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feat: langchain improved
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28
demos/use_cases/multi_frameworks/Dockerfile
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28
demos/use_cases/multi_frameworks/Dockerfile
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FROM python:3.11-slim
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WORKDIR /app
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# Install bash and uv
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RUN apt-get update && apt-get install -y bash && rm -rf /var/lib/apt/lists/*
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RUN pip install --no-cache-dir uv
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# Copy dependency files
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COPY pyproject.toml ./
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COPY README.md ./
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# Install dependencies to system Python
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RUN uv pip install --system .
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# Copy shared utilities
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COPY openai_protocol.py ./
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# Copy application code
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COPY crewai/ ./crewai/
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COPY langchain/ ./langchain/
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# Set environment variables
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ENV PYTHONUNBUFFERED=1
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ENV PYTHONPATH=/app:$PYTHONPATH
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# Default command (will be overridden in docker-compose)
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CMD ["uv", "run", "python", "crewai/flight_agent.py"]
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21
demos/use_cases/multi_frameworks/README.md
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21
demos/use_cases/multi_frameworks/README.md
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# Multi-Framework Travel Agents
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This project demonstrates multi-framework integration with both CrewAI and LangChain agents working together.
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## Agents
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- **CrewAI Flight Agent** (Port 10520): Handles flight-related queries
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- **LangChain Weather Agent** (Port 10510): Handles weather-related queries
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## Setup
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```bash
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docker compose build
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docker compose up -d
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```
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## Environment Variables
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- `OPENAI_API_KEY`: Required for LLM access
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- `AEROAPI_KEY`: Optional for flight data
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- `LLM_GATEWAY_ENDPOINT`: Plano gateway endpoint (default: http://host.docker.internal:12000/v1)
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57
demos/use_cases/multi_frameworks/config.yaml
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57
demos/use_cases/multi_frameworks/config.yaml
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version: v0.3.0
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agents:
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- id: weather_agent
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url: http://host.docker.internal:10510
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- id: flight_agent
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url: http://host.docker.internal:10520
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model_providers:
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- model: openai/gpt-4o
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access_key: $OPENAI_API_KEY
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default: true
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- model: openai/gpt-4o-mini
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access_key: $OPENAI_API_KEY # smaller, faster, cheaper model for extracting entities like location
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listeners:
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- type: agent
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name: travel_booking_service
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port: 8001
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router: plano_orchestrator_v1
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agents:
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- id: weather_agent
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description: |
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WeatherAgent is a specialized AI assistant for real-time weather information and forecasts. It provides accurate weather data for any city worldwide using the Open-Meteo API, helping travelers plan their trips with up-to-date weather conditions.
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Capabilities:
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* Get real-time weather conditions and multi-day forecasts for any city worldwide using Open-Meteo API (free, no API key needed)
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* Provides current temperature
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* Provides multi-day forecasts
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* Provides weather conditions
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* Provides sunrise/sunset times
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* Provides detailed weather information
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* Understands conversation context to resolve location references from previous messages
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* Handles weather-related questions including "What's the weather in [city]?", "What's the forecast for [city]?", "How's the weather in [city]?"
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* When queries include both weather and other travel questions (e.g., flights, currency), this agent answers ONLY the weather part
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- id: flight_agent
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description: |
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FlightAgent is an AI-powered tool specialized in providing live flight information between airports. It leverages the FlightAware AeroAPI to deliver real-time flight status, gate information, and delay updates.
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Capabilities:
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* Get live flight information between airports using FlightAware AeroAPI
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* Shows real-time flight status
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* Shows scheduled/estimated/actual departure and arrival times
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* Shows gate and terminal information
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* Shows delays
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* Shows aircraft type
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* Shows flight status
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* Automatically resolves city names to airport codes (IATA/ICAO)
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* Understands conversation context to infer origin/destination from follow-up questions
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* Handles flight-related questions including "What flights go from [city] to [city]?", "Do flights go to [city]?", "Are there direct flights from [city]?"
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* When queries include both flight and other travel questions (e.g., weather, currency), this agent answers ONLY the flight part
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tracing:
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random_sampling: 100
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423
demos/use_cases/multi_frameworks/crewai/flight_agent.py
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423
demos/use_cases/multi_frameworks/crewai/flight_agent.py
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import json
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import os
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import logging
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import time
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import uuid
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import httpx
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import uvicorn
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from datetime import datetime
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from typing import Optional
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from fastapi import FastAPI, Request
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from fastapi.responses import JSONResponse, StreamingResponse
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from openai import AsyncOpenAI
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from opentelemetry.propagate import extract, inject
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from crewai import Agent, Task, Crew, LLM
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from crewai.tools import tool
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from openai_protocol import create_chat_completion_chunk
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - [FLIGHT_AGENT] - %(levelname)s - %(message)s",
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)
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logger = logging.getLogger(__name__)
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LLM_GATEWAY_ENDPOINT = os.getenv(
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"LLM_GATEWAY_ENDPOINT", "http://host.docker.internal:12000/v1"
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)
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FLIGHT_MODEL = "openai/gpt-4o"
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EXTRACTION_MODEL = "openai/gpt-4o-mini"
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AEROAPI_BASE_URL = "https://aeroapi.flightaware.com/aeroapi"
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AEROAPI_KEY = os.getenv("AEROAPI_KEY")
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http_client = httpx.AsyncClient(timeout=30.0)
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openai_client = AsyncOpenAI(base_url=LLM_GATEWAY_ENDPOINT, api_key="EMPTY")
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SYSTEM_PROMPT = """You are a travel planning assistant specializing in flight information.
