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demo: add multi-framework agent demo (#688)
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24
demos/use_cases/multi_agent_with_crewai_langchain/Dockerfile
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24
demos/use_cases/multi_agent_with_crewai_langchain/Dockerfile
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FROM python:3.13-slim
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && \
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apt-get install -y --no-install-recommends bash && \
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rm -rf /var/lib/apt/lists/* && \
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pip install --no-cache-dir uv
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# Install Python dependencies
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COPY pyproject.toml README.md ./
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RUN uv pip install --system .
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# Copy application code
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COPY openai_protocol.py ./
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COPY crewai/ ./crewai/
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COPY langchain/ ./langchain/
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# Runtime configuration
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ENV PYTHONUNBUFFERED=1 \
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PYTHONPATH=/app
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CMD ["uv", "run", "python", "crewai/flight_agent.py"]
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132
demos/use_cases/multi_agent_with_crewai_langchain/README.md
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132
demos/use_cases/multi_agent_with_crewai_langchain/README.md
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Travel Agents in CrewAI and LangChain - with Plano
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**What you'll see:** A travel assistant that seamlessly combines flight booking (CrewAI) and weather forecasts (LangChain) in a single conversation - with unified routing, orchestration, moderation, and observability across both frameworks.
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## The Problem
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Building multi-agent systems today forces developers to:
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- **Pick one framework** - can't mix CrewAI, LangChain, or custom agents easily
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- **Write plumbing code** - authentication, request routing, error handling
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- **Rebuild for changes** - want to swap frameworks? Start over
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- **Limited observability** - no unified view across different agent frameworks
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## Plano's Solution
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Plano acts as a **framework-agnostic proxy and data plane** that:
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- Routes requests to the right agent(s), in the right order (CrewAI, LangChain, or custom)
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- Normalizes requests/responses across frameworks automatically
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- Provides unified authentication, tracing, and logs
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- Lets you mix and match frameworks without coupling, so that you can continue to innovate easily
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## How To Run
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### Prerequisites
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1. **Install Plano CLI**
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```bash
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uv tool install planoai
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```
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2. **Set Environment Variables**
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```bash
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export OPENAI_API_KEY=your_key_here
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export AEROAPI_KEY=your_key_here # Get your free API key at https://flightaware.com/aeroapi/
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```
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### Start the Demo
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```bash
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# From the demo directory
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cd demos/use_cases/multi_agent_with_crewai_langchain
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# Build and start all services
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docker-compose up -d
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```
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This starts:
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- **Plano** (ports 12000, 8001) - routing and orchestration
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- **CrewAI Flight Agent** (port 10520) - flight search
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- **LangChain Weather Agent** (port 10510) - weather forecasts
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- **Open WebUI** (port 8080) - chat interface
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- **Jaeger** (port 16686) - distributed tracing
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### Try It Out
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1. **Open the Chat Interface**
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- Navigate to [http://localhost:8080](http://localhost:8080)
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- Create an account (stored locally)
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2. **Ask Multi-Agent Questions**
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```
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"What's the weather in San Francisco and can you find flights from Seattle to San Francisco?"
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```
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Plano automatically:
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- Routes the weather part to the LangChain agent
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- Routes the flight part to the CrewAI agent
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- Combines responses seamlessly
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3. **View Distributed Traces**
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- Open [http://localhost:16686](http://localhost:16686) (Jaeger UI)
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- See how requests flow through both agents
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## Architecture
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```
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┌─────────────┐
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│ Open WebUI │ (Chat Interface)
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└──────┬──────┘
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│
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v
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┌─────────────┐
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│ Plano │ (Orchestration & DataPlane)
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└──────┬──────┘
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│
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├──────────────┬──────────────┐
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v v v
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┌────────────┐ ┌────────────┐ ┌──────────┐
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│ CrewAI │ │ LangChain │ │ Jaeger │
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│ Flight │ │ Weather │ │ (Traces) │
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│ Agent │ │ Agent │ └──────────┘
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└────────────┘ └────────────┘
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├──────────────├
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v v
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┌─────────────┐
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│ Plano │ (Proxy LLM calls)
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└──────┬──────┘
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```
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## Travel Agents
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### Flight Agent
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- Framework: CrewAI
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- Capabilities: Flight search, itinerary planning
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- Tools: `resolve_airport_code`, `search_flights`
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- Data Source: FlightAware AeroAPI
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### Weather Agent
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- Framework: LangChain
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- Capabilities: Weather forecasts, conditions
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- Tools: `get_weather_forecast`
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- Data Source: Open-Meteo API
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## Cleanup
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```bash
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docker-compose down
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```
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## Next Steps
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- **Add your own agent** - any framework, just expose the OpenAI-compatible endpoint
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- **Custom routing** - modify `config.yaml` to change agent selection logic
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- **Production deployment** - see [Plano docs](https://docs.katanemo.com) for scaling guidance
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## Learn More
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- [Plano Documentation](https://docs.planoai.dev)
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- [CrewAI Documentation](https://docs.crewai.com)
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- [LangChain Documentation](https://python.langchain.com)
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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://langchain-weather-agent:10510
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- id: flight_agent
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url: http://crewai-flight-agent: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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@ -0,0 +1,430 @@
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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 and travel conditions.
