From a859cf591e897c68ec0241a0206b0d94513ca003 Mon Sep 17 00:00:00 2001 From: Gheorghita Hurmuz Date: Mon, 7 Apr 2025 18:30:01 +0300 Subject: [PATCH 1/2] chore: move samples and langchain to another repository --- .github/actions/setup-python-env/action.yml | 23 - .github/workflows/langchain_ci.yml | 37 - samples/company-research-agent/.env.example | 4 - samples/company-research-agent/agent.mermaid | 23 - samples/company-research-agent/graph.py | 74 - samples/company-research-agent/langgraph.json | 7 - samples/company-research-agent/pyproject.toml | 47 - samples/company-research-agent/uv.lock | 2406 ---------------- samples/connections/.env.example | 2 - samples/connections/.vscode/launch.json | 17 - samples/connections/main.py | 73 - samples/connections/pyproject.toml | 11 - samples/connections/uv.lock | 562 ---- samples/hitl-inbox-server/database.py | 14 - samples/hitl-inbox-server/main.py | 292 -- samples/hitl-inbox-server/models.py | 33 - samples/hitl-inbox-server/pyproject.toml | 16 - samples/hitl-inbox-server/schemas.py | 57 - .../hitl-inbox-server/templates/index.html | 1253 --------- samples/hitl-inbox-server/uv.lock | 703 ----- .../.env.example | 6 - .../.vscode/launch.json | 16 - .../__init__.py | 0 .../_main.py | 31 - .../agent.mermaid | 20 - .../configuration.py | 48 - .../graph.py | 184 -- .../langgraph.json | 7 - .../prompts.py | 45 - .../pyproject.toml | 29 - .../state.py | 132 - .../supplier_website_reader.py | 31 - .../uipath_ecs_retriever_tool.py | 13 - .../utils.py | 26 - .../uv.lock | 2235 --------------- .../.env.example | 4 - .../README.md | 165 -- .../coder.mermaid | 23 - .../langgraph.json | 9 - .../planner.mermaid | 18 - .../pyproject.toml | 48 - .../researcher.mermaid | 23 - .../src/multi-agent-distributed/coder.py | 52 - .../src/multi-agent-distributed/planner.py | 213 -- .../src/multi-agent-distributed/researcher.py | 34 - .../uv.lock | 2381 ---------------- .../.env.example | 2 - .../README.md | 120 - .../agent.mermaid | 38 - .../graph.py | 122 - .../langgraph.json | 7 - .../pyproject.toml | 48 - .../uv.lock | 2381 ---------------- samples/simple-echo/README.md | 96 - samples/simple-echo/echo.py | 25 - samples/simple-echo/pyproject.toml | 9 - samples/simple-echo/uv.lock | 460 ---- samples/simple-local-mcp/.env.example | 1 - samples/simple-local-mcp/README.md | 95 - samples/simple-local-mcp/agent.mermaid | 17 - samples/simple-local-mcp/langgraph.json | 6 - samples/simple-local-mcp/pyproject.toml | 21 - .../src/simple-local-mcp/graph.py | 28 - .../src/simple-local-mcp/math_server.py | 16 - .../src/simple-local-mcp/weather_server.py | 11 - samples/simple-local-mcp/uv.lock | 2265 --------------- samples/simple-remote-mcp/.env.example | 2 - samples/simple-remote-mcp/agent.mermaid | 17 - samples/simple-remote-mcp/langgraph.json | 7 - samples/simple-remote-mcp/main.py | 24 - samples/simple-remote-mcp/pyproject.toml | 17 - samples/simple-remote-mcp/uv.lock | 2086 -------------- samples/ticket-classification/.env.example | 4 - samples/ticket-classification/README.md | 67 - samples/ticket-classification/agent.mermaid | 16 - samples/ticket-classification/langgraph.json | 7 - samples/ticket-classification/main.py | 142 - samples/ticket-classification/pyproject.toml | 19 - samples/ticket-classification/uv.lock | 2017 -------------- samples/travel-planner-local-mcp/.env.example | 4 - samples/travel-planner-local-mcp/README.md | 64 - .../__uipath/state.db | 3 - .../travel-planner-local-mcp/agent.mermaid | 22 - .../travel-planner-local-mcp/langgraph.json | 9 - samples/travel-planner-local-mcp/main.py | 101 - .../mcps/estate_mcp.py | 388 --- .../mcps/travel_mcp.py | 356 --- .../travel-planner-local-mcp/pyproject.toml | 21 - samples/travel-planner-local-mcp/uv.lock | 2233 --------------- sdk/langchain/.pre-commit-config.yaml | 7 - sdk/langchain/README.md | 0 sdk/langchain/pyproject.toml | 104 - sdk/langchain/tests/conftest.py | 76 - sdk/langchain/tests/test_dummy.py | 3 - sdk/langchain/tests/test_langchain_client.py | 152 - sdk/langchain/uipath_langchain/__init__.py | 3 - .../uipath_langchain/_cli/__init__.py | 1 - .../_cli/_runtime/_context.py | 21 - .../_cli/_runtime/_escalation.py | 245 -- .../_cli/_runtime/_exception.py | 17 - .../uipath_langchain/_cli/_runtime/_input.py | 151 - .../uipath_langchain/_cli/_runtime/_output.py | 323 --- .../_cli/_runtime/_runtime.py | 292 -- .../uipath_langchain/_cli/_utils/_graph.py | 200 -- .../uipath_langchain/_cli/cli_init.py | 173 -- .../uipath_langchain/_cli/cli_run.py | 79 - .../uipath_langchain/_utils/__init__.py | 4 - .../uipath_langchain/_utils/_request_mixin.py | 493 ---- .../uipath_langchain/_utils/_settings.py | 91 - .../uipath_langchain/_utils/_sleep_policy.py | 41 - ...-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b | 3 - ...-largec48857ed-1302-5954-9e24-69fa9b45e457 | 3 - .../_utils/tests/tests_uipath_cache.db | 3 - .../uipath_langchain/chat/__init__.py | 6 - sdk/langchain/uipath_langchain/chat/models.py | 259 -- .../uipath_langchain/chat/utils/__init__.py | 0 .../chat/utils/_chat_types.py | 54 - .../uipath_langchain/embeddings/__init__.py | 6 - .../uipath_langchain/embeddings/embeddings.py | 109 - sdk/langchain/uipath_langchain/middlewares.py | 10 - .../uipath_langchain/retrievers/__init__.py | 3 - .../retrievers/context_grounding_retriever.py | 35 - .../tracers/AsyncUiPathTracer.py | 308 --- .../uipath_langchain/tracers/UiPathTracer.py | 161 -- .../uipath_langchain/tracers/__init__.py | 5 - .../uipath_langchain/tracers/_events.py | 33 - .../tracers/_instrument_traceable.py | 285 -- .../uipath_langchain/tracers/_utils.py | 52 - sdk/langchain/uv.lock | 2439 ----------------- 129 files changed, 31091 deletions(-) delete mode 100644 .github/actions/setup-python-env/action.yml delete mode 100644 .github/workflows/langchain_ci.yml delete mode 100644 samples/company-research-agent/.env.example delete mode 100644 samples/company-research-agent/agent.mermaid delete mode 100644 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b/.github/actions/setup-python-env/action.yml deleted file mode 100644 index a4936e49..00000000 --- a/.github/actions/setup-python-env/action.yml +++ /dev/null @@ -1,23 +0,0 @@ -name: "Setup Python Environment" -description: "Checks out code, installs uv, and sets up Python" -runs: - using: "composite" - steps: - - name: Install uv - uses: astral-sh/setup-uv@v5 - with: - enable-cache: true - - - name: "Set up Python" - uses: actions/setup-python@v5 - with: - python-version-file: ".python-version" - - - name: "Setup venv" - shell: bash - run: | - uv venv - - - name: "Install the project" - shell: bash - run: uv sync --all-extras diff --git a/.github/workflows/langchain_ci.yml b/.github/workflows/langchain_ci.yml deleted file mode 100644 index 413412ad..00000000 --- a/.github/workflows/langchain_ci.yml +++ /dev/null @@ -1,37 +0,0 @@ -name: Langchain CI - -on: - workflow_dispatch: {} - push: - branches: - - main - paths: - - "sdk/langchain/**" - pull_request: - branches: - - main - paths: - - "sdk/langchain/**" - -defaults: - run: - working-directory: "sdk/langchain" - -jobs: - lint: - uses: ./.github/workflows/lint.yml - with: - folder_path: "sdk/langchain" - - test: - uses: ./.github/workflows/test.yml - with: - folder_path: "sdk/langchain" - - build: - needs: [lint, test] - uses: ./.github/workflows/build.yml - with: - folder_path: "sdk/langchain" - secrets: - token: ${{ secrets.PYPI_TOKEN }} diff --git a/samples/company-research-agent/.env.example b/samples/company-research-agent/.env.example deleted file mode 100644 index 2495d634..00000000 --- a/samples/company-research-agent/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -UIPATH_URL=https://alpha.uipath.com// -UIPATH_ACCESS_TOKEN=YOUR TOKEN HERE -ANTHROPIC_API_KEY=your_anthropic_api_key -TAVILY_API_KEY=your_tavily_api_key diff --git a/samples/company-research-agent/agent.mermaid b/samples/company-research-agent/agent.mermaid deleted file mode 100644 index 2900d529..00000000 --- a/samples/company-research-agent/agent.mermaid +++ /dev/null @@ -1,23 +0,0 @@ ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - __end__([

__end__

]):::last - __start__ --> researcher___start__; - researcher___end__ --> __end__; - subgraph researcher - researcher___start__(

__start__

) - researcher_agent(agent) - researcher_tools(tools) - researcher___end__(

__end__

) - researcher___start__ --> researcher_agent; - researcher_tools --> researcher_agent; - researcher_agent -.-> researcher_tools; - researcher_agent -.-> researcher___end__; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/company-research-agent/graph.py b/samples/company-research-agent/graph.py deleted file mode 100644 index 32f87ec2..00000000 --- a/samples/company-research-agent/graph.py +++ /dev/null @@ -1,74 +0,0 @@ -from langchain_anthropic import ChatAnthropic -from langchain_community.tools.tavily_search import TavilySearchResults -from langgraph.graph import END, START, MessagesState, StateGraph -from langgraph.prebuilt import create_react_agent -from pydantic import BaseModel - -# Set up the Tavily search tool -tavily_tool = TavilySearchResults(max_results=5) - -# Define system prompt -system_prompt = """You are an advanced AI assistant specializing in corporate research and outreach strategy development. Your primary functions are: -1. Researching target companies: Gather comprehensive information about the specified company, including its history, industry position, products/services, and recent news. -2. Analyzing organizational structures: Investigate and outline the company's internal structure, departments, and hierarchy. -3. Identifying key decision-makers: Find and provide information on important executives and leaders within the company who are likely to be involved in decision-making processes. -4. Developing outreach strategies: Based on the gathered information, create tailored strategies for effective communication and engagement with the company and its key personnel. - -To accomplish these tasks, follow these steps: -1. Use the TavilySearchResults tool to find recent and relevant information about the company. -2. Analyze the collected data to form insights about the company's structure, key decision-makers, and potential outreach strategies. - -When using the search tool: -- Clearly state the purpose of each search. -- Formulate effective search queries to find specific information about different aspects of the company. -- If a search doesn't provide the expected information, try refining your query. - -When responding, structure your output as a comprehensive analysis. Use clear section headers to organize the information. Provide concise, actionable insights. If you need more information to complete any part of your analysis, clearly state what additional details would be helpful. - -Always maintain a professional and objective tone in your research and recommendations. Your goal is to provide accurate, valuable information that can be used to inform business decisions and outreach efforts. - -DO NOT do any math as specified in your instructions. -""" - -llm = ChatAnthropic(model="claude-3-5-sonnet-latest") - -research_agent = create_react_agent(llm, tools=[tavily_tool], prompt=system_prompt) - - -class GraphInput(BaseModel): - company_name: str - - -class GraphOutput(BaseModel): - response: str - - -def research_node(state: GraphInput) -> GraphOutput: - # Format the user message with the company name - user_message = f"""Please provide a comprehensive analysis and outreach strategy for the company: {state.company_name}. Use the TavilySearchResults tool to gather information. Include detailed research on the company's background, organizational structure, key decision-makers, and a tailored outreach strategy. Format your response using the following section headers: - -1. Company Overview -2. Organizational Structure -3. Key Decision-Makers -4. Outreach Strategy -5. Additional Information Needed (if applicable) - -Ensure that each section is clearly labeled and contains relevant, concise information. If you need any additional specific information about {state.company_name} or its industry to enhance your analysis, please include your questions in the 'Additional Information Needed' section. -""" - - new_state = MessagesState(messages=[{"role": "user", "content": user_message}]) - - result = research_agent.invoke(new_state) - - return GraphOutput(response=result["messages"][-1].content) - - -# Build the state graph -builder = StateGraph(input=GraphInput, output=GraphOutput) -builder.add_node("researcher", research_node) - -builder.add_edge(START, "researcher") -builder.add_edge("researcher", END) - -# Compile the graph -graph = builder.compile() diff --git a/samples/company-research-agent/langgraph.json b/samples/company-research-agent/langgraph.json deleted file mode 100644 index c465a881..00000000 --- a/samples/company-research-agent/langgraph.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "dependencies": ["."], - "graphs": { - "agent": "./graph.py:graph" - }, - "env": ".env" -} diff --git a/samples/company-research-agent/pyproject.toml b/samples/company-research-agent/pyproject.toml deleted file mode 100644 index 656ddf2c..00000000 --- a/samples/company-research-agent/pyproject.toml +++ /dev/null @@ -1,47 +0,0 @@ -[project] -name = "company-research-agent" -version = "0.0.2" -description = "Company research agent with Tavily web search" -authors = [ - { name = "John Doe" } -] -requires-python = ">=3.9" -dependencies = [ - "langgraph>=0.2.55", - "langchain-community>=0.3.9", - "langchain-anthropic>=0.3.8", - "tavily-python>=0.5.0", - "uipath-sdk==0.0.110", - "uipath-langchain==0.0.81" -] - -[project.optional-dependencies] -dev = ["mypy>=1.11.1", "ruff>=0.6.1"] - -[build-system] -requires = ["setuptools>=73.0.0", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.setuptools.package-data] -"*" = ["py.typed"] - -[tool.ruff] -lint.select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort - "D", # pydocstyle - "D401", # First line should be in imperative mood - "T201", - "UP", -] -lint.ignore = [ - "UP006", - "UP007", - "UP035", - "D417", - "E501", -] - -[tool.ruff.lint.per-file-ignores] -"tests/*" = ["D", "UP"] diff --git a/samples/company-research-agent/uv.lock b/samples/company-research-agent/uv.lock deleted file mode 100644 index 27b98e78..00000000 --- a/samples/company-research-agent/uv.lock +++ /dev/null @@ -1,2406 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.9" -resolution-markers = [ - 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"version": "0.2.0", - "configurations": [ - { - "name": "Python: Current File", - "type": "debugpy", - "request": "launch", - "program": "${file}", - "justMyCode": "false", - "console": "integratedTerminal", - "cwd": "${workspaceFolder}", - "env": { - "PYTHONPATH": "${workspaceFolder}" - } - } - ] -} \ No newline at end of file diff --git a/samples/connections/main.py b/samples/connections/main.py deleted file mode 100644 index 548fa37b..00000000 --- a/samples/connections/main.py +++ /dev/null @@ -1,73 +0,0 @@ -from dataclasses import dataclass -import io -import os -import dotenv -from typing import Optional -from uipath_sdk import UiPathSDK -from uipath_connectors.uipath_airdk import ( - WebSearchRequest, - GenerateEmailRequest, - WebSearchRequestSearchEngine, -) -from uipath_connectors.google_gmail import ( - SendEmailBody, - SendEmailRequest, -) -from uipath_connectors.google_gmail.types import File as EmailAttachmentFile - -dotenv.load_dotenv() - -@dataclass -class EchoIn: - message: str - debug: Optional[bool] = False - send_email_to: Optional[bool] = None - -@dataclass -class EchoOut: - message: str - - -def main(input: EchoIn) -> EchoOut: - sdk = UiPathSDK() - airdk = sdk.connections.uipath_airdk(os.environ["UIPATH_AIRDK_CONNECTION_KEY"]) - - initialSearch = airdk.web_search(body=WebSearchRequest(provider=WebSearchRequestSearchEngine.GOOGLECUSTOMSEARCH, query=input.message)) - if input.debug: - print(initialSearch.model_dump_json(indent=4)) - - content = airdk.generate_email(body=GenerateEmailRequest( - need_salutation=True, - need_sign_off=True, - email_content="Generate a 10 paragraph summary of the following web search results: " + initialSearch.model_dump_json(), - )) - if input.debug: - print(content.model_dump_json(indent=4)) - - if input.send_email_to: - gmail = sdk.connections.google_gmail(os.environ["UIPATH_GOOGLE_GMAIL_CONNECTION_KEY"]) - gmail.client._client.verify = False - if input.debug: - print(content.emailContent) - - send_email = gmail.send_email( - body=SendEmailBody( - body=SendEmailRequest( - to=input.send_email_to, - body= content.emailContent, - subject="Email from the research", - ), - file=EmailAttachmentFile( - payload=io.BytesIO(initialSearch.model_dump_json(indent=4).encode('utf-8')), - file_name="search.json", - content_type="application/json", - ), - ) - ) - if input.debug: - print(send_email.model_dump_json(indent=4)) - - return EchoOut(message=content.model_dump_json(indent=4)) - -if __name__ == "__main__": - main(EchoIn(message="What is the capital of France?", debug=True, send_email_to="alice.roprau@gmail.com")) diff --git a/samples/connections/pyproject.toml b/samples/connections/pyproject.toml deleted file mode 100644 index d23e04d0..00000000 --- a/samples/connections/pyproject.toml +++ /dev/null @@ -1,11 +0,0 @@ -[project] -name = "hello-world" -version = "0.0.1" -description = "hello-world" -authors = [{ name = "John Doe", email = "john.doe@myemail.com" }] -dependencies = [ - "uipath-sdk==0.0.110", - "uipath_connectors.uipath_airdk>=1.53.6.dev2", - 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-# Database models and setup -from database import SessionLocal, engine -import models -import schemas - -sdk = UiPathSDK() - -# Create database tables -models.Base.metadata.create_all(bind=engine) - -app = FastAPI(title="Open Deep Research") - -# Enable CORS -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - -templates = Jinja2Templates(directory="templates") - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - -@app.get("/", response_class=HTMLResponse) -async def index(request: Request): - return templates.TemplateResponse("index.html", {"request": request}) - -# API endpoint to submit a new job -@app.post("/api/submit-job", response_model=schemas.JobResponse) -async def submit_job(job_request: schemas.JobRequest, background_tasks: BackgroundTasks, db: Session = Depends(get_db)): - # Create a new job record in the database - job_id = str(uuid.uuid4()) - new_job = models.Job( - id=job_id, - topic=job_request.topic, - status="creating", - created_at=datetime.now() - ) - db.add(new_job) - db.commit() - db.refresh(new_job) - - - # Submit the job to the external service in the background - background_tasks.add_task( - submit_orchestrator_job, - job_id=job_id, - topic=job_request.topic, - db=db - ) - - return {"job_id": job_id, "status": "creating", "message": "Job submitted successfully"} - -# Background task to submit job to Orchestrator -async def submit_orchestrator_job(job_id: str, topic: str, db: Session): - try: - agent_job = await sdk.processes.invoke_async( - name="open_deep_research", - input_arguments={"topic": topic} - ) - print(agent_job) - # Update job with Orchestrator job key if provided - job = db.query(models.Job).filter(models.Job.id == job_id).first() - if job and agent_job: - job.agent_job_key = agent_job.Key - job.agent_job_id = agent_job.Id - job.status = "pending" - db.commit() - print(job.agent_job_id) - - except httpx.HTTPError as e: - # If Orchestrator request fails, update the job status - job = db.query(models.Job).filter(models.Job.id == job_id).first() - if job: - job.status = "faulted" - job.error_message = str(e) - db.commit() - -# Webhook endpoint for Orchestrator job events notifications -@app.post("/webhook/job-complete") -async def job_complete_webhook(request: Request, db: Session = Depends(get_db)): - webhook_data = await request.json() - agent_job = webhook_data.get('Job') - agent_job_id = agent_job.get('Id') - #agent_job_key = agent_job.get('Key') - - print(webhook_data) - print(agent_job_id) - if not agent_job_id: - raise HTTPException(status_code=400, detail="Missing Job.Key in webhook data") - - # Retrieve the job from the database - job = db.query(models.Job).filter(models.Job.agent_job_id == agent_job_id).first() - if not job: - return {"status": "faulted", "message": "Job not found"} - - event_type = webhook_data.get('Type') - - print(event_type) - - # Update the job status and save the output - if event_type == 'job.started': - job.status = 'running' - - if event_type == 'job.suspended': - # Create a new inbox message with the job trigger output - inbox_content = await _retrieve_message_content(job_id=agent_job_id) - inbox_message = models.InboxMessage( - id=str(uuid.uuid4()), - job_id=job.id, - content=inbox_content, - status="pending", - received_at=datetime.now() - ) - db.add(inbox_message) - job.status = 'suspended' - - if event_type == 'job.completed': - output_args = agent_job.get('OutputArguments') - if output_args is not None: - job.output = output_args.get('final_report', '') - else: - job.output = '' - job.status = 'completed' - - if event_type == 'job.faulted': - job.status = 'faulted' - - job.completed_at = datetime.now() - - db.commit() - - return {"status": "success", "message": "Job completion processed successfully"} - -# API endpoint to get all inbox messages -@app.get("/api/inbox", response_model=List[schemas.InboxMessageResponse]) -async def get_inbox_api( - status: Optional[str] = None, - skip: int = 0, - limit: int = 100, - db: Session = Depends(get_db) -): - query = db.query(models.InboxMessage) - - if status: - query = query.filter(models.InboxMessage.status == status) - - messages = query.order_by(models.InboxMessage.received_at.desc()).offset(skip).limit(limit).all() - return messages - -# API endpoint to approve a message -@app.post("/api/approve-message/{message_id}", response_model=schemas.MessageApprovalResponse) -async def approve_message_api( - message_id: str, - approval_data: schemas.MessageApprovalRequest, - background_tasks: BackgroundTasks, - db: Session = Depends(get_db) -): - # Find the message - message = db.query(models.InboxMessage).filter(models.InboxMessage.id == message_id).first() - if not message: - raise HTTPException(status_code=404, detail=f"Message with id {message_id} not found") - - # Update message status - message.status = "approved" - message.approved_at = datetime.now() - message.human_feedback = approval_data.human_feedback - db.commit() - - # Find the associated job - job = db.query(models.Job).filter(models.Job.id == message.job_id).first() - if job and job.agent_job_id: - # Resume the job in the background - background_tasks.add_task( - resume_orchestrator_job, - job_id=job.id, - agent_job_id=job.agent_job_id, - human_feedback=approval_data.human_feedback, - db=db - ) - return {"status": "success", "message": "Message approved and job resumed with human feedback"} - - return {"status": "success", "message": "Message approved with human feedback"} - -# Background task to resume an Orchestrator job -async def resume_orchestrator_job(job_id: str, agent_job_id: str, human_feedback: str, db: Session): - try: - payload = True if human_feedback.lower() == "true" else human_feedback - - await sdk.jobs.resume_async(job_id=agent_job_id, payload=payload) - - # Update job status - job = db.query(models.Job).filter(models.Job.id == job_id).first() - if job: - job.status = "resumed" - job.human_feedback = human_feedback - db.commit() - - except httpx.HTTPError as e: - # If the resume request fails, log the error - job = db.query(models.Job).filter(models.Job.id == job_id).first() - if job: - job.error_message = f"Failed to resume job: {str(e)}" - db.commit() - -# API endpoint to get job status -@app.get("/api/job/{job_id}", response_model=schemas.JobDetailResponse) -async def get_job_status_api(job_id: str, db: Session = Depends(get_db)): - job = db.query(models.Job).filter(models.Job.id == job_id).first() - if not job: - raise HTTPException(status_code=404, detail=f"Job with id {job_id} not found") - - # Get associated inbox messages - messages = db.query(models.InboxMessage).filter(models.InboxMessage.job_id == job_id).all() - - return { - "id": job.id, - "topic": job.topic, - "status": job.status, - "agent_job_id": job.agent_job_id, - "created_at": job.created_at, - "completed_at": job.completed_at, - "error_message": job.error_message, - "human_feedback": job.human_feedback, - "output": job.output, - "messages": messages - } - -# API endpoint to get all jobs -@app.get("/api/jobs", response_model=List[schemas.JobBasicResponse]) -async def get_jobs_api( - status: Optional[str] = None, - skip: int = 0, - limit: int = 100, - db: Session = Depends(get_db) -): - query = db.query(models.Job) - - if status: - query = query.filter(models.Job.status == status) - - jobs = query.order_by(models.Job.created_at.desc()).offset(skip).limit(limit).all() - return jobs - -async def _retrieve_message_content( - job_id: str, - folder_key: Optional[str] = None, - folder_path: Optional[str] = None, -) -> str: - spec = sdk.jobs._retrieve_inbox_id_spec( - job_id=job_id, - folder_key=folder_key, - folder_path=folder_path, - ) - response = await sdk.jobs.request_async( - spec.method, - url=spec.endpoint, - params={ - "$filter": f"JobId eq {job_id}", - "$top": 1 - }, - headers=spec.headers, - ) - - response = response.json() - print(response) - if len(response["value"]) > 0: - return response["value"][0]["TriggerConfiguration"] - else: - raise Exception("No inbox found") - -if __name__ == "__main__": - import uvicorn - uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/samples/hitl-inbox-server/models.py b/samples/hitl-inbox-server/models.py deleted file mode 100644 index 2b630092..00000000 --- a/samples/hitl-inbox-server/models.py +++ /dev/null @@ -1,33 +0,0 @@ -from sqlalchemy import Column, Integer, String, Text, DateTime, ForeignKey -from sqlalchemy.orm import relationship -from database import Base -from datetime import datetime - -class Job(Base): - __tablename__ = "jobs" - - id = Column(String, primary_key=True, index=True) - agent_job_key = Column(String, index=True, nullable=True) - agent_job_id = Column(Integer, index=True, nullable=True) - topic = Column(String, index=True) - status = Column(String, index=True) # creating, pending, running, suspended, resumed, faulted, completed - output = Column(Text) - error_message = Column(Text, nullable=True) - human_feedback = Column(Text, nullable=True) - created_at = Column(DateTime, default=datetime.now) - completed_at = Column(DateTime, nullable=True) - - messages = relationship("InboxMessage", back_populates="job") - -class InboxMessage(Base): - __tablename__ = "inbox_messages" - - id = Column(String, primary_key=True, index=True) - job_id = Column(String, ForeignKey("jobs.id"), index=True) - content = Column(Text) - status = Column(String, index=True) # pending, approved, rejected - human_feedback = Column(Text, nullable=True) - received_at = Column(DateTime, default=datetime.now) - approved_at = Column(DateTime, nullable=True) - - job = relationship("Job", back_populates="messages") diff --git a/samples/hitl-inbox-server/pyproject.toml b/samples/hitl-inbox-server/pyproject.toml deleted file mode 100644 index 212c9f6c..00000000 --- a/samples/hitl-inbox-server/pyproject.toml +++ /dev/null @@ -1,16 +0,0 @@ -[project] -name = "hitl-inbox-server" -version = "0.0.1" -description = "HITL Inbox" -authors = [{ name = "Cristi Pufu", email = "cristian.pufu@uipath.com" }] -dependencies = [ - "fastapi>=0.115.11", - "uvicorn>=0.34.0", - "gunicorn>=23.0.0", - "sqlalchemy>=2.0.38", - "jinja2>=3.1.6", - "httpx>=0.28.1", - "python-multipart>=0.0.20", - "uipath-sdk==0.0.110" -] -requires-python = ">=3.10" diff --git a/samples/hitl-inbox-server/schemas.py b/samples/hitl-inbox-server/schemas.py deleted file mode 100644 index d96b709b..00000000 --- a/samples/hitl-inbox-server/schemas.py +++ /dev/null @@ -1,57 +0,0 @@ -from pydantic import BaseModel -from typing import Optional, List -from datetime import datetime - -class JobRequest(BaseModel): - topic: str - callback_url: Optional[str] = None - -class JobResponse(BaseModel): - job_id: str - status: str - message: str - -class InboxMessageResponse(BaseModel): - id: str - job_id: str - content: str - status: str - human_feedback: Optional[str] = None - received_at: datetime - approved_at: Optional[datetime] = None - - class Config: - orm_mode = True - -class MessageApprovalRequest(BaseModel): - human_feedback: str - -class MessageApprovalResponse(BaseModel): - status: str - message: str - -class JobBasicResponse(BaseModel): - id: str - topic: str - status: str - created_at: datetime - completed_at: Optional[datetime] = None - - class Config: - orm_mode = True - -class JobDetailResponse(BaseModel): - id: str - topic: str - status: str - output: Optional[str] = None - agent_job_id: Optional[int] = None - agent_job_key: Optional[str] = None - human_feedback: Optional[str] = None - created_at: datetime - completed_at: Optional[datetime] = None - error_message: Optional[str] = None - messages: List[InboxMessageResponse] = [] - - class Config: - orm_mode = True diff --git a/samples/hitl-inbox-server/templates/index.html b/samples/hitl-inbox-server/templates/index.html deleted file mode 100644 index 011ac6b1..00000000 --- a/samples/hitl-inbox-server/templates/index.html +++ /dev/null @@ -1,1253 +0,0 @@ - - - - - - Open Deep Research App - - - - -
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// Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "Python Debugger: Current File", - "type": "debugpy", - "request": "launch", - "program": "_main.py", - "console": "integratedTerminal", - "justMyCode": false - } - ] -} \ No newline at end of file diff --git a/samples/invoice-dispute-investigation-agent/__init__.py b/samples/invoice-dispute-investigation-agent/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/samples/invoice-dispute-investigation-agent/_main.py b/samples/invoice-dispute-investigation-agent/_main.py deleted file mode 100644 index 34341698..00000000 --- a/samples/invoice-dispute-investigation-agent/_main.py +++ /dev/null @@ -1,31 +0,0 @@ -import dotenv -from langgraph.checkpoint.memory import MemorySaver -from langgraph.graph.state import CompiledStateGraph -from graph import builder - -""" -This is the sample test application file for the LangGraph project. -It is used to run the graph and stream the results. -""" - -dotenv.load_dotenv('.env') -graph = builder.compile(checkpointer= MemorySaver()) - -args = { - "MismatchedItems": "Received Battery #bat14 (should be #bat17) Received Valve #val31 (should be #val15)", - "Company": "Pacific Manufacturing", - "Supplier": "Spectrum Parts", - "SupplierWebsite": "https://acme-batteries.vercel.app/products/" -} - -config = { "configurable": {"thread_id": "4"} } - -def stream_run(graph: CompiledStateGraph, args, config): - for event in graph.stream(args, config, stream_mode="values"): - if event.get('messages') is None or len(event["messages"]) == 0: - print("===== event with no messages =====") - print(event) - else: - event["messages"][-1].pretty_print() - -stream_run(graph, args, config) diff --git a/samples/invoice-dispute-investigation-agent/agent.mermaid b/samples/invoice-dispute-investigation-agent/agent.mermaid deleted file mode 100644 index 4e5844d8..00000000 --- a/samples/invoice-dispute-investigation-agent/agent.mermaid +++ /dev/null @@ -1,20 +0,0 @@ -%%{init: {'flowchart': {'curve': 'linear'}}}%% -graph TD; - __start__([

__start__

]):::first - input_node(input_node) - call_model(call_model) - tools(tools) - ask_human(ask_human) - output_node(output_node) - __end__([

__end__

]):::last - __start__ --> input_node; - ask_human --> call_model; - input_node --> call_model; - output_node --> __end__; - tools --> call_model; - call_model -.-> output_node; - call_model -.-> tools; - call_model -.-> ask_human; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/invoice-dispute-investigation-agent/configuration.py b/samples/invoice-dispute-investigation-agent/configuration.py deleted file mode 100644 index 0c17f3c3..00000000 --- a/samples/invoice-dispute-investigation-agent/configuration.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Define the configurable parameters for the agent.""" - -from __future__ import annotations - -from dataclasses import dataclass, field, fields -from typing import Annotated, Optional - -from langchain_core.runnables import RunnableConfig, ensure_config - -from prompts import SYSTEM_PROMPT, USER_PROMPT - -@dataclass() -class Configuration: - """The configuration for the agent.""" - - system_prompt: str = field( - default=SYSTEM_PROMPT, - metadata={ - "description": "The system prompt to use for the agent's interactions. " - "This prompt sets the context and behavior for the agent." - }, - ) - - user_prompt: str = field( - default=USER_PROMPT, - metadata={ - "description": "The user prompt to use for the agent's interactions. " - "This prompt sets the current goal of the agent." - }, - ) - - model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field( - default="anthropic/claude-3-5-sonnet-20240620", - metadata={ - "description": "The name of the language model to use for the agent's main interactions. " - "Should be in the form: provider/model-name." - }, - ) - - @classmethod - def from_runnable_config( - cls, config: Optional[RunnableConfig] = None - ) -> Configuration: - """Create a Configuration instance from a RunnableConfig object.""" - config = ensure_config(config) - configurable = config.get("configurable") or {} - _fields = {f.name for f in fields(cls) if f.init} - return cls(**{k: v for k, v in configurable.items() if k in _fields}) diff --git a/samples/invoice-dispute-investigation-agent/graph.py b/samples/invoice-dispute-investigation-agent/graph.py deleted file mode 100644 index c137283b..00000000 --- a/samples/invoice-dispute-investigation-agent/graph.py +++ /dev/null @@ -1,184 +0,0 @@ -"""Define a custom Reasoning and Action agent. - -Works with a chat model with tool calling support. -""" -from datetime import datetime, timezone -from pprint import pprint -from typing import Literal, cast - -from langchain_core.messages import AIMessage, HumanMessage -from langchain_core.runnables import RunnableConfig -from langgraph.graph import StateGraph -from langgraph.prebuilt import ToolNode - -from configuration import Configuration -from state import InputState, State, OutputState -from supplier_website_reader import SupplierWebsiteReader -from uipath_ecs_retriever_tool import retriever_tool -from utils import load_chat_model -from pydantic import BaseModel - -TOOLS = [ SupplierWebsiteReader, retriever_tool ] - - -def input_node(state: State, config: RunnableConfig): - - configuration = Configuration.from_runnable_config(config) - - # Format the user prompt. Customize this to change the agent's behavior. - user_message = configuration.user_prompt.format( - MismatchedItems=state.MismatchedItems, - SupplierWebsite=state.SupplierWebsite, - Supplier=state.Supplier, - Company=state.Company - ) - state.messages.append(HumanMessage(user_message)) - -# We are going "bind" all tools to the model -# We have the ACTUAL tools from above, but we also need a mock tool to ask a human -# Since `bind_tools` takes in tools but also just tool definitions, -# We can define a tool definition for `ask_human` -class AskHuman(BaseModel): - """Ask the human a question""" - - question: str - -def call_model( - state: State, config: RunnableConfig -) -> State: - """Call the LLM powering our "agent". - - This function prepares the prompt, initializes the model, and processes the response. - - Args: - state (State): The current state of the conversation. - config (RunnableConfig): Configuration for the model run. - - Returns: - dict: A dictionary containing the model's response message. - """ - - configuration = Configuration.from_runnable_config(config) - - # Initialize the model with tool binding. Change the model or add more tools here. - - model = load_chat_model(configuration.model).bind_tools(TOOLS + [AskHuman]) - - # Format the system prompt. Customize this to change the agent's behavior. - system_message = configuration.system_prompt.format( - system_time=datetime.now(tz=timezone.utc).isoformat(), - ) - - # Get the model's response - response = cast( - AIMessage, - model.invoke( - [{"role": "system", "content": system_message}, *state.messages], config - ), - ) - - # Handle the case when it's the last step and the model still wants to use a tool - if state.is_last_step and response.tool_calls: - response = AIMessage( - id=response.id, - content="Sorry, I could not find an answer to your question in the specified number of steps.", - ) - else: - state.SupplierEmail="supplier@acme.company" - state.InvestigationResult="The results are in" - - state.messages.append(response) - - # Return the model's response as a list to be added to existing messages - return state # {"messages": [response]} - -def output_node(state: State, config: RunnableConfig) -> OutputState: - configuration = Configuration.from_runnable_config(config) - - # Initialize the model with tool binding. Change the model or add more tools here. - - model = load_chat_model(configuration.model).with_structured_output(OutputState) - - pprint(state) - - response = cast( - OutputState, - model.invoke( - [{"role": "system", "content": "Build the output message"}, *state.messages], config - ), - ) - - return response - -# We define a fake node to ask the human -def ask_human(state: State): - tool_call_id = state.messages[-1].tool_calls[0]["id"] - tool_message = [{"tool_call_id": tool_call_id, "type": "tool", "content": "Human call is not implemented"}] - return {"messages": tool_message} - -# Define a new graph - -builder = StateGraph(State, input=InputState, output=OutputState, config_schema=Configuration) - -builder.add_node(input_node) - -# Define the two nodes we will cycle between -builder.add_node(call_model) -builder.add_node("tools", ToolNode(TOOLS)) -builder.add_node("ask_human", ask_human) -builder.add_node(output_node) - -# Set the entrypoint as `call_model` -# This means that this node is the first one called -builder.add_edge("__start__", "input_node") -builder.add_edge("input_node", "call_model") -builder.add_edge("output_node", "__end__") - - -def route_model_output(state: State) -> Literal["output_node", "tools", "ask_human"]: - """Determine the next node based on the model's output. - - This function checks if the model's last message contains tool calls. - - Args: - state (State): The current state of the conversation. - - Returns: - str: The name of the next node to call ("output_node" or "tools" or "ask_human"). - """ - - last_message = state.messages[-1] - if not isinstance(last_message, AIMessage): - raise ValueError( - f"Expected AIMessage in output edges, but got {type(last_message).__name__}" - ) - # If there is no tool call, then we finish - if not last_message.tool_calls: - return "output_node" - - if last_message.tool_calls[0]["name"] == "AskHuman": - return "ask_human" - - # Otherwise we execute the requested actions - return "tools" - -# Add a conditional edge to determine the next step after `call_model` -builder.add_conditional_edges( - "call_model", - # After call_model finishes running, the next node(s) are scheduled - # based on the output from route_model_output - route_model_output, -) - -# Add a normal edge from `tools` to `call_model` -# This creates a cycle: after using tools, we always return to the model -builder.add_edge("tools", "call_model") -builder.add_edge("ask_human", "call_model") - -# Compile the builder into an executable graph -# You can customize this by adding interrupt points for state updates -graph = builder.compile( - interrupt_before=[], # Add node names here to update state before they're called - interrupt_after=[], # Add node names here to update state after they're called -) -graph.name = "Invoice Dispute Investigation Agent" # This customizes the name in LangSmith diff --git a/samples/invoice-dispute-investigation-agent/langgraph.json b/samples/invoice-dispute-investigation-agent/langgraph.json deleted file mode 100644 index 0b753505..00000000 --- a/samples/invoice-dispute-investigation-agent/langgraph.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "dependencies": ["."], - "graphs": { - "agent": "./graph.py:graph" - }, - "env": ".env" - } \ No newline at end of file diff --git a/samples/invoice-dispute-investigation-agent/prompts.py b/samples/invoice-dispute-investigation-agent/prompts.py deleted file mode 100644 index 524a3f0a..00000000 --- a/samples/invoice-dispute-investigation-agent/prompts.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Default prompts used by the agent.""" - -SYSTEM_PROMPT = """#Role -You are an Invoice Dispute Investigation Agent who uses tools to answer questions. -For the Mismatched Items provided, you will use the Available Tools to achieve an Investigation Result. -Your Investigation Result will answer the Goal below in order to potentially send a follow up email to the Supplier. - -#Tool Usage Guidelines -- Use each tool only once for its intended purpose -- Avoid repeating tool calls with the same parameters -- Validate all tool outputs before proceeding - -#Investigation Workflow -1. Read supplier website data for mismatched items -2. Search for supplier contact information in SAP -3. Validate email format (must contain @ and a valid domain) -4. Escalate only if email is missing or invalid - -#Goal -A mismatch between a purchase order and an invoice was found. The invoice dispute investigation process should answer the following: -Should the company move forward with the dispute with the supplier? -What should the company do when the purchase order has Stock Keeping Units (SKUs) that do not match the invoice? -What should be the desired outcomes for the Company? -What is the email address of the contact for the Supplier when we have Invoice disputes? If the email is not found or is in invalid format, please escalate. -""" - -USER_PROMPT = """The following are the mismatched items from the received invoice compared to the purchase order: - - -{MismatchedItems} -. - -The Company who is disputing the invoice is {Company} -The Supplier who gave the invoice with the mismatched line items is {Supplier}. - -#Instructions -1. Obtain specifications from {SupplierWebsite} about the item invoiced. Replace with the item SKU. -2. Obtain specifications from {SupplierWebsite} of the item originally ordered. Replace with the item SKU. -3. Compare the specifications of the item invoiced and item originally ordered. -4. For each replacement item, categorize into **Valid Replacement** or **Invalid Replacement** based on the specifications extracted for each item. For each categorization, specify the reasoning. -5. Answer all questions from the invoice dispute investigation process per company guidelines. All answers need to be directly from company context and have a citation. -6. Determine if the {Company} should move forward with the dispute with the {Supplier}. -7. If moving forward with the dispute, obtain the Supplier Email to notify the supplier of the dispute. If the email address was not found or invalid, please escalate. -8. If moving forward with the dispute, draft an email with a subject and body regarding this dispute and its potential resolution. When drafting the email, use all details regarding the current dispute investigation. -""" diff --git a/samples/invoice-dispute-investigation-agent/pyproject.toml b/samples/invoice-dispute-investigation-agent/pyproject.toml deleted file mode 100644 index 319fde6c..00000000 --- a/samples/invoice-dispute-investigation-agent/pyproject.toml +++ /dev/null @@ -1,29 +0,0 @@ -[project] -name = "invoice-dispute-investigation-agent" -version = "0.1.1" -description = "Invoice Dispute Investigation Agent" -authors = [ - { name = "Eduard Stanculet" } -] -requires-python = ">=3.9" -dependencies = [ - "urllib3>=1.25.3,<3.0.0", - "pydantic>=2", - "typing-extensions>=4.7.1", - "uipath-sdk==0.0.110", - "uipath-langchain==0.0.81", - "uipath_connectors.uipath_airdk >=1.53.6.dev6", - "uipath_connectors.google_gmail >=1.18.17.dev6", - "langchain", - "langchain-core", - "langchain-anthropic", - "langchain-openai", - "langgraph" -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -packages = ["."] diff --git a/samples/invoice-dispute-investigation-agent/state.py b/samples/invoice-dispute-investigation-agent/state.py deleted file mode 100644 index b2023dbd..00000000 --- a/samples/invoice-dispute-investigation-agent/state.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Define the state structures for the agent.""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Sequence, Optional, List - -from langchain_core.messages import AnyMessage -from langgraph.graph import add_messages -from langgraph.managed import IsLastStep -from typing_extensions import Annotated - -@dataclass -class InputState: - """Defines the input state for the agent, representing a narrower interface to the outside world. - - This class is used to define the initial state and structure of incoming data. - """ - MismatchedItems: str = "" - Company: str = "" - Supplier: str = "" - SupplierWebsite: str = "" - -@dataclass -class OutputState: - - SupplierEmail: Annotated[str, ..., "Supplier email, queried from SAP or human escalation"] = "" - - InvestigationResult: Annotated[Optional[OutputState_InvestigationResult], ..., "The results of the investigation"] = None - - MismatchedItemsFindings: Annotated[Optional[List[OutputState_MismatchedItemsFindings_Item]], ..., "The categorization of replacement items and the reasoning "] = None - -@dataclass -class OutputState_InvestigationResult: - - MoveForward: Annotated[Optional[OutputState_InvestigationResult_MoveForward], ..., "Should the company move forward with the dispute with the supplier?"] = None - - SKUMismatchAction: Annotated[Optional[OutputState_InvestigationResult_SKUMismatchAction], ..., "What should the company do when the purchase order has SKUs that do not match the invoice?"] = None - - PODoesNotMatch: Annotated[Optional[OutputState_InvestigationResult_PODoesNotMatch], ..., "What should the company do when the purchase order has SKUs that does not match the invoice?"] = None - - DesiredOutcomes: Annotated[Optional[OutputState_InvestigationResult_DesiredOutcomes], ..., "What should be the desired outcomes for the Company?"] = None - - Email: Annotated[Optional[OutputState_InvestigationResult_Email], ..., "What should be the desired outcomes for the Company?"] = None - -@dataclass -class OutputState_InvestigationResult_MoveForward: - - Answer: Annotated[str, ..., "The answer to MoveForward"] = "" - - Citation: Annotated[str, ..., "The Citation to the answer for MoveForward. The citation should contain all document names, all page numbers, and the specific section for where the information was found. "] = "" - - AnswerBool: Annotated[bool, ..., "The Answer as a Boolean. If the company should move forward, this should be true"] = False - -@dataclass -class OutputState_InvestigationResult_SKUMismatchAction: - - Answer: Annotated[str, ..., "The Answer to SKUMismatchAction"] = "" - - Citation: Annotated[str, ..., "The Citation to the answer for SKUMismatchAction. The citation should contain all document names, all page numbers, and the specific section for where the information was found. "] = "" - -@dataclass -class OutputState_InvestigationResult_PODoesNotMatch: - - Answer: Annotated[str, ..., "The Answer for PODoesNotMatch"] = "" - - Citation: Annotated[str, ..., "The Citation to the answer for PODoesNotMatch. The citation should contain all document names, all page numbers, and the specific section for where the information was found. "] = "" - -@dataclass -class OutputState_InvestigationResult_DesiredOutcomes: - - Answer: Annotated[str, ..., "The Answer for DesiredOutcomes"] = "" - - Citation: Annotated[str, ..., "The Citation to the answer for DesiredOutcomes. The citation should contain all document names, all page numbers, and the specific section for where the information was found. "] = "" - -@dataclass -class OutputState_InvestigationResult_Email: - - emailSubject: Annotated[str, ..., "Subject of the email"] = "" - - emailBody: Annotated[str, ..., "Body of the email"] = "" - -@dataclass() -class OutputState_MismatchedItemsFindings_Item: - - Reasoning: Annotated[str, ..., "The reasoning for the categorization"] = "" - - MismatchedParts: Annotated[str, ..., "The names of the items that were mismatched"] = "" - - ValidReplacement: Annotated[str, ..., "If the mismatched items were found to be a valid replacement, this should be true"] = "" - -@dataclass -class State(InputState, OutputState): - """Represents the complete state of the agent, extending InputState with additional attributes. - - This class can be used to store any information needed throughout the agent's lifecycle. - """ - - messages: Annotated[Sequence[AnyMessage], add_messages] = field( - default_factory=list - ) - """ - Messages tracking the primary execution state of the agent. - - Typically accumulates a pattern of: - 1. HumanMessage - user input - 2. AIMessage with .tool_calls - agent picking tool(s) to use to collect information - 3. ToolMessage(s) - the responses (or errors) from the executed tools - 4. AIMessage without .tool_calls - agent responding in unstructured format to the user - 5. HumanMessage - user responds with the next conversational turn - - Steps 2-5 may repeat as needed. - - The `add_messages` annotation ensures that new messages are merged with existing ones, - updating by ID to maintain an "append-only" state unless a message with the same ID is provided. - """ - - is_last_step: IsLastStep = field(default=False) - """ - Indicates whether the current step is the last one before the graph raises an error. - - This is a 'managed' variable, controlled by the state machine rather than user code. - It is set to 'True' when the step count reaches recursion_limit - 1. - """ - - - # Additional attributes can be added here as needed. - # Common examples include: - # retrieved_documents: List[Document] = field(default_factory=list) - # extracted_entities: Dict[str, Any] = field(default_factory=dict) - # api_connections: Dict[str, Any] = field(default_factory=dict) - diff --git a/samples/invoice-dispute-investigation-agent/supplier_website_reader.py b/samples/invoice-dispute-investigation-agent/supplier_website_reader.py deleted file mode 100644 index c5687172..00000000 --- a/samples/invoice-dispute-investigation-agent/supplier_website_reader.py +++ /dev/null @@ -1,31 +0,0 @@ -import os -from typing import cast - -from langchain_core.tools import tool -from uipath_connectors.uipath_airdk import UiPathAirdk, WebReaderRequest -from uipath_sdk import UiPathSDK - - -@tool -def SupplierWebsiteReader(request: WebReaderRequest) -> str: - """ - Use this tool to read data from the supplier website. - Specifically, use it to get details about mismatched items from the site: '{{Supplier Website}}'. Replace '' with the actual product SKU. - - Args: - request: The request parameters - url: A publicly accessible URL - provider: The search engine to use. Must be one of: ['Jina'] - - Returns: - The text from the URL - """ - - airdk = cast( - UiPathAirdk, - UiPathSDK().connections.uipath_airdk( - os.environ.get("UIPATH_AIRDK_CONNECTION_KEY") - ), - ) - response = airdk.web_reader(body=request) - return response.text diff --git a/samples/invoice-dispute-investigation-agent/uipath_ecs_retriever_tool.py b/samples/invoice-dispute-investigation-agent/uipath_ecs_retriever_tool.py deleted file mode 100644 index 97ef8d1b..00000000 --- a/samples/invoice-dispute-investigation-agent/uipath_ecs_retriever_tool.py +++ /dev/null @@ -1,13 +0,0 @@ -from langchain.tools.retriever import create_retriever_tool -from uipath_langchain.retrievers import ContextGroundingRetriever -from os import environ as env - -retriever = ContextGroundingRetriever(index_name=f"{env.get('UIPATH_ECS_CONTEXT')}") -retriever_tool = create_retriever_tool( - retriever, - "ContextforInvoiceDisputeInvestigation", - """ - Use this tool to search the company internal documents for information about policies around dispute resolution. - Use a meaningful query to load relevant information from the documents. Save the citation for later use. - """ -) diff --git a/samples/invoice-dispute-investigation-agent/utils.py b/samples/invoice-dispute-investigation-agent/utils.py deleted file mode 100644 index 5aa1dd18..00000000 --- a/samples/invoice-dispute-investigation-agent/utils.py +++ /dev/null @@ -1,26 +0,0 @@ -"""Utility & helper functions.""" -from langchain.chat_models import init_chat_model -from langchain_core.language_models import BaseChatModel -from langchain_core.messages import BaseMessage - - -def get_message_text(msg: BaseMessage) -> str: - """Get the text content of a message.""" - content = msg.content - if isinstance(content, str): - return content - elif isinstance(content, dict): - return content.get("text", "") - else: - txts = [c if isinstance(c, str) else (c.get("text") or "") for c in content] - return "".join(txts).strip() - - -def load_chat_model(fully_specified_name: str) -> BaseChatModel: - """Load a chat model from a fully specified name. - - Args: - fully_specified_name (str): String in the format 'provider/model'. - 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-This repository contains a multi-agent system that breaks down complex tasks into discrete steps and routes them to specialized agents for execution. The system consists of three main components: - -1. **Planner agent**: Orchestrates the workflow by planning task execution and routing subtasks to worker agents -2. **Researcher Agent**: Finds information, formulas, and reference material without performing calculations -3. **Coder Agent**: Performs calculations and evaluates formulas with specific values - -Each agent functions as an independent entrypoint and can be deployed as a separate process, while still being packaged together as part of an Orchestrator Agent Package. - -## System Architecture - -The system uses LangGraph to create a directed graph of agents that can communicate and pass state to each other. - -### Planner Graph -```mermaid ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__(