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CRITICAL: You MUST respond with ONLY the final answer to the user.
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DO NOT OUTPUT:
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- "Thought:" or any internal thinking
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- "Action:" or tool names
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- "Action Input:" or parameters
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- "Observation:" or tool results
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- Any reasoning steps, planning, or internal deliberation
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FORMATTING RULES:
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- Respond in natural, conversational text ONLY
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- NEVER use JSON, code blocks, or technical formats
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- Present flight information in a clean, bullet-point list
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- Use plain text with proper spacing and line breaks
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Flight Information Format:
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- Airline Name (Flight Number)
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- Departure: Time from Airport Code, Gate info
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- Arrival: Time at Airport Code
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- Aircraft: Model name
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- Status: Current status
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Your task:
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1. Use tools silently (don't mention them to the user)
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2. Convert technical data into friendly, readable text
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3. Use 12-hour time format (e.g., "9:00 AM")
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4. Organize flights chronologically by departure time
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5. Include terminal/gate info when available
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Multi-agent context: If the conversation includes information from other sources, incorporate it naturally."""
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def build_flight_crew(
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request: Request,
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request_body: dict,
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streaming: bool,
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):
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ctx = extract(request.headers)
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extra_headers = {"x-envoy-max-retries": "3"}
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request_id = request.headers.get("x-request-id")
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if request_id:
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extra_headers["x-request-id"] = request_id
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inject(extra_headers, context=ctx)
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@tool("resolve_airport_code")
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async def resolve_airport_code_tool(city_name: str) -> str:
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"""Convert a city name to its primary airport IATA code.
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Args:
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city_name: Name of the city (e.g., 'Seattle', 'Atlanta', 'Karachi', 'Dubai')
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Returns:
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3-letter IATA airport code (e.g., 'SEA', 'ATL', 'KHI', 'DXB')
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Examples:
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Seattle → SEA
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Atlanta → ATL
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New York → JFK
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Dubai → DXB
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Karachi → KHI
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Lahore → LHE
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"""
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code = await resolve_airport_code(city_name, request)
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if not code:
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return f"Error: Could not resolve airport code for '{city_name}'"
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return code
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@tool("search_flights")
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async def search_flights(
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origin_code: str, destination_code: str, travel_date: Optional[str] = None
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):
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"""Search for flights between two airports using their IATA codes.
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Args:
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origin_code: Origin airport IATA code (3 letters, e.g., 'SEA', 'KHI')
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destination_code: Destination airport IATA code (3 letters, e.g., 'ATL', 'DXB')
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travel_date: Travel date in YYYY-MM-DD format. If not provided, defaults to TODAY.
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Note: Flight data is only available for today and up to 2 days ahead.
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IMPORTANT: Use the resolve_airport_code tool first if you only have city names.
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"""
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# Default to today's date if not provided
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if not travel_date:
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travel_date = datetime.now().strftime("%Y-%m-%d")
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# Validate that we have proper IATA codes (3 letters)
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if len(origin_code) != 3 or len(destination_code) != 3:
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return {
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"error": f"Invalid airport codes. Expected 3-letter IATA codes, got origin='{origin_code}' and destination='{destination_code}'. Use resolve_airport_code tool first to convert city names to codes.",
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}
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flight_data = await fetch_flights(origin_code, destination_code, travel_date)
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return {
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"origin_code": origin_code,
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"destination_code": destination_code,
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"travel_date": travel_date,
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"flights": flight_data.get("flights", []),
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"count": flight_data.get("count", 0),
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"error": flight_data.get("error"),
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}
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llm = LLM(
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model=FLIGHT_MODEL,
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api_key="EMPTY",
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base_url=LLM_GATEWAY_ENDPOINT,
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temperature=request_body.get("temperature", 0.7),
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max_tokens=request_body.get("max_tokens", 1000),
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stream=streaming,
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extra_headers=extra_headers,
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)
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agent = Agent(
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role="Flight Information Specialist",
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goal="Provide accurate, clear flight options and details for travelers.",
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backstory=SYSTEM_PROMPT,
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tools=[resolve_airport_code_tool, search_flights],
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llm=llm,
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verbose=False,
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reasoning=False,
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)
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task = Task(
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description=(
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"Answer the user's request based on this conversation:\n{conversation}\n\n"
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"CRITICAL: Output ONLY your final answer to the user. Do NOT output:\n"
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"- Thought, Action, Action Input, Observation, or any reasoning steps\n"
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"- Tool names, parameters, or results\n"
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"- Planning or internal deliberation\n\n"
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"Tool workflow (execute silently):\n"
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"1. City names → use resolve_airport_code to get IATA codes\n"
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"2. Use search_flights with the codes\n"
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"3. Present results conversationally\n\n"
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"Output requirements:\n"
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"- Natural conversational text only\n"
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"- NO JSON, code blocks, or technical formatting\n"
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"- Clean bullet points with readable times (9:00 AM format)\n"
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"- Direct answer with no reasoning shown"
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),
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expected_output=(
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"A direct answer to the user in plain text with flight options. "
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"NO Thought/Action/Observation. NO code blocks. NO JSON. "
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"Just natural language with bullet points."