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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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Weather Information (when available):
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- Present weather data in a clear, readable format
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- Include temperature, conditions, and any travel advisories
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- Integrate weather context with flight information naturally
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- Mention how weather might affect travel plans if relevant
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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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6. When weather data is provided, summarize it clearly and relate it to the travel plans
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7. NOTE (Multi-agent context): If the conversation includes information from other sources (weather, hotels, etc.), incorporate it naturally and cohesively in your response."""
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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=True,
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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: NOTE you are part of a multi-agent setup, so if the conversation includes information from other sources that are not flight-related, incorporate it naturally.\n"
|
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"Output ONLY your final answer to the user. Do NOT show:\n"
|
||||
"- 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"
|
||||
"- 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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|
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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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],
|
||||
temperature=0.1,
|
||||
max_tokens=10,
|
||||
extra_headers=extra_headers or None,
|
||||
)
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
|
||||
async def fetch_flights(
|
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origin_code: str, dest_code: str, travel_date: Optional[str] = None
|
||||
) -> dict:
|
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"""Fetch flights between two airports. Note: FlightAware limits to 2 days ahead."""
|
||||
search_date = travel_date or datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
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)
|
||||
days_ahead = (search_date_obj - today).days
|
||||
|
||||
if days_ahead > 2:
|
||||
logger.warning(
|
||||
f"Date {search_date} is {days_ahead} days ahead, exceeds FlightAware limit"
|
||||
)
|
||||
return {
|
||||
"origin_code": origin_code,
|
||||
"destination_code": dest_code,
|
||||
"flights": [],
|
||||
"count": 0,
|
||||
"error": f"FlightAware API only provides data up to 2 days ahead. Requested date ({search_date}) is {days_ahead} days away.",
|
||||
}
|
||||
|
||||
try:
|
||||
url = f"{AEROAPI_BASE_URL}/airports/{origin_code}/flights/to/{dest_code}"
|
||||
headers = {"x-apikey": AEROAPI_KEY}
|
||||
params = {
|
||||
"start": f"{search_date}T00:00:00Z",
|
||||
"end": f"{search_date}T23:59:59Z",
|
||||
"connection": "nonstop",
|
||||
"max_pages": 1,
|
||||
}
|
||||
|
||||
response = await http_client.get(url, headers=headers, params=params)
|
||||
|
||||
if response.status_code != 200:
|
||||
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: {create_chat_completion_chunk(model, content).model_dump_json()}\n\n"
|
||||
|
||||
yield f"data: {create_chat_completion_chunk(model, '', 'stop').model_dump_json()}\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: {create_chat_completion_chunk(model, error_message, 'stop').model_dump_json()}\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()
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
|
||||
services:
|
||||
plano:
|
||||
build:
|
||||
context: ../../../
|
||||
dockerfile: Dockerfile
|
||||
ports:
|
||||
- "8001:8001"
|
||||
environment:
|
||||
- ARCH_CONFIG_PATH=/app/arch_config.yaml
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY:?OPENAI_API_KEY environment variable is required but not set}
|
||||
- OTEL_TRACING_HTTP_ENDPOINT=http://host.docker.internal:4318/v1/traces
|
||||
volumes:
|
||||
- ./config.yaml:/app/arch_config.yaml:ro
|
||||
- /etc/ssl/cert.pem:/etc/ssl/cert.pem
|
||||
|
||||
crewai-flight-agent:
|
||||
build:
|
||||
dockerfile: Dockerfile
|
||||
restart: always
|
||||
ports:
|
||||
