import asyncio
from typing import List, Dict, Any, Tuple
from sqlalchemy.orm import Session, joinedload
from sqlalchemy.exc import SQLAlchemyError
import dspy
from app.core.vector_store.faiss_store import FaissVectorStore
from app.db import models
from app.core.retrievers.faiss_db_retriever import FaissDBRetriever
from app.core.retrievers.base_retriever import Retriever
from app.core.providers.factory import get_llm_provider
from app.core.pipelines.dspy_rag import DspyRagPipeline
class RAGService:
"""
Service class for managing conversational RAG sessions.
This class orchestrates the RAG pipeline and manages chat sessions.
"""
def __init__(self, retrievers: List[Retriever]):
self.retrievers = retrievers
self.faiss_retriever = next((r for r in retrievers if isinstance(r, FaissDBRetriever)), None)
# --- Session Management ---
def create_session(self, db: Session, user_id: str, provider_name: str) -> models.Session:
"""Creates a new chat session in the database."""
try:
new_session = models.Session(user_id=user_id, provider_name=provider_name, title=f"New Chat Session")
db.add(new_session)
db.commit()
db.refresh(new_session)
return new_session
except SQLAlchemyError as e:
db.rollback()
raise
async def chat_with_rag(
self,
db: Session,
session_id: int,
prompt: str,
provider_name: str,
load_faiss_retriever: bool = False
) -> Tuple[str, str]:
"""
Handles a message within a session, including saving history and getting a response.
"""
session = db.query(models.Session).options(
joinedload(models.Session.messages)
).filter(models.Session.id == session_id).first()
if not session:
raise ValueError(f"Session with ID {session_id} not found.")
user_message = models.Message(session_id=session_id, sender="user", content=prompt)
db.add(user_message)
db.commit()
db.refresh(user_message)
llm_provider = get_llm_provider(provider_name)
current_retrievers = []
if load_faiss_retriever:
if self.faiss_retriever:
current_retrievers.append(self.faiss_retriever)
else:
print("Warning: FaissDBRetriever requested but not available. Proceeding without it.")
rag_pipeline = DspyRagPipeline(retrievers=current_retrievers)
# Use dspy.context to configure the language model for this specific async task
with dspy.context(lm=llm_provider):
answer_text = await rag_pipeline.forward(
question=prompt,
history=session.messages,
db=db
)
assistant_message = models.Message(session_id=session_id, sender="assistant", content=answer_text)
db.add(assistant_message)
db.commit()
db.refresh(assistant_message)
return answer_text, provider_name
def get_message_history(self, db: Session, session_id: int) -> List[models.Message]:
"""
Retrieves all messages for a given session.
"""
session = db.query(models.Session).options(
joinedload(models.Session.messages)
).filter(models.Session.id == session_id).first()
return sorted(session.messages, key=lambda msg: msg.created_at) if session else None