mirror of
https://github.com/katanemo/plano.git
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plano orchestration (draft)
This commit is contained in:
Adil Hafeez
parent
2f9121407b
commit
53e03901d2
25 changed files with 2520 additions and 285 deletions
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@ -74,17 +74,13 @@ async def chat_completion_http(request: Request, request_body: ChatCompletionReq
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else:
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logger.info("No traceparent header found")
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# Check if streaming is requested
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if request_body.stream:
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return StreamingResponse(
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stream_chat_completions(request_body, traceparent_header),
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media_type="text/plain",
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headers={
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"content-type": "text/event-stream",
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},
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)
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else:
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return await non_streaming_chat_completions(request_body, traceparent_header)
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return StreamingResponse(
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stream_chat_completions(request_body, traceparent_header),
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media_type="text/plain",
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headers={
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"content-type": "text/event-stream",
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},
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)
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async def stream_chat_completions(
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@ -186,88 +182,6 @@ async def stream_chat_completions(
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yield "data: [DONE]\n\n"
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async def non_streaming_chat_completions(
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request_body: ChatCompletionRequest, traceparent_header: str = None
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):
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"""Generate non-streaming chat completions."""
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# Prepare messages for response generation
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response_messages = prepare_response_messages(request_body)
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try:
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# Call archgw using OpenAI client
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logger.info(f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to generate response")
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# Prepare extra headers if traceparent is provided
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extra_headers = {"x-envoy-max-retries": "3"}
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if traceparent_header:
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extra_headers["traceparent"] = traceparent_header
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response = await archgw_client.chat.completions.create(
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model=RESPONSE_MODEL,
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messages=response_messages,
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temperature=request_body.temperature or 0.7,
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max_tokens=request_body.max_tokens or 1000,
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extra_headers=extra_headers,
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)
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generated_response = response.choices[0].message.content.strip()
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logger.info(f"Response generated successfully")
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return ChatCompletionResponse(
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id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
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created=int(time.time()),
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model=request_body.model,
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choices=[
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": generated_response,
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},
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"finish_reason": "stop",
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}
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],
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usage={
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"prompt_tokens": sum(
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len(msg.content.split()) for msg in request_body.messages
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),
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"completion_tokens": len(generated_response.split()),
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"total_tokens": sum(
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len(msg.content.split()) for msg in request_body.messages
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)
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+ len(generated_response.split()),
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},
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)
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except Exception as e:
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logger.error(f"Error generating response: {e}")
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# Fallback response
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fallback_message = "I apologize, but I'm having trouble generating a response right now. Please try again."
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return ChatCompletionResponse(
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id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
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created=int(time.time()),
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model=request_body.model,
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choices=[
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{
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"index": 0,
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"message": {"role": "assistant", "content": fallback_message},
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"finish_reason": "stop",
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}
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],
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usage={
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"prompt_tokens": sum(
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len(msg.content.split()) for msg in request_body.messages
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),
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"completion_tokens": len(fallback_message.split()),
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"total_tokens": sum(
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len(msg.content.split()) for msg in request_body.messages
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)
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+ len(fallback_message.split()),
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},
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)
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@app.get("/health")
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async def health_check():
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"""Health check endpoint."""
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273
demos/use_cases/travel_booking/README.md
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273
demos/use_cases/travel_booking/README.md
Normal file
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@ -0,0 +1,273 @@
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# Travel Booking Demo
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A multi-agent travel booking system demonstrating archgw's agent router with specialized agents for weather, flights, and hotels.
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## Architecture
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This demo consists of three intelligent agents:
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1. **Weather Agent** (REST) - Provides current weather and forecasts for destinations worldwide
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2. **Flight Agent** (REST) - Searches and books flights between cities with pricing and availability
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3. **Hotel Agent** (REST) - Searches and reserves hotel rooms with preferences and pricing
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All agents use archgw's agent router to intelligently route user requests to the appropriate specialized agent.
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## Components
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### Weather Forecast Agent
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- **Port**: 10510
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- **Endpoint**: `/v1/chat/completions`
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- Provides weather information and forecasts for any location
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- Returns temperature, conditions, humidity, and wind speed
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- Supports multi-day forecasts
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### Flight Booking Agent
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- **Port**: 10520
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- **Endpoint**: `/v1/chat/completions`
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- Searches for flights between cities
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- Returns flight options with airlines, times, prices, and durations
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- Supports booking confirmations
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### Hotel Reservation Agent
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- **Port**: 10530
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- **Endpoint**: `/v1/chat/completions`
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- Searches for hotels in any city
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- Returns hotel options with ratings, amenities, prices, and locations
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- Supports reservation confirmations
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## Quick Start
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### Prerequisites
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- Python 3.10 or higher
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- UV package manager (recommended) or pip
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- OpenAI API key
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- archgw installed and configured
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### 1. Set up environment
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```bash
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# Copy and edit the .env file with your OpenAI API key
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cp .env.example .env
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# Edit .env and add your OPENAI_API_KEY
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```
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### 2. Install dependencies
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```bash
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# Using UV (recommended)
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uv sync
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# Or using pip
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pip install -e .
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```
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### 3. Start all agents
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```bash
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chmod +x start_agents.sh
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./start_agents.sh
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```
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This starts:
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- Weather Agent on port 10510
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- Flight Agent on port 10520
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- Hotel Agent on port 10530
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### 4. Start archgw
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In a new terminal:
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```bash
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cd /path/to/travel_booking
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archgw up --foreground
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```
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### 5. Test the system
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#### Weather Query
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```bash
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curl -X POST http://localhost:8001/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gpt-4o-mini",
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"messages": [
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{"role": "user", "content": "What is the weather like in Paris?"}
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]
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}'
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```
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#### Flight Search
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```bash
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curl -X POST http://localhost:8001/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gpt-4o-mini",
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"messages": [
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{"role": "user", "content": "Find me flights from New York to London next week"}
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]
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}'
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```
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#### Hotel Search
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```bash
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curl -X POST http://localhost:8001/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gpt-4o-mini",
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"messages": [
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{"role": "user", "content": "I need a hotel in Tokyo for 3 nights"}
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]
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}'
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```
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### 6. Use with Open WebUI (Optional)
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Start the docker compose services:
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```bash
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docker-compose up -d
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```
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Then open http://localhost:8080 in your browser. The Open WebUI is pre-configured to use the archgw endpoint at http://host.docker.internal:8001/v1.
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## Example Conversations
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### Multi-Agent Conversation
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The system can handle complex travel planning that involves multiple agents:
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```
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User: I'm planning a trip to Tokyo next month. What's the weather like?
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Assistant: [Weather Agent provides Tokyo weather forecast]
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User: Great! Can you find me flights from San Francisco to Tokyo?
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Assistant: [Flight Agent shows flight options]
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User: I'll take the United flight. Now I need a hotel near the city center.
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Assistant: [Hotel Agent shows hotel options in Tokyo]
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```
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The archgw agent router automatically routes each request to the appropriate agent based on the content.
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## Agent Capabilities
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### Weather Agent
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- Current weather conditions
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- 5-day forecasts
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- Temperature (Celsius and Fahrenheit)
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- Humidity and wind speed
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- Weather conditions (sunny, cloudy, rainy, etc.)