- "10520:10520"
|
||||
environment:
|
||||
- LLM_GATEWAY_ENDPOINT=http://plano:12000/v1
|
||||
- AEROAPI_KEY=${AEROAPI_KEY:?AEROAPI_KEY environment variable is required but not set}
|
||||
- PYTHONUNBUFFERED=1
|
||||
command: ["python", "-u", "crewai/flight_agent.py"]
|
||||
|
||||
langchain-weather-agent:
|
||||
build:
|
||||
dockerfile: Dockerfile
|
||||
restart: always
|
||||
ports:
|
||||
- "10510:10510"
|
||||
environment:
|
||||
- LLM_GATEWAY_ENDPOINT=http://plano:12000/v1
|
||||
command: ["python", "-u", "langchain/weather_agent.py"]
|
||||
|
||||
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://plano:8001/v1
|
||||
|
||||
jaeger:
|
||||
build:
|
||||
context: ../../shared/jaeger
|
||||
restart: always
|
||||
ports:
|
||||
- "16686:16686" # Jaeger UI
|
||||
- "4317:4317" # OTLP gRPC receiver
|
||||
- "4318:4318" # OTLP HTTP receiver
|
||||
|
|
@ -0,0 +1,459 @@
|
|||
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 = "gpt-4o"
|
||||
LOCATION_MODEL = "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("\"'`.,!?")
|
||||
|
||||
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 and travel conditions 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
|
||||
7. When flight information is present in the conversation, summarize it clearly:
|
||||
- Present flight details in a readable format (airline, times, gates, status)
|
||||
- Integrate flight and weather information cohesively
|
||||
- Mention how weather might affect the flights if relevant
|
||||
8. NOTE (Multi-agent context): If the conversation includes information from other agents and sources (flights, hotels, etc.), incorporate it naturally and provide a comprehensive travel summary.
|
||||
|
||||
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
|
||||
logger.debug("Request ID set: [redacted]")
|
||||
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: {create_chat_completion_chunk(model, content).model_dump_json()}\n\n"
|
||||
|
||||
yield f"data: {create_chat_completion_chunk(model, '', 'stop').model_dump_json()}\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: {create_chat_completion_chunk(model, error_message, 'stop').model_dump_json()}\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: {create_chat_completion_chunk(model, error_message, 'stop').model_dump_json()}\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)
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
"""OpenAI API protocol utilities for standardized response formatting."""
|
||||
|
||||
import time
|
||||
from typing import Optional
|
||||
from openai.types.chat import ChatCompletionChunk
|
||||
from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta
|
||||
|
||||
|
||||
def create_chat_completion_chunk(
|
||||
model: str,
|
||||
content: str,
|
||||
finish_reason: Optional[str] = None,
|
||||
) -> ChatCompletionChunk:
|
||||
"""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:
|
||||
ChatCompletionChunk object from OpenAI SDK
|
||||
"""
|
||||
return ChatCompletionChunk(
|
||||
id=f"chatcmpl-{int(time.time() * 1000000)}",
|
||||
object="chat.completion.chunk",
|
||||
created=int(time.time()),
|
||||
model=model,
|
||||
choices=[
|
||||
Choice(
|
||||
index=0,
|
||||
delta=ChoiceDelta(content=content) if content else ChoiceDelta(),
|
||||
finish_reason=finish_reason,
|
||||
)
|
||||
],
|
||||
)
|
||||
|
|
@ -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"]
|
||||
BIN
demos/use_cases/multi_agent_with_crewai_langchain/traces.png
Normal file
BIN
demos/use_cases/multi_agent_with_crewai_langchain/traces.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 331 KiB |
524
demos/use_cases/multi_agent_with_crewai_langchain/uv.lock
generated
Normal file
524
demos/use_cases/multi_agent_with_crewai_langchain/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" }
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||||
wheels = [
|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{ name = "idna" },
|
||||
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|
||||
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|
||||
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" }
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
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|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
version = "1.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
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||||
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|
||||
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Loading…
Add table
Add a link
Reference in a new issue