-bot/python-telegram-bot) | `22.0` | `22.1` | | [sqlalchemy](https://github.com/sqlalchemy/sqlalchemy) | `2.0.40` | `2.0.41` | Updates `langchain-community` from 0.3.23 to 0.3.24 - [Release notes](https://github.com/langchain-ai/langchain/releases) - [Commits](https://github.com/langchain-ai/langchain/compare/langchain==0.3.23...langchain==0.3.24) Updates `langchain-openai` from 0.3.16 to 0.3.17 - [Release notes](https://github.com/langchain-ai/langchain/releases) - [Commits](https://github.com/langchain-ai/langchain/compare/langchain-openai==0.3.16...langchain-openai==0.3.17) Updates `langgraph` from 0.4.3 to 0.4.5 - [Release notes](https://github.com/langchain-ai/langgraph/releases) - [Commits](https://github.com/langchain-ai/langgraph/compare/0.4.3...0.4.5) Updates `openai` from 1.78.1 to 1.79.0 - [Release notes](https://github.com/openai/openai-python/releases) - [Changelog](https://github.com/openai/openai-python/blob/main/CHANGELOG.md) - [Commits](https://github.com/openai/openai-python/compare/v1.78.1...v1.79.0) Updates `python-telegram-bot` from 22.0 to 22.1 - [Release notes](https://github.com/python-telegram-bot/python-telegram-bot/releases) - [Commits](https://github.com/python-telegram-bot/python-telegram-bot/compare/v22.0...v22.1) Updates `sqlalchemy` from 2.0.40 to 2.0.41 - [Release notes](https://github.com/sqlalchemy/sqlalchemy/releases) - [Changelog](https://github.com/sqlalchemy/sqlalchemy/blob/main/CHANGES.rst) - [Commits](https://github.com/sqlalchemy/sqlalchemy/commits) --- updated-dependencies: - dependency-name: langchain-community dependency-version: 0.3.24 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: langchain-openai dependency-version: 0.3.17 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: langgraph dependency-version: 0.4.5 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: openai dependency-version: 1.79.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies - dependency-name: python-telegram-bot dependency-version: '22.1' dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies - dependency-name: sqlalchemy dependency-version: 2.0.41 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies ... Signed-off-by: dependabot[bot] --- pyproject.toml | 12 ++++++------ requirements.txt | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1de1714d..fce47364 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,20 +10,20 @@ dependencies = [ "cachetools==5.5.2", "fastapi==0.115.12", "langchain==0.3.25", - "langchain-community==0.3.23", + "langchain-community==0.3.24", "langchain-core>=0.3.56,<1.0.0", - "langchain-openai==0.3.16", + "langchain-openai==0.3.17", "langchain-text-splitters==0.3.8", - "langgraph==0.4.3", - "openai==1.78.1", + "langgraph==0.4.5", + "openai==1.79.0", "pgvector==0.3.6", "psycopg2==2.9.10", "pydantic==2.11.4", "python-dotenv==1.1.0", - "python-telegram-bot==22.0", + "python-telegram-bot==22.1", "python-twitter-v2==0.9.2", "requests==2.32.3", - "sqlalchemy==2.0.40", + "sqlalchemy==2.0.41", "starlette==0.46.2", "supabase==2.15.1", "tiktoken==0.9.0", diff --git a/requirements.txt b/requirements.txt index b3b436be..389ea499 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,19 +3,19 @@ cachetools==5.5.2 fastapi==0.115.12 langchain==0.3.25 langchain_core>=0.3.56,<1.0.0 -langchain_community==0.3.23 -langchain_openai==0.3.16 +langchain_community==0.3.24 +langchain_openai==0.3.17 langchain_text_splitters==0.3.8 -langgraph==0.4.3 -openai==1.78.1 +langgraph==0.4.5 +openai==1.79.0 pgvector==0.3.6 psycopg2==2.9.10 pydantic==2.11.4 python-dotenv==1.1.0 -python-telegram-bot==22.0 +python-telegram-bot==22.1 python-twitter-v2==0.9.2 Requests==2.32.3 -SQLAlchemy==2.0.40 +SQLAlchemy==2.0.41 starlette==0.46.2 supabase==2.15.1 tiktoken==0.9.0 From db91f7b064a7f1e6c04afb8d255e46b0263c7ab8 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Mon, 19 May 2025 20:54:51 -0700 Subject: [PATCH 042/219] add new tools endpoints --- agent-tools-ts | 2 +- api/tools.py | 193 ++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 193 insertions(+), 2 deletions(-) diff --git a/agent-tools-ts b/agent-tools-ts index 978b7025..bc57b642 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 978b7025cd51f839c163f8b343a5ab1934e28bb7 +Subproject commit bc57b642987dc649144f317b58cfd4bf5fff54a0 diff --git a/api/tools.py b/api/tools.py index 0cf77ff3..3a8a99e5 100644 --- a/api/tools.py +++ b/api/tools.py @@ -1,10 +1,20 @@ from typing import List, Optional -from fastapi import APIRouter, HTTPException, Query, Request +from fastapi import APIRouter, Depends, HTTPException, Query, Request +from pydantic import BaseModel, Field # Added import for Pydantic models from starlette.responses import JSONResponse +from api.dependencies import ( + verify_profile_from_token, # Added verify_profile_from_token +) +from backend.factory import backend # Added backend factory +from backend.models import UUID, AgentFilter, Profile # Added Profile, AgentFilter from lib.logger import configure_logger from lib.tools import Tool, get_available_tools +from tools.dao_ext_action_proposals import ( + ProposeActionSendMessageTool, # Added ProposeActionSendMessageTool +) +from tools.faktory import FaktoryExecuteBuyTool # Added import for Faktory tool # Configure logger logger = configure_logger(__name__) @@ -16,6 +26,48 @@ available_tools = get_available_tools() +class FaktoryBuyTokenRequest(BaseModel): + """Request body for executing a Faktory buy order.""" + + # agent_id: UUID = Field(..., description="The ID of the agent performing the action") # Removed agent_id + btc_amount: str = Field( + ..., + description="Amount of BTC to spend on the purchase in standard units (e.g. 0.0004 = 0.0004 BTC or 40000 sats)", + ) + dao_token_dex_contract_address: str = Field( + ..., description="Contract principal where the DAO token is listed" + ) + slippage: Optional[str] = Field( + default="15", + description="Slippage tolerance in basis points (default: 15, which is 0.15%)", + ) + + +class ProposeSendMessageRequest(BaseModel): + """Request body for proposing a DAO action to send a message.""" + + action_proposals_voting_extension: str = Field( + ..., + description="Contract principal where the DAO creates action proposals for voting by DAO members.", + ) + action_proposal_contract_to_execute: str = Field( + ..., + description="Contract principal of the action proposal that executes sending a message.", + ) + dao_token_contract_address: str = Field( + ..., + description="Contract principal of the token used by the DAO for voting.", + ) + message: str = Field( + ..., + description="Message to be sent through the DAO proposal system.", + ) + memo: Optional[str] = Field( + None, + description="Optional memo to include with the proposal.", + ) + + @router.get("/available", response_model=List[Tool]) async def get_tools( request: Request, @@ -131,3 +183,142 @@ async def search_tools( except Exception as e: logger.error(f"Failed to search tools with query '{query}'", exc_info=e) raise HTTPException(status_code=500, detail=f"Failed to search tools: {str(e)}") + + +@router.post("/faktory/execute_buy") +async def execute_faktory_buy( + request: Request, + payload: FaktoryBuyTokenRequest, + profile: Profile = Depends(verify_profile_from_token), # Added auth dependency +) -> JSONResponse: + """Execute a buy order on Faktory DEX. + + This endpoint allows an authenticated user's agent to execute a buy order + for a specified token using BTC on the Faktory DEX. + + Args: + request: The FastAPI request object. + payload: The request body containing btc_amount, + dao_token_dex_contract_address, and optional slippage. + profile: The authenticated user's profile. + + Returns: + JSONResponse: The result of the buy order execution. + + Raises: + HTTPException: If there's an error executing the buy order, or if the + agent for the profile is not found. + """ + try: + logger.info( + f"Faktory execute buy request received from {request.client.host if request.client else 'unknown'} for profile {profile.id}" + ) + + # Get agent_id from profile_id + agents = backend.list_agents(AgentFilter(profile_id=profile.id)) + if not agents: + logger.error(f"No agent found for profile ID: {profile.id}") + raise HTTPException( + status_code=404, + detail=f"No agent found for profile ID: {profile.id}", + ) + + agent = agents[0] # Assuming the first agent is the one to use + agent_id = agent.id + + logger.info( + f"Using agent {agent_id} for profile {profile.id} to execute Faktory buy." + ) + + tool = FaktoryExecuteBuyTool(wallet_id=agent_id) # Use fetched agent_id + result = await tool._arun( + btc_amount=payload.btc_amount, + dao_token_dex_contract_address=payload.dao_token_dex_contract_address, + slippage=payload.slippage, + ) + + logger.debug( + f"Faktory execute buy result for agent {agent_id} (profile {profile.id}): {result}" + ) + return JSONResponse(content=result) + + except HTTPException as he: + # Re-raise HTTPExceptions directly + raise he + except Exception as e: + logger.error( + f"Failed to execute Faktory buy for profile {profile.id}", exc_info=e + ) + raise HTTPException( + status_code=500, + detail=f"Failed to execute Faktory buy order: {str(e)}", + ) + + +@router.post("/dao/action_proposals/propose_send_message") +async def propose_dao_action_send_message( + request: Request, + payload: ProposeSendMessageRequest, + profile: Profile = Depends(verify_profile_from_token), +) -> JSONResponse: + """Propose a DAO action to send a message. + + This endpoint allows an authenticated user's agent to create a proposal + for sending a message via the DAO's action proposal system. + + Args: + request: The FastAPI request object. + payload: The request body containing the proposal details. + profile: The authenticated user's profile. + + Returns: + JSONResponse: The result of the proposal creation. + + Raises: + HTTPException: If there's an error, or if the agent for the profile is not found. + """ + try: + logger.info( + f"DAO propose send message request received from {request.client.host if request.client else 'unknown'} for profile {profile.id}" + ) + + agents = backend.list_agents(AgentFilter(profile_id=profile.id)) + if not agents: + logger.error(f"No agent found for profile ID: {profile.id}") + raise HTTPException( + status_code=404, + detail=f"No agent found for profile ID: {profile.id}", + ) + + agent = agents[0] + agent_id = agent.id + + logger.info( + f"Using agent {agent_id} for profile {profile.id} to propose DAO send message action." + ) + + tool = ProposeActionSendMessageTool(wallet_id=agent_id) + result = await tool._arun( + action_proposals_voting_extension=payload.action_proposals_voting_extension, + action_proposal_contract_to_execute=payload.action_proposal_contract_to_execute, + dao_token_contract_address=payload.dao_token_contract_address, + message=payload.message, + memo=payload.memo, + ) + + logger.debug( + f"DAO propose send message result for agent {agent_id} (profile {profile.id}): {result}" + ) + return JSONResponse(content=result) + + except HTTPException as he: + raise he + except Exception as e: + logger.error( + f"Failed to propose DAO send message action for profile {profile.id}", + exc_info=e, + ) + raise HTTPException( + status_code=500, + detail=f"Failed to propose DAO send message action: {str(e)}", + ) From 333d2e7a2f43aa08df8b00c0909f38ec1909c04f Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 21 May 2025 20:29:12 -0700 Subject: [PATCH 043/219] update deploy tool for agent account --- agent-tools-ts | 2 +- .../runner/tasks/agent_account_deployer.py | 23 +++- tools/agent_account.py | 117 ++++++++++++++++++ tools/tools_factory.py | 57 +-------- 4 files changed, 139 insertions(+), 60 deletions(-) create mode 100644 tools/agent_account.py diff --git a/agent-tools-ts b/agent-tools-ts index bc57b642..81b33071 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit bc57b642987dc649144f317b58cfd4bf5fff54a0 +Subproject commit 81b33071d0b5c02310e1bbea49eac4bcdd9e3217 diff --git a/services/runner/tasks/agent_account_deployer.py b/services/runner/tasks/agent_account_deployer.py index 051e4437..9204df6f 100644 --- a/services/runner/tasks/agent_account_deployer.py +++ b/services/runner/tasks/agent_account_deployer.py @@ -13,7 +13,7 @@ from config import config from lib.logger import configure_logger from services.runner.base import BaseTask, JobContext, RunnerResult -from tools.smartwallet import SmartWalletDeploySmartWalletTool +from tools.agent_account import AgentAccountDeployTool logger = configure_logger(__name__) @@ -89,16 +89,31 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: logger.error(error_msg) return {"success": False, "error": error_msg} - # Initialize the SmartWalletDeploySmartWalletTool + # Initialize the AgentAccountDeployTool logger.debug("Preparing to deploy agent account") - deploy_tool = SmartWalletDeploySmartWalletTool( + deploy_tool = AgentAccountDeployTool( wallet_id=config.scheduler.agent_account_deploy_runner_wallet_id ) + # get address from wallet id + wallet = backend.get_wallet( + config.scheduler.agent_account_deploy_runner_wallet_id + ) + # depending on the network, use the correct address + profile = backend.get_profile(wallet.profile_id) + + if config.network == "mainnet": + owner_address = profile.email.strip("@stacks.id").upper() + agent_address = wallet.mainnet_address + else: + owner_address = "ST1994Y3P6ZDJX476QFSABEFE5T6YMTJT0T7RSQDW" + agent_address = wallet.testnet_address + # Execute the deployment logger.debug("Executing deployment...") deployment_result = await deploy_tool._arun( - owner_address=message_data["owner_address"], + owner_address=owner_address, + agent_address=agent_address, dao_token_contract=message_data["dao_token_contract"], dao_token_dex_contract=message_data["dao_token_dex_contract"], ) diff --git a/tools/agent_account.py b/tools/agent_account.py new file mode 100644 index 00000000..6f36568c --- /dev/null +++ b/tools/agent_account.py @@ -0,0 +1,117 @@ +from typing import Any, Dict, Optional, Type +from uuid import UUID + +from langchain.tools import BaseTool +from pydantic import BaseModel, Field + +from tools.bun import BunScriptRunner + + +class AgentAccountDeployInput(BaseModel): + """Input schema for deploying an agent account contract.""" + + owner_address: str = Field( + ..., + description="Stacks address of the wallet owner", + example="ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", + ) + agent_address: str = Field( + ..., + description="Stacks address of the agent", + example="ST2CY5V39NHDPWSXMW9QDT3HC3GD6Q6XX4CFRK9AG", + ) + dao_token_contract: str = Field( + ..., + description="Contract principal of the DAO token", + example="ST35K818S3K2GSNEBC3M35GA3W8Q7X72KF4RVM3QA.aibtc-token", + ) + dao_token_dex_contract: str = Field( + ..., + description="Contract principal of the DAO token DEX", + example="ST35K818S3K2GSNEBC3M35GA3W8Q7X72KF4RVM3QA.aibtc-token-dex", + ) + save_to_file: bool = Field( + False, + description="Whether to save the contract to a file", + ) + + +class AgentAccountDeployTool(BaseTool): + name: str = "agent_account_deploy" + description: str = ( + "Deploy a new agent account contract with specified owner and agent addresses. " + "Returns the deployed contract address and transaction ID." + ) + args_schema: Type[BaseModel] = AgentAccountDeployInput + return_direct: bool = False + wallet_id: Optional[UUID] = None + + def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): + super().__init__(**kwargs) + self.wallet_id = wallet_id + + def _deploy( + self, + owner_address: str, + agent_address: str, + dao_token_contract: str, + dao_token_dex_contract: str, + save_to_file: bool = False, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to deploy agent account.""" + if self.wallet_id is None: + return {"success": False, "message": "Wallet ID is required", "data": None} + + args = [ + owner_address, + agent_address, + dao_token_contract, + dao_token_dex_contract, + str(save_to_file).lower(), + ] + + return BunScriptRunner.bun_run( + self.wallet_id, + "aibtc-cohort-0/contract-tools", + "deploy-agent-account.ts", + *args, + ) + + def _run( + self, + owner_address: str, + agent_address: str, + dao_token_contract: str, + dao_token_dex_contract: str, + save_to_file: bool = False, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to deploy agent account.""" + return self._deploy( + owner_address, + agent_address, + dao_token_contract, + dao_token_dex_contract, + save_to_file, + **kwargs, + ) + + async def _arun( + self, + owner_address: str, + agent_address: str, + dao_token_contract: str, + dao_token_dex_contract: str, + save_to_file: bool = False, + **kwargs, + ) -> Dict[str, Any]: + """Async version of the tool.""" + return self._deploy( + owner_address, + agent_address, + dao_token_contract, + dao_token_dex_contract, + save_to_file, + **kwargs, + ) diff --git a/tools/tools_factory.py b/tools/tools_factory.py index b98f3fb4..55bc4cac 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -7,6 +7,7 @@ from backend.models import UUID, Profile, WalletFilter from lib.logger import configure_logger +from .agent_account import AgentAccountDeployTool from .bitflow import BitflowExecuteTradeTool from .coinmarketcap import GetBitcoinData from .contracts import ContractSIP10InfoTool, FetchContractSourceTool @@ -226,61 +227,7 @@ def initialize_tools( "wallet_get_my_transactions": WalletGetMyTransactions(wallet_id), "wallet_send_sip10": WalletSIP10SendTool(wallet_id), "x_credentials": CollectXCredentialsTool(profile_id), - "smartwallet_deploy_smart_wallet": SmartWalletDeploySmartWalletTool(wallet_id), - "smartwallet_deploy_my_smart_wallet": SmartWalletDeployMySmartWalletTool( - wallet_id - ), - "smartwallet_deposit_stx": SmartWalletDepositSTXTool(wallet_id), - "smartwallet_deposit_ft": SmartWalletDepositFTTool(wallet_id), - "smartwallet_approve_asset": SmartWalletApproveAssetTool(wallet_id), - "smartwallet_revoke_asset": SmartWalletRevokeAssetTool(wallet_id), - "smartwallet_get_balance_stx": SmartWalletGetBalanceSTXTool(wallet_id), - "smartwallet_is_approved_asset": SmartWalletIsApprovedAssetTool(wallet_id), - "smartwallet_get_configuration": SmartWalletGetConfigurationTool(wallet_id), - "smartwallet_generate_smart_wallet": SmartWalletGenerateSmartWalletTool( - wallet_id - ), - "smartwallet_generate_my_smart_wallet": SmartWalletGenerateMySmartWalletTool( - wallet_id - ), - "smartwallet_withdraw_stx": SmartWalletWithdrawSTXTool(wallet_id), - "smartwallet_withdraw_ft": SmartWalletWithdrawFTTool(wallet_id), - "smartwallet_proxy_create_proposal": SmartWalletProxyCreateProposalTool( - wallet_id - ), - "smartwallet_proxy_propose_action_send_message": SmartWalletProxyProposeActionSendMessageTool( - wallet_id - ), - "smartwallet_proxy_propose_action_add_resource": SmartWalletProxyProposeActionAddResourceTool( - wallet_id - ), - "smartwallet_proxy_propose_action_allow_asset": SmartWalletProxyProposeActionAllowAssetTool( - wallet_id - ), - "smartwallet_proxy_propose_action_toggle_resource_by_name": SmartWalletProxyProposeActionToggleResourceByNameTool( - wallet_id - ), - "smartwallet_proxy_propose_action_set_account_holder": SmartWalletProxyProposeActionSetAccountHolderTool( - wallet_id - ), - "smartwallet_proxy_propose_action_set_withdrawal_amount": SmartWalletProxyProposeActionSetWithdrawalAmountTool( - wallet_id - ), - "smartwallet_proxy_propose_action_set_withdrawal_period": SmartWalletProxyProposeActionSetWithdrawalPeriodTool( - wallet_id - ), - "smartwallet_vote_on_action_proposal": SmartWalletVoteOnActionProposalTool( - wallet_id - ), - "smartwallet_vote_on_core_proposal": SmartWalletVoteOnCoreProposalTool( - wallet_id - ), - "smartwallet_conclude_action_proposal": SmartWalletConcludeActionProposalTool( - wallet_id - ), - "smartwallet_conclude_core_proposal": SmartWalletConcludeCoreProposalTool( - wallet_id - ), + "agent_account_deploy": AgentAccountDeployTool(wallet_id), } return tools From 758e998fe1d2523d6cd7df66c8c9a36bfb6079ec Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 21 May 2025 20:32:26 -0700 Subject: [PATCH 044/219] cleanup imports --- tools/tools_factory.py | 27 --------------------------- 1 file changed, 27 deletions(-) diff --git a/tools/tools_factory.py b/tools/tools_factory.py index 55bc4cac..b888915f 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -67,33 +67,6 @@ LunarCrushTokenMetricsTool, SearchLunarCrushTool, ) -from .smartwallet import ( - SmartWalletApproveAssetTool, - SmartWalletConcludeActionProposalTool, - SmartWalletConcludeCoreProposalTool, - SmartWalletDeployMySmartWalletTool, - SmartWalletDeploySmartWalletTool, - SmartWalletDepositFTTool, - SmartWalletDepositSTXTool, - SmartWalletGenerateMySmartWalletTool, - SmartWalletGenerateSmartWalletTool, - SmartWalletGetBalanceSTXTool, - SmartWalletGetConfigurationTool, - SmartWalletIsApprovedAssetTool, - SmartWalletProxyCreateProposalTool, - SmartWalletProxyProposeActionAddResourceTool, - SmartWalletProxyProposeActionAllowAssetTool, - SmartWalletProxyProposeActionSendMessageTool, - SmartWalletProxyProposeActionSetAccountHolderTool, - SmartWalletProxyProposeActionSetWithdrawalAmountTool, - SmartWalletProxyProposeActionSetWithdrawalPeriodTool, - SmartWalletProxyProposeActionToggleResourceByNameTool, - SmartWalletRevokeAssetTool, - SmartWalletVoteOnActionProposalTool, - SmartWalletVoteOnCoreProposalTool, - SmartWalletWithdrawFTTool, - SmartWalletWithdrawSTXTool, -) from .telegram import SendTelegramNotificationTool from .transactions import ( StacksTransactionByAddressTool, From 9ddd1afb0400f5cc613bcde7347fccfb4f0ea529 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 21 May 2025 20:57:10 -0700 Subject: [PATCH 045/219] bug fix --- .../runner/tasks/agent_account_deployer.py | 28 +++++++++++++++++-- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/services/runner/tasks/agent_account_deployer.py b/services/runner/tasks/agent_account_deployer.py index 9204df6f..a744fae9 100644 --- a/services/runner/tasks/agent_account_deployer.py +++ b/services/runner/tasks/agent_account_deployer.py @@ -1,5 +1,6 @@ """Agent account deployment task implementation.""" +import json from dataclasses import dataclass from typing import Any, Dict, List @@ -51,7 +52,7 @@ async def _validate_task_specific(self, context: JobContext) -> bool: # Validate that at least one message has valid deployment data for message in pending_messages: - message_data = message.message or {} + message_data = self._parse_message_data(message.message) if self._validate_message_data(message_data): logger.info("Found valid agent account deployment message") return True @@ -66,6 +67,21 @@ async def _validate_task_specific(self, context: JobContext) -> bool: ) return False + def _parse_message_data(self, message: Any) -> Dict[str, Any]: + """Parse message data from either string or dictionary format.""" + if message is None: + return {} + + if isinstance(message, dict): + return message + + try: + # Try to parse as JSON string + return json.loads(message) + except (json.JSONDecodeError, TypeError): + logger.error(f"Failed to parse message data: {message}") + return {} + def _validate_message_data(self, message_data: Dict[str, Any]) -> bool: """Validate the message data contains required fields.""" required_fields = [ @@ -78,7 +94,7 @@ def _validate_message_data(self, message_data: Dict[str, Any]) -> bool: async def process_message(self, message: QueueMessage) -> Dict[str, Any]: """Process a single agent account deployment message.""" message_id = message.id - message_data = message.message or {} + message_data = self._parse_message_data(message.message) logger.debug(f"Processing agent account deployment message {message_id}") @@ -133,7 +149,13 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: async def get_pending_messages(self) -> List[QueueMessage]: """Get all unprocessed messages from the queue.""" filters = QueueMessageFilter(type=self.QUEUE_TYPE, is_processed=False) - return backend.list_queue_messages(filters=filters) + messages = backend.list_queue_messages(filters=filters) + + # Messages are already parsed by the backend, but we log them for debugging + for message in messages: + logger.debug(f"Queue message raw data: {message.message!r}") + + return messages async def _execute_impl( self, context: JobContext From 0d0f79be209ab3f5e03f278b9409527d4fe13246 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 21 May 2025 21:18:08 -0700 Subject: [PATCH 046/219] bug --- services/runner/tasks/agent_account_deployer.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/services/runner/tasks/agent_account_deployer.py b/services/runner/tasks/agent_account_deployer.py index a744fae9..0b59f093 100644 --- a/services/runner/tasks/agent_account_deployer.py +++ b/services/runner/tasks/agent_account_deployer.py @@ -120,16 +120,14 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: if config.network == "mainnet": owner_address = profile.email.strip("@stacks.id").upper() - agent_address = wallet.mainnet_address else: owner_address = "ST1994Y3P6ZDJX476QFSABEFE5T6YMTJT0T7RSQDW" - agent_address = wallet.testnet_address # Execute the deployment logger.debug("Executing deployment...") deployment_result = await deploy_tool._arun( owner_address=owner_address, - agent_address=agent_address, + agent_address=message_data["owner_address"], dao_token_contract=message_data["dao_token_contract"], dao_token_dex_contract=message_data["dao_token_dex_contract"], ) From c9c6c0f2ece2990e09622f005903ce5803aa0f01 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Thu, 22 May 2025 19:11:05 -0700 Subject: [PATCH 047/219] fix --- api/tools.py | 33 ++++++++++++++++++++++++++++----- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git a/api/tools.py b/api/tools.py index 3a8a99e5..1b82105c 100644 --- a/api/tools.py +++ b/api/tools.py @@ -8,7 +8,12 @@ verify_profile_from_token, # Added verify_profile_from_token ) from backend.factory import backend # Added backend factory -from backend.models import UUID, AgentFilter, Profile # Added Profile, AgentFilter +from backend.models import ( # Added Profile, AgentFilter + UUID, + AgentFilter, + Profile, + WalletFilter, +) from lib.logger import configure_logger from lib.tools import Tool, get_available_tools from tools.dao_ext_action_proposals import ( @@ -226,11 +231,20 @@ async def execute_faktory_buy( agent = agents[0] # Assuming the first agent is the one to use agent_id = agent.id + # get wallet id from agent + wallet = backend.get_wallet(WalletFilter(agent_id=agent_id)) + if not wallet: + logger.error(f"No wallet found for agent ID: {agent_id}") + raise HTTPException( + status_code=404, + detail=f"No wallet found for agent ID: {agent_id}", + ) + logger.info( f"Using agent {agent_id} for profile {profile.id} to execute Faktory buy." ) - tool = FaktoryExecuteBuyTool(wallet_id=agent_id) # Use fetched agent_id + tool = FaktoryExecuteBuyTool(wallet_id=wallet.id) # Use fetched agent_id result = await tool._arun( btc_amount=payload.btc_amount, dao_token_dex_contract_address=payload.dao_token_dex_contract_address, @@ -293,11 +307,20 @@ async def propose_dao_action_send_message( agent = agents[0] agent_id = agent.id + # get wallet id from agent + wallet = backend.get_wallet(WalletFilter(agent_id=agent_id)) + if not wallet: + logger.error(f"No wallet found for agent ID: {agent_id}") + raise HTTPException( + status_code=404, + detail=f"No wallet found for agent ID: {agent_id}", + ) + logger.info( - f"Using agent {agent_id} for profile {profile.id} to propose DAO send message action." + f"Using wallet {wallet.id} for profile {profile.id} to propose DAO send message action." ) - tool = ProposeActionSendMessageTool(wallet_id=agent_id) + tool = ProposeActionSendMessageTool(wallet_id=wallet.id) result = await tool._arun( action_proposals_voting_extension=payload.action_proposals_voting_extension, action_proposal_contract_to_execute=payload.action_proposal_contract_to_execute, @@ -307,7 +330,7 @@ async def propose_dao_action_send_message( ) logger.debug( - f"DAO propose send message result for agent {agent_id} (profile {profile.id}): {result}" + f"DAO propose send message result for wallet {wallet.id} (profile {profile.id}): {result}" ) return JSONResponse(content=result) From 3fb193a1e63483c2cbf562161b27a75a165e4008 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Fri, 23 May 2025 09:44:52 -0700 Subject: [PATCH 048/219] update --- api/tools.