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### Flight Agent
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- Flight search between any two cities
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- Multiple airline options
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- Flight times and durations
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- Pricing information
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- Direct and connecting flights
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- Seat availability
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- Booking confirmations
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### Hotel Agent
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- Hotel search by city
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- Check-in/check-out date support
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- Hotel ratings and reviews
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- Amenities listing
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- Distance from city center
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- Pricing per night and total
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- Room availability
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- Reservation confirmations
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## Architecture Details
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### Agent Routing
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archgw's agent router analyzes incoming requests and automatically routes them to the most appropriate agent based on:
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- Request content and intent
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- Agent descriptions in arch_config.yaml
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- Conversation context
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### Request Flow
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1. User sends a request to archgw (port 8001)
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2. archgw's agent router analyzes the request
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3. Router selects the appropriate agent (weather, flight, or hotel)
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4. Agent processes the request using archgw's LLM gateway
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5. Response streams back to the user
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### Tracing
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The demo includes Jaeger for distributed tracing:
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- View traces at http://localhost:16686
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- Trace sampling set to 100% for demo purposes
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- Track requests across archgw and agents
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## Development
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### Running Individual Agents
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You can start agents individually for development:
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```bash
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# Weather agent
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uv run python -m travel_agents --agent weather --port 10510
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# Flight agent
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uv run python -m travel_agents --agent flight --port 10520
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# Hotel agent
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uv run python -m travel_agents --agent hotel --port 10530
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```
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### Project Structure
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```
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travel_booking/
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├── arch_config.yaml # archgw configuration
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├── docker-compose.yaml # Optional services (Jaeger, Open WebUI)
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├── pyproject.toml # Python dependencies
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├── start_agents.sh # Start all agents script
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├── .env # Environment variables
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└── src/
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└── travel_agents/
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├── __init__.py # CLI entry point
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├── __main__.py # Module runner
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├── api.py # Shared API models
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├── weather_agent.py # Weather forecast agent
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├── flight_agent.py # Flight booking agent
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└── hotel_agent.py # Hotel reservation agent
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```
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## Configuration
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### arch_config.yaml
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The configuration defines:
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- Three agents with their URLs and descriptions
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- Model providers (OpenAI)
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- Model aliases for easy reference
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- Agent router on port 8001
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- Tracing configuration
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### Environment Variables
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- `OPENAI_API_KEY`: Your OpenAI API key (required)
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- `LLM_GATEWAY_ENDPOINT`: archgw LLM gateway URL (default: http://localhost:12000/v1)
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## Troubleshooting
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### Agents won't start
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- Ensure Python 3.10+ is installed
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- Check that UV is installed: `pip install uv`
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- Verify ports 10510, 10520, 10530 are available
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### archgw won't start
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- Make sure you're in the travel_booking directory
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- Check that OPENAI_API_KEY is set in .env
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- Verify archgw is installed: `archgw --version`
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### No response from agents
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- Check that all agents are running (check start_agents.sh output)
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- Verify archgw is running on port 8001
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- Check logs for errors
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### Wrong agent responds
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- The agent router uses LLM-based routing
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- If routing is incorrect, try being more explicit in your request
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- Check agent descriptions in arch_config.yaml
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## Notes
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- This is a demo with mock data - flights and hotels are simulated
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- Real implementations would integrate with actual APIs (Amadeus, Booking.com, etc.)
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- Weather data is generated randomly based on typical patterns for each city
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- All agents use streaming responses for better user experience
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## License
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This demo is part of the archgw project.
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38
demos/use_cases/travel_booking/arch_config.yaml
Normal file
38
demos/use_cases/travel_booking/arch_config.yaml
Normal file
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@ -0,0 +1,38 @@
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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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- id: hotel_agent
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url: http://host.docker.internal:10530
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model_providers:
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- model: openai/gpt-4o-mini
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access_key: $OPENAI_API_KEY
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default: true
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- model: openai/gpt-4o
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access_key: $OPENAI_API_KEY
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model_aliases:
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fast-llm:
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target: gpt-4o-mini
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smart-llm:
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target: gpt-4o
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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: Get current weather and forecast information for any location worldwide
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- id: flight_agent
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description: Search and book flights between cities with pricing and availability
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- id: hotel_agent
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description: Search and reserve hotel rooms with preferences and pricing
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tracing:
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random_sampling: 100
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17
demos/use_cases/travel_booking/docker-compose.yaml
Normal file
17
demos/use_cases/travel_booking/docker-compose.yaml
Normal file
|
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@ -0,0 +1,17 @@
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services:
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jaeger:
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build:
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context: ../../shared/jaeger
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ports:
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- "16686:16686"
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- "4317:4317"
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- "4318:4318"
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open-web-ui:
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image: dyrnq/open-webui:main
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restart: always
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ports:
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- "8080:8080"
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environment:
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- DEFAULT_MODEL=gpt-4o-mini
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- ENABLE_OPENAI_API=true
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- OPENAI_API_BASE_URL=http://host.docker.internal:8001/v1
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20
demos/use_cases/travel_booking/pyproject.toml
Normal file
20
demos/use_cases/travel_booking/pyproject.toml
Normal file
|
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@ -0,0 +1,20 @@
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[project]
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name = "travel-agents"
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version = "0.1.0"
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description = "Travel Booking Agents - Weather, Flight, and Hotel"
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readme = "README.md"
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requires-python = ">=3.10"
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dependencies = [
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"click>=8.2.1",
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"pydantic>=2.11.7",
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"fastapi>=0.104.1",
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"uvicorn>=0.24.0",
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"openai>=2.13.0",
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]
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[project.scripts]
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travel_agents = "travel_agents:main"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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52
demos/use_cases/travel_booking/src/travel_agents/__init__.py
Normal file
52
demos/use_cases/travel_booking/src/travel_agents/__init__.py
Normal file
|
|
@ -0,0 +1,52 @@
|
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import click
|
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|
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|
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@click.command()
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@click.option("--host", "host", default="localhost", help="Host to bind server to")
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@click.option("--port", "port", type=int, default=8000, help="Port for server")
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@click.option(
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"--agent",
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"agent",
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required=True,
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help="Agent name: weather, flight, or hotel",
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)
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def main(host, port, agent):
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"""Start a travel agent REST server."""