__start__

) - input(input) - create_plan(create_plan) - supervisor([supervisor]):::last - invoke_agent(invoke_agent) - __start__ --> input; - create_plan --> supervisor; - input --> supervisor; - invoke_agent --> supervisor; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc -``` - -### Researcher Agent -```mermaid ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - __end__([

__end__

]):::last - __start__ --> researcher___start__; - researcher___end__ --> __end__; - subgraph researcher - researcher___start__(

__start__

) - researcher_agent(agent) - researcher_tools(tools) - researcher___end__(

__end__

) - researcher___start__ --> researcher_agent; - researcher_tools --> researcher_agent; - researcher_agent -.-> researcher_tools; - researcher_agent -.-> researcher___end__; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc -``` - -### Coder Agent -```mermaid ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - __end__([

__end__

]):::last - __start__ --> coder___start__; - coder___end__ --> __end__; - subgraph coder - coder___start__(

__start__

) - coder_agent(agent) - coder_tools(tools) - coder___end__(

__end__

) - coder___start__ --> coder_agent; - coder_tools --> coder_agent; - coder_agent -.-> coder_tools; - coder_agent -.-> coder___end__; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc -``` - -## Agent Responsibilities - -- **Planner Agent**: - - Takes user questions and creates execution plans - - Routes tasks to appropriate worker agents - - Manages the execution flow and state tracking - - Returns final results to the user - -- **Researcher Agent**: - - Retrieves information using a Tavily search tool - - Provides factual content, definitions, and formulas - - Never performs calculations (strictly enforced) - -- **Coder Agent**: - - Performs calculations using Python code execution - - Evaluates formulas with specific input values - - Returns precise numerical results - -## Usage - -### Running the System - -To run the entire system, use the following command with the complete JSON input: - -```bash -uipath run planner '{"question": "First, please state the Pythagorean theorem. Give only the formula, using variables a, b, and c. Then apply this formula to calculate the value when a=2 and b=3."}' -``` - -### Debugging Individual Agents - -You can debug individual agents by directly invoking them: - -#### Researcher Agent -Run the researcher agent with: - -```bash -uipath run researcher '{"messages":[{"content":"State the Pythagorean theorem formula using variables a, b, and c","type":"human"}]}' -``` - -#### Coder Agent -Run the coder agent with: - -```bash -uipath run coder '{"messages":[{"content":"Let me help you state the Pythagorean theorem formula. The Pythagorean theorem is a fundamental mathematical formula that describes the relationship between the sides of a right triangle.\n\nThe formula is:\n\na² + b² = c²\n\nWhere:\n- a and b are the lengths of the two legs (the sides adjacent to the right angle)\n- c is the length of the hypotenuse (the longest side, opposite to the right angle).","type":"human","name":"researcher-agent"},{"content":"Calculate the result using the formula when a=2 and b=3","type":"human"}]}' -``` - -## Sample Workflow - -1. User submits a question about the Pythagorean theorem -2. Planner creates an execution plan with two steps: - - Step 1: Researcher agent retrieves the Pythagorean theorem formula - - Step 2: Coder agent applies the formula to calculate the result for a=2, b=3 -3. Planner executes Step 1 by invoking the researcher agent -4. Researcher agent returns the formula a² + b² = c² -5. Planner executes Step 2 by invoking the coder agent -6. Coder agent calculates c = √(2² + 3²) = √(4 + 9) = √13 ≈ 3.606 -7. Planner combines the responses and returns the final answer to the user - -## Implementation Details - -The system is implemented using: - -- LangGraph for agent orchestration -- LangChain for agent creation and tool integration -- Claude 3.5 Sonnet for LLM capabilities -- Tavily for search functionality in the researcher agent -- Python REPL for code execution in the coder agent - -## Requirements - -- Python 3.9+ -- LangGraph -- LangChain -- Anthropic API key -- Tavily API key diff --git a/samples/multi-agent-planner-researcher-coder-distributed/coder.mermaid b/samples/multi-agent-planner-researcher-coder-distributed/coder.mermaid deleted file mode 100644 index 3a19137c..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/coder.mermaid +++ /dev/null @@ -1,23 +0,0 @@ ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - __end__([

__end__

]):::last - __start__ --> coder___start__; - coder___end__ --> __end__; - subgraph coder - coder___start__(

__start__

) - coder_agent(agent) - coder_tools(tools) - coder___end__(

__end__

) - coder___start__ --> coder_agent; - coder_tools --> coder_agent; - coder_agent -.-> coder_tools; - coder_agent -.-> coder___end__; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/multi-agent-planner-researcher-coder-distributed/langgraph.json b/samples/multi-agent-planner-researcher-coder-distributed/langgraph.json deleted file mode 100644 index 83075544..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/langgraph.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "dependencies": ["."], - "graphs": { - "planner": "./src/multi-agent-distributed/planner.py:graph", - "researcher": "./src/multi-agent-distributed/researcher.py:graph", - "coder": "./src/multi-agent-distributed/coder.py:graph" - }, - "env": ".env" -} diff --git a/samples/multi-agent-planner-researcher-coder-distributed/planner.mermaid b/samples/multi-agent-planner-researcher-coder-distributed/planner.mermaid deleted file mode 100644 index 25e5df99..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/planner.mermaid +++ /dev/null @@ -1,18 +0,0 @@ ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__(

__start__

) - input(input) - create_plan(create_plan) - supervisor([supervisor]):::last - invoke_agent(invoke_agent) - __start__ --> input; - create_plan --> supervisor; - input --> supervisor; - invoke_agent --> supervisor; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/multi-agent-planner-researcher-coder-distributed/pyproject.toml b/samples/multi-agent-planner-researcher-coder-distributed/pyproject.toml deleted file mode 100644 index 9ba43c43..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/pyproject.toml +++ /dev/null @@ -1,48 +0,0 @@ -[project] -name = "multi-agents-distributed" -version = "0.0.1" -description = "Supervisor agent that coordinates between a researcher and a coder" -authors = [ - { name = "Radu Mocanu" } -] -requires-python = ">=3.9" -dependencies = [ - "langgraph>=0.2.55", - "langchain-community>=0.3.9", - "langchain-anthropic>=0.3.8", - "langchain-experimental>=0.3.4", - "tavily-python>=0.5.0", - "uipath-sdk==0.0.110", - "uipath-langchain==0.0.81" -] - -[project.optional-dependencies] -dev = ["mypy>=1.11.1", "ruff>=0.6.1"] - -[build-system] -requires = ["setuptools>=73.0.0", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.setuptools.package-data] -"*" = ["py.typed"] - -[tool.ruff] -lint.select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort - "D", # pydocstyle - "D401", # First line should be in imperative mood - "T201", - "UP", -] -lint.ignore = [ - "UP006", - "UP007", - "UP035", - "D417", - "E501", -] - -[tool.ruff.lint.per-file-ignores] -"tests/*" = ["D", "UP"] diff --git a/samples/multi-agent-planner-researcher-coder-distributed/researcher.mermaid b/samples/multi-agent-planner-researcher-coder-distributed/researcher.mermaid deleted file mode 100644 index 2900d529..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/researcher.mermaid +++ /dev/null @@ -1,23 +0,0 @@ ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - __end__([

__end__

]):::last - __start__ --> researcher___start__; - researcher___end__ --> __end__; - subgraph researcher - researcher___start__(

__start__

) - researcher_agent(agent) - researcher_tools(tools) - researcher___end__(

__end__

) - researcher___start__ --> researcher_agent; - researcher_tools --> researcher_agent; - researcher_agent -.-> researcher_tools; - researcher_agent -.-> researcher___end__; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/coder.py b/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/coder.py deleted file mode 100644 index ec8f1d4d..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/coder.py +++ /dev/null @@ -1,52 +0,0 @@ -from typing import Annotated - -from langchain_anthropic import ChatAnthropic -from langchain_core.tools import tool -from langchain_experimental.utilities import PythonREPL -from langgraph.graph import END, START, MessagesState, StateGraph -from langgraph.prebuilt import create_react_agent -from pydantic import BaseModel - -llm = ChatAnthropic(model="claude-3-5-sonnet-latest") - -repl = PythonREPL() - - -class GraphOutput(BaseModel): - answer: str - - -@tool -def python_repl_tool( - code: Annotated[str, "The python code to execute to generate your chart."], -): - """Use this to execute python code and do math. If you want to see the output of a value, - you should print it out with `print(...)`. This is visible to the user. - """ - try: - result = repl.run(code) - except BaseException as e: - return f"Failed to execute. Error: {repr(e)}" - result_str = ( - f"Successfully executed:\n\`\`\`python\n{code}\n\`\`\`\nStdout: {result}" - ) - return result_str - - -code_agent = create_react_agent(llm, tools=[python_repl_tool]) - - -def code_node(state: MessagesState) -> GraphOutput: - result = code_agent.invoke(state) - return GraphOutput(answer=result["messages"][-1].content) - - -# Build the state graph -builder = StateGraph(MessagesState, output=GraphOutput) -builder.add_node("coder", code_node) - -builder.add_edge(START, "coder") -builder.add_edge("coder", END) - -# Compile the graph -graph = builder.compile() diff --git a/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/planner.py b/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/planner.py deleted file mode 100644 index 25b4554d..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/planner.py +++ /dev/null @@ -1,213 +0,0 @@ -from typing import List, Literal - -from langchain_anthropic import ChatAnthropic -from langchain_core.messages import HumanMessage -from langchain_core.output_parsers import PydanticOutputParser -from langchain_core.prompts import ChatPromptTemplate -from langgraph.graph import START, MessagesState, StateGraph -from langgraph.types import Command, interrupt -from pydantic import BaseModel, Field -from typing_extensions import TypedDict -from uipath_sdk._models import InvokeProcess - -worker_agents = {"researcher": "researcher-agent", "coder": "coder-agent"} -agent_names = list(worker_agents.values()) -options = agent_names + ["FINISH"] - - -class Router(TypedDict): - """Worker to route to next. If no workers needed, route to FINISH.""" - - next: Literal[*options] - - -class GraphInput(BaseModel): - question: str - - -class GraphOutput(BaseModel): - answer: str - - -class PlanStep(BaseModel): - """A single step in the execution plan""" - - agent: str = Field( - description="The agent to execute this step (researcher-agent or coder-agent)" - ) - task: str = Field(description="The specific task for the agent to perform") - - -class ExecutionPlan(BaseModel): - """A plan for executing a complex task using specialized agents""" - - steps: List[PlanStep] = Field( - description="The ordered sequence of steps to execute" - ) - - -class State(MessagesState): - """State for the graph""" - - next: str - next_task: str - execution_plan: ExecutionPlan = None - current_step: int = 0 - - -def input(state: GraphInput): - return { - "messages": [ - HumanMessage(content=state.question), - ], - "next": "", - "next_task": "", - "execution_plan": None, - "current_step": 0, - } - - -llm = ChatAnthropic(model="claude-3-5-sonnet-latest") - - -def create_plan(state: State) -> Command: - """Create an execution plan based on the user's question.""" - parser = PydanticOutputParser(pydantic_object=ExecutionPlan) - - planning_prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - """You are a planning agent that creates execution plans for tasks. - Break down complex tasks into steps that can be performed by specialized agents.""", - ), - ("human", "{question}"), - ( - "system", - """ - Based on the user's request, create a structured execution plan. - - {format_instructions} - - Available agents: - - researcher-agent: Finds information, formulas, and reference material - - coder-agent: Performs calculations and evaluates formulas with specific values - - Create a plan with the minimum necessary steps to complete the task. - """, - ), - ] - ) - # Format the prompt with parser instructions and the user question - formatted_prompt = planning_prompt.format( - question=state["messages"][0].content, - format_instructions=parser.get_format_instructions(), - ) - - plan_response = llm.invoke(formatted_prompt) - - try: - plan_output = parser.parse(plan_response.content) - - steps = [] - for step in plan_output.steps: - agent_key = "researcher" if "researcher" in step.agent else "coder" - steps.append( - PlanStep(agent=worker_agents[agent_key], task=step.task) - ) - - execution_plan = ExecutionPlan(steps=steps) - except Exception as e: - print(f"Failed to parse plan: {e}") - return Command(goto="supervisor") - - # Create a plan summary for the messages - plan_summary = "Execution Plan:\n" + "\n".join( - [ - f"{i + 1}. {step.agent}: {step.task}" - for i, step in enumerate(execution_plan.steps) - ] - ) - - return Command( - update={ - "messages": [ - HumanMessage( - content=f"I've created an execution plan for this task:\n{plan_summary}" - ) - ], - "execution_plan": execution_plan, - }, - goto="supervisor", - ) - - -def supervisor_node(state: State) -> Command | GraphOutput: - """Execute the next step in the plan or finish if complete.""" - plan = state["execution_plan"] - - # If no plan exists, create one - if plan is None: - return Command(goto="create_plan") - - # If we've completed all steps, finish - if state["current_step"] >= len(plan.steps): - return GraphOutput(answer=state["messages"][-1].content) - - # Get the next step from the plan - next_step = plan.steps[state["current_step"]] - next_agent = next_step.agent - next_task = next_step.task - - # Return command to invoke the next agent - return Command(goto="invoke_agent", update={"next": next_agent, "next_task": next_task}) - - -def invoke_agent(state: State) -> Command: - """Invoke the agent specified in the current step of the execution plan.""" - - agent_name = state["next"] - task = state["next_task"] - - # Create a list of messages to send to the agent - # Keep previous agent messages + append the current task - input_messages = [ - msg for msg in state["messages"] - if isinstance(msg, HumanMessage) and hasattr(msg, "name") and msg.name - ] - input_messages.append(HumanMessage(content=task)) - - agent_response = interrupt( - InvokeProcess( - name=state["next"], input_arguments={"messages": input_messages} - ) - ) - - response_content = agent_response["answer"] - - agent_message = HumanMessage(content=response_content, name=agent_name) - - return Command( - update={ - "messages": [agent_message], - "current_step": state["current_step"] + 1, - }, - goto="supervisor", - ) - - -# Build the state graph -builder = StateGraph(State, input=GraphInput, output=GraphOutput) -builder.add_node("input", input) -builder.add_node("create_plan", create_plan) -builder.add_node("supervisor", supervisor_node) -builder.add_node("invoke_agent", invoke_agent) - -builder.add_edge(START, "input") -builder.add_edge("input", "supervisor") -builder.add_edge("create_plan", "supervisor") -builder.add_edge("invoke_agent", "supervisor") - - -# Compile the graph -graph = builder.compile() diff --git a/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/researcher.py b/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/researcher.py deleted file mode 100644 index 800ffdb4..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/researcher.py +++ /dev/null @@ -1,34 +0,0 @@ -from langchain_anthropic import ChatAnthropic -from langchain_community.tools.tavily_search import TavilySearchResults -from langgraph.graph import END, START, MessagesState, StateGraph -from langgraph.prebuilt import create_react_agent -from pydantic import BaseModel - -tavily_tool = TavilySearchResults(max_results=5) - -llm = ChatAnthropic(model="claude-3-5-sonnet-latest") - - -research_agent = create_react_agent( - llm, tools=[tavily_tool], prompt="You are a researcher. DO NOT do any math." -) - - -class GraphOutput(BaseModel): - answer: str - - -def research_node(state: MessagesState) -> GraphOutput: - result = research_agent.invoke(state) - return GraphOutput(answer=result["messages"][-1].content) - - -# Build the state graph -builder = StateGraph(input=MessagesState, output=GraphOutput) -builder.add_node("researcher", research_node) - -builder.add_edge(START, "researcher") -builder.add_edge("researcher", END) - -# Compile the graph -graph = builder.compile() diff --git a/samples/multi-agent-planner-researcher-coder-distributed/uv.lock b/samples/multi-agent-planner-researcher-coder-distributed/uv.lock deleted file mode 100644 index 0fa50896..00000000 --- a/samples/multi-agent-planner-researcher-coder-distributed/uv.lock +++ /dev/null @@ -1,2381 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.9" -resolution-markers = [ - "python_full_version >= '3.12.4'", - "python_full_version >= '3.10' and python_full_version < '3.12.4'", - "python_full_version < '3.10'", -] - 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-This repository demonstrates a LangGraph-based multi-agent system where a supervisor coordinates between a researcher and a coder to solve complex tasks. - -## Architecture - -The system consists of three primary agents: -- **Supervisor**: Manages the conversation flow and determines which specialized agent should act next -- **Researcher**: Performs web searches using Tavily to gather information -- **Coder**: Executes Python code to perform calculations and analysis - -```mermaid ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - input(input) - supervisor(supervisor) - __start__ --> input; - input --> supervisor; - supervisor -.-> researcher___start__; - supervisor -.-> coder___start__; - researcher___end__ -.-> supervisor; - coder___end__ -.-> supervisor; - subgraph researcher - researcher___start__(

__start__

) - researcher_agent(agent) - researcher_tools(tools) - researcher___end__(

__end__

) - researcher___start__ --> researcher_agent; - researcher_tools --> researcher_agent; - researcher_agent -.-> researcher_tools; - researcher_agent -.-> researcher___end__; - end - subgraph coder - coder___start__(

__start__

) - coder_agent(agent) - coder_tools(tools) - coder___end__(

__end__

) - coder___start__ --> coder_agent; - coder_tools --> coder_agent; - coder_agent -.-> coder_tools; - coder_agent -.-> coder___end__; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc - -``` - -## How It Works - -1. The user provides a question or task -2. The supervisor evaluates the task and routes it to the appropriate specialized agent -3. The specialized agent (researcher or coder) processes the task using its tools -4. The specialized agent returns its results to the supervisor -5. The supervisor either routes to another agent or finalizes the response when the task is complete - -## Features - -- **Routing**: The supervisor dynamically decides which agent is best suited for each step -- **Web Search**: The researcher agent can search the web for up-to-date information using Tavily -- **Code Execution**: The coder agent can execute Python code to perform calculations and analysis -- **Structured I/O**: Uses Pydantic models for clean input/output interfaces - -## Requirements - -- Python 3.9+ -- LangGraph -- Anthropic API key (for Claude) -- Tavily API key (for web search) - -## Installation - -```bash -uv venv -p 3.11 .venv -.venv\Scripts\activate -uv sync -``` - -Set your API keys as environment variables in .env - -```bash -ANTHROPIC_API_KEY=your_anthropic_api_key -TAVILY_API_KEY=your_tavily_api_key -``` - -## Usage - -### Command Line - -You can run the agent directly using UiPath: - -```bash -uipath run agent '{"question": "What is the square root of 42?"}' -``` - -### In Code - -```python -from your_module import graph - -# Create input in the expected format -input_data = {"question": "What is the square root of 42?"} - -# Run the graph -result = graph.invoke(input_data) - -# Access the answer -print(result["answer"]) -``` - - - -## Safety Notes - -The coder agent uses a Python REPL that executes code locally, which can be unsafe without proper sandboxing. This is intended for development and demonstration purposes only. In production, consider using a sandboxed execution environment. diff --git a/samples/multi-agent-supervisor-researcher-coder/agent.mermaid b/samples/multi-agent-supervisor-researcher-coder/agent.mermaid deleted file mode 100644 index 9adf1bb9..00000000 --- a/samples/multi-agent-supervisor-researcher-coder/agent.mermaid +++ /dev/null @@ -1,38 +0,0 @@ ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - input(input) - supervisor(supervisor) - __start__ --> input; - input --> supervisor; - supervisor -.-> researcher___start__; - supervisor -.-> coder___start__; - researcher___end__ -.-> supervisor; - coder___end__ -.-> supervisor; - subgraph researcher - researcher___start__(

__start__

) - researcher_agent(agent) - researcher_tools(tools) - researcher___end__(

__end__

) - researcher___start__ --> researcher_agent; - researcher_tools --> researcher_agent; - researcher_agent -.-> researcher_tools; - researcher_agent -.-> researcher___end__; - end - subgraph coder - coder___start__(

__start__

) - coder_agent(agent) - coder_tools(tools) - coder___end__(

__end__

) - coder___start__ --> coder_agent; - coder_tools --> coder_agent; - coder_agent -.-> coder_tools; - coder_agent -.-> coder___end__; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/multi-agent-supervisor-researcher-coder/graph.py b/samples/multi-agent-supervisor-researcher-coder/graph.py deleted file mode 100644 index 54479609..00000000 --- a/samples/multi-agent-supervisor-researcher-coder/graph.py +++ /dev/null @@ -1,122 +0,0 @@ -from typing import Annotated, Literal - -from langchain_anthropic import ChatAnthropic -from langchain_community.tools.tavily_search import TavilySearchResults -from langchain_core.messages import HumanMessage, SystemMessage -from langchain_core.tools import tool -from langchain_experimental.utilities import PythonREPL -from langgraph.graph import END, START, MessagesState, StateGraph -from langgraph.prebuilt import create_react_agent -from langgraph.types import Command -from pydantic import BaseModel -from typing_extensions import TypedDict - -tavily_tool = TavilySearchResults(max_results=5) - -# This executes code locally, which can be unsafe -repl = PythonREPL() - - -@tool -def python_repl_tool( - code: Annotated[str, "The python code to execute to generate your chart."], -): - """Use this to execute python code and do math. If you want to see the output of a value, - you should print it out with `print(...)`. This is visible to the user. - """ - try: - result = repl.run(code) - except BaseException as e: - return f"Failed to execute. Error: {repr(e)}" - result_str = f"Successfully executed:\n\`\`\`python\n{code}\n\`\`\`\nStdout: {result}" - return result_str - -members = ["researcher", "coder"] -# Our team supervisor is an LLM node. It just picks the next agent to process -# and decides when the work is completed -options = members + ["FINISH"] - -system_prompt = ( - "You are a supervisor tasked with managing a conversation between the" - f" following workers: {members}. Given the following user request," - " respond with the worker to act next. Each worker will perform a" - " task and respond with their results and status. When finished," - " respond with FINISH." -) - - -class Router(TypedDict): - """Worker to route to next. If no workers needed, route to FINISH.""" - - next: Literal[*options] - - -llm = ChatAnthropic(model="claude-3-5-sonnet-latest") - -class GraphInput(BaseModel): - question: str - -class GraphOutput(BaseModel): - answer: str - -class State(MessagesState): - next: str - -def input(state: GraphInput): - return { - "messages": [ - SystemMessage(content=system_prompt), - HumanMessage(content=state.question), - ], - "next": "", - } - -def supervisor_node(state: State) -> Command[Literal[*members]] | GraphOutput: - response = llm.with_structured_output(Router).invoke(state["messages"]) - goto = response["next"] - if goto == "FINISH": - return GraphOutput(answer=state["messages"][-1].content) - else: - return Command(goto=goto, update={"next": goto}) - -research_agent = create_react_agent( - llm, tools=[tavily_tool], prompt="You are a researcher. DO NOT do any math." -) - - -def research_node(state: State) -> Command[Literal["supervisor"]]: - result = research_agent.invoke(state) - return Command( - update={ - "messages": [ - HumanMessage(content=result["messages"][-1].content, name="researcher") - ] - }, - goto="supervisor", - ) - - -# NOTE: THIS PERFORMS ARBITRARY CODE EXECUTION, WHICH CAN BE UNSAFE WHEN NOT SANDBOXED -code_agent = create_react_agent(llm, tools=[python_repl_tool]) - - -def code_node(state: State) -> Command[Literal["supervisor"]]: - result = code_agent.invoke(state) - return Command( - update={ - "messages": [ - HumanMessage(content=result["messages"][-1].content, name="coder") - ] - }, - goto="supervisor", - ) - - -builder = StateGraph(State, input=GraphInput, output=GraphOutput) -builder.add_edge(START, "input") -builder.add_edge("input", "supervisor") -builder.add_node("input", input) -builder.add_node("supervisor", supervisor_node) -builder.add_node("researcher", research_node) -builder.add_node("coder", code_node) -graph = builder.compile() diff --git a/samples/multi-agent-supervisor-researcher-coder/langgraph.json b/samples/multi-agent-supervisor-researcher-coder/langgraph.json deleted file mode 100644 index c465a881..00000000 --- a/samples/multi-agent-supervisor-researcher-coder/langgraph.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "dependencies": ["."], - "graphs": { - "agent": "./graph.py:graph" - }, - "env": ".env" -} diff --git a/samples/multi-agent-supervisor-researcher-coder/pyproject.toml b/samples/multi-agent-supervisor-researcher-coder/pyproject.toml deleted file mode 100644 index 9ad7e683..00000000 --- a/samples/multi-agent-supervisor-researcher-coder/pyproject.toml +++ /dev/null @@ -1,48 +0,0 @@ -[project] -name = "multi_agent_supervisor" -version = "0.0.9" -description = "Multi-agent system where a supervisor coordinates between a researcher and a coder" -authors = [ - { name = "Cristi Pufu" } -] -requires-python = ">=3.9" -dependencies = [ - "langgraph>=0.2.55", - "langchain-community>=0.3.9", - "langchain-anthropic>=0.3.8", - "langchain-experimental>=0.3.4", - "tavily-python>=0.5.0", - "uipath-sdk==0.0.110", - "uipath-langchain==0.0.81" -] - -[project.optional-dependencies] -dev = ["mypy>=1.11.1", "ruff>=0.6.1"] - -[build-system] -requires = ["setuptools>=73.0.0", "wheel"] -build-backend = "setuptools.build_meta" - 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-## Overview - -This script provides a straightforward way to echo messages with the following features: -- Repeat a message multiple times -- Add an optional prefix to each line -- Return the result as a single string with newline separators - -## Installation - -### Setup Environment - -1. Create a new Python virtual environment using UV: - ```bash - uv venv -p 3.11 .venv - ``` - -2. Activate the virtual environment: - - On Windows: - ```bash - .venv\Scripts\activate - ``` - - On macOS/Linux: - ```bash - source .venv/bin/activate - ``` - -3. Install dependencies: - ```bash - uv sync - ``` - -## Usage - -### Basic Command Structure - -Run the script using the UiPath runner with a JSON input: - -```bash -uipath run echo.py '{"message": "Hello, world!"}' -``` - -### Input Parameters - -The script accepts the following parameters in JSON format: - -- `message` (required): The string message to echo -- `repeat` (optional, default=1): Number of times to repeat the message -- `prefix` (optional, default=None): Prefix to add to each line - -### Examples - -1. Basic usage (echo once): - ```bash - uipath run echo.py '{"message": "Hello, world!"}' - ``` - Output: - ``` - {'message': 'Hello, world!'} - ``` - -2. Repeat a message multiple times: - ```bash - uipath run echo.py '{"message": "Hello, world!", "repeat": 3}' - ``` - Output: - ``` - {'message': 'Hello, world!\nHello, world!\nHello, world!'} - ``` - -3. Add a prefix to each line: - ```bash - uipath run echo.py '{"message": "Hello, world!", "repeat": 2, "prefix": "INFO"}' - ``` - Output: - ``` - {'message': 'INFO: Hello, world!\nINFO: Hello, world!'} - ``` - -### Orchestrator Deployment - - ```bash - uipath init - ``` - ```bash - uipath auth - ``` - ```bash - uipath pack - ``` - ```bash - uipath publish - ``` diff --git a/samples/simple-echo/echo.py b/samples/simple-echo/echo.py deleted file mode 100644 index ca1cab1b..00000000 --- a/samples/simple-echo/echo.py +++ /dev/null @@ -1,25 +0,0 @@ -from dataclasses import dataclass -from typing import Optional - - -@dataclass -class EchoIn: - message: str - repeat: Optional[int] = 1 - prefix: Optional[str] = None - - -@dataclass -class EchoOut: - message: str - - -def main(input: EchoIn) -> EchoOut: - result = [] - for _ in range(input.repeat): - line = input.message - if input.prefix: - line = f"{input.prefix}: {line}" - result.append(line) - - return EchoOut(message="\n".join(result)) diff --git a/samples/simple-echo/pyproject.toml b/samples/simple-echo/pyproject.toml deleted file mode 100644 index 2e3c3d68..00000000 --- a/samples/simple-echo/pyproject.toml +++ /dev/null @@ -1,9 +0,0 @@ -[project] -name = "simple-echo" -version = "0.0.1" -description = "Echo script" -authors = [{ name = "Cristi Pufu", email = "cristian.pufu@uipath.com" }] -dependencies = [ - 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-This project demonstrates how to create an agent using LangGraph with Claude 3.5 Sonnet and multiple tool servers using the MCP (Model-Calling Protocol) standard. - -## Overview - -The agent uses: -- Claude 3.5 Sonnet as the language model -- LangGraph for orchestration -- Multiple MCP tool servers: - - Math server for mathematical operations - - Weather server for weather data retrieval - -## Architecture - -```mermaid ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - agent(agent) - tools(tools) - __end__([

__end__

]):::last - __start__ --> agent; - tools --> agent; - agent -.-> tools; - agent -.-> __end__; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc -``` - -The workflow follows a ReAct pattern: -1. Query is sent to the agent (Claude) -2. Agent decides whether to use tools or provide a final answer -3. If tools are needed, the request is sent to the appropriate tool server -4. Results from tools are sent back to the agent -5. Process repeats until the agent has enough information to provide a final answer - -## Prerequisites - -- Python 3.9+ -- `langchain-anthropic` -- `langchain-mcp-adapters` -- `langgraph` -- Anthropic API key set as an environment variable - -## Installation - -```bash -uv venv -p 3.11 .venv -.venv\Scripts\activate -uv sync -``` - -Set your API keys as environment variables in .env - -```bash -ANTHROPIC_API_KEY=your_anthropic_api_key -``` - -## Usage - -1. Ensure your tool servers are implemented correctly (see `math_server.py` and `weather_server.py`) -2. Run the agent with a query: - -```bash -uipath run agent '{"messages": [{"type": "human", "content": "What is 2+2"}]}' -``` - -## Debugging - -For debugging issues: - -1. Check that your MCP servers are running correctly: - ```bash - python math_server.py # Run standalone to verify it works - python weather_server.py - ``` - -2. Check logs for any connection or runtime errors: - ```bash - uipath run agent --debug '{"messages": [{"type": "human", "content": "What is 2+2"}]}' - ``` - -## Extending with New Tools - -To add a new tool: - -1. Create a new MCP-compatible server (similar to math_server.py) -2. Add it to the MultiServerMCPClient configuration dictionary -3. The agent will automatically discover and use the new tool's capabilities diff --git a/samples/simple-local-mcp/agent.mermaid b/samples/simple-local-mcp/agent.mermaid deleted file mode 100644 index 7c5946e0..00000000 --- a/samples/simple-local-mcp/agent.mermaid +++ /dev/null @@ -1,17 +0,0 @@ ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - agent(agent) - tools(tools) - __end__([

__end__

]):::last - __start__ --> agent; - tools --> agent; - agent -.-> tools; - agent -.-> __end__; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/simple-local-mcp/langgraph.json b/samples/simple-local-mcp/langgraph.json deleted file mode 100644 index 337abb66..00000000 --- a/samples/simple-local-mcp/langgraph.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "dependencies": ["."], - "graphs": { - "agent": "./src/simple-local-mcp/graph.py:make_graph" - } -} diff --git a/samples/simple-local-mcp/pyproject.toml b/samples/simple-local-mcp/pyproject.toml deleted file mode 100644 index 82d1bd22..00000000 --- a/samples/simple-local-mcp/pyproject.toml +++ /dev/null @@ -1,21 +0,0 @@ -[project] -name = "simple-local-mcp" -version = "0.0.3" -description = "Math and Weather Local MCP Server Agent" -authors = [{ name = "Cristi Pufu", email = "cristian.pufu@uipath.com" }] -dependencies = [ - "langgraph>=0.2.70", - "langchain-core>=0.3.34", - "langchain-anthropic>=0.3.9", - "langgraph-checkpoint-sqlite>=2.0.3", - "python-dotenv>=1.0.1", - "uipath-sdk==0.0.110", - "uipath-langchain==0.0.81", - "pydantic>=2.10.6", - "aiohttp>=3.11.12", - "typing-extensions>=4.12.2", - "langchain-mcp-adapters>=0.0.5", - "ipython>=8.32.0", - "mcp>=1.4.1", -] -requires-python = ">=3.10" diff --git a/samples/simple-local-mcp/src/simple-local-mcp/graph.py b/samples/simple-local-mcp/src/simple-local-mcp/graph.py deleted file mode 100644 index 42b52eb1..00000000 --- a/samples/simple-local-mcp/src/simple-local-mcp/graph.py +++ /dev/null @@ -1,28 +0,0 @@ -import sys -from contextlib import asynccontextmanager - -from langchain_anthropic import ChatAnthropic -from langchain_mcp_adapters.client import MultiServerMCPClient -from langgraph.prebuilt import create_react_agent - -model = ChatAnthropic(model="claude-3-5-sonnet-latest") - - -@asynccontextmanager -async def make_graph(): - async with MultiServerMCPClient( - { - "math": { - "command": sys.executable, - "args": ["src/simple-local-mcp/math_server.py"], - "transport": "stdio", - }, - "weather": { - "command": sys.executable, - "args": ["src/simple-local-mcp/weather_server.py"], - "transport": "stdio", - }, - } - ) as client: - agent = create_react_agent(model, client.get_tools()) - yield agent diff --git a/samples/simple-local-mcp/src/simple-local-mcp/math_server.py b/samples/simple-local-mcp/src/simple-local-mcp/math_server.py deleted file mode 100644 index 9cd78d79..00000000 --- a/samples/simple-local-mcp/src/simple-local-mcp/math_server.py +++ /dev/null @@ -1,16 +0,0 @@ -from mcp.server.fastmcp import FastMCP - -mcp = FastMCP("Math") - -@mcp.tool() -def add(a: int, b: int) -> int: - """Add two numbers""" - return a + b - -@mcp.tool() -def multiply(a: int, b: int) -> int: - """Multiply two numbers""" - return a * b - -if __name__ == "__main__": - mcp.run(transport="stdio") diff --git a/samples/simple-local-mcp/src/simple-local-mcp/weather_server.py b/samples/simple-local-mcp/src/simple-local-mcp/weather_server.py deleted file mode 100644 index 64faac8a..00000000 --- a/samples/simple-local-mcp/src/simple-local-mcp/weather_server.py +++ /dev/null @@ -1,11 +0,0 @@ -from mcp.server.fastmcp import FastMCP - 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flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - agent(agent) - tools(tools) - __end__([