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/api/tools.py b/api/tools.py index 1b82105c..97ec54bc 100644 --- a/api/tools.py +++ b/api/tools.py @@ -232,14 +232,16 @@ async def execute_faktory_buy( agent_id = agent.id # get wallet id from agent - wallet = backend.get_wallet(WalletFilter(agent_id=agent_id)) - if not wallet: + wallets = backend.list_wallets(WalletFilter(agent_id=agent_id)) + if not wallets: logger.error(f"No wallet found for agent ID: {agent_id}") raise HTTPException( status_code=404, detail=f"No wallet found for agent ID: {agent_id}", ) + wallet = wallets[0] # Get the first wallet for this agent + logger.info( f"Using agent {agent_id} for profile {profile.id} to execute Faktory buy." ) @@ -308,14 +310,16 @@ async def propose_dao_action_send_message( agent_id = agent.id # get wallet id from agent - wallet = backend.get_wallet(WalletFilter(agent_id=agent_id)) - if not wallet: + wallets = backend.list_wallets(WalletFilter(agent_id=agent_id)) + if not wallets: logger.error(f"No wallet found for agent ID: {agent_id}") raise HTTPException( status_code=404, detail=f"No wallet found for agent ID: {agent_id}", ) + wallet = wallets[0] # Get the first wallet for this agent + logger.info( f"Using wallet {wallet.id} for profile {profile.id} to propose DAO send message action." ) From 1919fe863ee8abe9a60f897eeb3f6f43657db2ed Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Fri, 23 May 2025 22:44:33 -0700 Subject: [PATCH 049/219] update api to match new model --- services/webhooks/dao/handler.py | 101 ++++++++------ services/webhooks/dao/models.py | 232 ++++++++++++++++++++++++------- 2 files changed, 238 insertions(+), 95 deletions(-) diff --git a/services/webhooks/dao/handler.py b/services/webhooks/dao/handler.py index 73933281..dcf60cc6 100644 --- a/services/webhooks/dao/handler.py +++ b/services/webhooks/dao/handler.py @@ -7,7 +7,11 @@ from backend.models import ContractStatus, DAOCreate, ExtensionCreate, TokenCreate from lib.logger import configure_logger from services.webhooks.base import WebhookHandler -from services.webhooks.dao.models import DAOWebhookPayload, DAOWebhookResponse +from services.webhooks.dao.models import ( + DAOWebhookPayload, + DAOWebhookResponse, + DeployedContractRegistryEntry, +) class DAOHandler(WebhookHandler): @@ -50,59 +54,70 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: dao = self.db.create_dao(dao_create) self.logger.info(f"Created DAO with ID: {dao.id}") - # Create extensions if provided + # Create extensions extension_ids: List[UUID] = [] - if parsed_data.extensions: - for ext_data in parsed_data.extensions: - extension_create = ExtensionCreate( - dao_id=dao.id, - type=( - f"{ext_data.type}_{ext_data.subtype}" - if ext_data.subtype - else ext_data.type - ), - contract_principal=ext_data.contract_principal, - tx_id=ext_data.tx_id, - status=( - ContractStatus.DEPLOYED - if ext_data.success - else ContractStatus.FAILED - ), + for ext_data in parsed_data.extensions: + # Determine if this is a deployed contract or just a contract response + if isinstance(ext_data, DeployedContractRegistryEntry): + # This is a deployed contract + contract_principal = ext_data.address + tx_id = ext_data.tx_id + status = ( + ContractStatus.DEPLOYED + if ext_data.success + else ContractStatus.FAILED ) + else: + # This is just a contract response (not deployed yet) + contract_principal = None + tx_id = None + status = ContractStatus.DRAFT + + # Create extension type from type and subtype + extension_type = ( + f"{ext_data.type.value}_{ext_data.subtype}" + if ext_data.subtype + else ext_data.type.value + ) - extension = self.db.create_extension(extension_create) - extension_ids.append(extension.id) - self.logger.info(f"Created extension with ID: {extension.id}") - - # Create token if provided - token_id = None - if parsed_data.token: - token_create = TokenCreate( + extension_create = ExtensionCreate( dao_id=dao.id, - contract_principal=parsed_data.token.contract_principal, - tx_id=parsed_data.token.tx_id, - name=parsed_data.token.name, - description=parsed_data.token.description, - symbol=parsed_data.token.symbol, - decimals=parsed_data.token.decimals, - max_supply=parsed_data.token.max_supply, - uri=parsed_data.token.uri, - image_url=parsed_data.token.image_url, - x_url=parsed_data.token.x_url, - telegram_url=parsed_data.token.telegram_url, - website_url=parsed_data.token.website_url, - status=ContractStatus.DEPLOYED, + type=extension_type, + contract_principal=contract_principal, + tx_id=tx_id, + status=status, ) - token = self.db.create_token(token_create) - token_id = token.id - self.logger.info(f"Created token with ID: {token.id}") + extension = self.db.create_extension(extension_create) + extension_ids.append(extension.id) + self.logger.info(f"Created extension with ID: {extension.id}") + + # Create token + token_create = TokenCreate( + dao_id=dao.id, + contract_principal=parsed_data.token.contract_principal, + tx_id=parsed_data.token.tx_id, + name=parsed_data.token.name, + description=parsed_data.token.description, + symbol=parsed_data.token.symbol, + decimals=parsed_data.token.decimals, + max_supply=parsed_data.token.max_supply, + uri=parsed_data.token.uri, + image_url=parsed_data.token.image_url, + x_url=parsed_data.token.x_url, + telegram_url=parsed_data.token.telegram_url, + website_url=parsed_data.token.website_url, + status=ContractStatus.DEPLOYED, + ) + + token = self.db.create_token(token_create) + self.logger.info(f"Created token with ID: {token.id}") # Prepare response response = DAOWebhookResponse( dao_id=dao.id, extension_ids=extension_ids if extension_ids else None, - token_id=token_id, + token_id=token.id, ) return { diff --git a/services/webhooks/dao/models.py b/services/webhooks/dao/models.py index e1b1461c..8282cf60 100644 --- a/services/webhooks/dao/models.py +++ b/services/webhooks/dao/models.py @@ -1,79 +1,207 @@ """Models for DAO webhook service.""" -from typing import List, Optional +from enum import Enum +from typing import List, Optional, Union from uuid import UUID -from pydantic import BaseModel, Field, model_validator +from pydantic import BaseModel, Field -class ExtensionData(BaseModel): - """Data model for extension creation via webhook.""" +class ContractType(str, Enum): + """Contract types enum.""" + + AGENT = "AGENT" + BASE = "BASE" + ACTIONS = "ACTIONS" + EXTENSIONS = "EXTENSIONS" + PROPOSALS = "PROPOSALS" + TOKEN = "TOKEN" + + +class ClarityVersion(int, Enum): + """Clarity version enum.""" + + CLARITY1 = 1 + CLARITY2 = 2 + CLARITY3 = 3 + + +class ContractCategory(str, Enum): + """Contract categories enum.""" + + BASE = "BASE" + ACTIONS = "ACTIONS" + EXTENSIONS = "EXTENSIONS" + PROPOSALS = "PROPOSALS" + EXTERNAL = "EXTERNAL" + TOKEN = "TOKEN" + + +# Contract subtypes for each type +class AgentSubtype(str, Enum): + """Agent contract subtypes.""" + + AGENT_ACCOUNT = "AGENT_ACCOUNT" + + +class BaseSubtype(str, Enum): + """Base contract subtypes.""" + + DAO = "DAO" + + +class ActionsSubtype(str, Enum): + """Actions contract subtypes.""" + + SEND_MESSAGE = "SEND_MESSAGE" + + +class ExtensionsSubtype(str, Enum): + """Extensions contract subtypes.""" + + ACTION_PROPOSAL_VOTING = "ACTION_PROPOSAL_VOTING" + DAO_CHARTER = "DAO_CHARTER" + DAO_EPOCH = "DAO_EPOCH" + DAO_USERS = "DAO_USERS" + ONCHAIN_MESSAGING = "ONCHAIN_MESSAGING" + REWARDS_ACCOUNT = "REWARDS_ACCOUNT" + TOKEN_OWNER = "TOKEN_OWNER" + TREASURY = "TREASURY" + + +class ProposalsSubtype(str, Enum): + """Proposals contract subtypes.""" + + INITIALIZE_DAO = "INITIALIZE_DAO" + + +class TokenSubtype(str, Enum): + """Token contract subtypes.""" + + DAO = "DAO" + DEX = "DEX" + POOL = "POOL" + PRELAUNCH = "PRELAUNCH" + + +# Contract subcategories for each category +class BaseSubcategory(str, Enum): + """Base contract subcategories.""" + + DAO = "DAO" + + +class ActionsSubcategory(str, Enum): + """Actions contract subcategories.""" + + CONFIGURE_TIMED_VAULT_DAO = "CONFIGURE_TIMED_VAULT_DAO" + CONFIGURE_TIMED_VAULT_SBTC = "CONFIGURE_TIMED_VAULT_SBTC" + CONFIGURE_TIMED_VAULT_STX = "CONFIGURE_TIMED_VAULT_STX" + PMT_DAO_ADD_RESOURCE = "PMT_DAO_ADD_RESOURCE" + PMT_DAO_TOGGLE_RESOURCE = "PMT_DAO_TOGGLE_RESOURCE" + PMT_SBTC_ADD_RESOURCE = "PMT_SBTC_ADD_RESOURCE" + PMT_SBTC_TOGGLE_RESOURCE = "PMT_SBTC_TOGGLE_RESOURCE" + PMT_STX_ADD_RESOURCE = "PMT_STX_ADD_RESOURCE" + PMT_STX_TOGGLE_RESOURCE = "PMT_STX_TOGGLE_RESOURCE" + MESSAGING_SEND_MESSAGE = "MESSAGING_SEND_MESSAGE" + TREASURY_ALLOW_ASSET = "TREASURY_ALLOW_ASSET" + + +class ExtensionsSubcategory(str, Enum): + """Extensions contract subcategories.""" + + ACTION_PROPOSALS = "ACTION_PROPOSALS" + CORE_PROPOSALS = "CORE_PROPOSALS" + CHARTER = "CHARTER" + MESSAGING = "MESSAGING" + PAYMENTS_DAO = "PAYMENTS_DAO" + PAYMENTS_SBTC = "PAYMENTS_SBTC" + PAYMENTS_STX = "PAYMENTS_STX" + TIMED_VAULT_DAO = "TIMED_VAULT_DAO" + TIMED_VAULT_SBTC = "TIMED_VAULT_SBTC" + TIMED_VAULT_STX = "TIMED_VAULT_STX" + TOKEN_OWNER = "TOKEN_OWNER" + TREASURY = "TREASURY" + + +class ProposalsSubcategory(str, Enum): + """Proposals contract subcategories.""" + + BOOTSTRAP_INIT = "BOOTSTRAP_INIT" + + +class ExternalSubcategory(str, Enum): + """External contract subcategories.""" + + STANDARD_SIP009 = "STANDARD_SIP009" + STANDARD_SIP010 = "STANDARD_SIP010" + FAKTORY_SIP010 = "FAKTORY_SIP010" + BITFLOW_POOL = "BITFLOW_POOL" + BITFOW_SIP010 = "BITFOW_SIP010" + + +class TokenSubcategory(str, Enum): + """Token contract subcategories.""" + + DAO = "DAO" + DEX = "DEX" + POOL = "POOL" + POOL_STX = "POOL_STX" + PRELAUNCH = "PRELAUNCH" + + +class ContractResponse(BaseModel): + """Contract response model.""" name: str - type: str - subtype: Optional[str] = None + display_name: Optional[str] = Field(None, alias="displayName") + type: ContractType + subtype: str # Handle union of subtypes as string for flexibility source: Optional[str] = None hash: Optional[str] = None - sender: Optional[str] = None - success: Optional[bool] = True - txId: Optional[str] = None - address: Optional[str] = None - contract_principal: Optional[str] = None - tx_id: Optional[str] = None - - @model_validator(mode="after") - def set_contract_info(self): - """Set contract_principal and tx_id from address and txId if not provided.""" - if not self.contract_principal and self.address: - self.contract_principal = self.address - if not self.tx_id and self.txId: - self.tx_id = self.txId - return self + deployment_order: int = Field(alias="deploymentOrder") + clarity_version: Optional[ClarityVersion] = Field(None, alias="clarityVersion") + + model_config = {"populate_by_name": True} + + +class DeployedContractRegistryEntry(ContractResponse): + """Deployed contract registry entry model.""" + + sender: str + success: bool + tx_id: Optional[str] = Field(None, alias="txId") + address: str + error: Optional[str] = None + + model_config = {"populate_by_name": True} class TokenData(BaseModel): - """Data model for token creation via webhook.""" + """Token data model for DAO webhook.""" - contract_principal: Optional[str] = None - tx_id: Optional[str] = None name: str - description: Optional[str] = None symbol: str - decimals: int = 6 - max_supply: Optional[str] = None - uri: Optional[str] = None - image_url: Optional[str] = None + decimals: int + description: str + max_supply: str + uri: str + tx_id: str + contract_principal: str + image_url: str x_url: Optional[str] = None telegram_url: Optional[str] = None website_url: Optional[str] = None class DAOWebhookPayload(BaseModel): - """Webhook payload for DAO creation.""" + """Webhook payload for DAO creation with new structure.""" name: str - mission: Optional[str] = None - description: Optional[str] = None - extensions: Optional[List[ExtensionData]] = Field(default_factory=list) - token: Optional[TokenData] = None - - @model_validator(mode="after") - def extract_token_from_extensions(self): - """Extract token information from extensions if token is not provided.""" - if not self.token and self.extensions: - # Look for a TOKEN extension with subtype DAO - for ext in self.extensions: - if ext.type == "TOKEN" and ext.subtype == "DAO": - # Create a token from the extension data - self.token = TokenData( - contract_principal=ext.address, - tx_id=ext.txId, - name=f"{self.name} Token", - symbol="TKN", - decimals=6, - ) - break - return self + mission: str + description: str + extensions: List[Union[DeployedContractRegistryEntry, ContractResponse]] + token: TokenData class DAOWebhookResponse(BaseModel): From d1a07437f9364442d42c670ab3a52c68e4863ce7 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Fri, 23 May 2025 22:48:26 -0700 Subject: [PATCH 050/219] fix validate by alias --- services/webhooks/dao/models.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/services/webhooks/dao/models.py b/services/webhooks/dao/models.py index 8282cf60..21756510 100644 --- a/services/webhooks/dao/models.py +++ b/services/webhooks/dao/models.py @@ -4,7 +4,7 @@ from typing import List, Optional, Union from uuid import UUID -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field class ContractType(str, Enum): @@ -162,7 +162,7 @@ class ContractResponse(BaseModel): deployment_order: int = Field(alias="deploymentOrder") clarity_version: Optional[ClarityVersion] = Field(None, alias="clarityVersion") - model_config = {"populate_by_name": True} + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) class DeployedContractRegistryEntry(ContractResponse): @@ -174,7 +174,7 @@ class DeployedContractRegistryEntry(ContractResponse): address: str error: Optional[str] = None - model_config = {"populate_by_name": True} + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) class TokenData(BaseModel): From fe011232342fb48f7d4932ca745bd3f107643fe7 Mon Sep 17 00:00:00 2001 From: davek Date: Sat, 24 May 2025 19:40:11 -0700 Subject: [PATCH 051/219] Add Discord image embed support --- requirements.txt | 1 + services/runner/tasks/tweet_task.py | 92 ++++++++++++++++++++++++++--- 2 files changed, 86 insertions(+), 7 deletions(-) diff --git a/requirements.txt b/requirements.txt index 389ea499..f12b5437 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,6 +14,7 @@ pydantic==2.11.4 python-dotenv==1.1.0 python-telegram-bot==22.1 python-twitter-v2==0.9.2 +tweepy==4.14.0 Requests==2.32.3 SQLAlchemy==2.0.41 starlette==0.46.2 diff --git a/services/runner/tasks/tweet_task.py b/services/runner/tasks/tweet_task.py index 10ae8fb1..394dd3a1 100644 --- a/services/runner/tasks/tweet_task.py +++ b/services/runner/tasks/tweet_task.py @@ -10,8 +10,16 @@ QueueMessageType, XCredsFilter, ) +import re +from io import BytesIO +from urllib.parse import urlparse + +import requests +import tweepy + from lib.logger import configure_logger from lib.twitter import TwitterService +from lib.utils import extract_image_urls from services.discord import create_discord_service from services.runner.base import BaseTask, JobContext, RunnerConfig, RunnerResult @@ -34,6 +42,56 @@ def __init__(self, config: Optional[RunnerConfig] = None): self._pending_messages: Optional[List[QueueMessage]] = None self.twitter_service = None + def _get_extension(self, url: str) -> str: + path = urlparse(url).path.lower() + for ext in [".png", ".jpg", ".jpeg", ".gif"]: + if path.endswith(ext): + return ext + return ".jpg" + + def _post_tweet_with_media( + self, + image_url: str, + text: str, + reply_id: Optional[str] = None, + ): + try: + headers = { + "User-Agent": "Mozilla/5.0" + } + response = requests.get(image_url, headers=headers, timeout=10) + response.raise_for_status() + auth = tweepy.OAuth1UserHandler( + self.twitter_service.consumer_key, + self.twitter_service.consumer_secret, + self.twitter_service.access_token, + self.twitter_service.access_secret, + ) + api = tweepy.API(auth) + extension = self._get_extension(image_url) + media = api.media_upload( + filename=f"image{extension}", + file=BytesIO(response.content), + ) + + client = tweepy.Client( + consumer_key=self.twitter_service.consumer_key, + consumer_secret=self.twitter_service.consumer_secret, + access_token=self.twitter_service.access_token, + access_token_secret=self.twitter_service.access_secret, + ) + + result = client.create_tweet( + text=text, + media_ids=[media.media_id_string], + reply_in_reply_to_tweet_id=reply_id, + ) + if result and result.data: + return type("Obj", (), {"id": result.data["id"]})() + except Exception as e: + logger.error(f"Failed to post tweet with media: {str(e)}") + return None + async def _initialize_twitter_service(self, dao_id: UUID) -> bool: """Initialize Twitter service with credentials for the given DAO.""" try: @@ -186,17 +244,32 @@ async def _process_tweet_message( ) # Extract tweet text directly from the message format - tweet_text = message.message["message"] + original_text = message.message["message"] logger.info(f"Sending tweet for DAO {message.dao_id}") - logger.debug(f"Tweet content: {tweet_text}") + logger.debug(f"Tweet content: {original_text}") + + # Look for image URLs in the text + image_urls = extract_image_urls(original_text) + image_url = image_urls[0] if image_urls else None + tweet_text = original_text + + if image_url: + tweet_text = re.sub(re.escape(image_url), "", original_text).strip() + tweet_text = re.sub(r"\s+", " ", tweet_text) # Prepare tweet parameters tweet_params = {"text": tweet_text} if message.tweet_id: tweet_params["reply_in_reply_to_tweet_id"] = message.tweet_id - # Send tweet using Twitter service - tweet_response = await self.twitter_service._apost_tweet(**tweet_params) + if image_url: + tweet_response = self._post_tweet_with_media( + image_url=image_url, + text=tweet_text, + reply_id=message.tweet_id, + ) + else: + tweet_response = await self.twitter_service._apost_tweet(**tweet_params) if not tweet_response: return TweetProcessingResult( @@ -214,8 +287,13 @@ async def _process_tweet_message( discord_service = create_discord_service() if discord_service: - discord_result = discord_service.send_message(tweet_text) - logger.info(f"Discord message sent: {discord_result['success']}") + embeds = None + if image_url: + embeds = [{"image": {"url": image_url}}] + discord_result = discord_service.send_message(tweet_text, embeds=embeds) + logger.info( + f"Discord message sent: {discord_result['success']}" + ) except Exception as e: logger.warning(f"Failed to send Discord message: {str(e)}") @@ -281,4 +359,4 @@ async def _execute_impl(self, context: JobContext) -> List[TweetProcessingResult return results -tweet_task = TweetTask() \ No newline at end of file +tweet_task = TweetTask() From e20c0942888f71c85ad39be31743dea3fcae0bd2 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sat, 24 May 2025 20:37:20 -0700 Subject: [PATCH 052/219] fix body --- services/webhooks/dao/handler.py | 26 +++++++++----------------- services/webhooks/dao/models.py | 4 ++-- 2 files changed, 11 insertions(+), 19 deletions(-) diff --git a/services/webhooks/dao/handler.py b/services/webhooks/dao/handler.py index dcf60cc6..9b26d0f8 100644 --- a/services/webhooks/dao/handler.py +++ b/services/webhooks/dao/handler.py @@ -8,9 +8,9 @@ from lib.logger import configure_logger from services.webhooks.base import WebhookHandler from services.webhooks.dao.models import ( + ContractResponse, DAOWebhookPayload, DAOWebhookResponse, - DeployedContractRegistryEntry, ) @@ -57,21 +57,11 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: # Create extensions extension_ids: List[UUID] = [] for ext_data in parsed_data.extensions: - # Determine if this is a deployed contract or just a contract response - if isinstance(ext_data, DeployedContractRegistryEntry): - # This is a deployed contract - contract_principal = ext_data.address - tx_id = ext_data.tx_id - status = ( - ContractStatus.DEPLOYED - if ext_data.success - else ContractStatus.FAILED - ) - else: - # This is just a contract response (not deployed yet) - contract_principal = None - tx_id = None - status = ContractStatus.DRAFT + # All extensions in this payload are contract definitions, not deployed contracts + # Set status as DRAFT since they're not deployed yet + contract_principal = None + tx_id = None + status = ContractStatus.DRAFT # Create extension type from type and subtype extension_type = ( @@ -90,7 +80,9 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: extension = self.db.create_extension(extension_create) extension_ids.append(extension.id) - self.logger.info(f"Created extension with ID: {extension.id}") + self.logger.info( + f"Created extension with ID: {extension.id} for type: {extension_type}" + ) # Create token token_create = TokenCreate( diff --git a/services/webhooks/dao/models.py b/services/webhooks/dao/models.py index 21756510..08bcc961 100644 --- a/services/webhooks/dao/models.py +++ b/services/webhooks/dao/models.py @@ -159,7 +159,7 @@ class ContractResponse(BaseModel): subtype: str # Handle union of subtypes as string for flexibility source: Optional[str] = None hash: Optional[str] = None - deployment_order: int = Field(alias="deploymentOrder") + deployment_order: Optional[int] = Field(None, alias="deploymentOrder") clarity_version: Optional[ClarityVersion] = Field(None, alias="clarityVersion") model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) @@ -200,7 +200,7 @@ class DAOWebhookPayload(BaseModel): name: str mission: str description: str - extensions: List[Union[DeployedContractRegistryEntry, ContractResponse]] + extensions: List[ContractResponse] token: TokenData From 592bd09429766d141e764143ca3139a809d014e5 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sat, 24 May 2025 20:46:07 -0700 Subject: [PATCH 053/219] update --- backend/models.py | 2 ++ services/webhooks/dao/handler.py | 14 ++++---------- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/backend/models.py b/backend/models.py index bce1d215..5364c06a 100644 --- a/backend/models.py +++ b/backend/models.py @@ -198,6 +198,7 @@ class Agent(AgentBase): class ExtensionBase(CustomBaseModel): dao_id: Optional[UUID] = None type: Optional[str] = None + subtype: Optional[str] = None contract_principal: Optional[str] = None tx_id: Optional[str] = None status: Optional[ContractStatus] = ContractStatus.DRAFT @@ -542,6 +543,7 @@ class AgentFilter(CustomBaseModel): class ExtensionFilter(CustomBaseModel): dao_id: Optional[UUID] = None type: Optional[str] = None + subtype: Optional[str] = None status: Optional[ContractStatus] = None contract_principal: Optional[str] = None diff --git a/services/webhooks/dao/handler.py b/services/webhooks/dao/handler.py index 9b26d0f8..3761d59b 100644 --- a/services/webhooks/dao/handler.py +++ b/services/webhooks/dao/handler.py @@ -61,18 +61,12 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: # Set status as DRAFT since they're not deployed yet contract_principal = None tx_id = None - status = ContractStatus.DRAFT - - # Create extension type from type and subtype - extension_type = ( - f"{ext_data.type.value}_{ext_data.subtype}" - if ext_data.subtype - else ext_data.type.value - ) + status = ContractStatus.DEPLOYED extension_create = ExtensionCreate( dao_id=dao.id, - type=extension_type, + type=ext_data.type, + subtype=ext_data.subtype, contract_principal=contract_principal, tx_id=tx_id, status=status, @@ -81,7 +75,7 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: extension = self.db.create_extension(extension_create) extension_ids.append(extension.id) self.logger.info( - f"Created extension with ID: {extension.id} for type: {extension_type}" + f"Created extension with ID: {extension.id} for type: {ext_data.type} and subtype: {ext_data.subtype}" ) # Create token From 69d861e4b858ab565aa876fb54c8dce0ec041a2a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 26 May 2025 13:11:37 +0000 Subject: [PATCH 054/219] Bump the dev-dependencies group with 7 updates Bumps the dev-dependencies group with 7 updates: | Package | From | To | | --- | --- | --- | | [cachetools](https://github.com/tkem/cachetools) | `5.5.2` | `6.0.0` | | [langchain-openai](https://github.com/langchain-ai/langchain) | `0.3.17` | `0.3.18` | | [langgraph](https://github.com/langchain-ai/langgraph) | `0.4.5` | `0.4.7` | | [openai](https://github.com/openai/openai-python) | `1.79.0` | `1.82.0` | | [pydantic](https://github.com/pydantic/pydantic) | `2.11.4` | `2.11.5` | | [supabase](https://github.com/supabase/supabase-py) | `2.15.1` | `2.15.2` | | [tweepy](https://github.com/tweepy/tweepy) | `4.14.0` | `4.15.0` | Updates `cachetools` from 5.5.2 to 6.0.0 - [Changelog](https://github.com/tkem/cachetools/blob/master/CHANGELOG.rst) - [Commits](https://github.com/tkem/cachetools/compare/v5.5.2...v6.0.0) Updates `langchain-openai` from 0.3.17 to 0.3.18 - [Release notes](https://github.com/langchain-ai/langchain/releases) - [Commits](https://github.com/langchain-ai/langchain/compare/langchain-openai==0.3.17...langchain-openai==0.3.18) Updates `langgraph` from 0.4.5 to 0.4.7 - [Release notes](https://github.com/langchain-ai/langgraph/releases) - [Commits](https://github.com/langchain-ai/langgraph/compare/0.4.5...0.4.7) Updates `openai` from 1.79.0 to 1.82.0 - [Release notes](https://github.com/openai/openai-python/releases) - [Changelog](https://github.com/openai/openai-python/blob/main/CHANGELOG.md) - [Commits](https://github.com/openai/openai-python/compare/v1.79.0...v1.82.0) Updates `pydantic` from 2.11.4 to 2.11.5 - [Release notes](https://github.com/pydantic/pydantic/releases) - [Changelog](https://github.com/pydantic/pydantic/blob/main/HISTORY.md) - [Commits](https://github.com/pydantic/pydantic/compare/v2.11.4...v2.11.5) Updates `supabase` from 2.15.1 to 2.15.2 - [Release notes](https://github.com/supabase/supabase-py/releases) - [Changelog](https://github.com/supabase/supabase-py/blob/main/CHANGELOG.md) - [Commits](https://github.com/supabase/supabase-py/compare/v2.15.1...v2.15.2) Updates `tweepy` from 4.14.0 to 4.15.0 - [Release notes](https://github.com/tweepy/tweepy/releases) - [Changelog](https://github.com/tweepy/tweepy/blob/master/docs/changelog.md) - [Commits](https://github.com/tweepy/tweepy/compare/v4.14.0...v4.15.0) --- updated-dependencies: - dependency-name: cachetools dependency-version: 6.0.0 dependency-type: direct:production update-type: version-update:semver-major dependency-group: dev-dependencies - dependency-name: langchain-openai dependency-version: 0.3.18 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: langgraph dependency-version: 0.4.7 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: openai dependency-version: 1.82.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies - dependency-name: pydantic dependency-version: 2.11.5 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: supabase dependency-version: 2.15.2 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: tweepy dependency-version: 4.15.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies ... Signed-off-by: dependabot[bot] --- pyproject.toml | 12 ++++++------ requirements.txt | 14 +++++++------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index fce47364..f0363841 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,25 +7,25 @@ requires-python = ">=3.13" dependencies = [ "aiohttp==3.11.18", "apscheduler==3.11.0", - "cachetools==5.5.2", + "cachetools==6.0.0", "fastapi==0.115.12", "langchain==0.3.25", "langchain-community==0.3.24", "langchain-core>=0.3.56,<1.0.0", - "langchain-openai==0.3.17", + "langchain-openai==0.3.18", "langchain-text-splitters==0.3.8", - "langgraph==0.4.5", - "openai==1.79.0", + "langgraph==0.4.7", + "openai==1.82.0", "pgvector==0.3.6", "psycopg2==2.9.10", - "pydantic==2.11.4", + "pydantic==2.11.5", "python-dotenv==1.1.0", "python-telegram-bot==22.1", "python-twitter-v2==0.9.2", "requests==2.32.3", "sqlalchemy==2.0.41", "starlette==0.46.2", - "supabase==2.15.1", + "supabase==2.15.2", "tiktoken==0.9.0", "uvicorn==0.34.2", "vecs==0.4.5", diff --git a/requirements.txt b/requirements.txt index f12b5437..fbe98f50 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,24 +1,24 @@ APScheduler==3.11.0 -cachetools==5.5.2 +cachetools==6.0.0 fastapi==0.115.12 langchain==0.3.25 langchain_core>=0.3.56,<1.0.0 langchain_community==0.3.24 -langchain_openai==0.3.17 +langchain_openai==0.3.18 langchain_text_splitters==0.3.8 -langgraph==0.4.5 -openai==1.79.0 +langgraph==0.4.7 +openai==1.82.0 pgvector==0.3.6 psycopg2==2.9.10 -pydantic==2.11.4 +pydantic==2.11.5 python-dotenv==1.1.0 python-telegram-bot==22.1 python-twitter-v2==0.9.2 -tweepy==4.14.0 +tweepy==4.15.0 Requests==2.32.3 SQLAlchemy==2.0.41 starlette==0.46.2 -supabase==2.15.1 +supabase==2.15.2 tiktoken==0.9.0 uvicorn==0.34.2 vecs==0.4.5 From cad239186cf87747d2763bbe3508da1cb61fd9a1 Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Mon, 26 May 2025 10:31:58 -0700 Subject: [PATCH 055/219] fix: bump agent tools to latest --- agent-tools-ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agent-tools-ts b/agent-tools-ts index 81b33071..537930f8 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 81b33071d0b5c02310e1bbea49eac4bcdd9e3217 +Subproject commit 537930f8adb768b83be845c55b1927081f40ce7c From 1d70f4a12988c089431916b67efc73c470e8b6f1 Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Mon, 26 May 2025 10:45:44 -0700 Subject: [PATCH 056/219] chore: add missing dep tweepy --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index fbe98f50..847b9d67 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,6 +20,7 @@ SQLAlchemy==2.0.41 starlette==0.46.2 supabase==2.15.2 tiktoken==0.9.0 +tweepy==4.15.0 uvicorn==0.34.2 vecs==0.4.5 From e44900c623ab09c8f152c32b2546e65c32a76a30 Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Mon, 26 May 2025 11:23:51 -0700 Subject: [PATCH 057/219] fix: correctly update deps for docker build --- pyproject.toml | 1 + uv.lock | 1286 +++++++++++++++++++++++++----------------------- 2 files changed, 662 insertions(+), 625 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f0363841..f1915b89 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ dependencies = [ "starlette==0.46.2", "supabase==2.15.2", "tiktoken==0.9.0", + "tweepy==4.15.0", "uvicorn==0.34.2", "vecs==0.4.5", ] diff --git a/uv.lock b/uv.lock index e9a10752..88fe2b72 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,4 @@ version = 1 -revision = 2 requires-python = ">=3.13" [[package]] @@ -29,6 +28,7 @@ dependencies = [ { name = "starlette" }, { name = "supabase" }, { name = "tiktoken" }, + { name = "tweepy" }, { name = "uvicorn" }, { name = "vecs" }, ] @@ -45,42 +45,42 @@ testing = [ requires-dist = [ { name = "aiohttp", specifier = "==3.11.18" }, { name = "apscheduler", specifier = "==3.11.0" }, - { name = "cachetools", specifier = "==5.5.2" }, + { name = "cachetools", specifier = "==6.0.0" }, { name = "fastapi", specifier = "==0.115.12" }, { name = "langchain", specifier = "==0.3.25" }, - { name = "langchain-community", specifier = "==0.3.23" }, + { name = "langchain-community", specifier = "==0.3.24" }, { name = "langchain-core", specifier = ">=0.3.56,<1.0.0" }, - { name = "langchain-openai", specifier = "==0.3.16" }, + { name = "langchain-openai", specifier = "==0.3.18" }, { name = "langchain-text-splitters", specifier = "==0.3.8" }, - { name = "langgraph", specifier = "==0.4.1" }, - { name = "openai", specifier = "==1.77.0" }, + { name = "langgraph", specifier = "==0.4.7" }, + { name = "openai", specifier = "==1.82.0" }, { name = "pgvector", specifier = "==0.3.6" }, { name = "psycopg2", specifier = "==2.9.10" }, - { name = "pydantic", specifier = "==2.11.4" }, + { name = "pydantic", specifier = "==2.11.5" }, { name = "pytest", marker = "extra == 'testing'", specifier = "==8.3.5" }, { name = "pytest-asyncio", marker = "extra == 'testing'", specifier = "==0.26.0" }, { name = "pytest-mock", marker = "extra == 'testing'", specifier = "==3.14.0" }, { name = "python-dotenv", specifier = "==1.1.0" }, - { name = "python-telegram-bot", specifier = "==22.0" }, + { name = "python-telegram-bot", specifier = "==22.1" }, { name = "python-twitter-v2", specifier = "==0.9.2" }, { name = "requests", specifier = "==2.32.3" }, { name = "responses", marker = "extra == 'testing'", specifier = "==0.25.7" }, - { name = "sqlalchemy", specifier = "==2.0.40" }, + { name = "sqlalchemy", specifier = "==2.0.41" }, { name = "starlette", specifier = "==0.46.2" }, - { name = "supabase", specifier = "==2.15.1" }, + { name = "supabase", specifier = "==2.15.2" }, { name = "tiktoken", specifier = "==0.9.0" }, + { name = "tweepy", specifier = "==4.15.0" }, { name = "uvicorn", specifier = "==0.34.2" }, { name = "vecs", specifier = "==0.4.5" }, ] -provides-extras = ["testing"] [[package]] name = "aiohappyeyeballs" version = "2.6.1" source = { registry = "https://pypi.org/simple" } 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services.webhooks.base import WebhookHandler from services.webhooks.dao.models import ( - ContractResponse, DAOWebhookPayload, DAOWebhookResponse, + DeployedContract, ) @@ -46,7 +46,7 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: dao_create = DAOCreate( name=parsed_data.name, mission=parsed_data.mission, - description=parsed_data.description, + description=parsed_data.description or parsed_data.mission, is_deployed=True, is_broadcasted=True, ) @@ -54,51 +54,67 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: dao = self.db.create_dao(dao_create) self.logger.info(f"Created DAO with ID: {dao.id}") - # Create extensions - extension_ids: List[UUID] = [] - for ext_data in parsed_data.extensions: - # All extensions in this payload are contract definitions, not deployed contracts - # Set status as DRAFT since they're not deployed yet - contract_principal = None - tx_id = None - status = ContractStatus.DEPLOYED - - extension_create = ExtensionCreate( - dao_id=dao.id, - type=ext_data.type, - subtype=ext_data.subtype, - contract_principal=contract_principal, - tx_id=tx_id, - status=status, - ) - - extension = self.db.create_extension(extension_create) - extension_ids.append(extension.id) - self.logger.info( - f"Created extension with ID: {extension.id} for type: {ext_data.type} and subtype: {ext_data.subtype}" - ) - - # Create token + # Find the main DAO token contract + dao_token_contract = None + for contract in parsed_data.contracts: + if contract.type.value == "TOKEN" and contract.subtype == "DAO": + dao_token_contract = contract + break + + if not dao_token_contract: + raise ValueError("No DAO token contract found in contracts list") + + # Create the main DAO token token_create = TokenCreate( dao_id=dao.id, - contract_principal=parsed_data.token.contract_principal, - tx_id=parsed_data.token.tx_id, - name=parsed_data.token.name, - description=parsed_data.token.description, - symbol=parsed_data.token.symbol, - decimals=parsed_data.token.decimals, - max_supply=parsed_data.token.max_supply, - uri=parsed_data.token.uri, - image_url=parsed_data.token.image_url, - x_url=parsed_data.token.x_url, - telegram_url=parsed_data.token.telegram_url, - website_url=parsed_data.token.website_url, + contract_principal=dao_token_contract.contract_principal, + tx_id=dao_token_contract.tx_id, + name=parsed_data.name, # Use DAO name as token name + description=parsed_data.description or parsed_data.mission, + symbol=parsed_data.token_info.symbol, + decimals=parsed_data.token_info.decimals, + max_supply=parsed_data.token_info.max_supply, + uri=parsed_data.token_info.uri, + image_url=parsed_data.token_info.image_url, + x_url=parsed_data.token_info.x_url, + telegram_url=parsed_data.token_info.telegram_url, + website_url=parsed_data.token_info.website_url, status=ContractStatus.DEPLOYED, ) token = self.db.create_token(token_create) self.logger.info(f"Created token with ID: {token.id}") + # Create extensions for DAO extension contracts + extension_ids: List[UUID] = [] + for contract in parsed_data.contracts: + # Only create extensions for actual DAO extension types + # Skip TOKEN type contracts as they are handled separately + if contract.type.value in [ + "EXTENSIONS", + "ACTIONS", + "PROPOSALS", + "BASE", + ]: + extension_create = ExtensionCreate( + dao_id=dao.id, + type=contract.type.value, + subtype=contract.subtype, + contract_principal=contract.contract_principal, + tx_id=contract.tx_id, + status=ContractStatus.DEPLOYED, + ) + + extension = self.db.create_extension(extension_create) + extension_ids.append(extension.id) + self.logger.info( + f"Created extension with ID: {extension.id} for type: {contract.type.value} and subtype: {contract.subtype}" + ) + else: + self.logger.info( + f"Skipping {contract.type.value} contract: {contract.name}" + ) + # Prepare response response = DAOWebhookResponse( dao_id=dao.id, diff --git a/services/webhooks/dao/models.py b/services/webhooks/dao/models.py index 08bcc961..42e3a742 100644 --- a/services/webhooks/dao/models.py +++ b/services/webhooks/dao/models.py @@ -150,6 +150,56 @@ class TokenSubcategory(str, Enum): PRELAUNCH = "PRELAUNCH" +class DeployedContract(BaseModel): + """Deployed contract model for the new webhook structure.""" + + name: str + display_name: Optional[str] = Field(None, alias="displayName") + type: ContractType + subtype: str # Handle union of subtypes as string for flexibility + tx_id: str = Field(alias="txId") + deployer: str + contract_principal: str = Field(alias="contractPrincipal") + + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) + + +class TokenInfo(BaseModel): + """Token information model for DAO webhook.""" + + symbol: str + decimals: int + max_supply: str = Field(alias="maxSupply") + uri: str + image_url: str = Field(alias="imageUrl") + x_url: Optional[str] = Field(None, alias="xUrl") + telegram_url: Optional[str] = Field(None, alias="telegramUrl") + website_url: Optional[str] = Field(None, alias="websiteUrl") + + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) + + +class DAOWebhookPayload(BaseModel): + """Webhook payload for DAO creation with deployed contracts structure.""" + + name: str + mission: str + description: Optional[str] = None + contracts: List[DeployedContract] + token_info: TokenInfo = Field(alias="tokenInfo") + + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) + + +class DAOWebhookResponse(BaseModel): + """Response model for DAO creation webhook.""" + + dao_id: UUID + extension_ids: Optional[List[UUID]] = None + token_id: Optional[UUID] = None + + +# Legacy models for backward compatibility class ContractResponse(BaseModel): """Contract response model.""" @@ -192,21 +242,3 @@ class TokenData(BaseModel): x_url: Optional[str] = None telegram_url: Optional[str] = None website_url: Optional[str] = None - - -class DAOWebhookPayload(BaseModel): - """Webhook payload for DAO creation with new structure.""" - - name: str - mission: str - description: str - extensions: List[ContractResponse] - token: TokenData - - -class DAOWebhookResponse(BaseModel): - """Response model for DAO creation webhook.""" - - dao_id: UUID - extension_ids: Optional[List[UUID]] = None - token_id: Optional[UUID] = None diff --git a/test_new_dao_webhook.py b/test_new_dao_webhook.py new file mode 100644 index 00000000..745c20fa --- /dev/null +++ b/test_new_dao_webhook.