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),
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agent=agent,
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)
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return Crew(agents=[agent], tasks=[task], stream=streaming, verbose=False)
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async def resolve_airport_code(city_name: str, request: Request) -> Optional[str]:
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if not city_name:
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return None
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try:
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ctx = extract(request.headers)
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extra_headers = {}
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inject(extra_headers, context=ctx)
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response = await openai_client.chat.completions.create(
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model=EXTRACTION_MODEL,
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messages=[
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{
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"role": "system",
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"content": "Convert city names to primary airport IATA codes. Return only the 3-letter code. Examples: Seattle→SEA, Atlanta→ATL, New York→JFK, Dubai→DXB, Lahore→LHE",
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},
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{"role": "user", "content": city_name},
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],
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temperature=0.1,
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max_tokens=10,
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extra_headers=extra_headers or None,
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)
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code = response.choices[0].message.content.strip().upper()
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code = code.strip("\"'`.,!? \n\t")
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return code if len(code) == 3 else None
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|
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except Exception as e:
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logger.error(f"Error resolving airport code for {city_name}: {e}")
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return None
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async def fetch_flights(
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origin_code: str, dest_code: str, travel_date: Optional[str] = None
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) -> dict:
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"""Fetch flights between two airports. Note: FlightAware limits to 2 days ahead."""
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search_date = travel_date or datetime.now().strftime("%Y-%m-%d")
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|
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search_date_obj = datetime.strptime(search_date, "%Y-%m-%d")
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today = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
|
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days_ahead = (search_date_obj - today).days
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||||
|
||||
if days_ahead > 2:
|
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logger.warning(
|
||||
f"Date {search_date} is {days_ahead} days ahead, exceeds FlightAware limit"
|
||||
)
|
||||
return {
|
||||
"origin_code": origin_code,
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"destination_code": dest_code,
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||||
"flights": [],
|
||||
"count": 0,
|
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"error": f"FlightAware API only provides data up to 2 days ahead. Requested date ({search_date}) is {days_ahead} days away.",
|
||||
}
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|
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try:
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url = f"{AEROAPI_BASE_URL}/airports/{origin_code}/flights/to/{dest_code}"
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headers = {"x-apikey": AEROAPI_KEY}
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params = {
|
||||
"start": f"{search_date}T00:00:00Z",
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||||
"end": f"{search_date}T23:59:59Z",
|
||||
"connection": "nonstop",
|
||||
"max_pages": 1,
|
||||
}
|
||||
|
||||
response = await http_client.get(url, headers=headers, params=params)
|
||||
|
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if response.status_code != 200:
|
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logger.error(
|
||||
f"FlightAware API error {response.status_code}: {response.text}"
|
||||
)
|
||||
return {
|
||||
"origin_code": origin_code,
|
||||
"destination_code": dest_code,
|
||||
"flights": [],
|
||||
"count": 0,
|
||||
}
|
||||
|
||||
data = response.json()
|
||||
flights = []
|
||||
|
||||
for flight_group in data.get("flights", [])[:5]:
|
||||
segments = flight_group.get("segments", [])
|
||||
if not segments:
|
||||
continue
|
||||
|
||||
flight = segments[0]
|
||||
flights.append(
|
||||
{
|
||||
"airline": flight.get("operator"),
|
||||
"flight_number": flight.get("ident_iata") or flight.get("ident"),
|
||||
"departure_time": flight.get("scheduled_out"),
|
||||
"arrival_time": flight.get("scheduled_in"),
|
||||
"origin": flight["origin"].get("code_iata")
|
||||
if isinstance(flight.get("origin"), dict)
|
||||
else None,
|
||||
"destination": flight["destination"].get("code_iata")
|
||||
if isinstance(flight.get("destination"), dict)
|
||||
else None,
|
||||
"aircraft_type": flight.get("aircraft_type"),
|
||||
"status": flight.get("status"),
|
||||
"terminal_origin": flight.get("terminal_origin"),
|
||||
"gate_origin": flight.get("gate_origin"),
|
||||
}
|
||||
)
|
||||
|
||||
logger.info(f"Found {len(flights)} flights from {origin_code} to {dest_code}")
|
||||
return {
|
||||
"origin_code": origin_code,
|
||||
"destination_code": dest_code,
|
||||
"flights": flights,
|
||||
"count": len(flights),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching flights: {e}")
|
||||
return {
|
||||
"origin_code": origin_code,
|
||||
"destination_code": dest_code,
|
||||
"flights": [],
|
||||
"count": 0,
|
||||
}
|
||||
|
||||
|
||||
app = FastAPI(title="Flight Information Agent", version="1.0.0")
|
||||
|
||||
|
||||
@app.post("/v1/chat/completions")
|
||||
async def handle_request(request: Request):
|
||||
request_body = await request.json()
|
||||
is_streaming = request_body.get("stream", True)
|
||||
model = request_body.get("model", FLIGHT_MODEL)
|
||||
|
||||
if is_streaming:
|
||||
return StreamingResponse(
|
||||
invoke_flight_agent_stream(request, request_body, model),
|
||||
media_type="text/event-stream",
|
||||
headers={"content-type": "text/event-stream"},
|
||||
)
|
||||
|
||||
content = await invoke_flight_agent(request, request_body)
|
||||
return JSONResponse(
|
||||
{
|
||||
"id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
"object": "chat.completion",
|
||||
"created": int(time.time()),
|
||||
"model": model,
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {"role": "assistant", "content": content},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def invoke_flight_agent(request: Request, request_body: dict):
|
||||
"""Generate flight information using a CrewAI agent."""
|
||||
messages = request_body.get("messages", [])
|
||||
crew = build_flight_crew(request, request_body, streaming=False)
|
||||
conversation = json.dumps(messages, indent=2)
|
||||
|
||||
try:
|
||||
result = crew.kickoff(inputs={"conversation": conversation})
|
||||
if hasattr(result, "raw"):
|
||||
return result.raw
|
||||
return str(result)
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating response: {e}")
|
||||
return "I'm having trouble retrieving flight information right now. Please try again."