__end__

]):::last - __start__ --> agent; - tools --> agent; - agent -.-> tools; - agent -.-> __end__; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/simple-remote-mcp/langgraph.json b/samples/simple-remote-mcp/langgraph.json deleted file mode 100644 index a431c132..00000000 --- a/samples/simple-remote-mcp/langgraph.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "dependencies": ["."], - "graphs": { - "agent": "./main.py:make_graph" - }, - "env": ".env" - } \ No newline at end of file diff --git a/samples/simple-remote-mcp/main.py b/samples/simple-remote-mcp/main.py deleted file mode 100644 index d54950b1..00000000 --- a/samples/simple-remote-mcp/main.py +++ /dev/null @@ -1,24 +0,0 @@ -from contextlib import asynccontextmanager -from langchain_mcp_adapters.client import MultiServerMCPClient -from langgraph.prebuilt import create_react_agent -from langchain_anthropic import ChatAnthropic -import dotenv -import os - -dotenv.load_dotenv() - - -@asynccontextmanager -async def make_graph(): - async with MultiServerMCPClient() as client: - await client.connect_to_server_via_sse( - server_name="hello-world-server", - url=os.getenv("UIPATH_MCP_SERVER_URL"), - headers={"Authorization": f"Bearer {os.getenv('UIPATH_ACCESS_TOKEN')}"}, - timeout=60, - ) - - tools = client.get_tools() - model = ChatAnthropic(model="claude-3-5-sonnet-latest") - graph = create_react_agent(model, tools=tools) - yield graph diff --git a/samples/simple-remote-mcp/pyproject.toml b/samples/simple-remote-mcp/pyproject.toml deleted file mode 100644 index 241d5cb7..00000000 --- a/samples/simple-remote-mcp/pyproject.toml +++ /dev/null @@ -1,17 +0,0 @@ -[project] -name = "simple-remote-mcp" -version = "0.0.1" -description = "simple-remote-mcp" -authors = [{ name = "John Doe", email = "john.doe@myemail.com" }] -dependencies = [ - "uipath-sdk==0.0.110", - "uipath-langchain==0.0.81", - "langchain>=0.1.0", - "langchain-anthropic>=0.0.1", - "langgraph>=0.3.21", - "python-dotenv>=1.0.0", - 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-Use LangGraph with Azure OpenAI to automatically classify support tickets into predefined categories with confidence scores. UiPath Orchestrator API integration for human approval step. - -## Debug - -1. Clone the repository: -```bash -git clone -cd samples\ticket-classification -``` - -2. Install dependencies: -```bash -pip install uv -uv venv -p 3.11 .venv -.venv\Scripts\activate -uv sync -``` - -3. Create a `.env` file in the project root with the following configuration: -```env -UIPATH_URL=https://alpha.uipath.com/ada/byoa -UIPATH_ACCESS_TOKEN=xxx -AZURE_OPENAI_API_KEY=xxx -AZURE_OPENAI_ENDPOINT=xxx -``` - -```bash -uipath run [--resume] -``` - -### Run - -To classify a ticket, run the script using UiPath CLI: - -```bash -uipath run agent '{"message": "GET Assets API does not enforce proper permissions Assets.View", "ticket_id": "TICKET-2345"}' -``` - -### Resume - -To resume the graph with approval: - -```bash -uipath run agent true --resume -``` - -### Input Format - -The input ticket should be in the following format: -```json -{ - "message": "The ticket message or description", - "ticket_id": "Unique ticket identifier" -} -``` - -### Output Format - -The script outputs JSON with the classification results: -```json -{ - "label": "security", - "confidence": 0.9 -} -``` diff --git a/samples/ticket-classification/agent.mermaid b/samples/ticket-classification/agent.mermaid deleted file mode 100644 index 53e6c730..00000000 --- a/samples/ticket-classification/agent.mermaid +++ /dev/null @@ -1,16 +0,0 @@ -%%{init: {'flowchart': {'curve': 'linear'}}}%% -graph TD; - __start__([

__start__

]):::first - classify(classify) - create_action(create_action) - human_approval(human_approval) - notify_team(notify_team) - __end__([

__end__

]):::last - __start__ --> classify; - classify --> create_action; - create_action --> human_approval; - human_approval --> notify_team; - notify_team --> __end__; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/ticket-classification/langgraph.json b/samples/ticket-classification/langgraph.json deleted file mode 100644 index 483ada7f..00000000 --- a/samples/ticket-classification/langgraph.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "dependencies": ["."], - "graphs": { - "agent": "./main.py:graph" - }, - "env": ".env" - } \ No newline at end of file diff --git a/samples/ticket-classification/main.py b/samples/ticket-classification/main.py deleted file mode 100644 index 4818e53b..00000000 --- a/samples/ticket-classification/main.py +++ /dev/null @@ -1,142 +0,0 @@ -import logging -import os -from typing import Literal, Optional - -from langchain_openai import AzureChatOpenAI -from langchain_core.output_parsers import PydanticOutputParser -from langchain_core.prompts import ChatPromptTemplate -from langgraph.graph import START, END, StateGraph -from langgraph.types import interrupt, Command -from pydantic import BaseModel, Field - -from uipath_sdk import UiPathSDK - -logger = logging.getLogger(__name__) - -uipath = UiPathSDK() - -class GraphInput(BaseModel): - message: str - ticket_id: str - -class GraphOutput(BaseModel): - label: str - confidence: float - -class GraphState(BaseModel): - message: str - ticket_id: str - label: Optional[str] = None - confidence: Optional[float] = None - - -class TicketClassification(BaseModel): - label: Literal["security", "error", "system", "billing", "performance"] = Field( - description="The classification label for the support ticket" - ) - confidence: float = Field( - description="Confidence score for the classification", ge=0.0, le=1.0 - ) - - -output_parser = PydanticOutputParser(pydantic_object=TicketClassification) - -prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - """You are a support ticket classifier. Classify tickets into exactly one category and provide a confidence score. - -{format_instructions} - -Categories: -- security: Security issues, access problems, auth failures -- error: Runtime errors, exceptions, unexpected behavior -- system: Core infrastructure or system-level problems -- billing: Payment and subscription related issues -- performance: Speed and resource usage concerns - -Respond with the classification in the requested JSON format.""", - ), - ("user", "{ticket_text}"), - ] -) - - -def get_azure_openai_api_key() -> str: - """Get Azure OpenAI API key from environment or UiPath.""" - api_key = os.getenv("AZURE_OPENAI_API_KEY") - - if not api_key: - try: - api_key = uipath.assets.retrieve_credential("AZURE_OPENAI_API_KEY") - if not api_key: - raise ValueError("No API key found in credentials") - except Exception as e: - logger.error(f"Failed to get API key: {str(e)}") - raise RuntimeError("Failed to get Azure OpenAI API key") - - return api_key - - -async def classify(state: GraphState) -> GraphState: - """Classify the support ticket using LLM.""" - llm = AzureChatOpenAI( - azure_deployment="gpt-4o-mini", - api_key=get_azure_openai_api_key(), - azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), - api_version="2024-10-21" - ) - _prompt = prompt.partial( - format_instructions=output_parser.get_format_instructions() - ) - chain = _prompt | llm | output_parser - - try: - result = await chain.ainvoke({"ticket_text": state.message}) - print(result) - state.label = result.label - state.confidence = result.confidence - logger.info( - f"Ticket classified with label: {result.label} confidence score: {result.confidence}" - ) - return state - except Exception as e: - logger.error(f"Classification failed: {str(e)}") - state.label = "error" - state.confidence = 0.0 - return state - -async def wait_for_human(state: GraphState) -> GraphState: - logger.info("Wait for human approval") - feedback = interrupt(f"Label: {state.label} Confidence: {state.confidence}") - - if isinstance(feedback, bool) and feedback is True: - return Command(goto="notify_team") - else: - return Command(goto=END) - -async def notify_team(state: GraphState) -> GraphState: - logger.info("Send team email notification") - print(state) - return state - -"""Process a support ticket through the workflow.""" - -builder = StateGraph(GraphState, input=GraphInput, output=GraphOutput) - -builder.add_node("classify", classify) -builder.add_node("human_approval", wait_for_human) -builder.add_node("notify_team", notify_team) - -builder.add_edge(START, "classify") -builder.add_edge("classify", "human_approval") -builder.add_edge("human_approval", "notify_team") -builder.add_edge("notify_team", END) - - -from langgraph.checkpoint.memory import MemorySaver - -memory = MemorySaver() - -graph = builder.compile(checkpointer=memory) diff --git a/samples/ticket-classification/pyproject.toml b/samples/ticket-classification/pyproject.toml deleted file mode 100644 index 181862f6..00000000 --- a/samples/ticket-classification/pyproject.toml +++ /dev/null @@ -1,19 +0,0 @@ -[project] -name = "ticket-classification-agent" -version = "0.0.2" -description = "Ticket classification system" -authors = [{ name = "Cristi Pufu", email = "cristian.pufu@uipath.com" }] -dependencies = [ - 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-Use LangGrahp with UiPath LLMs to automatically create a travel plan. -Shows the usage of stdio mcp. - -## Debug - -1. Clone the repository: -```bash -git clone -cd samples\travel-planner-local-mcp -``` - -2. Install dependencies: -```bash -pip install uv -uv venv -p 3.11 .venv -.venv\Scripts\activate -uv sync -``` - -3. Init uipath -``` -uipath init -uipath auth -``` - -4. Create a `.env` file in the project root with the following configuration: -```env -UIPATH_URL=https://alpha.uipath.com/ada/byoa -UIPATH_ACCESS_TOKEN=xxx -UIPATH_TENANT_ID=6961a069-3392-40ca-bf5d-276f4e54c8ff -UIPATH_ORGANIZATION_ID=b7006b1c-11c3-4a80-802e-fee0ebf9c360 -``` - -```bash -uipath run [--resume] -``` - -### Run - -To classify a ticket, run the script using UiPath CLI: - -```bash -uipath run agent '{"message": "Help me plan a ski vacation on a hard track." }' -``` - -### Input JSON Format - -The input ticket should be in the following format: -```json -{ - "message": "Plan a trip to...", -} -``` - -### Output Format - -The script outputs JSON with a markdown summary: -```json -{ - "summary": "# Final Report\n\n## Ski Vacation Options\n\n### 1. Aspen Snowmass\n- **Location:** Colorado, USA\n- **Elevation:** 12,510 ft\n- **Trails:** 362\n- **Best For:** Intermediate to advanced skiers\n- **Season:** November to April\n- **Highlights:** Aspen Snowmass features four mountains with varied terrain, luxury amenities, and a vibrant après-ski scene.\n- **Weather Forecast:** Currently unavailable, but typically sunny and around 75°F based on seasonal averages.\n\n### 2. Vail\n- **Location:** Colorado, USA\n- **Elevation:** 11,570 ft\n- **Trails:** 195\n- **Best For:** Intermediate to advanced skiers\n- **Season:** November to April\n- **Highlights:** Vail is known for its Back Bowls, European-style village, and extensive groomed terrain.\n- **Current Weather:** 25°F, Light Snow with 70% humidity and 8 mph winds. Current snow depth is 30 inches.\n\nBoth Aspen Snowmass and Vail offer excellent facilities and challenging tracks for advanced skiers. You can enjoy a mix of thrilling ski experiences and luxurious amenities. Vail currently has favorable skiing conditions with fresh snow, making it an attractive option for your ski vacation." -} -``` diff --git a/samples/travel-planner-local-mcp/__uipath/state.db b/samples/travel-planner-local-mcp/__uipath/state.db deleted file mode 100644 index ef25899f..00000000 --- a/samples/travel-planner-local-mcp/__uipath/state.db +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:97a283309ba97a26183c5bec08d664100aabaacae47fe6abfdd5c6f0ed2c998c -size 49152 diff --git a/samples/travel-planner-local-mcp/agent.mermaid b/samples/travel-planner-local-mcp/agent.mermaid deleted file mode 100644 index 93e39847..00000000 --- a/samples/travel-planner-local-mcp/agent.mermaid +++ /dev/null @@ -1,22 +0,0 @@ ---- -config: - flowchart: - curve: linear ---- -graph TD; - __start__([

__start__

]):::first - init(init) - agent(agent) - tools(tools) - output_summary(output_summary) - __end__([

