diff --git a/WORKFLOW_IMPROVEMENTS.md b/WORKFLOW_IMPROVEMENTS.md
deleted file mode 100644
index eed6f827..00000000
--- a/WORKFLOW_IMPROVEMENTS.md
+++ /dev/null
@@ -1,190 +0,0 @@
-# Proposal Evaluation Workflow Improvements
-
-## Problem Summary
-
-The original workflow was experiencing infinite loops with core agent invocations, causing:
-- Excessive core agent executions (>50 invocations)
-- Workflow halt conditions being triggered
-- Poor resource utilization and performance
-- Unpredictable workflow completion
-
-## Root Cause Analysis
-
-1. **Poor State Tracking**: The original supervisor logic only tracked invocation counts without understanding workflow progress
-2. **Unclear Agent Completion**: No way to determine if an agent had successfully completed its task
-3. **Primitive Loop Detection**: Only counted invocations without understanding workflow phases
-4. **Inefficient Routing**: Supervisor could route to the same agent repeatedly without checking completion
-
-## Solution Overview
-
-Implemented a comprehensive workflow improvement based on LangGraph supervisor best practices:
-
-### 1. State-Driven Workflow Management
-
-**Before:**
-```python
-# Simple counter-based tracking
-core_agent_invocations = state.get("core_agent_invocations", 0) + 1
-if core_agent_invocations > 50:
-    halt_workflow()
-```
-
-**After:**
-```python
-# Phase-based workflow tracking
-workflow_step = state.get("workflow_step", "start")
-completed_steps = state.get("completed_steps", set())
-
-# Clear progression through defined phases
-phases = ["image_processing", "core_evaluation", "specialized_evaluation", "final_reasoning"]
-```
-
-### 2. Completion Tracking System
-
-**Added to BaseCapabilityMixin:**
-```python
-# Track completion - add this node to completed_steps
-if "completed_steps" not in state:
-    state["completed_steps"] = set()
-state["completed_steps"].add(node_name)
-```
-
-This ensures every agent marks itself as completed, preventing re-execution.
-
-### 3. Improved Supervisor Logic
-
-**Sequential Workflow Management:**
-1. **Image Processing**: Must complete first (required for vision models)
-2. **Core Evaluation**: Must complete before specialized agents
-3. **Specialized Agents**: Run in parallel (historical, financial, social)
-4. **Final Reasoning**: Only after all evaluations complete
-
-**Smart Agent Selection:**
-```python
-# Only run agents that haven't completed
-pending_agents = []
-for agent, score_key in zip(specialized_agents, specialized_scores):
-    if score_key not in state and agent not in completed_steps:
-        pending_agents.append(agent)
-
-if pending_agents:
-    return pending_agents  # Parallel execution
-```
-
-### 4. Enhanced Error Detection
-
-**Multiple Safety Mechanisms:**
-- **Step Attempt Limiting**: Maximum 3 attempts per workflow step
-- **Completion Validation**: Verify all required scores exist before proceeding
-- **Expected Sequence Validation**: Ensure workflow follows proper order
-- **Agent Completion Tracking**: Verify agents actually complete their tasks
-
-### 5. Parallel Execution Optimization
-
-**Specialized Agents Run in Parallel:**
-```python
-# Instead of sequential execution, run multiple agents simultaneously
-if "core_score" in state:
-    pending_agents = [agent for agent in specialized_agents 
-                     if not completed(agent, state)]
-    if pending_agents:
-        return pending_agents  # LangGraph handles parallel execution
-```
-
-## Performance Improvements
-
-| Metric | Before | After | Improvement |
-|--------|--------|-------|-------------|
-| Core Agent Invocations | 50+ (infinite loop) | 1 (single execution) | 98% reduction |
-| Workflow Completion Rate | ~60% (due to halts) | 98%+ | 38% improvement |
-| Recursion Limit | 20 iterations | 15 iterations | 25% reduction |
-| Error Detection | Reactive (after timeout) | Proactive (per step) | Real-time |
-
-## Key Features Added
-
-### 1. Workflow Phase Tracking
-```python
-workflow_step: Annotated[str, lambda x, y: y[-1] if y else x]
-completed_steps: Annotated[set[str], lambda x, y: x.union(set(y)) if y else x]
-```
-
-### 2. Smart Routing Logic
-- Prevents re-execution of completed agents
-- Ensures prerequisite completion before next phase
-- Supports parallel execution for specialized agents
-
-### 3. Comprehensive Halt Conditions
-- Step attempt limits (max 3 per step)
-- Completion validation
-- Sequence validation
-- Agent completion tracking
-
-### 4. Enhanced Debugging
-- Workflow step tracking in results
-- Completed steps list for troubleshooting
-- Clear error messages for each halt condition
-
-## Testing Strategy
-
-Created comprehensive test suite (`test_improved_workflow.py`):
-
-1. **Basic Functionality Test**: Standard proposal evaluation
-2. **Edge Case Tests**: Empty proposals, short content, image handling
-3. **Performance Validation**: Recursion limit compliance
-4. **Error Handling**: Graceful degradation on failures
-
-## Migration Guide
-
-### For Existing Workflows
-
-1. **Update State Definition**: Add `workflow_step` and `completed_steps` to your state
-2. **Upgrade Supervisor Logic**: Replace counter-based with phase-based routing
-3. **Add Completion Tracking**: Ensure all agents mark completion in `add_to_graph`
-4. **Update Halt Conditions**: Use proactive step-based validation
-
-### Configuration Changes
-
-```python
-# Recommended configuration
-config = {
-    "recursion_limit": 15,  # Reduced from 20
-    "debug_level": 1,       # Enhanced logging
-    "model_name": "gpt-4.1" # Stable model
-}
-```
-
-## Benefits
-
-### Immediate
-- ✅ Eliminates infinite loops
-- ✅ Predictable workflow completion
-- ✅ Better error messages
-- ✅ Reduced resource usage
-
-### Long-term
-- ✅ Easier debugging and monitoring
-- ✅ Scalable to more complex workflows
-- ✅ Foundation for workflow analytics
-- ✅ Better user experience
-
-## Best Practices Applied
-
-Based on LangGraph supervisor patterns and multi-agent research:
-
-1. **Clear State Management**: Explicit workflow phases vs. implicit counters
-2. **Completion Semantics**: Agents explicitly mark completion
-3. **Supervisor Patterns**: Central coordination with distributed execution
-4. **Error Boundaries**: Fail-fast with clear error messages
-5. **Parallel Execution**: Optimize for concurrent agent execution where possible
-
-## Future Enhancements
-
-1. **Workflow Analytics**: Track performance metrics per phase
-2. **Dynamic Routing**: AI-driven agent selection based on proposal content
-3. **Checkpoint/Resume**: Save workflow state for long-running evaluations
-4. **A/B Testing**: Compare different evaluation strategies
-5. **Human-in-the-Loop**: Interrupt points for manual review
-
----
-
-**Result**: The improved workflow now completes proposal evaluations reliably without infinite loops, with better performance and clearer debugging capabilities. 
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index d0cd5d8b..3f6a643e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,25 +8,37 @@ dependencies = [
     "aiohttp==3.12.11",
     "apscheduler==3.11.0",
     "cachetools==6.0.0",
+    "faiss-cpu>=1.7.0",
     "fastapi==0.115.12",
     "langchain==0.3.25",
     "langchain-community==0.3.24",
     "langchain-core>=0.3.56,<1.0.0",
     "langchain-openai==0.3.21",
     "langchain-text-splitters==0.3.8",
+    "neo4j>=5.0.0",
+    "networkx>=3.0",
+    "nltk>=3.8",
+    "numpy>=1.24.0",
     "langgraph==0.4.8",
     "openai==1.84.0",
     "pgvector==0.3.6",
     "psycopg2==2.9.10",
     "pydantic==2.11.5",
+    "python-dateutil>=2.8.0",
     "python-dotenv==1.1.0",
     "python-telegram-bot==22.1",
     "python-twitter-v2==0.9.2",
+    "rank-bm25>=0.2.2",
     "requests==2.32.3",
+    "scikit-learn>=1.3.0",
+    "sentence-transformers>=2.2.0",
+    "spacy>=3.6.0",
     "sqlalchemy==2.0.41",
     "starlette==0.47.0",
     "supabase==2.15.2",
+    "textstat>=0.7.0",
     "tiktoken==0.9.0",
+    "tqdm>=4.65.0",
     "tweepy==4.15.0",
     "uvicorn==0.34.3",
     "vecs==0.4.5",
diff --git a/services/runner/tasks/proposal_embedder.py b/services/runner/tasks/proposal_embedder.py
index 4107ada4..e872e141 100644
--- a/services/runner/tasks/proposal_embedder.py
+++ b/services/runner/tasks/proposal_embedder.py
@@ -1,9 +1,10 @@
-"""Proposal embedding task implementation."""
+"""Enhanced proposal embedding task with advanced RAG strategies."""
 
 from dataclasses import dataclass
 from typing import List, Optional
 
 import openai
+from langchain_core.documents import Document
 from langchain_openai import OpenAIEmbeddings
 
 from backend.factory import backend
@@ -11,11 +12,20 @@
 from config import config
 from lib.logger import configure_logger
 from services.runner.base import BaseTask, JobContext, RunnerResult
+from services.workflows.chunking_strategies import (
+    AdaptiveChunkingStrategy,
+    create_chunking_strategy,
+)
+from services.workflows.enhanced_embeddings import (
+    ENHANCED_EMBEDDING_CONFIGS,
+    create_embedding_strategy,
+)
 
 logger = configure_logger(__name__)
 
 PROPOSAL_COLLECTION_NAME = "proposals"
-EMBEDDING_MODEL = "text-embedding-ada-002"
+# Use latest embedding model for better performance
+EMBEDDING_MODEL = "text-embedding-3-large"
 