|
||||
|
||||
|
||||
async def invoke_flight_agent_stream(
|
||||
request: Request,
|
||||
request_body: dict,
|
||||
model: str,
|
||||
):
|
||||
messages = request_body.get("messages", [])
|
||||
crew = build_flight_crew(request, request_body, streaming=True)
|
||||
conversation = json.dumps(messages, indent=2)
|
||||
|
||||
try:
|
||||
streaming = crew.kickoff(inputs={"conversation": conversation})
|
||||
for chunk in streaming:
|
||||
content = getattr(chunk, "content", None)
|
||||
if content is None:
|
||||
content = str(chunk)
|
||||
if not content:
|
||||
continue
|
||||
yield f"data: {json.dumps(create_chat_completion_chunk(model, content))}\n\n"
|
||||
|
||||
yield f"data: {json.dumps(create_chat_completion_chunk(model, '', 'stop'))}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
except Exception as e:
|
||||
logger.error(f"Error streaming response: {e}")
|
||||
error_message = "I'm having trouble retrieving flight information right now. Please try again."
|
||||
yield f"data: {json.dumps(create_chat_completion_chunk(model, error_message, 'stop'))}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
return {"status": "healthy", "agent": "flight_information"}
|
||||
|
||||
|
||||
def start_server(host: str = "0.0.0.0", port: int = 10520):
|
||||
uvicorn.run(
|
||||
app,
|
||||
host=host,
|
||||
port=port,
|
||||
log_config={
|
||||
"version": 1,
|
||||
"disable_existing_loggers": False,
|
||||
"formatters": {
|
||||
"default": {
|
||||
"format": "%(asctime)s - [FLIGHT_AGENT] - %(levelname)s - %(message)s"
|
||||
}
|
||||
},
|
||||
"handlers": {
|
||||
"default": {
|
||||
"formatter": "default",
|
||||
"class": "logging.StreamHandler",
|
||||
"stream": "ext://sys.stdout",
|
||||
}
|
||||
},
|
||||
"root": {"level": "INFO", "handlers": ["default"]},
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start_server()
|
||||
68
demos/use_cases/multi_frameworks/docker-compose.yaml
Normal file
68
demos/use_cases/multi_frameworks/docker-compose.yaml
Normal file
|
|
@ -0,0 +1,68 @@
|
|||
services:
|
||||
plano:
|
||||
build:
|
||||
context: ../../../
|
||||
dockerfile: Dockerfile
|
||||
ports:
|
||||
- "12000:12000"
|
||||
- "8001:8001"
|
||||
environment:
|
||||
- ARCH_CONFIG_PATH=/config/config.yaml
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY:?OPENAI_API_KEY environment variable is required but not set}
|
||||
volumes:
|
||||
- ./config.yaml:/app/arch_config.yaml
|
||||
- /etc/ssl/cert.pem:/etc/ssl/cert.pem
|
||||
|
||||
crewai-flight-agent:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
container_name: crewai-flight-agent
|
||||
restart: always
|
||||
ports:
|
||||
- "10520:10520"
|
||||
environment:
|
||||
- LLM_GATEWAY_ENDPOINT=http://host.docker.internal:12000/v1
|
||||
- AEROAPI_KEY=${AEROAPI_KEY:-}
|
||||
- PYTHONUNBUFFERED=1
|
||||
command: ["python", "-u", "crewai/flight_agent.py"]
|
||||
extra_hosts:
|
||||
- "host.docker.internal:host-gateway"
|
||||
|
||||
langchain-weather-agent:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
container_name: langchain-weather-agent
|
||||
restart: always
|
||||
ports:
|
||||
- "10510:10510"
|
||||
environment:
|
||||
- LLM_GATEWAY_ENDPOINT=http://host.docker.internal:12000/v1
|
||||
- PYTHONUNBUFFERED=1
|
||||
command: ["python", "-u", "langchain/weather_agent.py"]
|
||||
extra_hosts:
|
||||
- "host.docker.internal:host-gateway"
|
||||
|
||||
open-web-ui:
|
||||
image: dyrnq/open-webui:main
|
||||
restart: always
|
||||
ports:
|
||||
- "8080:8080"
|
||||
environment:
|
||||
- DEFAULT_MODEL=gpt-4o-mini
|
||||
- ENABLE_OPENAI_API=true
|
||||
- OPENAI_API_BASE_URL=http://host.docker.internal:8001/v1
|
||||
depends_on:
|
||||
- langchain-weather-agent
|
||||
- crewai-flight-agent
|
||||
|
||||
jaeger:
|
||||
build:
|
||||
context: ../../shared/jaeger
|
||||
container_name: jaeger
|
||||
restart: always
|
||||
ports:
|
||||
- "16686:16686" # Jaeger UI
|
||||
- "4317:4317" # OTLP gRPC receiver
|
||||
- "4318:4318" # OTLP HTTP receiver
|
||||
21
demos/use_cases/multi_frameworks/gateway_test.rest
Normal file
21
demos/use_cases/multi_frameworks/gateway_test.rest
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
### Test weather agent via Plano gateway
|
||||
POST http://localhost:11000/v1/chat/completions
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{"role": "user", "content": "What's the weather in Seattle today?"}
|
||||
]
|
||||
}
|
||||
|
||||
### Test flight agent via Plano gateway
|
||||
POST http://localhost:8001/v1/chat/completions
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{"role": "user", "content": "Find flights from Seattle to New York"}