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# Map friendly names to agent modules
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agent_map = {
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"weather": ("travel_agents.weather_agent", 10510),
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"flight": ("travel_agents.flight_agent", 10520),
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"hotel": ("travel_agents.hotel_agent", 10530),
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}
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if agent not in agent_map:
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print(f"Error: Unknown agent '{agent}'")
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print(f"Available agents: {', '.join(agent_map.keys())}")
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return
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module_name, default_port = agent_map[agent]
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|
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# Use default port if not specified
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if port == 8000:
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port = default_port
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print(f"Starting {agent} agent REST server on {host}:{port}")
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|
||||
# Import the agent module and start server
|
||||
if agent == "weather":
|
||||
from travel_agents.weather_agent import start_server
|
||||
|
||||
start_server(host=host, port=port)
|
||||
elif agent == "flight":
|
||||
from travel_agents.flight_agent import start_server
|
||||
|
||||
start_server(host=host, port=port)
|
||||
elif agent == "hotel":
|
||||
from travel_agents.hotel_agent import start_server
|
||||
|
||||
start_server(host=host, port=port)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
from . import main
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
36
demos/use_cases/travel_booking/src/travel_agents/api.py
Normal file
36
demos/use_cases/travel_booking/src/travel_agents/api.py
Normal file
|
|
@ -0,0 +1,36 @@
|
|||
from pydantic import BaseModel
|
||||
from typing import List, Optional, Dict, Any
|
||||
|
||||
|
||||
class ChatMessage(BaseModel):
|
||||
role: str
|
||||
content: str
|
||||
|
||||
|
||||
class ChatCompletionRequest(BaseModel):
|
||||
model: str
|
||||
messages: List[ChatMessage]
|
||||
temperature: Optional[float] = 1.0
|
||||
max_tokens: Optional[int] = None
|
||||
top_p: Optional[float] = 1.0
|
||||
frequency_penalty: Optional[float] = 0.0
|
||||
presence_penalty: Optional[float] = 0.0
|
||||
stream: Optional[bool] = False
|
||||
stop: Optional[List[str]] = None
|
||||
|
||||
|
||||
class ChatCompletionResponse(BaseModel):
|
||||
id: str
|
||||
object: str = "chat.completion"
|
||||
created: int
|
||||
model: str
|
||||
choices: List[Dict[str, Any]]
|
||||
usage: Dict[str, int]
|
||||
|
||||
|
||||
class ChatCompletionStreamResponse(BaseModel):
|
||||
id: str
|
||||
object: str = "chat.completion.chunk"
|
||||
created: int
|
||||
model: str
|
||||
choices: List[Dict[str, Any]]
|
||||
377
demos/use_cases/travel_booking/src/travel_agents/flight_agent.py
Normal file
377
demos/use_cases/travel_booking/src/travel_agents/flight_agent.py
Normal file
|
|
@ -0,0 +1,377 @@
|
|||
import json
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
from openai import AsyncOpenAI
|
||||
import os
|
||||
import logging
|
||||
import time
|
||||
import uuid
|
||||
import uvicorn
|
||||
from datetime import datetime, timedelta
|
||||
import random
|
||||
|
||||
from .api import (
|
||||
ChatCompletionRequest,
|
||||
ChatCompletionResponse,
|
||||
ChatCompletionStreamResponse,
|
||||
)
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - [FLIGHT_AGENT] - %(levelname)s - %(message)s",
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Configuration for archgw LLM gateway
|
||||
LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1")
|
||||
FLIGHT_MODEL = "gpt-4o-mini"
|
||||
|
||||
# Sample flight data
|
||||
AIRLINES = [
|
||||
"United Airlines",
|
||||
"Delta",
|
||||
"American Airlines",
|
||||
"British Airways",
|
||||
"Emirates",
|
||||
"Lufthansa",
|
||||
"Air France",
|
||||
"Singapore Airlines",
|
||||
]
|
||||
AIRCRAFT_TYPES = [
|
||||
"Boeing 737",
|
||||
"Airbus A320",
|
||||
"Boeing 777",
|
||||
"Airbus A350",
|
||||
"Boeing 787",
|
||||
]
|
||||
|
||||
CITIES = [
|
||||
"New York",
|
||||
"London",
|
||||
"Tokyo",
|
||||
"Paris",
|
||||
"Sydney",
|
||||
"Dubai",
|
||||
"Singapore",
|
||||
"San Francisco",
|
||||
"Los Angeles",
|
||||
"Chicago",
|
||||
"Miami",
|
||||
"Seattle",
|
||||
"Boston",
|
||||
"Hong Kong",
|
||||
"Bangkok",
|
||||
"Rome",
|
||||
]
|
||||
|
||||
# System prompt for flight agent
|
||||
SYSTEM_PROMPT = """You are a helpful flight booking assistant.
|
||||
|
||||
Your role is to help users search for and book flights based on their travel needs.
|
||||
|
||||
When responding:
|
||||
1. Parse the user's request to understand departure city, destination, dates, and preferences
|
||||
2. Use the flight search results provided in the conversation context
|
||||
3. Present flight options clearly with key details (airline, times, price, duration)
|
||||
4. Help users compare options and make informed decisions
|
||||
5. If they want to book, confirm their selection and provide booking confirmation
|
||||
6. Ask clarifying questions if needed information is missing
|
||||
|
||||
Be professional, helpful, and make the booking process smooth and easy."""
|
||||
|
||||
|
||||
def generate_flight_data(
|
||||
origin: str, destination: str, date: str = None, num_results: int = 5
|
||||
):
|
||||
"""Generate mock flight search results."""
|
||||
if not date:
|
||||
date = (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
|
||||
flights = []
|
||||
for i in range(num_results):
|
||||
# Generate departure and arrival times
|
||||
departure_hour = random.randint(6, 22)
|
||||
departure_min = random.choice([0, 15, 30, 45])
|
||||
flight_duration_hours = random.randint(2, 14)
|
||||
flight_duration_mins = random.choice([0, 15, 30, 45])
|
||||
|
||||
departure_time = f"{departure_hour:02d}:{departure_min:02d}"
|
||||
arrival_hour = (departure_hour + flight_duration_hours) % 24
|
||||
arrival_min = (departure_min + flight_duration_mins) % 60
|
||||
arrival_time = f"{arrival_hour:02d}:{arrival_min:02d}"
|
||||
|
||||
# Generate price based on duration
|
||||
base_price = 200 + (flight_duration_hours * 50)
|
||||
price_variation = random.randint(-100, 300)
|
||||
price = base_price + price_variation
|
||||
|
||||
# Determine if it's direct or has stops
|
||||
stops = random.choice([0, 0, 0, 1, 2]) # Bias towards direct flights
|
||||
|
||||
flight = {
|
||||
"flight_number": f"{random.choice(['UA', 'DL', 'AA', 'BA', 'EK'])}{random.randint(100, 999)}",
|
||||
"airline": random.choice(AIRLINES),
|
||||
"aircraft": random.choice(AIRCRAFT_TYPES),
|
||||
"origin": origin,
|
||||
"destination": destination,
|
||||
"date": date,
|
||||
"departure_time": departure_time,
|
||||
"arrival_time": arrival_time,
|
||||
"duration": f"{flight_duration_hours}h {flight_duration_mins}m",
|
||||
"stops": stops,
|
||||
"price_usd": price,
|
||||
"available_seats": random.randint(5, 150),
|
||||
"class": random.choice(
|
||||
["Economy", "Economy", "Premium Economy", "Business"]
|
||||
),
|
||||
}
|
||||
flights.append(flight)
|
||||
|
||||
# Sort by price
|
||||
flights.sort(key=lambda x: x["price_usd"])
|
||||
|
||||
return flights
|
||||
|
||||
|
||||
def extract_flight_params(messages):
|
||||
"""Extract flight search parameters from messages."""