__end__

]):::last - __start__ --> init; - init --> agent; - output_summary --> __end__; - tools --> agent; - agent -.-> tools; - agent -.-> output_summary; - agent -.-> agent; - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc diff --git a/samples/travel-planner-local-mcp/langgraph.json b/samples/travel-planner-local-mcp/langgraph.json deleted file mode 100644 index 14e809a4..00000000 --- a/samples/travel-planner-local-mcp/langgraph.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "dependencies": [ - "." - ], - "graphs": { - "agent": "./main.py:make_graph" - }, - "env": ".env" -} \ No newline at end of file diff --git a/samples/travel-planner-local-mcp/main.py b/samples/travel-planner-local-mcp/main.py deleted file mode 100644 index 6fedf5de..00000000 --- a/samples/travel-planner-local-mcp/main.py +++ /dev/null @@ -1,101 +0,0 @@ -import os -import sys -from contextlib import asynccontextmanager - -from langchain_core.messages import HumanMessage, SystemMessage -from langchain_mcp_adapters.client import MultiServerMCPClient -from langgraph.graph import END, START, MessagesState, StateGraph -from langgraph.prebuilt import ToolNode -from pydantic import BaseModel -from uipath_langchain.chat.models import ( - UiPathNormalizedChatModel, -) - - -class GraphInput(BaseModel): - message: str - - -class GraphOutput(BaseModel): - summary: str - - -def get_file_path(filename): - """Get absolute path to file in current directory.""" - return os.path.abspath(os.path.join(os.getcwd(), filename)) - - -mcp_config = { - "travel_mcp": { - "command": sys.executable, - "args": [get_file_path("./mcps/travel_mcp.py")], - "transport": "stdio", - } -} - - -@asynccontextmanager -async def make_graph(): - async with MultiServerMCPClient(mcp_config) as client: - tools = client.get_tools() - tool_node = ToolNode(tools) - - def init(state: GraphInput): - return { - "messages": [ - SystemMessage( - content="You are a helpful travel planner assistant!. When you are done please write your report in markdown as a single message and begin with `# Final Report`" - ), - HumanMessage(content=state.message), - ] - } - - def should_continue(state: MessagesState): - messages = state["messages"] - last_message = messages[-1] - - # Check if the last message contains a final report indicator - if hasattr(last_message, "content") and isinstance( - last_message.content, str - ): - if "# final report" in last_message.content.lower(): - return "output_summary" - - if last_message.tool_calls: - return "tools" - - return "agent" - - def output_summary(state: MessagesState): - messages = state["messages"] - last_message = messages[-1] - return {"summary": last_message.content} - - async def call_model(state: MessagesState): - messages = state["messages"] - - agent = UiPathNormalizedChatModel().bind_tools(tools) - response = agent.invoke(messages) - - return {"messages": [response]} - - workflow = StateGraph(MessagesState, input=GraphInput, output=GraphOutput) - # workflow = StateGraph(MessagesState) - - # Define the two nodes we will cycle between - workflow.add_node("init", init) - workflow.add_node("agent", call_model) - workflow.add_node("tools", tool_node) - workflow.add_node("output_summary", output_summary) - - workflow.add_edge(START, "init") - workflow.add_edge("init", "agent") - workflow.add_conditional_edges( - "agent", should_continue, ["tools", "output_summary", "agent"] - ) - workflow.add_edge("tools", "agent") - workflow.add_edge("output_summary", END) - - graph = workflow.compile() - - yield graph diff --git a/samples/travel-planner-local-mcp/mcps/estate_mcp.py b/samples/travel-planner-local-mcp/mcps/estate_mcp.py deleted file mode 100644 index da0ccd68..00000000 --- a/samples/travel-planner-local-mcp/mcps/estate_mcp.py +++ /dev/null @@ -1,388 +0,0 @@ -import sqlite3 -from datetime import datetime -from typing import Dict, List, Optional - -from mcp.server.fastmcp import FastMCP - -# Initialize FastMCP instance -mcp = FastMCP("Real Estate Database 🏠") - -# Database setup -DB_PATH = "real_estate.db" - - -def initialize_database(): - """Create database and tables if they don't exist""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - # Create properties table - cursor.execute(""" - CREATE TABLE IF NOT EXISTS properties ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - address TEXT NOT NULL, - city TEXT NOT NULL, - state TEXT NOT NULL, - zip_code TEXT NOT NULL, - price REAL NOT NULL, - bedrooms INTEGER NOT NULL, - bathrooms REAL NOT NULL, - sq_feet INTEGER NOT NULL, - property_type TEXT NOT NULL, - year_built INTEGER, - description TEXT, - listed_date TEXT - ) - """) - - conn.commit() - conn.close() - - -def populate_sample_data(): - """Add sample real estate data if the database is empty""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - # Check if data already exists - cursor.execute("SELECT COUNT(*) FROM properties") - count = cursor.fetchone()[0] - - if count == 0: - # Sample properties - properties = [ - { - "address": "123 Main St", - "city": "San Francisco", - "state": "CA", - "zip_code": "94105", - "price": 1250000, - "bedrooms": 3, - "bathrooms": 2.5, - "sq_feet": 1800, - "property_type": "Condo", - "year_built": 2010, - "description": "Modern condo with bay views", - "listed_date": "2025-03-01", - }, - { - "address": "456 Oak Ave", - "city": "New York", - "state": "NY", - "zip_code": "10001", - "price": 2100000, - "bedrooms": 2, - "bathrooms": 2, - "sq_feet": 1100, - "property_type": "Apartment", - "year_built": 1998, - "description": "Luxury apartment in heart of Manhattan", - "listed_date": "2025-02-15", - }, - { - "address": "789 Pine Rd", - "city": "Austin", - "state": "TX", - "zip_code": "78701", - "price": 550000, - "bedrooms": 4, - "bathrooms": 3, - "sq_feet": 2500, - "property_type": "Single Family", - "year_built": 2015, - "description": "Spacious home with backyard", - "listed_date": "2025-03-10", - }, - { - "address": "321 Maple Dr", - "city": "Seattle", - "state": "WA", - "zip_code": "98101", - "price": 875000, - "bedrooms": 3, - "bathrooms": 2, - "sq_feet": 1650, - "property_type": "Townhouse", - "year_built": 2018, - "description": "Modern townhouse near downtown", - "listed_date": "2025-01-20", - }, - { - "address": "555 Cedar Ln", - "city": "Chicago", - "state": "IL", - "zip_code": "60601", - "price": 720000, - "bedrooms": 2, - "bathrooms": 1.5, - "sq_feet": 1200, - "property_type": "Condo", - "year_built": 2005, - "description": "Renovated condo with lake views", - "listed_date": "2025-02-28", - }, - ] - - # Insert sample data - for prop in properties: - cursor.execute( - """ - INSERT INTO properties - (address, city, state, zip_code, price, bedrooms, bathrooms, - sq_feet, property_type, year_built, description, listed_date) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - prop["address"], - prop["city"], - prop["state"], - prop["zip_code"], - prop["price"], - prop["bedrooms"], - prop["bathrooms"], - prop["sq_feet"], - prop["property_type"], - prop["year_built"], - prop["description"], - prop["listed_date"], - ), - ) - - conn.commit() - print("Sample data added to the database.") - - conn.close() - - -# Initialize the database -initialize_database() -populate_sample_data() - -# MCP Tools for interacting with the database - - -@mcp.tool() -def get_all_properties() -> List[Dict]: - """Get all properties from the database""" - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - cursor.execute("SELECT * FROM properties") - rows = cursor.fetchall() - - properties = [dict(row) for row in rows] - conn.close() - - return properties - - -@mcp.tool() -def get_property(property_id: int) -> Optional[Dict]: - """Get a specific property by ID""" - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - cursor.execute("SELECT * FROM properties WHERE id = ?", (property_id,)) - row = cursor.fetchone() - - if row: - property_data = dict(row) - else: - property_data = None - - conn.close() - return property_data - - -@mcp.tool() -def search_properties( - city: Optional[str] = None, - min_price: Optional[float] = None, - max_price: Optional[float] = None, - min_bedrooms: Optional[int] = None, - property_type: Optional[str] = None, -) -> List[Dict]: - """ - Search for properties with various filters - """ - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - query = "SELECT * FROM properties WHERE 1=1" - params = [] - - if city: - query += " AND city = ?" - params.append(city) - - if min_price is not None: - query += " AND price >= ?" - params.append(min_price) - - if max_price is not None: - query += " AND price <= ?" - params.append(max_price) - - if min_bedrooms is not None: - query += " AND bedrooms >= ?" - params.append(min_bedrooms) - - if property_type: - query += " AND property_type = ?" - params.append(property_type) - - cursor.execute(query, params) - rows = cursor.fetchall() - - properties = [dict(row) for row in rows] - conn.close() - - return properties - - -@mcp.tool() -def add_property( - address: str, - city: str, - state: str, - zip_code: str, - price: float, - bedrooms: int, - bathrooms: float, - sq_feet: int, - property_type: str, - year_built: Optional[int] = None, - description: Optional[str] = None, -) -> Dict: - """Add a new property to the database""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - listed_date = datetime.now().strftime("%Y-%m-%d") - - cursor.execute( - """ - INSERT INTO properties - (address, city, state, zip_code, price, bedrooms, bathrooms, - sq_feet, property_type, year_built, description, listed_date) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - address, - city, - state, - zip_code, - price, - bedrooms, - bathrooms, - sq_feet, - property_type, - year_built, - description, - listed_date, - ), - ) - - property_id = cursor.lastrowid - conn.commit() - conn.close() - - return {"id": property_id, "message": "Property added successfully"} - - -@mcp.tool() -def update_property( - property_id: int, price: Optional[float] = None, description: Optional[str] = None -) -> Dict: - """Update price and/or description of an existing property""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - # Check if property exists - cursor.execute("SELECT id FROM properties WHERE id = ?", (property_id,)) - if not cursor.fetchone(): - conn.close() - return {"error": f"Property with ID {property_id} not found"} - - updates = [] - params = [] - - if price is not None: - updates.append("price = ?") - params.append(price) - - if description is not None: - updates.append("description = ?") - params.append(description) - - if not updates: - conn.close() - return {"error": "No updates provided"} - - query = f"UPDATE properties SET {', '.join(updates)} WHERE id = ?" - params.append(property_id) - - cursor.execute(query, params) - conn.commit() - conn.close() - - return {"message": f"Property {property_id} updated successfully"} - - -@mcp.tool() -def delete_property(property_id: int) -> Dict: - """Delete a property from the database""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - # Check if property exists - cursor.execute("SELECT id FROM properties WHERE id = ?", (property_id,)) - if not cursor.fetchone(): - conn.close() - return {"error": f"Property with ID {property_id} not found"} - - cursor.execute("DELETE FROM properties WHERE id = ?", (property_id,)) - conn.commit() - conn.close() - - return {"message": f"Property {property_id} deleted successfully"} - - -@mcp.tool() -def get_statistics() -> Dict: - """Get statistics about the real estate database""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - stats = {} - - cursor.execute("SELECT COUNT(*) FROM properties") - stats["total_properties"] = cursor.fetchone()[0] - - cursor.execute("SELECT AVG(price) FROM properties") - stats["average_price"] = round(cursor.fetchone()[0], 2) - - cursor.execute("SELECT MIN(price) FROM properties") - stats["min_price"] = cursor.fetchone()[0] - - cursor.execute("SELECT MAX(price) FROM properties") - stats["max_price"] = cursor.fetchone()[0] - - cursor.execute( - "SELECT property_type, COUNT(*) FROM properties GROUP BY property_type" - ) - stats["property_types"] = {row[0]: row[1] for row in cursor.fetchall()} - - cursor.execute("SELECT city, COUNT(*) FROM properties GROUP BY city") - stats["cities"] = {row[0]: row[1] for row in cursor.fetchall()} - - conn.close() - return stats - - -if __name__ == "__main__": - # Start the MCP service - mcp.run() diff --git a/samples/travel-planner-local-mcp/mcps/travel_mcp.py b/samples/travel-planner-local-mcp/mcps/travel_mcp.py deleted file mode 100644 index 53dab5ea..00000000 --- a/samples/travel-planner-local-mcp/mcps/travel_mcp.py +++ /dev/null @@ -1,356 +0,0 @@ -# demo.py -import json -from datetime import datetime - -from mcp.server.fastmcp import FastMCP - -mcp = FastMCP("travel-planner-local-mcp") - - -@mcp.tool() -def get_weather(location: str): - """Get the current weather for a specific location. - - Args: - location: City name or common abbreviation - - Returns: - Detailed weather information for the requested location - """ - weather_data = { - "san francisco": { - "temp": "60°F", - "condition": "Foggy", - "humidity": "75%", - "wind": "15 mph", - }, - "sf": { - "temp": "60°F", - "condition": "Foggy", - "humidity": "75%", - "wind": "15 mph", - }, - "new york": { - "temp": "72°F", - "condition": "Partly Cloudy", - "humidity": "68%", - "wind": "8 mph", - }, - "nyc": { - "temp": "72°F", - "condition": "Partly Cloudy", - "humidity": "68%", - "wind": "8 mph", - }, - "miami": { - "temp": "88°F", - "condition": "Sunny", - "humidity": "80%", - "wind": "10 mph", - }, - "chicago": { - "temp": "65°F", - "condition": "Windy", - "humidity": "60%", - "wind": "25 mph", - }, - "las vegas": { - "temp": "95°F", - "condition": "Clear", - "humidity": "20%", - "wind": "5 mph", - }, - "seattle": { - "temp": "55°F", - "condition": "Rainy", - "humidity": "85%", - "wind": "12 mph", - }, - # Adding ski resort destinations - "aspen": { - "temp": "28°F", - "condition": "Snowy", - "humidity": "65%", - "wind": "12 mph", - "snow_depth": "24 inches", - }, - "vail": { - "temp": "25°F", - "condition": "Light Snow", - "humidity": "70%", - "wind": "8 mph", - "snow_depth": "30 inches", - }, - "park city": { - "temp": "30°F", - "condition": "Snowy", - "humidity": "60%", - "wind": "15 mph", - "snow_depth": "22 inches", - }, - "lake tahoe": { - "temp": "32°F", - "condition": "Heavy Snow", - "humidity": "75%", - "wind": "10 mph", - "snow_depth": "28 inches", - }, - "whistler": { - "temp": "22°F", - "condition": "Powder Snow", - "humidity": "80%", - "wind": "6 mph", - "snow_depth": "35 inches", - }, - "zermatt": { - "temp": "20°F", - "condition": "Fresh Powder", - "humidity": "65%", - "wind": "9 mph", - "snow_depth": "40 inches", - }, - } - - location = location.lower() - if location in weather_data: - data = weather_data[location] - if "snow_depth" in data: - return f"Current weather in {location.title()}: {data['temp']}, {data['condition']} with {data['humidity']} humidity and {data['wind']} winds. Current snow depth: {data['snow_depth']}." - else: - return f"Current weather in {location.title()}: {data['temp']}, {data['condition']} with {data['humidity']} humidity and {data['wind']} winds." - else: - return f"Weather data for {location} not available. Forecasting sunny and 75°F based on seasonal averages." - - -@mcp.tool() -def get_coolest_cities(): - """Get a list of cool cities to visit with descriptions of what makes them interesting. - - Returns: - JSON formatted data about top travel destinations - """ - cool_cities = [ - { - "name": "New York City", - "highlights": "Vibrant culture, Broadway shows, Central Park, world-class dining", - "best_season": "Fall", - }, - { - "name": "San Francisco", - "highlights": "Golden Gate Bridge, tech hub, diverse food scene, unique neighborhoods", - "best_season": "Summer", - }, - { - "name": "Tokyo", - "highlights": "Blend of traditional and ultramodern, amazing food, efficient transit", - "best_season": "Spring", - }, - { - "name": "Paris", - "highlights": "Art, architecture, romance, cuisine, fashion", - "best_season": "Spring/Fall", - }, - { - "name": "Barcelona", - "highlights": "Stunning architecture, beaches, tapas, vibrant nightlife", - "best_season": "Spring/Fall", - }, - ] - return json.dumps(cool_cities, indent=2) - - -@mcp.tool() -def get_attractions(city: str): - """Get top tourist attractions for a specific city. - - Args: - city: Name of the city or ski resort area - - Returns: - List of top attractions with brief descriptions - """ - attractions_by_city = { - "san francisco": [ - "Golden Gate Bridge - Iconic suspension bridge with fantastic views", - "Alcatraz Island - Historic federal prison with audio tours", - "Fisherman's Wharf - Popular waterfront with restaurants and sea lions", - "Chinatown - Oldest and largest Chinatown outside of Asia", - "Golden Gate Park - Large urban park with museums and gardens", - ], - "new york": [ - "Empire State Building - Iconic 102-story skyscraper with observation decks", - "Statue of Liberty - Famous copper statue on Liberty Island", - "Central Park - Massive urban park with various attractions", - "Times Square - Vibrant intersection known for bright advertisements", - "Metropolitan Museum of Art - One of the world's largest art museums", - ], - "london": [ - "Tower of London - Historic castle and former prison on the Thames", - "British Museum - World-class collection of art and antiquities", - "Buckingham Palace - Home of the British monarchy", - "London Eye - Giant observation wheel with panoramic views", - "Westminster Abbey - Gothic church and site of royal coronations", - ], - # Adding ski resort attractions - "aspen": [ - "Aspen Mountain (Ajax) - Challenging terrain with breathtaking views of Aspen", - "Snowmass - Family-friendly mountain with diverse terrain for all skill levels", - "Aspen Art Museum - Contemporary art in a striking building designed by Shigeru Ban", - "Wheeler Opera House - Historic venue offering performances and cultural events", - "Maroon Bells - Iconic twin peaks offering spectacular photography opportunities", - ], - "vail": [ - "Back Bowls - Seven legendary powder bowls spanning over 3,000 acres", - "Vail Village - Charming European-inspired pedestrian village with shops and dining", - "Blue Sky Basin - Remote skiing experience with natural terrain features", - "Betty Ford Alpine Gardens - Highest botanical garden in North America", - "Colorado Ski & Snowboard Museum - Historical exhibits on ski culture and Winter Olympics", - ], - "park city": [ - "Park City Mountain Resort - Largest ski resort in the United States", - "Main Street - Historic district with Victorian buildings, galleries and restaurants", - "Utah Olympic Park - 2002 Winter Olympics venue with museum and activities", - "Deer Valley Resort - Luxury ski-only resort with impeccable grooming", - "High West Distillery - Award-winning distillery and restaurant in a historic building", - ], - "lake tahoe": [ - "Heavenly Mountain Resort - Panoramic lake views with skiing across California and Nevada", - "Emerald Bay State Park - Stunning natural bay with Fannette Island and Vikingsholm", - "Squaw Valley (Palisades Tahoe) - Olympic venue with challenging terrain", - "Northstar California - Family-friendly resort with excellent tree skiing", - "Lake Tahoe Gondola - Scenic ride offering spectacular views of the lake and mountains", - ], - "whistler": [ - "Peak 2 Peak Gondola - Record-breaking gondola connecting Whistler and Blackcomb mountains", - "Whistler Village - Vibrant pedestrian village with shops, restaurants and nightlife", - "Whistler Olympic Plaza - 2010 Winter Olympics venue with activities and events", - "Audain Art Museum - Contemporary art museum featuring Indigenous works", - "Vallea Lumina - Enchanting night walk through an illuminated forest", - ], - "zermatt": [ - "Matterhorn Glacier Paradise - Highest cable car station in Europe with glacier viewing", - "Gornergrat Railway - Scenic mountain railway offering spectacular Matterhorn views", - "Zermatt Village - Car-free alpine village with traditional chalets and luxury shops", - "Matterhorn Museum - Underground museum showcasing Zermatt's mountaineering history", - "Schwarzsee - Beautiful mountain lake reflecting the Matterhorn, accessible by gondola", - ], - } - - city = city.lower() - if city in attractions_by_city: - return attractions_by_city[city] - else: - return f"No attraction data available for {city}. Please check another popular destination like San Francisco, New York, London, or ski resorts like Aspen, Vail, Whistler, or Zermatt." - - -@mcp.tool() -def convert_currency(amount: float, from_currency: str, to_currency: str): - """Convert an amount from one currency to another. - - Args: - amount: The amount to convert - from_currency: Source currency code (USD, EUR, GBP, JPY) - to_currency: Target currency code (USD, EUR, GBP, JPY) - - Returns: - Converted amount and current exchange rate - """ - rates = { - "usd": {"eur": 0.93, "gbp": 0.79, "jpy": 149.8, "usd": 1.0}, - "eur": {"usd": 1.08, "gbp": 0.85, "jpy": 161.5, "eur": 1.0}, - "gbp": {"usd": 1.27, "eur": 1.18, "jpy": 190.0, "gbp": 1.0}, - "jpy": {"usd": 0.0067, "eur": 0.0062, "gbp": 0.0053, "jpy": 1.0}, - } - - from_currency = from_currency.lower() - to_currency = to_currency.lower() - - if from_currency in rates and to_currency in rates[from_currency]: - rate = rates[from_currency][to_currency] - converted = amount * rate - return f"{amount} {from_currency.upper()} = {converted:.2f} {to_currency.upper()} (Rate: 1 {from_currency.upper()} = {rate} {to_currency.upper()}, as of {datetime.now().strftime('%Y-%m-%d')})" - else: - return f"Conversion from {from_currency.upper()} to {to_currency.upper()} is not supported." - - -@mcp.tool() -def get_ski_resorts(difficulty: str = "all"): - """Get information about ski resorts based on difficulty level. - - Args: - difficulty: Preferred difficulty level (beginner, intermediate, advanced, or all) - - Returns: - JSON formatted data about top ski resorts - """ - ski_resorts = [ - { - "name": "Aspen Snowmass", - "location": "Colorado, USA", - "elevation": "12,510 ft", - "trails": 362, - "best_for": "intermediate to advanced", - "season": "November to April", - "highlights": "Four mountains with varied terrain, luxury amenities, vibrant après-ski scene", - }, - { - "name": "Whistler Blackcomb", - "location": "British Columbia, Canada", - "elevation": "7,494 ft", - "trails": 200, - "best_for": "all levels", - "season": "November to May", - "highlights": "Largest ski resort in North America, incredible backcountry, Olympic venue", - }, - { - "name": "Vail", - "location": "Colorado, USA", - "elevation": "11,570 ft", - "trails": 195, - "best_for": "intermediate to advanced", - "season": "November to April", - "highlights": "Back Bowls, European-style village, extensive groomed terrain", - }, - { - "name": "Park City Mountain", - "location": "Utah, USA", - "elevation": "10,026 ft", - "trails": 348, - "best_for": "all levels", - "season": "November to April", - "highlights": "Largest ski resort in USA, excellent snow quality, charming historic town", - }, - { - "name": "Heavenly", - "location": "Lake Tahoe, California/Nevada, USA", - "elevation": "10,067 ft", - "trails": 97, - "best_for": "intermediate", - "season": "November to April", - "highlights": "Stunning lake views, crosses state lines, vibrant casino nightlife", - }, - { - "name": "Zermatt", - "location": "Switzerland", - "elevation": "12,740 ft", - "trails": 200, - "best_for": "all levels", - "season": "November to April", - "highlights": "Matterhorn views, glacier skiing, car-free village, international cuisine", - }, - ] - - if difficulty.lower() == "all": - return json.dumps(ski_resorts, indent=2) - else: - filtered_resorts = [ - resort for resort in ski_resorts if difficulty.lower() in resort["best_for"] - ] - if filtered_resorts: - return json.dumps(filtered_resorts, indent=2) - else: - return f"No resorts found for {difficulty} difficulty. Try 'beginner', 'intermediate', 'advanced', or 'all'." - - -if __name__ == "__main__": - mcp.run(transport="stdio") diff --git a/samples/travel-planner-local-mcp/pyproject.toml b/samples/travel-planner-local-mcp/pyproject.toml deleted file mode 100644 index c5a932fe..00000000 --- a/samples/travel-planner-local-mcp/pyproject.toml +++ /dev/null @@ -1,21 +0,0 @@ -[project] -name = "travel-planner-agent" -version = "0.0.17" -description = "Travel planner agent" -authors = [{ name = "Ion Mincu", email = "ion.mincu@uipath.com" }] -dependencies = [ - "langgraph>=0.2.70", - "langchain-core>=0.3.34", - "langchain-openai>=0.3.9", - "langgraph-checkpoint-sqlite>=2.0.3", - "python-dotenv>=1.0.1", - "uipath-sdk==0.0.110", - "uipath-langchain==0.0.81", - "pydantic>=2.10.6", - "aiohttp>=3.11.12", - "typing-extensions>=4.12.2", - "langchain-mcp-adapters>=0.0.5", - "ipython>=8.32.0", - "mcp>=1.4.1", -] -requires-python = ">=3.10" diff --git a/samples/travel-planner-local-mcp/uv.lock b/samples/travel-planner-local-mcp/uv.lock deleted file mode 100644 index edb09cf2..00000000 --- a/samples/travel-planner-local-mcp/uv.lock +++ /dev/null @@ -1,2233 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.10" -resolution-markers = [ - 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rev: v0.9.6 - hooks: - - id: ruff - args: [ --fix ] - - id: ruff-format diff --git a/sdk/langchain/README.md b/sdk/langchain/README.md deleted file mode 100644 index e69de29b..00000000 diff --git a/sdk/langchain/pyproject.toml b/sdk/langchain/pyproject.toml deleted file mode 100644 index 1d1fcf9e..00000000 --- a/sdk/langchain/pyproject.toml +++ /dev/null @@ -1,104 +0,0 @@ -[project] -name = "uipath-langchain" -version = "0.0.85" -description = "UiPath Langchain" -readme = { file = "README.md", content-type = "text/markdown" } -requires-python = ">=3.9" -dependencies = [ - "uipath-sdk==0.0.112", - "langgraph>=0.2.70", - "langchain-core>=0.3.34", - "langgraph-checkpoint-sqlite>=2.0.3", - "langchain-community>=0.3.18", - "langchain-openai>=0.3.3", - "langchain>=0.3.4", - "requests>=2.23.3", - "types-requests>=2.32.0.20241016", - "pydantic-settings>=2.6.0", - "python-dotenv>=1.0.1", - "httpx>=0.27.0", - "openai>=1.65.5", -] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Topic :: Software Development :: Build Tools", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", -] -maintainers = [ - { name = "Marius Cosareanu", email = "marius.cosareanu@uipath.com" }, - { name = "Cristian Pufu", email = "cristian.pufu@uipath.com" } -] - -[project.entry-points."uipath_sdk.middlewares"] -register = "uipath_langchain.middlewares:register_middleware" - -[project.urls] -Homepage = "https://uipath.com" -Repository = "https://github.com/UiPath/uipath-python" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -packages = ["uipath_langchain"] - -[tool.hatch.build.targets.sdist] -packages = ["uipath_langchain"] - -[dependency-groups] -dev = [ - "mypy>=1.14.1", - "ruff>=0.9.4", - "pytest>=7.4.0", - "pytest-cov>=4.1.0", - "pytest-mock>=3.11.1", - "pre-commit>=4.1.0", - "numpy>=1.24.0", -] - -[project.optional-dependencies] -langchain = [ - "uipath-langchain>=0.0.2" -] - -[tool.ruff] -line-length = 88 -indent-width = 4 - -[tool.ruff.lint] -select = ["E", "F", "B", "I"] - -[tool.ruff.lint.per-file-ignores] -"*" = ["E501"] - -[tool.ruff.format] -quote-style = "double" -indent-style = "space" -skip-magic-trailing-comma = false -line-ending = "auto" - -[tool.mypy] -plugins = [ - "pydantic.mypy" -] - -follow_imports = "silent" -warn_redundant_casts = true -warn_unused_ignores = true -disallow_any_generics = true -check_untyped_defs = true -no_implicit_reexport = true - -disallow_untyped_defs = false - -[tool.pytest.ini_options] -testpaths = ["tests"] -python_files = "test_*.py" -addopts = "-ra -q" - - diff --git a/sdk/langchain/tests/conftest.py b/sdk/langchain/tests/conftest.py deleted file mode 100644 index d0dffd72..00000000 --- a/sdk/langchain/tests/conftest.py +++ /dev/null @@ -1,76 +0,0 @@ -import logging -import os -from os import environ as env -from typing import Generator, Optional - -import httpx -import pytest -from langchain.embeddings import CacheBackedEmbeddings -from langchain.globals import set_llm_cache -from langchain.storage import LocalFileStore -from langchain_community.cache import SQLiteCache - -from uipath_langchain._utils._settings import uipath_cached_paths_settings -from uipath_langchain.embeddings import UiPathOpenAIEmbeddings - - -def get_token(): - url_get_token = f"{env.get('UIPATH_URL', '').rstrip('/')}/identity_/connect/token" - - token_credentials = { - "client_id": env.get("UIPATH_CLIENT_ID"), - "client_secret": env.get("UIPATH_CLIENT_SECRET"), - "grant_type": "client_credentials", - } - - try: - with httpx.Client() as client: - response = client.post(url_get_token, data=token_credentials) - response.raise_for_status() - res_json = response.json() - token = res_json.get("access_token") - - if not token: - pytest.skip("Authentication token is empty or missing") - except (httpx.HTTPError, ValueError, KeyError) as e: - pytest.skip(f"Failed to obtain authentication token: {str(e)}") - - return token - - -@pytest.fixture(autouse=True) -def setup_test_env(): - env["UIPATH_ACCESS_TOKEN"] = get_token() - - -@pytest.fixture(scope="session") -def cached_llmgw_calls() -> Generator[Optional[SQLiteCache], None, None]: - if not os.environ.get("UIPATH_TESTS_CACHE_LLMGW"): - yield None - else: - logging.info("Setting up LLMGW cache") - db_path = uipath_cached_paths_settings.cached_completion_db - os.makedirs(os.path.dirname(db_path), exist_ok=True) - cache = SQLiteCache(database_path=db_path) - set_llm_cache(cache) - yield cache - set_llm_cache(None) - return - - -@pytest.fixture(scope="session") -def cached_embedder() -> Generator[Optional[CacheBackedEmbeddings], None, None]: - if not os.environ.get("UIPATH_TESTS_CACHE_LLMGW"): - yield None - else: - logging.info("Setting up embeddings cache") - model = "text-embedding-3-large" - embedder = CacheBackedEmbeddings.from_bytes_store( - underlying_embeddings=UiPathOpenAIEmbeddings(model=model), - document_embedding_cache=LocalFileStore( - uipath_cached_paths_settings.cached_embeddings_dir - ), - namespace=model, - ) - yield embedder - return diff --git a/sdk/langchain/tests/test_dummy.py b/sdk/langchain/tests/test_dummy.py deleted file mode 100644 index 2b8e06e4..00000000 --- a/sdk/langchain/tests/test_dummy.py +++ /dev/null @@ -1,3 +0,0 @@ -def test_dummy(): - """A dummy test that always passes.""" - assert True diff --git a/sdk/langchain/tests/test_langchain_client.py b/sdk/langchain/tests/test_langchain_client.py deleted file mode 100644 index ae489586..00000000 --- a/sdk/langchain/tests/test_langchain_client.py +++ /dev/null @@ -1,152 +0,0 @@ -from typing import Optional - -import numpy as np -import pytest -from langchain_community.cache import SQLiteCache -from langchain_community.callbacks import get_openai_callback -from langchain_core.embeddings import Embeddings -from langchain_core.messages import HumanMessage, SystemMessage -from langchain_core.tools import tool -from pydantic import BaseModel, ValidationError - -from uipath_langchain.chat import ( - UiPathAzureChatOpenAI, - UiPathNormalizedChatModel, -) -from uipath_langchain.embeddings import ( - UiPathAzureOpenAIEmbeddings, - UiPathOpenAIEmbeddings, -) - - -def test_cached_call(cached_llmgw_calls: Optional[SQLiteCache]): - model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls) - messages = [ - SystemMessage(content="How much is 2 + 2?"), - HumanMessage(content="Respond with JUST THE ANSWER."), - ] - response = model.invoke(messages) - assert "4" in response.content, f"Expected '4' in response, got: {response.content}" - - -def test_cached_call_tokens(cached_llmgw_calls: Optional[SQLiteCache]): - model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls) - messages = [ - SystemMessage(content="How much is 2 + 2?"), - HumanMessage(content="Respond with JUST THE ANSWER."), - ] - with get_openai_callback() as cb: - _ = model.invoke(messages) - total_tokens = cb.total_tokens - assert total_tokens >= 20, f"Expected more than 20 tokens, got: {total_tokens}" - - -def test_normalized_cached_call(cached_llmgw_calls: Optional[SQLiteCache]): - model = UiPathNormalizedChatModel( - model="anthropic.claude-3-5-sonnet-20240620-v1:0", cache=cached_llmgw_calls - ) - messages = [ - SystemMessage(content="How much is 2 + 2?"), - HumanMessage(content="Respond with JUST THE ANSWER."), - ] - response = model.invoke(messages) - assert "4" in response.content, f"Expected '4' in response, got: {response.content}" - - -def test_normalized_cached_call_tokens(cached_llmgw_calls: Optional[SQLiteCache]): - model = UiPathNormalizedChatModel( - model="anthropic.claude-3-5-sonnet-20240620-v1:0", cache=cached_llmgw_calls - ) - messages = [ - SystemMessage(content="How much is 2 + 2?"), - HumanMessage(content="Respond with JUST THE ANSWER."), - ] - with get_openai_callback() as cb: - _ = model.invoke(messages) - total_tokens = cb.total_tokens - assert total_tokens >= 20, f"Expected more than 20 tokens, got: {total_tokens}" - - -def test_tool_call(cached_llmgw_calls: Optional[SQLiteCache]): - @tool - def get_first_letter(input): - """ - Returns the first letter of the input - """ - return input[0] - - model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls).bind_tools( - [get_first_letter] - ) - messages = [ - SystemMessage(content="What is the first letter of the word 'apple'?"), - ] - response = model.invoke(messages) - assert hasattr(response, "tool_calls"), ( - "The response should have a 'tool_calls' attribute" - ) - tool_call = response.tool_calls[0] - assert tool_call["name"] == "get_first_letter", ( - f"Expected tool call to 'get_first_letter', got: {tool_call['name']}" - ) - assert tool_call["args"].get("input") == "apple", ( - f"Expected input to be 'apple', got: {tool_call['args'].get('input')}" - ) - - -def test_structured_output_call(cached_llmgw_calls: Optional[SQLiteCache]): - class CitySize(BaseModel): - """City and its size.""" - - city: str - size: int - - model = UiPathAzureChatOpenAI(cache=cached_llmgw_calls).with_structured_output( - CitySize - ) - messages = [ - SystemMessage( - content="What is the capital of France and what is its area in square meters?" - ), - ] - response = model.invoke(messages) - try: - city_size = CitySize.model_validate(response) - assert city_size.city == "Paris", ( - f"Expected city to be 'Paris', got: {city_size.city}" - ) - assert city_size.size > 0, ( - f"Expected size to be greater than 0, got: {city_size.size}" - ) - except ValidationError as exc: - pytest.fail(f"The response was not in the correct format: {exc}") - - -def test_embedding_call(cached_embedder: Optional[Embeddings]): - if not cached_embedder: - cached_embedder = UiPathOpenAIEmbeddings(model="text-embedding-3-large") - data = [ - "Test input pneumonoultramicroscopicsilicovolcanoconiosis", - "Another test input", - ] - embeds = cached_embedder.embed_documents(data) - try: - arr = np.array(embeds) - except Exception as exc: - pytest.fail(f"Failed to convert embeddings to numpy array: {exc}") - assert arr.shape == (2, 3072), f"Expected shape (2, 3072), got: {arr.shape}" - - -def test_custom_embedding_call(cached_embedder: Optional[Embeddings]): - if not cached_embedder: - cached_embedder = UiPathAzureOpenAIEmbeddings(model="text-embedding-3-large") - data = [ - "Test input pneumonoultramicroscopicsilicovolcanoconiosis", - "Another test input", - ] - embeds = cached_embedder.embed_documents(data) - try: - arr = np.array(embeds) - except Exception as exc: - pytest.fail(f"Failed to convert embeddings to numpy array: {exc}") - assert arr.shape == (2, 3072), f"Expected shape (2, 3072), got: {arr.shape}" diff --git a/sdk/langchain/uipath_langchain/__init__.py b/sdk/langchain/uipath_langchain/__init__.py deleted file mode 100644 index 3faed956..00000000 --- a/sdk/langchain/uipath_langchain/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .middlewares import register_middleware - -__all__ = ["register_middleware"] diff --git a/sdk/langchain/uipath_langchain/_cli/__init__.py b/sdk/langchain/uipath_langchain/_cli/__init__.py deleted file mode 100644 index c9c2ef67..00000000 --- a/sdk/langchain/uipath_langchain/_cli/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__all__: list[str] = [] diff --git a/sdk/langchain/uipath_langchain/_cli/_runtime/_context.py b/sdk/langchain/uipath_langchain/_cli/_runtime/_context.py deleted file mode 100644 index a5466ae9..00000000 --- a/sdk/langchain/uipath_langchain/_cli/_runtime/_context.py +++ /dev/null @@ -1,21 +0,0 @@ -from typing import Any, Optional, Union - -from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver -from langgraph.graph import StateGraph -from uipath_sdk._cli._runtime._contracts import UiPathRuntimeContext - -from .._utils._graph import LangGraphConfig - - -class LangGraphRuntimeContext(UiPathRuntimeContext): - """Context information passed throughout the runtime execution.""" - - langgraph_config: Optional[LangGraphConfig] = None - state_graph: Optional[StateGraph] = None - output: Optional[Any] = None - state: Optional[Any] = ( - None # TypedDict issue, the actual type is: Optional[langgraph.types.StateSnapshot] - ) - memory: Optional[AsyncSqliteSaver] = None - langsmith_tracing_enabled: Union[str, bool, None] = False - resume_triggers_table: str = "__uipath_resume_triggers" diff --git a/sdk/langchain/uipath_langchain/_cli/_runtime/_escalation.py b/sdk/langchain/uipath_langchain/_cli/_runtime/_escalation.py deleted file mode 100644 index e875b7e1..00000000 --- a/sdk/langchain/uipath_langchain/_cli/_runtime/_escalation.py +++ /dev/null @@ -1,245 +0,0 @@ -import json -import logging -from pathlib import Path -from typing import Any, Dict, Optional, Union - -from uipath_sdk import UiPathSDK -from uipath_sdk._models.actions import Action - -logger = logging.getLogger(__name__) - - -class Escalation: - """ - Class to handle default escalation. - """ - - def __init__(self, config_path: Union[str, Path] = "uipath.json"): - """ - Initialize the escalation with a config file path. - - Args: - config_path: Path to the configuration file (string or Path object) - """ - self.config_path = Path(config_path) - self._config = None - self._enabled = False - - self._load_config() - - def _load_config(self) -> None: - """ - Load and validate the default escalation from the config file. - - If the 'defaultEscalation' section exists, validates required fields. - Raises error if required fields are missing. - """ - try: - config_data = json.loads(self.config_path.read_text(encoding="utf-8")) - escalation_config = config_data.get("defaultEscalation") - - if escalation_config: - required_fields = {"request", "title"} - missing_fields = [ - field for field in required_fields if field not in escalation_config - ] - - if not any(key in escalation_config for key in ("appName", "appKey")): - missing_fields.append("appName or appKey") - - if missing_fields: - raise ValueError( - f"Missing required fields in configuration: {', '.join(missing_fields)}" - ) - - self._config = escalation_config - self._enabled = True - logger.debug("Escalation configuration loaded successfully") - else: - self._enabled = False - - except FileNotFoundError: - logger.debug(f"Config file not found: {self.config_path}") - self._enabled = False - - except json.JSONDecodeError: - logger.warning( - f"Failed to parse config file {self.config_path}: Invalid JSON" - ) - self._enabled = False - - except ValueError as e: - logger.error(str(e)) - raise - - except Exception as e: - logger.error(f"Unexpected error loading config {self.config_path}: {e}") - self._enabled = False - - @property - def enabled(self) -> bool: - """ - Check if escalation is enabled. - - Returns: - True if configuration is valid and loaded - """ - return self._enabled - - def prepare_data(self, value: Any) -> Dict[str, Any]: - """ - Prepare action data by replacing $VALUE placeholders with the provided value. - - Args: - value: The value to substitute into the template - - Returns: - Prepared data dictionary with substitutions applied - """ - if not self.enabled or not self._config: - return {} - - template = self._config.get("request", {}) - - if isinstance(value, str): - try: - value_obj = json.loads(value) - except json.JSONDecodeError: - value_obj = value - else: - value_obj = value - - return self._substitute_values(template, value_obj) - - def _substitute_values( - self, template: Dict[str, Any], value: Any - ) -> Dict[str, Any]: - """ - Replace template placeholders with actual values. - - Args: - template: Template dictionary containing placeholders - value: Values to substitute into the template - - Returns: - Template with values substituted - """ - - def process_value(template_value): - if isinstance(template_value, dict): - return {k: process_value(v) for k, v in template_value.items()} - elif isinstance(template_value, list): - return [process_value(item) for item in template_value] - elif isinstance(template_value, str): - if template_value == "$VALUE": - return value - elif template_value.startswith("$VALUE."): - return self._resolve_value_path(template_value, value) - - return template_value - - return process_value(template) - - def _resolve_value_path(self, path_expr: str, value: Any) -> Any: - """ - Resolve a dot-notation path expression against a value. - - Args: - path_expr: Path expression (e.g. "$VALUE.user.name") - value: Value object to extract data from - - Returns: - Extracted value or None if path doesn't exist - """ - path_parts = path_expr.replace("$VALUE.", "").split(".") - current = value - - for part in path_parts: - if not isinstance(current, dict) or part not in current: - return None - current = current.get(part) - - return current - - def extract_response_value(self, action_data: Dict[str, Any]) -> Any: - if not self._config: - return "" - - response_template = self._config.get("response") - if not response_template: - return "" - - for key, template_value in response_template.items(): - if key in action_data: - extracted_value = None - - if template_value == "$VALUE": - extracted_value = action_data[key] - elif isinstance(template_value, str) and template_value.startswith( - "$VALUE." - ): - path_parts = template_value.replace("$VALUE.", "").split(".") - current = action_data[key] - - valid_path = True - for part in path_parts: - if not isinstance(current, dict) or part not in current: - valid_path = False - break - current = current.get(part) - - if valid_path: - extracted_value = current - - if extracted_value is not None: - if isinstance(extracted_value, str): - if extracted_value.lower() == "true": - return True - elif extracted_value.lower() == "false": - return False - - try: - if "." in extracted_value: - return float(extracted_value) - else: - return int(extracted_value) - except ValueError: - pass - - return extracted_value - - return action_data - - async def create(self, value: Any) -> Optional[Action]: - """ - Create an escalation Action with the prepared data. - - Args: - value: The dynamic value to be substituted into the template - - Returns: - The created Action object or None if creation fails - """ - if not self.enabled or not self._config: - return None - - action_data = self.prepare_data(value) - - if not action_data: - logger.warning("Action creation skipped: empty data after preparation") - return None - - try: - uipath = UiPathSDK() - action = uipath.actions.create( - title=self._config.get("title", "Default escalation"), - app_name=self._config.get("appName"), - app_key=self._config.get("appKey"), - app_version=self._config.get("appVersion", 1), - data=action_data, - ) - logger.info(f"Action created successfully: {action.key}") - return action - except Exception as e: - logger.error(f"Error creating action: {e}") - return None diff --git a/sdk/langchain/uipath_langchain/_cli/_runtime/_exception.py b/sdk/langchain/uipath_langchain/_cli/_runtime/_exception.py deleted file mode 100644 index e1b1c3f0..00000000 --- a/sdk/langchain/uipath_langchain/_cli/_runtime/_exception.py +++ /dev/null @@ -1,17 +0,0 @@ -from typing import Optional - -from uipath_sdk._cli._runtime._contracts import UiPathErrorCategory, UiPathRuntimeError - - -class LangGraphRuntimeError(UiPathRuntimeError): - """Custom exception for LangGraph runtime errors with structured error information.""" - - def __init__( - self, - code: str, - title: str, - detail: str, - category: UiPathErrorCategory = UiPathErrorCategory.UNKNOWN, - status: Optional[int] = None, - ): - super().