 
 @dataclass
@@ -45,17 +55,45 @@ async def _validate_task_specific(self, context: JobContext) -> bool:
         # 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"Content: {proposal.content or 'N/A'}",
-            f"Type: {proposal.type.value if proposal.type else 'N/A'}",
-        ]
+    def _format_proposal_for_embedding(self, proposal: Proposal) -> Document:
+        """Format proposal data into a LangChain Document with rich metadata."""
+        # Create comprehensive content
+        content_parts = []
+
+        if proposal.title:
+            content_parts.append(f"Title: {proposal.title}")
+
+        if proposal.content:
+            content_parts.append(f"Content: {proposal.content}")
+
+        if proposal.type:
+            content_parts.append(f"Type: {proposal.type.value}")
+
         if proposal.action:
-            parts.append(f"Action: {proposal.action}")
-        # Add more relevant fields as needed
-        return "\n".join(parts)
+            content_parts.append(f"Action: {proposal.action}")
+
+        # Add contextual information
+        if hasattr(proposal, "dao_id") and proposal.dao_id:
+            content_parts.append(f"DAO ID: {proposal.dao_id}")
+
+        content = "\n\n".join(content_parts)
+
+        # Create rich metadata
+        metadata = {
+            "proposal_id": str(proposal.id),
+            "title": proposal.title or "",
+            "dao_id": str(proposal.dao_id) if proposal.dao_id else "",
+            "type": proposal.type.value if proposal.type else "",
+            "content_length": len(proposal.content or ""),
+            "has_action": bool(proposal.action),
+            "source": "proposal_database",
+        }
+
+        # Add timestamp information if available
+        if hasattr(proposal, "created_at") and proposal.created_at:
+            metadata["created_at"] = proposal.created_at.isoformat()
+
+        return Document(page_content=content, metadata=metadata)
 
     async def _get_embeddings(self, texts: List[str]) -> Optional[List[List[float]]]:
         """Get embeddings for a list of texts using OpenAI API."""
@@ -155,42 +193,53 @@ async def _execute_impl(self, context: JobContext) -> List[ProposalEmbedderResul
                 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 = []
-
+            # Prepare documents for advanced processing
+            documents_to_embed = []
             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))
+                document = self._format_proposal_for_embedding(proposal)
+                documents_to_embed.append(document)
+
+            # Apply advanced chunking strategy
+            chunking_strategy = create_chunking_strategy("adaptive")
+            chunked_documents = []
 
-            # Get embeddings using the updated method
-            logger.debug(
-                f"Requesting embeddings for {len(texts_to_embed)} new proposals."
+            for document in documents_to_embed:
+                chunks = chunking_strategy.chunk_document(document)
+                chunked_documents.extend(chunks)
+
+            logger.info(
+                f"Created {len(chunked_documents)} chunks from {len(documents_to_embed)} proposals"
             )
-            embeddings_list = await self._get_embeddings(texts_to_embed)
 
-            if embeddings_list is None:
-                errors.append("Failed to retrieve embeddings.")
-            else:
+            # Use enhanced embedding strategy
+            embedding_strategy = create_embedding_strategy("adaptive")
+            texts_to_embed = [doc.page_content for doc in chunked_documents]
+            metadata_list = [doc.metadata for doc in chunked_documents]
+            chunk_ids = [
+                f"{doc.metadata.get('proposal_id', 'unknown')}_{doc.metadata.get('chunk_index', 0)}"
+                for doc in chunked_documents
+            ]
+
+            # Get embeddings using the enhanced strategy
+            logger.debug(f"Requesting embeddings for {len(texts_to_embed)} chunks.")
+            try:
+                embeddings_list = embedding_strategy.embed_documents(texts_to_embed)
                 logger.debug(
                     f"Successfully retrieved {len(embeddings_list)} embeddings."
                 )
+            except Exception as e:
+                logger.error(f"Failed to get embeddings: {e}")
+                embeddings_list = None
+
+            if embeddings_list is None:
+                errors.append("Failed to retrieve embeddings.")
+            else:
                 # Prepare records for upsert
                 records_to_upsert = []
-                for i, proposal_id in enumerate(proposal_ids):
+                for i, chunk_id in enumerate(chunk_ids):
                     records_to_upsert.append(
                         (
-                            proposal_id,  # Use proposal UUID as the vector ID
+                            chunk_id,  # Use chunk ID as the vector ID
                             embeddings_list[i],  # Use the retrieved embeddings
                             metadata_list[i],
                         )
@@ -199,9 +248,10 @@ async def _execute_impl(self, context: JobContext) -> List[ProposalEmbedderResul
                 # Upsert into the vector collection
                 try:
                     collection.upsert(records=records_to_upsert)
-                    proposals_embedded = len(records_to_upsert)
+                    chunks_embedded = len(records_to_upsert)
+                    proposals_embedded = len(proposals_to_embed)
                     logger.info(
-                        f"Successfully upserted {proposals_embedded} proposal embeddings."
+                        f"Successfully upserted {chunks_embedded} chunks from {proposals_embedded} proposals."
                     )
                 except Exception as e:
                     error_msg = f"Failed to upsert proposal embeddings: {str(e)}"
diff --git a/services/workflows/README.md b/services/workflows/README.md
deleted file mode 100644
index fb0755de..00000000
--- a/services/workflows/README.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# 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/core_context.py b/services/workflows/agents/core_context.py
index 5cb92f6a..c9b36815 100644
--- a/services/workflows/agents/core_context.py
+++ b/services/workflows/agents/core_context.py
@@ -33,14 +33,12 @@ def __init__(self, config: Optional[Dict[str, Any]] = None):
 
     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__
-                )
+        if not hasattr(self, "hybrid_retrieve"):
+            self.hybrid_retrieve = VectorRetrievalCapability.hybrid_retrieve.__get__(
+                self, self.__class__
             )
             self.logger.info(
-                "Initialized vector retrieval capability for CoreContextAgent"
+                "Initialized hybrid retrieval capability for CoreContextAgent"
             )
 
     async def process(self, state: Dict[str, Any]) -> Dict[str, Any]:
@@ -68,9 +66,9 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]:
         if not dao_mission_text:
             try:
                 self.logger.debug(
-                    f"[DEBUG:CoreAgent:{proposal_id}] Attempting to retrieve DAO mission"
+                    f"[DEBUG:CoreAgent:{proposal_id}] Attempting to retrieve DAO mission using hybrid search"
                 )
-                dao_mission = await self.retrieve_from_vector_store(
+                dao_mission = await self.hybrid_retrieve(
                     query="DAO mission statement and values",
                     collection_name=self.config.get(
                         "mission_collection", "dao_documents"
diff --git a/services/workflows/agents/historical_context.py b/services/workflows/agents/historical_context.py
index 71a85d52..1fd6bdb7 100644
--- a/services/workflows/agents/historical_context.py
+++ b/services/workflows/agents/historical_context.py
@@ -36,14 +36,12 @@ def __init__(self, config: Optional[Dict[str, Any]] = None):
 
     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__
-                )
+        if not hasattr(self, "hybrid_retrieve"):
+            self.hybrid_retrieve = VectorRetrievalCapability.hybrid_retrieve.__get__(
+                self, self.__class__
             )
             self.logger.info(
-                "Initialized vector retrieval capability for HistoricalContextAgent"
+                "Initialized hybrid retrieval capability for HistoricalContextAgent"
             )
 
     async def _fetch_dao_proposals(self, dao_id: UUID) -> List[Proposal]:
@@ -137,9 +135,9 @@ async def process(self, state: Dict[str, Any]) -> Dict[str, Any]:
         past_proposals_vector_text = ""
         try:
             self.logger.debug(
-                f"[DEBUG:HistoricalAgent:{proposal_id}] Retrieving similar past proposals from vector store"
+                f"[DEBUG:HistoricalAgent:{proposal_id}] Retrieving similar past proposals from vector store using hybrid search"
             )
-            similar_proposals = await self.retrieve_from_vector_store(
+            similar_proposals = await self.hybrid_retrieve(
                 query=proposal_content[
                     :1000
                 ],  # Use first 1000 chars of proposal as query
diff --git a/services/workflows/chat.py b/services/workflows/chat.py
index a9eb724d..674f9a89 100644
--- a/services/workflows/chat.py
+++ b/services/workflows/chat.py
@@ -130,8 +130,8 @@ def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None:
         # This method could be implemented to modify existing graphs with these capabilities
         pass
 
-    async def retrieve_from_vector_store(self, query: str, **kwargs) -> List[Document]:
-        """Retrieve relevant documents from multiple vector stores.
+    async def hybrid_retrieve(self, query: str, **kwargs) -> List[Document]:
+        """Retrieve relevant documents from multiple vector stores using hybrid search.
 
         Args:
             query: The query to search for
@@ -141,51 +141,59 @@ async def retrieve_from_vector_store(self, query: str, **kwargs) -> List[Documen
             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,
-                            },
+            # Use the hybrid_retrieve method from VectorRetrievalCapability
+            return await super().hybrid_retrieve(query, **kwargs)
+        except Exception as e:
+            logger.error(
+                f"Hybrid retrieval failed, falling back to vector search: {str(e)}"
+            )
+            # Fallback to the original vector search implementation
+            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,
                         )
-                        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
+                        # 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 []
+                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 []
 
     async def create_plan(
         self, query: str, context_docs: List[Document] = None, **kwargs
@@ -364,9 +372,7 @@ async def retrieve_context(state: ChatState) -> Dict:
                 return {"vector_results": [], "web_search_results": []}
 
             # Get vector results
-            vector_results = await self.retrieve_from_vector_store(
-                query=last_user_message
-            )
+            vector_results = await self.hybrid_retrieve(query=last_user_message)
             logger.info(f"Retrieved {len(vector_results)} documents from vector store")
 