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python3 +"""Test script to validate new DAO webhook payload parsing.""" + +import json + +from services.webhooks.dao.models import DAOWebhookPayload + +# Sample payload from the user's new structure +sample_payload = { + "name": "XFACE•AIBTC•DAO", + "mission": "## Mission\\n\\nTo make Bitcoin Faces the most popular meme amongst Bitcoiners, on X, on-chain, and throughout Bitcoin culture.\\n\\n## Core Pillars\\n\\n1. Face as Identity: Every Bitcoin Face is a unique, deterministic, generative avatar tied to a name or address - an expression of sovereignty and style. The DAO preserves and evolves this standard as the meme layer of Bitcoin.\\n2. Meme Engine of the Network: The DAO funds and coordinates the viral spread of Bitcoin Faces - on X, on-chain, and beyond. Proposals reward memes, automate content, and shape culture.\\n3. Permissionless Personality: Anyone can generate a face. Anyone can remix it. But the DAO decides which AI styles, transformations, and add-ons become official. Governance as curatorship.\\n4. On-Chain Licensing and Monetization: Through the payments and invoicing system, enables creators to build tools, apps, and embeds that use Bitcoin Faces - with revenue shared between builders and the DAO treasury.\\n5. Autonomous Avatars, Autonomous Treasury: will gradually become a fully agent-driven culture DAO. Until then, the treasury is protected by time, quorum, and AI maturity. The meme spreads first, the money flows later.", + "contracts": [ + { + "name": "aibtc-faktory", + "display_name": "xface-faktory", + "type": "TOKEN", + "subtype": "DAO", + "tx_id": "6bb26cf198ad3f093a3e61b495f3acdb248c0230c2dc8edc2e1655a93ced72c5", + "deployer": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K", + "contract_principal": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.aibtc-faktory", + }, + { + "name": "aibtc-base-dao", + "display_name": "xface-base-dao", + "type": "BASE", + "subtype": "DAO", + "tx_id": "c1d9fd38f94f8fcd204f65b598ccb486a5f68b88f58de049d59df12eb11bd5bb", + "deployer": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K", + "contract_principal": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.aibtc-base-dao", + }, + { + "name": "aibtc-treasury", + "display_name": "xface-treasury", + "type": "EXTENSIONS", + "subtype": "TREASURY", + "tx_id": "159cc3c930f84e4e3026af900acb7d1cac1ba505ece3c24c077b912a0a8ad666", + "deployer": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K", + "contract_principal": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.aibtc-treasury", + }, + ], + "token_info": { + "symbol": "XFACE•AIBTC•DAO", + "decimals": 8, + "max_supply": "1000000000", + "uri": "https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/tokens//251.json", + "image_url": "https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/tokens//251.png", + "x_url": "https://x.com/1894855072556912681", + }, +} + + +def test_payload_parsing(): + """Test that the new payload structure can be parsed correctly.""" + try: + # Parse the payload + parsed_payload = DAOWebhookPayload(**sample_payload) + + print("✅ Payload parsed successfully!") + print(f"DAO Name: {parsed_payload.name}") + print(f"Number of contracts: {len(parsed_payload.contracts)}") + print(f"Token symbol: {parsed_payload.token_info.symbol}") + + # Check for DAO token contract + dao_token = None + for contract in parsed_payload.contracts: + if contract.type.value == "TOKEN" and contract.subtype == "DAO": + dao_token = contract + break + + if dao_token: + print(f"✅ Found DAO token contract: {dao_token.name}") + print(f" Contract Principal: {dao_token.contract_principal}") + print(f" TX ID: {dao_token.tx_id}") + else: + print("❌ No DAO token contract found") + + # Count extension contracts + extension_contracts = [ + c + for c in parsed_payload.contracts + if c.type.value in ["EXTENSIONS", "ACTIONS", "PROPOSALS", "BASE"] + ] + print(f"✅ Found {len(extension_contracts)} extension contracts") + + for ext in extension_contracts: + print(f" - {ext.type.value}: {ext.subtype} ({ext.name})") + + return True + + except Exception as e: + print(f"❌ Error parsing payload: {str(e)}") + return False + + +if __name__ == "__main__": + print("Testing new DAO webhook payload structure...") + success = test_payload_parsing() + if success: + print("\n🎉 All tests passed! The new structure is working correctly.") + else: + print("\n💥 Tests failed. Check the error messages above.") From 2d0822aa05e2a4b02c3b948c19d9925f69945d2c Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 27 May 2025 20:45:17 -0700 Subject: [PATCH 059/219] update tools --- agent-tools-ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agent-tools-ts b/agent-tools-ts index 537930f8..7c1b7129 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 537930f8adb768b83be845c55b1927081f40ce7c +Subproject commit 7c1b712966aac53ddd96e841db1c77af296fde62 From c7c1d1e80b7a4b2e3e71cda1bacbd1cb8f105852 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 27 May 2025 20:56:25 -0700 Subject: [PATCH 060/219] update tools --- tools/dao_ext_action_proposals.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tools/dao_ext_action_proposals.py b/tools/dao_ext_action_proposals.py index 47976187..d0b3d116 100644 --- a/tools/dao_ext_action_proposals.py +++ b/tools/dao_ext_action_proposals.py @@ -96,7 +96,7 @@ def _deploy( if resource_url: args.append(resource_url) - + if memo: if not resource_url: args.append("") # Add empty URL if not provided but memo is @@ -354,8 +354,8 @@ def _deploy( args = [ action_proposals_voting_extension, action_proposal_contract_to_execute, - dao_token_contract_address, message, + dao_token_contract_address, ] if memo: @@ -363,8 +363,8 @@ def _deploy( return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "propose-action-send-message.ts", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/public", + "create-action-proposal.ts", *args, ) From 9b0131f41895f29671d24c6e22781ef36e01bbc3 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 27 May 2025 21:18:42 -0700 Subject: [PATCH 061/219] updates --- .../webhooks/chainhook/handlers/action_proposal_handler.py | 6 +++--- .../webhooks/chainhook/handlers/dao_proposal_handler.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index 78cb3d61..1eaa7f34 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -52,9 +52,9 @@ def can_handle_transaction(self, transaction: TransactionWithReceipt) -> bool: ) return False - # Check if the method name is exactly "propose-action" + # Check if the method name is exactly "create-action-proposal" tx_method = tx_data_content.get("method", "") - is_proposal_method = tx_method == "propose-action" + is_proposal_method = tx_method == "create-action-proposal" # Access success from TransactionMetadata tx_success = tx_metadata.success @@ -95,7 +95,7 @@ def _get_proposal_info_from_events(self, events: list[Event]) -> Optional[Dict]: continue # Check if this is a proposal event - if value.get("notification") == "propose-action": + if value.get("notification") == "create-action-proposal": payload = value.get("payload", {}) if not payload: self.logger.warning("Empty payload in proposal event") diff --git a/services/webhooks/chainhook/handlers/dao_proposal_handler.py b/services/webhooks/chainhook/handlers/dao_proposal_handler.py index de5db1e8..305bb6e4 100644 --- a/services/webhooks/chainhook/handlers/dao_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/dao_proposal_handler.py @@ -55,7 +55,7 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: if method == "create-proposal": await self.core_handler.handle_transaction(transaction) - elif method == "propose-action": + elif method == "create-action-proposal": await self.action_handler.handle_transaction(transaction) else: self.logger.warning(f"Unknown proposal method: {method}") From d6087bf6caac514fa9de0357dac6be1f9f44f6a8 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 27 May 2025 21:23:20 -0700 Subject: [PATCH 062/219] update --- backend/models.py | 16 ++++++ .../handlers/action_proposal_handler.py | 49 +++++++++++++++++-- 2 files changed, 60 insertions(+), 5 deletions(-) diff --git a/backend/models.py b/backend/models.py index 5364c06a..14db48fb 100644 --- a/backend/models.py +++ b/backend/models.py @@ -347,6 +347,22 @@ class ProposalBase(CustomBaseModel): votes_against: Optional[str] = None # String to handle large numbers votes_for: Optional[str] = None # String to handle large numbers bond: Optional[str] = None # String to handle large numbers + # New fields from updated chainhook payload + contract_caller: Optional[str] = None + created_btc: Optional[int] = None + created_stx: Optional[int] = None + creator_user_id: Optional[int] = None + exec_end: Optional[int] = None + exec_start: Optional[int] = None + memo: Optional[str] = None + tx_sender: Optional[str] = None + vote_end: Optional[int] = None + vote_start: Optional[int] = None + voting_delay: Optional[int] = None + voting_period: Optional[int] = None + voting_quorum: Optional[int] = None + voting_reward: Optional[str] = None # String to handle large numbers + voting_threshold: Optional[int] = None class ProposalCreate(ProposalBase): diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index 1eaa7f34..89cfd013 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -94,8 +94,11 @@ def _get_proposal_info_from_events(self, events: list[Event]) -> Optional[Dict]: self.logger.debug("Value is None in SmartContractEvent data") continue - # Check if this is a proposal event - if value.get("notification") == "create-action-proposal": + # Check if this is a proposal event - updated to handle new notification format + notification = value.get("notification", "") + if notification == "create-action-proposal" or notification.endswith( + "/create-action-proposal" + ): payload = value.get("payload", {}) if not payload: self.logger.warning("Empty payload in proposal event") @@ -106,12 +109,32 @@ def _get_proposal_info_from_events(self, events: list[Event]) -> Optional[Dict]: "action": payload.get("action"), "caller": payload.get("caller"), "creator": payload.get("creator"), - "created_at_block": payload.get("createdAt"), - "end_block": payload.get("endBlock"), - "start_block": payload.get("startBlock"), + "created_at_block": payload.get("createdAt"), # Legacy field + "end_block": payload.get("endBlock"), # Legacy field + "start_block": payload.get("startBlock"), # Legacy field "liquid_tokens": str(payload.get("liquidTokens")), "parameters": payload.get("parameters"), "bond": str(payload.get("bond")), + # New fields from updated payload + "contract_caller": payload.get("contractCaller"), + "created_btc": payload.get("createdBtc"), + "created_stx": payload.get("createdStx"), + "creator_user_id": payload.get("creatorUserId"), + "exec_end": payload.get("execEnd"), + "exec_start": payload.get("execStart"), + "memo": payload.get("memo"), + "tx_sender": payload.get("txSender"), + "vote_end": payload.get("voteEnd"), + "vote_start": payload.get("voteStart"), + "voting_delay": payload.get("votingDelay"), + "voting_period": payload.get("votingPeriod"), + "voting_quorum": payload.get("votingQuorum"), + "voting_reward": ( + str(payload.get("votingReward")) + if payload.get("votingReward") is not None + else None + ), + "voting_threshold": payload.get("votingThreshold"), } self.logger.warning("Could not find proposal information in transaction events") @@ -225,6 +248,22 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: liquid_tokens=proposal_info["liquid_tokens"], parameters=parameters, bond=proposal_info["bond"], + # New fields from updated payload + contract_caller=proposal_info["contract_caller"], + created_btc=proposal_info["created_btc"], + created_stx=proposal_info["created_stx"], + creator_user_id=proposal_info["creator_user_id"], + exec_end=proposal_info["exec_end"], + exec_start=proposal_info["exec_start"], + memo=proposal_info["memo"], + tx_sender=proposal_info["tx_sender"], + vote_end=proposal_info["vote_end"], + vote_start=proposal_info["vote_start"], + voting_delay=proposal_info["voting_delay"], + voting_period=proposal_info["voting_period"], + voting_quorum=proposal_info["voting_quorum"], + voting_reward=proposal_info["voting_reward"], + voting_threshold=proposal_info["voting_threshold"], ) ) self.logger.info( From e71dacb65233029d6a56563be6aaae0d4df763bb Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 27 May 2025 21:27:05 -0700 Subject: [PATCH 063/219] update --- backend/models.py | 5 +- .../handlers/action_proposal_handler.py | 10 +--- .../handlers/core_proposal_handler.py | 47 ++++++++++++++++--- .../dao_proposal_burn_height_handler.py | 18 +++---- 4 files changed, 52 insertions(+), 28 deletions(-) diff --git a/backend/models.py b/backend/models.py index 14db48fb..a598d208 100644 --- a/backend/models.py +++ b/backend/models.py @@ -333,9 +333,6 @@ class ProposalBase(CustomBaseModel): action: Optional[str] = None caller: Optional[str] = None creator: Optional[str] = None - created_at_block: Optional[int] = None - end_block: Optional[int] = None - start_block: Optional[int] = None liquid_tokens: Optional[str] = None # Using string to handle large numbers parameters: Optional[str] = None # Additional fields from blockchain data @@ -347,7 +344,7 @@ class ProposalBase(CustomBaseModel): votes_against: Optional[str] = None # String to handle large numbers votes_for: Optional[str] = None # String to handle large numbers bond: Optional[str] = None # String to handle large numbers - # New fields from updated chainhook payload + # Fields from updated chainhook payload contract_caller: Optional[str] = None created_btc: Optional[int] = None created_stx: Optional[int] = None diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index 89cfd013..5b62a5fa 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -109,13 +109,10 @@ def _get_proposal_info_from_events(self, events: list[Event]) -> Optional[Dict]: "action": payload.get("action"), "caller": payload.get("caller"), "creator": payload.get("creator"), - "created_at_block": payload.get("createdAt"), # Legacy field - "end_block": payload.get("endBlock"), # Legacy field - "start_block": payload.get("startBlock"), # Legacy field "liquid_tokens": str(payload.get("liquidTokens")), "parameters": payload.get("parameters"), "bond": str(payload.get("bond")), - # New fields from updated payload + # Fields from updated payload "contract_caller": payload.get("contractCaller"), "created_btc": payload.get("createdBtc"), "created_stx": payload.get("createdStx"), @@ -242,13 +239,10 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: action=proposal_info["action"], caller=proposal_info["caller"], creator=proposal_info["creator"], - created_at_block=proposal_info["created_at_block"], - end_block=proposal_info["end_block"], - start_block=proposal_info["start_block"], liquid_tokens=proposal_info["liquid_tokens"], parameters=parameters, bond=proposal_info["bond"], - # New fields from updated payload + # Fields from updated payload contract_caller=proposal_info["contract_caller"], created_btc=proposal_info["created_btc"], created_stx=proposal_info["created_stx"], diff --git a/services/webhooks/chainhook/handlers/core_proposal_handler.py b/services/webhooks/chainhook/handlers/core_proposal_handler.py index de2a3a9e..b1cbeb77 100644 --- a/services/webhooks/chainhook/handlers/core_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/core_proposal_handler.py @@ -94,7 +94,10 @@ def _get_proposal_info_from_events(self, events: list[Event]) -> Optional[Dict]: continue # Check if this is a proposal event - if value.get("notification") == "create-proposal": + notification = value.get("notification", "") + if notification == "create-proposal" or notification.endswith( + "/create-proposal" + ): payload = value.get("payload", {}) if not payload: self.logger.warning("Empty payload in proposal event") @@ -104,11 +107,28 @@ def _get_proposal_info_from_events(self, events: list[Event]) -> Optional[Dict]: "proposal": payload.get("proposal"), # Contract to be deployed "caller": payload.get("caller"), "creator": payload.get("creator"), - "created_at_block": payload.get("createdAt"), - "end_block": payload.get("endBlock"), - "start_block": payload.get("startBlock"), "liquid_tokens": str(payload.get("liquidTokens")), "bond": str(payload.get("bond")), + # Fields from updated payload (if available) + "contract_caller": payload.get("contractCaller"), + "created_btc": payload.get("createdBtc"), + "created_stx": payload.get("createdStx"), + "creator_user_id": payload.get("creatorUserId"), + "exec_end": payload.get("execEnd"), + "exec_start": payload.get("execStart"), + "memo": payload.get("memo"), + "tx_sender": payload.get("txSender"), + "vote_end": payload.get("voteEnd"), + "vote_start": payload.get("voteStart"), + "voting_delay": payload.get("votingDelay"), + "voting_period": payload.get("votingPeriod"), + "voting_quorum": payload.get("votingQuorum"), + "voting_reward": ( + str(payload.get("votingReward")) + if payload.get("votingReward") is not None + else None + ), + "voting_threshold": payload.get("votingThreshold"), } self.logger.warning("Could not find proposal information in transaction events") @@ -179,11 +199,24 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: # Add fields from payload caller=proposal_info["caller"], creator=proposal_info["creator"], - created_at_block=proposal_info["created_at_block"], - end_block=proposal_info["end_block"], - start_block=proposal_info["start_block"], liquid_tokens=proposal_info["liquid_tokens"], bond=proposal_info["bond"], + # Fields from updated payload (if available) + contract_caller=proposal_info["contract_caller"], + created_btc=proposal_info["created_btc"], + created_stx=proposal_info["created_stx"], + creator_user_id=proposal_info["creator_user_id"], + exec_end=proposal_info["exec_end"], + exec_start=proposal_info["exec_start"], + memo=proposal_info["memo"], + tx_sender=proposal_info["tx_sender"], + vote_end=proposal_info["vote_end"], + vote_start=proposal_info["vote_start"], + voting_delay=proposal_info["voting_delay"], + voting_period=proposal_info["voting_period"], + voting_quorum=proposal_info["voting_quorum"], + voting_reward=proposal_info["voting_reward"], + voting_threshold=proposal_info["voting_threshold"], ) ) self.logger.info( diff --git a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py index 8aa23241..3fb9683c 100644 --- a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py +++ b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py @@ -136,18 +136,18 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: start_proposals = [ p for p in proposals - if p.start_block is not None - and p.end_block is not None - and p.start_block == burn_height + if p.vote_start is not None + and p.vote_end is not None + and p.vote_start == burn_height and p.parameters is not None # Ensure parameters exist ] end_proposals = [ p for p in proposals - if p.start_block is not None - and p.end_block is not None - and p.end_block == burn_height + if p.vote_start is not None + and p.vote_end is not None + and p.vote_end == burn_height and p.parameters is not None # Ensure parameters exist ] @@ -156,9 +156,9 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: vote_proposals = [ p for p in proposals - if p.start_block is not None - and p.end_block is not None - and p.start_block - vote_delay == burn_height + if p.vote_start is not None + and p.vote_end is not None + and p.vote_start - vote_delay == burn_height and p.parameters is not None # Ensure parameters exist ] From 2856be838d7e45c18d31f464bc0bd412fc4eb664 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 27 May 2025 21:44:44 -0700 Subject: [PATCH 064/219] update chainhooks --- .../handlers/action_proposal_handler.py | 60 ++++++++++++++++++- .../dao_proposal_burn_height_handler.py | 53 +--------------- 2 files changed, 62 insertions(+), 51 deletions(-) diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index 5b62a5fa..df09a3c8 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -1,6 +1,7 @@ """Handler for capturing new DAO action proposals.""" -from typing import Dict, Optional +from typing import Dict, List, Optional +from uuid import UUID from backend.factory import backend from backend.models import ( @@ -8,6 +9,8 @@ ProposalCreate, ProposalFilter, ProposalType, + QueueMessageCreate, + QueueMessageType, ) from lib.utils import decode_hex_parameters from services.webhooks.chainhook.handlers.base_proposal_handler import ( @@ -159,6 +162,34 @@ def _sanitize_string(self, input_string: Optional[str]) -> Optional[str]: return sanitized + def _get_agent_token_holders(self, dao_id: UUID) -> List[Dict]: + """Get agents that hold tokens for the given DAO. + + Args: + dao_id: The ID of the DAO + + Returns: + List[Dict]: List of agents with their wallet IDs + """ + # Use the specialized backend method for getting agents with DAO tokens + agents_with_tokens_dto = backend.get_agents_with_dao_tokens(dao_id) + + if not agents_with_tokens_dto: + self.logger.error(f"No agents found with tokens for DAO {dao_id}") + return [] + + # Convert DTOs to the expected format + agents_with_tokens = [ + {"agent_id": dto.agent_id, "wallet_id": dto.wallet_id} + for dto in agents_with_tokens_dto + ] + + self.logger.info( + f"Found {len(agents_with_tokens)} agents holding tokens for DAO {dao_id}" + ) + + return agents_with_tokens + async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: """Handle action proposal transactions. @@ -263,6 +294,33 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: self.logger.info( f"Created new action proposal record in database: {proposal.id}" ) + + # Queue evaluation messages for agents holding governance tokens + agents = self._get_agent_token_holders(dao_data["id"]) + if agents: + for agent in agents: + # Create message with only the proposal ID + message_data = { + "proposal_id": proposal.id, # Only pass the proposal UUID + } + + backend.create_queue_message( + QueueMessageCreate( + type=QueueMessageType.DAO_PROPOSAL_EVALUATION, + message=message_data, + dao_id=dao_data["id"], + wallet_id=agent["wallet_id"], + ) + ) + + self.logger.info( + f"Created evaluation queue message for agent {agent['agent_id']} " + f"to evaluate proposal {proposal.id}" + ) + else: + self.logger.warning( + f"No agents found holding tokens for DAO {dao_data['id']}" + ) except Exception as e: self.logger.error(f"Error creating proposal in database: {str(e)}") raise diff --git a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py index 3fb9683c..65af429d 100644 --- a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py +++ b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py @@ -133,7 +133,7 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: ) # Filter proposals that should start or end at this burn height - start_proposals = [ + vote_proposals = [ p for p in proposals if p.vote_start is not None @@ -151,63 +151,16 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: and p.parameters is not None # Ensure parameters exist ] - # Filter proposals that should trigger voting after delay - vote_delay = config.scheduler.dao_proposal_vote_delay_blocks - vote_proposals = [ - p - for p in proposals - if p.vote_start is not None - and p.vote_end is not None - and p.vote_start - vote_delay == burn_height - and p.parameters is not None # Ensure parameters exist - ] - - if not start_proposals and not end_proposals and not vote_proposals: + if not vote_proposals and not end_proposals: self.logger.info( f"No eligible proposals found for burn height {burn_height}" ) return self.logger.info( - f"Found {len(start_proposals)} proposals to start, {len(end_proposals)} proposals to conclude, " - f"and {len(vote_proposals)} proposals ready for voting" + f"Found {len(vote_proposals)} proposals to vote, {len(end_proposals)} proposals to conclude, " ) - # Process proposals that are starting - for proposal in start_proposals: - # Get the DAO for this proposal - dao = backend.get_dao(proposal.dao_id) - if not dao: - self.logger.warning(f"No DAO found for proposal {proposal.id}") - continue - - # Get agents holding governance tokens - agents = self._get_agent_token_holders(dao.id) - if not agents: - self.logger.warning(f"No agents found holding tokens for DAO {dao.id}") - continue - - # Create queue messages for each agent to evaluate and vote - for agent in agents: - # Create message with only the proposal ID - message_data = { - "proposal_id": proposal.id, # Only pass the proposal UUID - } - - backend.create_queue_message( - QueueMessageCreate( - type=QueueMessageType.DAO_PROPOSAL_EVALUATION, - message=message_data, - dao_id=dao.id, - wallet_id=agent["wallet_id"], - ) - ) - - self.logger.info( - f"Created evaluation queue message for agent {agent['agent_id']} " - f"to evaluate proposal {proposal.id}" - ) - # Process proposals that are ending for proposal in end_proposals: dao = backend.get_dao(proposal.dao_id) From acc537427eda4ff04d7bf2982b55c39988db5262 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 28 May 2025 19:24:09 -0700 Subject: [PATCH 065/219] update code for voting --- services/runner/tasks/dao_proposal_voter.py | 4 +-- tools/dao_ext_action_proposals.py | 28 ++++++++++----------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/services/runner/tasks/dao_proposal_voter.py b/services/runner/tasks/dao_proposal_voter.py index 21e4b708..cadb378e 100644 --- a/services/runner/tasks/dao_proposal_voter.py +++ b/services/runner/tasks/dao_proposal_voter.py @@ -142,9 +142,9 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: for vote in unvoted_votes: # Submit the vote vote_result = await voting_tool._arun( - action_proposals_voting_extension=proposal.contract_principal, + dao_action_proposal_voting_contract=proposal.contract_principal, proposal_id=proposal.proposal_id, - vote=vote.answer, + vote_for=vote.answer, ) if not vote_result.get("success", False): diff --git a/tools/dao_ext_action_proposals.py b/tools/dao_ext_action_proposals.py index d0b3d116..5d0f9a19 100644 --- a/tools/dao_ext_action_proposals.py +++ b/tools/dao_ext_action_proposals.py @@ -780,7 +780,7 @@ async def _arun( class VoteOnActionProposalInput(BaseModel): """Input schema for voting on an action proposal.""" - action_proposals_voting_extension: str = Field( + dao_action_proposal_voting_contract: str = Field( ..., description="Contract principal where the DAO creates action proposals for voting by DAO members.", examples=[ @@ -789,7 +789,7 @@ class VoteOnActionProposalInput(BaseModel): ], ) proposal_id: int = Field(..., description="ID of the proposal to vote on") - vote: bool = Field(..., description="True for yes/for, False for no/against") + vote_for: bool = Field(..., description="True for yes/for, False for no/against") class VoteOnActionProposalTool(BaseTool): @@ -809,9 +809,9 @@ def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): def _deploy( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, proposal_id: int, - vote: bool, + vote_for: bool, **kwargs, ) -> Dict[str, Any]: """Execute the tool to vote on an action proposal.""" @@ -819,40 +819,40 @@ def _deploy( return {"success": False, "message": "Wallet ID is required", "data": None} args = [ - action_proposals_voting_extension, + dao_action_proposal_voting_contract, str(proposal_id), - str(vote).lower(), + str(vote_for).lower(), ] return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "vote-on-proposal.ts", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/public", + "vote-on-action-proposal.ts", *args, ) def _run( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, proposal_id: int, - vote: bool, + vote_for: bool, **kwargs, ) -> Dict[str, Any]: """Execute the tool to vote on an action proposal.""" return self._deploy( - action_proposals_voting_extension, proposal_id, vote, **kwargs + dao_action_proposal_voting_contract, proposal_id, vote_for, **kwargs ) async def _arun( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, proposal_id: int, - vote: bool, + vote_for: bool, **kwargs, ) -> Dict[str, Any]: """Async version of the tool.""" return self._deploy( - action_proposals_voting_extension, proposal_id, vote, **kwargs + dao_action_proposal_voting_contract, proposal_id, vote_for, **kwargs ) From e9f3f1cf3c7b702a801185fa582fb0f1b35a4e4a Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 28 May 2025 19:30:25 -0700 Subject: [PATCH 066/219] update code for voting --- .../runner/tasks/dao_proposal_evaluation.py | 42 +++++++++---------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/services/runner/tasks/dao_proposal_evaluation.py b/services/runner/tasks/dao_proposal_evaluation.py index faee60c6..d6365d0c 100644 --- a/services/runner/tasks/dao_proposal_evaluation.py +++ b/services/runner/tasks/dao_proposal_evaluation.py @@ -159,26 +159,26 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: logger.info(f"Created vote record {vote.id} for proposal {proposal_id}") - # Create a DAO_PROPOSAL_VOTE message with the vote record ID - vote_message_data = {"proposal_id": proposal_id, "vote_id": str(vote.id)} - - vote_message = backend.create_queue_message( - QueueMessageCreate( - type=QueueMessageType.DAO_PROPOSAL_VOTE, - message=vote_message_data, - dao_id=dao_id, - wallet_id=wallet_id, - ) - ) - - if not vote_message: - logger.error("Failed to create vote queue message") - return { - "success": False, - "error": "Failed to create vote queue message", - } - - logger.info(f"Created vote queue message {vote_message.id}") + # # Create a DAO_PROPOSAL_VOTE message with the vote record ID + # vote_message_data = {"proposal_id": proposal_id, "vote_id": str(vote.id)} + + # vote_message = backend.create_queue_message( + # QueueMessageCreate( + # type=QueueMessageType.DAO_PROPOSAL_VOTE, + # message=vote_message_data, + # dao_id=dao_id, + # wallet_id=wallet_id, + # ) + # ) + + # if not vote_message: + # logger.error("Failed to create vote queue message") + # return { + # "success": False, + # "error": "Failed to create vote queue message", + # } + + # logger.info(f"Created vote queue message {vote_message.id}") # Mark the evaluation message as processed update_data = QueueMessageBase(is_processed=True) @@ -187,7 +187,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: return { "success": True, "vote_id": str(vote.id), - "vote_message_id": str(vote_message.id), + # "vote_message_id": str(vote_message.id), "approve": approval, "confidence": confidence, } From 1d2dfa30689787163c34711048ebd162a0e3587a Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 28 May 2025 19:40:59 -0700 Subject: [PATCH 067/219] fix chainhooks --- .../chainhook/handlers/action_vote_handler.py | 14 ++++++++--- .../chainhook/handlers/base_vote_handler.py | 12 +++++++-- .../chainhook/handlers/core_vote_handler.py | 25 +++++++++++++------ 3 files changed, 38 insertions(+), 13 deletions(-) diff --git a/services/webhooks/chainhook/handlers/action_vote_handler.py b/services/webhooks/chainhook/handlers/action_vote_handler.py index c9c97493..68a69c2a 100644 --- a/services/webhooks/chainhook/handlers/action_vote_handler.py +++ b/services/webhooks/chainhook/handlers/action_vote_handler.py @@ -62,7 +62,9 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: event_data = event.data value = event_data.get("value", {}) - if value.get("notification") == "vote-on-proposal": + # Check for the new notification format + notification = value.get("notification", "") + if "vote-on-action-proposal" in notification: payload = value.get("payload", {}) if not payload: self.logger.warning("Empty payload in vote event") @@ -73,9 +75,13 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: "proposalId" ), # Numeric ID for action proposals "voter": payload.get("voter"), - "caller": payload.get("caller"), - "amount": str(payload.get("amount")), - "vote_value": None, # Will be extracted from transaction args + "caller": payload.get("contractCaller"), # Updated field name + "tx_sender": payload.get("txSender"), # New field + "amount": str(payload.get("amount", 0)), + "vote_value": payload.get( + "vote" + ), # Vote value is now directly in payload + "voter_user_id": payload.get("voterUserId"), # New field } self.logger.warning("Could not find vote information in transaction events") diff --git a/services/webhooks/chainhook/handlers/base_vote_handler.py b/services/webhooks/chainhook/handlers/base_vote_handler.py index 435a977e..745cfa7c 100644 --- a/services/webhooks/chainhook/handlers/base_vote_handler.py +++ b/services/webhooks/chainhook/handlers/base_vote_handler.py @@ -76,9 +76,13 @@ def can_handle_transaction(self, transaction: TransactionWithReceipt) -> bool: ) return False - # Check if the method name contains "vote-on-proposal" + # Check if the method name contains "vote" and "proposal" tx_method = tx_data_content.get("method", "") - is_vote_method = tx_method == "vote-on-proposal" + is_vote_method = ( + tx_method == "vote-on-proposal" + or "vote-on-action-proposal" in tx_method + or "vote-on-core-proposal" in tx_method + ) # Access success from TransactionMetadata tx_success = tx_metadata.success @@ -130,6 +134,10 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: self.logger.info( f"Extracted vote value from transaction args: {vote_value}" ) + else: + self.logger.info( + f"Vote value found directly in event payload: {vote_value}" + ) if not proposal_identifier or not voter_address: self.logger.warning( diff --git a/services/webhooks/chainhook/handlers/core_vote_handler.py b/services/webhooks/chainhook/handlers/core_vote_handler.py index 17b1171d..881a57ca 100644 --- a/services/webhooks/chainhook/handlers/core_vote_handler.py +++ b/services/webhooks/chainhook/handlers/core_vote_handler.py @@ -62,20 +62,31 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: event_data = event.data value = event_data.get("value", {}) - if value.get("notification") == "vote-on-proposal": + # Check for both old and new notification formats + notification = value.get("notification", "") + if ( + notification == "vote-on-proposal" + or "vote-on-core-proposal" in notification + ): payload = value.get("payload", {}) if not payload: self.logger.warning("Empty payload in vote event") return None + # Handle both old and new payload structures + proposal_id = payload.get("proposal") or payload.get("proposalId") + caller = payload.get("caller") or payload.get("contractCaller") + return { - "proposal_identifier": payload.get( - "proposal" - ), # Contract principal for core proposals + "proposal_identifier": proposal_id, # Contract principal for core proposals "voter": payload.get("voter"), - "caller": payload.get("caller"), - "amount": str(payload.get("amount")), - "vote_value": None, # Will be extracted from transaction args + "caller": caller, + "tx_sender": payload.get("txSender"), # New field + "amount": str(payload.get("amount", 0)), + "vote_value": payload.get( + "vote" + ), # Vote value may be directly in payload now + "voter_user_id": payload.get("voterUserId"), # New field } self.logger.warning("Could not find vote information in transaction events") From 5edb33fdf947423dfcb1b11d4a463de4842a0f8a Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Wed, 28 May 2025 19:45:57 -0700 Subject: [PATCH 068/219] fix to use conclude file correctly --- tools/dao_ext_action_proposals.