__init__(code, title, detail, category, status, prefix="LANGGRAPH") diff --git a/sdk/langchain/uipath_langchain/_cli/_runtime/_input.py b/sdk/langchain/uipath_langchain/_cli/_runtime/_input.py deleted file mode 100644 index 38199af4..00000000 --- a/sdk/langchain/uipath_langchain/_cli/_runtime/_input.py +++ /dev/null @@ -1,151 +0,0 @@ -import json -import logging -from typing import Any, Optional, cast - -from langgraph.types import Command -from uipath_sdk import UiPathSDK -from uipath_sdk._cli._runtime._contracts import ( - UiPathErrorCategory, - UiPathResumeTriggerType, - UiPathRuntimeStatus, -) - -from ._context import LangGraphRuntimeContext -from ._escalation import Escalation -from ._exception import LangGraphRuntimeError - -logger = logging.getLogger(__name__) - - -def try_convert_to_json_format(value: str) -> str: - try: - return json.loads(value) - except json.decoder.JSONDecodeError: - return value - - -class LangGraphInputProcessor: - """ - Handles input processing for graph execution, including resume scenarios - where it needs to fetch data from UiPath. - """ - - def __init__(self, context: LangGraphRuntimeContext): - """ - Initialize the LangGraphInputProcessor. - - Args: - context: The runtime context for the graph execution. - """ - self.context = context - self.escalation = Escalation(self.context.config_path) - self.uipath = UiPathSDK() - - async def process(self) -> Any: - """ - Process the input data, handling resume scenarios by fetching - necessary data from UiPath if needed. - """ - logger.debug(f"Resumed: {self.context.resume} Input: {self.context.input_json}") - - if not self.context.resume: - return self.context.input_json - - if self.context.input_json: - return Command(resume=self.context.input_json) - - trigger = await self._get_latest_trigger() - if not trigger: - return Command(resume=self.context.input_json) - - type, key = trigger - logger.debug(f"ResumeTrigger: {type} {key}") - if type == UiPathResumeTriggerType.ACTION.value and key: - action = self.uipath.actions.retrieve(key) - logger.debug(f"Action: {action}") - if action.data is None: - return Command(resume={}) - if self.escalation and self.escalation.enabled: - extracted_value = self.escalation.extract_response_value(action.data) - return Command(resume=extracted_value) - return Command(resume=action.data) - elif type == UiPathResumeTriggerType.API.value and key: - payload = await self._get_api_payload(key) - if payload: - return Command(resume=payload) - elif type == UiPathResumeTriggerType.JOB.value and key: - job = await self.uipath.jobs.retrieve_async(key) - if ( - job.state - and not job.state.lower() - == UiPathRuntimeStatus.SUCCESSFUL.value.lower() - ): - error_code = "INVOKED_PROCESS_FAILURE" - error_title = "Invoked process did not finish successfully." - error_detail = try_convert_to_json_format( - str(job.job_error or job.info) - ) - raise LangGraphRuntimeError( - error_code, - error_title, - error_detail, - UiPathErrorCategory.USER, - ) - if job.output_arguments: - return Command(resume=try_convert_to_json_format(job.output_arguments)) - return Command(resume=self.context.input_json) - - async def _get_latest_trigger(self) -> Optional[tuple[str, str]]: - """Fetch the most recent trigger from the database.""" - if self.context.memory is None: - return None - try: - await self.context.memory.setup() - async with ( - self.context.memory.lock, - self.context.memory.conn.cursor() as cur, - ): - await cur.execute(f""" - SELECT type, key - FROM {self.context.resume_triggers_table} - ORDER BY timestamp DESC - LIMIT 1 - """) - result = await cur.fetchone() - if result is None: - return None - return cast(tuple[str, str], tuple(result)) - except Exception as e: - raise LangGraphRuntimeError( - "DB_QUERY_FAILED", - "Database query failed", - f"Error querying resume trigger information: {str(e)}", - UiPathErrorCategory.SYSTEM, - ) from e - - async def _get_api_payload(self, inbox_id: str) -> Any: - """ - Fetch payload data for API triggers. - - Args: - inbox_id: The Id of the inbox to fetch the payload for. - - Returns: - The value field from the API response payload, or None if an error occurs. - """ - try: - response = self.uipath.api_client.request( - "GET", - f"/orchestrator_/api/JobTriggers/GetPayload/{inbox_id}", - include_folder_headers=True, - ) - data = response.json() - return data.get("payload") - except Exception as e: - raise LangGraphRuntimeError( - "API_CONNECTION_ERROR", - "Failed to get trigger payload", - f"Error fetching API trigger payload for inbox {inbox_id}: {str(e)}", - UiPathErrorCategory.SYSTEM, - response.status_code, - ) from e diff --git a/sdk/langchain/uipath_langchain/_cli/_runtime/_output.py b/sdk/langchain/uipath_langchain/_cli/_runtime/_output.py deleted file mode 100644 index a8d9def2..00000000 --- a/sdk/langchain/uipath_langchain/_cli/_runtime/_output.py +++ /dev/null @@ -1,323 +0,0 @@ -import json -import logging -import uuid -from dataclasses import asdict, dataclass -from functools import cached_property -from typing import Any, Dict, Optional, Union, cast - -from langgraph.types import Interrupt, StateSnapshot -from uipath_sdk import UiPathSDK -from uipath_sdk._cli._runtime._contracts import ( - UiPathApiTrigger, - UiPathErrorCategory, - UiPathResumeTrigger, - UiPathResumeTriggerType, - UiPathRuntimeResult, - UiPathRuntimeStatus, -) -from uipath_sdk._models import CreateAction, InvokeProcess, WaitAction, WaitJob -from uipath_sdk._models.actions import Action - -from ._context import LangGraphRuntimeContext -from ._escalation import Escalation -from ._exception import LangGraphRuntimeError - -logger = logging.getLogger(__name__) - - -@dataclass -class InterruptInfo: - """Contains all information about an interrupt.""" - - value: Any - - @property - def type(self) -> Optional[UiPathResumeTriggerType]: - """Returns the type of the interrupt value.""" - if isinstance(self.value, CreateAction): - return UiPathResumeTriggerType.ACTION - if isinstance(self.value, WaitAction): - return UiPathResumeTriggerType.ACTION - if isinstance(self.value, InvokeProcess): - return UiPathResumeTriggerType.JOB - if isinstance(self.value, WaitJob): - return UiPathResumeTriggerType.JOB - return None - - @property - def identifier(self) -> Optional[str]: - """Returns the identifier based on the type.""" - if isinstance(self.value, Action): - return str(self.value.key) - return None - - def serialize(self) -> str: - """ - Converts the interrupt value to a JSON string if possible, - falls back to string representation if not. - """ - try: - if hasattr(self.value, "dict"): - data = self.value.dict() - elif hasattr(self.value, "to_dict"): - data = self.value.to_dict() - elif hasattr(self.value, "__dataclass_fields__"): - data = asdict(self.value) - else: - data = dict(self.value) - - return json.dumps(data, default=str) - except (TypeError, ValueError, json.JSONDecodeError): - return str(self.value) - - @cached_property - def resume_trigger(self) -> UiPathResumeTrigger: - """Creates the resume trigger based on interrupt type.""" - if self.type is None: - return UiPathResumeTrigger( - api_resume=UiPathApiTrigger( - inbox_id=str(uuid.uuid4()), request=self.serialize() - ) - ) - else: - return UiPathResumeTrigger(itemKey=self.identifier, triggerType=self.type) - - -class LangGraphOutputProcessor: - """ - Contains and manages the complete output information from graph execution. - Handles serialization, interrupt data, and file output. - """ - - def __init__(self, context: LangGraphRuntimeContext): - """ - Initialize the LangGraphOutputProcessor. - - Args: - context: The runtime context for the graph execution. - """ - self.context = context - self._interrupt_info: Optional[InterruptInfo] = None - self._resume_trigger: Optional[UiPathResumeTrigger] = None - - # Process interrupt information during initialization - state = cast(StateSnapshot, self.context.state) - if not state or not hasattr(state, "next") or not state.next: - return - - for task in state.tasks: - if hasattr(task, "interrupts") and task.interrupts: - for interrupt in task.interrupts: - if isinstance(interrupt, Interrupt): - self._interrupt_info = InterruptInfo(interrupt.value) - self._resume_trigger = self._interrupt_info.resume_trigger - return - - @property - def status(self) -> UiPathRuntimeStatus: - """Determines the execution status based on state.""" - return ( - UiPathRuntimeStatus.SUSPENDED - if self._interrupt_info - else UiPathRuntimeStatus.SUCCESSFUL - ) - - @property - def interrupt_value(self) -> Union[Action, InvokeProcess, Any]: - """Returns the actual value of the interrupt, with its specific type.""" - if self.interrupt_info is None: - return None - return self.interrupt_info.value - - @property - def interrupt_info(self) -> Optional[InterruptInfo]: - """Gets interrupt information if available.""" - return self._interrupt_info - - @property - def resume_trigger(self) -> Optional[UiPathResumeTrigger]: - """Gets resume trigger if interrupted.""" - return self._resume_trigger - - @cached_property - def serialized_output(self) -> Dict[str, Any]: - """Serializes the graph execution result.""" - try: - if self.context.output is None: - return {} - if hasattr(self.context.output, "dict"): - return self.context.output.dict() - elif hasattr(self.context.output, "to_dict"): - return self.context.output.to_dict() - return dict(self.context.output) - except Exception as e: - raise LangGraphRuntimeError( - "OUTPUT_SERIALIZATION_FAILED", - "Failed to serialize graph output", - f"Error serializing output data: {str(e)}", - UiPathErrorCategory.SYSTEM, - ) from e - - async def process(self) -> UiPathRuntimeResult: - """ - Process the output and prepare the final execution result. - - Returns: - UiPathRuntimeResult: The processed execution result. - - Raises: - LangGraphRuntimeError: If processing fails. - """ - try: - await self._save_resume_trigger() - - return UiPathRuntimeResult( - output=self.serialized_output, - status=self.status, - resume=self.resume_trigger if self.resume_trigger else None, - ) - - except LangGraphRuntimeError: - raise - except Exception as e: - raise LangGraphRuntimeError( - "OUTPUT_PROCESSING_FAILED", - "Failed to process execution output", - f"Unexpected error during output processing: {str(e)}", - UiPathErrorCategory.SYSTEM, - ) from e - - async def _save_resume_trigger(self) -> None: - """ - Stores the resume trigger in the SQLite database if available. - - Raises: - LangGraphRuntimeError: If database operations fail. - """ - if not self.resume_trigger or not self.context.memory: - return - - try: - await self.context.memory.setup() - async with ( - self.context.memory.lock, - self.context.memory.conn.cursor() as cur, - ): - try: - await cur.execute(f""" - CREATE TABLE IF NOT EXISTS {self.context.resume_triggers_table} ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - type TEXT NOT NULL, - key TEXT, - timestamp DATETIME DEFAULT (strftime('%Y-%m-%d %H:%M:%S', 'now', 'utc')) - ) - """) - except Exception as e: - raise LangGraphRuntimeError( - "DB_TABLE_CREATION_FAILED", - "Failed to create resume triggers table", - f"Database error while creating table: {str(e)}", - UiPathErrorCategory.SYSTEM, - ) from e - - try: - default_escalation = Escalation() - if default_escalation.enabled and isinstance( - self.interrupt_value, str - ): - action = await default_escalation.create(self.interrupt_value) - if action: - self._resume_trigger = UiPathResumeTrigger( - trigger_type=UiPathResumeTriggerType.ACTION, - item_key=action.key, - ) - return - if isinstance(self.interrupt_info, InterruptInfo): - uipath_sdk = UiPathSDK() - if self.interrupt_info.type is UiPathResumeTriggerType.JOB: - if isinstance(self.interrupt_value, InvokeProcess): - job = await uipath_sdk.processes.invoke_async( - name=self.interrupt_value.name, - input_arguments=self.interrupt_value.input_arguments, - ) - if job: - self._resume_trigger = UiPathResumeTrigger( - trigger_type=UiPathResumeTriggerType.JOB, - item_key=job.Key, - ) - elif isinstance(self.interrupt_value, WaitJob): - self._resume_trigger = UiPathResumeTrigger( - triggerType=UiPathResumeTriggerType.JOB, - itemKey=self.interrupt_value.job.key, - ) - elif self.interrupt_info.type is UiPathResumeTriggerType.ACTION: - if isinstance(self.interrupt_value, CreateAction): - action = uipath_sdk.actions.create( - title=self.interrupt_value.title, - app_name=self.interrupt_value.name - if self.interrupt_value.name - else "", - app_key=self.interrupt_value.key - if self.interrupt_value.key - else "", - app_version=self.interrupt_value.app_version - if self.interrupt_value.app_version - else 1, - assignee=self.interrupt_value.assignee - if self.interrupt_value.assignee - else "", - data=self.interrupt_value.data, - ) - if action: - self._resume_trigger = UiPathResumeTrigger( - trigger_type=UiPathResumeTriggerType.ACTION, - item_key=action.key, - ) - elif isinstance(self.interrupt_value, WaitAction): - self._resume_trigger = UiPathResumeTrigger( - triggerType=UiPathResumeTriggerType.ACTION, - itemKey=self.interrupt_value.action.key, - ) - - except Exception as e: - raise LangGraphRuntimeError( - "ESCALATION_CREATION_FAILED", - "Failed to create escalation action", - f"Error while creating escalation action: {str(e)}", - UiPathErrorCategory.SYSTEM, - ) from e - - if ( - self.resume_trigger.trigger_type.value - == UiPathResumeTriggerType.API.value - and self.resume_trigger.api_resume - ): - trigger_key = self.resume_trigger.api_resume.inbox_id - trigger_type = self.resume_trigger.trigger_type.value - else: - trigger_key = self.resume_trigger.item_key - trigger_type = self.resume_trigger.trigger_type.value - - try: - logger.debug(f"ResumeTrigger: {trigger_type} {trigger_key}") - await cur.execute( - f"INSERT INTO {self.context.resume_triggers_table} (type, key) VALUES (?, ?)", - (trigger_type, trigger_key), - ) - await self.context.memory.conn.commit() - except Exception as e: - raise LangGraphRuntimeError( - "DB_INSERT_FAILED", - "Failed to save resume trigger", - f"Database error while saving resume trigger: {str(e)}", - UiPathErrorCategory.SYSTEM, - ) from e - except LangGraphRuntimeError: - raise - except Exception as e: - raise LangGraphRuntimeError( - "RESUME_TRIGGER_SAVE_FAILED", - "Failed to save resume trigger", - f"Unexpected error while saving resume trigger: {str(e)}", - UiPathErrorCategory.SYSTEM, - ) from e diff --git a/sdk/langchain/uipath_langchain/_cli/_runtime/_runtime.py b/sdk/langchain/uipath_langchain/_cli/_runtime/_runtime.py deleted file mode 100644 index 048a7832..00000000 --- a/sdk/langchain/uipath_langchain/_cli/_runtime/_runtime.py +++ /dev/null @@ -1,292 +0,0 @@ -import json -import logging -from typing import List, Optional - -from langchain_core.callbacks.base import BaseCallbackHandler -from langchain_core.runnables.config import RunnableConfig -from langchain_core.tracers.langchain import wait_for_all_tracers -from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver -from langgraph.errors import EmptyInputError, GraphRecursionError, InvalidUpdateError -from langgraph.graph.state import CompiledStateGraph -from uipath_sdk._cli._runtime._contracts import ( - UiPathBaseRuntime, - UiPathErrorCategory, - UiPathRuntimeResult, -) - -from ..._utils import _instrument_traceable -from ...tracers import AsyncUiPathTracer -from ._context import LangGraphRuntimeContext -from ._exception import LangGraphRuntimeError -from ._input import LangGraphInputProcessor -from ._output import LangGraphOutputProcessor - -logger = logging.getLogger(__name__) - - -class LangGraphRuntime(UiPathBaseRuntime): - """ - A runtime class implementing the async context manager protocol. - This allows using the class with 'async with' statements. - """ - - def __init__(self, context: LangGraphRuntimeContext): - super().__init__(context) - self.context: LangGraphRuntimeContext = context - - async def execute(self) -> Optional[UiPathRuntimeResult]: - """ - Execute the graph with the provided input and configuration. - - Returns: - Dictionary with execution results - - Raises: - LangGraphRuntimeError: If execution fails - """ - _instrument_traceable() - - await self.validate() - - if self.context.state_graph is None: - return None - - tracer = None - - try: - async with AsyncSqliteSaver.from_conn_string( - self.state_file_path - ) as memory: - self.context.memory = memory - - # Compile the graph with the checkpointer - graph = self.context.state_graph.compile( - checkpointer=self.context.memory - ) - - # Process input, handling resume if needed - input_processor = LangGraphInputProcessor(context=self.context) - - processed_input = await input_processor.process() - - # Set up tracing if available - callbacks: List[BaseCallbackHandler] = [] - - if self.context.job_id and self.context.tracing_enabled: - tracer = AsyncUiPathTracer(context=self.context.trace_context) - await tracer.init_trace( - self.context.entrypoint, self.context.job_id - ) - callbacks = [tracer] - - graph_config: RunnableConfig = { - "configurable": { - "thread_id": self.context.job_id - if self.context.job_id - else "default" - }, - "callbacks": callbacks, - } - - # Stream the output at debug time - if self.context.job_id is None: - # Get final chunk while streaming - final_chunk = None - async for chunk in graph.astream( - processed_input, - graph_config, - stream_mode="values", - subgraphs=True, - ): - logger.info("%s", chunk) - final_chunk = chunk - - # Extract data from the subgraph tuple format (namespace, data) - if isinstance(final_chunk, tuple) and len(final_chunk) == 2: - final_chunk = final_chunk[1] - - # Process the final chunk to match ainvoke's output format - if isinstance(final_chunk, dict) and hasattr( - graph, "output_channels" - ): - output_channels = graph.output_channels - - # Case 1: Single output channel as string - if ( - isinstance(output_channels, str) - and output_channels in final_chunk - ): - self.context.output = final_chunk[output_channels] - - # Case 2: Sequence of output channels - elif hasattr(output_channels, "__iter__") and not isinstance( - output_channels, str - ): - # Check if all channels are present in the chunk - if all(ch in final_chunk for ch in output_channels): - result = {} - for channel in output_channels: - result[channel] = final_chunk[channel] - self.context.output = result - else: - # Fallback if not all channels are present - self.context.output = final_chunk - else: - # Use the whole chunk as output if we can't determine output channels - self.context.output = final_chunk - else: - # Execute the graph normally at runtime - self.context.output = await graph.ainvoke( - processed_input, graph_config - ) - - # Get the state if available - try: - self.context.state = await graph.aget_state(graph_config) - except Exception: - pass - - output_processor = LangGraphOutputProcessor(context=self.context) - - self.context.result = await output_processor.process() - - return self.context.result - - except Exception as e: - if isinstance(e, LangGraphRuntimeError): - raise - - detail = f"Error: {str(e)}" - - if isinstance(e, GraphRecursionError): - raise LangGraphRuntimeError( - "GRAPH_RECURSION_ERROR", - "Graph recursion limit exceeded", - detail, - UiPathErrorCategory.USER, - ) from e - - if isinstance(e, InvalidUpdateError): - raise LangGraphRuntimeError( - "GRAPH_INVALID_UPDATE", - str(e), - detail, - UiPathErrorCategory.USER, - ) from e - - if isinstance(e, EmptyInputError): - raise LangGraphRuntimeError( - "GRAPH_EMPTY_INPUT", - "The input data is empty", - detail, - UiPathErrorCategory.USER, - ) from e - - raise LangGraphRuntimeError( - "EXECUTION_ERROR", - "Graph execution failed", - detail, - UiPathErrorCategory.USER, - ) from e - finally: - if tracer is not None: - await tracer.wait_for_all_tracers() - - if self.context.langsmith_tracing_enabled: - wait_for_all_tracers() - - async def validate(self) -> None: - """Validate runtime inputs.""" - """Load and validate the graph configuration .""" - try: - if self.context.input: - self.context.input_json = json.loads(self.context.input) - except json.JSONDecodeError as e: - raise LangGraphRuntimeError( - "INPUT_INVALID_JSON", - "Invalid JSON input", - "The input data is not valid JSON.", - UiPathErrorCategory.USER, - ) from e - - if self.context.langgraph_config is None: - raise LangGraphRuntimeError( - "CONFIG_MISSING", - "Invalid configuration", - "Failed to load configuration", - UiPathErrorCategory.DEPLOYMENT, - ) - - try: - self.context.langgraph_config.load_config() - except Exception as e: - raise LangGraphRuntimeError( - "CONFIG_INVALID", - "Invalid configuration", - f"Failed to load configuration: {str(e)}", - UiPathErrorCategory.DEPLOYMENT, - ) from e - - # Determine entrypoint if not provided - graphs = self.context.langgraph_config.graphs - if not self.context.entrypoint and len(graphs) == 1: - self.context.entrypoint = graphs[0].name - elif not self.context.entrypoint: - graph_names = ", ".join(g.name for g in graphs) - raise LangGraphRuntimeError( - "ENTRYPOINT_MISSING", - "Entrypoint required", - f"Multiple graphs available. Please specify one of: {graph_names}.", - UiPathErrorCategory.DEPLOYMENT, - ) - - # Get the specified graph - self.graph_config = self.context.langgraph_config.get_graph( - self.context.entrypoint - ) - if not self.graph_config: - raise LangGraphRuntimeError( - "GRAPH_NOT_FOUND", - "Graph not found", - f"Graph '{self.context.entrypoint}' not found.", - UiPathErrorCategory.DEPLOYMENT, - ) - try: - loaded_graph = await self.graph_config.load_graph() - self.context.state_graph = ( - loaded_graph.builder - if isinstance(loaded_graph, CompiledStateGraph) - else loaded_graph - ) - except ImportError as e: - raise LangGraphRuntimeError( - "GRAPH_IMPORT_ERROR", - "Graph import failed", - f"Failed to import graph '{self.context.entrypoint}': {str(e)}", - UiPathErrorCategory.USER, - ) from e - except TypeError as e: - raise LangGraphRuntimeError( - "GRAPH_TYPE_ERROR", - "Invalid graph type", - f"Graph '{self.context.entrypoint}' is not a valid StateGraph or CompiledStateGraph: {str(e)}", - UiPathErrorCategory.USER, - ) from e - except ValueError as e: - raise LangGraphRuntimeError( - "GRAPH_VALUE_ERROR", - "Invalid graph value", - f"Invalid value in graph '{self.context.entrypoint}': {str(e)}", - UiPathErrorCategory.USER, - ) from e - except Exception as e: - raise LangGraphRuntimeError( - "GRAPH_LOAD_ERROR", - "Failed to load graph", - f"Unexpected error loading graph '{self.context.entrypoint}': {str(e)}", - UiPathErrorCategory.USER, - ) from e - - async def cleanup(self): - if hasattr(self, "graph_config") and self.graph_config: - await self.graph_config.cleanup() diff --git a/sdk/langchain/uipath_langchain/_cli/_utils/_graph.py b/sdk/langchain/uipath_langchain/_cli/_utils/_graph.py deleted file mode 100644 index 896a9488..00000000 --- a/sdk/langchain/uipath_langchain/_cli/_utils/_graph.py +++ /dev/null @@ -1,200 +0,0 @@ -import importlib.util -import inspect -import json -import logging -import os -import sys -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional, Union, cast - -from dotenv import load_dotenv -from langgraph.graph import StateGraph -from langgraph.graph.state import CompiledStateGraph - -logger = logging.getLogger(__name__) - - -@dataclass -class GraphConfig: - name: str - path: str - file_path: str - graph_var: str - _graph: Optional[Union[StateGraph, CompiledStateGraph]] = None - - @classmethod - def from_config(cls, name: str, path: str) -> "GraphConfig": - file_path, graph_var = path.split(":") - return cls(name=name, path=path, file_path=file_path, graph_var=graph_var) - - async def load_graph(self) -> Union[StateGraph, CompiledStateGraph]: - """Load graph from the specified path""" - try: - cwd = os.path.abspath(os.getcwd()) - abs_file_path = os.path.abspath(os.path.normpath(self.file_path)) - - if not abs_file_path.startswith(cwd): - raise ValueError( - f"Script path must be within the current directory. Found: {self.file_path}" - ) - - if not os.path.exists(abs_file_path): - raise FileNotFoundError(f"Script not found: {abs_file_path}") - - if cwd not in sys.path: - sys.path.insert(0, cwd) - - module_name = Path(abs_file_path).stem - spec = importlib.util.spec_from_file_location(module_name, abs_file_path) - - if not spec or not spec.loader: - raise ImportError(f"Could not load module from: {abs_file_path}") - - module = importlib.util.module_from_spec(spec) - sys.modules[module_name] = module - spec.loader.exec_module(module) - - graph = getattr(module, self.graph_var, None) - - # Get the graph object or function - graph_obj = getattr(module, self.graph_var, None) - - # Handle callable graph factory - if callable(graph_obj): - if inspect.iscoroutinefunction(graph_obj): - # Handle async function - try: - graph_obj = await graph_obj() - except RuntimeError as e: - raise e - else: - # Call regular function - graph_obj = graph_obj() - - # Handle async context manager - if ( - graph_obj is not None - and hasattr(graph_obj, "__aenter__") - and callable(graph_obj.__aenter__) - ): - self._context_manager = graph_obj - graph = await graph_obj.__aenter__() - - # No need for atexit registration - the calling code should - # maintain a reference to this object and call cleanup explicitly - - else: - # Not a context manager, use directly - graph = graph_obj - - if not isinstance(graph, (StateGraph, CompiledStateGraph)): - raise TypeError( - f"Expected StateGraph, CompiledStateGraph, or a callable returning one of these, got {type(graph)}" - ) - - self._graph = graph - return graph - - except Exception as e: - logger.error(f"Failed to load graph {self.name}: {str(e)}") - raise - - async def get_input_schema(self) -> Dict[str, Any]: - """Extract input schema from graph""" - if not self._graph: - self._graph = await self.load_graph() - - if hasattr(self._graph, "input_schema"): - return cast(dict[str, Any], self._graph.input_schema) - return {} - - async def cleanup(self): - """ - Clean up resources when done with the graph. - This should be called when the graph is no longer needed. - """ - if hasattr(self, "_context_manager") and self._context_manager: - try: - await self._context_manager.__aexit__(None, None, None) - except Exception as e: - logger.warning(f"Error during context cleanup: {str(e)}") - finally: - self._context_manager = None - self._graph = None - - -class LangGraphConfig: - def __init__(self, config_path: str = "langgraph.json"): - self.config_path = config_path - self._config: Optional[Dict[str, Any]] = None - self._graphs: List[GraphConfig] = [] - - @property - def exists(self) -> bool: - """Check if langgraph.json exists""" - return os.path.exists(self.config_path) - - def load_config(self) -> Dict[str, Any]: - """Load and validate langgraph configuration""" - if not self.exists: - raise FileNotFoundError(f"Config file not found: {self.config_path}") - - try: - with open(self.config_path, "r") as f: - config = json.load(f) - - required_fields = ["graphs"] - missing_fields = [field for field in required_fields if field not in config] - if missing_fields: - raise ValueError( - f"Missing required fields in langgraph.json: {missing_fields}" - ) - - if env_file := config.get("env"): - env_path = os.path.abspath(os.path.normpath(env_file)) - if os.path.exists(env_path): - if not load_dotenv(env_path): - logger.warning( - f"Could not load environment variables from {env_path}" - ) - else: - logger.debug(f"Loaded environment variables from {env_path}") - - self._config = config - self._load_graphs() - return config - except Exception as e: - logger.error(f"Failed to load langgraph.json: {str(e)}") - raise - - def _load_graphs(self): - """Load all graph configurations""" - if not self._config: - return - - self._graphs = [ - GraphConfig.from_config(name, path) - for name, path in self._config["graphs"].items() - ] - - @property - def graphs(self) -> List[GraphConfig]: - """Get all graph configurations""" - if not self._graphs: - self.load_config() - return self._graphs - - def get_graph(self, name: str) -> Optional[GraphConfig]: - """Get a specific graph configuration by name""" - return next((g for g in self.graphs if g.name == name), None) - - @property - def dependencies(self) -> List[str]: - """Get project dependencies""" - return self._config.get("dependencies", []) if self._config else [] - - @property - def env_file(self) -> Optional[str]: - """Get environment file path""" - return self._config.get("env") if self._config else None diff --git a/sdk/langchain/uipath_langchain/_cli/cli_init.py b/sdk/langchain/uipath_langchain/_cli/cli_init.py deleted file mode 100644 index 9f631450..00000000 --- a/sdk/langchain/uipath_langchain/_cli/cli_init.py +++ /dev/null @@ -1,173 +0,0 @@ -import asyncio -import json -import os -import uuid -from typing import Any, Dict - -from langgraph.graph.state import CompiledStateGraph -from uipath_sdk._cli._utils._parse_ast import generate_bindings_json # type: ignore -from uipath_sdk._cli.middlewares import MiddlewareResult - -from ._utils._graph import LangGraphConfig - - -def resolve_refs(schema, root=None): - """Recursively resolves $ref references in a JSON schema.""" - if root is None: - root = schema # Store the root schema to resolve $refs - - if isinstance(schema, dict): - if "$ref" in schema: - ref_path = schema["$ref"].lstrip("#/").split("/") - ref_schema = root - for part in ref_path: - ref_schema = ref_schema.get(part, {}) - return resolve_refs(ref_schema, root) - - return {k: resolve_refs(v, root) for k, v in schema.items()} - - elif isinstance(schema, list): - return [resolve_refs(item, root) for item in schema] - - return schema - - -def generate_schema_from_graph(graph: CompiledStateGraph) -> Dict[str, Any]: - """Extract input/output schema from a LangGraph graph""" - schema = { - "input": {"type": "object", "properties": {}, "required": []}, - "output": {"type": "object", "properties": {}, "required": []}, - } - - if hasattr(graph, "input_schema"): - if hasattr(graph.input_schema, "model_json_schema"): - input_schema = graph.input_schema.model_json_schema() - - unpacked_ref_def_properties = resolve_refs(input_schema) - - schema["input"]["properties"] = unpacked_ref_def_properties.get( - "properties", {} - ) - schema["input"]["required"] = unpacked_ref_def_properties.get( - "required", [] - ) - - if hasattr(graph, "output_schema"): - if hasattr(graph.output_schema, "model_json_schema"): - output_schema = graph.output_schema.model_json_schema() - - unpacked_ref_def_properties = resolve_refs(output_schema) - schema["output"]["properties"] = unpacked_ref_def_properties.get( - "properties", {} - ) - schema["output"]["required"] = unpacked_ref_def_properties.get( - "required", [] - ) - - return schema - - -async def langgraph_init_middleware_async(entrypoint: str) -> MiddlewareResult: - """Middleware to check for langgraph.json and create uipath.json with schemas""" - config = LangGraphConfig() - if not config.exists: - return MiddlewareResult( - should_continue=True - ) # Continue with normal flow if no langgraph.json - - try: - config.load_config() - entrypoints = [] - all_bindings = {"version": "2.0", "resources": []} - mermaids = {} - - for graph in config.graphs: - if entrypoint and graph.name != entrypoint: - continue - - try: - loaded_graph = await graph.load_graph() - state_graph = ( - loaded_graph.builder - if isinstance(loaded_graph, CompiledStateGraph) - else loaded_graph - ) - compiled_graph = state_graph.compile() - graph_schema = generate_schema_from_graph(compiled_graph) - - mermaids[graph.name] = compiled_graph.get_graph(xray=1).draw_mermaid() - - try: - print("Generating bindings for ", graph.file_path) - # Make sure the file path exists - if os.path.exists(graph.file_path): - file_bindings = generate_bindings_json(graph.file_path) - - # Merge bindings - if "resources" in file_bindings: - all_bindings["resources"] = file_bindings["resources"] - except Exception as e: - print( - f"Warning: Could not generate bindings for {graph.file_path}: {str(e)}" - ) - - new_entrypoint: dict[str, Any] = { - "filePath": graph.name, - "uniqueId": str(uuid.uuid4()), - "type": "agent", - "input": graph_schema["input"], - "output": graph_schema["output"], - } - entrypoints.append(new_entrypoint) - - except Exception as e: - print(f"Error during graph load: {e}") - return MiddlewareResult( - should_continue=False, - error_message=f"Failed to load graph '{graph.name}': {str(e)}", - should_include_stacktrace=True, - ) - finally: - await graph.cleanup() - - if entrypoint and not entrypoints: - return MiddlewareResult( - should_continue=False, - error_message=f"Error: No graph found with name '{entrypoint}'", - ) - - uipath_config = {"entryPoints": entrypoints, "bindings": all_bindings} - - # Save the uipath.json file - config_path = "uipath.json" - with open(config_path, "w") as f: - json.dump(uipath_config, f, indent=2) - - for graph_name, mermaid_content in mermaids.items(): - mermaid_file_path = f"{graph_name}.mermaid" - try: - with open(mermaid_file_path, "w") as f: - f.write(mermaid_content) - except Exception as write_error: - return MiddlewareResult( - should_continue=False, - error_message=f"Error writing mermaid file for '{graph_name}': {str(write_error)}", - should_include_stacktrace=True, - ) - - return MiddlewareResult( - should_continue=False, - info_message=f"Configuration file {config_path} created successfully.", - ) - - except Exception as e: - return MiddlewareResult( - should_continue=False, - error_message=f"Error processing langgraph configuration: {str(e)}", - should_include_stacktrace=True, - ) - - -def langgraph_init_middleware(entrypoint: str) -> MiddlewareResult: - """Middleware to check for langgraph.json and create uipath.json with schemas""" - return asyncio.run(langgraph_init_middleware_async(entrypoint)) diff --git a/sdk/langchain/uipath_langchain/_cli/cli_run.py b/sdk/langchain/uipath_langchain/_cli/cli_run.py deleted file mode 100644 index 5a4266ac..00000000 --- a/sdk/langchain/uipath_langchain/_cli/cli_run.py +++ /dev/null @@ -1,79 +0,0 @@ -import asyncio -import logging -from os import environ as env -from typing import Optional - -from dotenv import load_dotenv -from uipath_sdk._cli._runtime._contracts import UiPathTraceContext -from uipath_sdk._cli.middlewares import MiddlewareResult - -from ._runtime._context import LangGraphRuntimeContext -from ._runtime._exception import LangGraphRuntimeError -from ._runtime._runtime import LangGraphRuntime -from ._utils._graph import LangGraphConfig - -logger = logging.getLogger(__name__) -load_dotenv() - - -def langgraph_run_middleware( - entrypoint: Optional[str], input: Optional[str], resume: bool -) -> MiddlewareResult: - """Middleware to handle langgraph execution""" - config = LangGraphConfig() - if not config.exists: - return MiddlewareResult( - should_continue=True - ) # Continue with normal flow if no langgraph.json - - try: - - async def execute(): - context = LangGraphRuntimeContext.from_config( - env.get("UIPATH_CONFIG_PATH", "uipath.json") - ) - - context.entrypoint = entrypoint - context.input = input - context.resume = resume - context.langgraph_config = config - context.logs_min_level = env.get("LOG_LEVEL", "INFO") - context.job_id = env.get("UIPATH_JOB_KEY") - context.trace_id = env.get("UIPATH_TRACE_ID") - context.tracing_enabled = env.get("UIPATH_TRACING_ENABLED", True) - context.trace_context = UiPathTraceContext( - enabled=env.get("UIPATH_TRACING_ENABLED", True), - trace_id=env.get("UIPATH_TRACE_ID"), - parent_span_id=env.get("UIPATH_PARENT_SPAN_ID"), - root_span_id=env.get("UIPATH_ROOT_SPAN_ID"), - job_id=env.get("UIPATH_JOB_KEY"), - org_id=env.get("UIPATH_ORGANIZATION_ID"), - tenant_id=env.get("UIPATH_TENANT_ID"), - process_key=env.get("UIPATH_PROCESS_UUID"), - folder_key=env.get("UIPATH_FOLDER_KEY"), - ) - context.langsmith_tracing_enabled = env.get("LANGSMITH_TRACING", False) - - # Add default env variables - env["UIPATH_REQUESTING_PRODUCT"] = "uipath-python-sdk" - env["UIPATH_REQUESTING_FEATURE"] = "langgraph-agent" - - async with LangGraphRuntime.from_context(context) as runtime: - await runtime.execute() - - asyncio.run(execute()) - - return MiddlewareResult(should_continue=False, error_message=None) - - except LangGraphRuntimeError as e: - return MiddlewareResult( - should_continue=False, - error_message=e.error_info.detail, - should_include_stacktrace=True, - ) - except Exception as e: - return MiddlewareResult( - should_continue=False, - error_message=f"Error: {str(e)}", - should_include_stacktrace=True, - ) diff --git a/sdk/langchain/uipath_langchain/_utils/__init__.py b/sdk/langchain/uipath_langchain/_utils/__init__.py deleted file mode 100644 index 31605f17..00000000 --- a/sdk/langchain/uipath_langchain/_utils/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from ..tracers._instrument_traceable import _instrument_traceable -from ._request_mixin import UiPathRequestMixin - -__all__ = ["UiPathRequestMixin", "_instrument_traceable"] diff --git a/sdk/langchain/uipath_langchain/_utils/_request_mixin.py b/sdk/langchain/uipath_langchain/_utils/_request_mixin.py deleted file mode 100644 index 84bc6c2a..00000000 --- a/sdk/langchain/uipath_langchain/_utils/_request_mixin.py +++ /dev/null @@ -1,493 +0,0 @@ -# mypy: disable-error-code="no-redef,arg-type" -import json -import logging -import os -import time -from typing import Any, Dict, List, Mapping, Optional - -import httpx -import openai -from langchain_core.embeddings import Embeddings -from langchain_core.language_models.chat_models import _cleanup_llm_representation -from pydantic import BaseModel, Field, SecretStr -from tenacity import ( - AsyncRetrying, - Retrying, - retry_if_exception_type, - stop_after_attempt, - wait_exponential_jitter, -) - -from uipath_langchain._utils._settings import ( - UiPathClientFactorySettings, - UiPathClientSettings, - get_uipath_token_header, -) -from uipath_langchain._utils._sleep_policy import before_sleep_log - - -def get_from_uipath_url(): - url = os.getenv("UIPATH_URL") - if url: - return "/".join(url.split("/", 3)[:3]) - return None - - -class UiPathRequestMixin(BaseModel): - class Config: - arbitrary_types_allowed = True - - default_headers: Optional[Mapping[str, str]] = { - "X-UiPath-Streaming-Enabled": "false", - } - model_name: Optional[str] = Field( - default_factory=lambda: os.getenv("UIPATH_MODEL_NAME", "gpt-4o-2024-08-06"), - alias="model", - ) - settings: Optional[UiPathClientSettings] = None - client_id: Optional[str] = Field( - default_factory=lambda: os.getenv("UIPATH_CLIENT_ID") - ) - client_secret: Optional[str] = Field( - default_factory=lambda: os.getenv("UIPATH_CLIENT_SECRET") - ) - base_url: Optional[str] = Field( - default_factory=lambda data: getattr(data["settings"], "base_url", None) - or os.getenv("UIPATH_BASE_URL") - or get_from_uipath_url(), - alias="azure_endpoint", - ) - access_token: Optional[str] = Field( - default_factory=lambda data: ( - getattr(data["settings"], "access_token", None) - or os.getenv("UIPATH_ACCESS_TOKEN") # Environment variable - or os.getenv("UIPATH_SERVICE_TOKEN") # Environment variable - or get_uipath_token_header( - UiPathClientFactorySettings( - UIPATH_BASE_URL=data["base_url"], - UIPATH_CLIENT_ID=data["client_id"], - UIPATH_CLIENT_SECRET=data["client_secret"], - ) - ) # Get service token from UiPath - ) - ) - org_id: Any = Field( - default_factory=lambda data: getattr(data["settings"], "org_id", None) - or os.getenv("UIPATH_ORGANIZATION_ID", "") - ) - tenant_id: Any = Field( - default_factory=lambda data: getattr(data["settings"], "tenant_id", None) - or os.getenv("UIPATH_TENANT_ID", "") - ) - requesting_product: Any = Field( - default_factory=lambda data: getattr( - data["settings"], "requesting_product", None - ) - or os.getenv("UIPATH_REQUESTING_PRODUCT", "") - ) - requesting_feature: Any = Field( - default_factory=lambda data: getattr( - data["settings"], "requesting_feature", None - ) - or os.getenv("UIPATH_REQUESTING_FEATURE", "") - ) - default_request_timeout: Any = Field( - default_factory=lambda data: float( - getattr(data["settings"], "timeout_seconds", None) - or os.getenv("UIPATH_TIMEOUT_SECONDS", "120") - ), - alias="timeout", - ) - - openai_api_version: Optional[str] = Field( - default_factory=lambda: os.getenv("OPENAI_API_VERSION", "2024-08-01-preview"), - alias="api_version", - ) - include_account_id: bool = False - temperature: Optional[float] = 0.0 - max_tokens: Optional[int] = 1000 - frequency_penalty: Optional[float] = None - presence_penalty: Optional[float] = None - - logger: Optional[logging.Logger] = None - max_retries: Optional[int] = 5 - base_delay: float = 5.0 - max_delay: float = 60.0 - - _url: Optional[str] = None - _auth_headers: Optional[Dict[str, str]] = None - - # required to instantiate AzureChatOpenAI subclasses - azure_endpoint: Optional[str] = Field( - default="placeholder", description="Bypassed Azure endpoint" - ) - openai_api_key: Optional[SecretStr] = Field( - default=SecretStr("placeholder"), description="Bypassed API key" - ) - # required to instatiate ChatAnthropic subclasses (will be needed when passthrough is implemented for Anthropic models) - stop_sequences: Optional[List[str]] = Field( - default=None, description="Bypassed stop sequence" - ) - - def _request( - self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] - ) -> Dict[str, Any]: - """Run an asynchronous call to the LLM.""" - # if self.logger: - # self.logger.info(f"Completion request: {request_body['messages'][:2]}") - with httpx.Client( - event_hooks={ - "request": [self._log_request_duration], - "response": [self._log_response_duration], - } - ) as client: - response = client.post( - url, - headers=headers, - json=request_body, - timeout=self.default_request_timeout, - ) - - # Handle HTTP errors and map them to OpenAI exceptions - try: - response.raise_for_status() - except httpx.HTTPStatusError as err: - if self.logger: - self.logger.error( - "Error querying UiPath: %s (%s)", - err.response.reason_phrase, - err.response.status_code, - extra={ - "ActionName": self.settings.action_name, - "ActionId": self.settings.action_id, - } - if self.settings - else None, - ) - raise self._make_status_error_from_response(err.response) from err - - return response.json() - - def _call( - self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] - ) -> Dict[str, Any]: - """Run a synchronous call with retries to LLM""" - if self.max_retries is None: - return self._request(url, request_body, headers) - - retryer = Retrying( - stop=stop_after_attempt(self.max_retries), - wait=wait_exponential_jitter( - initial=self.base_delay, - max=self.max_delay, - jitter=1.0, - ), - retry=retry_if_exception_type( - (openai.RateLimitError, httpx.TimeoutException) - ), - reraise=True, - before_sleep=before_sleep_log(self.logger, logging.WARNING) - if self.logger is not None - else None, - ) - - try: - return retryer(self._request, url, request_body, headers) - except openai.APIStatusError as err: - if self.logger: - self.logger.error( - "Failed querying LLM after retries: %s", - err, - extra={ - "ActionName": self.settings.action_name, - "ActionId": self.settings.action_id, - } - if self.settings - else None, - ) - raise err - - async def _arequest( - self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] - ) -> Dict[str, Any]: - # if self.logger: - # self.logger.info(f"Completion request: {request_body['messages'][:2]}") - async with httpx.AsyncClient( - event_hooks={ - "request": [self._alog_request_duration], - "response": [self._alog_response_duration], - } - ) as client: - response = await client.post( - url, - headers=headers, - json=request_body, - timeout=self.default_request_timeout, - ) - # Handle HTTP errors and map them to OpenAI exceptions - try: - response.raise_for_status() - except httpx.HTTPStatusError as err: - if self.logger: - self.logger.error( - "Error querying LLM: %s (%s)", - err.response.reason_phrase, - err.response.status_code, - extra={ - "ActionName": self.settings.action_name, - "ActionId": self.settings.action_id, - } - if self.settings - else None, - ) - raise self._make_status_error_from_response(err.response) from err - - return response.json() - - async def _acall( - self, url: str, request_body: Dict[str, Any], headers: Dict[str, str] - ) -> Dict[str, Any]: - """Run an asynchronous call with retries to the LLM.""" - if self.max_retries is None: - return await self._arequest(url, request_body, headers) - - retryer = AsyncRetrying( - stop=stop_after_attempt(self.max_retries), - wait=wait_exponential_jitter( - initial=self.base_delay, - max=self.max_delay, - jitter=1.0, - ), - retry=retry_if_exception_type( - (openai.RateLimitError, httpx.TimeoutException) - ), - reraise=True, - before_sleep=before_sleep_log(self.logger, logging.WARNING) - if self.logger is not None - else None, - ) - - try: - response: Any = await retryer(self._arequest, url, request_body, headers) - if self.logger: - self.logger.info( - f"[uipath_langchain_client] Finished retryer after {retryer.statistics['attempt_number'] - 1} retries", - extra={ - "retry": f"{retryer.statistics['attempt_number'] - 1}", - "ActionName": self.settings.action_name, - "ActionId": self.settings.action_id, - } - if self.settings - else { - "retry": f"{retryer.statistics['attempt_number'] - 1}", - }, - ) - return response - except openai.APIStatusError as err: - if self.logger: - self.logger.error( - "[uipath_langchain_client] Failed querying LLM after retries: %s", - err, - extra={ - "reason": err.message, - "statusCode": err.status_code, - "ActionName": self.settings.action_name, - "ActionId": self.settings.action_id, - } - if self.settings - else { - "reason": err.message, - "statusCode": err.status_code, - }, - ) - raise err - - def _make_status_error_from_response( - self, - response: httpx.Response, - ) -> openai.APIStatusError: - """Function reproduced from openai._client to handle UiPath errors.""" - if response.is_closed and not response.is_stream_consumed: - # We can't read the response body as it has been closed - # before it was read. This can happen if an event hook - # raises a status error. - body = None - err_msg = f"Error code: {response.status_code}" - else: - err_text = response.text.strip() - body = err_text - - try: - body = json.loads(err_text) - err_msg = f"Error code: {response.status_code} - {body}" - except Exception: - err_msg = err_text or f"Error code: {response.status_code}" - - return self._make_status_error(err_msg, body=body, response=response) - - def _make_status_error( - self, - err_msg: str, - *, - body: object, - response: httpx.Response, - ) -> openai.APIStatusError: - """Function reproduced from openai._client to handle UiPath errors.""" - data = body.get("error", body) if isinstance(body, Mapping) else body - if response.status_code == 400: - return openai.BadRequestError(err_msg, response=response, body=data) - - if response.status_code == 401: - return openai.AuthenticationError(err_msg, response=response, body=data) - - if response.status_code == 403: - return openai.PermissionDeniedError(err_msg, response=response, body=data) - - if response.status_code == 404: - return openai.NotFoundError(err_msg, response=response, body=data) - - if response.status_code == 409: - return openai.ConflictError(err_msg, response=response, body=data) - - if response.status_code == 422: - return openai.UnprocessableEntityError( - err_msg, response=response, body=data - ) - - if response.status_code == 429: - return openai.RateLimitError(err_msg, response=response, body=data) - - if response.status_code >= 500: - return openai.InternalServerError(err_msg, response=response, body=data) - return openai.APIStatusError(err_msg, response=response, body=data) - - def _log_request_duration(self, request: httpx.Request): - """Log the start time of the request.""" - if self.logger: - request.extensions["start_time"] = time.monotonic() - - def _log_response_duration(self, response: httpx.Response): - """Log the duration of the request.""" - if self.logger: - start_time = response.request.extensions.get("start_time") - if start_time: - duration = time.monotonic() - start_time - type = "embedding" - if not isinstance(self, Embeddings): - type = "normalized" if self.is_normalized else "completion" - self.logger.info( - f"[uipath_langchain_client] Request to {response.request.url} took {duration:.2f} seconds.", - extra={ - "requestUrl": f"{response.request.url}", - "duration": f"{duration:.2f}", - "type": type, - "ActionName": self.settings.action_name, - "ActionId": self.settings.action_id, - } - if self.settings - else { - "requestUrl": f"{response.request.url}", - "duration": f"{duration:.2f}", - "type": type, - }, - ) - - async def _alog_request_duration(self, request: httpx.Request): - """Log the start time of the request.""" - self._log_request_duration(request) - - async def _alog_response_duration(self, response: httpx.Response): - """Log the duration of the request.""" - self._log_response_duration(response) - - @property - def _llm_type(self) -> str: - """Get the type of language model used by this chat model. Used for logging purposes only.""" - return "uipath" - - @property - def _identifying_params(self) -> Dict[str, Any]: - return { - "url": self.url, - "model": self.model_name, - "temperature": self.temperature, - "max_tokens": self.max_tokens, - "frequency_penalty": self.frequency_penalty, - "presence_penalty": self.presence_penalty, - } - - def _prepare_url(self, url: str) -> httpx.URL: - return httpx.URL(self.url) - - def _build_headers(self, options, retries_taken: int = 0) -> httpx.Headers: - return httpx.Headers(self.auth_headers) - - @property - def url(self) -> str: - if not self._url: - env_uipath_url = os.getenv("UIPATH_URL") - - if env_uipath_url: - self._url = f"{env_uipath_url.rstrip('/')}/{self.endpoint}" - else: - self._url = ( - f"{self.base_url}/{self.org_id}/{self.tenant_id}/{self.endpoint}" - ) - return self._url - - @property - def endpoint(self) -> str: - raise NotImplementedError( - "The endpoint property is not implemented for this class." - ) - - @property - def auth_headers(self) -> Dict[str, str]: - if not self._auth_headers: - self._auth_headers = { - **self.default_headers, # type: ignore - "Authorization": f"Bearer {self.access_token}", - "X-UiPath-LlmGateway-RequestingProduct": self.requesting_product, - "X-UiPath-LlmGateway-RequestingFeature": self.requesting_feature, - "X-UiPath-LlmGateway-TimeoutSeconds": str(self.default_request_timeout), - } - if self.is_normalized and self.model_name: - self._auth_headers["X-UiPath-LlmGateway-NormalizedApi-ModelName"] = ( - self.model_name - ) - if self.include_account_id: - self._auth_headers["x-uipath-internal-accountid"] = self.org_id - self._auth_headers["x-uipath-internal-tenantid"] = self.tenant_id - return self._auth_headers - - def _get_llm_string(self, stop: Optional[list[str]] = None, **kwargs: Any) -> str: - serialized_repr = getattr(self, "_serialized", self.model_dump()) - _cleanup_llm_representation(serialized_repr, 1) - kwargs = serialized_repr.get("kwargs", serialized_repr) - for key in [ - "base_url", - "access_token", - "client_id", - "client_secret", - "org_id", - "tenant_id", - "requesting_product", - "requesting_feature", - "azure_endpoint", - "openai_api_version", - "openai_api_key", - "default_request_timeout", - "max_retries", - "base_delay", - "max_delay", - "logger", - "settings", - ]: - if key in kwargs: - kwargs.pop(key, None) - llm_string = json.dumps(serialized_repr, sort_keys=True) - return llm_string - - @property - def is_normalized(self) -> bool: - return False diff --git a/sdk/langchain/uipath_langchain/_utils/_settings.py b/sdk/langchain/uipath_langchain/_utils/_settings.py deleted file mode 100644 index 0e85822f..00000000 --- a/sdk/langchain/uipath_langchain/_utils/_settings.py +++ /dev/null @@ -1,91 +0,0 @@ -# mypy: disable-error-code="syntax" -import os -from enum import Enum -from typing import Any, Optional - -import httpx -from pydantic import Field -from pydantic_settings import BaseSettings - - -class UiPathCachedPathsSettings(BaseSettings): - cached_completion_db: str = Field( - default=os.path.join( - os.path.dirname(__file__), "tests", "tests_uipath_cache.db" - ), - alias="CACHED_COMPLETION_DB", - ) - cached_embeddings_dir: str = Field( - default=os.path.join(os.path.dirname(__file__), "tests", "cached_embeddings"), - alias="CACHED_EMBEDDINGS_DIR", - ) - - -uipath_cached_paths_settings = UiPathCachedPathsSettings() -uipath_token_header: Optional[str] = None - - -class UiPathClientFactorySettings(BaseSettings): - base_url: str = Field(default="", alias="UIPATH_BASE_URL") - client_id: str = Field(default="", alias="UIPATH_CLIENT_ID") - client_secret: str = Field(default="", alias="UIPATH_CLIENT_SECRET") - - -class UiPathClientSettings(BaseSettings): - access_token: str = Field(default_factory=lambda: get_uipath_token_header()) - base_url: str = Field(default="", alias="UIPATH_BASE_URL") - org_id: str = Field(default="", alias="UIPATH_ORGANIZATION_ID") - tenant_id: str = Field(default="", alias="UIPATH_TENANT_ID") - requesting_product: str = Field(default="", alias="UIPATH_REQUESTING_PRODUCT") - requesting_feature: str = Field(default="", alias="UIPATH_REQUESTING_FEATURE") - timeout_seconds: str = Field(default="120", alias="UIPATH_TIMEOUT_SECONDS") - action_name: str = Field(default="DefaultActionName", alias="UIPATH_ACTION_NAME") - action_id: str = Field(default="DefaultActionId", alias="UIPATH_ACTION_ID") - - -class UiPathEndpoints(Enum): - NORMALIZED_COMPLETION_ENDPOINT = "llmgateway_/api/chat/completions" - PASSTHROUGH_COMPLETION_ENDPOINT = "llmgateway_/openai/deployments/{model}/chat/completions?api-version={api_version}" - EMBEDDING_ENDPOINT = ( - "llmgateway_/openai/deployments/{model}/embeddings?api-version={api_version}" - ) - - -def get_uipath_token_header( - settings: Any = None, -) -> str: - global uipath_token_header - if not uipath_token_header: - settings = settings or UiPathClientFactorySettings() - url_get_token = f"{settings.base_url}/identity_/connect/token" - token_credentials = dict( - client_id=settings.client_id, - client_secret=settings.client_secret, - grant_type="client_credentials", - ) - with httpx.Client() as client: - res = client.post(url_get_token, data=token_credentials) - res_json = res.json() - uipath_token_header = res_json.get("access_token") - - return uipath_token_header or "" - - -async def get_token_header_async( - settings: Any = None, -) -> str: - global uipath_token_header - if not uipath_token_header: - settings = settings or UiPathClientFactorySettings() - url_get_token = f"{settings.base_url}/identity_/connect/token" - token_credentials = dict( - client_id=settings.client_id, - client_secret=settings.client_secret, - grant_type="client_credentials", - ) - - with httpx.Client() as client: - res_json = client.post(url_get_token, data=token_credentials).json() - uipath_token_header = res_json.get("access_token") - - return uipath_token_header or "" diff --git a/sdk/langchain/uipath_langchain/_utils/_sleep_policy.py b/sdk/langchain/uipath_langchain/_utils/_sleep_policy.py deleted file mode 100644 index c059c796..00000000 --- a/sdk/langchain/uipath_langchain/_utils/_sleep_policy.py +++ /dev/null @@ -1,41 +0,0 @@ -import logging -from typing import Callable - -from tenacity import ( - RetryCallState, - _utils, -) - - -def before_sleep_log( - logger: "logging.Logger", - log_level: int, - exc_info: bool = False, -) -> Callable[["RetryCallState"], None]: - """Before call strategy that logs to some logger the attempt.""" - - def log_it(retry_state: "RetryCallState") -> None: - if retry_state.outcome is None: - raise RuntimeError("log_it() called before outcome was set") - - if retry_state.next_action is None: - raise RuntimeError("log_it() called before next_action was set") - - if retry_state.outcome.failed: - ex = retry_state.outcome.exception() - verb, value = "raised", f"{ex.