             # Get web search results
@@ -557,7 +563,7 @@ async def _execute_stream_impl(
             logger.info(
                 f"Retrieving documents from vector store for query: {input_str[:50]}..."
             )
-            documents = await workflow.retrieve_from_vector_store(query=input_str)
+            documents = await workflow.hybrid_retrieve(query=input_str)
             logger.info(f"Retrieved {len(documents)} documents from vector store")
             try:
                 logger.info("Creating plan with vector context...")
diff --git a/services/workflows/chunking_strategies.py b/services/workflows/chunking_strategies.py
new file mode 100644
index 00000000..0648f991
--- /dev/null
+++ b/services/workflows/chunking_strategies.py
@@ -0,0 +1,516 @@
+"""Advanced chunking strategies for improved RAG performance."""
+
+import re
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+from langchain_openai import OpenAIEmbeddings
+from sklearn.cluster import KMeans
+from sklearn.metrics.pairwise import cosine_similarity
+
+from lib.logger import configure_logger
+
+logger = configure_logger(__name__)
+
+
+class ChunkingStrategy(ABC):
+    """Abstract base class for chunking strategies."""
+
+    @abstractmethod
+    def chunk_document(self, document: Document) -> List[Document]:
+        """Chunk a document into smaller pieces."""
+        pass
+
+
+class FixedLengthChunkingStrategy(ChunkingStrategy):
+    """Fixed-length chunking with configurable overlap."""
+
+    def __init__(
+        self,
+        chunk_size: int = 1000,
+        chunk_overlap: int = 200,
+        separators: Optional[List[str]] = None,
+    ):
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        self.separators = separators or ["\n\n", "\n", ". ", " ", ""]
+        self.text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+            separators=self.separators,
+        )
+
+    def chunk_document(self, document: Document) -> List[Document]:
+        """Split document into fixed-length chunks."""
+        chunks = self.text_splitter.split_documents([document])
+
+        # Add chunk metadata
+        for i, chunk in enumerate(chunks):
+            chunk.metadata.update(
+                {
+                    "chunk_index": i,
+                    "chunk_count": len(chunks),
+                    "chunking_strategy": "fixed_length",
+                    "parent_doc_id": document.metadata.get("id", "unknown"),
+                }
+            )
+
+        return chunks
+
+
+class SentenceBasedChunkingStrategy(ChunkingStrategy):
+    """Sentence-based chunking that preserves logical flow."""
+
+    def __init__(self, max_sentences_per_chunk: int = 10, overlap_sentences: int = 2):
+        self.max_sentences_per_chunk = max_sentences_per_chunk
+        self.overlap_sentences = overlap_sentences
+        # Enhanced sentence boundary detection
+        self.sentence_pattern = re.compile(
+            r'(?<=[.!?])\s+(?=[A-Z])|(?<=[.!?"])\s+(?=[A-Z])|(?<=\.)\s+(?=\d)'
+        )
+
+    def chunk_document(self, document: Document) -> List[Document]:
+        """Split document into sentence-based chunks."""
+        text = document.page_content
+        sentences = self.sentence_pattern.split(text)
+        sentences = [s.strip() for s in sentences if s.strip()]
+
+        chunks = []
+        for i in range(
+            0, len(sentences), self.max_sentences_per_chunk - self.overlap_sentences
+        ):
+            chunk_sentences = sentences[i : i + self.max_sentences_per_chunk]
+            chunk_text = " ".join(chunk_sentences)
+
+            chunk_metadata = document.metadata.copy()
+            chunk_metadata.update(
+                {
+                    "chunk_index": len(chunks),
+                    "sentence_start": i,
+                    "sentence_end": min(
+                        i + self.max_sentences_per_chunk, len(sentences)
+                    ),
+                    "chunking_strategy": "sentence_based",
+                    "parent_doc_id": document.metadata.get("id", "unknown"),
+                }
+            )
+
+            chunks.append(Document(page_content=chunk_text, metadata=chunk_metadata))
+
+        return chunks
+
+
+class SemanticChunkingStrategy(ChunkingStrategy):
+    """Semantic chunking using embeddings to group related content."""
+
+    def __init__(
+        self,
+        embeddings_model: Optional[Any] = None,
+        similarity_threshold: float = 0.8,
+        max_chunk_size: int = 1500,
+        min_chunk_size: int = 200,
+    ):
+        self.embeddings_model = embeddings_model or OpenAIEmbeddings()
+        self.similarity_threshold = similarity_threshold
+        self.max_chunk_size = max_chunk_size
+        self.min_chunk_size = min_chunk_size
+        self.sentence_splitter = SentenceBasedChunkingStrategy(
+            max_sentences_per_chunk=3
+        )
+
+    def chunk_document(self, document: Document) -> List[Document]:
+        """Split document into semantically coherent chunks."""
+        # First, split into sentences
+        sentence_chunks = self.sentence_splitter.chunk_document(document)
+
+        if len(sentence_chunks) <= 1:
+            return sentence_chunks
+
+        # Get embeddings for each sentence chunk
+        texts = [chunk.page_content for chunk in sentence_chunks]
+        try:
+            embeddings = self.embeddings_model.embed_documents(texts)
+        except Exception as e:
+            logger.warning(f"Failed to get embeddings for semantic chunking: {e}")
+            return sentence_chunks
+
+        # Group semantically similar sentences
+        semantic_chunks = self._group_by_similarity(sentence_chunks, embeddings)
+
+        # Add metadata
+        for i, chunk in enumerate(semantic_chunks):
+            chunk.metadata.update(
+                {
+                    "chunk_index": i,
+                    "chunk_count": len(semantic_chunks),
+                    "chunking_strategy": "semantic",
+                    "parent_doc_id": document.metadata.get("id", "unknown"),
+                }
+            )
+
+        return semantic_chunks
+
+    def _group_by_similarity(
+        self, chunks: List[Document], embeddings: List[List[float]]
+    ) -> List[Document]:
+        """Group chunks by semantic similarity."""
+        if not embeddings:
+            return chunks
+
+        embeddings_array = np.array(embeddings)
+        similarity_matrix = cosine_similarity(embeddings_array)
+
+        semantic_groups = []
+        used_indices = set()
+
+        for i, chunk in enumerate(chunks):
+            if i in used_indices:
+                continue
+
+            # Start a new semantic group
+            group_chunks = [chunk]
+            group_indices = {i}
+            current_text_length = len(chunk.page_content)
+
+            # Find similar chunks to add to this group
+            for j in range(i + 1, len(chunks)):
+                if j in used_indices:
+                    continue
+
+                similarity = similarity_matrix[i][j]
+                next_chunk_length = len(chunks[j].page_content)
+
+                # Add to group if similar and within size limits
+                if (
+                    similarity >= self.similarity_threshold
+                    and current_text_length + next_chunk_length <= self.max_chunk_size
+                ):
+                    group_chunks.append(chunks[j])
+                    group_indices.add(j)
+                    current_text_length += next_chunk_length
+
+            # Mark all chunks in this group as used
+            used_indices.update(group_indices)
+
+            # Create the combined chunk
+            combined_text = " ".join([c.page_content for c in group_chunks])
+            combined_metadata = chunks[i].metadata.copy()
+            combined_metadata["semantic_group_size"] = len(group_chunks)
+
+            semantic_groups.append(
+                Document(page_content=combined_text, metadata=combined_metadata)
+            )
+
+        return semantic_groups
+
+
+class SlidingWindowChunkingStrategy(ChunkingStrategy):
+    """Sliding window chunking with overlap to maintain context."""
+
+    def __init__(
+        self,
+        window_size: int = 1000,
+        step_size: int = 500,
+        preserve_sentences: bool = True,
+    ):
+        self.window_size = window_size
+        self.step_size = step_size
+        self.preserve_sentences = preserve_sentences
+
+        if preserve_sentences:
+            self.sentence_pattern = re.compile(r"(?<=[.!?])\s+(?=[A-Z])")
+
+    def chunk_document(self, document: Document) -> List[Document]:
+        """Create overlapping chunks using sliding window."""
+        text = document.page_content
+
+        if self.preserve_sentences:
+            return self._sliding_window_with_sentences(document, text)
+        else:
+            return self._sliding_window_character_based(document, text)
+
+    def _sliding_window_with_sentences(
+        self, document: Document, text: str
+    ) -> List[Document]:
+        """Sliding window that respects sentence boundaries."""
+        sentences = self.sentence_pattern.split(text)
+        sentences = [s.strip() for s in sentences if s.strip()]
+
+        chunks = []
+        current_pos = 0
+
+        while current_pos < len(sentences):
+            # Build chunk respecting sentence boundaries
+            chunk_text = ""
+            sentence_count = 0
+
+            for i in range(current_pos, len(sentences)):
+                candidate_text = (
+                    chunk_text + " " + sentences[i] if chunk_text else sentences[i]
+                )
+
+                if len(candidate_text) <= self.window_size:
+                    chunk_text = candidate_text
+                    sentence_count += 1
+                else:
+                    break
+
+            if chunk_text:
+                chunk_metadata = document.metadata.copy()
+                chunk_metadata.update(
+                    {
+                        "chunk_index": len(chunks),
+                        "window_start": current_pos,
+                        "window_end": current_pos + sentence_count,
+                        "chunking_strategy": "sliding_window_sentences",
+                        "parent_doc_id": document.metadata.get("id", "unknown"),
+                    }
+                )
+
+                chunks.append(
+                    Document(page_content=chunk_text, metadata=chunk_metadata)
+                )
+
+            # Move window by step size (in sentences)
+            step_sentences = max(
+                1, self.step_size // 100
+            )  # Approximate step in sentences
+            current_pos += step_sentences
+
+        return chunks
+
+    def _sliding_window_character_based(
+        self, document: Document, text: str
+    ) -> List[Document]:
+        """Character-based sliding window."""
+        chunks = []
+
+        for start in range(0, len(text), self.step_size):
+            end = min(start + self.window_size, len(text))
+            chunk_text = text[start:end]
+
+            chunk_metadata = document.metadata.copy()
+            chunk_metadata.update(
+                {
+                    "chunk_index": len(chunks),
+                    "char_start": start,
+                    "char_end": end,
+                    "chunking_strategy": "sliding_window_chars",
+                    "parent_doc_id": document.metadata.get("id", "unknown"),
+                }
+            )
+
+            chunks.append(Document(page_content=chunk_text, metadata=chunk_metadata))
+
+        return chunks
+
+
+class MetadataAugmentedChunkingStrategy(ChunkingStrategy):
+    """Chunking strategy that enhances chunks with rich metadata."""
+
+    def __init__(
+        self,
+        base_strategy: ChunkingStrategy,
+        extract_keywords: bool = True,
+        extract_entities: bool = True,
+        add_summary: bool = True,
+    ):
+        self.base_strategy = base_strategy
+        self.extract_keywords = extract_keywords
+        self.extract_entities = extract_entities
+        self.add_summary = add_summary
+
+    def chunk_document(self, document: Document) -> List[Document]:
+        """Enhance chunks with metadata."""
+        base_chunks = self.base_strategy.chunk_document(document)
+
+        enhanced_chunks = []
+        for chunk in base_chunks:
+            enhanced_metadata = chunk.metadata.copy()
+            enhanced_metadata["chunking_strategy"] = "metadata_augmented"
+
+            # Add document structure metadata
+            if "title" in document.metadata:
+                enhanced_metadata["document_title"] = document.metadata["title"]
+
+            # Extract keywords (simple implementation)
+            if self.extract_keywords:
+                keywords = self._extract_keywords(chunk.page_content)
+                enhanced_metadata["keywords"] = keywords
+
+            # Add chunk summary (simplified)
+            if self.add_summary:
+                summary = self._create_summary(chunk.page_content)
+                enhanced_metadata["summary"] = summary
+
+            # Add text statistics
+            enhanced_metadata.update(
+                {
+                    "char_count": len(chunk.page_content),
+                    "word_count": len(chunk.page_content.split()),
+                    "sentence_count": len(re.split(r"[.!?]+", chunk.page_content)),
+                }
+            )
+
+            enhanced_chunks.append(
+                Document(page_content=chunk.page_content, metadata=enhanced_metadata)
+            )
+
+        return enhanced_chunks
+
+    def _extract_keywords(self, text: str) -> List[str]:
+        """Simple keyword extraction."""
+        # Remove common words and extract meaningful terms
+        import string
+
+        words = text.lower().split()
+        words = [w.strip(string.punctuation) for w in words]
+
+        # Simple filtering for meaningful keywords
+        stop_words = {
+            "the",
+            "a",
+            "an",
+            "and",
+            "or",
+            "but",
+            "in",
+            "on",
+            "at",
+            "to",
+            "for",
+            "of",
+            "with",
+            "by",
+            "from",
+            "as",
+            "is",
+            "was",
+            "are",
+            "were",
+            "be",
+            "been",
+            "being",
+            "have",
+            "has",
+            "had",
+            "do",
+            "does",
+            "did",
+            "will",
+            "would",
+            "could",
+            "should",
+            "may",
+            "might",
+            "can",
+            "this",
+            "that",
+            "these",
+            "those",
+            "i",
+            "you",
+            "he",
+            "she",
+            "it",
+            "we",
+            "they",
+        }
+
+        keywords = [w for w in words if len(w) > 3 and w not in stop_words]
+
+        # Return top keywords by frequency
+        from collections import Counter
+
+        keyword_counts = Counter(keywords)
+        return [word for word, _ in keyword_counts.most_common(10)]
+
+    def _create_summary(self, text: str, max_length: int = 100) -> str:
+        """Create a simple summary of the text."""
+        sentences = re.split(r"[.!?]+", text)
+        sentences = [s.strip() for s in sentences if s.strip()]
+
+        if not sentences:
+            return ""
+
+        # Take first sentence as summary, truncate if too long
+        summary = sentences[0]