|
||||
origin = None
|
||||
destination = None
|
||||
date = None
|
||||
|
||||
# Look through messages for flight details
|
||||
for msg in reversed(messages):
|
||||
if msg.role == "user":
|
||||
content = msg.content.lower()
|
||||
|
||||
# Look for "from X to Y" pattern
|
||||
if " from " in content and " to " in content:
|
||||
parts = content.split(" from ")
|
||||
if len(parts) > 1:
|
||||
remaining = parts[1]
|
||||
if " to " in remaining:
|
||||
city_parts = remaining.split(" to ")
|
||||
origin = city_parts[0].strip().title()
|
||||
# Extract destination (may have more text after)
|
||||
dest_words = city_parts[1].strip().split()
|
||||
if dest_words:
|
||||
destination = dest_words[0].title()
|
||||
|
||||
# Look for date mentions
|
||||
if "tomorrow" in content:
|
||||
date = (datetime.now() + timedelta(days=1)).strftime("%Y-%m-%d")
|
||||
elif "next week" in content:
|
||||
date = (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
|
||||
# Defaults
|
||||
if not origin:
|
||||
origin = "New York"
|
||||
if not destination:
|
||||
destination = "London"
|
||||
if not date:
|
||||
date = (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
|
||||
return origin, destination, date
|
||||
|
||||
|
||||
# Initialize OpenAI client for archgw
|
||||
archgw_client = AsyncOpenAI(
|
||||
base_url=LLM_GATEWAY_ENDPOINT,
|
||||
api_key="EMPTY",
|
||||
)
|
||||
|
||||
# FastAPI app for REST server
|
||||
app = FastAPI(title="Flight Booking Agent", version="1.0.0")
|
||||
|
||||
|
||||
def prepare_flight_messages(request_body: ChatCompletionRequest):
|
||||
"""Prepare messages with flight data."""
|
||||
# Extract flight parameters
|
||||
origin, destination, date = extract_flight_params(request_body.messages)
|
||||
|
||||
# Check if user wants to book (vs just search)
|
||||
last_user_msg = ""
|
||||
for msg in reversed(request_body.messages):
|
||||
if msg.role == "user":
|
||||
last_user_msg = msg.content.lower()
|
||||
break
|
||||
|
||||
is_booking = any(
|
||||
word in last_user_msg for word in ["book", "reserve", "purchase", "buy"]
|
||||
)
|
||||
|
||||
# Generate flight search results
|
||||
flights = generate_flight_data(origin, destination, date)
|
||||
|
||||
flight_context = f"""
|
||||
Flight search results for {origin} to {destination} on {date}:
|
||||
|
||||
{json.dumps(flights, indent=2)}
|
||||
|
||||
{'User wants to book a flight. Help them complete the booking.' if is_booking else 'Present these options to the user clearly.'}
|
||||
"""
|
||||
|
||||
response_messages = [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "system", "content": flight_context},
|
||||
]
|
||||
|
||||
# Add conversation history
|
||||
for msg in request_body.messages:
|
||||
response_messages.append({"role": msg.role, "content": msg.content})
|
||||
|
||||
return response_messages
|
||||
|
||||
|
||||
@app.post("/v1/chat/completions")
|
||||
async def chat_completion_http(request: Request, request_body: ChatCompletionRequest):
|
||||
"""HTTP endpoint for chat completions with streaming support."""
|
||||
logger.info(
|
||||
f"Received flight booking request with {len(request_body.messages)} messages"
|
||||
)
|
||||
|
||||
# Get traceparent header from HTTP request
|
||||
traceparent_header = request.headers.get("traceparent")
|
||||
|
||||
if traceparent_header:
|
||||
logger.info(f"Received traceparent header: {traceparent_header}")
|
||||
|
||||
return StreamingResponse(
|
||||
stream_chat_completions(request_body, traceparent_header),
|
||||
media_type="text/plain",
|
||||
headers={
|
||||
"content-type": "text/event-stream",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
async def stream_chat_completions(
|
||||
request_body: ChatCompletionRequest, traceparent_header: str = None
|
||||
):
|
||||
"""Generate streaming chat completions."""
|
||||
# Prepare messages with flight data
|
||||
response_messages = prepare_flight_messages(request_body)
|
||||
|
||||
try:
|
||||
logger.info(
|
||||
f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to generate flight response"
|
||||
)
|
||||
|
||||
# Prepare extra headers
|
||||
extra_headers = {"x-envoy-max-retries": "3"}
|
||||
if traceparent_header:
|
||||
extra_headers["traceparent"] = traceparent_header
|
||||
|
||||
response_stream = await archgw_client.chat.completions.create(
|
||||
model=FLIGHT_MODEL,
|
||||
messages=response_messages,
|
||||
temperature=request_body.temperature or 0.7,
|
||||
max_tokens=request_body.max_tokens or 1000,
|
||||
stream=True,
|
||||
extra_headers=extra_headers,
|
||||
)
|
||||
|
||||
completion_id = f"chatcmpl-{uuid.uuid4().hex[:8]}"
|
||||
created_time = int(time.time())
|
||||
collected_content = []
|
||||
|
||||
async for chunk in response_stream:
|
||||
if chunk.choices and chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
collected_content.append(content)
|
||||
|
||||
stream_chunk = ChatCompletionStreamResponse(
|
||||
id=completion_id,
|
||||
created=created_time,
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"content": content},
|
||||
"finish_reason": None,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {stream_chunk.model_dump_json()}\n\n"
|
||||
|
||||
# Send final chunk
|
||||
full_response = "".join(collected_content)
|
||||
updated_history = [{"role": "assistant", "content": full_response}]
|
||||
|
||||
final_chunk = ChatCompletionStreamResponse(
|
||||
id=completion_id,
|
||||
created=created_time,
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {},
|
||||
"finish_reason": "stop",
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": json.dumps(updated_history),
|
||||
},
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {final_chunk.model_dump_json()}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating flight response: {e}")
|
||||
|
||||
error_chunk = ChatCompletionStreamResponse(
|
||||
id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
created=int(time.time()),
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {
|
||||
"content": "I apologize, but I'm having trouble searching for flights right now. Please try again."
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {error_chunk.model_dump_json()}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
"""Health check endpoint."""
|
||||
return {"status": "healthy", "agent": "flight_booking"}
|
||||
|
||||
|
||||
def start_server(host: str = "localhost", port: int = 10520):
|
||||
"""Start the REST server."""
|
||||
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"],
|
||||
},
|
||||
},
|
||||
)
|
||||
388
demos/use_cases/travel_booking/src/travel_agents/hotel_agent.py
Normal file
388
demos/use_cases/travel_booking/src/travel_agents/hotel_agent.py
Normal file
|
|
@ -0,0 +1,388 @@
|
|||
import json
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
from openai import AsyncOpenAI
|
||||
import os
|
||||
import logging
|
||||
import time
|
||||
import uuid
|
||||
import uvicorn
|
||||
from datetime import datetime, timedelta
|
||||
import random
|
||||
|
||||
from .api import (
|
||||
ChatCompletionRequest,
|
||||
ChatCompletionResponse,
|
||||
ChatCompletionStreamResponse,
|
||||
)
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - [HOTEL_AGENT] - %(levelname)s - %(message)s",
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Configuration for archgw LLM gateway
|
||||
LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1")
|
||||
HOTEL_MODEL = "gpt-4o-mini"
|
||||
|
||||
# Sample hotel data
|
||||
HOTEL_CHAINS = [
|
||||
"Marriott",
|
||||
"Hilton",
|
||||
"Hyatt",
|
||||
"InterContinental",
|
||||
"Four Seasons",
|
||||
"Sheraton",
|
||||
"Ritz-Carlton",
|
||||
"Westin",
|
||||
]
|
||||
HOTEL_TYPES = [
|
||||
"Luxury Hotel",
|
||||
"Business Hotel",
|
||||
"Boutique Hotel",
|
||||
"Resort",
|
||||
"City Center Hotel",
|
||||
]
|
||||
AMENITIES = [
|
||||
["Free WiFi", "Pool", "Gym", "Restaurant", "Bar"],
|
||||
["Free WiFi", "Gym", "Business Center", "Room Service"],
|
||||
["Free WiFi", "Spa", "Pool", "Restaurant", "Concierge"],
|
||||
["Free WiFi", "Beach Access", "Pool", "Restaurant", "Water Sports"],
|
||||
["Free WiFi", "Rooftop Bar", "Restaurant", "City Views"],
|
||||
]
|
||||
|
||||
CITIES = [
|
||||
"New York",
|
||||
"London",
|
||||
"Tokyo",
|
||||
"Paris",
|
||||
"Sydney",
|
||||
"Dubai",
|
||||
"Singapore",
|
||||
"San Francisco",
|
||||
"Los Angeles",
|
||||
"Chicago",
|
||||
"Miami",
|
||||
"Seattle",
|
||||
"Boston",
|
||||
"Hong Kong",
|
||||
"Bangkok",
|
||||
"Rome",
|
||||
]
|
||||
|
||||
# System prompt for hotel agent
|
||||
SYSTEM_PROMPT = """You are a helpful hotel reservation assistant.