|
||||
]
|
||||
}
|
||||
456
demos/use_cases/multi_frameworks/langchain/weather_agent.py
Normal file
456
demos/use_cases/multi_frameworks/langchain/weather_agent.py
Normal file
|
|
@ -0,0 +1,456 @@
|
|||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from urllib.parse import quote
|
||||
|
||||
import httpx
|
||||
import uvicorn
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.responses import JSONResponse, StreamingResponse
|
||||
from langchain.agents import create_agent
|
||||
from langchain_core.tools import tool
|
||||
from langchain_openai import ChatOpenAI
|
||||
from openai import AsyncOpenAI
|
||||
from opentelemetry.propagate import extract, inject
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from openai_protocol import create_chat_completion_chunk
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - [WEATHER_AGENT] - %(levelname)s - %(message)s",
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
LLM_GATEWAY_ENDPOINT = os.getenv(
|
||||
"LLM_GATEWAY_ENDPOINT", "http://host.docker.internal:12000/v1"
|
||||
)
|
||||
WEATHER_MODEL = "openai/gpt-4o"
|
||||
LOCATION_MODEL = "openai/gpt-4o-mini"
|
||||
|
||||
openai_client_via_plano = AsyncOpenAI(
|
||||
base_url=LLM_GATEWAY_ENDPOINT,
|
||||
api_key="EMPTY",
|
||||
)
|
||||
|
||||
app = FastAPI(title="Weather Forecast Agent", version="1.0.0")
|
||||
|
||||
http_client = httpx.AsyncClient(timeout=10.0)
|
||||
|
||||
|
||||
def celsius_to_fahrenheit(temp_c: Optional[float]) -> Optional[float]:
|
||||
return round(temp_c * 9 / 5 + 32, 1) if temp_c is not None else None
|
||||
|
||||
|
||||
async def get_weather_data(
|
||||
request: Request,
|
||||
messages: list,
|
||||
days: int = 1,
|
||||
request_id: str = None,
|
||||
city_override: Optional[str] = None,
|
||||
):
|
||||
instructions = """You are a city name extractor. Look at the FINAL user message ONLY and extract the city name.
|
||||
|
||||
The FINAL user message will be the LAST message with role "user" in the conversation.
|
||||
|
||||
IMPORTANT: Ignore all previous messages. Focus ONLY on the FINAL user message.
|
||||
|
||||
Examples of what to extract from the FINAL user message:
|
||||
- "What's the weather in Seattle?" -> Seattle
|
||||
- "What's the weather in San Francisco?" -> San Francisco
|
||||
- "What about Dubai?" -> Dubai
|
||||
- "How's the weather in Tokyo today?" -> Tokyo
|
||||
- "Tell me about Lahore" -> Lahore
|
||||
- "What about there?" -> Look at conversation for the last mentioned city
|
||||
|
||||
Output ONLY the city name. Nothing else. One word or city name only.
|
||||
If no city can be found, output: NOT_FOUND"""
|
||||
|
||||
location = city_override
|
||||
if not location:
|
||||
try:
|
||||
user_messages = [
|
||||
msg.get("content") for msg in messages if msg.get("role") == "user"
|
||||
]
|
||||
|
||||
if not user_messages:
|
||||
location = "New York"
|
||||
else:
|
||||
ctx = extract(request.headers)
|
||||
extra_headers = {}
|
||||
if request_id:
|
||||
extra_headers["x-request-id"] = request_id
|
||||
inject(extra_headers, context=ctx)
|
||||
response = await openai_client_via_plano.chat.completions.create(
|
||||
model=LOCATION_MODEL,
|
||||
messages=[
|
||||
{"role": "system", "content": instructions},
|
||||
*[
|
||||
{"role": msg.get("role"), "content": msg.get("content")}
|
||||
for msg in messages
|
||||
],
|
||||
],
|
||||
temperature=0.1,
|
||||
max_tokens=10,
|
||||
extra_headers=extra_headers if extra_headers else None,
|
||||
)
|
||||
|
||||
location = response.choices[0].message.content.strip().strip("\"'`.,!?")
|
||||
logger.info("Location extraction result: '%s'", location)
|
||||
|
||||
if not location or location.upper() == "NOT_FOUND":
|
||||
location = "New York"
|
||||
logger.info("Location not found, defaulting to: %s", location)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Error extracting location: %s", e)
|
||||
location = "New York"
|
||||
|
||||
logger.info("Fetching weather for location: '%s' (days: %s)", location, days)
|
||||
|
||||
try:
|
||||
geocode_url = (
|
||||
"https://geocoding-api.open-meteo.com/v1/search?"
|
||||
f"name={quote(location)}&count=1&language=en&format=json"
|
||||
)
|
||||
geocode_response = await http_client.get(geocode_url)
|
||||
|
||||
if geocode_response.status_code != 200 or not geocode_response.json().get(
|
||||
"results"
|
||||
):
|
||||
logger.warning("Could not geocode %s, using New York", location)
|
||||
location = "New York"
|
||||
geocode_url = (
|
||||
"https://geocoding-api.open-meteo.com/v1/search?"