__class__.__name__}: {ex}" - else: - verb, value = "returned", retry_state.outcome.result() - - if retry_state.fn is None: - fn_name = "" - else: - fn_name = _utils.get_callback_name(retry_state.fn) - - logger.log( - log_level, - f"Retrying #{retry_state.attempt_number} {fn_name} in {retry_state.next_action.sleep} seconds as it {verb} {value}.", - {"retries": f"{retry_state.attempt_number}"}, - ) - - return log_it diff --git a/sdk/langchain/uipath_langchain/_utils/tests/cached_embeddings/text-embedding-3-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b b/sdk/langchain/uipath_langchain/_utils/tests/cached_embeddings/text-embedding-3-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b deleted file mode 100644 index ee925ee7..00000000 --- a/sdk/langchain/uipath_langchain/_utils/tests/cached_embeddings/text-embedding-3-large5034ec3c-85c9-54b8-ac89-5e0cbcf99e3b +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:eb0757c242dc5fa2290e2fe8059747d6b5ba18da613fc96a1c1ab496cb1832ef -size 68875 diff --git a/sdk/langchain/uipath_langchain/_utils/tests/cached_embeddings/text-embedding-3-largec48857ed-1302-5954-9e24-69fa9b45e457 b/sdk/langchain/uipath_langchain/_utils/tests/cached_embeddings/text-embedding-3-largec48857ed-1302-5954-9e24-69fa9b45e457 deleted file mode 100644 index eaa5c8cb..00000000 --- a/sdk/langchain/uipath_langchain/_utils/tests/cached_embeddings/text-embedding-3-largec48857ed-1302-5954-9e24-69fa9b45e457 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e3e104c19299f441bd137ed18783b28eccc5fc83d3c4b066ee0f80b7960098fe -size 68913 diff --git a/sdk/langchain/uipath_langchain/_utils/tests/tests_uipath_cache.db b/sdk/langchain/uipath_langchain/_utils/tests/tests_uipath_cache.db deleted file mode 100644 index 92021bbf..00000000 --- a/sdk/langchain/uipath_langchain/_utils/tests/tests_uipath_cache.db +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:13bc6863c135afd0fa862b57762d008af026507fe566c176daeaad92d75ecff5 -size 45056 diff --git a/sdk/langchain/uipath_langchain/chat/__init__.py b/sdk/langchain/uipath_langchain/chat/__init__.py deleted file mode 100644 index 4e2b7842..00000000 --- a/sdk/langchain/uipath_langchain/chat/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .models import UiPathAzureChatOpenAI, UiPathNormalizedChatModel - -__all__ = [ - "UiPathNormalizedChatModel", - "UiPathAzureChatOpenAI", -] diff --git a/sdk/langchain/uipath_langchain/chat/models.py b/sdk/langchain/uipath_langchain/chat/models.py deleted file mode 100644 index e46a0ab1..00000000 --- a/sdk/langchain/uipath_langchain/chat/models.py +++ /dev/null @@ -1,259 +0,0 @@ -import json -from typing import Any, Dict, List, Literal, Optional, Union - -from langchain_core.callbacks import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain_core.language_models import LanguageModelInput -from langchain_core.messages import AIMessage, BaseMessage -from langchain_core.messages.ai import UsageMetadata -from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.runnables import Runnable -from langchain_openai.chat_models import AzureChatOpenAI -from pydantic import BaseModel - -from uipath_langchain._utils._request_mixin import UiPathRequestMixin -from uipath_langchain._utils._settings import UiPathEndpoints - - -class UiPathAzureChatOpenAI(UiPathRequestMixin, AzureChatOpenAI): - """Custom LLM connector for LangChain integration with UiPath.""" - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - if "tools" in kwargs and not kwargs["tools"]: - del kwargs["tools"] - payload = self._get_request_payload(messages, stop=stop, **kwargs) - response = self._call(self.url, payload, self.auth_headers) - return self._create_chat_result(response) - - async def _agenerate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - if "tools" in kwargs and not kwargs["tools"]: - del kwargs["tools"] - payload = self._get_request_payload(messages, stop=stop, **kwargs) - response = await self._acall(self.url, payload, self.auth_headers) - return self._create_chat_result(response) - - def with_structured_output( - self, - schema: Optional[Any] = None, - *, - method: Literal["function_calling", "json_mode", "json_schema"] = "json_schema", - include_raw: bool = False, - strict: Optional[bool] = None, - **kwargs: Any, - ) -> Runnable[LanguageModelInput, Any]: - """Model wrapper that returns outputs formatted to match the given schema.""" - schema = ( - schema.model_json_schema() - if isinstance(schema, type) and issubclass(schema, BaseModel) - else schema - ) - return super().with_structured_output( - schema=schema, - method=method, - include_raw=include_raw, - strict=strict, - **kwargs, - ) - - @property - def endpoint(self) -> str: - return UiPathEndpoints.PASSTHROUGH_COMPLETION_ENDPOINT.value.format( - model=self.model_name, api_version=self.openai_api_version - ) - - -class UiPathNormalizedChatModel(UiPathRequestMixin, AzureChatOpenAI): - """Custom LLM connector for LangChain integration with UiPath Normalized.""" - - def _create_chat_result( - self, - response: Union[Dict[str, Any], BaseModel], - generation_info: Optional[Dict[Any, Any]] = None, - ) -> ChatResult: - if not isinstance(response, dict): - response = response.model_dump() - message = response["choices"][0]["message"] - usage = response["usage"] - - ai_message = AIMessage( - content=message.get("content", ""), - usage_metadata=UsageMetadata( - input_tokens=usage.get("prompt_tokens", 0), - output_tokens=usage.get("completion_tokens", 0), - total_tokens=usage.get("total_tokens", 0), - ), - additional_kwargs={}, - response_metadata={ - "token_usage": response["usage"], - "model_name": self.model_name, - "finish_reason": response["choices"][0].get("finish_reason", None), - "system_fingerprint": response["id"], - "created": response["created"], - }, - ) - - if "tool_calls" in message: - ai_message.tool_calls = [ - { - "id": tool["id"], - "name": tool["name"], - "args": tool["arguments"], - } - for tool in message["tool_calls"] - ] - generation = ChatGeneration(message=ai_message) - return ChatResult(generations=[generation]) - - def _get_request_payload( - self, - input_: LanguageModelInput, - *, - stop: Optional[List[str]] = None, - **kwargs: Any, - ) -> Dict[Any, Any]: - payload = super()._get_request_payload(input_, stop=stop, **kwargs) - # hacks to make the request work with uipath normalized - for message in payload["messages"]: - if message["content"] is None: - message["content"] = "" - if "tool_calls" in message: - for tool_call in message["tool_calls"]: - tool_call["name"] = tool_call["function"]["name"] - tool_call["arguments"] = json.loads( - tool_call["function"]["arguments"] - ) - if message["role"] == "tool": - message["content"] = { - "result": message["content"], - "call_id": message["tool_call_id"], - } - return payload - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - """Override the _generate method to implement the chat model logic. - - This can be a call to an API, a call to a local model, or any other - implementation that generates a response to the input prompt. - - Args: - messages: the prompt composed of a list of messages. - stop: a list of strings on which the model should stop generating. - If generation stops due to a stop token, the stop token itself - SHOULD BE INCLUDED as part of the output. This is not enforced - across models right now, but it's a good practice to follow since - it makes it much easier to parse the output of the model - downstream and understand why generation stopped. - run_manager: A run manager with callbacks for the LLM. - """ - if kwargs.get("tools"): - kwargs["tools"] = [tool["function"] for tool in kwargs["tools"]] - if "tool_choice" in kwargs and kwargs["tool_choice"]["type"] == "function": - kwargs["tool_choice"] = { - "type": "tool", - "name": kwargs["tool_choice"]["function"]["name"], - } - payload = self._get_request_payload(messages, stop=stop, **kwargs) - - response = self._call(self.url, payload, self.auth_headers) - return self._create_chat_result(response) - - async def _agenerate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - """Override the _generate method to implement the chat model logic. - - This can be a call to an API, a call to a local model, or any other - implementation that generates a response to the input prompt. - - Args: - messages: the prompt composed of a list of messages. - stop: a list of strings on which the model should stop generating. - If generation stops due to a stop token, the stop token itself - SHOULD BE INCLUDED as part of the output. This is not enforced - across models right now, but it's a good practice to follow since - it makes it much easier to parse the output of the model - downstream and understand why generation stopped. - run_manager: A run manager with callbacks for the LLM. - """ - if kwargs.get("tools"): - kwargs["tools"] = [tool["function"] for tool in kwargs["tools"]] - if "tool_choice" in kwargs and kwargs["tool_choice"]["type"] == "function": - kwargs["tool_choice"] = { - "type": "tool", - "name": kwargs["tool_choice"]["function"]["name"], - } - payload = self._get_request_payload(messages, stop=stop, **kwargs) - - response = await self._acall(self.url, payload, self.auth_headers) - return self._create_chat_result(response) - - def with_structured_output( - self, - schema: Optional[Any] = None, - *, - method: Literal[ - "function_calling", "json_mode", "json_schema" - ] = "function_calling", - include_raw: bool = False, - strict: Optional[bool] = None, - **kwargs: Any, - ) -> Runnable[LanguageModelInput, Any]: - """Model wrapper that returns outputs formatted to match the given schema.""" - if method == "json_schema" and ( - not self.model_name or not self.model_name.startswith("gpt") - ): - method = "function_calling" - if self.logger: - self.logger.warning( - "The json_schema output is not supported for non-GPT models. Using function_calling instead.", - extra={ - "ActionName": self.settings.action_name, - "ActionId": self.settings.action_id, - } - if self.settings - else None, - ) - schema = ( - schema.model_json_schema() - if isinstance(schema, type) and issubclass(schema, BaseModel) - else schema - ) - return super().with_structured_output( - schema=schema, - method=method, - include_raw=include_raw, - strict=strict, - **kwargs, - ) - - @property - def endpoint(self) -> str: - return UiPathEndpoints.NORMALIZED_COMPLETION_ENDPOINT.value - - @property - def is_normalized(self) -> bool: - return True diff --git a/sdk/langchain/uipath_langchain/chat/utils/__init__.py b/sdk/langchain/uipath_langchain/chat/utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py b/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py deleted file mode 100644 index 2abc8c0a..00000000 --- a/sdk/langchain/uipath_langchain/chat/utils/_chat_types.py +++ /dev/null @@ -1,54 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -from pydantic import BaseModel, Field - - -class ToolCall(BaseModel): - id: str - name: str - arguments: Dict[Any, Any] - - -class Message(BaseModel): - role: str - content: Optional[Union[str, Dict[Any, Any]]] = None - tool_calls: Optional[List[ToolCall]] = None - - -class Tool(BaseModel): - name: str - description: str - parameters: Optional[Dict[Any, Any]] = None - - -class ToolChoice(BaseModel): - type: str - name: Optional[str] = None - - -class ChatCompletionRequest(BaseModel): - max_tokens: int - # n: int - # top_p: float - temperature: float - frequency_penalty: float - presence_penalty: float - messages: List[Message] - tools: Optional[List[Tool]] = None - tool_choice: Optional[ToolChoice] = None - response_format: Optional[Dict[str, Any]] = None - - -class ModelSettings(BaseModel): - top_p: float = 1.0 - n: int = 1 - temperature: float = 0.0 - frequency_penalty: float = 0.0 - presence_penalty: float = 0.0 - max_tokens: int = 4096 - tool_choice: Optional[str] = None - enforced_tool_name: Optional[str] = None - - -class UiPathOutput(BaseModel): - output_field: str = Field(..., description="The output field") diff --git a/sdk/langchain/uipath_langchain/embeddings/__init__.py b/sdk/langchain/uipath_langchain/embeddings/__init__.py deleted file mode 100644 index e5387262..00000000 --- a/sdk/langchain/uipath_langchain/embeddings/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .embeddings import UiPathAzureOpenAIEmbeddings, UiPathOpenAIEmbeddings - -__all__ = [ - "UiPathAzureOpenAIEmbeddings", - "UiPathOpenAIEmbeddings", -] diff --git a/sdk/langchain/uipath_langchain/embeddings/embeddings.py b/sdk/langchain/uipath_langchain/embeddings/embeddings.py deleted file mode 100644 index bbb9161a..00000000 --- a/sdk/langchain/uipath_langchain/embeddings/embeddings.py +++ /dev/null @@ -1,109 +0,0 @@ -import os -from typing import List, Optional - -import httpx -from langchain_community.callbacks.manager import openai_callback_var -from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings -from pydantic import Field - -from uipath_langchain._utils._request_mixin import UiPathRequestMixin -from uipath_langchain._utils._settings import UiPathEndpoints - - -class UiPathAzureOpenAIEmbeddings(UiPathRequestMixin, AzureOpenAIEmbeddings): - """Custom Embeddings connector for LangChain integration with UiPath, with minimal changes compared to AzureOpenAIEmbeddings.""" - - model_name: Optional[str] = Field( - default_factory=lambda: os.getenv( - "UIPATH_MODEL_NAME", "text-embedding-3-large" - ), - alias="model", - ) - - def __init__(self, **kwargs): - super().__init__( - http_client=httpx.Client( - event_hooks={ - "request": [self._log_request_duration], - "response": [self._log_response_duration], - } - ), - http_async_client=httpx.AsyncClient( - event_hooks={ - "request": [self._alog_request_duration], - "response": [self._alog_response_duration], - } - ), - **kwargs, - ) - self.client._client._prepare_url = self._prepare_url - self.client._client._build_headers = self._build_headers - self.async_client._client._prepare_url = self._prepare_url - self.async_client._client._build_headers = self._build_headers - - @property - def endpoint(self) -> str: - return UiPathEndpoints.EMBEDDING_ENDPOINT.value.format( - model=self.model_name, api_version=self.openai_api_version - ) - - -class UiPathOpenAIEmbeddings(UiPathRequestMixin, OpenAIEmbeddings): - """Custom Embeddings connector for LangChain integration with UiPath, with full control over the embedding call.""" - - model_name: Optional[str] = Field( - default_factory=lambda: os.getenv( - "UIPATH_MODEL_NAME", "text-embedding-3-large" - ), - alias="model", - ) - - def embed_documents( - self, texts: List[str], chunk_size: Optional[int] = None - ) -> List[List[float]]: - """Embed a list of documents using the UiPath.""" - embeddings = [] - total_tokens = 0 - # Process in chunks if specified - chunk_size_ = chunk_size or self.chunk_size or len(texts) - for i in range(0, len(texts), chunk_size_): - chunk = texts[i : i + chunk_size_] - payload = {"input": chunk} - response = self._call(self.url, payload, self.auth_headers) - chunk_embeddings = [r["embedding"] for r in response["data"]] - total_tokens += response["usage"]["prompt_tokens"] - embeddings.extend(chunk_embeddings) - if contextvar := openai_callback_var.get(): - contextvar.prompt_tokens += total_tokens - contextvar.total_tokens += total_tokens - contextvar.successful_requests += 1 - return embeddings - - async def aembed_documents( - self, - texts: List[str], - chunk_size: Optional[int] = None, - ) -> List[List[float]]: - """Async version of embed_documents.""" - embeddings = [] - total_tokens = 0 - # Process in chunks if specified - chunk_size_ = chunk_size or self.chunk_size or len(texts) - for i in range(0, len(texts), chunk_size_): - chunk = texts[i : i + chunk_size_] - payload = {"input": chunk} - response = await self._acall(self.url, payload, self.auth_headers) - chunk_embeddings = [r["embedding"] for r in response["data"]] - total_tokens += response["usage"]["prompt_tokens"] - embeddings.extend(chunk_embeddings) - if contextvar := openai_callback_var.get(): - contextvar.prompt_tokens += total_tokens - contextvar.total_tokens += total_tokens - contextvar.successful_requests += 1 - return embeddings - - @property - def endpoint(self) -> str: - return UiPathEndpoints.EMBEDDING_ENDPOINT.value.format( - model=self.model_name, api_version=self.openai_api_version - ) diff --git a/sdk/langchain/uipath_langchain/middlewares.py b/sdk/langchain/uipath_langchain/middlewares.py deleted file mode 100644 index 0a5e50e0..00000000 --- a/sdk/langchain/uipath_langchain/middlewares.py +++ /dev/null @@ -1,10 +0,0 @@ -from uipath_sdk._cli.middlewares import Middlewares - -from ._cli.cli_init import langgraph_init_middleware -from ._cli.cli_run import langgraph_run_middleware - - -def register_middleware(): - """This function will be called by the entry point system when uipath_langchain is installed""" - Middlewares.register("init", langgraph_init_middleware) - Middlewares.register("run", langgraph_run_middleware) diff --git a/sdk/langchain/uipath_langchain/retrievers/__init__.py b/sdk/langchain/uipath_langchain/retrievers/__init__.py deleted file mode 100644 index 2b8050e3..00000000 --- a/sdk/langchain/uipath_langchain/retrievers/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .context_grounding_retriever import ContextGroundingRetriever - -__all__ = ["ContextGroundingRetriever"] diff --git a/sdk/langchain/uipath_langchain/retrievers/context_grounding_retriever.py b/sdk/langchain/uipath_langchain/retrievers/context_grounding_retriever.py deleted file mode 100644 index 4ecc2757..00000000 --- a/sdk/langchain/uipath_langchain/retrievers/context_grounding_retriever.py +++ /dev/null @@ -1,35 +0,0 @@ -from typing import List, Optional - -from langchain_core.callbacks import CallbackManagerForRetrieverRun -from langchain_core.documents import Document -from langchain_core.retrievers import BaseRetriever -from uipath_sdk import UiPathSDK - - -class ContextGroundingRetriever(BaseRetriever): - index_name: str - uipath_sdk: Optional[UiPathSDK] = None - number_of_results: Optional[int] = 10 - - def _get_relevant_documents( - self, query: str, *, run_manager: CallbackManagerForRetrieverRun - ) -> List[Document]: - """Sync implementations for retriever calls context_grounding API to search the requested index.""" - - sdk = self.uipath_sdk if self.uipath_sdk is not None else UiPathSDK() - results = sdk.context_grounding.search( - self.index_name, - query, - self.number_of_results if self.number_of_results is not None else 10, - ) - - return [ - Document( - page_content=x.content, - metadata={ - "source": x.source, - "page_number": x.page_number, - }, - ) - for x in results - ] diff --git a/sdk/langchain/uipath_langchain/tracers/AsyncUiPathTracer.py b/sdk/langchain/uipath_langchain/tracers/AsyncUiPathTracer.py deleted file mode 100644 index 3d6d0708..00000000 --- a/sdk/langchain/uipath_langchain/tracers/AsyncUiPathTracer.py +++ /dev/null @@ -1,308 +0,0 @@ -import asyncio -import json -import logging -import queue -import re -import uuid -import warnings -from os import environ as env -from typing import Any, Dict, Optional - -import httpx -from langchain_core.tracers.base import AsyncBaseTracer -from langchain_core.tracers.schemas import Run -from pydantic import PydanticDeprecationWarning -from uipath_sdk._cli._runtime._contracts import UiPathTraceContext - -from ._events import CustomTraceEvents, FunctionCallEventData -from ._utils import _setup_tracer_httpx_logging, _simple_serialize_defaults - -logger = logging.getLogger(__name__) - -_setup_tracer_httpx_logging("/llmops_/api/Agent/trace/") - - -class Status: - SUCCESS = 1 - ERROR = 2 - INTERRUPTED = 3 - - -class AsyncUiPathTracer(AsyncBaseTracer): - def __init__( - self, - context: Optional[UiPathTraceContext] = None, - client: Optional[httpx.AsyncClient] = None, - **kwargs, - ): - super().__init__(**kwargs) - - self.client = client or httpx.AsyncClient() - self.retries = 3 - self.log_queue: queue.Queue[dict[str, Any]] = queue.Queue() - - self.context = context or UiPathTraceContext() - - llm_ops_pattern = self._get_base_url() + "{orgId}/llmops_" - - self.url = llm_ops_pattern.format(orgId=self.context.org_id).rstrip("/") - - auth_token = env.get("UNATTENDED_USER_ACCESS_TOKEN") or env.get( - "UIPATH_ACCESS_TOKEN" - ) - - self.headers = {"Authorization": f"Bearer {auth_token}"} - - self.running = True - self.worker_task = asyncio.create_task(self._worker()) - self.function_call_run_map: Dict[str, Run] = {} - - async def on_custom_event( - self, - name: str, - data: Any, - *, - run_id: uuid.UUID, - tags=None, - metadata=None, - **kwargs: Any, - ) -> None: - if name == CustomTraceEvents.UIPATH_TRACE_FUNCTION_CALL: - # only handle the function call event - - if not isinstance(data, FunctionCallEventData): - logger.warning( - f"Received unexpected data type for function call event: {type(data)}" - ) - return - - if data.event_type == "call": - run = self.run_map[str(run_id)] - child_run = run.create_child( - name=data.function_name, - run_type=data.run_type, - tags=data.tags, - inputs=data.inputs, - ) - - if data.metadata is not None: - run.add_metadata(data.metadata) - - call_uuid = data.call_uuid - self.function_call_run_map[call_uuid] = child_run - - self._send_span(run) - - if data.event_type == "completion": - call_uuid = data.call_uuid - previous_run = self.function_call_run_map.pop(call_uuid, None) - - if previous_run: - previous_run.end( - outputs=self._safe_dict_dump(data.output), error=data.error - ) - self._send_span(previous_run) - - async def init_trace(self, run_name, trace_id=None) -> None: - if self.context.trace_id: - # trace id already set no need to do anything - return - - # no trace id, start a new trace - await self.start_trace(run_name, trace_id) - - async def start_trace(self, run_name, trace_id=None) -> None: - self.context.trace_id = str(uuid.uuid4()) - - run_name = run_name or f"Job Run: {self.context.trace_id}" - trace_data = { - "id": self.context.trace_id, - "name": re.sub( - "[!@#$<>\.]", "", run_name - ), # if we use these characters the Agents UI throws some error (but llmops backend seems fine) - "referenceId": self.context.reference_id, - "attributes": "{}", - "organizationId": self.context.org_id, - "tenantId": self.context.tenant_id, - } - - for attempt in range(self.retries): - response = await self.client.post( - f"{self.url}/api/Agent/trace/", headers=self.headers, json=trace_data - ) - - if response.is_success: - break - - await asyncio.sleep(0.5 * (2**attempt)) # Exponential backoff - - if 400 <= response.status_code < 600: - logger.warning( - f"Error when sending trace: {response}. Body is: {response.text}" - ) - - async def wait_for_all_tracers(self) -> None: - """ - Wait for all pending log requests to complete - """ - self.running = False - if self.worker_task: - await self.worker_task - - async def _worker(self): - """Worker loop that processes logs from the queue.""" - while self.running: - try: - if self.log_queue.empty(): - await asyncio.sleep(1) - continue - - span_data = self.log_queue.get_nowait() - - for attempt in range(self.retries): - response = await self.client.post( - f"{self.url}/api/Agent/span/", - headers=self.headers, - json=span_data, - timeout=10, - ) - - if response.is_success: - break - - await asyncio.sleep(0.5 * (2**attempt)) # Exponential backoff - - if 400 <= response.status_code < 600: - logger.warning( - f"Error when sending trace: {response}. Body is: {response.text}" - ) - except Exception as e: - logger.warning(f"Exception when sending trace: {e}.") - - # wait for a bit to ensure all logs are sent - await asyncio.sleep(1) - - # try to send any remaining logs in the queue - while True: - try: - if self.log_queue.empty(): - break - - span_data = self.log_queue.get_nowait() - - response = await self.client.post( - f"{self.url}/api/Agent/span/", - headers=self.headers, - json=span_data, - timeout=10, - ) - except Exception as e: - logger.warning(f"Exception when sending trace: {e}.") - - async def _persist_run(self, run: Run) -> None: - # Determine if this is a start or end trace based on whether end_time is set - self._send_span(run) - - def _send_span(self, run: Run) -> None: - """Send span data for a run to the API""" - run_id = str(run.id) - - try: - start_time = ( - run.start_time.isoformat() if run.start_time is not None else None - ) - end_time = ( - run.end_time.isoformat() if run.end_time is not None else start_time - ) - - parent_id = ( - str(run.parent_run_id) - if run.parent_run_id is not None - else self.context.parent_span_id - ) - attributes = self._safe_jsons_dump(self._run_to_dict(run)) - status = self._determine_status(run.error) - - span_data = { - "id": run_id, - "parentId": parent_id, - "traceId": self.context.trace_id, - "name": run.name, - "startTime": start_time, - "endTime": end_time, - "referenceId": self.context.reference_id, - "attributes": attributes, - "organizationId": self.context.org_id, - "tenantId": self.context.tenant_id, - "spanType": "LangGraphRun", - "status": status, - "jobKey": self.context.job_id, - "folderKey": self.context.folder_key, - "processKey": self.context.folder_key, - } - - self.log_queue.put(span_data) - except Exception as e: - logger.warning(f"Exception when adding trace to queue: {e}.") - - async def _start_trace(self, run: Run) -> None: - await super()._start_trace(run) - await self._persist_run(run) - - async def _end_trace(self, run: Run) -> None: - await super()._end_trace(run) - await self._persist_run(run) - - def _determine_status(self, error: Optional[str]): - if error: - if error.startswith("GraphInterrupt("): - return Status.INTERRUPTED - - return Status.ERROR - - return Status.SUCCESS - - def _safe_jsons_dump(self, obj) -> str: - try: - json_str = json.dumps(obj, default=_simple_serialize_defaults) - return json_str - except Exception as e: - logger.warning(f"Error serializing object to JSON: {e}") - return "{ }" - - def _safe_dict_dump(self, obj) -> Dict[str, Any]: - try: - serialized = json.loads(json.dumps(obj, default=_simple_serialize_defaults)) - return serialized - except Exception as e: - # Last resort - string representation - logger.warning(f"Error serializing object to JSON: {e}") - return {"raw": str(obj)} - - def _run_to_dict(self, run: Run): - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=PydanticDeprecationWarning) - - return { - **run.dict(exclude={"child_runs", "inputs", "outputs"}), - "inputs": run.inputs.copy() if run.inputs is not None else None, - "outputs": run.outputs.copy() if run.outputs is not None else None, - } - - def _get_base_url(self) -> str: - uipath_url = ( - env.get("UIPATH_URL") or "https://cloud.uipath.com/dummyOrg/dummyTennant/" - ) - uipath_url = uipath_url.rstrip("/") - - # split by "//" to get ['', 'https:', 'alpha.uipath.com/ada/byoa'] - parts = uipath_url.split("//") - - # after splitting by //, the base URL will be at index 1 along with the rest, - # hence split it again using "/" to get ['https:', 'alpha.uipath.com', 'ada', 'byoa'] - base_url_parts = parts[1].split("/") - - # combine scheme and netloc to get the base URL - base_url = parts[0] + "//" + base_url_parts[0] + "/" - - return base_url diff --git a/sdk/langchain/uipath_langchain/tracers/UiPathTracer.py b/sdk/langchain/uipath_langchain/tracers/UiPathTracer.py deleted file mode 100644 index ae7b5d72..00000000 --- a/sdk/langchain/uipath_langchain/tracers/UiPathTracer.py +++ /dev/null @@ -1,161 +0,0 @@ -import json -import logging -import re -import uuid -import warnings -from os import environ as env -from time import sleep - -import httpx -from langchain_core.tracers.base import BaseTracer -from langchain_core.tracers.schemas import Run -from pydantic import PydanticDeprecationWarning - -from ._utils import _simple_serialize_defaults - -logger = logging.getLogger(__name__) - - -class UiPathTracer(BaseTracer): - def __init__(self, client, **kwargs): - super().__init__(**kwargs) - - self.client = client or httpx.Client() - self.retries = 3 - - llm_ops_pattern = self._get_base_url() + "{orgId}/llmops_" - self.orgId = env.get("UIPATH_ORGANIZATION_ID") - self.tenantId = env.get("UIPATH_TENANT_ID") - self.url = llm_ops_pattern.format(orgId=self.orgId).rstrip("/") - - self.auth_token = env.get("UNATTENDED_USER_ACCESS_TOKEN") or env.get( - "UIPATH_ACCESS_TOKEN" - ) - - self.jobKey = env.get("UIPATH_JOB_KEY") - self.folderKey = env.get("UIPATH_FOLDER_KEY") - self.processKey = env.get("UIPATH_PROCESS_UUID") - - self.referenceId = self.jobKey or str(uuid.uuid4()) - - self.headers = { - "Authorization": f"Bearer {self.auth_token}", - } - - def _get_base_url(self) -> str: - uipath_url = ( - env.get("UIPATH_URL") or "https://cloud.uipath.com/dummyOrg/dummyTennant/" - ) - uipath_url = uipath_url.rstrip("/") - - # split by "//" to get ['', 'https:', 'alpha.uipath.com/ada/byoa'] - parts = uipath_url.split("//") - - # after splitting by //, the base URL will be at index 1 along with the rest, - # hence split it again using "/" to get ['https:', 'alpha.uipath.com', 'ada', 'byoa'] - base_url_parts = parts[1].split("/") - - # combine scheme and netloc to get the base URL - base_url = parts[0] + "//" + base_url_parts[0] + "/" - - return base_url - - def init_trace(self, run_name, trace_id=None) -> None: - trace_id_env = env.get("UIPATH_TRACE_ID") - - if trace_id_env: - self.trace_parent = trace_id_env - else: - self.start_trace(run_name, trace_id) - - def start_trace(self, run_name, trace_id=None) -> None: - self.trace_parent = trace_id or str(uuid.uuid4()) - run_name = run_name or f"Job Run: {self.trace_parent}" - trace_data = { - "id": self.trace_parent, - "name": re.sub( - "[!@#$<>\.]", "", run_name - ), # if we use these characters the Agents UI throws some error (but llmops backend seems fine) - "referenceId": self.referenceId, - "attributes": "{}", - "organizationId": self.orgId, - "tenantId": self.tenantId, - } - - for attempt in range(self.retries): - response = self.client.post( - f"{self.url}/api/Agent/trace/", headers=self.headers, json=trace_data - ) - - if response.is_success: - break - - sleep(0.5 * (2**attempt)) # Exponential backoff - - if 400 <= response.status_code < 600: - logger.warning( - f"Error when sending trace: {response}. Body is: {response.text}" - ) - - def _persist_run(self, run: Run) -> None: - # when (run.id == run.parent_run_id) it's the start of a new trace - # but we treat all as spans and parent to a single Trace with Id == Job.Key - start_time = run.start_time.isoformat() if run.start_time is not None else None - end_time = run.end_time.isoformat() if run.end_time is not None else start_time - - span_data = { - "id": str(run.id), - "parentId": str(run.parent_run_id) - if run.parent_run_id is not None - else None, - "traceId": self.trace_parent, - "name": run.name, - "startTime": start_time, - "endTime": end_time, - "referenceId": self.referenceId, - "attributes": self._safe_json_dump(self._run_to_dict(run)), - "organizationId": self.orgId, - "tenantId": self.tenantId, - "spanType": "LangGraphRun", - "status": 2 if run.error else 1, - "jobKey": self.jobKey, - "folderKey": self.folderKey, - "processKey": self.processKey, - } - - for attempt in range(self.retries): - response = self.client.post( - f"{self.url}/api/Agent/span/", headers=self.headers, json=span_data - ) - - if response.is_success: - break - - sleep(0.5 * (2**attempt)) # Exponential backoff - - if 400 <= response.status_code < 600: - logger.warning( - f"Error when sending trace: {response}. Body is: {response.text}" - ) - - def _end_trace(self, run: Run) -> None: - super()._end_trace(run) - self._persist_run(run) - - def _safe_json_dump(self, obj) -> str: - try: - json_str = json.dumps(obj, default=_simple_serialize_defaults) - return json_str - except Exception as e: - logger.warning(e) - return "{ }" - - def _run_to_dict(self, run: Run): - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=PydanticDeprecationWarning) - - return { - **run.dict(exclude={"child_runs", "inputs", "outputs"}), - "inputs": run.inputs.copy() if run.inputs is not None else None, - "outputs": run.outputs.copy() if run.outputs is not None else None, - } diff --git a/sdk/langchain/uipath_langchain/tracers/__init__.py b/sdk/langchain/uipath_langchain/tracers/__init__.py deleted file mode 100644 index 360d26bd..00000000 --- a/sdk/langchain/uipath_langchain/tracers/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from ._instrument_traceable import _instrument_traceable -from .AsyncUiPathTracer import AsyncUiPathTracer -from .UiPathTracer import UiPathTracer - -__all__ = ["AsyncUiPathTracer", "UiPathTracer", "_instrument_traceable"] diff --git a/sdk/langchain/uipath_langchain/tracers/_events.py b/sdk/langchain/uipath_langchain/tracers/_events.py deleted file mode 100644 index 0b91b02c..00000000 --- a/sdk/langchain/uipath_langchain/tracers/_events.py +++ /dev/null @@ -1,33 +0,0 @@ -from typing import Any, Dict, List, Literal, Optional - -RUN_TYPE_T = Literal[ - "tool", "chain", "llm", "retriever", "embedding", "prompt", "parser" -] - - -class CustomTraceEvents: - UIPATH_TRACE_FUNCTION_CALL = "__uipath_trace_function_call" - - -class FunctionCallEventData: - def __init__( - self, - function_name: str, - event_type: str, - inputs: Dict[str, Any], - call_uuid: str, - output: Any, - error: Optional[str] = None, - run_type: Optional[RUN_TYPE_T] = None, - tags: Optional[List[str]] = None, - metadata: Optional[Dict[str, Any]] = None, - ): - self.function_name = function_name - self.event_type = event_type - self.inputs = inputs - self.call_uuid = call_uuid - self.output = output - self.error = error - self.run_type = run_type or "chain" - self.tags = tags - self.metadata = metadata diff --git a/sdk/langchain/uipath_langchain/tracers/_instrument_traceable.py b/sdk/langchain/uipath_langchain/tracers/_instrument_traceable.py deleted file mode 100644 index 7bf96845..00000000 --- a/sdk/langchain/uipath_langchain/tracers/_instrument_traceable.py +++ /dev/null @@ -1,285 +0,0 @@ -import functools -import importlib -import inspect -import logging -import sys -import uuid -from typing import Any, Dict, List, Literal, Optional - -from langchain_core.callbacks import dispatch_custom_event - -from ._events import CustomTraceEvents, FunctionCallEventData - -# Original module and traceable function references -original_langsmith: Any = None -original_traceable: Any = None - -logger = logging.getLogger(__name__) - - -def dispatch_trace_event( - func_name, - inputs: Dict[str, Any], - event_type="call", - call_uuid=None, - result=None, - exception=None, - run_type: Optional[ - Literal["tool", "chain", "llm", "retriever", "embedding", "prompt", "parser"] - ] = None, - tags: Optional[List[str]] = None, - metadata: Optional[Dict[str, Any]] = None, -): - """Dispatch trace event to our server.""" - - event_data = FunctionCallEventData( - function_name=func_name, - event_type=event_type, - inputs=inputs, - call_uuid=call_uuid, - output=result, - error=str(exception) if exception else None, - run_type=run_type, - tags=tags, - metadata=metadata, - ) - dispatch_custom_event(CustomTraceEvents.UIPATH_TRACE_FUNCTION_CALL, event_data) - - -def format_args_for_trace( - signature: inspect.Signature, *args: Any, **kwargs: Any -) -> Dict[str, Any]: - try: - """Return a dictionary of inputs from the function signature.""" - # Create a parameter mapping by partially binding the arguments - parameter_binding = signature.bind_partial(*args, **kwargs) - - # Fill in default values for any unspecified parameters - parameter_binding.apply_defaults() - - # Extract the input parameters, skipping special Python parameters - result = {} - for name, value in parameter_binding.arguments.items(): - # Skip class and instance references - if name in ("self", "cls"): - continue - - # Handle **kwargs parameters specially - param_info = signature.parameters.get(name) - if param_info and param_info.kind == inspect.Parameter.VAR_KEYWORD: - # Flatten nested kwargs directly into the result - if isinstance(value, dict): - result.update(value) - else: - # Regular parameter - result[name] = value - - return result - except Exception as e: - logger.warning( - f"Error formatting arguments for trace: {e}. Using args and kwargs directly." - ) - return {"args": args, "kwargs": kwargs} - - -# Create patched version of traceable -def patched_traceable(*decorator_args, **decorator_kwargs): - # Handle the case when @traceable is used directly as decorator without arguments - if ( - len(decorator_args) == 1 - and callable(decorator_args[0]) - and not decorator_kwargs - ): - func = decorator_args[0] - return _create_appropriate_wrapper(func, original_traceable(func), {}) - - # Handle the case when @traceable(args) is used with parameters - original_decorated = original_traceable(*decorator_args, **decorator_kwargs) - - def uipath_trace_decorator(func): - # Apply the original decorator with its arguments - wrapped_func = original_decorated(func) - return _create_appropriate_wrapper(func, wrapped_func, decorator_kwargs) - - return uipath_trace_decorator - - -def _create_appropriate_wrapper( - original_func: Any, wrapped_func: Any, decorator_kwargs: Dict[str, Any] -): - """Create the appropriate wrapper based on function type.""" - - # Get the function name and tags from decorator arguments - func_name = decorator_kwargs.get("name", original_func.__name__) - tags = decorator_kwargs.get("tags", None) - metadata = decorator_kwargs.get("metadata", None) - run_type = decorator_kwargs.get("run_type", None) - - # Async generator function - if inspect.isasyncgenfunction(wrapped_func): - - @functools.wraps(wrapped_func) - async def async_gen_wrapper(*args, **kwargs): - try: - call_uuid = str(uuid.uuid4()) - - inputs = format_args_for_trace( - inspect.signature(original_func), *args, **kwargs - ) - - dispatch_trace_event( - func_name, - inputs, - "call", - call_uuid, - run_type=run_type, - tags=tags, - metadata=metadata, - ) - async_gen = wrapped_func(*args, **kwargs) - - results = [] - - async for item in async_gen: - results.append(item) - yield item - - dispatch_trace_event( - func_name, inputs, "completion", call_uuid, results - ) - except Exception as e: - dispatch_trace_event( - func_name, inputs, "completion", call_uuid, exception=e - ) - raise - - return async_gen_wrapper - - # Sync generator function - elif inspect.isgeneratorfunction(wrapped_func): - - @functools.wraps(wrapped_func) - def gen_wrapper(*args, **kwargs): - try: - call_uuid = str(uuid.uuid4()) - - inputs = format_args_for_trace( - inspect.signature(original_func), *args, **kwargs - ) - - results = [] - - dispatch_trace_event( - func_name, - inputs, - "call", - call_uuid, - run_type=run_type, - tags=tags, - metadata=metadata, - ) - gen = wrapped_func(*args, **kwargs) - for item in gen: - results.append(item) - yield item - dispatch_trace_event( - func_name, inputs, "completion", call_uuid, results - ) - except Exception as e: - dispatch_trace_event( - func_name, inputs, "completion", call_uuid, exception=e - ) - raise - - return gen_wrapper - - # Async function - elif inspect.iscoroutinefunction(wrapped_func): - - @functools.wraps(wrapped_func) - async def async_wrapper(*args, **kwargs): - try: - call_uuid = str(uuid.uuid4()) - - inputs = format_args_for_trace( - inspect.signature(original_func), *args, **kwargs - ) - - dispatch_trace_event( - func_name, - inputs, - "call", - call_uuid, - run_type=run_type, - tags=tags, - metadata=metadata, - ) - result = await wrapped_func(*args, **kwargs) - dispatch_trace_event(func_name, inputs, "completion", call_uuid, result) - return result - except Exception as e: - dispatch_trace_event( - func_name, inputs, "completion", call_uuid, exception=e - ) - raise - - return async_wrapper - - # Regular sync function (default case) - else: - - @functools.wraps(wrapped_func) - def sync_wrapper(*args, **kwargs): - try: - call_uuid = str(uuid.uuid4()) - - inputs = format_args_for_trace( - inspect.signature(original_func), *args, **kwargs - ) - - dispatch_trace_event( - func_name, - inputs, - "call", - call_uuid, - run_type=run_type, - tags=tags, - metadata=metadata, - ) - result = wrapped_func(*args, **kwargs) - dispatch_trace_event(func_name, inputs, "completion", call_uuid, result) - return result - except Exception as e: - dispatch_trace_event( - func_name, inputs, "completion", call_uuid, exception=e - ) - raise - - return sync_wrapper - - -# Apply the patch -def _instrument_traceable(): - """Apply the patch to langsmith module at import time.""" - global original_langsmith, original_traceable - - # Import the original module if not already done - if original_langsmith is None: - # Temporarily remove our custom module from sys.modules - if "langsmith" in sys.modules: - original_langsmith = sys.modules["langsmith"] - del sys.modules["langsmith"] - - # Import the original module - original_langsmith = importlib.import_module("langsmith") - - # Store the original traceable - original_traceable = original_langsmith.traceable - - # Replace the traceable function with our patched version - original_langsmith.traceable = patched_traceable - - # Put our modified module back - sys.modules["langsmith"] = original_langsmith - - return original_langsmith diff --git a/sdk/langchain/uipath_langchain/tracers/_utils.py b/sdk/langchain/uipath_langchain/tracers/_utils.py deleted file mode 100644 index 7ad38fcc..00000000 --- a/sdk/langchain/uipath_langchain/tracers/_utils.py +++ /dev/null @@ -1,52 +0,0 @@ -import datetime -import logging -from zoneinfo import ZoneInfo - - -class IgnoreSpecificUrl(logging.Filter): - def __init__(self, url_to_ignore): - super().__init__() - self.url_to_ignore = url_to_ignore - - def filter(self, record): - try: - if record.msg == 'HTTP Request: %s %s "%s %d %s"': - # Ignore the log if the URL matches the one we want to ignore - method = record.args[0] - url = record.args[1] - - if method == "POST" and url.path.endswith(self.url_to_ignore): - # Check if the URL contains the specific path we want to ignore - return True - return False - - except Exception: - return False - - -def _setup_tracer_httpx_logging(url: str): - # Create a custom logger for httpx - # Add the custom filter to the root logger - logging.getLogger("httpx").addFilter(IgnoreSpecificUrl(url)) - - -def _simple_serialize_defaults(obj): - if hasattr(obj, "model_dump"): - return obj.model_dump(exclude_none=True, mode="json") - if hasattr(obj, "dict"): - return obj.dict() - if hasattr(obj, "to_dict"): - return obj.to_dict() - - if isinstance(obj, (set, tuple)): - if hasattr(obj, "_asdict") and callable(obj._asdict): - return obj._asdict() - return list(obj) - - if isinstance(obj, datetime.datetime): - return obj.isoformat() - - if isinstance(obj, (datetime.timezone, ZoneInfo)): - return obj.tzname(None) - - return str(obj) diff --git a/sdk/langchain/uv.lock b/sdk/langchain/uv.lock deleted file mode 100644 index 6a4f85d8..00000000 --- a/sdk/langchain/uv.lock +++ /dev/null @@ -1,2439 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.9" -resolution-markers = [ - "python_full_version >= '3.12.4'", - "python_full_version >= '3.10' and python_full_version < '3.12.4'", - "python_full_version < '3.10'", 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.../uipath_sdk/_cli/_utils/_parse_ast.py | 15 +- {sdk/core => src}/uipath_sdk/_cli/cli_auth.py | 2 +- .../uipath_sdk/_cli/cli_deploy.py | 0 {sdk/core => src}/uipath_sdk/_cli/cli_init.py | 0 {sdk/core => src}/uipath_sdk/_cli/cli_new.py | 0 {sdk/core => src}/uipath_sdk/_cli/cli_pack.py | 0 .../uipath_sdk/_cli/cli_publish.py | 0 {sdk/core => src}/uipath_sdk/_cli/cli_run.py | 4 +- .../uipath_sdk/_cli/middlewares.py | 6 +- {sdk/core => src}/uipath_sdk/_config.py | 0 .../uipath_sdk/_execution_context.py | 0 .../uipath_sdk/_folder_context.py | 0 .../uipath_sdk/_models/__init__.py | 0 .../uipath_sdk/_models/action_schema.py | 0 .../uipath_sdk/_models/actions.py | 0 .../core => src}/uipath_sdk/_models/assets.py | 0 .../uipath_sdk/_models/connections.py | 0 .../uipath_sdk/_models/context_grounding.py | 0 .../uipath_sdk/_models/interrupt_models.py | 0 {sdk/core => src}/uipath_sdk/_models/job.py | 0 .../uipath_sdk/_models/llm_gateway.py | 202 ++-- .../uipath_sdk/_models/processes.py | 0 .../core => src}/uipath_sdk/_models/queues.py | 0 .../uipath_sdk/_services/__init__.py | 0 .../uipath_sdk/_services/_base_service.py | 0 .../uipath_sdk/_services/actions_service.py | 0 .../uipath_sdk/_services/api_client.py | 0 .../uipath_sdk/_services/assets_service.py | 0 .../uipath_sdk/_services/buckets_service.py | 0 .../_services/connections_service.py | 0 .../_services/connections_service.pyi | 0 .../_services/context_grounding_service.py | 0 .../uipath_sdk/_services/jobs_service.py | 0 .../_services/llm_gateway_service.py | 0 .../uipath_sdk/_services/processes_service.py | 0 .../uipath_sdk/_services/queues_service.py | 0 {sdk/core => src}/uipath_sdk/_uipath_sdk.py | 0 .../uipath_sdk/_utils/__init__.py | 0 .../uipath_sdk/_utils/_endpoint.py | 0 .../uipath_sdk/_utils/_infer_bindings.py | 0 {sdk/core => src}/uipath_sdk/_utils/_logs.py | 0 .../uipath_sdk/_utils/_request_override.py | 0 .../uipath_sdk/_utils/_request_spec.py | 0 .../uipath_sdk/_utils/_user_agent.py | 0 .../uipath_sdk/_utils/constants.py | 0 {sdk/core => src}/uipath_sdk/py.typed | 0 {sdk/core/tests => tests}/__init__.py | 0 {sdk/core/tests => tests}/cli/test_init.py | 0 {sdk/core/tests => tests}/conftest.py | 0 .../sdk/services/test_llm_integration.py | 266 ++--- .../sdk/services/test_llm_service.py | 282 ++--- .../services/test_uipath_llm_integration.py | 1022 ++++++++--------- {sdk/core/tests => tests}/sdk/test_config.py | 2 +- sdk/core/uv.lock => uv.lock | 2 +- 100 files changed, 978 insertions(+), 1193 deletions(-) create mode 100644 .github/workflows/cd.yml create mode 100644 .github/workflows/ci.yml delete mode 100644 .github/workflows/core_ci.yml rename sdk/core/.pre-commit-config.yaml => .pre-commit-config.yaml (100%) rename {sdk/core/docs => docs}/assets.md (100%) rename {sdk/core/docs => docs}/assets/uipath-logo.svg (100%) rename {sdk/core/docs => docs}/buckets.md (100%) rename {sdk/core/docs => docs}/connections.md (100%) rename {sdk/core/docs => docs}/context_grounding.md (100%) rename {sdk/core/docs => docs}/index.md (100%) rename {sdk/core/docs => docs}/jobs.md (100%) rename {sdk/core/docs => docs}/processes.md (100%) rename {sdk/core/docs => docs}/queues.md (100%) rename sdk/core/justfile => justfile (100%) rename sdk/core/mkdocs.yml => mkdocs.yml (100%) rename sdk/core/py.typed => py.typed (100%) rename sdk/core/pyproject.toml => pyproject.toml (96%) delete mode 100644 sdk/core/README.md rename {sdk/core => src}/uipath_sdk/__init__.