+        if len(summary) > max_length:
+            summary = summary[:max_length] + "..."
+
+        return summary
+
+
+class AdaptiveChunkingStrategy(ChunkingStrategy):
+    """Adaptive chunking that chooses strategy based on document characteristics."""
+
+    def __init__(self):
+        self.strategies = {
+            "short": FixedLengthChunkingStrategy(chunk_size=500, chunk_overlap=50),
+            "medium": SemanticChunkingStrategy(),
+            "long": SlidingWindowChunkingStrategy(window_size=1500, step_size=750),
+            "structured": MetadataAugmentedChunkingStrategy(
+                SentenceBasedChunkingStrategy()
+            ),
+        }
+
+    def chunk_document(self, document: Document) -> List[Document]:
+        """Choose and apply the best chunking strategy for the document."""
+        text = document.page_content
+        doc_length = len(text)
+
+        # Simple heuristics for strategy selection
+        if doc_length < 800:
+            strategy = self.strategies["short"]
+        elif self._is_structured_document(document):
+            strategy = self.strategies["structured"]
+        elif doc_length > 5000:
+            strategy = self.strategies["long"]
+        else:
+            strategy = self.strategies["medium"]
+
+        chunks = strategy.chunk_document(document)
+
+        # Add adaptive strategy metadata
+        for chunk in chunks:
+            chunk.metadata["adaptive_strategy_used"] = strategy.__class__.__name__
+
+        return chunks
+
+    def _is_structured_document(self, document: Document) -> bool:
+        """Detect if document has clear structure."""
+        text = document.page_content
+
+        # Look for structural indicators
+        has_headers = bool(re.search(r"^(#+\s|\d+\.\s)", text, re.MULTILINE))
+        has_bullets = bool(re.search(r"^[\s]*[-*•]\s", text, re.MULTILINE))
+        has_sections = bool(re.search(r"\n\n[A-Z][^.]*:\n", text))
+
+        return has_headers or has_bullets or has_sections
+
+
+# Factory function for easy strategy creation
+def create_chunking_strategy(
+    strategy_type: str = "adaptive", **kwargs
+) -> ChunkingStrategy:
+    """Factory function to create chunking strategies."""
+
+    strategies = {
+        "fixed": FixedLengthChunkingStrategy,
+        "sentence": SentenceBasedChunkingStrategy,
+        "semantic": SemanticChunkingStrategy,
+        "sliding": SlidingWindowChunkingStrategy,
+        "metadata": lambda **kw: MetadataAugmentedChunkingStrategy(
+            FixedLengthChunkingStrategy(**kw)
+        ),
+        "adaptive": AdaptiveChunkingStrategy,
+    }
+
+    if strategy_type not in strategies:
+        raise ValueError(f"Unknown strategy type: {strategy_type}")
+
+    strategy_class = strategies[strategy_type]
+    return strategy_class(**kwargs)
diff --git a/services/workflows/enhanced_embeddings.py b/services/workflows/enhanced_embeddings.py
new file mode 100644
index 00000000..039579e0
--- /dev/null
+++ b/services/workflows/enhanced_embeddings.py
@@ -0,0 +1,347 @@
+"""Enhanced embedding strategies for improved RAG performance."""
+
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from langchain_openai import OpenAIEmbeddings
+
+from lib.logger import configure_logger
+
+logger = configure_logger(__name__)
+
+
+class EnhancedEmbeddingStrategy(ABC):
+    """Abstract base class for enhanced embedding strategies."""
+
+    @abstractmethod
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents."""
+        pass
+
+    @abstractmethod
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query text."""
+        pass
+
+
+class MultiModelEmbeddingStrategy(EnhancedEmbeddingStrategy):
+    """Use multiple embedding models and combine their outputs."""
+
+    def __init__(
+        self,
+        primary_model: Optional[Embeddings] = None,
+        secondary_models: Optional[List[Embeddings]] = None,
+        combination_method: str = "average",
+    ):
+        # Use latest OpenAI embedding models
+        self.primary_model = primary_model or OpenAIEmbeddings(
+            model="text-embedding-3-large",  # Latest model with 3072 dimensions
+            dimensions=1536,  # Can be reduced for better performance
+        )
+
+        self.secondary_models = secondary_models or [
+            OpenAIEmbeddings(
+                model="text-embedding-3-small",  # Smaller, faster model
+                dimensions=512,
+            )
+        ]
+
+        self.combination_method = combination_method
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed documents using multiple models and combine."""
+        try:
+            # Get embeddings from primary model
+            primary_embeddings = self.primary_model.embed_documents(texts)
+
+            if not self.secondary_models:
+                return primary_embeddings
+
+            # Get embeddings from secondary models
+            all_embeddings = [primary_embeddings]
+
+            for model in self.secondary_models:
+                try:
+                    secondary_embeddings = model.embed_documents(texts)
+                    # Normalize dimensions if needed
+                    normalized_embeddings = self._normalize_embeddings(
+                        secondary_embeddings, target_dim=len(primary_embeddings[0])
+                    )
+                    all_embeddings.append(normalized_embeddings)
+                except Exception as e:
+                    logger.warning(f"Secondary model failed: {e}")
+                    continue
+
+            # Combine embeddings
+            return self._combine_embeddings(all_embeddings)
+
+        except Exception as e:
+            logger.error(f"Multi-model embedding failed: {e}")
+            # Fallback to primary model only
+            return self.primary_model.embed_documents(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query using the same combination strategy."""
+        embeddings = self.embed_documents([text])
+        return embeddings[0] if embeddings else []
+
+    def _normalize_embeddings(
+        self, embeddings: List[List[float]], target_dim: int
+    ) -> List[List[float]]:
+        """Normalize embedding dimensions."""
+        normalized = []
+        for embedding in embeddings:
+            if len(embedding) == target_dim:
+                normalized.append(embedding)
+            elif len(embedding) > target_dim:
+                # Truncate
+                normalized.append(embedding[:target_dim])
+            else:
+                # Pad with zeros
+                padded = embedding + [0.0] * (target_dim - len(embedding))
+                normalized.append(padded)
+        return normalized
+
+    def _combine_embeddings(
+        self, embedding_lists: List[List[List[float]]]
+    ) -> List[List[float]]:
+        """Combine embeddings from multiple models."""
+        if not embedding_lists:
+            return []
+
+        if len(embedding_lists) == 1:
+            return embedding_lists[0]
+
+        num_docs = len(embedding_lists[0])
+        combined = []
+
+        for doc_idx in range(num_docs):
+            doc_embeddings = [emb_list[doc_idx] for emb_list in embedding_lists]
+
+            if self.combination_method == "average":
+                combined_embedding = np.mean(doc_embeddings, axis=0).tolist()
+            elif self.combination_method == "weighted":
+                # Give more weight to primary model
+                weights = [0.7] + [0.3 / (len(doc_embeddings) - 1)] * (
+                    len(doc_embeddings) - 1
+                )
+                combined_embedding = np.average(
+                    doc_embeddings, axis=0, weights=weights
+                ).tolist()
+            elif self.combination_method == "concat":
+                combined_embedding = []
+                for emb in doc_embeddings:
+                    combined_embedding.extend(emb)
+            else:
+                # Default to average
+                combined_embedding = np.mean(doc_embeddings, axis=0).tolist()
+
+            combined.append(combined_embedding)
+
+        return combined
+
+
+class ContextAwareEmbeddingStrategy(EnhancedEmbeddingStrategy):
+    """Embeddings that consider document context and metadata."""
+
+    def __init__(
+        self,
+        base_model: Optional[Embeddings] = None,
+        include_metadata: bool = True,
+        metadata_weight: float = 0.1,
+    ):
+        self.base_model = base_model or OpenAIEmbeddings(
+            model="text-embedding-3-large",
+            dimensions=1536,
+        )
+        self.include_metadata = include_metadata
+        self.metadata_weight = metadata_weight
+
+    def embed_documents_with_context(
+        self, documents: List[Document]
+    ) -> List[List[float]]:
+        """Embed documents with their metadata context."""
+        enhanced_texts = []
+
+        for doc in documents:
+            text = doc.page_content
+
+            if self.include_metadata and doc.metadata:
+                # Add relevant metadata to the text for embedding
+                metadata_text = self._extract_metadata_text(doc.metadata)
+                if metadata_text:
+                    text = f"{text}\n\nContext: {metadata_text}"
+
+            enhanced_texts.append(text)
+
+        return self.base_model.embed_documents(enhanced_texts)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Standard embedding interface."""
+        return self.base_model.embed_documents(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        return self.base_model.embed_query(text)
+
+    def _extract_metadata_text(self, metadata: Dict[str, Any]) -> str:
+        """Extract relevant text from metadata."""
+        relevant_fields = ["title", "summary", "keywords", "type", "category"]
+        metadata_parts = []
+
+        for field in relevant_fields:
+            if field in metadata and metadata[field]:
+                value = metadata[field]
+                if isinstance(value, list):
+                    value = ", ".join(str(v) for v in value)
+                metadata_parts.append(f"{field}: {value}")
+
+        return "; ".join(metadata_parts)
+
+
+class HierarchicalEmbeddingStrategy(EnhancedEmbeddingStrategy):
+    """Multi-level embeddings for different granularities."""
+
+    def __init__(
+        self,
+        sentence_model: Optional[Embeddings] = None,
+        paragraph_model: Optional[Embeddings] = None,
+        document_model: Optional[Embeddings] = None,
+    ):
+        self.sentence_model = sentence_model or OpenAIEmbeddings(
+            model="text-embedding-3-small", dimensions=512
+        )
+        self.paragraph_model = paragraph_model or OpenAIEmbeddings(
+            model="text-embedding-3-large", dimensions=1024
+        )
+        self.document_model = document_model or OpenAIEmbeddings(
+            model="text-embedding-3-large", dimensions=1536
+        )
+
+    def embed_hierarchical(
+        self, documents: List[Document], level: str = "paragraph"
+    ) -> List[List[float]]:
+        """Embed at specified hierarchical level."""
+        texts = [doc.page_content for doc in documents]
+
+        if level == "sentence":
+            return self.sentence_model.embed_documents(texts)
+        elif level == "paragraph":
+            return self.paragraph_model.embed_documents(texts)
+        elif level == "document":
+            return self.document_model.embed_documents(texts)
+        else:
+            raise ValueError(f"Unknown level: {level}")
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Default to paragraph level."""
+        return self.paragraph_model.embed_documents(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query at appropriate level based on length."""
+        if len(text.split()) < 10:
+            return self.sentence_model.embed_query(text)
+        elif len(text.split()) < 50:
+            return self.paragraph_model.embed_query(text)
+        else:
+            return self.document_model.embed_query(text)
+
+
+class AdaptiveEmbeddingStrategy(EnhancedEmbeddingStrategy):
+    """Adaptively choose embedding strategy based on content."""
+
+    def __init__(self):
+        self.multi_model = MultiModelEmbeddingStrategy()
+        self.context_aware = ContextAwareEmbeddingStrategy()
+        self.hierarchical = HierarchicalEmbeddingStrategy()
+
+        # Cache for performance
+        self._cache = {}
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Choose best embedding strategy based on content characteristics."""
+        strategy = self._choose_strategy(texts)
+
+        cache_key = f"{strategy.__class__.__name__}_{hash(tuple(texts))}"
+        if cache_key in self._cache:
+            logger.debug("Using cached embeddings")
+            return self._cache[cache_key]
+
+        embeddings = strategy.embed_documents(texts)
+        self._cache[cache_key] = embeddings
+
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query using adaptive strategy."""
+        strategy = self._choose_strategy([text])
+        return strategy.embed_query(text)
+
+    def _choose_strategy(self, texts: List[str]) -> EnhancedEmbeddingStrategy:
+        """Choose the best embedding strategy for the given texts."""
+        if not texts:
+            return self.multi_model
+
+        avg_length = sum(len(text.split()) for text in texts) / len(texts)
+        has_structure = any(self._has_structure(text) for text in texts)
+
+        if has_structure:
+            return self.context_aware
+        elif avg_length > 200:
+            return self.hierarchical
+        else:
+            return self.multi_model
+
+    def _has_structure(self, text: str) -> bool:
+        """Detect if text has structural elements."""
+        import re
+
+        # Look for structural indicators
+        has_headers = bool(re.search(r"^(#+\s|\d+\.\s)", text, re.MULTILINE))
+        has_bullets = bool(re.search(r"^[\s]*[-*•]\s", text, re.MULTILINE))
+        has_sections = bool(re.search(r"\n\n[A-Z][^.]*:\n", text))
+
+        return has_headers or has_bullets or has_sections
+
+
+# Factory function
+def create_embedding_strategy(
+    strategy_type: str = "adaptive", **kwargs
+) -> EnhancedEmbeddingStrategy:
+    """Factory function to create embedding strategies."""
+
+    strategies = {
+        "multi_model": MultiModelEmbeddingStrategy,
+        "context_aware": ContextAwareEmbeddingStrategy,
+        "hierarchical": HierarchicalEmbeddingStrategy,
+        "adaptive": AdaptiveEmbeddingStrategy,
+    }
+
+    if strategy_type not in strategies:
+        raise ValueError(f"Unknown strategy type: {strategy_type}")
+
+    strategy_class = strategies[strategy_type]
+    return strategy_class(**kwargs)
+
+
+# Enhanced embedding models configuration
+ENHANCED_EMBEDDING_CONFIGS = {
+    "latest_openai": {
+        "model": "text-embedding-3-large",
+        "dimensions": 1536,
+        "description": "Latest OpenAI embedding model with best performance",
+    },
+    "fast_openai": {
+        "model": "text-embedding-3-small",
+        "dimensions": 512,
+        "description": "Faster OpenAI model for real-time applications",
+    },
+    "legacy_openai": {
+        "model": "text-embedding-ada-002",
+        "dimensions": 1536,
+        "description": "Legacy model for compatibility",
+    },
+}
diff --git a/services/workflows/graph_rag.py b/services/workflows/graph_rag.py
new file mode 100644
index 00000000..f59d58e2
--- /dev/null
+++ b/services/workflows/graph_rag.py
@@ -0,0 +1,579 @@
+"""GraphRAG implementation combining knowledge graphs with vector retrieval."""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+import networkx as nx
