API-Based RAG Services

In production, your RAG system won't be a Python script running on your laptop. It will be a service accessed via a REST API. FastAPI is the preferred choice for Python-based AI services due to its speed and native-support for asynchronous programming.

Architecting the API

A typical RAG request looks like this: POST /ask

{
  "query": "How do I install the SDK?",
  "stream": true,
  "metadata_filter": {"user_plan": "Pro"}
}

Implementation with FastAPI

from fastapi import FastAPI
from fastapi.responses import StreamingResponse

app = FastAPI()

@app.post("/ask")
async def ask_rag(request: RAGRequest):
    # 1. Retrieve Context
    context = await search_vector_db(request.query)
    
    # 2. Generate Stream
    return StreamingResponse(
        generate_claude_stream(context, request.query),
        media_type="text/event-stream"
    )

Key Considerations for RAG APIs

1. Authentication: Use JWT or API Keys to protect the endpoint.
2. Rate Limiting: AI inference is expensive. Don't let a single user send 10,000 requests and drain your budget.
3. CORS: Ensure your web app can securely call your API.
4. Timeout Management: RAG can take several seconds. Ensure your API gateway (like Nginx or AWS Gateway) doesn't timeout while waiting for the LLM.

Document Ingestion via API

Don't just upload files manually. Create an endpoint to handle dynamic data: POST /ingest (Handles PDF, Image, or Audio uploads).

Exercises

1. Create a simple FastAPI server that returns "Hello World."
2. Add a POST endpoint that accepts a query and returns a "Faked" RAG response.
3. Why is "Streaming" (Server-Sent Events) preferred for RAG over a standard JSON response?