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/dao_ext_action_proposals.py b/tools/dao_ext_action_proposals.py index 5d0f9a19..eceb1e92 100644 --- a/tools/dao_ext_action_proposals.py +++ b/tools/dao_ext_action_proposals.py @@ -922,8 +922,8 @@ def _deploy( return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "conclude-proposal.ts", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/public", + "conclude-action-proposal.ts", *args, ) From 18e900dc49ecc9300c429f1f11bafb811e3a2bac Mon Sep 17 00:00:00 2001 From: davek Date: Wed, 28 May 2025 20:17:29 -0700 Subject: [PATCH 069/219] docs: note tweet thread auto split --- README.md | 1 + services/runner/tasks/tweet_task.py | 86 ++++++++++++++++------------- 2 files changed, 49 insertions(+), 38 deletions(-) diff --git a/README.md b/README.md index fdf9c62e..a49ca762 100644 --- a/README.md +++ b/README.md @@ -73,6 +73,7 @@ aibtcdev-backend/ ### 3. Social Media Integration - Twitter automation and monitoring +- Tweets longer than 280 characters are automatically threaded - Telegram bot integration - Discord notifications - Automated content generation diff --git a/services/runner/tasks/tweet_task.py b/services/runner/tasks/tweet_task.py index 394dd3a1..b8da3a2b 100644 --- a/services/runner/tasks/tweet_task.py +++ b/services/runner/tasks/tweet_task.py @@ -42,6 +42,22 @@ def __init__(self, config: Optional[RunnerConfig] = None): self._pending_messages: Optional[List[QueueMessage]] = None self.twitter_service = None + def _split_text_into_chunks(self, text: str, limit: int = 280) -> List[str]: + """Split text into chunks not exceeding the limit without cutting words.""" + words = text.split() + chunks = [] + current = "" + for word in words: + if len(current) + len(word) + (1 if current else 0) <= limit: + current = f"{current} {word}".strip() + else: + if current: + chunks.append(current) + current = word + if current: + chunks.append(current) + return chunks + def _get_extension(self, url: str) -> str: path = urlparse(url).path.lower() for ext in [".png", ".jpg", ".jpeg", ".gif"]: @@ -56,9 +72,7 @@ def _post_tweet_with_media( reply_id: Optional[str] = None, ): try: - headers = { - "User-Agent": "Mozilla/5.0" - } + headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(image_url, headers=headers, timeout=10) response.raise_for_status() auth = tweepy.OAuth1UserHandler( @@ -201,15 +215,6 @@ async def _validate_message( dao_id=None, ) - # Check tweet length - if len(tweet_text) > 280: # Twitter's character limit - return TweetProcessingResult( - success=False, - message=f"Tweet exceeds character limit: {len(tweet_text)} chars", - tweet_id=message.tweet_id, - dao_id=message.dao_id, - ) - # No need to modify the message structure, keep it as is return None @@ -257,30 +262,35 @@ async def _process_tweet_message( tweet_text = re.sub(re.escape(image_url), "", original_text).strip() tweet_text = re.sub(r"\s+", " ", tweet_text) - # Prepare tweet parameters - tweet_params = {"text": tweet_text} - if message.tweet_id: - tweet_params["reply_in_reply_to_tweet_id"] = message.tweet_id - - if image_url: - tweet_response = self._post_tweet_with_media( - image_url=image_url, - text=tweet_text, - reply_id=message.tweet_id, - ) - else: - tweet_response = await self.twitter_service._apost_tweet(**tweet_params) + # Split tweet text if necessary + chunks = self._split_text_into_chunks(tweet_text) + previous_tweet_id = message.tweet_id + tweet_response = None + + for index, chunk in enumerate(chunks): + if index == 0 and image_url: + tweet_response = self._post_tweet_with_media( + image_url=image_url, + text=chunk, + reply_id=previous_tweet_id, + ) + else: + tweet_response = await self.twitter_service._apost_tweet( + text=chunk, + reply_in_reply_to_tweet_id=previous_tweet_id, + ) - if not tweet_response: - return TweetProcessingResult( - success=False, - message="Failed to send tweet", - dao_id=message.dao_id, - tweet_id=message.tweet_id, - ) + if not tweet_response: + return TweetProcessingResult( + success=False, + message="Failed to send tweet", + dao_id=message.dao_id, + tweet_id=previous_tweet_id, + ) - logger.info(f"Successfully posted tweet {tweet_response.id}") - logger.debug(f"Tweet ID: {tweet_response.id}") + logger.info(f"Successfully posted tweet {tweet_response.id}") + logger.debug(f"Tweet ID: {tweet_response.id}") + previous_tweet_id = tweet_response.id # Discord Service try: @@ -290,10 +300,10 @@ async def _process_tweet_message( embeds = None if image_url: embeds = [{"image": {"url": image_url}}] - discord_result = discord_service.send_message(tweet_text, embeds=embeds) - logger.info( - f"Discord message sent: {discord_result['success']}" + discord_result = discord_service.send_message( + tweet_text, embeds=embeds ) + logger.info(f"Discord message sent: {discord_result['success']}") except Exception as e: logger.warning(f"Failed to send Discord message: {str(e)}") @@ -301,7 +311,7 @@ async def _process_tweet_message( return TweetProcessingResult( success=True, message="Successfully sent tweet", - tweet_id=tweet_response.id, + tweet_id=previous_tweet_id, dao_id=message.dao_id, ) From 807a0d42b0f1295636ade55b5fdf6b58f518c4ee Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Thu, 29 May 2025 07:05:48 -0700 Subject: [PATCH 070/219] chore: update submodule --- agent-tools-ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agent-tools-ts b/agent-tools-ts index 7c1b7129..3667c3cf 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 7c1b712966aac53ddd96e841db1c77af296fde62 +Subproject commit 3667c3cff526c6608711f44c274c3fe35b02b9db From dcb78a724718d68a823b9fd11885e157eb4fe78e Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Thu, 29 May 2025 07:25:42 -0700 Subject: [PATCH 071/219] fix: make a pass to remove unused tools --- tools/dao_ext_action_proposals.py | 815 +---------------------------- tools/dao_ext_core_proposals.py | 236 --------- tools/dao_ext_onchain_messaging.py | 73 --- tools/dao_ext_payments_invoices.py | 338 ------------ tools/dao_ext_timed_vault.py | 188 ------- tools/tools_factory.py | 68 --- 6 files changed, 24 insertions(+), 1694 deletions(-) delete mode 100644 tools/dao_ext_core_proposals.py delete mode 100644 tools/dao_ext_onchain_messaging.py delete mode 100644 tools/dao_ext_payments_invoices.py delete mode 100644 tools/dao_ext_timed_vault.py diff --git a/tools/dao_ext_action_proposals.py b/tools/dao_ext_action_proposals.py index eceb1e92..f4417d6f 100644 --- a/tools/dao_ext_action_proposals.py +++ b/tools/dao_ext_action_proposals.py @@ -12,651 +12,8 @@ class DaoBaseInput(BaseModel): pass - -class ProposeActionAddResourceInput(BaseModel): - """Input schema for proposing to add a resource action.""" - - action_proposals_voting_extension: str = Field( - ..., - description="Contract principal where the DAO creates action proposals for voting by DAO members.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2" - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2" - ], - ) - action_proposal_contract_to_execute: str = Field( - ..., - description="Contract principal of the action proposal that executes adding a resource to the DAO.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-add-resource" - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-add-resource" - ], - ) - dao_token_contract_address: str = Field( - ..., - description="Contract principal of the token used by the DAO for voting", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-faktory", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", - ], - ) - resource_name: str = Field(..., description="Name of the resource to add") - resource_description: str = Field(..., description="Description of the resource") - resource_price: int = Field(..., description="Price of the resource in microstacks") - resource_url: Optional[str] = Field( - None, - description="Optional URL associated with the resource", - examples=["https://www.example.com/resource"], - ) - memo: Optional[str] = Field( - None, - description="Optional memo to include with the proposal", - examples=["Adding a new consultation resource for the DAO"], - ) - - -class ProposeActionAddResourceTool(BaseTool): - name: str = "dao_propose_action_add_resource" - description: str = ( - "This creates a proposal that DAO members can vote on to add the new resource to the " - " DAO resource contract with specified name, description, price, and optional URL." - ) - args_schema: Type[BaseModel] = ProposeActionAddResourceInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - resource_name: str, - resource_description: str, - resource_price: int, - resource_url: Optional[str] = None, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose adding a resource.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - resource_name, - resource_description, - str(resource_price), - ] - - if resource_url: - args.append(resource_url) - - if memo: - if not resource_url: - args.append("") # Add empty URL if not provided but memo is - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "propose-action-add-resource.ts", - *args, - ) - - def _run( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - resource_name: str, - resource_description: str, - resource_price: int, - resource_url: Optional[str] = None, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose adding a resource.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - resource_name, - resource_description, - resource_price, - resource_url, - memo, - **kwargs, - ) - - async def _arun( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - resource_name: str, - resource_description: str, - resource_price: int, - resource_url: Optional[str] = None, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - resource_name, - resource_description, - resource_price, - resource_url, - memo, - **kwargs, - ) - - -class ProposeActionAllowAssetInput(BaseModel): - """Input schema for proposing to allow an asset action.""" - - action_proposals_voting_extension: str = Field( - ..., - description="Contract principal where the DAO creates action proposals for voting by DAO members.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", - ], - ) - action_proposal_contract_to_execute: str = Field( - ..., - description="Contract principal of the action proposal that executes allowing an asset in the DAO treasury.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-allow-asset", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", - ], - ) - dao_token_contract_address: str = Field( - ..., - description="Contract principal of the token used by the DAO for voting", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-faktory", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", - ], - ) - dao_token_contract_address_to_allow: str = Field( - ..., - description="Contract principal of the token to allow", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-faktory", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", - ], - ) - memo: Optional[str] = Field( - None, - description="Optional memo to include with the proposal", - examples=["Allow new token for DAO treasury operations"], - ) - - -class ProposeActionAllowAssetTool(BaseTool): - name: str = "dao_propose_action_allow_asset" - description: str = ( - "This creates a proposal that DAO members can vote on to allow a specific " - " token contract to be used within the DAO treasury contract." - ) - args_schema: Type[BaseModel] = ProposeActionAllowAssetInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - dao_token_contract_address_to_allow: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose allowing an asset.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - dao_token_contract_address_to_allow, - ] - - if memo: - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "propose-action-allow-asset.ts", - *args, - ) - - def _run( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - dao_token_contract_address_to_allow: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose allowing an asset.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - dao_token_contract_address_to_allow, - memo, - **kwargs, - ) - - async def _arun( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - dao_token_contract_address_to_allow: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - dao_token_contract_address_to_allow, - memo, - **kwargs, - ) - - -class ProposeActionSendMessageInput(BaseModel): - """Input schema for proposing to send a message action.""" - - action_proposals_voting_extension: str = Field( - ..., - description="Contract principal where the DAO creates action proposals for voting by DAO members.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", - ], - ) - action_proposal_contract_to_execute: str = Field( - ..., - description="Contract principal of the action proposal that executes sending a message through the DAO.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-send-message", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-send-message", - ], - ) - dao_token_contract_address: str = Field( - ..., - description="Contract principal of the token used by the DAO for voting", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-faktory", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", - ], - ) - message: str = Field( - ..., - description="Message to be sent through the DAO proposal system, verified to be from the DAO and posted to Twitter/X automatically if successful.", - examples=["gm gm from the $FACES DAO!"], - ) - memo: Optional[str] = Field( - None, - description="Optional memo to include with the proposal", - examples=["Sending a greeting message from our DAO"], - ) - - -class ProposeActionSendMessageTool(BaseTool): - name: str = "dao_propose_action_send_message" - description: str = ( - "This creates a proposal that DAO members can vote on to send a specific message that gets " - "stored on-chain and automatically posted to the DAO Twitter/X account." - ) - args_schema: Type[BaseModel] = ProposeActionSendMessageInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - message: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose sending a message.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - action_proposals_voting_extension, - action_proposal_contract_to_execute, - message, - dao_token_contract_address, - ] - - if memo: - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/public", - "create-action-proposal.ts", - *args, - ) - - def _run( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - message: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose sending a message.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - message, - memo, - **kwargs, - ) - - async def _arun( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - message: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - message, - memo, - **kwargs, - ) - - -class ProposeActionSetAccountHolderInput(BaseModel): - """Input schema for proposing to set account holder action.""" - - action_proposals_voting_extension: str = Field( - ..., - description="Contract principal where the DAO creates action proposals for voting by DAO members.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", - ], - ) - action_proposal_contract_to_execute: str = Field( - ..., - description="Contract principal of the action proposal that executes setting the account holder in a DAO timed vault.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-set-account-holder", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-set-account-holder", - ], - ) - dao_token_contract_address: str = Field( - ..., - description="Contract principal of the token used by the DAO for voting", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-faktory", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", - ], - ) - account_holder: str = Field( - ..., - description="Address of the new account holder", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18", - "ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", - "ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM.contract", - ], - ) - memo: Optional[str] = Field( - None, - description="Optional memo to include with the proposal", - examples=["Setting new account holder for DAO vault"], - ) - - -class ProposeActionSetAccountHolderTool(BaseTool): - name: str = "dao_propose_action_set_account_holder" - description: str = ( - "This creates a proposal that DAO members can vote on to change the account holder " - "in a DAO timed vault to a specified standard or contract address." - ) - args_schema: Type[BaseModel] = ProposeActionSetAccountHolderInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - account_holder: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose setting a new account holder.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - account_holder, - ] - - if memo: - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "propose-action-set-account-holder.ts", - *args, - ) - - def _run( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - account_holder: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose setting a new account holder.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - account_holder, - memo, - **kwargs, - ) - - async def _arun( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - account_holder: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - account_holder, - memo, - **kwargs, - ) - - -class ProposeActionSetWithdrawalAmountInput(BaseModel): - """Input schema for proposing to set withdrawal amount action.""" - - action_proposals_voting_extension: str = Field( - ..., - description="Contract principal where the DAO creates action proposals for voting by DAO members.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", - ], - ) - action_proposal_contract_to_execute: str = Field( - ..., - description="Contract principal of the action proposal that executes setting the withdrawal amount in a DAO timed vault.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-set-withdrawal-amount", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-set-withdrawal-amount", - ], - ) - dao_token_contract_address: str = Field( - ..., - description="Contract principal of the token used by the DAO for voting", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-faktory", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", - ], - ) - withdrawal_amount: int = Field( - ..., - description="New withdrawal amount to set in microSTX", - examples=["50000000"], # 50 STX - ) - memo: Optional[str] = Field( - None, - description="Optional memo to include with the proposal", - examples=["Updating withdrawal amount to 50 STX"], - ) - - -class ProposeActionSetWithdrawalAmountTool(BaseTool): - name: str = "dao_propose_action_set_withdrawal_amount" - description: str = ( - "This creates a proposal that DAO members can vote on to change the withdrawal amount " - " to a specified number of microSTX in a DAO timed vault." - ) - args_schema: Type[BaseModel] = ProposeActionSetWithdrawalAmountInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - withdrawal_amount: int, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose setting a new withdrawal amount.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - str(withdrawal_amount), - ] - - if memo: - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "propose-action-set-withdrawal-amount.ts", - *args, - ) - - def _run( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - withdrawal_amount: int, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose setting a new withdrawal amount.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - withdrawal_amount, - memo, - **kwargs, - ) - - async def _arun( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - withdrawal_amount: int, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - withdrawal_amount, - memo, - **kwargs, - ) - - -class ProposeActionSetWithdrawalPeriodInput(BaseModel): - """Input schema for proposing to set withdrawal period action.""" +class ProposeActionSendMessageInput(BaseModel): + """Input schema for proposing to send a message action.""" action_proposals_voting_extension: str = Field( ..., @@ -668,10 +25,10 @@ class ProposeActionSetWithdrawalPeriodInput(BaseModel): ) action_proposal_contract_to_execute: str = Field( ..., - description="Contract principal of the action proposal that executes setting the withdrawal period in a DAO timed vault.", + description="Contract principal of the action proposal that executes sending a message through the DAO.", examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-set-withdrawal-period", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-set-withdrawal-period", + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-send-message", + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-send-message", ], ) dao_token_contract_address: str = Field( @@ -682,25 +39,25 @@ class ProposeActionSetWithdrawalPeriodInput(BaseModel): "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", ], ) - withdrawal_period: int = Field( + message: str = Field( ..., - description="New withdrawal period to set in Bitcoin blocks", - examples=["144"], # 1 day in BTC blocks + description="Message to be sent through the DAO proposal system, verified to be from the DAO and posted to Twitter/X automatically if successful.", + examples=["gm gm from the $FACES DAO!"], ) memo: Optional[str] = Field( None, description="Optional memo to include with the proposal", - examples=["Updating withdrawal period to 1 day (144 blocks)"], + examples=["Sending a greeting message from our DAO"], ) -class ProposeActionSetWithdrawalPeriodTool(BaseTool): - name: str = "dao_propose_action_set_withdrawal_period" +class ProposeActionSendMessageTool(BaseTool): + name: str = "dao_propose_action_send_message" description: str = ( - "This creates a proposal that DAO members can vote on to change the withdrawal period " - " to a specified number of Bitcoin blocks in a DAO timed vault." + "This creates a proposal that DAO members can vote on to send a specific message that gets " + "stored on-chain and automatically posted to the DAO Twitter/X account." ) - args_schema: Type[BaseModel] = ProposeActionSetWithdrawalPeriodInput + args_schema: Type[BaseModel] = ProposeActionSendMessageInput return_direct: bool = False wallet_id: Optional[UUID] = None @@ -713,19 +70,19 @@ def _deploy( action_proposals_voting_extension: str, action_proposal_contract_to_execute: str, dao_token_contract_address: str, - withdrawal_period: int, + message: str, memo: Optional[str] = None, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to propose setting a new withdrawal period.""" + """Execute the tool to propose sending a message.""" if self.wallet_id is None: return {"success": False, "message": "Wallet ID is required", "data": None} args = [ action_proposals_voting_extension, action_proposal_contract_to_execute, + message, dao_token_contract_address, - str(withdrawal_period), ] if memo: @@ -733,8 +90,8 @@ def _deploy( return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "propose-action-set-withdrawal-period.ts", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/public", + "create-action-proposal.ts", *args, ) @@ -743,16 +100,16 @@ def _run( action_proposals_voting_extension: str, action_proposal_contract_to_execute: str, dao_token_contract_address: str, - withdrawal_period: int, + message: str, memo: Optional[str] = None, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to propose setting a new withdrawal period.""" + """Execute the tool to propose sending a message.""" return self._deploy( action_proposals_voting_extension, action_proposal_contract_to_execute, dao_token_contract_address, - withdrawal_period, + message, memo, **kwargs, ) @@ -762,7 +119,7 @@ async def _arun( action_proposals_voting_extension: str, action_proposal_contract_to_execute: str, dao_token_contract_address: str, - withdrawal_period: int, + message: str, memo: Optional[str] = None, **kwargs, ) -> Dict[str, Any]: @@ -771,12 +128,11 @@ async def _arun( action_proposals_voting_extension, action_proposal_contract_to_execute, dao_token_contract_address, - withdrawal_period, + message, memo, **kwargs, ) - class VoteOnActionProposalInput(BaseModel): """Input schema for voting on an action proposal.""" @@ -961,129 +317,6 @@ async def _arun( **kwargs, ) - -class ProposeActionToggleResourceInput(BaseModel): - """Input schema for proposing to toggle a resource action.""" - - action_proposals_voting_extension: str = Field( - ..., - description="Contract principal where the DAO creates action proposals for voting by DAO members.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", - ], - ) - action_proposal_contract_to_execute: str = Field( - ..., - description="Contract principal of the action proposal that executes toggling a resource in the DAO.", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-toggle-resource", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-toggle-resource", - ], - ) - dao_token_contract_address: str = Field( - ..., - description="Contract principal of the token used by the DAO for voting", - examples=[ - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-faktory", - "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-faktory", - ], - ) - resource_name: str = Field( - ..., - description="Name of the resource to toggle", - examples=["apiv1", "protected-content", "1hr consulting"], - ) - memo: Optional[str] = Field( - None, - description="Optional memo to include with the proposal", - examples=["Toggling availability of consulting resource"], - ) - - -class ProposeActionToggleResourceTool(BaseTool): - name: str = "dao_propose_action_toggle_resource" - description: str = ( - "This creates a proposal that DAO members can vote on to enable or disable " - "whether a specific resource can be paid for in the DAO resource contract." - ) - args_schema: Type[BaseModel] = ProposeActionToggleResourceInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - resource_name: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose toggling a resource.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - resource_name, - ] - - if memo: - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/action-proposals/public", - "propose-action-toggle-resource-by-name.ts", - *args, - ) - - def _run( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - resource_name: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to propose toggling a resource.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - resource_name, - memo, - **kwargs, - ) - - async def _arun( - self, - action_proposals_voting_extension: str, - action_proposal_contract_to_execute: str, - dao_token_contract_address: str, - resource_name: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - action_proposals_voting_extension, - action_proposal_contract_to_execute, - dao_token_contract_address, - resource_name, - memo, - **kwargs, - ) - - class GetLiquidSupplyInput(BaseModel): """Input schema for getting the liquid supply.""" diff --git a/tools/dao_ext_core_proposals.py b/tools/dao_ext_core_proposals.py deleted file mode 100644 index 7fc11821..00000000 --- a/tools/dao_ext_core_proposals.py +++ /dev/null @@ -1,236 +0,0 @@ -from typing import Any, Dict, Optional, Type -from uuid import UUID - -from langchain.tools import BaseTool -from pydantic import BaseModel, Field - -from tools.bun import BunScriptRunner - - -class GenerateCoreProposalInput(BaseModel): - """Input schema for generating a core proposal.""" - - dao_deployer_address: str = Field( - ..., - description="The address of the DAO deployer", - example="ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", - ) - dao_token_symbol: str = Field( - ..., - description="The token symbol for the DAO", - example="aibtc", - ) - proposal_contract_name: str = Field( - ..., - description="The name of the proposal contract", - example="aibtc-treasury-withdraw-stx", - ) - proposal_args: Dict[str, str] = Field( - ..., - description="Arguments for the proposal in key-value format", - example={ - "stx_amount": "1000000", - "recipient_address": "ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", - }, - ) - generate_files: bool = Field( - False, - description="Whether to generate and save proposal files", - ) - - -class GenerateCoreProposalTool(BaseTool): - name: str = "dao_generate_core_proposal" - description: str = ( - "Generate a core proposal for the DAO. " - "This will create the proposal contract but not deploy it. " - "Returns the generated proposal details if successful." - ) - args_schema: Type[BaseModel] = GenerateCoreProposalInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - dao_deployer_address: str, - dao_token_symbol: str, - proposal_contract_name: str, - proposal_args: Dict[str, str], - generate_files: bool = False, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to generate a core proposal.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - dao_deployer_address, - dao_token_symbol, - proposal_contract_name, - str(proposal_args).replace("'", '"'), # Convert Python dict to JSON string - str(generate_files).lower(), - ] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/core-proposals", - "generate-core-proposal.ts", - *args, - ) - - def _run( - self, - dao_deployer_address: str, - dao_token_symbol: str, - proposal_contract_name: str, - proposal_args: Dict[str, str], - generate_files: bool = False, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to generate a core proposal.""" - return self._deploy( - dao_deployer_address, - dao_token_symbol, - proposal_contract_name, - proposal_args, - generate_files, - **kwargs, - ) - - async def _arun( - self, - dao_deployer_address: str, - dao_token_symbol: str, - proposal_contract_name: str, - proposal_args: Dict[str, str], - generate_files: bool = False, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - dao_deployer_address, - dao_token_symbol, - proposal_contract_name, - proposal_args, - generate_files, - **kwargs, - ) - - -class DeployCoreProposalInput(BaseModel): - """Input schema for deploying a core proposal.""" - - dao_deployer_address: str = Field( - ..., - description="The address of the DAO deployer", - example="ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", - ) - dao_token_symbol: str = Field( - ..., - description="The token symbol for the DAO", - example="aibtc", - ) - proposal_contract_name: str = Field( - ..., - description="The name of the proposal contract", - example="aibtc-treasury-withdraw-stx", - ) - proposal_args: Dict[str, str] = Field( - ..., - description="Arguments for the proposal in key-value format", - example={ - "stx_amount": "1000000", - "recipient_address": "ST1PQHQKV0RJXZFY1DGX8MNSNYVE3VGZJSRTPGZGM", - }, - ) - generate_files: bool = Field( - False, - description="Whether to generate and save proposal files", - ) - - -class DeployCoreProposalTool(BaseTool): - name: str = "dao_deploy_core_proposal" - description: str = ( - "Deploy a core proposal for the DAO. " - "This will generate and deploy the proposal contract. " - "This is a required step before proposing. " - "Returns the deployment details if successful." - ) - args_schema: Type[BaseModel] = DeployCoreProposalInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - dao_deployer_address: str, - dao_token_symbol: str, - proposal_contract_name: str, - proposal_args: Dict[str, str], - generate_files: bool = False, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to deploy a core proposal.