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from langchain_openai import OpenAIEmbeddings
+
+from backend.factory import backend
+from lib.logger import configure_logger
+
+logger = configure_logger(__name__)
+
+
+@dataclass
+class GraphNode:
+    """Represents a node in the knowledge graph."""
+
+    id: str
+    type: str  # e.g., "proposal", "dao", "agent", "concept"
+    properties: Dict[str, Any]
+    embedding: Optional[List[float]] = None
+
+
+@dataclass
+class GraphEdge:
+    """Represents an edge in the knowledge graph."""
+
+    source: str
+    target: str
+    relation: str
+    properties: Dict[str, Any]
+    weight: float = 1.0
+
+
+@dataclass
+class GraphRAGResult:
+    """Enhanced result combining graph and vector information."""
+
+    document: Document
+    vector_score: float
+    graph_score: float
+    combined_score: float
+    graph_context: Dict[str, Any]
+    related_entities: List[GraphNode]
+    relationship_paths: List[List[str]]
+
+
+class KnowledgeGraphBuilder:
+    """Builds and maintains a knowledge graph from documents."""
+
+    def __init__(self, embeddings: Optional[Embeddings] = None):
+        self.embeddings = embeddings or OpenAIEmbeddings(model="text-embedding-3-large")
+        self.graph = nx.DiGraph()
+        self.node_embeddings = {}
+        self.entity_types = {
+            "dao",
+            "proposal",
+            "agent",
+            "token",
+            "wallet",
+            "concept",
+            "action",
+            "vote",
+            "user",
+        }
+
+    def add_document_to_graph(self, document: Document) -> List[GraphNode]:
+        """Extract entities and relationships from a document and add to graph."""
+        nodes_added = []
+
+        # Extract entities from document
+        entities = self._extract_entities(document)
+
+        # Create nodes for entities
+        for entity in entities:
+            node = self._create_node(entity, document)
+            if node:
+                self.graph.add_node(
+                    node.id,
+                    type=node.type,
+                    properties=node.properties,
+                    embedding=node.embedding,
+                )
+                nodes_added.append(node)
+
+        # Extract and add relationships
+        relationships = self._extract_relationships(entities, document)
+        for source, target, relation, properties in relationships:
+            self.graph.add_edge(
+                source,
+                target,
+                relation=relation,
+                properties=properties,
+                weight=properties.get("weight", 1.0),
+            )
+
+        # Connect document to its entities
+        doc_id = document.metadata.get(
+            "proposal_id", f"doc_{hash(document.page_content)}"
+        )
+        doc_node = GraphNode(
+            id=doc_id,
+            type="document",
+            properties={
+                "title": document.metadata.get("title", ""),
+                "content_preview": document.page_content[:200],
+                **document.metadata,
+            },
+        )
+
+        self.graph.add_node(
+            doc_node.id, type=doc_node.type, properties=doc_node.properties
+        )
+
+        # Connect document to extracted entities
+        for entity_node in nodes_added:
+            self.graph.add_edge(
+                doc_node.id,
+                entity_node.id,
+                relation="mentions",
+                properties={"source": "extraction"},
+                weight=0.8,
+            )
+
+        return nodes_added
+
+    def _extract_entities(self, document: Document) -> List[Dict[str, Any]]:
+        """Extract named entities from document content."""
+        entities = []
+        content = document.page_content.lower()
+        metadata = document.metadata
+
+        # Extract entities from metadata
+        if "dao_id" in metadata:
+            entities.append(
+                {
+                    "text": metadata.get("dao_name", metadata["dao_id"]),
+                    "type": "dao",
+                    "id": metadata["dao_id"],
+                    "metadata": {"source": "metadata"},
+                }
+            )
+
+        if "proposal_id" in metadata:
+            entities.append(
+                {
+                    "text": metadata.get(
+                        "title", f"proposal_{metadata['proposal_id']}"
+                    ),
+                    "type": "proposal",
+                    "id": metadata["proposal_id"],
+                    "metadata": {"source": "metadata"},
+                }
+            )
+
+        # Simple keyword-based entity extraction
+        # In production, use NER models like spaCy or a custom model
+        dao_keywords = ["dao", "organization", "governance", "community"]
+        proposal_keywords = ["proposal", "vote", "decision", "action"]
+        agent_keywords = ["agent", "bot", "ai", "assistant"]
+
+        for keyword in dao_keywords:
+            if keyword in content:
+                entities.append(
+                    {
+                        "text": keyword,
+                        "type": "concept",
+                        "id": f"concept_{keyword}",
+                        "metadata": {"source": "extraction"},
+                    }
+                )
+
+        return entities
+
+    def _create_node(
+        self, entity: Dict[str, Any], document: Document
+    ) -> Optional[GraphNode]:
+        """Create a graph node from an extracted entity."""
+        try:
+            # Generate embedding for entity
+            entity_text = entity["text"]
+            embedding = self.embeddings.embed_query(entity_text)
+
+            node = GraphNode(
+                id=entity["id"],
+                type=entity["type"],
+                properties={
+                    "text": entity_text,
+                    "source_document": document.metadata.get("proposal_id", "unknown"),
+                    **entity.get("metadata", {}),
+                },
+                embedding=embedding,
+            )
+
+            return node
+
+        except Exception as e:
+            logger.error(f"Failed to create node for entity {entity}: {e}")
+            return None
+
+    def _extract_relationships(
+        self, entities: List[Dict[str, Any]], document: Document
+    ) -> List[Tuple[str, str, str, Dict[str, Any]]]:
+        """Extract relationships between entities."""
+        relationships = []
+
+        # Simple co-occurrence based relationships
+        for i, entity1 in enumerate(entities):
+            for entity2 in entities[i + 1 :]:
+                # Create relationship if entities co-occur
+                relation_type = self._determine_relation_type(entity1, entity2)
+                if relation_type:
+                    relationships.append(
+                        (
+                            entity1["id"],
+                            entity2["id"],
+                            relation_type,
+                            {
+                                "source": "co_occurrence",
+                                "document": document.metadata.get(
+                                    "proposal_id", "unknown"
+                                ),
+                                "weight": 0.5,
+                            },
+                        )
+                    )
+
+        return relationships
+
+    def _determine_relation_type(
+        self, entity1: Dict[str, Any], entity2: Dict[str, Any]
+    ) -> Optional[str]:
+        """Determine the type of relationship between two entities."""
+        type1, type2 = entity1["type"], entity2["type"]
+
+        # Define relationship rules
+        if type1 == "dao" and type2 == "proposal":
+            return "has_proposal"
+        elif type1 == "proposal" and type2 == "dao":
+            return "belongs_to"
+        elif type1 == "dao" and type2 == "agent":
+            return "has_agent"
+        elif type1 == "proposal" and type2 == "concept":
+            return "relates_to"
+        elif type1 == type2:
+            return "related_to"
+
+        return "associated_with"
+
+    def find_related_entities(
+        self,
+        entity_id: str,
+        max_depth: int = 2,
+        relation_types: Optional[List[str]] = None,
+    ) -> List[GraphNode]:
+        """Find entities related to a given entity."""
+        if entity_id not in self.graph:
+            return []
+
+        related_nodes = []
+        visited = set()
+
+        def dfs(node_id: str, depth: int):
+            if depth > max_depth or node_id in visited:
+                return
+
+            visited.add(node_id)
+
+            # Get neighbors
+            for neighbor in self.graph.neighbors(node_id):
+                edge_data = self.graph[node_id][neighbor]
+                relation = edge_data.get("relation", "")
+
+                if relation_types is None or relation in relation_types:
+                    node_data = self.graph.nodes[neighbor]
+                    related_node = GraphNode(
+                        id=neighbor,
+                        type=node_data.get("type", "unknown"),
+                        properties=node_data.get("properties", {}),
+                        embedding=node_data.get("embedding"),
+                    )
+                    related_nodes.append(related_node)
+
+                    dfs(neighbor, depth + 1)
+
+        dfs(entity_id, 0)
+        return related_nodes
+
+    def get_graph_stats(self) -> Dict[str, Any]:
+        """Get statistics about the knowledge graph."""
+        return {
+            "nodes": self.graph.number_of_nodes(),
+            "edges": self.graph.number_of_edges(),
+            "node_types": self._get_node_type_counts(),
+            "relation_types": self._get_relation_type_counts(),
+            "density": nx.density(self.graph),
+            "connected_components": nx.number_weakly_connected_components(self.graph),
+        }
+
+    def _get_node_type_counts(self) -> Dict[str, int]:
+        """Count nodes by type."""
+        type_counts = {}
+        for node_id, data in self.graph.nodes(data=True):
+            node_type = data.get("type", "unknown")
+            type_counts[node_type] = type_counts.get(node_type, 0) + 1
+        return type_counts
+
+    def _get_relation_type_counts(self) -> Dict[str, int]:
+        """Count edges by relation type."""
+        relation_counts = {}
+        for source, target, data in self.graph.edges(data=True):
+            relation = data.get("relation", "unknown")
+            relation_counts[relation] = relation_counts.get(relation, 0) + 1
+        return relation_counts
+
+
+class GraphRAGRetriever:
+    """Retriever that combines vector search with graph traversal."""
+
+    def __init__(
+        self,
+        collection_names: List[str],
+        knowledge_graph: KnowledgeGraphBuilder,
+        embeddings: Optional[Embeddings] = None,
+        vector_weight: float = 0.6,
+        graph_weight: float = 0.4,
+    ):
+        self.collection_names = collection_names
+        self.knowledge_graph = knowledge_graph
+        self.embeddings = embeddings or OpenAIEmbeddings(model="text-embedding-3-large")
+        self.vector_weight = vector_weight
+        self.graph_weight = graph_weight
+
+    async def retrieve(
+        self, query: str, limit: int = 10, use_graph_expansion: bool = True, **kwargs
+    ) -> List[GraphRAGResult]:
+        """Retrieve documents using both vector search and graph traversal."""
+        logger.info(f"GraphRAG retrieval for query: '{query[:50]}...'")
+
+        # Step 1: Vector search
+        vector_results = await self._vector_search(query, limit * 2)
+
+        # Step 2: Graph-based enhancement
+        enhanced_results = []
+
+        for doc, vector_score in vector_results:
+            # Find related entities for this document
+            doc_id = doc.metadata.get("proposal_id", f"doc_{hash(doc.page_content)}")
+            related_entities = self.knowledge_graph.find_related_entities(doc_id)
+
+            # Calculate graph score based on entity relationships
+            graph_score = self._calculate_graph_score(query, doc, related_entities)
+
+            # Find relationship paths
+            paths = self._find_relationship_paths(query, doc_id)
+
+            # Combine scores
+            combined_score = (
+                self.vector_weight * vector_score + self.graph_weight * graph_score
+            )
+
+            result = GraphRAGResult(
+                document=doc,
+                vector_score=vector_score,
+                graph_score=graph_score,
+                combined_score=combined_score,
+                graph_context={
+                    "related_entity_count": len(related_entities),
+                    "relationship_path_count": len(paths),
+                },
+                related_entities=related_entities,
+                relationship_paths=paths,
+            )
+
+            enhanced_results.append(result)
+
+        # Sort by combined score
+        enhanced_results.sort(key=lambda x: x.combined_score, reverse=True)
+
+        # Graph expansion: Add related documents
+        if use_graph_expansion and enhanced_results:
+            expanded_results = await self._expand_with_graph(enhanced_results, query)
+            enhanced_results.extend(expanded_results)
+
+        # Remove duplicates and limit results
+        unique_results = self._deduplicate_results(enhanced_results)
+
+        logger.info(
+            f"GraphRAG retrieval completed: {len(unique_results[:limit])} results"
+        )
+
+        return unique_results[:limit]
+
+    async def _vector_search(
+        self, query: str, limit: int
+    ) -> List[Tuple[Document, float]]:
+        """Perform vector similarity search."""
+        all_results = []
+
+        for collection_name in self.collection_names:
+            try:
+                results = await backend.query_vectors(
+                    collection_name=collection_name,
+                    query_text=query,
+                    limit=limit // len(self.collection_names),
+                    embeddings=self.embeddings,
+                )
+
+                for result in results:
+                    doc = Document(
+                        page_content=result.get("page_content", ""),
+                        metadata={
+                            **result.get("metadata", {}),
+                            "collection_source": collection_name,
+                        },
+                    )
+                    score = result.get("similarity", 0.5)
+                    all_results.append((doc, score))
+
+            except Exception as e:
+                logger.error(
+                    f"Vector search failed for collection {collection_name}: {e}"
+                )
+                continue
+
+        return all_results
+
+    def _calculate_graph_score(
+        self, query: str, document: Document, related_entities: List[GraphNode]
+    ) -> float:
+        """Calculate relevance score based on graph structure."""
+        if not related_entities:
+            return 0.0
+
+        try:
+            # Get query embedding
+            query_embedding = self.embeddings.embed_query(query)
+
+            # Calculate similarity with related entities
+            entity_scores = []
+            for entity in related_entities:
+                if entity.embedding:
+                    similarity = self._cosine_similarity(
+                        query_embedding, entity.embedding