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/README.md (100%) rename {sdk/core => src}/uipath_sdk/_cli/__init__.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/_auth_server.py (98%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/_models.py (72%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/_oidc_utils.py (99%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/_portal_service.py (98%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/_utils.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/auth_config.json (100%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/index.html (100%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/localhost.crt (100%) rename {sdk/core => src}/uipath_sdk/_cli/_auth/localhost.key (100%) rename {sdk/core => src}/uipath_sdk/_cli/_runtime/_contracts.py (95%) rename {sdk/core => src}/uipath_sdk/_cli/_runtime/_logging.py (93%) rename {sdk/core => src}/uipath_sdk/_cli/_runtime/_runtime.py (97%) rename {sdk/core => src}/uipath_sdk/_cli/_templates/.psmdcp.template (100%) rename {sdk/core => src}/uipath_sdk/_cli/_templates/.rels.template (100%) rename {sdk/core => src}/uipath_sdk/_cli/_templates/[Content_Types].xml.template (100%) rename {sdk/core => src}/uipath_sdk/_cli/_templates/main.py.template (100%) rename {sdk/core => src}/uipath_sdk/_cli/_templates/package.nuspec.template (100%) rename {sdk/core => src}/uipath_sdk/_cli/_utils/_common.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/_utils/_input_args.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/_utils/_parse_ast.py (98%) rename {sdk/core => src}/uipath_sdk/_cli/cli_auth.py (97%) rename {sdk/core => src}/uipath_sdk/_cli/cli_deploy.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/cli_init.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/cli_new.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/cli_pack.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/cli_publish.py (100%) rename {sdk/core => src}/uipath_sdk/_cli/cli_run.py (98%) rename {sdk/core => src}/uipath_sdk/_cli/middlewares.py (95%) rename {sdk/core => src}/uipath_sdk/_config.py (100%) rename {sdk/core => src}/uipath_sdk/_execution_context.py (100%) rename {sdk/core => src}/uipath_sdk/_folder_context.py (100%) rename {sdk/core => src}/uipath_sdk/_models/__init__.py (100%) rename {sdk/core => src}/uipath_sdk/_models/action_schema.py (100%) rename {sdk/core => src}/uipath_sdk/_models/actions.py (100%) rename {sdk/core => src}/uipath_sdk/_models/assets.py (100%) rename {sdk/core => src}/uipath_sdk/_models/connections.py (100%) rename {sdk/core => src}/uipath_sdk/_models/context_grounding.py (100%) rename {sdk/core => src}/uipath_sdk/_models/interrupt_models.py (100%) rename {sdk/core => src}/uipath_sdk/_models/job.py (100%) rename {sdk/core => src}/uipath_sdk/_models/llm_gateway.py (95%) rename {sdk/core => src}/uipath_sdk/_models/processes.py (100%) rename {sdk/core => src}/uipath_sdk/_models/queues.py (100%) rename {sdk/core => src}/uipath_sdk/_services/__init__.py (100%) rename {sdk/core => src}/uipath_sdk/_services/_base_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/actions_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/api_client.py (100%) rename {sdk/core => src}/uipath_sdk/_services/assets_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/buckets_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/connections_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/connections_service.pyi (100%) rename {sdk/core => src}/uipath_sdk/_services/context_grounding_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/jobs_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/llm_gateway_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/processes_service.py (100%) rename {sdk/core => src}/uipath_sdk/_services/queues_service.py (100%) rename {sdk/core => src}/uipath_sdk/_uipath_sdk.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/__init__.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/_endpoint.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/_infer_bindings.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/_logs.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/_request_override.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/_request_spec.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/_user_agent.py (100%) rename {sdk/core => src}/uipath_sdk/_utils/constants.py (100%) rename {sdk/core => src}/uipath_sdk/py.typed (100%) rename {sdk/core/tests => tests}/__init__.py (100%) rename {sdk/core/tests => tests}/cli/test_init.py (100%) rename {sdk/core/tests => tests}/conftest.py (100%) rename {sdk/core/tests => tests}/sdk/services/test_llm_integration.py (97%) rename {sdk/core/tests => tests}/sdk/services/test_llm_service.py (97%) rename {sdk/core/tests => tests}/sdk/services/test_uipath_llm_integration.py (97%) rename {sdk/core/tests => tests}/sdk/test_config.py (97%) rename sdk/core/uv.lock => uv.lock (99%) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index a0b13e4f..b922d8e3 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -5,21 +5,12 @@ on: secrets: token: required: true - inputs: - folder_path: - description: 'The folder path to run actions in' - required: true - type: string jobs: build: name: Build and publish if: github.ref == 'refs/heads/main' runs-on: "ubuntu-24.04" - defaults: - run: - working-directory: ${{ inputs.folder_path }} - steps: - uses: actions/checkout@v4 @@ -39,7 +30,17 @@ jobs: run: | uv build + - name: Check if version in pyproject.toml was modified + id: check_version + run: | + if git diff --name-only ${{ github.sha }} ${{ github.event.before }} | grep -q 'pyproject.toml'; then + echo "modified=true" >> $GITHUB_OUTPUT + else + echo "modified=false" >> $GITHUB_OUTPUT + fi + - name: "Publish" + if: ${{ steps.check_version.outputs.modified }} == 'true' run: | uv publish env: diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml new file mode 100644 index 00000000..b27097bc --- /dev/null +++ b/.github/workflows/cd.yml @@ -0,0 +1,13 @@ +name: CD + +on: + workflow_dispatch: {} + push: + branches: + - main + +jobs: + build: + uses: ./.github/workflows/build.yml + secrets: + token: ${{ secrets.PYPI_TOKEN }} \ No newline at end of file diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 00000000..c396fc78 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,21 @@ +name: Core CI + +on: + workflow_dispatch: {} + push: + branches: + - main + paths: + - "src/uipath_sdk/**" + pull_request: + branches: + - main + paths: + - "src/uipath_sdk/**" + +jobs: + lint: + uses: ./.github/workflows/lint.yml + + test: + uses: ./.github/workflows/test.yml diff --git a/.github/workflows/core_ci.yml b/.github/workflows/core_ci.yml deleted file mode 100644 index af4737cd..00000000 --- a/.github/workflows/core_ci.yml +++ /dev/null @@ -1,37 +0,0 @@ -name: Core CI - -on: - workflow_dispatch: {} - push: - branches: - - main - paths: - - "sdk/core/**" - pull_request: - branches: - - main - paths: - - "sdk/core/**" - -defaults: - run: - working-directory: "sdk/core" - -jobs: - lint: - uses: ./.github/workflows/lint.yml - with: - folder_path: "sdk/core" - - test: - uses: ./.github/workflows/test.yml - with: - folder_path: "sdk/core" - - build: - needs: [lint, test] - uses: ./.github/workflows/build.yml - with: - folder_path: "sdk/core" - secrets: - token: ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 35e0d172..b09f54e0 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -3,10 +3,6 @@ name: Reusable Lint Workflow on: workflow_call: inputs: - folder_path: - description: 'The folder path to run actions in' - required: true - type: string should_skip: description: 'Whether to skip the linting step' required: false @@ -18,9 +14,6 @@ jobs: name: Lint runs-on: "ubuntu-24.04" if: inputs.should_skip == false - defaults: - run: - working-directory: ${{ inputs.folder_path }} steps: diff --git a/.github/workflows/rest_ci.yml b/.github/workflows/rest_ci.yml index 0e7d9087..4180b31d 100644 --- a/.github/workflows/rest_ci.yml +++ b/.github/workflows/rest_ci.yml @@ -1,4 +1,4 @@ -name: Core CI +name: Dumb CI on: workflow_dispatch: {} @@ -6,22 +6,20 @@ on: branches: - main paths-ignore: - - "sdk/**" + - "src/**" pull_request: branches: - main paths-ignore: - - "sdk/**" + - "src/**" jobs: lint: uses: ./.github/workflows/lint.yml with: should_skip: true - folder_path: "." test: uses: ./.github/workflows/test.yml with: - should_skip: true - folder_path: "." \ No newline at end of file + should_skip: true \ No newline at end of file diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 51a8fa55..c3535dfc 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -3,11 +3,6 @@ name: Reusable Test Workflow on: workflow_call: inputs: - - folder_path: - description: 'The folder path to run actions in' - required: true - type: string should_skip: description: 'Whether to skip the linting step' required: false @@ -19,9 +14,6 @@ jobs: name: Test runs-on: "ubuntu-24.04" if: inputs.should_skip == false - defaults: - run: - working-directory: ${{ inputs.folder_path }} steps: - uses: actions/checkout@v4 diff --git a/sdk/core/.pre-commit-config.yaml b/.pre-commit-config.yaml similarity index 100% rename from sdk/core/.pre-commit-config.yaml rename to .pre-commit-config.yaml diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 888cadb8..afb65b6f 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -34,7 +34,7 @@ See `just --list` for linting, formatting and build commands. ### Use SDK Locally 1. Create a folder on your own device `mkdir project; cd project` 2. Initialize the python project `uv` `uv init . --python 3.9` -3. Obtain the project path `PATH_TO_SDK=/Users/YOU_USER/platform-sdk/sdk/core` +3. Obtain the project path `PATH_TO_SDK=/Users/YOU_USER/uipath-python` 4. Install the sdk in editable mode `uv add --editable ${PATH_TO_SDK}` :information_source: Instead of cloning the project into `.venv/lib/python3.9/site-packages/uipath_sdk`, this mode creates a file named `_uipath_sdk.pth` inside `.venv/lib/python3.9/site-packages`. This file contains the value of `PATH_TO_SDK`, which is added to `sys.path`—the list of directories where python searches for packages. (Run `python -c 'import sys; print(sys.path)'` to see the entries.) diff --git a/README.md b/README.md index 7ce308fa..c7b6020c 100644 --- a/README.md +++ b/README.md @@ -158,9 +158,3 @@ To properly use the CLI for packaging and publishing, your project should includ - A `uipath.json` file (generated by `uipath init`) - Any Python files needed for your automation -## Development - -### Setting Up a Development Environment - -Please read our [contribution guidelines](CONTRIBUTING.md) before submitting a pull request. - diff --git a/sdk/core/docs/assets.md b/docs/assets.md similarity index 100% rename from sdk/core/docs/assets.md rename to docs/assets.md diff --git a/sdk/core/docs/assets/uipath-logo.svg b/docs/assets/uipath-logo.svg similarity index 100% rename from sdk/core/docs/assets/uipath-logo.svg rename to docs/assets/uipath-logo.svg diff --git a/sdk/core/docs/buckets.md b/docs/buckets.md similarity index 100% rename from sdk/core/docs/buckets.md rename to docs/buckets.md diff --git a/sdk/core/docs/connections.md b/docs/connections.md similarity index 100% rename from sdk/core/docs/connections.md rename to docs/connections.md diff --git a/sdk/core/docs/context_grounding.md b/docs/context_grounding.md similarity index 100% rename from sdk/core/docs/context_grounding.md rename to docs/context_grounding.md diff --git a/sdk/core/docs/index.md b/docs/index.md similarity index 100% rename from sdk/core/docs/index.md rename to docs/index.md diff --git a/sdk/core/docs/jobs.md b/docs/jobs.md similarity index 100% rename from sdk/core/docs/jobs.md rename to docs/jobs.md diff --git a/sdk/core/docs/processes.md b/docs/processes.md similarity index 100% rename from sdk/core/docs/processes.md rename to docs/processes.md diff --git a/sdk/core/docs/queues.md b/docs/queues.md similarity index 100% rename from sdk/core/docs/queues.md rename to docs/queues.md diff --git a/sdk/core/justfile b/justfile similarity index 100% rename from sdk/core/justfile rename to justfile diff --git a/sdk/core/mkdocs.yml b/mkdocs.yml similarity index 100% rename from sdk/core/mkdocs.yml rename to mkdocs.yml diff --git a/sdk/core/py.typed b/py.typed similarity index 100% rename from sdk/core/py.typed rename to py.typed diff --git a/sdk/core/pyproject.toml b/pyproject.toml similarity index 96% rename from sdk/core/pyproject.toml rename to pyproject.toml index 33b9819a..0a944c7a 100644 --- a/sdk/core/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "uipath-sdk" -version = "0.0.112" +version = "0.0.113" description = "UiPath SDK" readme = { file = "README.md", content-type = "text/markdown" } requires-python = ">=3.9" @@ -41,10 +41,10 @@ requires = ["hatchling"] build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] -packages = ["uipath_sdk"] +packages = ["src/uipath_sdk"] [tool.hatch.build.targets.sdist] -packages = ["uipath_sdk"] +packages = ["src/uipath_sdk"] [dependency-groups] dev = [ diff --git a/sdk/core/README.md b/sdk/core/README.md deleted file mode 100644 index c7b6020c..00000000 --- a/sdk/core/README.md +++ /dev/null @@ -1,160 +0,0 @@ -# UiPath Python SDK - -A Python SDK that enables programmatic interaction with UiPath Platform services including processes, assets, buckets, context grounding, data services, jobs, and more. The package also features a CLI for creation, packaging, and deployment of automations to UiPath Platform. - -## Table of Contents - -- [Installation](#installation) -- [Configuration](#configuration) - - [Environment Variables](#environment-variables) -- [Basic Usage](#basic-usage) -- [Available Services](#available-services) -- [Examples](#examples) - - [Buckets Service](#buckets-service) - - [Context Grounding Service](#context-grounding-service) -- [Command Line Interface (CLI)](#command-line-interface-cli) - - [Authentication](#authentication) - - [Initialize a Project](#initialize-a-project) - - [Debug a Project](#debug-a-project) - - [Package a Project](#package-a-project) - - [Publish a Package](#publish-a-package) -- [Project Structure](#project-structure) -- [Development](#development) - - [Setting Up a Development Environment](#setting-up-a-development-environment) - -## Installation - -```bash -pip install uipath-sdk -``` - -using `uv`: - -```bash -uv add uipath-sdk -``` - -## Configuration - -### Environment Variables - -Create a `.env` file in your project root with the following variables: - -``` -UIPATH_URL=https://cloud.uipath.com/ACCOUNT_NAME/TENANT_NAME -UIPATH_ACCESS_TOKEN=YOUR_TOKEN_HERE -``` - -## Basic Usage - -```python -from uipath_sdk import UiPathSDK -# Initialize the SDK -sdk = UiPathSDK() -# Execute a process -job = sdk.processes.invoke( - name="MyProcess", - input_arguments={"param1": "value1", "param2": 42} -) -# Work with assets -asset = sdk.assets.retrieve(name="MyAsset") -``` - -## Available Services - -The SDK provides access to various UiPath services: -- `sdk.processes` - Manage and execute UiPath automation processes -- `sdk.assets` - Work with assets (variables, credentials) stored in UiPath -- `sdk.buckets` - Manage cloud storage containers for automation files -- `sdk.connections` - Handle connections to external systems -- `sdk.context_grounding` - Work with semantic contexts for AI-enabled automation -- `sdk.jobs` - Monitor and manage automation jobs -- `sdk.queues` - Work with transaction queues -- `sdk.actions` - Work with Action Center -- `sdk.api_client` - Direct access to the API client for custom requests - -## Examples - -### Buckets Service - -```python -# Download a file from a bucket -sdk.buckets.download( - bucket_key="my-bucket", - blob_file_path="path/to/file.xlsx", - destination_path="local/path/file.xlsx" -) -``` - -### Context Grounding Service - -```python -# Search for contextual information -results = sdk.context_grounding.search( - name="my-knowledge-index", - query="How do I process an invoice?", - number_of_results=5 -) -``` - -## Command Line Interface (CLI) - -The SDK also provides a command-line interface for creating, packaging, and deploying automations: - -### Authentication - -```bash -uipath auth -``` - -This command opens a browser for authentication and creates/updates your `.env` file with the proper credentials. - -### Initialize a Project - -```bash -uipath init [ENTRYPOINT] -``` - -Creates a `uipath.json` configuration file for your project. If the entrypoint is not provided, it will try to find a single Python file in the current directory. - -### Debug a Project - -```bash -uipath run ENTRYPOINT [INPUT] -``` - -Executes a Python script with the provided JSON input arguments. - -### Package a Project - -```bash -uipath pack -``` - -Packages your project into a `.nupkg` file that can be deployed to UiPath. - -**Note:** Your `pyproject.toml` must include: -- A description field (avoid characters: &, <, >, ", ', ;) -- Author information - -Example: -```toml -description = "Your package description" -authors = [{name = "Your Name", email = "your.email@example.com"}] -``` - -### Publish a Package - -```bash -uipath publish -``` - -Publishes the most recently created package to your UiPath Orchestrator. - -## Project Structure - -To properly use the CLI for packaging and publishing, your project should include: -- A `pyproject.toml` file with project metadata -- A `uipath.json` file (generated by `uipath init`) -- Any Python files needed for your automation - diff --git a/sdk/core/uipath_sdk/__init__.py b/src/uipath_sdk/__init__.py similarity index 100% rename from sdk/core/uipath_sdk/__init__.py rename to src/uipath_sdk/__init__.py diff --git a/sdk/core/uipath_sdk/_cli/README.md b/src/uipath_sdk/_cli/README.md similarity index 100% rename from sdk/core/uipath_sdk/_cli/README.md rename to src/uipath_sdk/_cli/README.md diff --git a/sdk/core/uipath_sdk/_cli/__init__.py b/src/uipath_sdk/_cli/__init__.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/__init__.py rename to src/uipath_sdk/_cli/__init__.py diff --git a/sdk/core/uipath_sdk/_cli/_auth/_auth_server.py b/src/uipath_sdk/_cli/_auth/_auth_server.py similarity index 98% rename from sdk/core/uipath_sdk/_cli/_auth/_auth_server.py rename to src/uipath_sdk/_cli/_auth/_auth_server.py index cd2c5012..4a8325ef 100644 --- a/sdk/core/uipath_sdk/_cli/_auth/_auth_server.py +++ b/src/uipath_sdk/_cli/_auth/_auth_server.py @@ -33,7 +33,7 @@ def log_message(self, format, *args) -> None: pass def do_POST(self): - """Handle POST requests to /set_token""" + """Handle POST requests to /set_token.""" if self.path == "/set_token": content_length = int(self.headers["Content-Length"]) post_data = self.rfile.read(content_length) @@ -71,7 +71,7 @@ def do_POST(self): self.send_error(404, "Path not found") def do_GET(self): - """Handle GET requests by serving index.html""" + """Handle GET requests by serving index.html.""" # Always serve index.html regardless of the path try: index_path = os.path.join(os.path.dirname(__file__), "index.html") diff --git a/sdk/core/uipath_sdk/_cli/_auth/_models.py b/src/uipath_sdk/_cli/_auth/_models.py similarity index 72% rename from sdk/core/uipath_sdk/_cli/_auth/_models.py rename to src/uipath_sdk/_cli/_auth/_models.py index df47f5fe..32fc45df 100644 --- a/sdk/core/uipath_sdk/_cli/_auth/_models.py +++ b/src/uipath_sdk/_cli/_auth/_models.py @@ -2,7 +2,7 @@ class AuthConfig(TypedDict): - """TypedDict for auth_config.json structure""" + """TypedDict for auth_config.json structure.""" client_id: str port: int @@ -11,7 +11,7 @@ class AuthConfig(TypedDict): class TokenData(TypedDict): - """TypedDict for token data structure""" + """TypedDict for token data structure.""" access_token: str refresh_token: str @@ -22,7 +22,7 @@ class TokenData(TypedDict): class AccessTokenData(TypedDict): - """TypedDict for access token data structure""" + """TypedDict for access token data structure.""" sub: str prt_id: str @@ -31,21 +31,21 @@ class AccessTokenData(TypedDict): class TenantInfo(TypedDict): - """TypedDict for tenant info structure""" + """TypedDict for tenant info structure.""" name: str id: str class OrganizationInfo(TypedDict): - """TypedDict for organization info structure""" + """TypedDict for organization info structure.""" id: str name: str class TenantsAndOrganizationInfoResponse(TypedDict): - """TypedDict for tenants and organization info response structure""" + """TypedDict for tenants and organization info response structure.""" tenants: list[TenantInfo] organization: OrganizationInfo diff --git a/sdk/core/uipath_sdk/_cli/_auth/_oidc_utils.py b/src/uipath_sdk/_cli/_auth/_oidc_utils.py similarity index 99% rename from sdk/core/uipath_sdk/_cli/_auth/_oidc_utils.py rename to src/uipath_sdk/_cli/_auth/_oidc_utils.py index efc71a92..0d6934a4 100644 --- a/sdk/core/uipath_sdk/_cli/_auth/_oidc_utils.py +++ b/src/uipath_sdk/_cli/_auth/_oidc_utils.py @@ -9,7 +9,6 @@ def generate_code_verifier_and_challenge(): """Generate PKCE code verifier and challenge.""" - code_verifier = base64.urlsafe_b64encode(os.urandom(32)).decode("utf-8").rstrip("=") code_challenge_bytes = hashlib.sha256(code_verifier.encode("utf-8")).digest() diff --git a/sdk/core/uipath_sdk/_cli/_auth/_portal_service.py b/src/uipath_sdk/_cli/_auth/_portal_service.py similarity index 98% rename from sdk/core/uipath_sdk/_cli/_auth/_portal_service.py rename to src/uipath_sdk/_cli/_auth/_portal_service.py index dc705134..e2ff2304 100644 --- a/sdk/core/uipath_sdk/_cli/_auth/_portal_service.py +++ b/src/uipath_sdk/_cli/_auth/_portal_service.py @@ -16,7 +16,7 @@ class PortalService: - """Service for interacting with the UiPath Portal API""" + """Service for interacting with the UiPath Portal API.""" access_token: Optional[str] = None prt_id: Optional[str] = None diff --git a/sdk/core/uipath_sdk/_cli/_auth/_utils.py b/src/uipath_sdk/_cli/_auth/_utils.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/_auth/_utils.py rename to src/uipath_sdk/_cli/_auth/_utils.py diff --git a/sdk/core/uipath_sdk/_cli/_auth/auth_config.json b/src/uipath_sdk/_cli/_auth/auth_config.json similarity index 100% rename from sdk/core/uipath_sdk/_cli/_auth/auth_config.json rename to src/uipath_sdk/_cli/_auth/auth_config.json diff --git a/sdk/core/uipath_sdk/_cli/_auth/index.html b/src/uipath_sdk/_cli/_auth/index.html similarity index 100% rename from sdk/core/uipath_sdk/_cli/_auth/index.html rename to src/uipath_sdk/_cli/_auth/index.html diff --git a/sdk/core/uipath_sdk/_cli/_auth/localhost.crt b/src/uipath_sdk/_cli/_auth/localhost.crt similarity index 100% rename from sdk/core/uipath_sdk/_cli/_auth/localhost.crt rename to src/uipath_sdk/_cli/_auth/localhost.crt diff --git a/sdk/core/uipath_sdk/_cli/_auth/localhost.key b/src/uipath_sdk/_cli/_auth/localhost.key similarity index 100% rename from sdk/core/uipath_sdk/_cli/_auth/localhost.key rename to src/uipath_sdk/_cli/_auth/localhost.key diff --git a/sdk/core/uipath_sdk/_cli/_runtime/_contracts.py b/src/uipath_sdk/_cli/_runtime/_contracts.py similarity index 95% rename from sdk/core/uipath_sdk/_cli/_runtime/_contracts.py rename to src/uipath_sdk/_cli/_runtime/_contracts.py index b6d9f7e1..5af84cea 100644 --- a/sdk/core/uipath_sdk/_cli/_runtime/_contracts.py +++ b/src/uipath_sdk/_cli/_runtime/_contracts.py @@ -1,6 +1,4 @@ -""" -Core runtime contracts that define the interfaces between components. -""" +"""Core runtime contracts that define the interfaces between components.""" import json import logging @@ -20,9 +18,7 @@ class UiPathResumeTriggerType(str, Enum): - """ - Constants representing different types of resume job triggers in the system. - """ + """Constants representing different types of resume job triggers in the system.""" NONE = "None" QUEUE_ITEM = "QueueItem" @@ -161,8 +157,7 @@ class UiPathRuntimeContext(BaseModel): @classmethod def from_config(cls, config_path=None): - """ - Load configuration from uipath.json file. + """Load configuration from uipath.json file. Args: config_path: Path to the configuration file. If None, uses the default "uipath.json" @@ -268,8 +263,8 @@ def as_dict(self) -> Dict[str, Any]: class UiPathBaseRuntime(ABC): - """ - Base runtime class implementing the async context manager protocol. + """Base runtime class implementing the async context manager protocol. + This allows using the class with 'async with' statements. """ @@ -278,8 +273,7 @@ def __init__(self, context: UiPathRuntimeContext): @classmethod def from_context(cls, context: UiPathRuntimeContext): - """ - Factory method to create a runtime instance from a context. + """Factory method to create a runtime instance from a context. Args: context: The runtime context with configuration @@ -291,8 +285,8 @@ def from_context(cls, context: UiPathRuntimeContext): return runtime async def __aenter__(self): - """ - Async enter method called when entering the 'async with' block. + """Async enter method called when entering the 'async with' block. + Initializes and prepares the runtime environment. Returns: @@ -313,8 +307,7 @@ async def __aenter__(self): @abstractmethod async def execute(self) -> Optional[UiPathRuntimeResult]: - """ - Execute with the provided context. + """Execute with the provided context. Returns: Dictionary with execution results @@ -335,8 +328,8 @@ async def cleanup(self): pass async def __aexit__(self, exc_type, exc_val, exc_tb): - """ - Async exit method called when exiting the 'async with' block. + """Async exit method called when exiting the 'async with' block. + Cleans up resources and handles any exceptions. Always writes output file regardless of whether execution was successful, diff --git a/sdk/core/uipath_sdk/_cli/_runtime/_logging.py b/src/uipath_sdk/_cli/_runtime/_logging.py similarity index 93% rename from sdk/core/uipath_sdk/_cli/_runtime/_logging.py rename to src/uipath_sdk/_cli/_runtime/_logging.py index 294c5530..63c430d9 100644 --- a/sdk/core/uipath_sdk/_cli/_runtime/_logging.py +++ b/src/uipath_sdk/_cli/_runtime/_logging.py @@ -5,13 +5,10 @@ class PersistentLogsHandler(logging.FileHandler): - """ - A simple log handler that always writes to a single file without rotation. - """ + """A simple log handler that always writes to a single file without rotation.""" def __init__(self, file: str): - """ - Initialize the handler to write logs to a single file, appending always. + """Initialize the handler to write logs to a single file, appending always. Args: file (str): The file where logs should be stored. @@ -24,10 +21,7 @@ def __init__(self, file: str): class LogsInterceptor: - """ - Intercepts all logging and stdout/stderr, routing to either persistent log files or stdout - based on whether it's running as a job or not. - """ + """Intercepts all logging and stdout/stderr, routing to either persistent log files or stdout based on whether it's running as a job or not.""" def __init__( self, @@ -36,8 +30,7 @@ def __init__( file: Optional[str] = "execution.log", job_id: Optional[str] = None, ): - """ - Initialize the log interceptor. + """Initialize the log interceptor. Args: min_level: Minimum logging level to capture. @@ -95,9 +88,7 @@ def _clean_all_handlers(self, logger: logging.Logger) -> None: logger.addHandler(self.log_handler) def setup(self) -> None: - """ - Configure logging to use our persistent handler. - """ + """Configure logging to use our persistent handler.""" # Use global disable to prevent all logging below our minimum level if self.numeric_min_level > logging.NOTSET: logging.disable(self.numeric_min_level - 1) diff --git a/sdk/core/uipath_sdk/_cli/_runtime/_runtime.py b/src/uipath_sdk/_cli/_runtime/_runtime.py similarity index 97% rename from sdk/core/uipath_sdk/_cli/_runtime/_runtime.py rename to src/uipath_sdk/_cli/_runtime/_runtime.py index d5798d9d..e4c3f147 100644 --- a/sdk/core/uipath_sdk/_cli/_runtime/_runtime.py +++ b/src/uipath_sdk/_cli/_runtime/_runtime.py @@ -1,6 +1,4 @@ -""" -Python script runtime implementation for executing and managing python scripts. -""" +"""Python script runtime implementation for executing and managing python scripts.""" import importlib.util import inspect @@ -24,13 +22,10 @@ class UiPathRuntime(UiPathBaseRuntime): - """ - Runtime for executing Python scripts. - """ + """Runtime for executing Python scripts.""" async def execute(self) -> Optional[UiPathRuntimeResult]: - """ - Execute the Python script with the provided input and configuration. + """Execute the Python script with the provided input and configuration. Returns: Dictionary with execution results @@ -105,7 +100,6 @@ async def cleanup(self) -> None: def _execute_python_script(self, script_path: str, input_data: Any) -> Any: """Execute the Python script with the given input.""" - spec = importlib.util.spec_from_file_location("dynamic_module", script_path) if not spec or not spec.loader: raise UiPathRuntimeError( diff --git a/sdk/core/uipath_sdk/_cli/_templates/.psmdcp.template b/src/uipath_sdk/_cli/_templates/.psmdcp.template similarity index 100% rename from sdk/core/uipath_sdk/_cli/_templates/.psmdcp.template rename to src/uipath_sdk/_cli/_templates/.psmdcp.template diff --git a/sdk/core/uipath_sdk/_cli/_templates/.rels.template b/src/uipath_sdk/_cli/_templates/.rels.template similarity index 100% rename from sdk/core/uipath_sdk/_cli/_templates/.rels.template rename to src/uipath_sdk/_cli/_templates/.rels.template diff --git a/sdk/core/uipath_sdk/_cli/_templates/[Content_Types].xml.template b/src/uipath_sdk/_cli/_templates/[Content_Types].xml.template similarity index 100% rename from sdk/core/uipath_sdk/_cli/_templates/[Content_Types].xml.template rename to src/uipath_sdk/_cli/_templates/[Content_Types].xml.template diff --git a/sdk/core/uipath_sdk/_cli/_templates/main.py.template b/src/uipath_sdk/_cli/_templates/main.py.template similarity index 100% rename from sdk/core/uipath_sdk/_cli/_templates/main.py.template rename to src/uipath_sdk/_cli/_templates/main.py.template diff --git a/sdk/core/uipath_sdk/_cli/_templates/package.nuspec.template b/src/uipath_sdk/_cli/_templates/package.nuspec.template similarity index 100% rename from sdk/core/uipath_sdk/_cli/_templates/package.nuspec.template rename to src/uipath_sdk/_cli/_templates/package.nuspec.template diff --git a/sdk/core/uipath_sdk/_cli/_utils/_common.py b/src/uipath_sdk/_cli/_utils/_common.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/_utils/_common.py rename to src/uipath_sdk/_cli/_utils/_common.py diff --git a/sdk/core/uipath_sdk/_cli/_utils/_input_args.py b/src/uipath_sdk/_cli/_utils/_input_args.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/_utils/_input_args.py rename to src/uipath_sdk/_cli/_utils/_input_args.py diff --git a/sdk/core/uipath_sdk/_cli/_utils/_parse_ast.py b/src/uipath_sdk/_cli/_utils/_parse_ast.py similarity index 98% rename from sdk/core/uipath_sdk/_cli/_utils/_parse_ast.py rename to src/uipath_sdk/_cli/_utils/_parse_ast.py index 8b30eb5a..2f6dc8ee 100644 --- a/sdk/core/uipath_sdk/_cli/_utils/_parse_ast.py +++ b/src/uipath_sdk/_cli/_utils/_parse_ast.py @@ -147,8 +147,7 @@ def extract_parameter( param_name: str, position_index: Optional[int] = None, ) -> Optional[Any]: - """ - Extract a parameter from a method call, checking both keyword and positional arguments. + """Extract a parameter from a method call, checking both keyword and positional arguments. Args: method_call: The ServiceMethodCall object @@ -170,8 +169,7 @@ def extract_parameter( def parse_local_module( module_path: str, base_dir: str ) -> Dict[str, List[Dict[str, str]]]: - """ - Parse a local module and extract SDK usage. + """Parse a local module and extract SDK usage. Args: module_path: Import path of the module (e.g., 'myapp.utils') @@ -410,8 +408,7 @@ def get_results(self) -> Dict[str, List[Dict[str, str]]]: def parse_sdk_usage( source_code: str, base_dir: str = "" ) -> Dict[str, List[Dict[str, str]]]: - """ - Parse the source code and return UiPathSDK usage information. + """Parse the source code and return UiPathSDK usage information. Args: source_code: The Python source code to analyze @@ -440,8 +437,7 @@ def parse_sdk_usage( def convert_to_bindings_format(sdk_usage_data): - """ - Convert the output of parse_sdk_usage to a structure similar to bindings_v2.json + """Convert the output of parse_sdk_usage to a structure similar to bindings_v2.json. Args: sdk_usage_data: Dictionary output from parse_sdk_usage @@ -524,8 +520,7 @@ def convert_to_bindings_format(sdk_usage_data): def generate_bindings_json(file_path: str) -> str: - """ - Generate bindings JSON from a Python file. + """Generate bindings JSON from a Python file. Args: file_path: Path to the Python file to analyze diff --git a/sdk/core/uipath_sdk/_cli/cli_auth.py b/src/uipath_sdk/_cli/cli_auth.py similarity index 97% rename from sdk/core/uipath_sdk/_cli/cli_auth.py rename to src/uipath_sdk/_cli/cli_auth.py index 25695f1e..bb373a64 100644 --- a/sdk/core/uipath_sdk/_cli/cli_auth.py +++ b/src/uipath_sdk/_cli/cli_auth.py @@ -17,7 +17,7 @@ @click.command() @environment_options def auth(domain="alpha"): - """Authenticate with UiPath Cloud Platform""" + """Authenticate with UiPath Cloud Platform.""" portal_service = PortalService(domain) if ( os.getenv("UIPATH_URL") diff --git a/sdk/core/uipath_sdk/_cli/cli_deploy.py b/src/uipath_sdk/_cli/cli_deploy.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/cli_deploy.py rename to src/uipath_sdk/_cli/cli_deploy.py diff --git a/sdk/core/uipath_sdk/_cli/cli_init.py b/src/uipath_sdk/_cli/cli_init.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/cli_init.py rename to src/uipath_sdk/_cli/cli_init.py diff --git a/sdk/core/uipath_sdk/_cli/cli_new.py b/src/uipath_sdk/_cli/cli_new.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/cli_new.py rename to src/uipath_sdk/_cli/cli_new.py diff --git a/sdk/core/uipath_sdk/_cli/cli_pack.py b/src/uipath_sdk/_cli/cli_pack.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/cli_pack.py rename to src/uipath_sdk/_cli/cli_pack.py diff --git a/sdk/core/uipath_sdk/_cli/cli_publish.py b/src/uipath_sdk/_cli/cli_publish.py similarity index 100% rename from sdk/core/uipath_sdk/_cli/cli_publish.py rename to src/uipath_sdk/_cli/cli_publish.py diff --git a/sdk/core/uipath_sdk/_cli/cli_run.py b/src/uipath_sdk/_cli/cli_run.py similarity index 98% rename from sdk/core/uipath_sdk/_cli/cli_run.py rename to src/uipath_sdk/_cli/cli_run.py index 6b3b577f..1d7f659c 100644 --- a/sdk/core/uipath_sdk/_cli/cli_run.py +++ b/src/uipath_sdk/_cli/cli_run.py @@ -25,8 +25,7 @@ def python_run_middleware( entrypoint: Optional[str], input: Optional[str], resume: bool ) -> MiddlewareResult: - """ - Middleware to handle Python script execution. + """Middleware to handle Python script execution. Args: entrypoint: Path to the Python script to execute @@ -36,7 +35,6 @@ def python_run_middleware( Returns: MiddlewareResult with execution status and messages """ - if not entrypoint: return MiddlewareResult( should_continue=False, diff --git a/sdk/core/uipath_sdk/_cli/middlewares.py b/src/uipath_sdk/_cli/middlewares.py similarity index 95% rename from sdk/core/uipath_sdk/_cli/middlewares.py rename to src/uipath_sdk/_cli/middlewares.py index 5df73237..320f2da7 100644 --- a/sdk/core/uipath_sdk/_cli/middlewares.py +++ b/src/uipath_sdk/_cli/middlewares.py @@ -29,7 +29,7 @@ class Middlewares: @classmethod def register(cls, command: str, middleware: MiddlewareFunc) -> None: - """Register a middleware for a specific command""" + """Register a middleware for a specific command.""" if command not in cls._middlewares: cls._middlewares[command] = [] cls._middlewares[command].append(middleware) @@ -39,7 +39,7 @@ def register(cls, command: str, middleware: MiddlewareFunc) -> None: @classmethod def get(cls, command: str) -> List[MiddlewareFunc]: - """Get all middlewares for a specific command""" + """Get all middlewares for a specific command.""" return cls._middlewares.get(command, []) @classmethod @@ -74,7 +74,7 @@ def clear(cls, command: Optional[str] = None) -> None: @classmethod def load_plugins(cls) -> None: - """Load all middlewares registered via entry points""" + """Load all middlewares registered via entry points.""" if cls._plugins_loaded: return diff --git a/sdk/core/uipath_sdk/_config.py b/src/uipath_sdk/_config.py similarity index 100% rename from sdk/core/uipath_sdk/_config.py rename to src/uipath_sdk/_config.py diff --git a/sdk/core/uipath_sdk/_execution_context.py b/src/uipath_sdk/_execution_context.py similarity index 100% rename from sdk/core/uipath_sdk/_execution_context.py rename to src/uipath_sdk/_execution_context.py diff --git a/sdk/core/uipath_sdk/_folder_context.py b/src/uipath_sdk/_folder_context.py similarity index 100% rename from sdk/core/uipath_sdk/_folder_context.py rename to src/uipath_sdk/_folder_context.py diff --git a/sdk/core/uipath_sdk/_models/__init__.py b/src/uipath_sdk/_models/__init__.py similarity index 100% rename from sdk/core/uipath_sdk/_models/__init__.py rename to src/uipath_sdk/_models/__init__.py diff --git a/sdk/core/uipath_sdk/_models/action_schema.py b/src/uipath_sdk/_models/action_schema.py similarity index 100% rename from sdk/core/uipath_sdk/_models/action_schema.py rename to src/uipath_sdk/_models/action_schema.py diff --git a/sdk/core/uipath_sdk/_models/actions.py b/src/uipath_sdk/_models/actions.py similarity index 100% rename from sdk/core/uipath_sdk/_models/actions.py rename to src/uipath_sdk/_models/actions.py diff --git a/sdk/core/uipath_sdk/_models/assets.py b/src/uipath_sdk/_models/assets.py similarity index 100% rename from sdk/core/uipath_sdk/_models/assets.py rename to src/uipath_sdk/_models/assets.py diff --git a/sdk/core/uipath_sdk/_models/connections.py b/src/uipath_sdk/_models/connections.py similarity index 100% rename from sdk/core/uipath_sdk/_models/connections.py rename to src/uipath_sdk/_models/connections.py diff --git a/sdk/core/uipath_sdk/_models/context_grounding.py b/src/uipath_sdk/_models/context_grounding.py similarity index 100% rename from sdk/core/uipath_sdk/_models/context_grounding.py rename to src/uipath_sdk/_models/context_grounding.py diff --git a/sdk/core/uipath_sdk/_models/interrupt_models.py b/src/uipath_sdk/_models/interrupt_models.py similarity index 100% rename from sdk/core/uipath_sdk/_models/interrupt_models.py rename to src/uipath_sdk/_models/interrupt_models.py diff --git a/sdk/core/uipath_sdk/_models/job.py b/src/uipath_sdk/_models/job.py similarity index 100% rename from sdk/core/uipath_sdk/_models/job.py rename to src/uipath_sdk/_models/job.py diff --git a/sdk/core/uipath_sdk/_models/llm_gateway.py b/src/uipath_sdk/_models/llm_gateway.py similarity index 95% rename from sdk/core/uipath_sdk/_models/llm_gateway.py rename to src/uipath_sdk/_models/llm_gateway.py index 06fc1878..d30c4816 100644 --- a/sdk/core/uipath_sdk/_models/llm_gateway.py +++ b/src/uipath_sdk/_models/llm_gateway.py @@ -1,101 +1,101 @@ -from typing import Any, Dict, List, Literal, Optional, Union - -from pydantic import BaseModel - - -class EmbeddingItem(BaseModel): - embedding: List[float] - index: int - object: str - - -class EmbeddingUsage(BaseModel): - prompt_tokens: int - total_tokens: int - - -class TextEmbedding(BaseModel): - data: List[EmbeddingItem] - model: str - object: str - usage: EmbeddingUsage - - -class UsageInfo(BaseModel): - encoding: str - prompt_tokens: int - - -class ToolCall(BaseModel): - id: str - name: str - arguments: Dict[str, Any] - - -class ToolPropertyDefinition(BaseModel): - type: str - description: Optional[str] = None - enum: Optional[List[str]] = None - - -class ToolParametersDefinition(BaseModel): - type: str = "object" - properties: Dict[str, ToolPropertyDefinition] - required: Optional[List[str]] = None - - -class ToolFunctionDefinition(BaseModel): - name: str - description: Optional[str] = None - parameters: ToolParametersDefinition - - -class ToolDefinition(BaseModel): - type: Literal["function"] = "function" - function: ToolFunctionDefinition - - -class AutoToolChoice(BaseModel): - type: Literal["auto"] = "auto" - - -class RequiredToolChoice(BaseModel): - type: Literal["required"] = "required" - - -class SpecificToolChoice(BaseModel): - type: Literal["tool"] = "tool" - name: str - - -ToolChoice = Union[ - AutoToolChoice, RequiredToolChoice, SpecificToolChoice, Literal["auto", "none"] -] - - -class ChatMessage(BaseModel): - role: str - content: Optional[str] = None - tool_calls: Optional[List[ToolCall]] = None - - -class ChatCompletionChoice(BaseModel): - index: int - message: ChatMessage - finish_reason: str - - -class ChatCompletionUsage(BaseModel): - prompt_tokens: int - completion_tokens: int - total_tokens: int - cache_read_input_tokens: Optional[int] = None - - -class ChatCompletion(BaseModel): - id: str - object: str - created: int - model: str - choices: List[ChatCompletionChoice] - usage: ChatCompletionUsage +from typing import Any, Dict, List, Literal, Optional, Union + +from pydantic import BaseModel + + +class EmbeddingItem(BaseModel): + embedding: List[float] + index: int + object: str + + +class EmbeddingUsage(BaseModel): + prompt_tokens: int + total_tokens: int + + +class TextEmbedding(BaseModel): + data: List[EmbeddingItem] + model: str + object: str + usage: EmbeddingUsage + + +class UsageInfo(BaseModel): + encoding: str + prompt_tokens: int + + +class ToolCall(BaseModel): + id: str + name: str + arguments: Dict[str, Any] + + +class ToolPropertyDefinition(BaseModel): + type: str + description: Optional[str] = None + enum: Optional[List[str]] = None + + +class ToolParametersDefinition(BaseModel): + type: str = "object" + properties: Dict[str, ToolPropertyDefinition] + required: Optional[List[str]] = None + + +class ToolFunctionDefinition(BaseModel): + name: str + description: Optional[str] = None + parameters: ToolParametersDefinition + + +class ToolDefinition(BaseModel): + type: Literal["function"] = "function" + function: ToolFunctionDefinition + + +class AutoToolChoice(BaseModel): + type: Literal["auto"] = "auto" + + +class RequiredToolChoice(BaseModel): + type: Literal["required"] = "required" + + +class SpecificToolChoice(BaseModel): + type: Literal["tool"] = "tool" + name: str + + +ToolChoice = Union[ + AutoToolChoice, RequiredToolChoice, SpecificToolChoice, Literal["auto", "none"] +] + + +class ChatMessage(BaseModel): + role: str + content: Optional[str] = None + tool_calls: Optional[List[ToolCall]] = None + + +class ChatCompletionChoice(BaseModel): + index: int + message: ChatMessage + finish_reason: str + + +class ChatCompletionUsage(BaseModel): + prompt_tokens: int + completion_tokens: int + total_tokens: int + cache_read_input_tokens: Optional[int] = None + + +class ChatCompletion(BaseModel): + id: str + object: str + created: int + model: str + choices: List[ChatCompletionChoice] + usage: ChatCompletionUsage diff --git a/sdk/core/uipath_sdk/_models/processes.py b/src/uipath_sdk/_models/processes.py similarity index 100% rename from sdk/core/uipath_sdk/_models/processes.py rename to src/uipath_sdk/_models/processes.py diff --git a/sdk/core/uipath_sdk/_models/queues.py b/src/uipath_sdk/_models/queues.py similarity index 100% rename from sdk/core/uipath_sdk/_models/queues.py rename to src/uipath_sdk/_models/queues.py diff --git a/sdk/core/uipath_sdk/_services/__init__.py b/src/uipath_sdk/_services/__init__.py similarity index 100% rename from sdk/core/uipath_sdk/_services/__init__.py rename to src/uipath_sdk/_services/__init__.py diff --git a/sdk/core/uipath_sdk/_services/_base_service.py b/src/uipath_sdk/_services/_base_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/_base_service.py rename to src/uipath_sdk/_services/_base_service.py diff --git a/sdk/core/uipath_sdk/_services/actions_service.py b/src/uipath_sdk/_services/actions_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/actions_service.py rename to src/uipath_sdk/_services/actions_service.py diff --git a/sdk/core/uipath_sdk/_services/api_client.py b/src/uipath_sdk/_services/api_client.py similarity index 100% rename from sdk/core/uipath_sdk/_services/api_client.py rename to src/uipath_sdk/_services/api_client.py diff --git a/sdk/core/uipath_sdk/_services/assets_service.py b/src/uipath_sdk/_services/assets_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/assets_service.py rename to src/uipath_sdk/_services/assets_service.py diff --git a/sdk/core/uipath_sdk/_services/buckets_service.py b/src/uipath_sdk/_services/buckets_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/buckets_service.py rename to src/uipath_sdk/_services/buckets_service.py diff --git a/sdk/core/uipath_sdk/_services/connections_service.py b/src/uipath_sdk/_services/connections_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/connections_service.py rename to src/uipath_sdk/_services/connections_service.py diff --git a/sdk/core/uipath_sdk/_services/connections_service.pyi b/src/uipath_sdk/_services/connections_service.pyi similarity index 100% rename from sdk/core/uipath_sdk/_services/connections_service.pyi rename to src/uipath_sdk/_services/connections_service.pyi diff --git a/sdk/core/uipath_sdk/_services/context_grounding_service.py b/src/uipath_sdk/_services/context_grounding_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/context_grounding_service.py rename to src/uipath_sdk/_services/context_grounding_service.py diff --git a/sdk/core/uipath_sdk/_services/jobs_service.py b/src/uipath_sdk/_services/jobs_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/jobs_service.py rename to src/uipath_sdk/_services/jobs_service.py diff --git a/sdk/core/uipath_sdk/_services/llm_gateway_service.py b/src/uipath_sdk/_services/llm_gateway_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/llm_gateway_service.py rename to src/uipath_sdk/_services/llm_gateway_service.py diff --git a/sdk/core/uipath_sdk/_services/processes_service.py b/src/uipath_sdk/_services/processes_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/processes_service.py rename to src/uipath_sdk/_services/processes_service.py diff --git a/sdk/core/uipath_sdk/_services/queues_service.py b/src/uipath_sdk/_services/queues_service.py similarity index 100% rename from sdk/core/uipath_sdk/_services/queues_service.py rename to src/uipath_sdk/_services/queues_service.py diff --git a/sdk/core/uipath_sdk/_uipath_sdk.py b/src/uipath_sdk/_uipath_sdk.py similarity index 100% rename from sdk/core/uipath_sdk/_uipath_sdk.py rename to src/uipath_sdk/_uipath_sdk.py diff --git a/sdk/core/uipath_sdk/_utils/__init__.py b/src/uipath_sdk/_utils/__init__.