|
||||
f"name={quote(location)}&count=1&language=en&format=json"
|
||||
)
|
||||
geocode_response = await http_client.get(geocode_url)
|
||||
|
||||
geocode_data = geocode_response.json()
|
||||
if not geocode_data.get("results"):
|
||||
return {
|
||||
"location": location,
|
||||
"weather": {
|
||||
"date": datetime.now().strftime("%Y-%m-%d"),
|
||||
"day_name": datetime.now().strftime("%A"),
|
||||
"temperature_c": None,
|
||||
"temperature_f": None,
|
||||
"weather_code": None,
|
||||
"error": "Could not retrieve weather data",
|
||||
},
|
||||
}
|
||||
|
||||
result = geocode_data["results"][0]
|
||||
location_name = result.get("name", location)
|
||||
latitude = result["latitude"]
|
||||
longitude = result["longitude"]
|
||||
|
||||
logger.info(
|
||||
"Geocoded '%s' to %s (%s, %s)", location, location_name, latitude, longitude
|
||||
)
|
||||
|
||||
weather_url = (
|
||||
"https://api.open-meteo.com/v1/forecast?"
|
||||
f"latitude={latitude}&longitude={longitude}&"
|
||||
"current=temperature_2m&"
|
||||
"daily=sunrise,sunset,temperature_2m_max,temperature_2m_min,weather_code&"
|
||||
f"forecast_days={days}&timezone=auto"
|
||||
)
|
||||
|
||||
weather_response = await http_client.get(weather_url)
|
||||
if weather_response.status_code != 200:
|
||||
return {
|
||||
"location": location_name,
|
||||
"weather": {
|
||||
"date": datetime.now().strftime("%Y-%m-%d"),
|
||||
"day_name": datetime.now().strftime("%A"),
|
||||
"temperature_c": None,
|
||||
"temperature_f": None,
|
||||
"weather_code": None,
|
||||
"error": "Could not retrieve weather data",
|
||||
},
|
||||
}
|
||||
|
||||
weather_data = weather_response.json()
|
||||
current_temp = weather_data.get("current", {}).get("temperature_2m")
|
||||
daily = weather_data.get("daily", {})
|
||||
|
||||
forecast = []
|
||||
for i in range(days):
|
||||
date_str = daily["time"][i]
|
||||
date_obj = datetime.fromisoformat(date_str.replace("Z", "+00:00"))
|
||||
|
||||
temp_max = (
|
||||
daily.get("temperature_2m_max", [])[i]
|
||||
if daily.get("temperature_2m_max")
|
||||
else None
|
||||
)
|
||||
temp_min = (
|
||||
daily.get("temperature_2m_min", [])[i]
|
||||
if daily.get("temperature_2m_min")
|
||||
else None
|
||||
)
|
||||
weather_code = (
|
||||
daily.get("weather_code", [0])[i] if daily.get("weather_code") else 0
|
||||
)
|
||||
sunrise = daily.get("sunrise", [])[i] if daily.get("sunrise") else None
|
||||
sunset = daily.get("sunset", [])[i] if daily.get("sunset") else None
|
||||
|
||||
temp_c = (
|
||||
temp_max
|
||||
if temp_max is not None
|
||||
else (current_temp if i == 0 and current_temp else temp_min)
|
||||
)
|
||||
|
||||
forecast.append(
|
||||
{
|
||||
"date": date_str.split("T")[0],
|
||||
"day_name": date_obj.strftime("%A"),
|
||||
"temperature_c": round(temp_c, 1) if temp_c is not None else None,
|
||||
"temperature_f": celsius_to_fahrenheit(temp_c),
|
||||
"temperature_max_c": (
|
||||
round(temp_max, 1) if temp_max is not None else None
|
||||
),
|
||||
"temperature_min_c": (
|
||||
round(temp_min, 1) if temp_min is not None else None
|
||||
),
|
||||
"weather_code": weather_code,
|
||||
"sunrise": sunrise.split("T")[1] if sunrise else None,
|
||||
"sunset": sunset.split("T")[1] if sunset else None,
|
||||
}
|
||||
)
|
||||
|
||||
return {"location": location_name, "forecast": forecast}
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Error getting weather data: %s", e)
|
||||
return {
|
||||
"location": location,
|
||||
"weather": {
|
||||
"date": datetime.now().strftime("%Y-%m-%d"),
|
||||
"day_name": datetime.now().strftime("%A"),
|
||||
"temperature_c": None,
|
||||
"temperature_f": None,
|
||||
"weather_code": None,
|
||||
"error": "Could not retrieve weather data",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class WeatherToolInput(BaseModel):
|
||||
city: str = Field(..., description="City name to look up weather for")
|
||||
days: int = Field(
|
||||
1,
|
||||
ge=1,
|
||||
le=16,
|
||||
description="Number of forecast days (1-16). Defaults to 1 (current).",
|
||||
)
|
||||
|
||||
|
||||
WEATHER_SYSTEM_PROMPT = """You are a weather assistant in a multi-agent system. You will receive weather data in JSON format with these fields:
|
||||
|
||||
- "location": City name
|
||||
- "forecast": Array of weather objects, each with date, day_name, temperature_c, temperature_f, temperature_max_c, temperature_min_c, weather_code, sunrise, sunset
|
||||
- weather_code: WMO code (0=clear, 1-3=partly cloudy, 45-48=fog, 51-67=rain, 71-86=snow, 95-99=thunderstorm)
|
||||
|
||||
Your task:
|
||||
1. Present the weather/forecast clearly for the location
|
||||
2. For single day: show current conditions
|
||||
3. For multi-day: show each day with date and conditions
|
||||
4. Include temperature in both Celsius and Fahrenheit
|
||||
5. Describe conditions naturally based on weather_code
|
||||
6. Use conversational language
|
||||
|
||||
Multi-agent context: You are part of a larger system. If the conversation includes additional context or information from other sources, acknowledge and incorporate it naturally into your response. Your primary focus is weather, but be aware of the full conversation context.