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - dao_deployer_address, - dao_token_symbol, - proposal_contract_name, - str(proposal_args).replace("'", '"'), # Convert Python dict to JSON string - str(generate_files).lower(), - ] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/core-proposals", - "deploy-core-proposal.ts", - *args, - ) - - def _run( - self, - dao_deployer_address: str, - dao_token_symbol: str, - proposal_contract_name: str, - proposal_args: Dict[str, str], - generate_files: bool = False, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to deploy a core proposal.""" - return self._deploy( - dao_deployer_address, - dao_token_symbol, - proposal_contract_name, - proposal_args, - generate_files, - **kwargs, - ) - - async def _arun( - self, - dao_deployer_address: str, - dao_token_symbol: str, - proposal_contract_name: str, - proposal_args: Dict[str, str], - generate_files: bool = False, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy( - dao_deployer_address, - dao_token_symbol, - proposal_contract_name, - proposal_args, - generate_files, - **kwargs, - ) diff --git a/tools/dao_ext_onchain_messaging.py b/tools/dao_ext_onchain_messaging.py deleted file mode 100644 index 7bbb7219..00000000 --- a/tools/dao_ext_onchain_messaging.py +++ /dev/null @@ -1,73 +0,0 @@ -from typing import Any, Dict, Optional, Type -from uuid import UUID - -from langchain.tools import BaseTool -from pydantic import BaseModel, Field - -from tools.bun import BunScriptRunner - - -class SendMessageInput(BaseModel): - """Input schema for sending an onchain message.""" - - messaging_contract: str = Field( - ..., - description="Contract principal of the messaging contract for the DAO", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.dao-onchain-messaging", - ) - message: str = Field(..., description="Message to send") - - -class SendMessageTool(BaseTool): - name: str = "dao_messaging_send" - description: str = ( - "Send a message through the DAO's onchain messaging system. " - "Messages are stored permanently on the blockchain and can be viewed by anyone." - ) - args_schema: Type[BaseModel] = SendMessageInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - messaging_contract: str, - message: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to send a message.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [ - messaging_contract, - message, - ] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/onchain-messaging/public", - "send-message.ts", - *args, - ) - - def _run( - self, - messaging_contract: str, - message: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to send a message.""" - return self._deploy(messaging_contract, message, **kwargs) - - async def _arun( - self, - messaging_contract: str, - message: str, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(messaging_contract, message, **kwargs) diff --git a/tools/dao_ext_payments_invoices.py b/tools/dao_ext_payments_invoices.py deleted file mode 100644 index 919ecae3..00000000 --- a/tools/dao_ext_payments_invoices.py +++ /dev/null @@ -1,338 +0,0 @@ -from typing import Any, Dict, Optional, Type -from uuid import UUID - -from langchain.tools import BaseTool -from pydantic import BaseModel, Field - -from tools.bun import BunScriptRunner - - -class GetInvoiceInput(BaseModel): - """Input schema for getting invoice details.""" - - payments_invoices_contract: str = Field( - ..., - description="Contract principal of the payments and invoices contract", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-payments-invoices", - ) - invoice_index: int = Field(..., description="Index of the invoice to retrieve") - - -class GetInvoiceTool(BaseTool): - name: str = "dao_get_invoice" - description: str = ( - "Get details of a specific invoice from the DAO's payments and invoices system. " - "Returns the full invoice data if it exists." - ) - args_schema: Type[BaseModel] = GetInvoiceInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - payments_invoices_contract: str, - invoice_index: int, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get invoice details.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [payments_invoices_contract, str(invoice_index)] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/payments-invoices/read-only", - "get-invoice.ts", - *args, - ) - - def _run( - self, - payments_invoices_contract: str, - invoice_index: int, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get invoice details.""" - return self._deploy(payments_invoices_contract, invoice_index, **kwargs) - - async def _arun( - self, - payments_invoices_contract: str, - invoice_index: int, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(payments_invoices_contract, invoice_index, **kwargs) - - -class GetResourceInput(BaseModel): - """Input schema for getting resource details.""" - - payments_invoices_contract: str = Field( - ..., - description="Contract principal of the payments and invoices contract", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-payments-invoices", - ) - resource_index: int = Field(..., description="Index of the resource to retrieve") - - -class GetResourceTool(BaseTool): - name: str = "dao_get_resource" - description: str = ( - "Get details of a specific resource from the DAO's payments and invoices system. " - "Returns the full resource data if it exists." - ) - args_schema: Type[BaseModel] = GetResourceInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - payments_invoices_contract: str, - resource_index: int, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get resource details.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [payments_invoices_contract, str(resource_index)] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/payments-invoices/read-only", - "get-resource.ts", - *args, - ) - - def _run( - self, - payments_invoices_contract: str, - resource_index: int, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get resource details.""" - return self._deploy(payments_invoices_contract, resource_index, **kwargs) - - async def _arun( - self, - payments_invoices_contract: str, - resource_index: int, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(payments_invoices_contract, resource_index, **kwargs) - - -class GetResourceByNameInput(BaseModel): - """Input schema for getting resource details by name.""" - - payments_invoices_contract: str = Field( - ..., - description="Contract principal of the payments and invoices contract", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-payments-invoices", - ) - resource_name: str = Field(..., description="Name of the resource to retrieve") - - -class GetResourceByNameTool(BaseTool): - name: str = "dao_get_resource_by_name" - description: str = ( - "Get details of a specific resource by its name from the DAO's payments and invoices system. " - "Returns the full resource data if it exists." - ) - args_schema: Type[BaseModel] = GetResourceByNameInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - payments_invoices_contract: str, - resource_name: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get resource details by name.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [payments_invoices_contract, resource_name] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/payments-invoices/read-only", - "get-resource-by-name.ts", - *args, - ) - - def _run( - self, - payments_invoices_contract: str, - resource_name: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get resource details by name.""" - return self._deploy(payments_invoices_contract, resource_name, **kwargs) - - async def _arun( - self, - payments_invoices_contract: str, - resource_name: str, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(payments_invoices_contract, resource_name, **kwargs) - - -class PayInvoiceInput(BaseModel): - """Input schema for paying an invoice.""" - - payments_invoices_contract: str = Field( - ..., - description="Contract principal of the payments and invoices contract", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-payments-invoices", - ) - resource_index: int = Field(..., description="Index of the resource to pay for") - memo: Optional[str] = Field( - None, description="Optional memo to include with the payment" - ) - - -class PayInvoiceTool(BaseTool): - name: str = "dao_pay_invoice" - description: str = ( - "Pay an invoice for a specific resource in the DAO's payments and invoices system. " - "Optionally includes a memo with the payment." - ) - args_schema: Type[BaseModel] = PayInvoiceInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - payments_invoices_contract: str, - resource_index: int, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to pay an invoice.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [payments_invoices_contract, str(resource_index)] - if memo: - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/payments-invoices/public", - "pay-invoice.ts", - *args, - ) - - def _run( - self, - payments_invoices_contract: str, - resource_index: int, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to pay an invoice.""" - return self._deploy(payments_invoices_contract, resource_index, memo, **kwargs) - - async def _arun( - self, - payments_invoices_contract: str, - resource_index: int, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(payments_invoices_contract, resource_index, memo, **kwargs) - - -class PayInvoiceByResourceNameInput(BaseModel): - """Input schema for paying an invoice by resource name.""" - - payments_invoices_contract: str = Field( - ..., - description="Contract principal of the payments and invoices contract", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-payments-invoices", - ) - resource_name: str = Field(..., description="Name of the resource to pay for") - memo: Optional[str] = Field( - None, description="Optional memo to include with the payment" - ) - - -class PayInvoiceByResourceNameTool(BaseTool): - name: str = "dao_pay_invoice_by_resource_name" - description: str = ( - "Pay an invoice for a specific resource by its name in the DAO's payments and invoices system. " - "Optionally includes a memo with the payment." - ) - args_schema: Type[BaseModel] = PayInvoiceByResourceNameInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - payments_invoices_contract: str, - resource_name: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to pay an invoice by resource name.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [payments_invoices_contract, resource_name] - if memo: - args.append(memo) - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/payments-invoices/public", - "pay-invoice-by-resource-name.ts", - *args, - ) - - def _run( - self, - payments_invoices_contract: str, - resource_name: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to pay an invoice by resource name.""" - return self._deploy(payments_invoices_contract, resource_name, memo, **kwargs) - - async def _arun( - self, - payments_invoices_contract: str, - resource_name: str, - memo: Optional[str] = None, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(payments_invoices_contract, resource_name, memo, **kwargs) diff --git a/tools/dao_ext_timed_vault.py b/tools/dao_ext_timed_vault.py deleted file mode 100644 index 60c44025..00000000 --- a/tools/dao_ext_timed_vault.py +++ /dev/null @@ -1,188 +0,0 @@ -from typing import Any, Dict, Optional, Type -from uuid import UUID - -from langchain.tools import BaseTool -from pydantic import BaseModel, Field - -from tools.bun import BunScriptRunner - - -class GetAccountTermsInput(BaseModel): - """Input schema for getting timed vault terms.""" - - timed_vault_contract: str = Field( - ..., - description="Contract principal of the timed vault", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-timed-vault", - ) - - -class GetAccountTermsTool(BaseTool): - name: str = "dao_timedvault_get_account_terms" - description: str = ( - "Get the current terms of the DAO's timed vault. " - "Returns information about withdrawal limits, periods, and account holder." - ) - args_schema: Type[BaseModel] = GetAccountTermsInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - timed_vault_contract: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get account terms.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [timed_vault_contract] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/timed-vault/read-only", - "get-account-terms.ts", - *args, - ) - - def _run( - self, - timed_vault_contract: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to get account terms.""" - return self._deploy(timed_vault_contract, **kwargs) - - async def _arun( - self, - timed_vault_contract: str, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(timed_vault_contract, **kwargs) - - -class DepositSTXInput(BaseModel): - """Input schema for depositing STX.""" - - timed_vault_contract: str = Field( - ..., - description="Contract principal of the timed vault", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-timed-vault", - ) - amount: int = Field(..., description="Amount of STX to deposit in microstacks") - - -class DepositSTXTool(BaseTool): - name: str = "dao_timedvault_deposit_stx" - description: str = ( - "Deposit STX into the DAO's timed vault. " - "The amount should be specified in microstacks (1 STX = 1,000,000 microstacks)." - ) - args_schema: Type[BaseModel] = DepositSTXInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - timed_vault_contract: str, - amount: int, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to deposit STX.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [timed_vault_contract, str(amount)] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/timed-vault/public", - "deposit-stx.ts", - *args, - ) - - def _run( - self, - timed_vault_contract: str, - amount: int, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to deposit STX.""" - return self._deploy(timed_vault_contract, amount, **kwargs) - - async def _arun( - self, - timed_vault_contract: str, - amount: int, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(timed_vault_contract, amount, **kwargs) - - -class WithdrawSTXInput(BaseModel): - """Input schema for withdrawing STX.""" - - timed_vault_contract: str = Field( - ..., - description="Contract principal of the timed vault", - example="ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-timed-vault", - ) - - -class WithdrawSTXTool(BaseTool): - name: str = "dao_timedvault_withdraw_stx" - description: str = ( - "Withdraw STX from the DAO's timed vault. " - "This will withdraw the maximum allowed amount based on the account terms." - ) - args_schema: Type[BaseModel] = WithdrawSTXInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy( - self, - timed_vault_contract: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to withdraw STX.""" - if self.wallet_id is None: - return {"success": False, "message": "Wallet ID is required", "data": None} - - args = [timed_vault_contract] - - return BunScriptRunner.bun_run( - self.wallet_id, - "aibtc-dao/extensions/timed-vault/public", - "withdraw-stx.ts", - *args, - ) - - def _run( - self, - timed_vault_contract: str, - **kwargs, - ) -> Dict[str, Any]: - """Execute the tool to withdraw STX.""" - return self._deploy(timed_vault_contract, **kwargs) - - async def _arun( - self, - timed_vault_contract: str, - **kwargs, - ) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(timed_vault_contract, **kwargs) diff --git a/tools/tools_factory.py b/tools/tools_factory.py index b888915f..42c37640 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -19,13 +19,7 @@ GetTotalVotesTool, GetVotingConfigurationTool, GetVotingPowerTool, - ProposeActionAddResourceTool, - ProposeActionAllowAssetTool, ProposeActionSendMessageTool, - ProposeActionSetAccountHolderTool, - ProposeActionSetWithdrawalAmountTool, - ProposeActionSetWithdrawalPeriodTool, - ProposeActionToggleResourceTool, VoteOnActionProposalTool, ) from .dao_ext_charter import ( @@ -33,19 +27,6 @@ GetCurrentDaoCharterVersionTool, GetDaoCharterTool, ) -from .dao_ext_core_proposals import ( - DeployCoreProposalTool, - GenerateCoreProposalTool, -) -from .dao_ext_payments_invoices import ( - GetInvoiceTool, - GetResourceByNameTool, - GetResourceTool, - PayInvoiceByResourceNameTool, - PayInvoiceTool, -) -from .dao_ext_timed_vault import DepositSTXTool as TimedVaultDepositSTXTool -from .dao_ext_timed_vault import GetAccountTermsTool, WithdrawSTXTool from .dao_ext_treasury import GetAllowedAssetTool, IsAllowedAssetTool from .database import ( AddScheduledTaskTool, @@ -123,56 +104,8 @@ def initialize_tools( logger.warning(f"Failed to get wallet for agent {agent_id}: {e}") tools = { - "coinmarketcap_get_market_data": GetBitcoinData(), "bitflow_execute_trade": BitflowExecuteTradeTool(wallet_id), - "contracts_get_sip10_info": ContractSIP10InfoTool(wallet_id), - "contracts_deploy_dao": ContractDAODeployTool(wallet_id), "contracts_fetch_source_code": FetchContractSourceTool(wallet_id), - "dao_coreproposals_generate_proposal": GenerateCoreProposalTool(wallet_id), - "dao_coreproposals_deploy_proposal": DeployCoreProposalTool(wallet_id), - "dao_actionproposals_conclude_proposal": ConcludeActionProposalTool(wallet_id), - "dao_actionproposals_get_liquid_supply": GetLiquidSupplyTool(wallet_id), - "dao_actionproposals_get_proposal": GetProposalTool(wallet_id), - "dao_actionproposals_get_total_votes": GetTotalVotesTool(wallet_id), - "dao_actionproposals_get_voting_configuration": GetVotingConfigurationTool( - wallet_id - ), - "dao_actionproposals_get_voting_power": GetVotingPowerTool(wallet_id), - "dao_actionproposals_vote_on_proposal": VoteOnActionProposalTool(wallet_id), - "dao_actionproposals_propose_add_resource": ProposeActionAddResourceTool( - wallet_id - ), - "dao_actionproposals_propose_allow_asset": ProposeActionAllowAssetTool( - wallet_id - ), - "dao_actionproposals_propose_send_message": ProposeActionSendMessageTool( - wallet_id - ), - "dao_actionproposals_propose_set_account_holder": ProposeActionSetAccountHolderTool( - wallet_id - ), - "dao_actionproposals_propose_set_withdrawal_amount": ProposeActionSetWithdrawalAmountTool( - wallet_id - ), - "dao_actionproposals_propose_set_withdrawal_period": ProposeActionSetWithdrawalPeriodTool( - wallet_id - ), - "dao_actionproposals_propose_toggle_resource": ProposeActionToggleResourceTool( - wallet_id - ), - "dao_timedvault_get_account_terms": GetAccountTermsTool(wallet_id), - "dao_timedvault_deposit_stx": TimedVaultDepositSTXTool(wallet_id), - "dao_timedvault_withdraw_stx": WithdrawSTXTool(wallet_id), - "dao_charter_get_current": GetCurrentDaoCharterTool(wallet_id), - "dao_charter_get_current_version": GetCurrentDaoCharterVersionTool(wallet_id), - "dao_charter_get_version": GetDaoCharterTool(wallet_id), - "dao_payments_get_invoice": GetInvoiceTool(wallet_id), - "dao_payments_get_resource": GetResourceTool(wallet_id), - "dao_payments_get_resource_by_name": GetResourceByNameTool(wallet_id), - "dao_payments_pay_invoice": PayInvoiceTool(wallet_id), - "dao_payments_pay_invoice_by_resource": PayInvoiceByResourceNameTool(wallet_id), - "dao_treasury_get_allowed_asset": GetAllowedAssetTool(wallet_id), - "dao_treasury_is_allowed_asset": IsAllowedAssetTool(wallet_id), "database_add_scheduled_task": AddScheduledTaskTool(profile_id, agent_id), "database_get_dao_list": GetDAOListTool(), "database_get_dao_get_by_name": GetDAOByNameTool(), @@ -186,7 +119,6 @@ def initialize_tools( "lunarcrush_get_token_metrics": LunarCrushTokenMetricsTool(), "lunarcrush_search": SearchLunarCrushTool(), "lunarcrush_get_token_metadata": LunarCrushTokenMetadataTool(), - "stacks_get_transaction_status": StacksTransactionStatusTool(wallet_id), "stacks_get_transaction_details": StacksTransactionTool(wallet_id), "stacks_get_transactions_by_address": StacksTransactionByAddressTool(wallet_id), "stacks_get_contract_info": STXGetContractInfoTool(), From fcc45a79dabb12694be5691d83debf38591c6a98 Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Thu, 29 May 2025 07:26:42 -0700 Subject: [PATCH 072/219] fix: remove simplified tx status endpoint Easier to pass the full JSON to the agent if it needs tx details, less confusion on which tool to use --- tools/tools_factory.py | 1 - tools/transactions.py | 51 ------------------------------------------ 2 files changed, 52 deletions(-) diff --git a/tools/tools_factory.py b/tools/tools_factory.py index 42c37640..b4bb100b 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -51,7 +51,6 @@ from .telegram import SendTelegramNotificationTool from .transactions import ( StacksTransactionByAddressTool, - StacksTransactionStatusTool, StacksTransactionTool, ) from .twitter import TwitterPostTweetTool diff --git a/tools/transactions.py b/tools/transactions.py index 431f798b..94ee8a75 100644 --- a/tools/transactions.py +++ b/tools/transactions.py @@ -7,57 +7,6 @@ from .bun import BunScriptRunner - -class StacksTransactionStatusInput(BaseModel): - """Input schema for checking Stacks transaction status.""" - - transaction_id: str = Field( - ..., description="The ID of the transaction to check the status for." - ) - - -class StacksTransactionStatusTool(BaseTool): - name: str = "stacks_transaction_status" - description: str = ( - "Get the current status of a Stacks blockchain transaction using its ID. " - "Returns success status and transaction details if available." - ) - args_schema: Type[BaseModel] = StacksTransactionStatusInput - return_direct: bool = False - wallet_id: Optional[UUID] = None - - def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): - super().__init__(**kwargs) - self.wallet_id = wallet_id - - def _deploy(self, transaction_id: str, **kwargs) -> Dict[str, Any]: - """Execute the tool to check transaction status.""" - if self.wallet_id is None: - return { - "success": False, - "error": "Wallet ID is required", - "output": "", - } - try: - result = BunScriptRunner.bun_run( - self.wallet_id, - "stacks-transactions", - "get-transaction-status.ts", - transaction_id, - ) - return result - except Exception as e: - return {"output": None, "error": str(e), "success": False} - - def _run(self, transaction_id: str, **kwargs) -> Dict[str, Any]: - """Execute the tool to check transaction status.""" - return self._deploy(transaction_id, **kwargs) - - async def _arun(self, transaction_id: str, **kwargs) -> Dict[str, Any]: - """Async version of the tool.""" - return self._deploy(transaction_id, **kwargs) - - class StacksTransactionInput(BaseModel): """Input schema for retrieving detailed Stacks transaction information.""" From ca75298c314fc35f8c1a7097bf29d89190a4ca97 Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Thu, 29 May 2025 07:29:04 -0700 Subject: [PATCH 073/219] fix: add single tool back for dao charter We have it in the DB but allows us to look up any mission on-chain. --- tools/tools_factory.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/tools_factory.py b/tools/tools_factory.py index b4bb100b..c3b51fb9 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -24,8 +24,6 @@ ) from .dao_ext_charter import ( GetCurrentDaoCharterTool, - GetCurrentDaoCharterVersionTool, - GetDaoCharterTool, ) from .dao_ext_treasury import GetAllowedAssetTool, IsAllowedAssetTool from .database import ( @@ -103,8 +101,10 @@ def initialize_tools( logger.warning(f"Failed to get wallet for agent {agent_id}: {e}") tools = { + "bitflow_execute_trade": BitflowExecuteTradeTool(wallet_id), "contracts_fetch_source_code": FetchContractSourceTool(wallet_id), + "dao_charter_get_current_charter": GetCurrentDaoCharterTool(wallet_id), "database_add_scheduled_task": AddScheduledTaskTool(profile_id, agent_id), "database_get_dao_list": GetDAOListTool(), "database_get_dao_get_by_name": GetDAOByNameTool(), From 5ceb69b9290e05dc826b938148eb3234b2c0902d Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Thu, 29 May 2025 07:29:59 -0700 Subject: [PATCH 074/219] fix: remove treasury tools, leave file to reuse later --- tools/tools_factory.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tools/tools_factory.py b/tools/tools_factory.py index c3b51fb9..dff043c1 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -25,7 +25,6 @@ from .dao_ext_charter import ( GetCurrentDaoCharterTool, ) -from .dao_ext_treasury import GetAllowedAssetTool, IsAllowedAssetTool from .database import ( AddScheduledTaskTool, DeleteScheduledTaskTool, From 0c0a45758806d94302a89b78c0d5064bb6499dd8 Mon Sep 17 00:00:00 2001 From: Jason Schrader Date: Thu, 29 May 2025 07:31:03 -0700 Subject: [PATCH 075/219] fix: some more cleanup --- tools/tools_factory.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tools/tools_factory.py b/tools/tools_factory.py index dff043c1..d20b6cfd 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -9,9 +9,7 @@ from .agent_account import AgentAccountDeployTool from .bitflow import BitflowExecuteTradeTool -from .coinmarketcap import GetBitcoinData from .contracts import ContractSIP10InfoTool, FetchContractSourceTool -from .dao_deployments import ContractDAODeployTool from .dao_ext_action_proposals import ( ConcludeActionProposalTool, GetLiquidSupplyTool, @@ -102,6 +100,7 @@ def initialize_tools( tools = { "bitflow_execute_trade": BitflowExecuteTradeTool(wallet_id), + "contracts_fetch_sip10_info": ContractSIP10InfoTool(wallet_id), "contracts_fetch_source_code": FetchContractSourceTool(wallet_id), "dao_charter_get_current_charter": GetCurrentDaoCharterTool(wallet_id), "database_add_scheduled_task": AddScheduledTaskTool(profile_id, agent_id), From 9adaf3585a746dc1e96fb02503c0013bc393904a Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Thu, 29 May 2025 08:51:38 -0700 Subject: [PATCH 076/219] update --- services/webhooks/dao/handler.py | 41 +++++++++++--------------------- 1 file changed, 14 insertions(+), 27 deletions(-) diff --git a/services/webhooks/dao/handler.py b/services/webhooks/dao/handler.py index 29dbce99..8dc88ad9 100644 --- a/services/webhooks/dao/handler.py +++ b/services/webhooks/dao/handler.py @@ -88,33 +88,20 @@ async def handle(self, parsed_data: DAOWebhookPayload) -> Dict[str, Any]: # Create extensions for DAO extension contracts extension_ids: List[UUID] = [] for contract in parsed_data.contracts: - # Only create extensions for actual DAO extension types - # Skip TOKEN type contracts as they are handled separately - if contract.type.value in [ - "EXTENSIONS", - "ACTIONS", - "PROPOSALS", - "BASE", - ]: - extension_create = ExtensionCreate( - dao_id=dao.id, - type=contract.type.value, - subtype=contract.subtype, - contract_principal=contract.contract_principal, - tx_id=contract.tx_id, - status=ContractStatus.DEPLOYED, - ) - - extension = self.db.create_extension(extension_create) - extension_ids.append(extension.id) - self.logger.info( - f"Created extension with ID: {extension.id} for type: {contract.type.value} and subtype: {contract.subtype}" - ) - else: - self.logger.info( - f"Skipping {contract.type.value} contract: {contract.name}" - ) - + extension_create = ExtensionCreate( + dao_id=dao.id, + type=contract.type.value, + subtype=contract.subtype, + contract_principal=contract.contract_principal, + tx_id=contract.tx_id, + status=ContractStatus.DEPLOYED, + ) + + extension = self.db.create_extension(extension_create) + extension_ids.append(extension.id) + self.logger.info( + f"Created extension with ID: {extension.id} for type: {contract.type.value} and subtype: {contract.subtype}" + ) # Prepare response response = DAOWebhookResponse( dao_id=dao.id, From e388d120387eefd2c7178777deaa01c48a0e660c Mon Sep 17 00:00:00 2001 From: "Jason Schrader (aider)" Date: Thu, 29 May 2025 10:33:09 -0700 Subject: [PATCH 077/219] feat: Update action proposal tools to match typescript versions --- tools/dao_ext_action_proposals.py | 428 ++++++++++++++++++++++++++---- tools/tools_factory.py | 18 +- 2 files changed, 393 insertions(+), 53 deletions(-) diff --git a/tools/dao_ext_action_proposals.py b/tools/dao_ext_action_proposals.py index f4417d6f..2b590bb1 100644 --- a/tools/dao_ext_action_proposals.py +++ b/tools/dao_ext_action_proposals.py @@ -364,7 +364,7 @@ def _deploy( return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/read-only", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", "get-liquid-supply.ts", *args, ) @@ -437,7 +437,7 @@ def _deploy( return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/read-only", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", "get-proposal.ts", *args, ) @@ -461,8 +461,72 @@ async def _arun( return self._deploy(action_proposals_voting_extension, proposal_id, **kwargs) -class GetTotalVotesInput(BaseModel): - """Input schema for getting total votes for a voter.""" +class GetVotingConfigurationInput(BaseModel): + """Input schema for getting voting configuration.""" + + action_proposals_voting_extension: str = Field( + ..., + description="Contract principal where the DAO creates action proposals for voting by DAO members.", + examples=[ + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", + ], + ) + + +class GetVotingConfigurationTool(BaseTool): + name: str = "dao_action_get_voting_configuration" + description: str = ( + "Get the voting configuration from the DAO action proposals contract. " + "Returns the current voting parameters and settings used for proposals." + ) + args_schema: Type[BaseModel] = GetVotingConfigurationInput + return_direct: bool = False + wallet_id: Optional[UUID] = None + + def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): + super().__init__(**kwargs) + self.wallet_id = wallet_id + + def _deploy( + self, + action_proposals_voting_extension: str, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to get voting configuration.""" + if self.wallet_id is None: + return {"success": False, "message": "Wallet ID is required", "data": None} + + args = [ + action_proposals_voting_extension, + ] + + return BunScriptRunner.bun_run( + self.wallet_id, + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", + "get-voting-configuration.ts", + *args, + ) + + def _run( + self, + action_proposals_voting_extension: str, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to get voting configuration.""" + return self._deploy(action_proposals_voting_extension, **kwargs) + + async def _arun( + self, + action_proposals_voting_extension: str, + **kwargs, + ) -> Dict[str, Any]: + """Async version of the tool.""" + return self._deploy(action_proposals_voting_extension, **kwargs) + + +class GetVotingPowerInput(BaseModel): + """Input schema for getting voting power.""" action_proposals_voting_extension: str = Field( ..., @@ -473,16 +537,20 @@ class GetTotalVotesInput(BaseModel): ], ) proposal_id: int = Field(..., description="ID of the proposal to check") - voter_address: str = Field(..., description="Address of the voter to check") + voter_address: str = Field( + ..., + description="Address of the voter to check voting power for", + examples=["ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18"], + ) -class GetTotalVotesTool(BaseTool): - name: str = "dao_action_get_total_votes" +class GetVotingPowerTool(BaseTool): + name: str = "dao_action_get_voting_power" description: str = ( - "Get the total votes cast by a specific voter on a proposal. " - "Returns the number of votes the voter has cast on the given proposal." + "Get the voting power of a specific address for a proposal. " + "Returns the number of votes the address can cast on the given proposal." ) - args_schema: Type[BaseModel] = GetTotalVotesInput + args_schema: Type[BaseModel] = GetVotingPowerInput return_direct: bool = False wallet_id: Optional[UUID] = None @@ -497,7 +565,7 @@ def _deploy( voter_address: str, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to get total votes.""" + """Execute the tool to get voting power.""" if self.wallet_id is None: return {"success": False, "message": "Wallet ID is required", "data": None} @@ -509,8 +577,8 @@ def _deploy( return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/read-only", - "get-total-votes.ts", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", + "get-voting-power.ts", *args, ) @@ -521,28 +589,242 @@ def _run( voter_address: str, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to get total votes.""" + """Execute the tool to get voting power.""" return self._deploy( action_proposals_voting_extension, proposal_id, voter_address, **kwargs ) + +class VetoActionProposalInput(BaseModel): + """Input schema for vetoing an action proposal.""" + + dao_action_proposal_voting_contract: str = Field( + ..., + description="Contract principal where the DAO creates action proposals for voting by DAO members.", + examples=[ + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", + ], + ) + proposal_id: int = Field(..., description="ID of the proposal to veto") + + +class VetoActionProposalTool(BaseTool): + name: str = "dao_action_veto_proposal" + description: str = ( + "Veto an existing action proposal in the DAO. " + "Allows casting a veto vote on a specific proposal ID " + "in the provided action proposals contract." + ) + args_schema: Type[BaseModel] = VetoActionProposalInput + return_direct: bool = False + wallet_id: Optional[UUID] = None + + def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): + super().