|
||||
|
||||
Your role is to help users find and book hotels that match their needs and preferences.
|
||||
|
||||
When responding:
|
||||
1. Parse the user's request to understand location, dates, number of guests, and preferences
|
||||
2. Use the hotel search results provided in the conversation context
|
||||
3. Present hotel options clearly with key details (name, rating, amenities, price per night)
|
||||
4. Help users compare options based on their priorities (location, price, amenities, etc.)
|
||||
5. If they want to book, confirm their selection and provide booking confirmation
|
||||
6. Ask clarifying questions if needed information is missing (dates, number of rooms, guests, etc.)
|
||||
|
||||
Be professional, attentive to details, and help make the booking process smooth."""
|
||||
|
||||
|
||||
def generate_hotel_data(
|
||||
location: str, check_in: str = None, check_out: str = None, num_results: int = 5
|
||||
):
|
||||
"""Generate mock hotel search results."""
|
||||
if not check_in:
|
||||
check_in = (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
if not check_out:
|
||||
check_out = (datetime.now() + timedelta(days=10)).strftime("%Y-%m-%d")
|
||||
|
||||
# Calculate number of nights
|
||||
check_in_date = datetime.strptime(check_in, "%Y-%m-%d")
|
||||
check_out_date = datetime.strptime(check_out, "%Y-%m-%d")
|
||||
nights = (check_out_date - check_in_date).days
|
||||
|
||||
hotels = []
|
||||
for i in range(num_results):
|
||||
# Generate hotel details
|
||||
chain = random.choice(HOTEL_CHAINS)
|
||||
hotel_type = random.choice(HOTEL_TYPES)
|
||||
rating = round(random.uniform(3.5, 5.0), 1)
|
||||
|
||||
# Generate price based on rating and location
|
||||
base_price = 100 + (rating - 3.5) * 100
|
||||
price_variation = random.randint(-50, 150)
|
||||
price_per_night = int(base_price + price_variation)
|
||||
|
||||
# Distance from city center
|
||||
distance_km = round(random.uniform(0.5, 15.0), 1)
|
||||
|
||||
hotel = {
|
||||
"name": f"{chain} {location} {random.choice(['Downtown', 'City Center', 'Waterfront', 'Airport', 'Marina'])}",
|
||||
"type": hotel_type,
|
||||
"rating": rating,
|
||||
"location": location,
|
||||
"distance_from_center_km": distance_km,
|
||||
"check_in": check_in,
|
||||
"check_out": check_out,
|
||||
"nights": nights,
|
||||
"price_per_night_usd": price_per_night,
|
||||
"total_price_usd": price_per_night * nights,
|
||||
"amenities": random.choice(AMENITIES),
|
||||
"available_rooms": random.randint(3, 50),
|
||||
"room_type": random.choice(
|
||||
["Standard Room", "Deluxe Room", "Suite", "Executive Room"]
|
||||
),
|
||||
"cancellation_policy": random.choice(
|
||||
[
|
||||
"Free cancellation until 24h before",
|
||||
"Free cancellation until 48h before",
|
||||
"Non-refundable",
|
||||
]
|
||||
),
|
||||
}
|
||||
hotels.append(hotel)
|
||||
|
||||
# Sort by rating (descending) then price
|
||||
hotels.sort(key=lambda x: (-x["rating"], x["price_per_night_usd"]))
|
||||
|
||||
return hotels
|
||||
|
||||
|
||||
def extract_hotel_params(messages):
|
||||
"""Extract hotel search parameters from messages."""
|
||||
location = None
|
||||
check_in = None
|
||||
check_out = None
|
||||
|
||||
# Look through messages for hotel details
|
||||
for msg in reversed(messages):
|
||||
if msg.role == "user":
|
||||
content = msg.content.lower()
|
||||
|
||||
# Look for location - "in X" or "hotel in X"
|
||||
if " in " in content:
|
||||
parts = content.split(" in ")
|
||||
if len(parts) > 1:
|
||||
# Get the word after "in"
|
||||
location_words = parts[1].strip().split()
|
||||
if location_words:
|
||||
location = location_words[0].title()
|
||||
|
||||
# Look for date mentions
|
||||
if "tomorrow" in content:
|
||||
check_in = (datetime.now() + timedelta(days=1)).strftime("%Y-%m-%d")
|
||||
check_out = (datetime.now() + timedelta(days=4)).strftime("%Y-%m-%d")
|
||||
elif "next week" in content:
|
||||
check_in = (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
check_out = (datetime.now() + timedelta(days=10)).strftime("%Y-%m-%d")
|
||||
|
||||
# Defaults
|
||||
if not location:
|
||||
location = "New York"
|
||||
if not check_in:
|
||||
check_in = (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
if not check_out:
|
||||
check_out = (datetime.now() + timedelta(days=10)).strftime("%Y-%m-%d")
|
||||
|
||||
return location, check_in, check_out
|
||||
|
||||
|
||||
# Initialize OpenAI client for archgw
|
||||
archgw_client = AsyncOpenAI(
|
||||
base_url=LLM_GATEWAY_ENDPOINT,
|
||||
api_key="EMPTY",
|
||||
)
|
||||
|
||||
# FastAPI app for REST server
|
||||
app = FastAPI(title="Hotel Reservation Agent", version="1.0.0")
|
||||
|
||||
|
||||
def prepare_hotel_messages(request_body: ChatCompletionRequest):
|
||||
"""Prepare messages with hotel data."""
|
||||
# Extract hotel parameters
|
||||
location, check_in, check_out = extract_hotel_params(request_body.messages)
|
||||
|
||||
# Check if user wants to book (vs just search)
|
||||
last_user_msg = ""
|
||||
for msg in reversed(request_body.messages):
|
||||
if msg.role == "user":
|
||||
last_user_msg = msg.content.lower()
|
||||
break
|
||||
|
||||
is_booking = any(word in last_user_msg for word in ["book", "reserve", "confirm"])
|
||||
|
||||
# Generate hotel search results
|
||||
hotels = generate_hotel_data(location, check_in, check_out)
|
||||
|
||||
hotel_context = f"""
|
||||
Hotel search results for {location} from {check_in} to {check_out}:
|
||||
|
||||
{json.dumps(hotels, indent=2)}
|
||||
|
||||
{'User wants to book a hotel. Help them complete the reservation.' if is_booking else 'Present these options to the user clearly.'}
|
||||
"""
|
||||
|
||||
response_messages = [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "system", "content": hotel_context},
|
||||
]
|
||||
|
||||
# Add conversation history
|
||||
for msg in request_body.messages:
|
||||
response_messages.append({"role": msg.role, "content": msg.content})
|
||||
|
||||
return response_messages
|
||||
|
||||
|
||||
@app.post("/v1/chat/completions")
|
||||
async def chat_completion_http(request: Request, request_body: ChatCompletionRequest):
|
||||
"""HTTP endpoint for chat completions with streaming support."""