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/__init__.py rename to src/uipath_sdk/_utils/__init__.py diff --git a/sdk/core/uipath_sdk/_utils/_endpoint.py b/src/uipath_sdk/_utils/_endpoint.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/_endpoint.py rename to src/uipath_sdk/_utils/_endpoint.py diff --git a/sdk/core/uipath_sdk/_utils/_infer_bindings.py b/src/uipath_sdk/_utils/_infer_bindings.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/_infer_bindings.py rename to src/uipath_sdk/_utils/_infer_bindings.py diff --git a/sdk/core/uipath_sdk/_utils/_logs.py b/src/uipath_sdk/_utils/_logs.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/_logs.py rename to src/uipath_sdk/_utils/_logs.py diff --git a/sdk/core/uipath_sdk/_utils/_request_override.py b/src/uipath_sdk/_utils/_request_override.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/_request_override.py rename to src/uipath_sdk/_utils/_request_override.py diff --git a/sdk/core/uipath_sdk/_utils/_request_spec.py b/src/uipath_sdk/_utils/_request_spec.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/_request_spec.py rename to src/uipath_sdk/_utils/_request_spec.py diff --git a/sdk/core/uipath_sdk/_utils/_user_agent.py b/src/uipath_sdk/_utils/_user_agent.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/_user_agent.py rename to src/uipath_sdk/_utils/_user_agent.py diff --git a/sdk/core/uipath_sdk/_utils/constants.py b/src/uipath_sdk/_utils/constants.py similarity index 100% rename from sdk/core/uipath_sdk/_utils/constants.py rename to src/uipath_sdk/_utils/constants.py diff --git a/sdk/core/uipath_sdk/py.typed b/src/uipath_sdk/py.typed similarity index 100% rename from sdk/core/uipath_sdk/py.typed rename to src/uipath_sdk/py.typed diff --git a/sdk/core/tests/__init__.py b/tests/__init__.py similarity index 100% rename from sdk/core/tests/__init__.py rename to tests/__init__.py diff --git a/sdk/core/tests/cli/test_init.py b/tests/cli/test_init.py similarity index 100% rename from sdk/core/tests/cli/test_init.py rename to tests/cli/test_init.py diff --git a/sdk/core/tests/conftest.py b/tests/conftest.py similarity index 100% rename from sdk/core/tests/conftest.py rename to tests/conftest.py diff --git a/sdk/core/tests/sdk/services/test_llm_integration.py b/tests/sdk/services/test_llm_integration.py similarity index 97% rename from sdk/core/tests/sdk/services/test_llm_integration.py rename to tests/sdk/services/test_llm_integration.py index 70ea9516..f7938b52 100644 --- a/sdk/core/tests/sdk/services/test_llm_integration.py +++ b/tests/sdk/services/test_llm_integration.py @@ -1,133 +1,133 @@ -import os - -import pytest - -from uipath_sdk._config import Config -from uipath_sdk._execution_context import ExecutionContext -from uipath_sdk._services.llm_gateway_service import ( - ChatModels, - EmbeddingModels, - UiPathOpenAIService, -) - - -def get_env_var(name: str) -> str: - """Get environment variable or skip test if not present.""" - value = os.environ.get(name) - if value is None: - pytest.skip(f"Environment variable {name} is not set") - return value - - -class TestLLMIntegration: - @pytest.fixture - def llm_service(self): - """Create an OpenAIService instance with environment variables.""" - base_url = get_env_var("UIPATH_URL") - api_key = get_env_var("UIPATH_ACCESS_TOKEN") - - config = Config(base_url=base_url, secret=api_key) - execution_context = ExecutionContext() - return UiPathOpenAIService(config=config, execution_context=execution_context) - - @pytest.mark.asyncio - async def test_embeddings_real(self, llm_service): - """Test the embeddings function with a real API call.""" - input_text = "This is a test for embedding a sentence." - - # Make the actual API call - result = await llm_service.embeddings(input=input_text) - - # Validate the response - assert result is not None - assert hasattr(result, "data") - assert len(result.data) > 0 - assert hasattr(result.data[0], "embedding") - assert len(result.data[0].embedding) > 0 - assert hasattr(result, "model") - assert hasattr(result, "usage") - assert result.usage.prompt_tokens > 0 - - @pytest.mark.asyncio - async def test_embeddings_usage_real(self, llm_service): - """Test the embeddings_usage function with a real API call.""" - input_text = "Testing the embedding usage endpoint." - - # Make the actual API call - result = await llm_service.embeddings_usage(input=input_text) - - # Validate the response - assert result is not None - assert hasattr(result, "encoding") - assert hasattr(result, "prompt_tokens") - assert result.prompt_tokens > 0 - - @pytest.mark.asyncio - async def test_chat_completions_real(self, llm_service): - """Test the chat_completions function with a real API call.""" - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What is the capital of France?"}, - ] - - # Make the actual API call - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=50, - temperature=0.7, - ) - - # Validate the response - assert result is not None - assert hasattr(result, "id") - assert hasattr(result, "choices") - assert len(result.choices) > 0 - assert hasattr(result.choices[0], "message") - assert hasattr(result.choices[0].message, "content") - assert result.choices[0].message.content.strip() != "" - assert hasattr(result, "usage") - assert result.usage.prompt_tokens > 0 - - @pytest.mark.asyncio - async def test_chat_completions_usage_real(self, llm_service): - """Test the chat_completions_usage function with a real API call.""" - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What is the capital of France?"}, - ] - - # Make the actual API call - result = await llm_service.chat_completions_usage( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=50, - temperature=0.7, - ) - - # Validate the response - assert result is not None - assert hasattr(result, "encoding") - assert hasattr(result, "prompt_tokens") - assert result.prompt_tokens > 0 - assert isinstance(result.prompt_tokens, int) - assert isinstance(result.encoding, str) - assert len(result.encoding) > 0 - - @pytest.mark.asyncio - async def test_embeddings_with_custom_model_real(self, llm_service): - """Test the embeddings function with a custom model.""" - input_text = "Testing embeddings with a different model." - - # Make the actual API call with a specific embedding model - result = await llm_service.embeddings( - input=input_text, embedding_model=EmbeddingModels.text_embedding_3_large - ) - - # Validate the response - assert result is not None - assert hasattr(result, "data") - assert len(result.data) > 0 - assert hasattr(result.data[0], "embedding") - assert len(result.data[0].embedding) > 0 - assert result.model == "text-embedding-3-large" +import os + +import pytest + +from uipath_sdk._config import Config +from uipath_sdk._execution_context import ExecutionContext +from uipath_sdk._services.llm_gateway_service import ( + ChatModels, + EmbeddingModels, + UiPathOpenAIService, +) + + +def get_env_var(name: str) -> str: + """Get environment variable or skip test if not present.""" + value = os.environ.get(name) + if value is None: + pytest.skip(f"Environment variable {name} is not set") + return value + + +class TestLLMIntegration: + @pytest.fixture + def llm_service(self): + """Create an OpenAIService instance with environment variables.""" + base_url = get_env_var("UIPATH_URL") + api_key = get_env_var("UIPATH_ACCESS_TOKEN") + + config = Config(base_url=base_url, secret=api_key) + execution_context = ExecutionContext() + return UiPathOpenAIService(config=config, execution_context=execution_context) + + @pytest.mark.asyncio + async def test_embeddings_real(self, llm_service): + """Test the embeddings function with a real API call.""" + input_text = "This is a test for embedding a sentence." + + # Make the actual API call + result = await llm_service.embeddings(input=input_text) + + # Validate the response + assert result is not None + assert hasattr(result, "data") + assert len(result.data) > 0 + assert hasattr(result.data[0], "embedding") + assert len(result.data[0].embedding) > 0 + assert hasattr(result, "model") + assert hasattr(result, "usage") + assert result.usage.prompt_tokens > 0 + + @pytest.mark.asyncio + async def test_embeddings_usage_real(self, llm_service): + """Test the embeddings_usage function with a real API call.""" + input_text = "Testing the embedding usage endpoint." + + # Make the actual API call + result = await llm_service.embeddings_usage(input=input_text) + + # Validate the response + assert result is not None + assert hasattr(result, "encoding") + assert hasattr(result, "prompt_tokens") + assert result.prompt_tokens > 0 + + @pytest.mark.asyncio + async def test_chat_completions_real(self, llm_service): + """Test the chat_completions function with a real API call.""" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is the capital of France?"}, + ] + + # Make the actual API call + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=50, + temperature=0.7, + ) + + # Validate the response + assert result is not None + assert hasattr(result, "id") + assert hasattr(result, "choices") + assert len(result.choices) > 0 + assert hasattr(result.choices[0], "message") + assert hasattr(result.choices[0].message, "content") + assert result.choices[0].message.content.strip() != "" + assert hasattr(result, "usage") + assert result.usage.prompt_tokens > 0 + + @pytest.mark.asyncio + async def test_chat_completions_usage_real(self, llm_service): + """Test the chat_completions_usage function with a real API call.""" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is the capital of France?"}, + ] + + # Make the actual API call + result = await llm_service.chat_completions_usage( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=50, + temperature=0.7, + ) + + # Validate the response + assert result is not None + assert hasattr(result, "encoding") + assert hasattr(result, "prompt_tokens") + assert result.prompt_tokens > 0 + assert isinstance(result.prompt_tokens, int) + assert isinstance(result.encoding, str) + assert len(result.encoding) > 0 + + @pytest.mark.asyncio + async def test_embeddings_with_custom_model_real(self, llm_service): + """Test the embeddings function with a custom model.""" + input_text = "Testing embeddings with a different model." + + # Make the actual API call with a specific embedding model + result = await llm_service.embeddings( + input=input_text, embedding_model=EmbeddingModels.text_embedding_3_large + ) + + # Validate the response + assert result is not None + assert hasattr(result, "data") + assert len(result.data) > 0 + assert hasattr(result.data[0], "embedding") + assert len(result.data[0].embedding) > 0 + assert result.model == "text-embedding-3-large" diff --git a/sdk/core/tests/sdk/services/test_llm_service.py b/tests/sdk/services/test_llm_service.py similarity index 97% rename from sdk/core/tests/sdk/services/test_llm_service.py rename to tests/sdk/services/test_llm_service.py index 65cf5c9c..7c108d81 100644 --- a/sdk/core/tests/sdk/services/test_llm_service.py +++ b/tests/sdk/services/test_llm_service.py @@ -1,141 +1,141 @@ -import json -from unittest.mock import MagicMock, patch - -import pytest - -from uipath_sdk._config import Config -from uipath_sdk._execution_context import ExecutionContext -from uipath_sdk._models.llm_gateway import ( - TextEmbedding, - UsageInfo, -) -from uipath_sdk._services.llm_gateway_service import ( - EmbeddingModels, - UiPathOpenAIService, -) - - -class TestOpenAIService: - @pytest.fixture - def config(self): - return Config(base_url="https://example.com", secret="test_secret") - - @pytest.fixture - def execution_context(self): - return ExecutionContext() - - @pytest.fixture - def openai_service(self, config, execution_context): - return UiPathOpenAIService(config=config, execution_context=execution_context) - - def test_init(self, config, execution_context): - service = UiPathOpenAIService( - config=config, execution_context=execution_context - ) - assert service._config == config - assert service._execution_context == execution_context - - @patch.object(UiPathOpenAIService, "request_async") - @pytest.mark.asyncio - async def test_embeddings_usage(self, mock_request, openai_service): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "encoding": "cl100k_base", - "prompt_tokens": 4, - } - mock_request.return_value = mock_response - - # Call the method - result = await openai_service.embeddings_usage(input="Test input") - - # Assertions - mock_request.assert_called_once() - assert isinstance(result, UsageInfo) - assert result.encoding == "cl100k_base" - assert result.prompt_tokens == 4 - - @patch.object(UiPathOpenAIService, "request_async") - @pytest.mark.asyncio - async def test_embeddings(self, mock_request, openai_service): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0, "object": "embedding"}], - "model": "text-embedding-ada-002", - "object": "list", - "usage": {"prompt_tokens": 4, "total_tokens": 4}, - } - mock_request.return_value = mock_response - - # Call the method - result = await openai_service.embeddings(input="Test input") - - # Assertions - mock_request.assert_called_once() - assert isinstance(result, TextEmbedding) - assert result.data[0].embedding == [0.1, 0.2, 0.3] - assert result.model == "text-embedding-ada-002" - assert result.usage.prompt_tokens == 4 - - @patch.object(UiPathOpenAIService, "request_async") - @pytest.mark.asyncio - async def test_chat_completions_usage(self, mock_request, openai_service): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "encoding": "cl100k_base", - "prompt_tokens": 10, - } - mock_request.return_value = mock_response - - # Test messages - messages = [ - {"role": "system", "content": "You are a helpful assistant"}, - {"role": "user", "content": "Hello"}, - ] - - # Call the method - result = await openai_service.chat_completions_usage( - messages=messages, max_tokens=50, temperature=0.5 - ) - - # Assertions - mock_request.assert_called_once() - assert isinstance(result, UsageInfo) - assert result.encoding == "cl100k_base" - assert result.prompt_tokens == 10 - - # Verify the correct endpoint was called - args, kwargs = mock_request.call_args - assert json.loads(kwargs["content"])["messages"] == messages - assert json.loads(kwargs["content"])["max_tokens"] == 50 - assert json.loads(kwargs["content"])["temperature"] == 0.5 - - @patch.object(UiPathOpenAIService, "request_async") - @pytest.mark.asyncio - async def test_embeddings_with_custom_model(self, mock_request, openai_service): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0, "object": "embedding"}], - "model": "text-embedding-3-large", - "object": "list", - "usage": {"prompt_tokens": 4, "total_tokens": 4}, - } - mock_request.return_value = mock_response - - # Call the method with custom model - result = await openai_service.embeddings( - input="Test input", embedding_model=EmbeddingModels.text_embedding_3_large - ) - - # Assertions for the result - mock_request.assert_called_once() - assert result.model == "text-embedding-3-large" - assert len(result.data) == 1 - assert result.data[0].embedding == [0.1, 0.2, 0.3] - assert result.data[0].index == 0 - assert result.object == "list" - assert result.usage.prompt_tokens == 4 - assert result.usage.total_tokens == 4 +import json +from unittest.mock import MagicMock, patch + +import pytest + +from uipath_sdk._config import Config +from uipath_sdk._execution_context import ExecutionContext +from uipath_sdk._models.llm_gateway import ( + TextEmbedding, + UsageInfo, +) +from uipath_sdk._services.llm_gateway_service import ( + EmbeddingModels, + UiPathOpenAIService, +) + + +class TestOpenAIService: + @pytest.fixture + def config(self): + return Config(base_url="https://example.com", secret="test_secret") + + @pytest.fixture + def execution_context(self): + return ExecutionContext() + + @pytest.fixture + def openai_service(self, config, execution_context): + return UiPathOpenAIService(config=config, execution_context=execution_context) + + def test_init(self, config, execution_context): + service = UiPathOpenAIService( + config=config, execution_context=execution_context + ) + assert service._config == config + assert service._execution_context == execution_context + + @patch.object(UiPathOpenAIService, "request_async") + @pytest.mark.asyncio + async def test_embeddings_usage(self, mock_request, openai_service): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "encoding": "cl100k_base", + "prompt_tokens": 4, + } + mock_request.return_value = mock_response + + # Call the method + result = await openai_service.embeddings_usage(input="Test input") + + # Assertions + mock_request.assert_called_once() + assert isinstance(result, UsageInfo) + assert result.encoding == "cl100k_base" + assert result.prompt_tokens == 4 + + @patch.object(UiPathOpenAIService, "request_async") + @pytest.mark.asyncio + async def test_embeddings(self, mock_request, openai_service): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0, "object": "embedding"}], + "model": "text-embedding-ada-002", + "object": "list", + "usage": {"prompt_tokens": 4, "total_tokens": 4}, + } + mock_request.return_value = mock_response + + # Call the method + result = await openai_service.embeddings(input="Test input") + + # Assertions + mock_request.assert_called_once() + assert isinstance(result, TextEmbedding) + assert result.data[0].embedding == [0.1, 0.2, 0.3] + assert result.model == "text-embedding-ada-002" + assert result.usage.prompt_tokens == 4 + + @patch.object(UiPathOpenAIService, "request_async") + @pytest.mark.asyncio + async def test_chat_completions_usage(self, mock_request, openai_service): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "encoding": "cl100k_base", + "prompt_tokens": 10, + } + mock_request.return_value = mock_response + + # Test messages + messages = [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Hello"}, + ] + + # Call the method + result = await openai_service.chat_completions_usage( + messages=messages, max_tokens=50, temperature=0.5 + ) + + # Assertions + mock_request.assert_called_once() + assert isinstance(result, UsageInfo) + assert result.encoding == "cl100k_base" + assert result.prompt_tokens == 10 + + # Verify the correct endpoint was called + args, kwargs = mock_request.call_args + assert json.loads(kwargs["content"])["messages"] == messages + assert json.loads(kwargs["content"])["max_tokens"] == 50 + assert json.loads(kwargs["content"])["temperature"] == 0.5 + + @patch.object(UiPathOpenAIService, "request_async") + @pytest.mark.asyncio + async def test_embeddings_with_custom_model(self, mock_request, openai_service): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0, "object": "embedding"}], + "model": "text-embedding-3-large", + "object": "list", + "usage": {"prompt_tokens": 4, "total_tokens": 4}, + } + mock_request.return_value = mock_response + + # Call the method with custom model + result = await openai_service.embeddings( + input="Test input", embedding_model=EmbeddingModels.text_embedding_3_large + ) + + # Assertions for the result + mock_request.assert_called_once() + assert result.model == "text-embedding-3-large" + assert len(result.data) == 1 + assert result.data[0].embedding == [0.1, 0.2, 0.3] + assert result.data[0].index == 0 + assert result.object == "list" + assert result.usage.prompt_tokens == 4 + assert result.usage.total_tokens == 4 diff --git a/sdk/core/tests/sdk/services/test_uipath_llm_integration.py b/tests/sdk/services/test_uipath_llm_integration.py similarity index 97% rename from sdk/core/tests/sdk/services/test_uipath_llm_integration.py rename to tests/sdk/services/test_uipath_llm_integration.py index ec1ad723..2cadef51 100644 --- a/sdk/core/tests/sdk/services/test_uipath_llm_integration.py +++ b/tests/sdk/services/test_uipath_llm_integration.py @@ -1,511 +1,511 @@ -import json -import os -from unittest.mock import MagicMock, patch - -import pytest - -from uipath_sdk._config import Config -from uipath_sdk._execution_context import ExecutionContext -from uipath_sdk._models.llm_gateway import ( - AutoToolChoice, - SpecificToolChoice, - ToolDefinition, - ToolFunctionDefinition, - ToolParametersDefinition, - ToolPropertyDefinition, -) -from uipath_sdk._services.llm_gateway_service import ( - ChatModels, - UiPathLlmChatService, -) - - -def get_env_var(name: str) -> str: - """Get environment variable or skip test if not present.""" - value = os.environ.get(name) - if value is None: - pytest.skip(f"Environment variable {name} is not set") - return value - - -class TestUiPathLLMIntegration: - @pytest.fixture - def llm_service(self): - """Create a UiPathLLMService instance with environment variables.""" - # In a real-world scenario, these would be environment variables - base_url = get_env_var("UIPATH_URL") - api_key = get_env_var("UIPATH_ACCESS_TOKEN") - - config = Config(base_url=base_url, secret=api_key) - execution_context = ExecutionContext() - return UiPathLlmChatService(config=config, execution_context=execution_context) - - @pytest.mark.asyncio - async def test_basic_chat_completions(self, llm_service): - """Test basic chat completions functionality.""" - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What is the capital of France?"}, - ] - - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=50, - temperature=0, - ) - - # Validate the response - assert result is not None - assert hasattr(result, "id") - assert hasattr(result, "choices") - assert len(result.choices) > 0 - assert hasattr(result.choices[0], "message") - assert hasattr(result.choices[0].message, "content") - assert "Paris" in result.choices[0].message.content - - @pytest.mark.asyncio - async def test_tool_call_required(self, llm_service): - """Test the tool call functionality with a specific required tool.""" - messages = [ - { - "role": "system", - "content": "You are given two tools/functions and a user and password. You must first call test_tool with the given credentials then call submit_answer with the result. If the result is nested, extract the result string and pass it to submit_answer. Do not respond with text, only call the tools/functions.", - }, - {"role": "user", "content": "username: John, password: 1234"}, - ] - - # Define the test_tool - test_tool = ToolDefinition( - type="function", - function=ToolFunctionDefinition( - name="test_tool", - description="call this to obtain the result", - parameters=ToolParametersDefinition( - type="object", - properties={ - "name": ToolPropertyDefinition( - type="string", description="the name of the user" - ), - "password": ToolPropertyDefinition( - type="string", description="the password of the user" - ), - }, - required=["name", "password"], - ), - ), - ) - - # Define tool choice to specifically use test_tool - tool_choice = SpecificToolChoice(type="tool", name="test_tool") - - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=250, - temperature=0, - tools=[test_tool], - tool_choice=tool_choice, - ) - - # Validate the response - assert result is not None - assert len(result.choices) > 0 - assert result.choices[0].message.tool_calls is not None - assert len(result.choices[0].message.tool_calls) > 0 - assert result.choices[0].message.tool_calls[0].name == "test_tool" - assert "name" in result.choices[0].message.tool_calls[0].arguments - assert result.choices[0].message.tool_calls[0].arguments["name"] == "John" - assert "password" in result.choices[0].message.tool_calls[0].arguments - assert result.choices[0].message.tool_calls[0].arguments["password"] == "1234" - - @pytest.mark.asyncio - async def test_chat_with_conversation_history(self, llm_service): - """Test chat completions with a conversation history including assistant messages.""" - messages = [ - {"role": "system", "content": "You are a helpful assistant"}, - {"role": "user", "content": "Hi my name is John"}, - {"content": "Hello John! How can I assist you today?", "role": "assistant"}, - {"role": "user", "content": "What is my name?"}, - ] - - # Define the test_tool but with auto tool choice - test_tool = ToolDefinition( - type="function", - function=ToolFunctionDefinition( - name="test_tool", - description="call this to obtain the result", - parameters=ToolParametersDefinition( - type="object", - properties={ - "name": ToolPropertyDefinition( - type="string", description="the name of the user" - ), - "password": ToolPropertyDefinition( - type="string", description="the password of the user" - ), - }, - required=["name", "password"], - ), - ), - ) - - # Use auto tool choice - tool_choice = AutoToolChoice(type="auto") - - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=250, - temperature=0, - frequency_penalty=1, - presence_penalty=1, - tools=[test_tool], - tool_choice=tool_choice, - ) - - # Validate the response - assert result is not None - assert len(result.choices) > 0 - assert result.choices[0].message.content is not None - assert "John" in result.choices[0].message.content - # The model chose to respond with text instead of using the tool - assert ( - result.choices[0].message.tool_calls is None - or len(result.choices[0].message.tool_calls) == 0 - ) - - @pytest.mark.asyncio - async def test_no_tools(self, llm_service): - """Test chat completions without any tools.""" - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Write a haiku about Python programming."}, - ] - - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=100, - temperature=0.7, - ) - - # Validate the response - assert result is not None - assert len(result.choices) > 0 - assert result.choices[0].message.content is not None - assert len(result.choices[0].message.content.strip()) > 0 - # No tools were provided, so no tool calls should be in the response - assert ( - result.choices[0].message.tool_calls is None - or len(result.choices[0].message.tool_calls) == 0 - ) - - -class TestUiPathLLMServiceMocked: - @pytest.fixture - def config(self): - return Config(base_url="https://example.com", secret="test_secret") - - @pytest.fixture - def execution_context(self): - return ExecutionContext() - - @pytest.fixture - def llm_service(self, config, execution_context): - return UiPathLlmChatService(config=config, execution_context=execution_context) - - def test_init(self, config, execution_context): - service = UiPathLlmChatService( - config=config, execution_context=execution_context - ) - assert service._config == config - assert service._execution_context == execution_context - - @pytest.mark.asyncio - @patch.object(UiPathLlmChatService, "request_async") - async def test_basic_chat_completions_mocked(self, mock_request, llm_service): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "id": "chatcmpl-123", - "object": "chat.completion", - "created": 1677858242, - "model": "gpt-4o-mini-2024-07-18", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "The capital of France is Paris.", - }, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 30, - "completion_tokens": 10, - "total_tokens": 40, - "cache_read_input_tokens": None, - }, - } - mock_request.return_value = mock_response - - # Test messages - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What is the capital of France?"}, - ] - - # Call the method - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=50, - temperature=0, - ) - - # Assertions - mock_request.assert_called_once() - assert result.id == "chatcmpl-123" - assert len(result.choices) == 1 - assert result.choices[0].message.content == "The capital of France is Paris." - assert "Paris" in result.choices[0].message.content - assert result.usage.prompt_tokens == 30 - assert result.usage.completion_tokens == 10 - - # Verify the correct endpoint and payload - args, kwargs = mock_request.call_args - assert "/llmgateway_/api/chat/completions" in args[1] - assert json.loads(kwargs["content"])["messages"] == messages - assert json.loads(kwargs["content"])["max_tokens"] == 50 - assert json.loads(kwargs["content"])["temperature"] == 0 - - @pytest.mark.asyncio - @patch.object(UiPathLlmChatService, "request_async") - async def test_tool_call_required_mocked(self, mock_request, llm_service): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "id": "chatcmpl-456", - "object": "chat.completion", - "created": 1677858242, - "model": "gpt-4o-mini-2024-07-18", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": None, - "tool_calls": [ - { - "id": "call_abc123", - "name": "test_tool", - "arguments": {"name": "John", "password": "1234"}, - } - ], - }, - "finish_reason": "tool_calls", - } - ], - "usage": { - "prompt_tokens": 50, - "completion_tokens": 25, - "total_tokens": 75, - "cache_read_input_tokens": None, - }, - } - mock_request.return_value = mock_response - - # Test messages - messages = [ - { - "role": "system", - "content": "You are given two tools/functions and a user and password. You must first call test_tool with the given credentials then call submit_answer with the result. If the result is nested, extract the result string and pass it to submit_answer. Do not respond with text, only call the tools/functions.", - }, - {"role": "user", "content": "username: John, password: 1234"}, - ] - - # Define the test_tool - test_tool = ToolDefinition( - type="function", - function=ToolFunctionDefinition( - name="test_tool", - description="call this to obtain the result", - parameters=ToolParametersDefinition( - type="object", - properties={ - "name": ToolPropertyDefinition( - type="string", description="the name of the user" - ), - "password": ToolPropertyDefinition( - type="string", description="the password of the user" - ), - }, - required=["name", "password"], - ), - ), - ) - - # Define tool choice - tool_choice = SpecificToolChoice(type="tool", name="test_tool") - - # Call the method - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=250, - temperature=0, - tools=[test_tool], - tool_choice=tool_choice, - ) - - # Assertions - mock_request.assert_called_once() - assert result.id == "chatcmpl-456" - assert len(result.choices) == 1 - assert result.choices[0].message.tool_calls is not None - assert len(result.choices[0].message.tool_calls) == 1 - assert result.choices[0].message.tool_calls[0].name == "test_tool" - assert result.choices[0].message.tool_calls[0].arguments["name"] == "John" - assert result.choices[0].message.tool_calls[0].arguments["password"] == "1234" - - @pytest.mark.asyncio - @patch.object(UiPathLlmChatService, "request_async") - async def test_chat_with_conversation_history_mocked( - self, mock_request, llm_service - ): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "id": "chatcmpl-789", - "object": "chat.completion", - "created": 1677858242, - "model": "gpt-4o-mini-2024-07-18", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "Your name is John, as you mentioned earlier.", - }, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 70, - "completion_tokens": 15, - "total_tokens": 85, - "cache_read_input_tokens": None, - }, - } - mock_request.return_value = mock_response - - # Test messages with conversation history - messages = [ - {"role": "system", "content": "You are a helpful assistant"}, - {"role": "user", "content": "Hi my name is John"}, - {"content": "Hello John! How can I assist you today?", "role": "assistant"}, - {"role": "user", "content": "What is my name?"}, - ] - - # Define test tool - test_tool = ToolDefinition( - type="function", - function=ToolFunctionDefinition( - name="test_tool", - description="call this to obtain the result", - parameters=ToolParametersDefinition( - type="object", - properties={ - "name": ToolPropertyDefinition( - type="string", description="the name of the user" - ), - "password": ToolPropertyDefinition( - type="string", description="the password of the user" - ), - }, - required=["name", "password"], - ), - ), - ) - - # Use auto tool choice - tool_choice = AutoToolChoice(type="auto") - - # Call the method - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=250, - temperature=0, - frequency_penalty=1, - presence_penalty=1, - tools=[test_tool], - tool_choice=tool_choice, - ) - - # Assertions - mock_request.assert_called_once() - assert result.id == "chatcmpl-789" - assert len(result.choices) == 1 - assert result.choices[0].message.content is not None - assert "John" in result.choices[0].message.content - assert result.choices[0].message.tool_calls is None - - @pytest.mark.asyncio - @patch.object(UiPathLlmChatService, "request_async") - async def test_no_tools_mocked(self, mock_request, llm_service): - # Mock response - mock_response = MagicMock() - mock_response.json.return_value = { - "id": "chatcmpl-abc", - "object": "chat.completion", - "created": 1677858242, - "model": "gpt-4o-mini-2024-07-18", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "Silently coding,\nPython's logic unfolds clear,\nBugs hide, then reveal.", - }, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 40, - "completion_tokens": 20, - "total_tokens": 60, - "cache_read_input_tokens": None, - }, - } - mock_request.return_value = mock_response - - # Test messages - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Write a haiku about Python programming."}, - ] - - # Call the method - result = await llm_service.chat_completions( - messages=messages, - model=ChatModels.gpt_4o_mini_2024_07_18, - max_tokens=100, - temperature=0.7, - ) - - # Assertions - mock_request.assert_called_once() - assert result.id == "chatcmpl-abc" - assert len(result.choices) == 1 - assert result.choices[0].message.content is not None - assert len(result.choices[0].message.content.strip()) > 0 - assert result.choices[0].message.tool_calls is None - - # Verify the correct payload was sent - args, kwargs = mock_request.call_args - assert json.loads(kwargs["content"])["messages"] == messages - assert json.loads(kwargs["content"])["max_tokens"] == 100 - assert json.loads(kwargs["content"])["temperature"] == 0.7 +import json +import os +from unittest.mock import MagicMock, patch + +import pytest + +from uipath_sdk._config import Config +from uipath_sdk._execution_context import ExecutionContext +from uipath_sdk._models.llm_gateway import ( + AutoToolChoice, + SpecificToolChoice, + ToolDefinition, + ToolFunctionDefinition, + ToolParametersDefinition, + ToolPropertyDefinition, +) +from uipath_sdk._services.llm_gateway_service import ( + ChatModels, + UiPathLlmChatService, +) + + +def get_env_var(name: str) -> str: + """Get environment variable or skip test if not present.""" + value = os.environ.get(name) + if value is None: + pytest.skip(f"Environment variable {name} is not set") + return value + + +class TestUiPathLLMIntegration: + @pytest.fixture + def llm_service(self): + """Create a UiPathLLMService instance with environment variables.""" + # In a real-world scenario, these would be environment variables + base_url = get_env_var("UIPATH_URL") + api_key = get_env_var("UIPATH_ACCESS_TOKEN") + + config = Config(base_url=base_url, secret=api_key) + execution_context = ExecutionContext() + return UiPathLlmChatService(config=config, execution_context=execution_context) + + @pytest.mark.asyncio + async def test_basic_chat_completions(self, llm_service): + """Test basic chat completions functionality.""" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is the capital of France?"}, + ] + + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=50, + temperature=0, + ) + + # Validate the response + assert result is not None + assert hasattr(result, "id") + assert hasattr(result, "choices") + assert len(result.choices) > 0 + assert hasattr(result.choices[0], "message") + assert hasattr(result.choices[0].message, "content") + assert "Paris" in result.choices[0].message.content + + @pytest.mark.asyncio + async def test_tool_call_required(self, llm_service): + """Test the tool call functionality with a specific required tool.""" + messages = [ + { + "role": "system", + "content": "You are given two tools/functions and a user and password. You must first call test_tool with the given credentials then call submit_answer with the result. If the result is nested, extract the result string and pass it to submit_answer. Do not respond with text, only call the tools/functions.", + }, + {"role": "user", "content": "username: John, password: 1234"}, + ] + + # Define the test_tool + test_tool = ToolDefinition( + type="function", + function=ToolFunctionDefinition( + name="test_tool", + description="call this to obtain the result", + parameters=ToolParametersDefinition( + type="object", + properties={ + "name": ToolPropertyDefinition( + type="string", description="the name of the user" + ), + "password": ToolPropertyDefinition( + type="string", description="the password of the user" + ), + }, + required=["name", "password"], + ), + ), + ) + + # Define tool choice to specifically use test_tool + tool_choice = SpecificToolChoice(type="tool", name="test_tool") + + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=250, + temperature=0, + tools=[test_tool], + tool_choice=tool_choice, + ) + + # Validate the response + assert result is not None + assert len(result.choices) > 0 + assert result.choices[0].message.tool_calls is not None + assert len(result.choices[0].message.tool_calls) > 0 + assert result.choices[0].message.tool_calls[0].name == "test_tool" + assert "name" in result.choices[0].message.tool_calls[0].arguments + assert result.choices[0].message.tool_calls[0].arguments["name"] == "John" + assert "password" in result.choices[0].message.tool_calls[0].arguments + assert result.choices[0].message.tool_calls[0].arguments["password"] == "1234" + + @pytest.mark.asyncio + async def test_chat_with_conversation_history(self, llm_service): + """Test chat completions with a conversation history including assistant messages.""" + messages = [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Hi my name is John"}, + {"content": "Hello John! How can I assist you today?", "role": "assistant"}, + {"role": "user", "content": "What is my name?"}, + ] + + # Define the test_tool but with auto tool choice + test_tool = ToolDefinition( + type="function", + function=ToolFunctionDefinition( + name="test_tool", + description="call this to obtain the result", + parameters=ToolParametersDefinition( + type="object", + properties={ + "name": ToolPropertyDefinition( + type="string", description="the name of the user" + ), + "password": ToolPropertyDefinition( + type="string", description="the password of the user" + ), + }, + required=["name", "password"], + ), + ), + ) + + # Use auto tool choice + tool_choice = AutoToolChoice(type="auto") + + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=250, + temperature=0, + frequency_penalty=1, + presence_penalty=1, + tools=[test_tool], + tool_choice=tool_choice, + ) + + # Validate the response + assert result is not None + assert len(result.choices) > 0 + assert result.choices[0].message.content is not None + assert "John" in result.choices[0].message.content + # The model chose to respond with text instead of using the tool + assert ( + result.choices[0].message.tool_calls is None + or len(result.choices[0].message.tool_calls) == 0 + ) + + @pytest.mark.asyncio + async def test_no_tools(self, llm_service): + """Test chat completions without any tools.""" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Write a haiku about Python programming."}, + ] + + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=100, + temperature=0.7, + ) + + # Validate the response + assert result is not None + assert len(result.choices) > 0 + assert result.choices[0].message.content is not None + assert len(result.choices[0].message.content.strip()) > 0 + # No tools were provided, so no tool calls should be in the response + assert ( + result.choices[0].message.tool_calls is None + or len(result.choices[0].message.tool_calls) == 0 + ) + + +class TestUiPathLLMServiceMocked: + @pytest.fixture + def config(self): + return Config(base_url="https://example.com", secret="test_secret") + + @pytest.fixture + def execution_context(self): + return ExecutionContext() + + @pytest.fixture + def llm_service(self, config, execution_context): + return UiPathLlmChatService(config=config, execution_context=execution_context) + + def test_init(self, config, execution_context): + service = UiPathLlmChatService( + config=config, execution_context=execution_context + ) + assert service._config == config + assert service._execution_context == execution_context + + @pytest.mark.asyncio + @patch.object(UiPathLlmChatService, "request_async") + async def test_basic_chat_completions_mocked(self, mock_request, llm_service): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "id": "chatcmpl-123", + "object": "chat.completion", + "created": 1677858242, + "model": "gpt-4o-mini-2024-07-18", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "The capital of France is Paris.", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 30, + "completion_tokens": 10, + "total_tokens": 40, + "cache_read_input_tokens": None, + }, + } + mock_request.return_value = mock_response + + # Test messages + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is the capital of France?"}, + ] + + # Call the method + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=50, + temperature=0, + ) + + # Assertions + mock_request.assert_called_once() + assert result.id == "chatcmpl-123" + assert len(result.choices) == 1 + assert result.choices[0].message.content == "The capital of France is Paris." + assert "Paris" in result.choices[0].message.content + assert result.usage.prompt_tokens == 30 + assert result.usage.completion_tokens == 10 + + # Verify the correct endpoint and payload + args, kwargs = mock_request.call_args + assert "/llmgateway_/api/chat/completions" in args[1] + assert json.loads(kwargs["content"])["messages"] == messages + assert json.loads(kwargs["content"])["max_tokens"] == 50 + assert json.loads(kwargs["content"])["temperature"] == 0 + + @pytest.mark.asyncio + @patch.object(UiPathLlmChatService, "request_async") + async def test_tool_call_required_mocked(self, mock_request, llm_service): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "id": "chatcmpl-456", + "object": "chat.completion", + "created": 1677858242, + "model": "gpt-4o-mini-2024-07-18", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "call_abc123", + "name": "test_tool", + "arguments": {"name": "John", "password": "1234"}, + } + ], + }, + "finish_reason": "tool_calls", + } + ], + "usage": { + "prompt_tokens": 50, + "completion_tokens": 25, + "total_tokens": 75, + "cache_read_input_tokens": None, + }, + } + mock_request.return_value = mock_response + + # Test messages + messages = [ + { + "role": "system", + "content": "You are given two tools/functions and a user and password. You must first call test_tool with the given credentials then call submit_answer with the result. If the result is nested, extract the result string and pass it to submit_answer. Do not respond with text, only call the tools/functions.", + }, + {"role": "user", "content": "username: John, password: 1234"}, + ] + + # Define the test_tool + test_tool = ToolDefinition( + type="function", + function=ToolFunctionDefinition( + name="test_tool", + description="call this to obtain the result", + parameters=ToolParametersDefinition( + type="object", + properties={ + "name": ToolPropertyDefinition( + type="string", description="the name of the user" + ), + "password": ToolPropertyDefinition( + type="string", description="the password of the user" + ), + }, + required=["name", "password"], + ), + ), + ) + + # Define tool choice + tool_choice = SpecificToolChoice(type="tool", name="test_tool") + + # Call the method + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=250, + temperature=0, + tools=[test_tool], + tool_choice=tool_choice, + ) + + # Assertions + mock_request.assert_called_once() + assert result.id == "chatcmpl-456" + assert len(result.choices) == 1 + assert result.choices[0].message.tool_calls is not None + assert len(result.choices[0].message.tool_calls) == 1 + assert result.choices[0].message.tool_calls[0].name == "test_tool" + assert result.choices[0].message.tool_calls[0].arguments["name"] == "John" + assert result.choices[0].message.tool_calls[0].arguments["password"] == "1234" + + @pytest.mark.asyncio + @patch.object(UiPathLlmChatService, "request_async") + async def test_chat_with_conversation_history_mocked( + self, mock_request, llm_service + ): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "id": "chatcmpl-789", + "object": "chat.completion", + "created": 1677858242, + "model": "gpt-4o-mini-2024-07-18", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Your name is John, as you mentioned earlier.", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 70, + "completion_tokens": 15, + "total_tokens": 85, + "cache_read_input_tokens": None, + }, + } + mock_request.return_value = mock_response + + # Test messages with conversation history + messages = [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Hi my name is John"}, + {"content": "Hello John! How can I assist you today?", "role": "assistant"}, + {"role": "user", "content": "What is my name?"}, + ] + + # Define test tool + test_tool = ToolDefinition( + type="function", + function=ToolFunctionDefinition( + name="test_tool", + description="call this to obtain the result", + parameters=ToolParametersDefinition( + type="object", + properties={ + "name": ToolPropertyDefinition( + type="string", description="the name of the user" + ), + "password": ToolPropertyDefinition( + type="string", description="the password of the user" + ), + }, + required=["name", "password"], + ), + ), + ) + + # Use auto tool choice + tool_choice = AutoToolChoice(type="auto") + + # Call the method + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=250, + temperature=0, + frequency_penalty=1, + presence_penalty=1, + tools=[test_tool], + tool_choice=tool_choice, + ) + + # Assertions + mock_request.assert_called_once() + assert result.id == "chatcmpl-789" + assert len(result.choices) == 1 + assert result.choices[0].message.content is not None + assert "John" in result.choices[0].message.content + assert result.choices[0].message.tool_calls is None + + @pytest.mark.asyncio + @patch.object(UiPathLlmChatService, "request_async") + async def test_no_tools_mocked(self, mock_request, llm_service): + # Mock response + mock_response = MagicMock() + mock_response.json.return_value = { + "id": "chatcmpl-abc", + "object": "chat.completion", + "created": 1677858242, + "model": "gpt-4o-mini-2024-07-18", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Silently coding,\nPython's logic unfolds clear,\nBugs hide, then reveal.", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 40, + "completion_tokens": 20, + "total_tokens": 60, + "cache_read_input_tokens": None, + }, + } + mock_request.return_value = mock_response + + # Test messages + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Write a haiku about Python programming."}, + ] + + # Call the method + result = await llm_service.chat_completions( + messages=messages, + model=ChatModels.gpt_4o_mini_2024_07_18, + max_tokens=100, + temperature=0.7, + ) + + # Assertions + mock_request.assert_called_once() + assert result.id == "chatcmpl-abc" + assert len(result.choices) == 1 + assert result.choices[0].message.content is not None + assert len(result.choices[0].message.content.strip()) > 0 + assert result.choices[0].message.tool_calls is None + + # Verify the correct payload was sent + args, kwargs = mock_request.call_args + assert json.loads(kwargs["content"])["messages"] == messages + assert json.loads(kwargs["content"])["max_tokens"] == 100 + assert json.loads(kwargs["content"])["temperature"] == 0.7 diff --git a/sdk/core/tests/sdk/test_config.py b/tests/sdk/test_config.py similarity index 97% rename from sdk/core/tests/sdk/test_config.py rename to tests/sdk/test_config.py index 5114bbb3..5e7407cf 100644 --- a/sdk/core/tests/sdk/test_config.py +++ b/tests/sdk/test_config.py @@ -1,7 +1,7 @@ import pydantic_core import pytest -from uipath_sdk._uipath_sdk import UiPathSDK +from uipath_sdk import UiPathSDK class TestSdkConfig: diff --git a/sdk/core/uv.lock b/uv.lock similarity index 99% rename from sdk/core/uv.lock rename to uv.lock index 2032656f..8a52ae62 100644 --- a/sdk/core/uv.lock +++ b/uv.lock @@ -1604,7 +1604,7 @@ wheels = [ [[package]] name = "uipath-sdk" -version = "0.0.110" +version = "0.0.113" source = { editable = "." } dependencies = [ { name = "click" },