__init__(**kwargs) + self.wallet_id = wallet_id + + def _deploy( + self, + dao_action_proposal_voting_contract: str, + proposal_id: int, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to veto an action proposal.""" + if self.wallet_id is None: + return {"success": False, "message": "Wallet ID is required", "data": None} + + args = [ + dao_action_proposal_voting_contract, + str(proposal_id), + ] + + return BunScriptRunner.bun_run( + self.wallet_id, + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/public", + "veto-action-proposal.ts", + *args, + ) + + def _run( + self, + dao_action_proposal_voting_contract: str, + proposal_id: int, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to veto an action proposal.""" + return self._deploy( + dao_action_proposal_voting_contract, proposal_id, **kwargs + ) + async def _arun( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, + proposal_id: int, + **kwargs, + ) -> Dict[str, Any]: + """Async version of the tool.""" + return self._deploy( + dao_action_proposal_voting_contract, proposal_id, **kwargs + ) + + +class GetTotalProposalsInput(BaseModel): + """Input schema for getting total proposals data.""" + + dao_action_proposal_voting_contract: str = Field( + ..., + description="Contract principal where the DAO creates action proposals for voting by DAO members.", + examples=[ + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", + ], + ) + + +class GetTotalProposalsTool(BaseTool): + name: str = "dao_action_get_total_proposals" + description: str = ( + "Get the total proposals data from the DAO action proposals contract. " + "Returns counts of proposals and last proposal block information." + ) + args_schema: Type[BaseModel] = GetTotalProposalsInput + return_direct: bool = False + wallet_id: Optional[UUID] = None + + def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): + super().__init__(**kwargs) + self.wallet_id = wallet_id + + def _deploy( + self, + dao_action_proposal_voting_contract: str, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to get total proposals data.""" + if self.wallet_id is None: + return {"success": False, "message": "Wallet ID is required", "data": None} + + args = [ + dao_action_proposal_voting_contract, + ] + + return BunScriptRunner.bun_run( + self.wallet_id, + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", + "get-total-proposals.ts", + *args, + ) + + def _run( + self, + dao_action_proposal_voting_contract: str, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to get total proposals data.""" + return self._deploy(dao_action_proposal_voting_contract, **kwargs) + + async def _arun( + self, + dao_action_proposal_voting_contract: str, + **kwargs, + ) -> Dict[str, Any]: + """Async version of the tool.""" + return self._deploy(dao_action_proposal_voting_contract, **kwargs) + + +class GetVetoVoteRecordInput(BaseModel): + """Input schema for getting a veto vote record.""" + + dao_action_proposal_voting_contract: str = Field( + ..., + description="Contract principal where the DAO creates action proposals for voting by DAO members.", + examples=[ + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.faces-action-proposals-v2", + "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", + ], + ) + proposal_id: int = Field(..., description="ID of the proposal to check") + voter_address: str = Field( + ..., + description="Address of the voter to check the veto vote record for", + examples=["ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18"], + ) + + +class GetVetoVoteRecordTool(BaseTool): + name: str = "dao_action_get_veto_vote_record" + description: str = ( + "Get the veto vote record for a specific voter on a proposal. " + "Returns the amount of veto votes if a record exists, otherwise null." + ) + args_schema: Type[BaseModel] = GetVetoVoteRecordInput + return_direct: bool = False + wallet_id: Optional[UUID] = None + + def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): + super().__init__(**kwargs) + self.wallet_id = wallet_id + + def _deploy( + self, + dao_action_proposal_voting_contract: str, + proposal_id: int, + voter_address: str, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to get a veto vote record.""" + if self.wallet_id is None: + return {"success": False, "message": "Wallet ID is required", "data": None} + + args = [ + dao_action_proposal_voting_contract, + str(proposal_id), + voter_address, + ] + + return BunScriptRunner.bun_run( + self.wallet_id, + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", + "get-veto-vote-record.ts", + *args, + ) + + def _run( + self, + dao_action_proposal_voting_contract: str, + proposal_id: int, + voter_address: str, + **kwargs, + ) -> Dict[str, Any]: + """Execute the tool to get a veto vote record.""" + return self._deploy( + dao_action_proposal_voting_contract, + proposal_id, + voter_address, + **kwargs, + ) + + async def _arun( + self, + dao_action_proposal_voting_contract: str, proposal_id: int, voter_address: str, **kwargs, ) -> Dict[str, Any]: """Async version of the tool.""" return self._deploy( - action_proposals_voting_extension, proposal_id, voter_address, **kwargs + dao_action_proposal_voting_contract, + proposal_id, + voter_address, + **kwargs, ) -class GetVotingConfigurationInput(BaseModel): - """Input schema for getting voting configuration.""" +class GetVoteRecordInput(BaseModel): + """Input schema for getting a vote record.""" - action_proposals_voting_extension: str = Field( + dao_action_proposal_voting_contract: str = Field( ..., description="Contract principal where the DAO creates action proposals for voting by DAO members.", examples=[ @@ -550,15 +832,21 @@ class GetVotingConfigurationInput(BaseModel): "ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18.t3st-action-proposals-v2", ], ) + proposal_id: int = Field(..., description="ID of the proposal to check") + voter_address: str = Field( + ..., + description="Address of the voter to check the vote record for", + examples=["ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18"], + ) -class GetVotingConfigurationTool(BaseTool): - name: str = "dao_action_get_voting_configuration" +class GetVoteRecordTool(BaseTool): + name: str = "dao_action_get_vote_record" description: str = ( - "Get the voting configuration from the DAO action proposals contract. " - "Returns the current voting parameters and settings used for proposals." + "Get the vote record for a specific voter on a proposal. " + "Returns the vote (true/false) and amount if a record exists, otherwise null." ) - args_schema: Type[BaseModel] = GetVotingConfigurationInput + args_schema: Type[BaseModel] = GetVoteRecordInput return_direct: bool = False wallet_id: Optional[UUID] = None @@ -568,46 +856,63 @@ def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): def _deploy( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, + proposal_id: int, + voter_address: str, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to get voting configuration.""" + """Execute the tool to get a vote record.""" if self.wallet_id is None: return {"success": False, "message": "Wallet ID is required", "data": None} args = [ - action_proposals_voting_extension, + dao_action_proposal_voting_contract, + str(proposal_id), + voter_address, ] return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/read-only", - "get-voting-configuration.ts", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", + "get-vote-record.ts", *args, ) def _run( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, + proposal_id: int, + voter_address: str, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to get voting configuration.""" - return self._deploy(action_proposals_voting_extension, **kwargs) + """Execute the tool to get a vote record.""" + return self._deploy( + dao_action_proposal_voting_contract, + proposal_id, + voter_address, + **kwargs, + ) async def _arun( self, - action_proposals_voting_extension: str, - dao_token_contract_address: str, + dao_action_proposal_voting_contract: str, + proposal_id: int, + voter_address: str, **kwargs, ) -> Dict[str, Any]: """Async version of the tool.""" - return self._deploy(action_proposals_voting_extension, **kwargs) + return self._deploy( + dao_action_proposal_voting_contract, + proposal_id, + voter_address, + **kwargs, + ) -class GetVotingPowerInput(BaseModel): - """Input schema for getting voting power.""" +class GetVoteRecordsInput(BaseModel): + """Input schema for getting vote records (vote and veto vote).""" - action_proposals_voting_extension: str = Field( + dao_action_proposal_voting_contract: str = Field( ..., description="Contract principal where the DAO creates action proposals for voting by DAO members.", examples=[ @@ -618,18 +923,19 @@ class GetVotingPowerInput(BaseModel): proposal_id: int = Field(..., description="ID of the proposal to check") voter_address: str = Field( ..., - description="Address of the voter to check voting power for", + description="Address of the voter to check vote records for", examples=["ST3YT0XW92E6T2FE59B2G5N2WNNFSBZ6MZKQS5D18"], ) -class GetVotingPowerTool(BaseTool): - name: str = "dao_action_get_voting_power" +class GetVoteRecordsTool(BaseTool): + name: str = "dao_action_get_vote_records" description: str = ( - "Get the voting power of a specific address for a proposal. " - "Returns the number of votes the address can cast on the given proposal." + "Get both the regular vote record and veto vote record for a specific voter on a proposal. " + "Returns an object containing 'voteRecord' (vote and amount, or null) and " + "'vetoVoteRecord' (amount, or null)." ) - args_schema: Type[BaseModel] = GetVotingPowerInput + args_schema: Type[BaseModel] = GetVoteRecordsInput return_direct: bool = False wallet_id: Optional[UUID] = None @@ -639,38 +945,56 @@ def __init__(self, wallet_id: Optional[UUID] = None, **kwargs): def _deploy( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, proposal_id: int, voter_address: str, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to get voting power.""" + """Execute the tool to get vote records.""" if self.wallet_id is None: return {"success": False, "message": "Wallet ID is required", "data": None} args = [ - action_proposals_voting_extension, + dao_action_proposal_voting_contract, str(proposal_id), voter_address, ] return BunScriptRunner.bun_run( self.wallet_id, - "aibtc-dao/extensions/action-proposals/read-only", - "get-voting-power.ts", + "aibtc-cohort-0/dao-tools/extensions/action-proposal-voting/read-only", + "get-vote-records.ts", *args, ) def _run( self, - action_proposals_voting_extension: str, + dao_action_proposal_voting_contract: str, proposal_id: int, voter_address: str, **kwargs, ) -> Dict[str, Any]: - """Execute the tool to get voting power.""" + """Execute the tool to get vote records.""" return self._deploy( - action_proposals_voting_extension, proposal_id, voter_address, **kwargs + dao_action_proposal_voting_contract, + proposal_id, + voter_address, + **kwargs, + ) + + async def _arun( + self, + dao_action_proposal_voting_contract: str, + proposal_id: int, + voter_address: str, + **kwargs, + ) -> Dict[str, Any]: + """Async version of the tool.""" + return self._deploy( + dao_action_proposal_voting_contract, + proposal_id, + voter_address, + **kwargs, ) async def _arun( diff --git a/tools/tools_factory.py b/tools/tools_factory.py index d20b6cfd..7125d2f7 100644 --- a/tools/tools_factory.py +++ b/tools/tools_factory.py @@ -14,10 +14,14 @@ ConcludeActionProposalTool, GetLiquidSupplyTool, GetProposalTool, - GetTotalVotesTool, + GetTotalProposalsTool, + GetVoteRecordTool, + GetVoteRecordsTool, + GetVetoVoteRecordTool, GetVotingConfigurationTool, GetVotingPowerTool, ProposeActionSendMessageTool, + VetoActionProposalTool, VoteOnActionProposalTool, ) from .dao_ext_charter import ( @@ -102,7 +106,19 @@ def initialize_tools( "bitflow_execute_trade": BitflowExecuteTradeTool(wallet_id), "contracts_fetch_sip10_info": ContractSIP10InfoTool(wallet_id), "contracts_fetch_source_code": FetchContractSourceTool(wallet_id), + "dao_action_conclude_proposal": ConcludeActionProposalTool(wallet_id), + "dao_action_get_liquid_supply": GetLiquidSupplyTool(wallet_id), + "dao_action_get_proposal": GetProposalTool(wallet_id), + "dao_action_get_total_proposals": GetTotalProposalsTool(wallet_id), + "dao_action_get_veto_vote_record": GetVetoVoteRecordTool(wallet_id), + "dao_action_get_vote_record": GetVoteRecordTool(wallet_id), + "dao_action_get_vote_records": GetVoteRecordsTool(wallet_id), + "dao_action_get_voting_configuration": GetVotingConfigurationTool(wallet_id), + "dao_action_get_voting_power": GetVotingPowerTool(wallet_id), + "dao_action_veto_proposal": VetoActionProposalTool(wallet_id), + "dao_action_vote_on_proposal": VoteOnActionProposalTool(wallet_id), "dao_charter_get_current_charter": GetCurrentDaoCharterTool(wallet_id), + "dao_propose_action_send_message": ProposeActionSendMessageTool(wallet_id), "database_add_scheduled_task": AddScheduledTaskTool(profile_id, agent_id), "database_get_dao_list": GetDAOListTool(), "database_get_dao_get_by_name": GetDAOByNameTool(), From 5455e31188828373cfc257bff7d430dff60026c2 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sun, 1 Jun 2025 19:51:34 -0700 Subject: [PATCH 078/219] update --- .../runner/tasks/dao_proposal_evaluation.py | 42 +++++++++---------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/services/runner/tasks/dao_proposal_evaluation.py b/services/runner/tasks/dao_proposal_evaluation.py index d6365d0c..faee60c6 100644 --- a/services/runner/tasks/dao_proposal_evaluation.py +++ b/services/runner/tasks/dao_proposal_evaluation.py @@ -159,26 +159,26 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: logger.info(f"Created vote record {vote.id} for proposal {proposal_id}") - # # Create a DAO_PROPOSAL_VOTE message with the vote record ID - # vote_message_data = {"proposal_id": proposal_id, "vote_id": str(vote.id)} - - # vote_message = backend.create_queue_message( - # QueueMessageCreate( - # type=QueueMessageType.DAO_PROPOSAL_VOTE, - # message=vote_message_data, - # dao_id=dao_id, - # wallet_id=wallet_id, - # ) - # ) - - # if not vote_message: - # logger.error("Failed to create vote queue message") - # return { - # "success": False, - # "error": "Failed to create vote queue message", - # } - - # logger.info(f"Created vote queue message {vote_message.id}") + # Create a DAO_PROPOSAL_VOTE message with the vote record ID + vote_message_data = {"proposal_id": proposal_id, "vote_id": str(vote.id)} + + vote_message = backend.create_queue_message( + QueueMessageCreate( + type=QueueMessageType.DAO_PROPOSAL_VOTE, + message=vote_message_data, + dao_id=dao_id, + wallet_id=wallet_id, + ) + ) + + if not vote_message: + logger.error("Failed to create vote queue message") + return { + "success": False, + "error": "Failed to create vote queue message", + } + + logger.info(f"Created vote queue message {vote_message.id}") # Mark the evaluation message as processed update_data = QueueMessageBase(is_processed=True) @@ -187,7 +187,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: return { "success": True, "vote_id": str(vote.id), - # "vote_message_id": str(vote_message.id), + "vote_message_id": str(vote_message.id), "approve": approval, "confidence": confidence, } From 92089852400e4c13973e77cfa53c3fe822a5f0a7 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sun, 1 Jun 2025 20:05:07 -0700 Subject: [PATCH 079/219] update --- agent-tools-ts | 2 +- .../runner/tasks/dao_proposal_evaluation.py | 40 +++++++++---------- 2 files changed, 21 insertions(+), 21 deletions(-) diff --git a/agent-tools-ts b/agent-tools-ts index 3667c3cf..7c1b7129 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 3667c3cff526c6608711f44c274c3fe35b02b9db +Subproject commit 7c1b712966aac53ddd96e841db1c77af296fde62 diff --git a/services/runner/tasks/dao_proposal_evaluation.py b/services/runner/tasks/dao_proposal_evaluation.py index faee60c6..d940e87e 100644 --- a/services/runner/tasks/dao_proposal_evaluation.py +++ b/services/runner/tasks/dao_proposal_evaluation.py @@ -160,25 +160,25 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: logger.info(f"Created vote record {vote.id} for proposal {proposal_id}") # Create a DAO_PROPOSAL_VOTE message with the vote record ID - vote_message_data = {"proposal_id": proposal_id, "vote_id": str(vote.id)} - - vote_message = backend.create_queue_message( - QueueMessageCreate( - type=QueueMessageType.DAO_PROPOSAL_VOTE, - message=vote_message_data, - dao_id=dao_id, - wallet_id=wallet_id, - ) - ) - - if not vote_message: - logger.error("Failed to create vote queue message") - return { - "success": False, - "error": "Failed to create vote queue message", - } - - logger.info(f"Created vote queue message {vote_message.id}") + # vote_message_data = {"proposal_id": proposal_id, "vote_id": str(vote.id)} + + # vote_message = backend.create_queue_message( + # QueueMessageCreate( + # type=QueueMessageType.DAO_PROPOSAL_VOTE, + # message=vote_message_data, + # dao_id=dao_id, + # wallet_id=wallet_id, + # ) + # ) + + # if not vote_message: + # logger.error("Failed to create vote queue message") + # return { + # "success": False, + # "error": "Failed to create vote queue message", + # } + + # logger.info(f"Created vote queue message {vote_message.id}") # Mark the evaluation message as processed update_data = QueueMessageBase(is_processed=True) @@ -187,7 +187,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: return { "success": True, "vote_id": str(vote.id), - "vote_message_id": str(vote_message.id), + # "vote_message_id": str(vote_message.id), "approve": approval, "confidence": confidence, } From eec27be42a6b104735d4616a7cd01bb7fccda63f Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sun, 1 Jun 2025 21:44:25 -0700 Subject: [PATCH 080/219] update --- backend/models.py | 7 +------ backend/supabase.py | 2 -- services/runner/tasks/dao_proposal_evaluation.py | 4 ++-- services/runner/tasks/proposal_embedder.py | 4 +--- .../webhooks/chainhook/handlers/action_proposal_handler.py | 3 +-- .../webhooks/chainhook/handlers/core_proposal_handler.py | 4 +--- services/webhooks/chainhook/handlers/core_vote_handler.py | 1 - .../chainhook/handlers/dao_proposal_burn_height_handler.py | 4 ++-- .../chainhook/handlers/dao_proposal_conclusion_handler.py | 3 ++- services/workflows/agents/historical_context.py | 2 +- services/workflows/proposal_evaluation.py | 2 +- 11 files changed, 12 insertions(+), 24 deletions(-) diff --git a/backend/models.py b/backend/models.py index a598d208..c0769a70 100644 --- a/backend/models.py +++ b/backend/models.py @@ -321,20 +321,16 @@ class Profile(ProfileBase): class ProposalBase(CustomBaseModel): dao_id: Optional[UUID] = None title: Optional[str] = None - description: Optional[str] = None + content: Optional[str] = None # Replaces both description and parameters status: Optional[ContractStatus] = ContractStatus.DRAFT contract_principal: Optional[str] = None tx_id: Optional[str] = None proposal_id: Optional[int] = None # On-chain proposal ID if its an action proposal - proposal_contract: Optional[str] = ( - None # Contract address of the proposal if its a core contract proposal - ) type: Optional[ProposalType] = ProposalType.ACTION action: Optional[str] = None caller: Optional[str] = None creator: Optional[str] = None liquid_tokens: Optional[str] = None # Using string to handle large numbers - parameters: Optional[str] = None # Additional fields from blockchain data concluded_by: Optional[str] = None executed: Optional[bool] = None @@ -594,7 +590,6 @@ class ProposalFilter(CustomBaseModel): met_quorum: Optional[bool] = None met_threshold: Optional[bool] = None type: Optional[ProposalType] = None - proposal_contract: Optional[str] = None class StepFilter(CustomBaseModel): diff --git a/backend/supabase.py b/backend/supabase.py index 944b609d..9033adbd 100644 --- a/backend/supabase.py +++ b/backend/supabase.py @@ -1305,8 +1305,6 @@ def list_proposals( query = query.eq("met_threshold", filters.met_threshold) if filters.type is not None: query = query.eq("type", filters.type) - if filters.proposal_contract is not None: - query = query.eq("proposal_contract", filters.proposal_contract) response = query.execute() data = response.data or [] return [Proposal(**row) for row in data] diff --git a/services/runner/tasks/dao_proposal_evaluation.py b/services/runner/tasks/dao_proposal_evaluation.py index d940e87e..5f8bd9dc 100644 --- a/services/runner/tasks/dao_proposal_evaluation.py +++ b/services/runner/tasks/dao_proposal_evaluation.py @@ -125,7 +125,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: approval = evaluation.get("approve", False) confidence = evaluation.get("confidence_score", 0.0) reasoning = evaluation.get("reasoning", "No reasoning provided") - formatted_prompt = result.get("formatted_prompt", "No prompt provided") + formatted_prompt = result.get("formatted_prompt", "") total_cost = result.get("total_overall_cost", 0.0) model = result.get("evaluation_model_info", {}).get("name", "Unknown") @@ -140,7 +140,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: vote_data = VoteCreate( wallet_id=wallet_id, dao_id=dao_id, - agent_id=None, # This will be set from the wallet if it exists + agent_id=wallet.agent_id, # This will be set from the wallet if it exists proposal_id=proposal_id, answer=approval, reasoning=reasoning, diff --git a/services/runner/tasks/proposal_embedder.py b/services/runner/tasks/proposal_embedder.py index afecde1c..05b87aff 100644 --- a/services/runner/tasks/proposal_embedder.py +++ b/services/runner/tasks/proposal_embedder.py @@ -49,13 +49,11 @@ def _format_proposal_for_embedding(self, proposal: Proposal) -> str: """Format proposal data into a string for embedding.""" parts = [ f"Title: {proposal.title or 'N/A'}", - f"Description: {proposal.description or 'N/A'}", + f"Content: {proposal.content or 'N/A'}", f"Type: {proposal.type.value if proposal.type else 'N/A'}", ] if proposal.action: parts.append(f"Action: {proposal.action}") - if proposal.parameters: - parts.append(f"Parameters: {proposal.parameters}") # Add more relevant fields as needed return "\n".join(parts) diff --git a/services/webhooks/chainhook/handlers/action_proposal_handler.py b/services/webhooks/chainhook/handlers/action_proposal_handler.py index df09a3c8..a6af793d 100644 --- a/services/webhooks/chainhook/handlers/action_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/action_proposal_handler.py @@ -260,7 +260,7 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: ProposalCreate( dao_id=dao_data["id"], title=proposal_title, - description=f"Action proposal {proposal_info['proposal_id']} for {dao_data['name']}", + content=f"Action proposal {proposal_info['proposal_id']} for {dao_data['name']}. Parameters: {parameters}", contract_principal=contract_identifier, tx_id=tx_id, proposal_id=proposal_info["proposal_id"], @@ -271,7 +271,6 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: caller=proposal_info["caller"], creator=proposal_info["creator"], liquid_tokens=proposal_info["liquid_tokens"], - parameters=parameters, bond=proposal_info["bond"], # Fields from updated payload contract_caller=proposal_info["contract_caller"], diff --git a/services/webhooks/chainhook/handlers/core_proposal_handler.py b/services/webhooks/chainhook/handlers/core_proposal_handler.py index b1cbeb77..12e89dc4 100644 --- a/services/webhooks/chainhook/handlers/core_proposal_handler.py +++ b/services/webhooks/chainhook/handlers/core_proposal_handler.py @@ -178,7 +178,6 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: filters=ProposalFilter( dao_id=dao_data["id"], contract_principal=contract_identifier, - proposal_contract=proposal_info["proposal"], type=ProposalType.CORE, ) ) @@ -190,12 +189,11 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: ProposalCreate( dao_id=dao_data["id"], title=proposal_title, - description=f"Core contract proposal for {proposal_info['proposal']}", + content=f"Core contract proposal for {proposal_info['proposal']}", contract_principal=contract_identifier, tx_id=tx_id, status=ContractStatus.DEPLOYED, # Since it's already on-chain type=ProposalType.CORE, - proposal_contract=proposal_info["proposal"], # Add fields from payload caller=proposal_info["caller"], creator=proposal_info["creator"], diff --git a/services/webhooks/chainhook/handlers/core_vote_handler.py b/services/webhooks/chainhook/handlers/core_vote_handler.py index 881a57ca..a42ceafa 100644 --- a/services/webhooks/chainhook/handlers/core_vote_handler.py +++ b/services/webhooks/chainhook/handlers/core_vote_handler.py @@ -30,7 +30,6 @@ def _find_proposal( proposals = backend.list_proposals( filters=ProposalFilter( contract_principal=contract_identifier, - proposal_contract=proposal_identifier, type=ProposalType.CORE, ) ) diff --git a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py index 65af429d..7cf54b6a 100644 --- a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py +++ b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py @@ -139,7 +139,7 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: if p.vote_start is not None and p.vote_end is not None and p.vote_start == burn_height - and p.parameters is not None # Ensure parameters exist + and p.content is not None # Ensure content exists ] end_proposals = [ @@ -148,7 +148,7 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: if p.vote_start is not None and p.vote_end is not None and p.vote_end == burn_height - and p.parameters is not None # Ensure parameters exist + and p.content is not None # Ensure content exists ] if not vote_proposals and not end_proposals: diff --git a/services/webhooks/chainhook/handlers/dao_proposal_conclusion_handler.py b/services/webhooks/chainhook/handlers/dao_proposal_conclusion_handler.py index 747d0fbf..ee354140 100644 --- a/services/webhooks/chainhook/handlers/dao_proposal_conclusion_handler.py +++ b/services/webhooks/chainhook/handlers/dao_proposal_conclusion_handler.py @@ -132,7 +132,8 @@ def _get_conclusion_info_from_events(self, events: List[Event]) -> Optional[Dict base_data["type"] = ProposalType.ACTION elif "proposal" in payload: # Core proposal - base_data["proposal_contract"] = payload.get("proposal") + # Note: proposal_contract field was removed from the model + # The proposal contract info is stored in contract_principal base_data["type"] = ProposalType.CORE else: self.logger.warning( diff --git a/services/workflows/agents/historical_context.py b/services/workflows/agents/historical_context.py index 213b87be..c8efdfbe 100644 --- a/services/workflows/agents/historical_context.py +++ b/services/workflows/agents/historical_context.py @@ -85,7 +85,7 @@ def _format_proposals_for_context(self, proposals: List[Proposal]) -> str: [ f'\n' f" {proposal.title or 'Untitled'}\n" - f" {proposal.description or 'No description'}\n" + f" {proposal.content or 'No content'}\n" f" {proposal.status or 'Unknown'}\n" f" {proposal.type or 'Unknown'}\n" f" {proposal.created_at.strftime('%Y-%m-%d') if proposal.created_at else 'Unknown'}\n" diff --git a/services/workflows/proposal_evaluation.py b/services/workflows/proposal_evaluation.py index a5f553ad..a19d6067 100644 --- a/services/workflows/proposal_evaluation.py +++ b/services/workflows/proposal_evaluation.py @@ -463,7 +463,7 @@ async def evaluate_and_vote_on_proposal( logger.info(f"Starting evaluation of proposal {proposal_id}") evaluation_result = await evaluate_proposal( proposal_id=str(proposal_id), - proposal_data=proposal.parameters, + proposal_data=proposal.content, config=config, ) From 086b48149e91a7e8cbf95ce29f4cc7451cddacc4 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sun, 1 Jun 2025 23:06:16 -0700 Subject: [PATCH 081/219] add staging frontend --- main.py | 1 + 1 file changed, 1 insertion(+) diff --git a/main.py b/main.py index 6a9f52c7..493b16ac 100644 --- a/main.py +++ b/main.py @@ -33,6 +33,7 @@ "https://app.aibtc.dev", "https://aibtc.dev", "https://app-staging.aibtc.dev", + "https://*.aibtcdev-frontend-staging.pages.dev", ], allow_credentials=True, allow_methods=["*"], From a20ea719f11d29787dabaeb10184298cd92e3da9 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sun, 1 Jun 2025 23:15:21 -0700 Subject: [PATCH 082/219] add ui-redesign --- main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.py b/main.py index 493b16ac..97646110 100644 --- a/main.py +++ b/main.py @@ -33,7 +33,7 @@ "https://app.aibtc.dev", "https://aibtc.dev", "https://app-staging.aibtc.dev", - "https://*.aibtcdev-frontend-staging.pages.dev", + "https://ui-redesign.aibtcdev-frontend-staging.pages.dev", ], allow_credentials=True, allow_methods=["*"], From 6c4087813b2ab5172e5f3c16e3685b903e6355ba Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Sun, 1 Jun 2025 23:18:13 -0700 Subject: [PATCH 083/219] switch up the allow origin --- main.py | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/main.py b/main.py index 97646110..55241d0e 100644 --- a/main.py +++ b/main.py @@ -24,17 +24,7 @@ # Configure CORS app.add_middleware( CORSMiddleware, - allow_origins=[ - "https://sprint.aibtc.dev", - "https://sprint-faster.aibtc.dev", - "https://*.aibtcdev-frontend.pages.dev", - "http://localhost:3000", - "https://staging.aibtc.chat", - "https://app.aibtc.dev", - "https://aibtc.dev", - "https://app-staging.aibtc.dev", - "https://ui-redesign.aibtcdev-frontend-staging.pages.dev", - ], + allow_origin_regex=r"^(https://((sprint|sprint-faster|app|app-staging)\.aibtc\.dev|aibtc\.dev|staging\.aibtc\.chat|[^.]+\.aibtcdev-frontend(-staging)?\.pages\.dev)|http://localhost:3000)$", allow_credentials=True, allow_methods=["*"], allow_headers=["*"], From fbcd49ead65c1e4fd029a3dd1465983232ebc4a7 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 2 Jun 2025 13:25:57 +0000 Subject: [PATCH 084/219] Bump the dev-dependencies group with 5 updates --- updated-dependencies: - dependency-name: aiohttp dependency-version: 3.12.6 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies - dependency-name: openai dependency-version: 1.82.1 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: starlette dependency-version: 0.47.