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