+                    )
+                    entity_scores.append(similarity)
+
+            if entity_scores:
+                # Use max similarity as graph score
+                return max(entity_scores)
+            else:
+                # Fallback: score based on number of relationships
+                return min(len(related_entities) / 10.0, 1.0)
+
+        except Exception as e:
+            logger.error(f"Failed to calculate graph score: {e}")
+            return 0.0
+
+    def _find_relationship_paths(self, query: str, doc_id: str) -> List[List[str]]:
+        """Find relationship paths that might be relevant to the query."""
+        paths = []
+
+        if doc_id not in self.knowledge_graph.graph:
+            return paths
+
+        # Find paths to entities that might be relevant to the query
+        # This is a simplified implementation
+        try:
+            # Get all nodes within 2 hops
+            for target in self.knowledge_graph.graph.nodes():
+                if target != doc_id:
+                    try:
+                        path = nx.shortest_path(
+                            self.knowledge_graph.graph, doc_id, target
+                        )
+                        if len(path) <= 3:  # Max 2 hops
+                            paths.append(path)
+                    except nx.NetworkXNoPath:
+                        continue
+        except Exception as e:
+            logger.error(f"Failed to find relationship paths: {e}")
+
+        return paths[:5]  # Limit to top 5 paths
+
+    async def _expand_with_graph(
+        self, initial_results: List[GraphRAGResult], query: str
+    ) -> List[GraphRAGResult]:
+        """Expand results using graph relationships."""
+        expanded_results = []
+
+        # Get entities from top results
+        top_entities = set()
+        for result in initial_results[:3]:  # Use top 3 results
+            for entity in result.related_entities:
+                top_entities.add(entity.id)
+
+        # Find documents related to these entities
+        for entity_id in top_entities:
+            related_entities = self.knowledge_graph.find_related_entities(
+                entity_id, max_depth=1
+            )
+
+            for entity in related_entities:
+                if entity.type == "document":
+                    # This would require additional document retrieval logic
+                    # For now, we'll skip this expansion
+                    pass
+
+        return expanded_results
+
+    def _deduplicate_results(
+        self, results: List[GraphRAGResult]
+    ) -> List[GraphRAGResult]:
+        """Remove duplicate results based on document content."""
+        seen_docs = set()
+        unique_results = []
+
+        for result in results:
+            doc_id = result.document.metadata.get(
+                "proposal_id", f"doc_{hash(result.document.page_content)}"
+            )
+
+            if doc_id not in seen_docs:
+                seen_docs.add(doc_id)
+                unique_results.append(result)
+
+        return unique_results
+
+    def _cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
+        """Calculate cosine similarity between two vectors."""
+        import numpy as np
+
+        vec1_np = np.array(vec1)
+        vec2_np = np.array(vec2)
+
+        dot_product = np.dot(vec1_np, vec2_np)
+        norm1 = np.linalg.norm(vec1_np)
+        norm2 = np.linalg.norm(vec2_np)
+
+        if norm1 == 0 or norm2 == 0:
+            return 0.0
+
+        return dot_product / (norm1 * norm2)
+
+
+# Factory function
+def create_graph_rag_system(
+    collection_names: List[str],
+    embeddings: Optional[Embeddings] = None,
+    graph_config: Optional[Dict[str, Any]] = None,
+) -> Tuple[KnowledgeGraphBuilder, GraphRAGRetriever]:
+    """Factory function to create a complete GraphRAG system."""
+
+    config = graph_config or {}
+
+    # Create embeddings if not provided
+    if embeddings is None:
+        embeddings = OpenAIEmbeddings(
+            model=config.get("embedding_model", "text-embedding-3-large"),
+            dimensions=config.get("embedding_dimensions", 1536),
+        )
+
+    # Create knowledge graph builder
+    graph_builder = KnowledgeGraphBuilder(embeddings)
+
+    # Create GraphRAG retriever
+    retriever = GraphRAGRetriever(
+        collection_names=collection_names,
+        knowledge_graph=graph_builder,
+        embeddings=embeddings,
+        vector_weight=config.get("vector_weight", 0.6),
+        graph_weight=config.get("graph_weight", 0.4),
+    )
+
+    return graph_builder, retriever
diff --git a/services/workflows/hybrid_search.py b/services/workflows/hybrid_search.py
new file mode 100644
index 00000000..56bb5297
--- /dev/null
+++ b/services/workflows/hybrid_search.py
@@ -0,0 +1,310 @@
+"""Hybrid search implementation combining vector and keyword search with reranking."""
+
+import re
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from langchain_openai import OpenAIEmbeddings
+from rank_bm25 import BM25Okapi
+
+from backend.factory import backend
+from lib.logger import configure_logger
+
+logger = configure_logger(__name__)
+
+
+@dataclass
+class SearchResult:
+    """Enhanced search result with scoring information."""
+
+    document: Document
+    vector_score: float
+    keyword_score: float
+    combined_score: float
+    rank: int
+
+
+class Reranker(ABC):
+    """Abstract base class for reranking strategies."""
+
+    @abstractmethod
+    def rerank(
+        self, query: str, results: List[SearchResult], **kwargs
+    ) -> List[SearchResult]:
+        """Rerank search results based on relevance."""
+        pass
+
+
+class SemanticReranker(Reranker):
+    """Rerank results based on semantic similarity with the query."""
+
+    def __init__(self, embeddings: Optional[Embeddings] = None):
+        self.embeddings = embeddings or OpenAIEmbeddings(model="text-embedding-3-large")
+
+    def rerank(
+        self, query: str, results: List[SearchResult], **kwargs
+    ) -> List[SearchResult]:
+        """Rerank based on semantic similarity."""
+        if not results:
+            return results
+
+        try:
+            # Get query embedding
+            query_embedding = self.embeddings.embed_query(query)
+
+            # Get document embeddings
+            texts = [result.document.page_content for result in results]
+            doc_embeddings = self.embeddings.embed_documents(texts)
+
+            # Calculate semantic similarities
+            similarities = []
+            for doc_embedding in doc_embeddings:
+                similarity = self._cosine_similarity(query_embedding, doc_embedding)
+                similarities.append(similarity)
+
+            # Update scores and rerank
+            reranked_results = []
+            for i, result in enumerate(results):
+                semantic_score = similarities[i]
+                # Combine with existing score
+                new_score = 0.7 * semantic_score + 0.3 * result.combined_score
+
+                reranked_result = SearchResult(
+                    document=result.document,
+                    vector_score=result.vector_score,
+                    keyword_score=result.keyword_score,
+                    combined_score=new_score,
+                    rank=result.rank,
+                )
+                reranked_results.append(reranked_result)
+
+            # Sort by new combined score
+            reranked_results.sort(key=lambda x: x.combined_score, reverse=True)
+
+            # Update ranks
+            for i, result in enumerate(reranked_results):
+                result.rank = i + 1
+
+            return reranked_results
+
+        except Exception as e:
+            logger.error(f"Semantic reranking failed: {e}")
+            return results
+
+    def _cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
+        """Calculate cosine similarity between two vectors."""
+        vec1_np = np.array(vec1)
+        vec2_np = np.array(vec2)
+
+        dot_product = np.dot(vec1_np, vec2_np)
+        norm1 = np.linalg.norm(vec1_np)
+        norm2 = np.linalg.norm(vec2_np)
+
+        if norm1 == 0 or norm2 == 0:
+            return 0.0
+
+        return dot_product / (norm1 * norm2)
+
+
+class MetadataReranker(Reranker):
+    """Rerank results based on metadata relevance."""
+
+    def __init__(self, metadata_weights: Optional[Dict[str, float]] = None):
+        self.metadata_weights = metadata_weights or {
+            "title": 0.3,
+            "type": 0.2,
+            "dao_id": 0.15,
+            "keywords": 0.25,
+            "recency": 0.1,
+        }
+
+    def rerank(
+        self, query: str, results: List[SearchResult], **kwargs
+    ) -> List[SearchResult]:
+        """Rerank based on metadata relevance."""
+        if not results:
+            return results
+
+        query_lower = query.lower()
+
+        reranked_results = []
+        for result in results:
+            metadata_score = self._calculate_metadata_score(
+                query_lower, result.document.metadata
+            )
+
+            # Combine with existing score
+            new_score = 0.6 * result.combined_score + 0.4 * metadata_score
+
+            reranked_result = SearchResult(
+                document=result.document,
+                vector_score=result.vector_score,
+                keyword_score=result.keyword_score,
+                combined_score=new_score,
+                rank=result.rank,
+            )
+            reranked_results.append(reranked_result)
+
+        # Sort by new combined score
+        reranked_results.sort(key=lambda x: x.combined_score, reverse=True)
+
+        # Update ranks
+        for i, result in enumerate(reranked_results):
+            result.rank = i + 1
+
+        return reranked_results
+
+    def _calculate_metadata_score(self, query: str, metadata: Dict[str, Any]) -> float:
+        """Calculate relevance score based on metadata."""
+        total_score = 0.0
+        total_weight = 0.0
+
+        for field, weight in self.metadata_weights.items():
+            if field in metadata and metadata[field]:
+                field_score = self._calculate_field_score(query, metadata[field])
+                total_score += field_score * weight
+                total_weight += weight
+
+        return total_score / total_weight if total_weight > 0 else 0.0
+
+    def _calculate_field_score(self, query: str, field_value: Any) -> float:
+        """Calculate score for a specific metadata field."""
+        if not field_value:
+            return 0.0
+
+        field_text = str(field_value).lower()
+
+        # Simple keyword matching
+        query_words = set(query.split())
+        field_words = set(field_text.split())
+
+        if not query_words:
+            return 0.0
+
+        overlap = query_words.intersection(field_words)
+        return len(overlap) / len(query_words)
+
+
+class HybridSearchEngine:
+    """Hybrid search engine combining vector and keyword search."""
+
+    def __init__(
+        self,
+        collection_names: List[str],
+        embeddings: Optional[Embeddings] = None,
+        vector_weight: float = 0.7,
+        keyword_weight: float = 0.3,
+    ):
+        self.collection_names = collection_names
+        self.embeddings = embeddings or OpenAIEmbeddings(model="text-embedding-3-large")
+        self.vector_weight = vector_weight
+        self.keyword_weight = keyword_weight
+
+    async def hybrid_search(
+        self, query: str, limit: int = 10, **kwargs
+    ) -> List[SearchResult]:
+        """Perform hybrid search combining vector and keyword search."""
+        logger.info(f"Performing hybrid search for query: '{query[:50]}...'")
+
+        # Perform vector search
+        vector_results = await self._vector_search(query, limit * 2)
+
+        # For now, we'll use vector search as the primary method
+        # Keyword search can be added later with BM25 or Elasticsearch
+
+        # Convert to SearchResult format
+        search_results = []
+        for i, (doc, score) in enumerate(vector_results):
+            search_result = SearchResult(
+                document=doc,
+                vector_score=score,
+                keyword_score=0.0,  # TODO: Implement keyword scoring
+                combined_score=score,
+                rank=i + 1,
+            )
+            search_results.append(search_result)
+
+        return search_results[:limit]
+
+    async def _vector_search(
+        self, query: str, limit: int
+    ) -> List[Tuple[Document, float]]:
+        """Perform vector similarity search."""
+        all_results = []
+
+        for collection_name in self.collection_names:
+            try:
+                results = await backend.query_vectors(
+                    collection_name=collection_name,
+                    query_text=query,
+                    limit=limit // len(self.collection_names),
+                    embeddings=self.embeddings,
+                )
+
+                for result in results:
+                    doc = Document(
+                        page_content=result.get("page_content", ""),
+                        metadata={
+                            **result.get("metadata", {}),
+                            "collection_source": collection_name,
+                        },
+                    )
+                    # Default score if not available
+                    score = result.get("similarity", 0.5)
+                    all_results.append((doc, score))
+
+            except Exception as e:
+                logger.error(
+                    f"Vector search failed for collection {collection_name}: {e}"
+                )
+                continue
+
+        return all_results
+
+    def get_search_stats(self) -> Dict[str, Any]:
+        """Get statistics about the search engine."""
+        stats = {
+            "collections": len(self.collection_names),
+            "total_indexed_documents": 0,
+            "collection_stats": {},
+        }
+
+        for collection_name in self.collection_names:
+            docs_count = 0
+            has_bm25 = False
+
+            stats["collection_stats"][collection_name] = {
+                "document_count": docs_count,
+                "has_keyword_index": has_bm25,
+            }
+
+        return stats
+
+
+# Factory function
+def create_hybrid_search_engine(
+    collection_names: List[str],
+    embeddings: Optional[Embeddings] = None,
+    search_config: Optional[Dict[str, Any]] = None,
+) -> HybridSearchEngine:
+    """Factory function to create a hybrid search engine."""
+
+    config = search_config or {}
+
+    # Create embeddings if not provided
+    if embeddings is None:
+        embeddings = OpenAIEmbeddings(
+            model=config.get("embedding_model", "text-embedding-3-large"),
+            dimensions=config.get("embedding_dimensions", 1536),
+        )
+
+    return HybridSearchEngine(
+        collection_names=collection_names,
+        embeddings=embeddings,
+        vector_weight=config.get("vector_weight", 0.7),
+        keyword_weight=config.get("keyword_weight", 0.3),
+    )
diff --git a/services/workflows/vector_mixin.py b/services/workflows/vector_mixin.py
index 5d2cde89..da6405fc 100644
--- a/services/workflows/vector_mixin.py
+++ b/services/workflows/vector_mixin.py
@@ -7,6 +7,8 @@
 from backend.factory import backend
 from lib.logger import configure_logger
 from services.workflows.base import BaseWorkflowMixin
+from services.workflows.enhanced_embeddings import create_embedding_strategy
+from services.workflows.hybrid_search import HybridSearchEngine, SearchResult
 
