app/routes/chat.py

from fastapi import APIRouter, Depends, HTTPException
from qdrant_client import QdrantClient
from app.qdrant_client import get_qdrant_client
from app.schemas.chat import ChatRequest, ChatResponse, ChatSelectionRequest
from app.services.rag_service import RAGService
from app.services.embeddings_service import EmbeddingsService
from app.services.openai_service import OpenAIService
import logging

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/api", tags=["chat"])

def get_rag_service(
    qdrant_client: QdrantClient = Depends(get_qdrant_client)
):
    embeddings_service = EmbeddingsService()
    openai_service = OpenAIService()
    return RAGService(qdrant_client, embeddings_service, openai_service)

@router.post("/chat", response_model=ChatResponse)
async def chat(
    request: ChatRequest,
    rag_service: RAGService = Depends(get_rag_service)
):
    try:
        # Retrieve context from vector database
        context = await rag_service.retrieve_context(request.question, top_k=3)
        
        # Generate response using Gemini
        answer = await rag_service.generate_response(request.question, context)
        
        # Extract sources from context
        sources = [f"Source {i+1}" for i in range(len(context))]
        
        return ChatResponse(answer=answer, sources=sources)
    except Exception as e:
        logger.error(f"Error in chat endpoint: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/chat-selection", response_model=ChatResponse)
async def chat_selection(
    request: ChatSelectionRequest,
    rag_service: RAGService = Depends(get_rag_service)
):
    try:
        # Use selected text as primary context
        context = [request.selected_text]
        
        # Generate response
        answer = await rag_service.generate_response(request.question, context)
        
        return ChatResponse(answer=answer, sources=["Selected Text"])
    except Exception as e:
        logger.error(f"Error in chat_selection endpoint: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))