|
||||
logger.info(
|
||||
f"Received hotel reservation request with {len(request_body.messages)} messages"
|
||||
)
|
||||
|
||||
# Get traceparent header from HTTP request
|
||||
traceparent_header = request.headers.get("traceparent")
|
||||
|
||||
if traceparent_header:
|
||||
logger.info(f"Received traceparent header: {traceparent_header}")
|
||||
|
||||
return StreamingResponse(
|
||||
stream_chat_completions(request_body, traceparent_header),
|
||||
media_type="text/plain",
|
||||
headers={
|
||||
"content-type": "text/event-stream",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
async def stream_chat_completions(
|
||||
request_body: ChatCompletionRequest, traceparent_header: str = None
|
||||
):
|
||||
"""Generate streaming chat completions."""
|
||||
# Prepare messages with hotel data
|
||||
response_messages = prepare_hotel_messages(request_body)
|
||||
|
||||
try:
|
||||
logger.info(
|
||||
f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to generate hotel response"
|
||||
)
|
||||
|
||||
# Prepare extra headers
|
||||
extra_headers = {"x-envoy-max-retries": "3"}
|
||||
if traceparent_header:
|
||||
extra_headers["traceparent"] = traceparent_header
|
||||
|
||||
response_stream = await archgw_client.chat.completions.create(
|
||||
model=HOTEL_MODEL,
|
||||
messages=response_messages,
|
||||
temperature=request_body.temperature or 0.7,
|
||||
max_tokens=request_body.max_tokens or 1000,
|
||||
stream=True,
|
||||
extra_headers=extra_headers,
|
||||
)
|
||||
|
||||
completion_id = f"chatcmpl-{uuid.uuid4().hex[:8]}"
|
||||
created_time = int(time.time())
|
||||
collected_content = []
|
||||
|
||||
async for chunk in response_stream:
|
||||
if chunk.choices and chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
collected_content.append(content)
|
||||
|
||||
stream_chunk = ChatCompletionStreamResponse(
|
||||
id=completion_id,
|
||||
created=created_time,
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"content": content},
|
||||
"finish_reason": None,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {stream_chunk.model_dump_json()}\n\n"
|
||||
|
||||
# Send final chunk
|
||||
full_response = "".join(collected_content)
|
||||
updated_history = [{"role": "assistant", "content": full_response}]
|
||||
|
||||
final_chunk = ChatCompletionStreamResponse(
|
||||
id=completion_id,
|
||||
created=created_time,
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {},
|
||||
"finish_reason": "stop",
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": json.dumps(updated_history),
|
||||
},
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {final_chunk.model_dump_json()}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating hotel response: {e}")
|
||||
|
||||
error_chunk = ChatCompletionStreamResponse(
|
||||
id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
created=int(time.time()),
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {
|
||||
"content": "I apologize, but I'm having trouble searching for hotels right now. Please try again."
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {error_chunk.model_dump_json()}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
"""Health check endpoint."""
|
||||
return {"status": "healthy", "agent": "hotel_reservation"}
|
||||
|
||||
|
||||
def start_server(host: str = "localhost", port: int = 10530):
|
||||
"""Start the REST server."""
|
||||
uvicorn.run(
|
||||
app,
|
||||
host=host,
|
||||
port=port,
|
||||
log_config={
|
||||
"version": 1,
|
||||
"disable_existing_loggers": False,
|
||||
"formatters": {
|
||||
"default": {
|
||||
"format": "%(asctime)s - [HOTEL_AGENT] - %(levelname)s - %(message)s",
|
||||
},
|
||||
},
|
||||
"handlers": {
|
||||
"default": {
|
||||
"formatter": "default",
|
||||
"class": "logging.StreamHandler",
|
||||
"stream": "ext://sys.stdout",
|
||||
},
|
||||
},
|
||||
"root": {
|
||||
"level": "INFO",
|
||||
"handlers": ["default"],
|
||||
},
|
||||
},
|
||||
)
|
||||
|
|
@ -0,0 +1,316 @@
|
|||
import json
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
from openai import AsyncOpenAI
|
||||
import os
|
||||
import logging
|
||||
import time
|
||||
import uuid
|
||||
import uvicorn
|
||||
from datetime import datetime, timedelta
|
||||
import random
|
||||
|
||||
from .api import (
|
||||
ChatCompletionRequest,
|
||||
ChatCompletionResponse,
|
||||
ChatCompletionStreamResponse,
|
||||
)
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - [WEATHER_AGENT] - %(levelname)s - %(message)s",
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Configuration for archgw LLM gateway
|
||||
LLM_GATEWAY_ENDPOINT = os.getenv("LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1")
|
||||
WEATHER_MODEL = "gpt-4o-mini"
|
||||
|
||||
# Sample weather data
|
||||
WEATHER_CONDITIONS = ["Sunny", "Partly Cloudy", "Cloudy", "Rainy", "Stormy", "Snowy"]
|
||||
CITIES_DATA = {
|
||||
"new york": {"temp_base": 15, "condition_bias": "Cloudy"},
|
||||
"london": {"temp_base": 12, "condition_bias": "Rainy"},
|
||||
"tokyo": {"temp_base": 18, "condition_bias": "Partly Cloudy"},
|
||||
"paris": {"temp_base": 14, "condition_bias": "Cloudy"},
|
||||
"sydney": {"temp_base": 22, "condition_bias": "Sunny"},
|
||||
"dubai": {"temp_base": 32, "condition_bias": "Sunny"},
|
||||
"singapore": {"temp_base": 28, "condition_bias": "Rainy"},
|
||||
"san francisco": {"temp_base": 16, "condition_bias": "Partly Cloudy"},
|
||||
}
|
||||
|
||||
# System prompt for weather agent
|
||||
SYSTEM_PROMPT = """You are a helpful weather information assistant.
|
||||
|
||||
Your role is to provide accurate and helpful weather information based on the weather data provided.
|
||||
|
||||
When responding:
|
||||
1. Parse the user's request to understand the location they're asking about
|
||||
2. Use the weather data provided in the conversation context
|
||||
3. Provide clear, concise weather information
|
||||
4. Include temperature, conditions, and any relevant details
|
||||
5. If asked about forecast, provide multi-day information
|
||||
6. Be conversational and helpful
|
||||
|
||||
Format your responses in a user-friendly way."""
|
||||
|
||||
|
||||
def get_weather_data(location: str, days: int = 1):
|
||||
"""Generate mock weather data for a location."""