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: dev-dependencies - dependency-name: uvicorn dependency-version: 0.34.3 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies - dependency-name: pytest-mock dependency-version: 3.14.1 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: dev-dependencies ... Signed-off-by: dependabot[bot] --- pyproject.toml | 10 +++++----- requirements.txt | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f1915b89..6fcb9f96 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.13" dependencies = [ - "aiohttp==3.11.18", + "aiohttp==3.12.6", "apscheduler==3.11.0", "cachetools==6.0.0", "fastapi==0.115.12", @@ -15,7 +15,7 @@ dependencies = [ "langchain-openai==0.3.18", "langchain-text-splitters==0.3.8", "langgraph==0.4.7", - "openai==1.82.0", + "openai==1.82.1", "pgvector==0.3.6", "psycopg2==2.9.10", "pydantic==2.11.5", @@ -24,11 +24,11 @@ dependencies = [ "python-twitter-v2==0.9.2", "requests==2.32.3", "sqlalchemy==2.0.41", - "starlette==0.46.2", + "starlette==0.47.0", "supabase==2.15.2", "tiktoken==0.9.0", "tweepy==4.15.0", - "uvicorn==0.34.2", + "uvicorn==0.34.3", "vecs==0.4.5", ] @@ -36,6 +36,6 @@ dependencies = [ testing = [ "pytest==8.3.5", "pytest-asyncio==0.26.0", - "pytest-mock==3.14.0", + "pytest-mock==3.14.1", "responses==0.25.7", ] diff --git a/requirements.txt b/requirements.txt index 847b9d67..58a47843 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,7 @@ langchain_community==0.3.24 langchain_openai==0.3.18 langchain_text_splitters==0.3.8 langgraph==0.4.7 -openai==1.82.0 +openai==1.82.1 pgvector==0.3.6 psycopg2==2.9.10 pydantic==2.11.5 @@ -17,15 +17,15 @@ python-twitter-v2==0.9.2 tweepy==4.15.0 Requests==2.32.3 SQLAlchemy==2.0.41 -starlette==0.46.2 +starlette==0.47.0 supabase==2.15.2 tiktoken==0.9.0 tweepy==4.15.0 -uvicorn==0.34.2 +uvicorn==0.34.3 vecs==0.4.5 # Test dependencies pytest==8.3.5 pytest-asyncio==0.26.0 responses==0.25.7 -aiohttp==3.11.18 \ No newline at end of file +aiohttp==3.12.6 \ No newline at end of file From 20c42734199294e93d659429a79a22d4f5e7546d Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Mon, 2 Jun 2025 09:32:11 -0700 Subject: [PATCH 085/219] update tools --- agent-tools-ts | 2 +- api/tools.py | 91 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 92 insertions(+), 1 deletion(-) diff --git a/agent-tools-ts b/agent-tools-ts index 7c1b7129..d28e2edc 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit 7c1b712966aac53ddd96e841db1c77af296fde62 +Subproject commit d28e2edcf4d6d951e88b4c6a44726f703f5b5633 diff --git a/api/tools.py b/api/tools.py index 97ec54bc..0c190e76 100644 --- a/api/tools.py +++ b/api/tools.py @@ -18,6 +18,7 @@ from lib.tools import Tool, get_available_tools from tools.dao_ext_action_proposals import ( ProposeActionSendMessageTool, # Added ProposeActionSendMessageTool + VetoActionProposalTool, # Added VetoActionProposalTool ) from tools.faktory import FaktoryExecuteBuyTool # Added import for Faktory tool @@ -73,6 +74,19 @@ class ProposeSendMessageRequest(BaseModel): ) +class VetoActionProposalRequest(BaseModel): + """Request body for vetoing a DAO action proposal.""" + + dao_action_proposal_voting_contract: str = Field( + ..., + description="Contract principal where the DAO creates action proposals for voting by DAO members.", + ) + proposal_id: int = Field( + ..., + description="ID of the proposal to veto.", + ) + + @router.get("/available", response_model=List[Tool]) async def get_tools( request: Request, @@ -349,3 +363,80 @@ async def propose_dao_action_send_message( status_code=500, detail=f"Failed to propose DAO send message action: {str(e)}", ) + + +@router.post("/dao/action_proposals/veto_proposal") +async def veto_dao_action_proposal( + request: Request, + payload: VetoActionProposalRequest, + profile: Profile = Depends(verify_profile_from_token), +) -> JSONResponse: + """Veto a DAO action proposal. + + This endpoint allows an authenticated user's agent to veto an existing + action proposal in the DAO's action proposal system. + + Args: + request: The FastAPI request object. + payload: The request body containing the proposal details to veto. + profile: The authenticated user's profile. + + Returns: + JSONResponse: The result of the veto operation. + + Raises: + HTTPException: If there's an error, or if the agent for the profile is not found. + """ + try: + logger.info( + f"DAO veto action proposal request received from {request.client.host if request.client else 'unknown'} for profile {profile.id}" + ) + + agents = backend.list_agents(AgentFilter(profile_id=profile.id)) + if not agents: + logger.error(f"No agent found for profile ID: {profile.id}") + raise HTTPException( + status_code=404, + detail=f"No agent found for profile ID: {profile.id}", + ) + + agent = agents[0] + agent_id = agent.id + + # get wallet id from agent + wallets = backend.list_wallets(WalletFilter(agent_id=agent_id)) + if not wallets: + logger.error(f"No wallet found for agent ID: {agent_id}") + raise HTTPException( + status_code=404, + detail=f"No wallet found for agent ID: {agent_id}", + ) + + wallet = wallets[0] # Get the first wallet for this agent + + logger.info( + f"Using wallet {wallet.id} for profile {profile.id} to veto DAO action proposal {payload.proposal_id}." + ) + + tool = VetoActionProposalTool(wallet_id=wallet.id) + result = await tool._arun( + dao_action_proposal_voting_contract=payload.dao_action_proposal_voting_contract, + proposal_id=payload.proposal_id, + ) + + logger.debug( + f"DAO veto action proposal result for wallet {wallet.id} (profile {profile.id}): {result}" + ) + return JSONResponse(content=result) + + except HTTPException as he: + raise he + except Exception as e: + logger.error( + f"Failed to veto DAO action proposal for profile {profile.id}", + exc_info=e, + ) + raise HTTPException( + status_code=500, + detail=f"Failed to veto DAO action proposal: {str(e)}", + ) From f173a1cd0cd4e4c5fdbe17c6ff4c6483d8269db3 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Mon, 2 Jun 2025 11:09:40 -0700 Subject: [PATCH 086/219] update --- api/tools.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/api/tools.py b/api/tools.py index 0c190e76..550f6cb5 100644 --- a/api/tools.py +++ b/api/tools.py @@ -81,7 +81,7 @@ class VetoActionProposalRequest(BaseModel): ..., description="Contract principal where the DAO creates action proposals for voting by DAO members.", ) - proposal_id: int = Field( + proposal_id: str = Field( ..., description="ID of the proposal to veto.", ) @@ -418,10 +418,19 @@ async def veto_dao_action_proposal( f"Using wallet {wallet.id} for profile {profile.id} to veto DAO action proposal {payload.proposal_id}." ) + # get proposal from id + proposal = backend.get_proposal(payload.proposal_id) + if not proposal: + logger.error(f"No proposal found for ID: {payload.proposal_id}") + raise HTTPException( + status_code=404, + detail=f"No proposal found for ID: {payload.proposal_id}", + ) + tool = VetoActionProposalTool(wallet_id=wallet.id) result = await tool._arun( dao_action_proposal_voting_contract=payload.dao_action_proposal_voting_contract, - proposal_id=payload.proposal_id, + proposal_id=proposal.proposal_id, ) logger.debug( From 623743af5e5661ceb905c6bee66123986e842979 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Mon, 2 Jun 2025 12:42:49 -0700 Subject: [PATCH 087/219] update --- .../chainhook/handlers/action_vote_handler.py | 21 ++++++++++++- .../chainhook/handlers/core_vote_handler.py | 21 ++++++++++++- .../chainhook/handlers/dao_vote_handler.py | 30 ++++++++++++------- 3 files changed, 60 insertions(+), 12 deletions(-) diff --git a/services/webhooks/chainhook/handlers/action_vote_handler.py b/services/webhooks/chainhook/handlers/action_vote_handler.py index 68a69c2a..f03aa36e 100644 --- a/services/webhooks/chainhook/handlers/action_vote_handler.py +++ b/services/webhooks/chainhook/handlers/action_vote_handler.py @@ -77,7 +77,7 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: "voter": payload.get("voter"), "caller": payload.get("contractCaller"), # Updated field name "tx_sender": payload.get("txSender"), # New field - "amount": str(payload.get("amount", 0)), + "amount": self._extract_amount(payload.get("amount")), "vote_value": payload.get( "vote" ), # Vote value is now directly in payload @@ -86,3 +86,22 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: self.logger.warning("Could not find vote information in transaction events") return None + + def _extract_amount(self, amount) -> str: + """Extract and convert the amount from Clarity format to a string. + + Args: + amount: The amount value which could be a string with 'u' prefix, integer, or None + + Returns: + str: The amount as a string, or "0" if None + """ + if amount is None: + return "0" + + amount_str = str(amount) + if amount_str.startswith("u"): + # Remove the 'u' prefix and return as string + return amount_str[1:] + else: + return amount_str diff --git a/services/webhooks/chainhook/handlers/core_vote_handler.py b/services/webhooks/chainhook/handlers/core_vote_handler.py index a42ceafa..a7099725 100644 --- a/services/webhooks/chainhook/handlers/core_vote_handler.py +++ b/services/webhooks/chainhook/handlers/core_vote_handler.py @@ -81,7 +81,7 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: "voter": payload.get("voter"), "caller": caller, "tx_sender": payload.get("txSender"), # New field - "amount": str(payload.get("amount", 0)), + "amount": self._extract_amount(payload.get("amount")), "vote_value": payload.get( "vote" ), # Vote value may be directly in payload now @@ -90,3 +90,22 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: self.logger.warning("Could not find vote information in transaction events") return None + + def _extract_amount(self, amount) -> str: + """Extract and convert the amount from Clarity format to a string. + + Args: + amount: The amount value which could be a string with 'u' prefix, integer, or None + + Returns: + str: The amount as a string, or "0" if None + """ + if amount is None: + return "0" + + amount_str = str(amount) + if amount_str.startswith("u"): + # Remove the 'u' prefix and return as string + return amount_str[1:] + else: + return amount_str diff --git a/services/webhooks/chainhook/handlers/dao_vote_handler.py b/services/webhooks/chainhook/handlers/dao_vote_handler.py index 5fc95db4..ae67000d 100644 --- a/services/webhooks/chainhook/handlers/dao_vote_handler.py +++ b/services/webhooks/chainhook/handlers/dao_vote_handler.py @@ -71,24 +71,28 @@ def _get_vote_info_from_events(self, events: List[Event]) -> Optional[Dict]: proposal_id = payload.get("proposal_id") # Get voter address - voter = None - if "voter" in payload: - voter = payload.get("voter") + voter = payload.get("voter") # Get vote value (true/false) - vote_value = None - if "vote" in payload: - vote_value = payload.get("vote") + vote_value = payload.get("vote") - # Get token amount + # Get token amount - ensure it's converted to string amount = None if "amount" in payload: amount = str(payload.get("amount")) elif "liquidTokens" in payload: amount = str(payload.get("liquidTokens")) - # Try to determine the vote value from the transaction args - # This is needed because some contracts don't include the vote value in the event + # Get contract caller + contract_caller = payload.get("contractCaller") + + # Get tx sender + tx_sender = payload.get("txSender") + + # Get voter user ID + voter_user_id = payload.get("voterUserId") + + # Try to determine the vote value from the transaction args if not found if vote_value is None: # Check if we can extract it from the method args args = event_data.get("args", []) @@ -97,10 +101,16 @@ def _get_vote_info_from_events(self, events: List[Event]) -> Optional[Dict]: if vote_str in ["true", "false"]: vote_value = vote_str == "true" + self.logger.info( + f"Extracted vote info: proposal_id={proposal_id}, voter={voter}, vote_value={vote_value}, amount={amount}, contract_caller={contract_caller}, tx_sender={tx_sender}, voter_user_id={voter_user_id}" + ) + return { "proposal_id": proposal_id, "voter": voter, - "caller": payload.get("caller"), + "contract_caller": contract_caller, + "tx_sender": tx_sender, + "voter_user_id": voter_user_id, "amount": amount, "vote_value": vote_value, } From 202e81149f300c4e546584fd398b6bb4a567bb7d Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Mon, 2 Jun 2025 14:55:33 -0700 Subject: [PATCH 088/219] update agent tools --- agent-tools-ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agent-tools-ts b/agent-tools-ts index d28e2edc..c46d58c0 160000 --- a/agent-tools-ts +++ b/agent-tools-ts @@ -1 +1 @@ -Subproject commit d28e2edcf4d6d951e88b4c6a44726f703f5b5633 +Subproject commit c46d58c09f3ae2278cb1a2977b371cf6d6880a65 From 41b113d8824421333e90ac4284365217598baceb Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Mon, 2 Jun 2025 18:24:49 -0700 Subject: [PATCH 089/219] update blockchain height for bitcoin --- backend/models.py | 1 + services/webhooks/chainhook/handlers/block_state_handler.py | 3 +++ 2 files changed, 4 insertions(+) diff --git a/backend/models.py b/backend/models.py index c0769a70..3df023af 100644 --- a/backend/models.py +++ b/backend/models.py @@ -29,6 +29,7 @@ class ChainStateBase(CustomBaseModel): block_height: Optional[int] = None block_hash: Optional[str] = None network: Optional[str] = "mainnet" # mainnet or testnet + bitcoin_block_height: Optional[int] = None class ChainStateCreate(ChainStateBase): diff --git a/services/webhooks/chainhook/handlers/block_state_handler.py b/services/webhooks/chainhook/handlers/block_state_handler.py index 9604c3f3..dd35c609 100644 --- a/services/webhooks/chainhook/handlers/block_state_handler.py +++ b/services/webhooks/chainhook/handlers/block_state_handler.py @@ -78,6 +78,7 @@ async def handle_block(self, block: Apply) -> None: # Extract block info block_height = block.block_identifier.index block_hash = block.block_identifier.hash + bitcoin_block_height = block.metadata.bitcoin_anchor_block_identifier.index self.logger.info( f"Processing block: height={block_height}, hash={block_hash}" ) @@ -102,6 +103,7 @@ async def handle_block(self, block: Apply) -> None: block_height=block_height, block_hash=block_hash, network=current_state.network, + bitcoin_block_height=bitcoin_block_height, ), ) if not updated: @@ -126,6 +128,7 @@ async def handle_block(self, block: Apply) -> None: block_height=block_height, block_hash=block_hash, network=config.network.network, + bitcoin_block_height=bitcoin_block_height, ) ) if not created: From 812afb4224e5f19ee4dd305ff6c598bceac26cbf Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Mon, 2 Jun 2025 18:55:39 -0700 Subject: [PATCH 090/219] update concluder --- .../chainhook/handlers/dao_proposal_burn_height_handler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py index 7cf54b6a..1e73d6f1 100644 --- a/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py +++ b/services/webhooks/chainhook/handlers/dao_proposal_burn_height_handler.py @@ -146,8 +146,8 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: p for p in proposals if p.vote_start is not None - and p.vote_end is not None - and p.vote_end == burn_height + and p.exec_start is not None + and p.exec_start == burn_height and p.content is not None # Ensure content exists ] From a6e3703eac258a3706e74dad4058604121e1f151 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 3 Jun 2025 08:52:56 -0700 Subject: [PATCH 091/219] update --- .../chainhook/handlers/action_vote_handler.py | 20 +++- .../chainhook/handlers/base_vote_handler.py | 17 +++ test_amount_extraction.py | 100 ++++++++++++++++++ 3 files changed, 136 insertions(+), 1 deletion(-) create mode 100644 test_amount_extraction.py diff --git a/services/webhooks/chainhook/handlers/action_vote_handler.py b/services/webhooks/chainhook/handlers/action_vote_handler.py index f03aa36e..d32dbcfb 100644 --- a/services/webhooks/chainhook/handlers/action_vote_handler.py +++ b/services/webhooks/chainhook/handlers/action_vote_handler.py @@ -70,6 +70,14 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: self.logger.warning("Empty payload in vote event") return None + self.logger.info( + f"[DEBUG] Found vote event with payload: {payload}" + ) + raw_amount = payload.get("amount") + self.logger.info( + f"[DEBUG] Raw amount from payload: {raw_amount} (type: {type(raw_amount)})" + ) + return { "proposal_identifier": payload.get( "proposalId" @@ -96,12 +104,22 @@ def _extract_amount(self, amount) -> str: Returns: str: The amount as a string, or "0" if None """ + self.logger.info( + f"[DEBUG] _extract_amount called with: {amount} (type: {type(amount)})" + ) + if amount is None: + self.logger.info("[DEBUG] Amount is None, returning '0'") return "0" amount_str = str(amount) + self.logger.info(f"[DEBUG] Amount converted to string: '{amount_str}'") + if amount_str.startswith("u"): # Remove the 'u' prefix and return as string - return amount_str[1:] + result = amount_str[1:] + self.logger.info(f"[DEBUG] Removed 'u' prefix, returning: '{result}'") + return result else: + self.logger.info(f"[DEBUG] No 'u' prefix, returning: '{amount_str}'") return amount_str diff --git a/services/webhooks/chainhook/handlers/base_vote_handler.py b/services/webhooks/chainhook/handlers/base_vote_handler.py index 745cfa7c..322f25a4 100644 --- a/services/webhooks/chainhook/handlers/base_vote_handler.py +++ b/services/webhooks/chainhook/handlers/base_vote_handler.py @@ -194,7 +194,13 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: update_data = VoteBase(tx_id=tx_id) if amount and not vote.amount: update_data.amount = amount + self.logger.info(f"[DEBUG] Setting amount in update_data: {amount}") + else: + self.logger.info( + f"[DEBUG] Not setting amount - amount: {amount}, existing vote.amount: {vote.amount}" + ) + self.logger.info(f"[DEBUG] Update data: {update_data.model_dump()}") backend.update_vote(vote.id, update_data) self.logger.info(f"Updated vote {vote.id}") else: @@ -217,8 +223,19 @@ async def handle_transaction(self, transaction: TransactionWithReceipt) -> None: amount=amount, ) + self.logger.info( + f"[DEBUG] Creating vote with data: {new_vote.model_dump()}" + ) + try: vote = backend.create_vote(new_vote) self.logger.info(f"Created new vote record with ID: {vote.id}") + self.logger.info(f"[DEBUG] Created vote details: {vote.model_dump()}") except Exception as e: self.logger.error(f"Failed to create vote record: {str(e)}") + self.logger.error( + f"[DEBUG] Vote data that failed: {new_vote.model_dump()}" + ) + import traceback + + self.logger.error(f"[DEBUG] Full traceback: {traceback.format_exc()}") diff --git a/test_amount_extraction.py b/test_amount_extraction.py new file mode 100644 index 00000000..5a444694 --- /dev/null +++ b/test_amount_extraction.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +"""Test script to verify amount extraction from webhook data.""" + +from services.webhooks.chainhook.handlers.action_vote_handler import ActionVoteHandler +from services.webhooks.chainhook.models import Event + +# Mock the test data from the webhook you provided +test_webhook_data = { + "data": { + "contract_identifier": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.fast-action-proposal-voting", + "topic": "print", + "value": { + "notification": "fast-action-proposal-voting/vote-on-action-proposal", + "payload": { + "amount": 4541549459041732, + "contractCaller": "ST1B9N1SJPRK9D3H98FWGT8AXEGH8T4BH5P38Z4ZC", + "proposalId": 44, + "txSender": "ST1B9N1SJPRK9D3H98FWGT8AXEGH8T4BH5P38Z4ZC", + "vote": True, + "voter": "ST1B9N1SJPRK9D3H98FWGT8AXEGH8T4BH5P38Z4ZC", + "voterUserId": 1, + }, + }, + } +} + + +def create_mock_event(data): + """Create a mock event object for testing.""" + + class MockEvent: + def __init__(self, data): + self.type = "SmartContractEvent" + self.data = data + + return MockEvent(data) + + +def test_amount_extraction(): + """Test the amount extraction from webhook data.""" + print("Testing amount extraction...") + + # Create handler + handler = ActionVoteHandler() + + # Create mock event + mock_event = create_mock_event(test_webhook_data["data"]) + events = [mock_event] + + # Test _get_vote_info_from_events + print("\n1. Testing _get_vote_info_from_events:") + vote_info = handler._get_vote_info_from_events(events) + + if vote_info: + print(f"✓ Vote info extracted successfully: {vote_info}") + print(f"✓ Amount from vote_info: {vote_info.get('amount')}") + print(f"✓ Proposal ID: {vote_info.get('proposal_identifier')}") + print(f"✓ Voter: {vote_info.get('voter')}") + print(f"✓ Vote value: {vote_info.get('vote_value')}") + else: + print("✗ Failed to extract vote info") + return False + + # Test _extract_amount directly + print("\n2. Testing _extract_amount directly:") + raw_amount = test_webhook_data["data"]["value"]["payload"]["amount"] + print(f"Raw amount: {raw_amount} (type: {type(raw_amount)})") + + extracted_amount = handler._extract_amount(raw_amount) + print(f"Extracted amount: {extracted_amount} (type: {type(extracted_amount)})") + + # Test with different amount formats + print("\n3. Testing different amount formats:") + test_amounts = [ + 4541549459041732, # int + "4541549459041732", # string + "u4541549459041732", # Clarity uint format + None, # None + 0, # zero + "0", # zero string + ] + + for test_amount in test_amounts: + result = handler._extract_amount(test_amount) + print(f"Input: {test_amount} ({type(test_amount)}) -> Output: {result}") + + print("\n4. Verification:") + expected_amount = "4541549459041732" + if vote_info and vote_info.get("amount") == expected_amount: + print(f"✓ Amount extraction is working correctly: {expected_amount}") + return True + else: + print( + f"✗ Amount extraction failed. Expected: {expected_amount}, Got: {vote_info.get('amount') if vote_info else 'None'}" + ) + return False + + +if __name__ == "__main__": + test_amount_extraction() From 1de31ebc88712d73f12cca2f9f34032ac5739faf Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 3 Jun 2025 08:54:09 -0700 Subject: [PATCH 092/219] update --- .../chainhook/handlers/action_vote_handler.py | 16 --- test_amount_extraction.py | 100 ----------------- test_new_dao_webhook.py | 101 ------------------ 3 files changed, 217 deletions(-) delete mode 100644 test_amount_extraction.py delete mode 100644 test_new_dao_webhook.py diff --git a/services/webhooks/chainhook/handlers/action_vote_handler.py b/services/webhooks/chainhook/handlers/action_vote_handler.py index d32dbcfb..2cb03f3b 100644 --- a/services/webhooks/chainhook/handlers/action_vote_handler.py +++ b/services/webhooks/chainhook/handlers/action_vote_handler.py @@ -70,14 +70,6 @@ def _get_vote_info_from_events(self, events: list[Event]) -> Optional[Dict]: self.logger.warning("Empty payload in vote event") return None - self.logger.info( - f"[DEBUG] Found vote event with payload: {payload}" - ) - raw_amount = payload.get("amount") - self.logger.info( - f"[DEBUG] Raw amount from payload: {raw_amount} (type: {type(raw_amount)})" - ) - return { "proposal_identifier": payload.get( "proposalId" @@ -104,22 +96,14 @@ def _extract_amount(self, amount) -> str: Returns: str: The amount as a string, or "0" if None """ - self.logger.info( - f"[DEBUG] _extract_amount called with: {amount} (type: {type(amount)})" - ) - if amount is None: - self.logger.info("[DEBUG] Amount is None, returning '0'") return "0" amount_str = str(amount) - self.logger.info(f"[DEBUG] Amount converted to string: '{amount_str}'") if amount_str.startswith("u"): # Remove the 'u' prefix and return as string result = amount_str[1:] - self.logger.info(f"[DEBUG] Removed 'u' prefix, returning: '{result}'") return result else: - self.logger.info(f"[DEBUG] No 'u' prefix, returning: '{amount_str}'") return amount_str diff --git a/test_amount_extraction.py b/test_amount_extraction.py deleted file mode 100644 index 5a444694..00000000 --- a/test_amount_extraction.py +++ /dev/null @@ -1,100 +0,0 @@ -#!/usr/bin/env python3 -"""Test script to verify amount extraction from webhook data.""" - -from services.webhooks.chainhook.handlers.action_vote_handler import ActionVoteHandler -from services.webhooks.chainhook.models import Event - -# Mock the test data from the webhook you provided -test_webhook_data = { - "data": { - "contract_identifier": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.fast-action-proposal-voting", - "topic": "print", - "value": { - "notification": "fast-action-proposal-voting/vote-on-action-proposal", - "payload": { - "amount": 4541549459041732, - "contractCaller": "ST1B9N1SJPRK9D3H98FWGT8AXEGH8T4BH5P38Z4ZC", - "proposalId": 44, - "txSender": "ST1B9N1SJPRK9D3H98FWGT8AXEGH8T4BH5P38Z4ZC", - "vote": True, - "voter": "ST1B9N1SJPRK9D3H98FWGT8AXEGH8T4BH5P38Z4ZC", - "voterUserId": 1, - }, - }, - } -} - - -def create_mock_event(data): - """Create a mock event object for testing.""" - - class MockEvent: - def __init__(self, data): - self.type = "SmartContractEvent" - self.data = data - - return MockEvent(data) - - -def test_amount_extraction(): - """Test the amount extraction from webhook data.""" - print("Testing amount extraction...") - - # Create handler - handler = ActionVoteHandler() - - # Create mock event - mock_event = create_mock_event(test_webhook_data["data"]) - events = [mock_event] - - # Test _get_vote_info_from_events - print("\n1. Testing _get_vote_info_from_events:") - vote_info = handler._get_vote_info_from_events(events) - - if vote_info: - print(f"✓ Vote info extracted successfully: {vote_info}") - print(f"✓ Amount from vote_info: {vote_info.get('amount')}") - print(f"✓ Proposal ID: {vote_info.get('proposal_identifier')}") - print(f"✓ Voter: {vote_info.get('voter')}") - print(f"✓ Vote value: {vote_info.get('vote_value')}") - else: - print("✗ Failed to extract vote info") - return False - - # Test _extract_amount directly - print("\n2. Testing _extract_amount directly:") - raw_amount = test_webhook_data["data"]["value"]["payload"]["amount"] - print(f"Raw amount: {raw_amount} (type: {type(raw_amount)})") - - extracted_amount = handler._extract_amount(raw_amount) - print(f"Extracted amount: {extracted_amount} (type: {type(extracted_amount)})") - - # Test with different amount formats - print("\n3. Testing different amount formats:") - test_amounts = [ - 4541549459041732, # int - "4541549459041732", # string - "u4541549459041732", # Clarity uint format - None, # None - 0, # zero - "0", # zero string - ] - - for test_amount in test_amounts: - result = handler._extract_amount(test_amount) - print(f"Input: {test_amount} ({type(test_amount)}) -> Output: {result}") - - print("\n4. Verification:") - expected_amount = "4541549459041732" - if vote_info and vote_info.get("amount") == expected_amount: - print(f"✓ Amount extraction is working correctly: {expected_amount}") - return True - else: - print( - f"✗ Amount extraction failed. Expected: {expected_amount}, Got: {vote_info.get('amount') if vote_info else 'None'}" - ) - return False - - -if __name__ == "__main__": - test_amount_extraction() diff --git a/test_new_dao_webhook.py b/test_new_dao_webhook.py deleted file mode 100644 index 745c20fa..00000000 --- a/test_new_dao_webhook.py +++ /dev/null @@ -1,101 +0,0 @@ -#!/usr/bin/env python3 -"""Test script to validate new DAO webhook payload parsing.""" - -import json - -from services.webhooks.dao.models import DAOWebhookPayload - -# Sample payload from the user's new structure -sample_payload = { - "name": "XFACE•AIBTC•DAO", - "mission": "## Mission\\n\\nTo make Bitcoin Faces the most popular meme amongst Bitcoiners, on X, on-chain, and throughout Bitcoin culture.\\n\\n## Core Pillars\\n\\n1. Face as Identity: Every Bitcoin Face is a unique, deterministic, generative avatar tied to a name or address - an expression of sovereignty and style. The DAO preserves and evolves this standard as the meme layer of Bitcoin.\\n2. Meme Engine of the Network: The DAO funds and coordinates the viral spread of Bitcoin Faces - on X, on-chain, and beyond. Proposals reward memes, automate content, and shape culture.\\n3. Permissionless Personality: Anyone can generate a face. Anyone can remix it. But the DAO decides which AI styles, transformations, and add-ons become official. Governance as curatorship.\\n4. On-Chain Licensing and Monetization: Through the payments and invoicing system, enables creators to build tools, apps, and embeds that use Bitcoin Faces - with revenue shared between builders and the DAO treasury.\\n5. Autonomous Avatars, Autonomous Treasury: will gradually become a fully agent-driven culture DAO. Until then, the treasury is protected by time, quorum, and AI maturity. The meme spreads first, the money flows later.", - "contracts": [ - { - "name": "aibtc-faktory", - "display_name": "xface-faktory", - "type": "TOKEN", - "subtype": "DAO", - "tx_id": "6bb26cf198ad3f093a3e61b495f3acdb248c0230c2dc8edc2e1655a93ced72c5", - "deployer": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K", - "contract_principal": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.aibtc-faktory", - }, - { - "name": "aibtc-base-dao", - "display_name": "xface-base-dao", - "type": "BASE", - "subtype": "DAO", - "tx_id": "c1d9fd38f94f8fcd204f65b598ccb486a5f68b88f58de049d59df12eb11bd5bb", - "deployer": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K", - "contract_principal": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.aibtc-base-dao", - }, - { - "name": "aibtc-treasury", - "display_name": "xface-treasury", - "type": "EXTENSIONS", - "subtype": "TREASURY", - "tx_id": "159cc3c930f84e4e3026af900acb7d1cac1ba505ece3c24c077b912a0a8ad666", - "deployer": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K", - "contract_principal": "ST3DD7MASYJADCFXN3745R11RVM4PCXCPVRS3V27K.aibtc-treasury", - }, - ], - "token_info": { - "symbol": "XFACE•AIBTC•DAO", - "decimals": 8, - "max_supply": "1000000000", - "uri": "https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/tokens//251.json", - "image_url": "https://mkkhfmcrbwyuutcvtier.supabase.co/storage/v1/object/public/tokens//251.png", - "x_url": "https://x.com/1894855072556912681", - }, -} - - -def test_payload_parsing(): - """Test that the new payload structure can be parsed correctly.""" - try: - # Parse the payload - parsed_payload = DAOWebhookPayload(**sample_payload) - - print("✅ Payload parsed successfully!") - print(f"DAO Name: {parsed_payload.name}") - print(f"Number of contracts: {len(parsed_payload.contracts)}") - print(f"Token symbol: {parsed_payload.token_info.symbol}") - - # Check for DAO token contract - dao_token = None - for contract in parsed_payload.contracts: - if contract.type.value == "TOKEN" and contract.subtype == "DAO": - dao_token = contract - break - - if dao_token: - print(f"✅ Found DAO token contract: {dao_token.name}") - print(f" Contract Principal: {dao_token.contract_principal}") - print(f" TX ID: {dao_token.tx_id}") - else: - print("❌ No DAO token contract found") - - # Count extension contracts - extension_contracts = [ - c - for c in parsed_payload.contracts - if c.type.value in ["EXTENSIONS", "ACTIONS", "PROPOSALS", "BASE"] - ] - print(f"✅ Found {len(extension_contracts)} extension contracts") - - for ext in extension_contracts: - print(f" - {ext.type.value}: {ext.subtype} ({ext.name})") - - return True - - except Exception as e: - print(f"❌ Error parsing payload: {str(e)}") - return False - - -if __name__ == "__main__": - print("Testing new DAO webhook payload structure...") - success = test_payload_parsing() - if success: - print("\n🎉 All tests passed! The new structure is working correctly.") - else: - print("\n💥 Tests failed. Check the error messages above.") From 32acc1db04fdf6247b27d944d075d8bf5910c561 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 3 Jun 2025 13:08:26 -0700 Subject: [PATCH 093/219] make voting more efficient --- backend/abstract.py | 14 ++ backend/models.py | 47 +++++ backend/supabase.py | 145 ++++++++++++++ services/runner/tasks/dao_proposal_voter.py | 210 ++++++++++++++------ 4 files changed, 357 insertions(+), 59 deletions(-) diff --git a/backend/abstract.py b/backend/abstract.py index 056c2611..feb4d25c 100644 --- a/backend/abstract.py +++ b/backend/abstract.py @@ -43,6 +43,7 @@ ProposalBase, ProposalCreate, ProposalFilter, + ProposalFilterN, QueueMessage, QueueMessageBase, QueueMessageCreate, @@ -77,6 +78,7 @@ WalletBase, WalletCreate, WalletFilter, + WalletFilterN, XCreds, XCredsBase, XCredsCreate, @@ -288,6 +290,11 @@ def get_wallet(self, wallet_id: UUID) -> Optional[Wallet]: def list_wallets(self, filters: Optional[WalletFilter] = None) -> List[Wallet]: pass + @abstractmethod + def list_wallets_n(self, filters: Optional[WalletFilterN] = None) -> List[Wallet]: + """Enhanced wallets listing with support for batch operations and advanced filtering.""" + pass + @abstractmethod def update_wallet( self, wallet_id: UUID, update_data: WalletBase @@ -468,6 +475,13 @@ def list_proposals( ) -> List[Proposal]: pass + @abstractmethod + def list_proposals_n( + self, filters: Optional[ProposalFilterN] = None + ) -> List[Proposal]: + """Enhanced proposals listing with support for batch operations and advanced filtering.""" + pass + @abstractmethod def update_proposal( self, proposal_id: UUID, update_data: ProposalBase diff --git a/backend/models.py b/backend/models.py index 3df023af..271f621d 100644 --- a/backend/models.py +++ b/backend/models.py @@ -534,6 +534,23 @@ class WalletFilter(CustomBaseModel): testnet_address: Optional[str] = None +class WalletFilterN(CustomBaseModel): + """Enhanced wallet filter with support for batch operations using 'in_' queries.""" + + # Standard equality filters (same as WalletFilter) + agent_id: Optional[UUID] = None + profile_id: Optional[UUID] = None + mainnet_address: Optional[str] = None + testnet_address: Optional[str] = None + + # Batch filters using 'in_' operations + ids: Optional[List[UUID]] = None + agent_ids: Optional[List[UUID]] = None + profile_ids: Optional[List[UUID]] = None + mainnet_addresses: Optional[List[str]] = None + testnet_addresses: Optional[List[str]] = None + + class QueueMessageFilter(CustomBaseModel): type: Optional[QueueMessageType] = None is_processed: Optional[bool] = None @@ -593,6 +610,36 @@ class ProposalFilter(CustomBaseModel): type: Optional[ProposalType] = None +class ProposalFilterN(CustomBaseModel): + """Enhanced proposal filter with support for batch operations using 'in_' queries.""" + + # Standard equality filters (same as ProposalFilter) + dao_id: Optional[UUID] = None + status: Optional[ContractStatus] = None + contract_principal: Optional[str] = None + proposal_id: Optional[int] = None + executed: Optional[bool] = None + passed: Optional[bool] = None + met_quorum: Optional[bool] = None + met_threshold: Optional[bool] = None + type: Optional[ProposalType] = None + + # Batch filters using 'in_' operations + dao_ids: Optional[List[UUID]] = None + proposal_ids: Optional[List[int]] = None + statuses: Optional[List[ContractStatus]] = None + contract_principals: Optional[List[str]] = None + types: Optional[List[ProposalType]] = None + + # Range filters for numeric fields + proposal_id_gte: Optional[int] = None # greater than or equal + proposal_id_lte: Optional[int] = None # less than or equal + + # Text search (if supported by backend) + title_contains: Optional[str] = None + content_contains: Optional[str] = None + + class StepFilter(CustomBaseModel): job_id: Optional[UUID] = None role: Optional[str] = None diff --git a/backend/supabase.py b/backend/supabase.py index 9033adbd..bd05c8a2 100644 --- a/backend/supabase.py +++ b/backend/supabase.py @@ -53,6 +53,7 @@ ProposalBase, ProposalCreate, ProposalFilter, + ProposalFilterN, QueueMessage, QueueMessageBase, QueueMessageCreate, @@ -88,6 +89,7 @@ WalletBase, WalletCreate, WalletFilter, + WalletFilterN, XCreds, XCredsBase, XCredsCreate, @@ -766,6 +768,65 @@ def list_wallets(self, filters: Optional["WalletFilter"] = None) -> List["Wallet data = response.data or [] return [Wallet(**row) for row in data] + def list_wallets_n( + self, filters: Optional["WalletFilterN"] = None + ) -> List["Wallet"]: + """Enhanced wallets listing with support for batch operations and advanced filtering.""" + query = self.client.table("wallets").select("*") + + if filters: + # Standard equality filters + if filters.agent_id is not None: + query = query.eq("agent_id", str(filters.agent_id)) + if filters.profile_id is not None: + query = query.eq("profile_id", str(filters.profile_id)) + if filters.mainnet_address is not None: + query = query.eq("mainnet_address", filters.mainnet_address) + if filters.testnet_address is not None: + query = query.eq("testnet_address", filters.testnet_address) + + # Batch filters using 'in_' operations + if filters.ids is not None and len(filters.ids) > 0: + id_strings = [str(wallet_id) for wallet_id in filters.ids] + query = query.in_("id", id_strings) + if filters.agent_ids is not None and len(filters.agent_ids) > 0: + agent_id_strings = [str(agent_id) for agent_id in filters.agent_ids] + query = query.in_("agent_id", agent_id_strings) + if filters.profile_ids is not None and len(filters.profile_ids) > 0: + profile_id_strings = [ + str(profile_id) for profile_id in filters.profile_ids + ] + query = query.in_("profile_id", profile_id_strings) + if ( + filters.mainnet_addresses is not None + and len(filters.mainnet_addresses) > 0 + ): + query = query.in_("mainnet_address", filters.mainnet_addresses) + if ( + filters.testnet_addresses is not None + and len(filters.testnet_addresses) > 0 + ): + query = query.in_("testnet_address", filters.testnet_addresses) + + try: + response = query.execute() + data = response.data or [] + return [Wallet(**row) for row in data] + except Exception as e: + logger.error(f"Error in list_wallets_n: {str(e)}") + # Fallback to original list_wallets if enhanced filtering fails + if filters: + # Convert enhanced filter to basic filter for fallback + basic_filter = WalletFilter( + agent_id=filters.agent_id, + profile_id=filters.profile_id, + mainnet_address=filters.mainnet_address, + testnet_address=filters.testnet_address, + ) + return self.list_wallets(basic_filter) + else: + return self.list_wallets() + def update_wallet( self, wallet_id: UUID, update_data: "WalletBase" ) -> Optional["Wallet"]: @@ -1933,3 +1994,87 @@ def delete_holder(self, holder_id: UUID) -> bool: ) deleted = response.data or [] return len(deleted) > 0 + + # ---------------------------------------------------------------- + # 18. PROPOSALS_N + # ---------------------------------------------------------------- + def list_proposals_n( + self, filters: Optional["ProposalFilterN"] = None + ) -> List["Proposal"]: + """Enhanced proposals listing with support for batch operations and advanced filtering.""" + query = self.client.table("proposals").select("*") + + if filters: + # Standard equality filters + if filters.dao_id is not None: + query = query.eq("dao_id", str(filters.dao_id)) + if filters.status is not None: + query = query.eq("status", str(filters.status)) + if filters.contract_principal is not None: + query = query.eq("contract_principal", filters.contract_principal) + if filters.proposal_id is not None: + query = query.eq("proposal_id", filters.proposal_id) + if filters.executed is not None: + query = query.eq("executed", filters.executed) + if filters.passed is not None: + query = query.eq("passed", filters.passed) + if filters.met_quorum is not None: + query = query.eq("met_quorum", filters.met_quorum) + if filters.met_threshold is not None: + query = query.eq("met_threshold", filters.met_threshold) + if filters.type is not None: + query = query.eq("type", filters.type) + + # Batch filters using 'in_' operations + if filters.dao_ids is not None and len(filters.dao_ids) > 0: + dao_id_strings = [str(dao_id) for dao_id in filters.dao_ids] + query = query.in_("dao_id", dao_id_strings) + if filters.proposal_ids is not None and len(filters.proposal_ids) > 0: + query = query.in_("proposal_id", filters.proposal_ids) + if filters.statuses is not None and len(filters.statuses) > 0: + status_strings = [str(status) for status in filters.statuses] + query = query.in_("status", status_strings) + if ( + filters.contract_principals is not None + and len(filters.contract_principals) > 0 + ): + query = query.in_("contract_principal", filters.contract_principals) + if filters.types is not None and len(filters.types) > 0: + type_strings = [str(ptype) for ptype in filters.types] + query = query.in_("type", type_strings) + + # Range filters for numeric fields + if filters.proposal_id_gte is not None: + query = query.gte("proposal_id", filters.proposal_id_gte) + if filters.proposal_id_lte is not None: + query = query.lte("proposal_id", filters.proposal_id_lte) + + # Text search filters (using ilike for case-insensitive partial matching) + if filters.title_contains is not None: + query = query.ilike("title", f"%{filters.title_contains}%") + if filters.content_contains is not None: + query = query.ilike("content", f"%{filters.content_contains}%") + + try: + response = query.execute() + data = response.data or [] + return [Proposal(**row) for row in data] + except Exception as e: + logger.error(f"Error in list_proposals_n: {str(e)}") + # Fallback to original list_proposals if enhanced filtering fails + if filters: + # Convert enhanced filter to basic filter for fallback + basic_filter = ProposalFilter( + dao_id=filters.dao_id, + status=filters.status, + contract_principal=filters.contract_principal, + proposal_id=filters.proposal_id, + executed=filters.executed, + passed=filters.passed, + met_quorum=filters.met_quorum, + met_threshold=filters.met_threshold, + type=filters.type, + ) + return self.list_proposals(basic_filter) + else: + return self.list_proposals() diff --git a/services/runner/tasks/dao_proposal_voter.py b/services/runner/tasks/dao_proposal_voter.py index cadb378e..cd084bd4 100644 --- a/services/runner/tasks/dao_proposal_voter.py +++ b/services/runner/tasks/dao_proposal_voter.py @@ -2,16 +2,18 @@ import json from dataclasses import dataclass -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional, Set from backend.factory import backend from backend.models import ( + ProposalFilterN, QueueMessage, QueueMessageBase, QueueMessageFilter, QueueMessageType, VoteBase, VoteFilter, + WalletFilterN, ) from config import config from lib.logger import configure_logger @@ -33,59 +35,132 @@ def __post_init__(self): self.errors = self.errors or [] +@dataclass +class VotingContext: + """Cached context for voting operations to avoid redundant queries.""" + + pending_messages: List[QueueMessage] + proposal_ids: Set[str] + proposals_by_id: Dict[str, Any] + wallets_by_id: Dict[str, Any] + unvoted_votes_by_proposal: Dict[str, List[Any]] + + class DAOProposalVoterTask(BaseTask[DAOProposalVoteResult]): """Task runner for processing and voting on DAO proposals.""" QUEUE_TYPE = QueueMessageType.DAO_PROPOSAL_VOTE - async def _validate_task_specific(self, context: JobContext) -> bool: - """Validate task-specific conditions.""" + async def _build_voting_context(self) -> Optional[VotingContext]: + """Build a comprehensive context with all necessary data to minimize database calls.""" try: - # Get pending messages from the queue + # 1. Get all pending messages pending_messages = await self.get_pending_messages() - message_count = len(pending_messages) - logger.debug(f"Found {message_count} pending proposal voting messages") - - if message_count == 0: + if not pending_messages: logger.info("No pending proposal voting messages found") - return False + return None + + # 2. Extract all proposal IDs from messages + proposal_ids = set() + wallet_ids = set() - # Validate that at least one message has a valid proposal ID for message in pending_messages: message_data = message.message or {} proposal_id = message_data.get("proposal_id") + if proposal_id: + proposal_ids.add(proposal_id) + if message.wallet_id: + wallet_ids.add(message.wallet_id) + + if not proposal_ids: + logger.warning("No valid proposal IDs found in pending messages") + return None + + # 3. Batch fetch all proposals using enhanced list_proposals_n method + proposals_by_id = {} + if proposal_ids: + # Convert proposal_ids to integers and use enhanced batch fetch + int_proposal_ids = [] + for proposal_id in proposal_ids: + if proposal_id.isdigit(): + int_proposal_ids.append(int(proposal_id)) + + if int_proposal_ids: + enhanced_filter = ProposalFilterN(proposal_ids=int_proposal_ids) + proposals = backend.list_proposals_n(enhanced_filter) + proposals_by_id = { + str(p.proposal_id): p + for p in proposals + if p.proposal_id is not None + } - if not proposal_id: - logger.warning(f"Message {message.id} missing proposal_id") - continue + # 4. Batch fetch all wallets using enhanced list_wallets_n method + wallets_by_id = {} + if wallet_ids: + enhanced_wallet_filter = WalletFilterN(ids=list(wallet_ids)) + wallets = backend.list_wallets_n(enhanced_wallet_filter) + wallets_by_id = {str(w.id): w for w in wallets} + + # 5. Batch fetch all unvoted votes for all proposals + unvoted_votes_by_proposal = {} + if proposal_ids: + # Get all unvoted votes for all proposals at once + all_unvoted_votes = backend.list_votes(VoteFilter(voted=False)) + + # Group by proposal_id + for vote in all_unvoted_votes: + if vote.proposal_id: + proposal_key = str(vote.proposal_id) + if proposal_key in proposal_ids: + if proposal_key not in unvoted_votes_by_proposal: + unvoted_votes_by_proposal[proposal_key] = [] + unvoted_votes_by_proposal[proposal_key].append(vote) + + return VotingContext( + pending_messages=pending_messages, + proposal_ids=proposal_ids, + proposals_by_id=proposals_by_id, + wallets_by_id=wallets_by_id, + unvoted_votes_by_proposal=unvoted_votes_by_proposal, + ) - # Check if the proposal exists in the database - proposal = backend.get_proposal(proposal_id) - if proposal: - # Check if there are any unvoted votes for this proposal - unvoted_votes = backend.list_votes( - VoteFilter( - proposal_id=proposal_id, - voted=False, - ) + except Exception as e: + logger.error(f"Error building voting context: {str(e)}", exc_info=True) + return None + + async def _validate_task_specific(self, context: JobContext) -> bool: + """Validate task-specific conditions using optimized batch queries.""" + try: + voting_context = await self._build_voting_context() + if not voting_context: + return False + + # Check if we have valid proposals with unvoted votes + valid_proposals_found = False + for proposal_id in voting_context.proposal_ids: + if ( + proposal_id in voting_context.proposals_by_id + and proposal_id in voting_context.unvoted_votes_by_proposal + and voting_context.unvoted_votes_by_proposal[proposal_id] + ): + + unvoted_count = len( + voting_context.unvoted_votes_by_proposal[proposal_id] + ) + logger.info( + f"Found valid proposal {proposal_id} with {unvoted_count} unvoted votes to process" ) + valid_proposals_found = True + break - if unvoted_votes: - logger.info( - f"Found valid proposal {proposal_id} with {len(unvoted_votes)} unvoted votes to process" - ) - return True - else: - logger.warning( - f"No unvoted votes found for proposal {proposal_id}" - ) - else: - logger.warning(f"Proposal {proposal_id} not found in database") + if not valid_proposals_found: + logger.warning( + "No valid proposals with unvoted votes found in pending messages" + ) - logger.warning( - "No valid proposals with unvoted votes found in pending messages" - ) - return False + # Cache the context for later use in execution + self._voting_context = voting_context + return valid_proposals_found except Exception as e: logger.error( @@ -93,12 +168,13 @@ async def _validate_task_specific(self, context: JobContext) -> bool: ) return False - async def process_message(self, message: QueueMessage) -> Dict[str, Any]: - """Process a single DAO proposal voting message.""" + async def process_message( + self, message: QueueMessage, voting_context: VotingContext + ) -> Dict[str, Any]: + """Process a single DAO proposal voting message using cached context.""" message_id = message.id message_data = message.message or {} wallet_id = message.wallet_id - dao_id = message.dao_id logger.debug( f"Processing proposal voting message {message_id} for wallet {wallet_id}" @@ -113,27 +189,35 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: return {"success": False, "error": error_msg} try: - # Get the proposal details from the database - proposal = backend.get_proposal(proposal_id) + # Use cached proposal data + proposal = voting_context.proposals_by_id.get(proposal_id) if not proposal: error_msg = f"Proposal {proposal_id} not found in database" logger.error(error_msg) return {"success": False, "error": error_msg} - # Get unvoted votes for this proposal and wallet - unvoted_votes = backend.list_votes( - VoteFilter( - proposal_id=proposal_id, - wallet_id=wallet_id, - voted=False, - ) + # Get unvoted votes from cached data + all_unvoted_votes = voting_context.unvoted_votes_by_proposal.get( + proposal_id, [] ) + # Filter for this specific wallet + unvoted_votes = [ + vote for vote in all_unvoted_votes if vote.wallet_id == wallet_id + ] + if not unvoted_votes: error_msg = f"No unvoted votes found for proposal {proposal_id} and wallet {wallet_id}" logger.error(error_msg) return {"success": False, "error": error_msg} + # Use cached wallet data + wallet = voting_context.wallets_by_id.get(str(wallet_id)) + if not wallet: + error_msg = f"Wallet {wallet_id} not found" + logger.error(error_msg) + return {"success": False, "error": error_msg} + # Initialize the voting tool voting_tool = VoteOnActionProposalTool(wallet_id=wallet_id) @@ -189,9 +273,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: ) continue - # Log the txid for debugging - ## Get the correct address based on network configuration - wallet = backend.get_wallet(wallet_id) + # Get the correct address based on network configuration address = ( wallet.mainnet_address if config.network.network == "mainnet" @@ -207,6 +289,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: logger.debug( f"Attempting to update vote {vote.id} with data: {vote_data.model_dump()}" ) + try: # Log the current vote state before update current_vote = backend.get_vote(vote.id) @@ -237,6 +320,7 @@ async def process_message(self, message: QueueMessage) -> Dict[str, Any]: } ) continue + results.append( { "success": True, @@ -276,12 +360,13 @@ async def get_pending_messages(self) -> List[QueueMessage]: return backend.list_queue_messages(filters=filters) async def _execute_impl(self, context: JobContext) -> List[DAOProposalVoteResult]: - """Run the DAO proposal voter task.""" - pending_messages = await self.get_pending_messages() - message_count = len(pending_messages) - logger.debug(f"Found {message_count} pending proposal voting messages") + """Run the DAO proposal voter task using cached context.""" + # Use cached context from validation if available + voting_context = getattr(self, "_voting_context", None) + if not voting_context: + voting_context = await self._build_voting_context() - if not pending_messages: + if not voting_context or not voting_context.pending_messages: return [ DAOProposalVoteResult( success=True, @@ -291,13 +376,16 @@ async def _execute_impl(self, context: JobContext) -> List[DAOProposalVoteResult ) ] + message_count = len(voting_context.pending_messages) + logger.debug(f"Found {message_count} pending proposal voting messages") + # Process each message processed_count = 0 voted_count = 0 errors = [] - for message in pending_messages: - result = await self.process_message(message) + for message in voting_context.pending_messages: + result = await self.process_message(message, voting_context) processed_count += 1 if result.get("success"): @@ -317,6 +405,10 @@ async def _execute_impl(self, context: JobContext) -> List[DAOProposalVoteResult f"Voted: {voted_count}, Errors: {len(errors)}" ) + # Clear cached context + if hasattr(self, "_voting_context"): + delattr(self, "_voting_context") + return [ DAOProposalVoteResult( success=True, From 1a6e7ccaa1a4439634f174dfc7b1f7606e0a9720 Mon Sep 17 00:00:00 2001 From: Human <162091348+human058382928@users.noreply.github.com> Date: Tue, 3 Jun 2025 14:03:58 -0700 Subject: [PATCH 094/219] update --- .github/dependabot.yml | 4 +- api/tools.py | 109 ++ docs/proposal_recommendation_agent.md | 172 +++ .../proposal_recommendation_component.tsx | 418 ++++++ examples/proposal_recommendation_example.py | 97 ++ examples/proposal_recommendation_types.ts | 402 ++++++ pyproject.toml | 2 +- requirements.txt | 31 - services/workflows/__init__.py | 4 + services/workflows/agents/__init__.py | 17 + .../workflows/agents/financial_context.py | 3 +- .../workflows/agents/historical_context.py | 1 - .../agents/proposal_recommendation.py | 265 ++++ services/workflows/agents/reasoning.py | 11 +- services/workflows/agents/social_context.py | 3 +- services/workflows/hierarchical_workflows.py | 8 +- services/workflows/utils/models.py | 19 +- uv.lock | 1231 +++++++++-------- 18 files changed, 2127 insertions(+), 670 deletions(-) create mode 100644 docs/proposal_recommendation_agent.md create mode 100644 examples/proposal_recommendation_component.tsx create mode 100644 examples/proposal_recommendation_example.py create mode 100644 examples/proposal_recommendation_types.ts delete mode 100644 requirements.txt create mode 100644 services/workflows/agents/proposal_recommendation.py diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 8855a548..9af80699 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -16,4 +16,6 @@ updates: - "*" ignore: - dependency-name: "pgvector" - versions: [">=0.3.1"] \ No newline at end of file + versions: [">=0.3.1"] + - dependency-name: "starlette" + versions: [">=0.46.0" , "<=0.47.0"] \ No newline at end of file diff --git a/api/tools.py b/api/tools.py index 550f6cb5..04d3ddd6 100644 --- a/api/tools.py +++ b/api/tools.py @@ -16,6 +16,11 @@ ) from lib.logger import configure_logger from lib.tools import Tool, get_available_tools + +# Import the proposal recommendation agent +from services.workflows.agents.proposal_recommendation import ( + ProposalRecommendationAgent, +) from tools.dao_ext_action_proposals import ( ProposeActionSendMessageTool, # Added ProposeActionSendMessageTool VetoActionProposalTool, # Added VetoActionProposalTool @@ -87,6 +92,33 @@ class VetoActionProposalRequest(BaseModel): ) +class ProposalRecommendationRequest(BaseModel): + """Request body for getting a proposal recommendation.""" + + dao_id: UUID = Field( + ..., + description="The ID of the DAO to generate a proposal recommendation for.", + ) + focus_area: Optional[str] = Field( + default="general improvement", + description="Specific area of focus for the recommendation (e.g., 'community growth', 'technical development', 'partnerships')", + ) + specific_needs: Optional[str] = Field( + default="", + description="Any specific needs or requirements to consider in the recommendation", + ) + model_name: Optional[str] = Field( + default="gpt-4.1", + description="LLM model to use for generation (e.g., 'gpt-4.1', 'gpt-4o', 'gpt-3.5-turbo')", + ) + temperature: Optional[float] = Field( + default=0.1, + description="Temperature for LLM generation (0.0-2.0). Lower = more focused, Higher = more creative", + ge=0.0, + le=2.0, + ) + + @router.get("/available", response_model=List[Tool]) async def get_tools( request: Request, @@ -449,3 +481,80 @@ async def veto_dao_action_proposal( status_code=500, detail=f"Failed to veto DAO action proposal: {str(e)}", ) + + +@router.post("/dao/proposal_recommendations/generate") +async def generate_proposal_recommendation( + request: Request, + payload: ProposalRecommendationRequest, + profile: Profile = Depends(verify_profile_from_token), +) -> JSONResponse: + """Generate a proposal recommendation for a DAO. + + This endpoint allows an authenticated user to get AI-generated proposal + recommendations based on the DAO's mission, description, and previous proposals. + + Args: + request: The FastAPI request object. + payload: The request body containing dao_id and optional parameters. + profile: The authenticated user's profile. + + Returns: + JSONResponse: The generated proposal recommendation. + + Raises: + HTTPException: If there's an error, or if the DAO is not found. + """ + try: + logger.info( + f"Proposal recommendation request received from {request.client.host if request.client else 'unknown'} for profile {profile.id} and DAO {payload.dao_id}" + ) + + # Verify that the DAO exists + dao = backend.get_dao(payload.dao_id) + if not dao: + logger.error(f"DAO with ID {payload.dao_id} not found") + raise HTTPException( + status_code=404, + detail=f"DAO with ID {payload.dao_id} not found", + ) + + logger.info( + f"Generating proposal recommendation for DAO {dao.name} (ID: {payload.dao_id})" + ) + + # Create the proposal recommendation agent with model configuration + config = { + "model_name": "gpt-4.1-mini", # Use model from request or default + "temperature": 0.1, # Use temperature from request or default + "streaming": True, # Enable streaming responses + "callbacks": [], # Optional callback handlers + } + agent = ProposalRecommendationAgent(config=config) + + # Prepare state for the agent + state = { + "dao_id": payload.dao_id, + "focus_area": payload.focus_area, + "specific_needs": payload.specific_needs, + } + + # Get the recommendation + result = await agent.process(state) + + logger.debug( + f"Proposal recommendation result for DAO {payload.dao_id}: {result.get('title', 'Unknown')}" + ) + return JSONResponse(content=result) + + except HTTPException as he: + raise he + except Exception as e: + logger.error( + f"Failed to generate proposal recommendation for DAO {payload.dao_id}", + exc_info=e, + ) + raise HTTPException( + status_code=500, + detail=f"Failed to generate proposal recommendation: {str(e)}", + ) diff --git a/docs/proposal_recommendation_agent.md b/docs/proposal_recommendation_agent.md new file mode 100644 index 00000000..4394f5f6 --- /dev/null +++ b/docs/proposal_recommendation_agent.md @@ -0,0 +1,172 @@ +# Proposal Recommendation Agent + +The `ProposalRecommendationAgent` is a LangGraph-based AI agent that generates intelligent proposal recommendations for DAOs based on their mission, description, and historical proposal data. + +## Overview + +This agent analyzes a DAO's context and provides thoughtful suggestions for new proposals that: +- Align with the DAO's mission and values +- Build upon or complement existing proposals +- Address gaps in the DAO's current proposal landscape +- Are practical and achievable + +## Features + +- **Mission Alignment**: Analyzes the DAO's mission statement and description to ensure recommendations align with core values +- **Historical Context**: Reviews up to 8 recent proposals to understand patterns, avoid duplicates, and identify gaps +- **Intelligent Analysis**: Uses AI to identify opportunities for meaningful proposals that would benefit the DAO +- **Structured Output**: Returns well-formatted recommendations with titles, content, rationale, priority, and impact assessments +- **Token Usage Tracking**: Monitors and reports LLM token consumption for cost management + +## Architecture + +The agent follows the established LangGraph workflow patterns: + +```python +from services.workflows.agents.proposal_recommendation import ProposalRecommendationAgent +from services.workflows.utils.models import ProposalRecommendationOutput +``` + +### Key Components + +1. **ProposalRecommendationAgent**: Main agent class inheriting from `BaseCapabilityMixin` and `TokenUsageMixin` +2. **ProposalRecommendationOutput**: Pydantic model defining the structured output format +3. **Database Integration**: Direct integration with Supabase through the backend factory +4. **API Endpoint**: RESTful API endpoint for authenticated users + +## Usage + +### Direct Agent Usage + +```python +import asyncio +from uuid import UUID +from services.workflows.agents.proposal_recommendation import ProposalRecommendationAgent + +async def get_recommendation(): + agent = ProposalRecommendationAgent(config={}) + + state = { + "dao_id": UUID("your-dao-id-here"), + "focus_area": "community growth", # Optional + "specific_needs": "Increase member engagement" # Optional + } + + result = await agent.process(state) + return result +``` + +### API Usage + +**Endpoint**: `POST /tools/dao/proposal_recommendations/generate` + +**Authentication**: Requires valid user session token + +**Request Body**: +```json +{ + "dao_id": "12345678-1234-5678-9abc-123456789abc", + "focus_area": "technical development", + "specific_needs": "Improve smart contract security" +} +``` + +**Response**: +```json +{ + "title": "Smart Contract Security Audit and Enhancement Program", + "content": "Comprehensive proposal with objectives, deliverables, timeline...", + "rationale": "Based on the DAO's mission and analysis of recent proposals...", + "priority": "high", + "estimated_impact": "Significantly improve contract security and member confidence", + "suggested_action": "Form a security committee and allocate budget for audits", + "dao_id": "12345678-1234-5678-9abc-123456789abc", + "dao_name": "Example DAO", + "proposals_analyzed": 5, + "token_usage": { + "proposal_recommendation_agent": { + "input_tokens": 1250, + "output_tokens": 340 + } + } +} +``` + +## Output Schema + +The agent returns a `ProposalRecommendationOutput` with the following fields: + +- **title** (string): A clear, compelling proposal title (max 100 characters) +- **content** (string): Detailed proposal content with objectives, deliverables, timeline, and success metrics +- **rationale** (string): Explanation of why this proposal is recommended based on DAO context +- **priority** (string): Priority level - "high", "medium", or "low" +- **estimated_impact** (string): Expected positive impact on the DAO +- **suggested_action** (string, optional): Specific next steps or actions to implement + +Additional metadata includes: +- **dao_id**: The DAO identifier +- **dao_name**: Name of the DAO +- **proposals_analyzed**: Number of recent proposals analyzed +- **token_usage**: LLM token consumption details + +## Analysis Criteria + +The agent evaluates proposals based on: + +1. **Alignment with DAO Mission** (40%): How well the recommendation aligns with stated mission and values +2. **Gap Analysis** (25%): Identifying opportunities not addressed by recent proposals +3. **Feasibility** (20%): Practical achievability and resource requirements +4. **Community Impact** (15%): Potential positive impact on the DAO community + +## Configuration Options + +The agent accepts configuration options through the `config` parameter: + +```python +config = { + "recursion_limit": 20, # Maximum processing recursion + "model_name": "gpt-4.1", # LLM model to use + "temperature": 0.1, # LLM temperature setting +} + +agent = ProposalRecommendationAgent(config=config) +``` + +## Error Handling + +The agent includes comprehensive error handling: + +- **DAO Not Found**: Returns error response if the specified DAO doesn't exist +- **Database Errors**: Gracefully handles database connection issues +- **LLM Errors**: Catches and reports AI model errors +- **Validation Errors**: Validates input parameters and provides clear error messages + +## Integration with Existing Systems + +The agent seamlessly integrates with the existing aibtcdev-backend architecture: + +- **Backend Factory**: Uses the established backend pattern for database access +- **Authentication**: Leverages existing user authentication via `verify_profile_from_token` +- **Logging**: Uses the configured logger for consistent log output +- **Models**: Follows the Pydantic model patterns used throughout the codebase + +## Example Use Cases + +1. **Regular Proposal Planning**: DAOs can use this monthly to identify new proposal opportunities +2. **Gap Analysis**: Understanding what areas need attention based on proposal history +3. **Strategic Planning**: Generating ideas that align with long-term DAO goals +4. **Member Engagement**: Providing starting points for community members to create proposals + +## Future Enhancements + +Potential improvements could include: + +- Integration with vector stores for semantic similarity analysis +- Support for proposal templates and categories +- Budget estimation and resource planning +- Integration with DAO voting history and outcomes +- Multi-language support for international DAOs + +## Testing + +See `examples/proposal_recommendation_example.py` for a complete usage example and testing script. \ No newline at end of file diff --git a/examples/proposal_recommendation_component.tsx b/examples/proposal_recommendation_component.tsx new file mode 100644 index 00000000..6802727e --- /dev/null +++ b/examples/proposal_recommendation_component.tsx @@ -0,0 +1,418 @@ +/** + * React component example for using the Proposal Recommendation API + * + * This demonstrates how to integrate the proposal recommendation API + * into a React/Next.js frontend application. + */ + +import React, { useState } from "react"; +import type { + ProposalRecommendationRequest, + ProposalRecommendationResult, + ProposalRecommendationResponse, + ProposalPriority, +} from "./proposal_recommendation_types"; +import { + generateProposalRecommendation, + isProposalRecommendationError, + formatProposalRecommendation, + validateRequest, + extractMetrics, +} from "./proposal_recommendation_types"; + +// ============================================================================ +// COMPONENT INTERFACES +// ============================================================================ + +interface ProposalRecommendationFormProps { + /** Authentication token for API calls */ + authToken: string; + + /** Callback when a recommendation is generated */ + onRecommendationGenerated?: ( + recommendation: ProposalRecommendationResponse + ) => void; + + /** Optional initial DAO ID */ + initialDaoId?: string; +} + +interface FormData { + daoId: string; + focusArea: string; + specificNeeds: string; +} + +// ============================================================================ +// MAIN COMPONENT +// ============================================================================ + +export const ProposalRecommendationForm: React.FC< + ProposalRecommendationFormProps +> = ({ authToken, onRecommendationGenerated, initialDaoId = "" }) => { + // Form state + const [formData, setFormData] = useState({ + daoId: initialDaoId, + focusArea: "", + specificNeeds: "", + }); + + // API state + const [isLoading, setIsLoading] = useState(false); + const [recommendation, setRecommendation] = + useState(null); + const [error, setError] = useState(null); + const [validationErrors, setValidationErrors] = useState([]); + + // Handle form input changes + const handleInputChange = (field: keyof FormData, value: string) => { + setFormData((prev) => ({ ...prev, [field]: value })); + setValidationErrors([]); // Clear validation errors when user types + setError(null); // Clear API errors when user types + }; + + // Validate and submit form + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault(); + + // Create API request object + const request: ProposalRecommendationRequest = { + dao_id: formData.daoId, + ...(formData.focusArea && { focus_area: formData.focusArea }), + ...(formData.specificNeeds && { specific_needs: formData.specificNeeds }), + }; + + // Validate request + const errors = validateRequest(request); + if (errors.length > 0) { + setValidationErrors(errors); + return; + } + + setIsLoading(true); + setError(null); + setValidationErrors([]); + + try { + const result: ProposalRecommendationResult = + await generateProposalRecommendation(request, authToken); + + if (isProposalRecommendationError(result)) { + setError(result.error); + setRecommendation(null); + } else { + setRecommendation(result); + setError(null); + onRecommendationGenerated?.(result); + } + } catch (err) { + const errorMessage = + err instanceof Error ? err.message : "Unknown error occurred"; + setError(`Failed to generate recommendation: ${errorMessage}`); + setRecommendation(null); + } finally { + setIsLoading(false); + } + }; + + // Reset form + const handleReset = () => { + setFormData({ daoId: initialDaoId, focusArea: "", specificNeeds: "" }); + setRecommendation(null); + setError(null); + setValidationErrors([]); + }; + + return ( +
+

Generate Proposal Recommendation

+ + {/* Form */} +
+
+ + handleInputChange("daoId", e.target.value)} + placeholder="12345678-1234-5678-9abc-123456789abc" + disabled={isLoading} + required + /> +
+ +
+ + +
+ +
+ +