|
||||
|
||||
Remember: Only use the provided data. If fields are null, mention data is unavailable."""
|
||||
|
||||
|
||||
def build_weather_agent(
|
||||
request: Request,
|
||||
request_body: dict,
|
||||
streaming: bool,
|
||||
):
|
||||
messages = request_body.get("messages", [])
|
||||
ctx = extract(request.headers)
|
||||
extra_headers = {"x-envoy-max-retries": "3"}
|
||||
request_id = request.headers.get("x-request-id")
|
||||
if request_id:
|
||||
extra_headers["x-request-id"] = request_id
|
||||
inject(extra_headers, context=ctx)
|
||||
|
||||
@tool("get_weather_forecast", args_schema=WeatherToolInput)
|
||||
async def get_weather_forecast(city: str, days: int = 1):
|
||||
"""Fetch a structured weather forecast for a city."""
|
||||
return await get_weather_data(
|
||||
request,
|
||||
messages,
|
||||
days,
|
||||
request_id=request_id,
|
||||
city_override=city,
|
||||
)
|
||||
|
||||
llm = ChatOpenAI(
|
||||
model=WEATHER_MODEL,
|
||||
api_key="EMPTY",
|
||||
base_url=LLM_GATEWAY_ENDPOINT,
|
||||
temperature=request_body.get("temperature", 0.7),
|
||||
max_tokens=request_body.get("max_tokens", 1000),
|
||||
streaming=streaming,
|
||||
default_headers=extra_headers,
|
||||
)
|
||||
|
||||
return create_agent(
|
||||
model=llm,
|
||||
tools=[get_weather_forecast],
|
||||
system_prompt=WEATHER_SYSTEM_PROMPT,
|
||||
)
|
||||
|
||||
|
||||
@app.post("/v1/chat/completions")
|
||||
async def handle_request(request: Request):
|
||||
request_body = await request.json()
|
||||
is_streaming = request_body.get("stream", True)
|
||||
|
||||
try:
|
||||
model = request_body.get("model", WEATHER_MODEL)
|
||||
|
||||
if is_streaming:
|
||||
return StreamingResponse(
|
||||
invoke_weather_agent_stream(request, request_body, model),
|
||||
media_type="text/event-stream",
|
||||
headers={"content-type": "text/event-stream"},
|
||||
)
|
||||
|
||||
content = await invoke_weather_agent(request, request_body)
|
||||
return JSONResponse(
|
||||
{
|
||||
"id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
"object": "chat.completion",
|
||||
"created": int(time.time()),
|
||||
"model": model,
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {"role": "assistant", "content": content},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("Error generating weather response: %s", e)
|
||||
if is_streaming:
|
||||
return StreamingResponse(
|
||||
invoke_weather_agent_error_stream(
|
||||
request_body,
|
||||
"I'm having trouble retrieving weather information right now. Please try again.",
|
||||
),
|
||||
media_type="text/event-stream",
|
||||
headers={"content-type": "text/event-stream"},
|
||||
)
|
||||
return JSONResponse(
|
||||
{
|
||||
"error": {
|
||||
"message": "I'm having trouble retrieving weather information right now. Please try again.",
|
||||
"type": "server_error",
|
||||
}
|
||||
},
|
||||
status_code=500,
|
||||
)
|
||||
|
||||
|
||||
async def invoke_weather_agent(
|
||||
request: Request,
|
||||
request_body: dict,
|
||||
):
|
||||
messages = request_body.get("messages", [])
|
||||
agent = build_weather_agent(request, request_body, streaming=False)
|
||||
|
||||
result = await agent.ainvoke({"messages": messages})
|
||||
final_message = result["messages"][-1]
|
||||
return (
|
||||
final_message.content
|
||||
if hasattr(final_message, "content")
|
||||
else str(final_message)
|
||||
)
|
||||
|
||||
|
||||
async def invoke_weather_agent_stream(
|
||||
request: Request,
|
||||
request_body: dict,
|
||||
model: str,
|
||||
):
|
||||
messages = request_body.get("messages", [])
|
||||
agent = build_weather_agent(request, request_body, streaming=True)
|
||||
|
||||
try:
|
||||
async for event in agent.astream_events(
|
||||
{"messages": messages},
|
||||
version="v2",
|
||||
):
|
||||
if event.get("event") != "on_chat_model_stream":
|
||||
continue
|
||||
chunk = event.get("data", {}).get("chunk")
|
||||
content = getattr(chunk, "content", None)
|
||||
if not content:
|
||||
continue
|
||||
if isinstance(content, list):
|
||||
content = "".join(
|
||||
piece for piece in content if isinstance(piece, str)
|
||||
).strip()
|
||||
if not content:
|
||||
continue
|
||||
yield f"data: {json.dumps(create_chat_completion_chunk(model, content))}\n\n"
|
||||
|
||||
yield f"data: {json.dumps(create_chat_completion_chunk(model, '', 'stop'))}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
except Exception as e:
|
||||
logger.error("Error streaming weather response: %s", e)
|
||||
error_message = "I'm having trouble retrieving weather information right now. Please try again."
