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RAG Agent API Service
FastAPI application providing a RAG (Retrieval-Augmented Generation) endpoint
for querying documentation and generating grounded answers.
Endpoints:
POST /query - Submit a question and get an answer with sources
GET /health - Health check endpoint
Features:
- Query validation with Pydantic
- Integration with Qdrant for document retrieval
- OpenAI integration for answer generation
- Source attribution and deduplication
- Structured logging
- CORS support for web clients
Usage:
uvicorn rag_agent:app --reload --host 0.0.0.0 --port 8000
"""
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import logging
from datetime import datetime
from models.request_models import QueryRequest
from models.response_models import QueryResponse, ErrorResponse
from services.integration_service import IntegrationService
from exceptions import RAGException, ValidationError, RetrievalError, GenerationError, RateLimitError
from config.logging_config import setup_logging, get_logger
# Try to import translation router (optional - requires PyTorch)
translation_router = None
try:
from routers.translation import router as translation_router
except Exception as e:
print(f"[WARNING] Translation router not available: {e}")
print("[INFO] RAG agent will run without translation support")
# Initialize FastAPI app
app = FastAPI(
title="RAG Agent API",
description="Retrieval-Augmented Generation API for documentation Q&A",
version="1.0.0",
docs_url="/docs",
redoc_url="/redoc"
)
# Configure CORS for development (allow all origins)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # In production, restrict to specific origins
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Configure logging
setup_logging(level="INFO", json_format=False)
logger = get_logger('rag_agent')
# Initialize integration service
integration_service = IntegrationService()
# Register routers (only if available)
if translation_router is not None:
app.include_router(translation_router)
print("[INFO] Translation router registered")
else:
print("[INFO] Running without translation support - use mock_translation_server.py for translations")
@app.get("/health")
async def health_check():
"""
Health check endpoint.
Returns:
dict: Health status with version and timestamp
"""
return {
"status": "healthy",
"version": "1.0.0",
"timestamp": datetime.utcnow().isoformat() + "Z"
}
@app.get("/")
async def root():
"""
Root endpoint with API information.
Returns:
dict: API info and documentation links
"""
return {
"name": "RAG Agent API",
"version": "1.0.0",
"description": "Retrieval-Augmented Generation API for documentation Q&A",
"docs": "/docs",
"redoc": "/redoc",
"health": "/health"
}
@app.post("/query", response_model=QueryResponse, responses={
422: {"model": ErrorResponse, "description": "Validation Error"},
503: {"model": ErrorResponse, "description": "Retrieval Error"},
502: {"model": ErrorResponse, "description": "Generation Error"},
429: {"model": ErrorResponse, "description": "Rate Limit Error"}
})
async def query_documentation(request: QueryRequest):
"""
Submit a question and get an answer with source attribution.
This endpoint implements the full RAG pipeline:
1. Validates the query
2. Retrieves relevant documentation chunks from Qdrant
3. Generates a grounded answer using OpenAI
4. Returns the answer with deduplicated source references
Args:
request: QueryRequest with query, top_k, and model
Returns:
QueryResponse with answer, sources, and metadata
Raises:
HTTPException: For validation, retrieval, generation, or rate limit errors
"""
start_time = datetime.utcnow()
logger.info(
"Received query request",
extra={
"query": request.query[:50] + "..." if len(request.query) > 50 else request.query,
"top_k": request.top_k,
"model": request.model
}
)
try:
# Process query through RAG pipeline
response = await integration_service.process_query(
query=request.query,
top_k=request.top_k,
model=request.model
)
# Log success
latency_ms = int((datetime.utcnow() - start_time).total_seconds() * 1000)
logger.info(
"Query processed successfully",
extra={
"query": request.query[:50] + "..." if len(request.query) > 50 else request.query,
"latency_ms": latency_ms,
"tokens_used": response.tokens_used,
"sources_count": len(response.sources)
}
)
return response
except ValidationError as e:
# Log validation error
logger.warning(
f"Validation error: {e.message}",
extra={
"query": request.query,
"error_type": e.error_type,
"status_code": e.status_code
}
)
return JSONResponse(
status_code=e.status_code,
content=ErrorResponse(
error_type=e.error_type,
error_message=e.message,
query=request.query,
status_code=e.status_code
).model_dump()
)
except RetrievalError as e:
# Log retrieval error
logger.error(
f"Retrieval error: {e.message}",
extra={
"query": request.query,
"error_type": e.error_type,
"status_code": e.status_code
}
)
return JSONResponse(
status_code=e.status_code,
content=ErrorResponse(
error_type=e.error_type,
error_message=e.message,
query=request.query,
status_code=e.status_code
).model_dump()
)
except GenerationError as e:
# Log generation error
logger.error(
f"Generation error: {e.message}",
extra={
"query": request.query,
"error_type": e.error_type,
"status_code": e.status_code
}
)
return JSONResponse(
status_code=e.status_code,
content=ErrorResponse(
error_type=e.error_type,
error_message=e.message,
query=request.query,
status_code=e.status_code
).model_dump()
)
except RateLimitError as e:
# Log rate limit error
logger.warning(
f"Rate limit error: {e.message}",
extra={
"query": request.query,
"error_type": e.error_type,
"status_code": e.status_code
}
)
return JSONResponse(
status_code=e.status_code,
content=ErrorResponse(
error_type=e.error_type,
error_message=e.message,
query=request.query,
status_code=e.status_code
).model_dump()
)
except Exception as e:
# Log unexpected error
logger.error(
f"Unexpected error: {str(e)}",
extra={
"query": request.query,
"error_type": "InternalServerError"
},
exc_info=True
)
return JSONResponse(
status_code=500,
content=ErrorResponse(
error_type="InternalServerError",
error_message=f"An unexpected error occurred: {str(e)}",
query=request.query,
status_code=500
).model_dump()
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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