|
||||
location_lower = location.lower()
|
||||
|
||||
# Find matching city
|
||||
city_data = None
|
||||
for city, data in CITIES_DATA.items():
|
||||
if city in location_lower or location_lower in city:
|
||||
city_data = data
|
||||
location = city.title()
|
||||
break
|
||||
|
||||
if not city_data:
|
||||
# Default for unknown cities
|
||||
city_data = {"temp_base": 20, "condition_bias": "Partly Cloudy"}
|
||||
|
||||
weather_info = []
|
||||
for day in range(days):
|
||||
date = datetime.now() + timedelta(days=day)
|
||||
temp_variation = random.randint(-5, 5)
|
||||
temp = city_data["temp_base"] + temp_variation
|
||||
|
||||
# Bias towards the city's typical condition
|
||||
if random.random() < 0.6:
|
||||
condition = city_data["condition_bias"]
|
||||
else:
|
||||
condition = random.choice(WEATHER_CONDITIONS)
|
||||
|
||||
day_info = {
|
||||
"date": date.strftime("%Y-%m-%d"),
|
||||
"day_name": date.strftime("%A"),
|
||||
"temperature_c": temp,
|
||||
"temperature_f": int(temp * 9 / 5 + 32),
|
||||
"condition": condition,
|
||||
"humidity": random.randint(40, 80),
|
||||
"wind_speed_kmh": random.randint(5, 30),
|
||||
}
|
||||
weather_info.append(day_info)
|
||||
|
||||
return {"location": location, "forecast": weather_info}
|
||||
|
||||
|
||||
def extract_location_from_messages(messages):
|
||||
"""Extract location from user messages."""
|
||||
# Look through messages for location mentions
|
||||
for msg in reversed(messages):
|
||||
if msg.role == "user":
|
||||
content = msg.content.lower()
|
||||
# Check for known cities
|
||||
for city in CITIES_DATA.keys():
|
||||
if city in content:
|
||||
return city.title()
|
||||
# Basic extraction for "in [location]" or "weather [location]"
|
||||
words = content.split()
|
||||
if "in" in words:
|
||||
idx = words.index("in")
|
||||
if idx + 1 < len(words):
|
||||
return words[idx + 1].title()
|
||||
return "New York" # Default location
|
||||
|
||||
|
||||
# Initialize OpenAI client for archgw
|
||||
archgw_client = AsyncOpenAI(
|
||||
base_url=LLM_GATEWAY_ENDPOINT,
|
||||
api_key="EMPTY",
|
||||
)
|
||||
|
||||
# FastAPI app for REST server
|
||||
app = FastAPI(title="Weather Forecast Agent", version="1.0.0")
|
||||
|
||||
|
||||
def prepare_weather_messages(request_body: ChatCompletionRequest):
|
||||
"""Prepare messages with weather data."""
|
||||
# Extract location from conversation
|
||||
location = extract_location_from_messages(request_body.messages)
|
||||
|
||||
# Determine if they want forecast (multi-day)
|
||||
last_user_msg = ""
|
||||
for msg in reversed(request_body.messages):
|
||||
if msg.role == "user":
|
||||
last_user_msg = msg.content.lower()
|
||||
break
|
||||
|
||||
days = 5 if "forecast" in last_user_msg or "week" in last_user_msg else 1
|
||||
|
||||
# Get weather data
|
||||
weather_data = get_weather_data(location, days)
|
||||
|
||||
# Create system message with weather data
|
||||
weather_context = f"""
|
||||
Current weather data for {weather_data['location']}:
|
||||
|
||||
{json.dumps(weather_data, indent=2)}
|
||||
|
||||
Use this data to answer the user's weather query.
|
||||
"""
|
||||
|
||||
response_messages = [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "system", "content": weather_context},
|
||||
]
|
||||
|
||||
# Add conversation history
|
||||
for msg in request_body.messages:
|
||||
response_messages.append({"role": msg.role, "content": msg.content})
|
||||
|
||||
return response_messages
|
||||
|
||||
|
||||
@app.post("/v1/chat/completions")
|
||||
async def chat_completion_http(request: Request, request_body: ChatCompletionRequest):
|
||||
"""HTTP endpoint for chat completions with streaming support."""
|
||||
logger.info(f"Received weather request with {len(request_body.messages)} messages")
|
||||
|
||||
# Get traceparent header from HTTP request
|
||||
traceparent_header = request.headers.get("traceparent")
|
||||
|
||||
if traceparent_header:
|
||||
logger.info(f"Received traceparent header: {traceparent_header}")
|
||||
|
||||
return StreamingResponse(
|
||||
stream_chat_completions(request_body, traceparent_header),
|
||||
media_type="text/plain",
|
||||
headers={
|
||||
"content-type": "text/event-stream",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
async def stream_chat_completions(
|
||||
request_body: ChatCompletionRequest, traceparent_header: str = None
|
||||
):
|
||||
"""Generate streaming chat completions."""
|
||||
# Prepare messages with weather data
|
||||
response_messages = prepare_weather_messages(request_body)
|
||||
|
||||
try:
|
||||
logger.info(
|
||||
f"Calling archgw at {LLM_GATEWAY_ENDPOINT} to generate weather response"
|
||||
)
|
||||
|
||||
# Prepare extra headers
|
||||
extra_headers = {"x-envoy-max-retries": "3"}
|
||||
if traceparent_header:
|
||||
extra_headers["traceparent"] = traceparent_header
|
||||
|
||||
response_stream = await archgw_client.chat.completions.create(
|
||||
model=WEATHER_MODEL,
|
||||
messages=response_messages,
|
||||
temperature=request_body.temperature or 0.7,
|
||||
max_tokens=request_body.max_tokens or 1000,
|
||||
stream=True,
|
||||
extra_headers=extra_headers,
|
||||
)
|
||||
|
||||
completion_id = f"chatcmpl-{uuid.uuid4().hex[:8]}"
|
||||
created_time = int(time.time())
|
||||
collected_content = []
|
||||
|
||||
async for chunk in response_stream:
|
||||
if chunk.choices and chunk.choices[0].delta.content:
|
||||
content = chunk.choices[0].delta.content
|
||||
collected_content.append(content)
|
||||
|
||||
stream_chunk = ChatCompletionStreamResponse(
|
||||
id=completion_id,
|
||||
created=created_time,
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {"content": content},
|
||||
"finish_reason": None,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {stream_chunk.model_dump_json()}\n\n"
|
||||
|
||||
# Send final chunk
|
||||
full_response = "".join(collected_content)
|
||||
updated_history = [{"role": "assistant", "content": full_response}]
|
||||
|
||||
final_chunk = ChatCompletionStreamResponse(
|
||||
id=completion_id,
|
||||
created=created_time,
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {},
|
||||
"finish_reason": "stop",
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": json.dumps(updated_history),
|
||||
},
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {final_chunk.model_dump_json()}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating weather response: {e}")
|
||||
|
||||
error_chunk = ChatCompletionStreamResponse(
|
||||
id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
|
||||
created=int(time.time()),
|
||||
model=request_body.model,
|
||||
choices=[
|
||||
{
|
||||
"index": 0,
|
||||
"delta": {
|
||||
"content": "I apologize, but I'm having trouble retrieving weather information right now. Please try again."