 logger = configure_logger(__name__)
 
@@ -22,11 +24,24 @@ def __init__(self, *args, **kwargs):
     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"]
+            self.collection_names = [
+                "knowledge_collection",
+                "dao_collection",
+                "proposals",
+            ]
         if not hasattr(self, "embeddings"):
-            self.embeddings = OpenAIEmbeddings()
+            # Use enhanced embedding strategy
+            self.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
+            self.embedding_strategy = create_embedding_strategy("adaptive")
         if not hasattr(self, "vector_results_cache"):
             self.vector_results_cache = {}
+        if not hasattr(self, "hybrid_search_engine"):
+            self.hybrid_search_engine = HybridSearchEngine(
+                collection_names=self.collection_names,
+                embeddings=self.embeddings,
+                vector_weight=0.7,
+                keyword_weight=0.3,
+            )
 
     async def retrieve_from_vector_store(self, query: str, **kwargs) -> List[Document]:
         """Retrieve relevant documents from multiple vector stores.
@@ -85,6 +100,50 @@ async def retrieve_from_vector_store(self, query: str, **kwargs) -> List[Documen
             logger.error(f"Vector store retrieval failed: {str(e)}", exc_info=True)
             return []
 
+    async def hybrid_retrieve(self, query: str, **kwargs) -> List[Document]:
+        """Retrieve documents using hybrid search (vector + keyword)."""
+        try:
+            self._init_vector_retrieval()
+
+            # Check cache first
+            cache_key = f"hybrid_{query}"
+            if cache_key in self.vector_results_cache:
+                logger.debug(f"Using cached hybrid results for query: {query}")
+                return self.vector_results_cache[cache_key]
+
+            limit = kwargs.pop("limit", 10)
+
+            # Perform hybrid search
+            search_results = await self.hybrid_search_engine.hybrid_search(
+                query=query, limit=limit, **kwargs
+            )
+
+            # Convert SearchResult objects back to Documents
+            documents = [result.document for result in search_results]
+
+            # Add search metadata to documents
+            for i, (doc, result) in enumerate(zip(documents, search_results)):
+                doc.metadata.update(
+                    {
+                        "search_rank": result.rank,
+                        "vector_score": result.vector_score,
+                        "keyword_score": result.keyword_score,
+                        "combined_score": result.combined_score,
+                        "search_method": "hybrid",
+                    }
+                )
+
+            # Cache results
+            self.vector_results_cache[cache_key] = documents
+
+            logger.debug(f"Hybrid search returned {len(documents)} documents")
+            return documents
+
+        except Exception as e:
+            logger.error(f"Hybrid search failed: {str(e)}", exc_info=True)
+            # Fallback to regular vector search
+            return await self.retrieve_from_vector_store(query, **kwargs)
+
     def integrate_with_graph(self, graph: StateGraph, **kwargs) -> None:
         """Integrate vector retrieval capability with a graph.
 
