app/services/rag_service.py

# import os
# from qdrant_client import QdrantClient
# from qdrant_client.models import NamedVector
# from typing import List

# from app.services.openai_service import OpenAIService
# from app.services.embeddings_service import EmbeddingsService

# class RAGService:
#     def __init__(self, qdrant_client: QdrantClient, embeddings_service: EmbeddingsService, gemini_service: OpenAIService):
#         self.qdrant_client = qdrant_client
#         self.embeddings_service = embeddings_service
#         self.gemini_service = gemini_service
#         self.collection_name = os.getenv("QDRANT_COLLECTION_NAME", "book_embeddings")

#     async def retrieve_context(self, query: str, top_k: int = 3) -> List[str]:
#         query_vector = self.embeddings_service.create_embedding(query)

#         search_result = self.qdrant_client.query_points(
#             collection_name=self.collection_name,
#             query=query_vector,
#             limit=top_k,
#             with_payload=True,
#         ).points

#         context = [point.payload.get("content", "") for point in search_result if point.payload]
#         return context

#     async def generate_response(self, query: str, context: List[str]) -> str:
#         full_prompt = f"""Context: {' '.join(context)}

# Question: {query}

# Answer:"""
#         response = await self.gemini_service.get_chat_response(full_prompt)
#         return response


import os
from qdrant_client import QdrantClient
from qdrant_client.models import NamedVector
from typing import List

from app.services.openai_service import OpenAIService
from app.services.embeddings_service import EmbeddingsService

class RAGService:
    def __init__(self, qdrant_client: QdrantClient, embeddings_service: EmbeddingsService, gemini_service: OpenAIService):
        self.qdrant_client = qdrant_client
        self.embeddings_service = embeddings_service
        self.gemini_service = gemini_service
        self.collection_name = os.getenv("QDRANT_COLLECTION_NAME", "book_embeddings")

    async def retrieve_context(self, query: str, top_k: int = 3) -> List[str]:
        query_vector = self.embeddings_service.create_embedding(query)

        search_result = self.qdrant_client.query_points(
            collection_name=self.collection_name,
            query=query_vector,
            limit=top_k,
            with_payload=True,
        ).points

        context = [point.payload.get("content", "") for point in search_result if point.payload]
        return context

    async def generate_response(self, query: str, context: List[str]) -> str:
        full_prompt = f"""Context: {' '.join(context)}

Question: {query}

Answer:"""
        response = await self.gemini_service.get_chat_response(full_prompt)
        return response