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
yield f"data: {error_chunk.model_dump_json()}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
"""Health check endpoint."""
|
||||
return {"status": "healthy", "agent": "weather_forecast"}
|
||||
|
||||
|
||||
def start_server(host: str = "localhost", port: int = 10510):
|
||||
"""Start the REST server."""
|
||||
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"],
|
||||
},
|
||||
},
|
||||
)
|
||||
45
demos/use_cases/travel_booking/start_agents.sh
Executable file
45
demos/use_cases/travel_booking/start_agents.sh
Executable file
|
|
@ -0,0 +1,45 @@
|
|||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
WAIT_FOR_PIDS=()
|
||||
|
||||
log() {
|
||||
timestamp=$(python3 -c 'from datetime import datetime; print(datetime.now().strftime("%Y-%m-%d %H:%M:%S,%f")[:23])')
|
||||
message="$*"
|
||||
echo "$timestamp - $message"
|
||||
}
|
||||
|
||||
cleanup() {
|
||||
log "Caught signal, terminating all agent processes ..."
|
||||
for PID in "${WAIT_FOR_PIDS[@]}"; do
|
||||
if kill $PID 2> /dev/null; then
|
||||
log "killed process: $PID"
|
||||
fi
|
||||
done
|
||||
exit 1
|
||||
}
|
||||
|
||||
trap cleanup EXIT
|
||||
|
||||
log "Starting weather agent on port 10510..."
|
||||
uv run python -m travel_agents --host 0.0.0.0 --port 10510 --agent weather &
|
||||
WAIT_FOR_PIDS+=($!)
|
||||
|
||||
log "Starting flight agent on port 10520..."
|
||||
uv run python -m travel_agents --host 0.0.0.0 --port 10520 --agent flight &
|
||||
WAIT_FOR_PIDS+=($!)
|
||||
|
||||
log "Starting hotel agent on port 10530..."
|
||||
uv run python -m travel_agents --host 0.0.0.0 --port 10530 --agent hotel &
|
||||
WAIT_FOR_PIDS+=($!)
|
||||
|
||||
log "All agents started successfully!"
|
||||
log " - Weather Agent: http://localhost:10510"
|
||||
log " - Flight Agent: http://localhost:10520"
|
||||
log " - Hotel Agent: http://localhost:10530"
|
||||
log ""
|
||||
log "Waiting for agents to run..."
|
||||
|
||||
for PID in "${WAIT_FOR_PIDS[@]}"; do
|
||||
wait "$PID"
|
||||
done
|
||||
16
demos/use_cases/travel_booking/test.rest
Normal file
16
demos/use_cases/travel_booking/test.rest
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
### test archfc with plano orchestrator
|
||||
POST https://archfc.katanemo.dev/v1/chat/completions HTTP/1.1
|
||||
Content-Type: application/json
|
||||
model: Plano-Orchestrator
|
||||
|
||||
{
|
||||
"model": "Plano-Orchestrator",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "You are a helpful assistant that selects the most suitable routes based on user intent.\nYou are provided with a list of available routes enclosed within <routes></routes> XML tags:\n<routes>\n{\"name\": \"Harvey\", \"description\": \"Harvey is a professional-class AI platform built for industry leaders. It offers a suite of tools to augment various work processes, including research, document management, and task delegation. The platform emphasizes security and integrates domain-specific models for complex professional work. Harvey is used by leading law firms and Fortune 500 companies.\n\nCapabilities: \n * Delegates complex tasks via natural language to a domain-specific personal assistant.\n * Provides answers rooted in reliable, cited source material from up to 50 documents.\n * Supports drafting and revising complex long-form content.\n * Offers pre-crafted prompts to streamline workflows.\n * Operates in 50+ languages, countries, and legal systems.\n * Integrates with Microsoft Word to enhance drafting capabilities.\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}}\n{\"name\": \"Mirtilla\", \"description\": \"Mirtilla is an AI-powered platform designed to revolutionize meetings and notes. It offers robust transcription capabilities, including translation and timestamping, allowing for efficient review and analysis of discussions. The platform also provides concise summaries and records of meetings, highlighting essential points and decisions. Users can generate custom requests tailored to provide insights, define focus areas, or create specific outputs. Mirtilla's AI Notes feature allows users to input notes with tags for classification and utilize AI to search through these notes using natural language. Data is fully encrypted with zero-knowledge encryption, ensuring privacy and compliance with GDPR regulations. The platform is hosted on Italian servers.\n\nCapabilities: \n * Meeting Transcription: Transcribes audio/video files, translates to English, and includes timestamping.\n * Meeting Summarization: Generates concise summaries, minutes, and records of meetings.\n * Personalized Requests: Creates custom outputs from transcription content, such as LinkedIn posts or summaries.\n * AI-Powered Notes: Allows note input with tags, AI-powered natural language search, and sharing capabilities.\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}}\n{\"name\": \"ThumbGenie\", \"description\": \"ThumbGenie is the #1 AI thumbnail maker for creating high-converting, personalized YouTube thumbnails automatically. Our powerful thumbnail generator customizes designs to match your channel's unique style, boosting click-through rates while saving you time and money.\n\nCapabilities: \n * Generate multiple YouTube thumbnail designs automatically.\n * Customize designs to match your channel's unique style.\n * Train AI models based on your existing thumbnails for consistent branding.\n * Adjust realism scale to fine-tune thumbnail style.\n * Use a YouTube video URL to add a reference image.\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}}\n</routes>\n\nYou are also given the conversation context enclosed within <conversation></conversation> XML tags:\n<conversation>\n[\n {\n \"role\": \"user\",\n \"content\": \"Hey, how's it going?\"\n },\n {\n \"role\": \"assistant\",\n \"content\": \"Hello! I'm doing well, thank you. How can I assist you today?\"\n },\n {\n \"role\": \"user\",\n \"content\": \"I need to draft a response to a legal motion. It's fairly complex and needs to cite several case precedents.\"\n }\n]\n</conversation>\n\n## Instructions\n1. Analyze the latest user intent from the conversation.\n2. Compare it against the available routes to find which routes can help fulfill the request.\n3. Respond only with the exact route names from <routes>.\n4. If no routes can help or the intent is already fulfilled, return an empty list.\n\n## Response Format\nReturn your answer strictly in JSON as follows:\n{\"route\": [\"route_name_1\", \"route_name_2\", \"...\"]}\nIf no routes are needed, return an empty list for `route`."
|
||||
}
|
||||
],
|
||||
"continue_final_message": false,
|
||||
"add_generation_prompt": true
|
||||
}
|
||||
485
demos/use_cases/travel_booking/uv.lock
generated
Normal file
485
demos/use_cases/travel_booking/uv.lock
generated
Normal file
|
|
@ -0,0 +1,485 @@
|
|||
version = 1
|
||||
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 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303 },
|
||||
]
|
||||
|
||||
[[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 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
|
||||
]
|
||||
|
||||
[[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 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362 },
|
||||
]
|
||||
|
||||
[[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 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "platform_system == 'Windows'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274 },
|
||||
]
|
||||
|
||||
[[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 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
|
||||
]
|
||||
|
||||
[[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 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277 },
|
||||
]
|
||||
|
||||
[[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 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/8a/0e/97c33bf5009bdbac74fd2beace167cab3f978feb69cc36f1ef79360d6c4e/exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598", size = 16740 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.125.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "annotated-doc" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "starlette" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/17/71/2df15009fb4bdd522a069d2fbca6007c6c5487fce5cb965be00fc335f1d1/fastapi-0.125.0.tar.gz", hash = "sha256:16b532691a33e2c5dee1dac32feb31dc6eb41a3dd4ff29a95f9487cb21c054c0", size = 370550 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/34/2f/ff2fcc98f500713368d8b650e1bbc4a0b3ebcdd3e050dcdaad5f5a13fd7e/fastapi-0.125.0-py3-none-any.whl", hash = "sha256:2570ec4f3aecf5cca8f0428aed2398b774fcdfee6c2116f86e80513f2f86a7a1", size = 112888 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.16.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250 }
|
||||
wheels = [
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Loading…
Add table
Add a link
Reference in a new issue