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import logging
from typing import Any, Dict, List
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from app.internal.bdd_manager import (
create_collection,
get_ensemble_retriever,
get_retriever,
get_vector_store,
)
from app.internal.embedder import get_embedder
from app.internal.export_report import create_pdf_report
from app.internal.llm_chat import (
generate_summary,
get_chat_llm,
get_conversational_rag_chain,
get_documents_retrieve,
get_format_output,
get_history_retriever,
get_llm_answer,
get_rag_chain,
get_session_history,
get_system_prompt_chain,
question_to_conversational_rag_chain,
)
from app.internal.template_prompt import contextualize_q_prompt, qa_prompt
from app.settings import settings
# Initialisation du logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
chat_router = APIRouter(
prefix="/chatting",
tags=["question_anwser"],
responses={404: {"description": "Not found"}},
)
class QueryRequest(BaseModel):
user_query: str
session_id: str = settings.session_id
class ResponseOutput(BaseModel):
answer: str
context: List[str]
formatted_output: str
class Conversation(BaseModel):
messages: List[Any]
class ResponseOutputSum(BaseModel):
summary: str
# Initialisation des ressources
user_collection_name = settings.user_collection_name
logger.info("Initializing collection: %s", user_collection_name)
create_collection(user_collection_name)
doc_collection_name = settings.doc_collection_name
logger.info("Initializing collection: %s", doc_collection_name)
create_collection(doc_collection_name)
embedder = get_embedder(provider=settings.provider)
logger.info("Embedder initialized.")
doc_vector_store = get_vector_store(embedder, doc_collection_name)
logger.info("Vector store initialized with collection: %s", doc_collection_name)
user_vector_store = get_vector_store(embedder, user_collection_name)
logger.info("Vector store initialized with collection: %s", user_collection_name)
logger.info("Initializing LLM and retrievers...")
llm = get_chat_llm()
user_retriever = get_retriever(user_vector_store)
doc_retriever = get_retriever(doc_vector_store)
retriever = get_ensemble_retriever(doc_retriever, user_retriever)
logger.info("Creating history-aware retriever...")
history_retriever = get_history_retriever(llm, retriever, contextualize_q_prompt)
logger.info("Creating system prompt chain...")
qa_chain = get_system_prompt_chain(llm, qa_prompt)
logger.info("Creating RAG chain...")
rag_chain = get_rag_chain(history_retriever, qa_chain)
logger.info("Initializing conversational RAG chain...")
conversational_chain = get_conversational_rag_chain(
rag_chain,
lambda sid: get_session_history(settings.session_id, settings.history_store),
)
@chat_router.post("/chat", response_model=ResponseOutput)
async def chat_with_rag_chain(request: QueryRequest):
"""
Route pour interagir avec le RAG (Retrieval-Augmented Generation) Chain.
"""
logger.info("Received chat request with session_id: %s", request.session_id)
logger.info("User query: %s", request.user_query)
try:
logger.info("Processing user query...")
response = question_to_conversational_rag_chain(
request.user_query, conversational_chain, request.session_id
)
logger.info("LLM response received: %s", response)
answer = get_llm_answer(response)
documents = get_documents_retrieve(response)
logger.info("Formatting output...")
formatted_output = get_format_output(answer, documents)
logger.info(
"Successfully processed chat request for session_id: %s", request.session_id
)
return {
"answer": answer,
"context": documents,
"formatted_output": formatted_output,
}
except ValueError as e:
logger.error("Validation error: %s", str(e))
raise HTTPException(status_code=400, detail=str(e))
except Exception as e:
logger.error("Internal server error: %s", str(e))
raise HTTPException(status_code=500, detail=f"Internal server error: {e}")
@chat_router.get("/history/{session_id}")
async def get_chat_history(session_id: str):
"""
Route pour récupérer l'historique des messages pour une session donnée.
"""
logger.info("Fetching chat history for session_id: %s", session_id)
try:
history = get_session_history(session_id, settings.history_store)
logger.info(
"Successfully retrieved chat history for session_id: %s", session_id
)
return {"session_id": session_id, "history": history.messages}
except ValueError as e:
logger.error("Validation error: %s", str(e))
raise HTTPException(status_code=400, detail=str(e))
except Exception as e:
logger.error("Internal server error while fetching history: %s", str(e))
raise HTTPException(status_code=500, detail=f"Internal server error: {e}")
@chat_router.post("/chat", response_model=ResponseOutput)
async def chat_with_rag_chain(request: QueryRequest):
"""
Route pour interagir avec le RAG (Retrieval-Augmented Generation) Chain.
"""
logger.info("Received chat request with session_id: %s", request.session_id)
logger.info("User query: %s", request.user_query)
try:
logger.info("Processing user query...")
response = question_to_conversational_rag_chain(
request.user_query, conversational_chain, request.session_id
)
answer = get_llm_answer(response)
documents = get_documents_retrieve(response)
logger.info("Formatting output...")
formatted_output = get_format_output(answer, documents)
logger.info(
"Successfully processed chat request for session_id: %s", request.session_id
)
return {
"answer": answer,
"context": documents,
"formatted_output": formatted_output,
}
except ValueError as e:
logger.error("Validation error: %s", str(e))
raise HTTPException(status_code=400, detail=str(e))
except Exception as e:
logger.error("Internal server error: %s", str(e))
raise HTTPException(status_code=500, detail=f"Internal server error: {e}")
@chat_router.post("/summary", response_model=ResponseOutputSum)
async def summarize_conversation(conversation: Conversation):
"""
Génère un résumé de la conversation et liste les documents PDF référencés.
Args:
conversation (Conversation): Objet contenant les messages de la conversation.
Returns:
dict: Résumé de la conversation et liste des documents PDF référencés.
"""
outpur_path = r"..\Shared_data\export.pdf"
# outpur_path = r"C:\Users\jeanb\Documents\kzs-team\Shared_data\export.pdf"
logo_path = r"app\resources\logo_ademe.png"
summary_text = generate_summary(llm, conversation.messages)
create_pdf_report(outpur_path, logo_path, summary_text)
return {"summary": summary_text}
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