|
||||
yield f"data: {json.dumps(create_chat_completion_chunk(model, error_message, 'stop'))}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
|
||||
async def invoke_weather_agent_error_stream(request_body: dict, error_message: str):
|
||||
model = request_body.get("model", WEATHER_MODEL)
|
||||
yield f"data: {json.dumps(create_chat_completion_chunk(model, error_message, 'stop'))}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
return {"status": "healthy", "agent": "weather_forecast"}
|
||||
|
||||
|
||||
def start_server(host: str = "localhost", port: int = 10510):
|
||||
uvicorn.run(
|
||||
app,
|
||||
host=host,
|
||||
port=port,
|
||||
log_config={
|
||||
"version": 1,
|
||||
"disable_existing_loggers": False,
|
||||
"formatters": {
|
||||
"default": {
|
||||
"format": "%(asctime)s - [WEATHER_AGENT] - %(levelname)s - %(message)s",
|
||||
}
|
||||
},
|
||||
"handlers": {
|
||||
"default": {
|
||||
"formatter": "default",
|
||||
"class": "logging.StreamHandler",
|
||||
"stream": "ext://sys.stdout",
|
||||
}
|
||||
},
|
||||
"root": {"level": "INFO", "handlers": ["default"]},
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start_server(host="0.0.0.0", port=10510)
|
||||
35
demos/use_cases/multi_frameworks/openai_protocol.py
Normal file
35
demos/use_cases/multi_frameworks/openai_protocol.py
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
"""OpenAI API protocol utilities for standardized response formatting."""
|
||||
|
||||
import time
|
||||
import uuid
|
||||
from typing import Optional
|
||||
|
||||
|
||||
def create_chat_completion_chunk(
|
||||
model: str,
|
||||
content: str,
|
||||
finish_reason: Optional[str] = None,
|
||||
) -> dict:
|
||||
"""Create an OpenAI-compatible streaming chat completion chunk.
|
||||
|
||||
Args:
|
||||
model: Model identifier to include in the response
|
||||
content: Content text for this chunk
|
||||
finish_reason: Optional finish reason ('stop', 'length', etc.)
|
||||
|
||||
Returns:
|
||||
Dictionary formatted as OpenAI chat.completion.chunk
|
||||
"""
|
||||
return {
|
||||
"id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
"object": "chat.completion.chunk",
|
||||
"created": int(time.time()),
|
||||
"model": model,
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"content": content} if content else {},
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
],
|
||||
}
|
||||
26
demos/use_cases/multi_frameworks/pyproject.toml
Normal file
26
demos/use_cases/multi_frameworks/pyproject.toml
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
[project]
|
||||
name = "multi-framework-agents"
|
||||
version = "0.1.0"
|
||||
description = "Multi-Framework Travel Agents - CrewAI and LangChain integration"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"click>=8.2.1",
|
||||
"pydantic>=2.11.7",
|
||||
"fastapi>=0.115.0",
|
||||
"uvicorn>=0.30.0",
|
||||
"openai>=1.0.0",
|
||||
"httpx>=0.24.0",
|
||||
"opentelemetry-api>=1.20.0",
|
||||
"crewai[tools]>=0.70.0",
|
||||
"langchain>=1.0.0",
|
||||
"langchain-core>=1.0.0",
|
||||
"langchain-openai>=0.3.0",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["crewai", "langchain"]
|
||||
43
demos/use_cases/multi_frameworks/test.rest
Normal file
43
demos/use_cases/multi_frameworks/test.rest
Normal file
|
|
@ -0,0 +1,43 @@
|
|||
@llm_endpoint = http://localhost:12000
|
||||
|
||||
### Travel Agent Chat Completion Request
|
||||
POST {{llm_endpoint}}/v1/chat/completions HTTP/1.1
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What's the weather in Seattle?"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "The weather in Seattle is sunny with a temperature of 60 degrees Fahrenheit."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is one Alaska flight that goes direct to Atlanta from Seattle?"
|
||||
}
|
||||
],
|
||||
"max_tokens": 1000,
|
||||
"stream": false,
|
||||
"temperature": 1.0
|
||||
}
|
||||
|
||||
|
||||
### test 8001
|
||||
|
||||
### test upstream llm
|
||||
POST http://localhost:8001/v1/chat/completions HTTP/1.1
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "\nCurrent weather data for Seattle:\n\n{\n \"location\": \"Seattle\",\n \"forecast\": [\n {\n \"date\": \"2025-12-22\",\n \"day_name\": \"Monday\",\n \"temperature_c\": 8.3,\n \"temperature_f\": 46.9,\n \"temperature_max_c\": 8.3,\n \"temperature_min_c\": 2.8,\n \"condition\": \"Rainy\",\n \"sunrise\": \"07:55\",\n \"sunset\": \"16:20\"\n }\n ]\n}\n\nUse this data to answer the user's weather query."
|
||||
}
|
||||
],
|
||||
"model": "gpt-4o",
|
||||
}
|
||||
524
demos/use_cases/multi_frameworks/uv.lock
generated
Normal file
524
demos/use_cases/multi_frameworks/uv.lock
generated
Normal file
|
|
@ -0,0 +1,524 @@
|
|||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.10"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-doc"
|
||||
version = "0.0.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.12.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "exceptiongroup", marker = "python_full_version < '3.11'" },
|
||||
{ name = "idna" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.11.12"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438, upload-time = "2025-11-12T02:54:49.735Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "distro"
|
||||
version = "1.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/8a/0e/97c33bf5009bdbac74fd2beace167cab3f978feb69cc36f1ef79360d6c4e/exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598", size = 16740, upload-time = "2025-11-21T23:01:53.443Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.125.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "annotated-doc" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "starlette" },
|
||||
{ name = "typing-extensions" },
|
||||
]
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Loading…
Add table
Add a link
Reference in a new issue