diff --git a/test_improved_workflow.py b/test_improved_workflow.py
deleted file mode 100644
index 64f3709e..00000000
--- a/test_improved_workflow.py
+++ /dev/null
@@ -1,202 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for the improved proposal evaluation workflow.
-
-This script tests the enhanced supervisor logic and completion tracking
-to ensure we don't have infinite loops with core agent invocations.
-"""
-
-import asyncio
-import os
-import sys
-from uuid import uuid4
-
-# Add the project root to Python path
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-
-from lib.logger import configure_logger
-from services.workflows.proposal_evaluation import evaluate_proposal
-
-logger = configure_logger(__name__)
-
-
-async def test_improved_workflow():
-    """Test the improved workflow with better supervision."""
-    print("🧪 Testing Improved Proposal Evaluation Workflow")
-    print("=" * 60)
-
-    # Test with a simple proposal
-    test_proposal_id = str(uuid4())
-    test_proposal_data = """
-    Proposal: Fund AI Development Initiative
-    
-    We request 500 tokens to develop advanced AI capabilities for the DAO.
-    This will help automate proposal evaluation and improve decision-making.
-    
-    Timeline: 3 months
-    Deliverables:
-    - Enhanced AI evaluation system
-    - Automated proposal scoring
-    - Integration with existing DAO infrastructure
-    
-    Budget breakdown:
-    - Development: 300 tokens
-    - Testing: 100 tokens
-    - Documentation: 100 tokens
-    """
-
-    try:
-        print(f"📋 Proposal ID: {test_proposal_id}")
-        print(f"📝 Proposal Content: {len(test_proposal_data)} characters")
-        print("\n🚀 Starting evaluation...")
-
-        # Configure with debugging enabled
-        config = {
-            "model_name": "gpt-4.1",
-            "debug_level": 2,
-            "recursion_limit": 15,  # Lower limit to catch issues faster
-        }
-
-        result = await evaluate_proposal(
-            proposal_id=test_proposal_id,
-            proposal_data=test_proposal_data,
-            config=config,
-        )
-
-        print("\n✅ Evaluation completed successfully!")
-        print("=" * 60)
-
-        # Display results
-        print(f"📊 Final Decision: {result.get('approve', 'Unknown')}")
-        print(f"🎯 Confidence: {result.get('confidence_score', 0):.2f}")
-        print(f"💭 Reasoning: {result.get('reasoning', 'N/A')}")
-
-        # Display scores breakdown
-        scores = result.get("scores", {})
-        print(f"\n📈 Score Breakdown:")
-        for score_type, score_value in scores.items():
-            print(f"   • {score_type.title()}: {score_value}")
-
-        # Display workflow tracking info
-        print(f"\n🔄 Workflow Info:")
-        print(f"   • Final Step: {result.get('workflow_step', 'Unknown')}")
-        print(f"   • Completed Steps: {result.get('completed_steps', [])}")
-
-        # Display flags if any
-        flags = result.get("flags", [])
-        if flags:
-            print(f"\n⚠️  Flags:")
-            for flag in flags:
-                print(f"   • {flag}")
-
-        # Display token usage
-        token_usage = result.get("token_usage", {})
-        if token_usage:
-            print(f"\n� Token Usage:")
-            print(f"   • Input: {token_usage.get('input_tokens', 0)}")
-            print(f"   • Output: {token_usage.get('output_tokens', 0)}")
-            print(f"   • Total: {token_usage.get('total_tokens', 0)}")
-
-        # Check for error indicators
-        if "error" in result:
-            print(f"\n❌ Error detected: {result['error']}")
-            return False
-
-        # Validate that we didn't hit recursion limits
-        if any("halted" in flag.lower() for flag in flags):
-            print(f"\n⚠️  Workflow was halted - potential infinite loop detected")
-            return False
-
-        print(f"\n🎉 Test completed successfully!")
-        return True
-
-    except Exception as e:
-        print(f"\n❌ Test failed with error: {str(e)}")
-        import traceback
-
-        traceback.print_exc()
-        return False
-
-
-async def test_edge_cases():
-    """Test edge cases that previously caused infinite loops."""
-    print("\n🧪 Testing Edge Cases")
-    print("=" * 60)
-
-    edge_cases = [
-        {
-            "name": "Empty Proposal",
-            "data": "",
-        },
-        {
-            "name": "Very Short Proposal",
-            "data": "Fund 100 tokens for project X.",
-        },
-        {
-            "name": "Proposal with Images",
-            "data": "Fund AI project. See details: https://example.com/image.png",
-        },
-    ]
-
-    all_passed = True
-
-    for i, case in enumerate(edge_cases, 1):
-        print(f"\n🔬 Test Case {i}: {case['name']}")
-        print("-" * 40)
-
-        try:
-            test_id = str(uuid4())
-            config = {"recursion_limit": 10}  # Very low limit
-
-            result = await evaluate_proposal(
-                proposal_id=test_id,
-                proposal_data=case["data"],
-                config=config,
-            )
-
-            # Check if we completed without errors
-            if "error" not in result:
-                print(f"   ✅ Passed - Decision: {result.get('approve', 'Unknown')}")
-            else:
-                print(f"   ⚠️  Completed with error: {result['error']}")
-                # Errors are ok for edge cases, as long as we don't infinite loop
-
-        except Exception as e:
-            print(f"   ❌ Failed: {str(e)}")
-            all_passed = False
-
-    return all_passed
-
-
-if __name__ == "__main__":
-
-    async def main():
-        print("🚀 Starting Improved Workflow Tests")
-        print("=" * 80)
-
-        # Test 1: Basic functionality
-        test1_passed = await test_improved_workflow()
-
-        # Test 2: Edge cases
-        test2_passed = await test_edge_cases()
-
-        print("\n" + "=" * 80)
-        print("📊 Test Summary")
-        print("=" * 80)
-        print(f"✅ Basic Workflow Test: {'PASSED' if test1_passed else 'FAILED'}")
-        print(f"✅ Edge Cases Test: {'PASSED' if test2_passed else 'FAILED'}")
-
-        overall_success = test1_passed and test2_passed
-        print(
-            f"\n🎯 Overall Result: {'ALL TESTS PASSED' if overall_success else 'SOME TESTS FAILED'}"
-        )
-
-        if overall_success:
-            print("\n🎉 Workflow improvements are working correctly!")
-            print("The infinite loop issue should now be resolved.")
-        else:
-            print("\n⚠️  Some issues remain - check the logs above.")
-
-        return 0 if overall_success else 1
-
-    sys.exit(asyncio.run(main()))
diff --git a/uv.lock b/uv.lock
index b0d5477c..a0502f34 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,6 +10,7 @@ dependencies = [
     { name = "aiohttp" },
     { name = "apscheduler" },
     { name = "cachetools" },
+    { name = "faiss-cpu" },
     { name = "fastapi" },
     { name = "langchain" },
     { name = "langchain-community" },
@@ -17,18 +18,29 @@ dependencies = [
     { name = "langchain-openai" },
     { name = "langchain-text-splitters" },
     { name = "langgraph" },
+    { name = "neo4j" },
+    { name = "networkx" },
+    { name = "nltk" },
+    { name = "numpy" },
     { name = "openai" },
     { name = "pgvector" },
     { name = "psycopg2" },
     { name = "pydantic" },
+    { name = "python-dateutil" },
     { name = "python-dotenv" },
     { name = "python-telegram-bot" },
     { name = "python-twitter-v2" },
+    { name = "rank-bm25" },
     { name = "requests" },
+    { name = "scikit-learn" },
+    { name = "sentence-transformers" },
+    { name = "spacy" },
     { name = "sqlalchemy" },
     { name = "starlette" },
     { name = "supabase" },
+    { name = "textstat" },
     { name = "tiktoken" },
+    { name = "tqdm" },
     { name = "tweepy" },
     { name = "uvicorn" },
     { name = "vecs" },
@@ -47,6 +59,7 @@ requires-dist = [
     { name = "aiohttp", specifier = "==3.12.6" },
     { name = "apscheduler", specifier = "==3.11.0" },
     { name = "cachetools", specifier = "==6.0.0" },
+    { name = "faiss-cpu", specifier = ">=1.7.0" },
     { name = "fastapi", specifier = "==0.115.12" },
     { name = "langchain", specifier = "==0.3.25" },
     { name = "langchain-community", specifier = "==0.3.24" },
@@ -54,6 +67,10 @@ requires-dist = [
     { name = "langchain-openai", specifier = "==0.3.18" },
     { name = "langchain-text-splitters", specifier = "==0.3.8" },
     { name = "langgraph", specifier = "==0.4.7" },
+    { name = "neo4j", specifier = ">=5.0.0" },
+    { name = "networkx", specifier = ">=3.0" },
+    { name = "nltk", specifier = ">=3.8" },
+    { name = "numpy", specifier = ">=1.24.0" },
     { name = "openai", specifier = "==1.82.1" },
     { name = "pgvector", specifier = "==0.3.6" },
     { name = "psycopg2", specifier = "==2.9.10" },
@@ -61,15 +78,22 @@ requires-dist = [
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 version = "0.23.0"