manual push

.env

@@ -1 +1 @@
-NVIDIA_API_KEY = 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
+NVIDIA_API_KEY=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

.gitignore

@@ -0,0 +1,4 @@
+.env
+.idea/
+__pycache__/
+*.pyc

app.py

@@ -1,4 +1,6 @@
-import os, zipfile, urllib.request
+import os, urllib.request
+from contextlib import asynccontextmanager
+from dotenv import load_dotenv
 from fastapi import FastAPI
 from pydantic import BaseModel
 from langchain_community.document_loaders import PyPDFLoader
@@ -7,53 +9,28 @@ from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings
 from langchain_chroma import Chroma
 from langchain_core.prompts import ChatPromptTemplate
 
-# Dossier inscriptible dans le conteneur du Space
+load_dotenv()
+
 DOCS_DIR = "/tmp/documents/"
 
-def telecharger_documents():
-    # --- code de téléchargement du notebook, adapté au conteneur ---
-    # ============================================================
-    # TELECHARGEMENT DU DOCUMENT
-    # ============================================================
-    # Telechargement direct (Python pur — compatible Windows/Mac/Colab)
+rag_state = {}
 
-    import os, urllib.request
 
+def telecharger_documents():
     os.makedirs(DOCS_DIR, exist_ok=True)
     dest = os.path.join(DOCS_DIR, "CONVENTION_SYNTEC.pdf")
-
     if os.path.exists(dest):
         print(f"Le fichier {dest} existe deja — telechargement ignore.")
     else:
         print("Telechargement de CONVENTION_SYNTEC.pdf...")
-        urllib.request.urlretrieve("https://github.com/archiducarmel/SupDeVinci_M1_MachineLearning_DeepLearning/releases/download/datas/CONVENTION_SYNTEC.pdf", dest)
+        urllib.request.urlretrieve(
+            "https://github.com/archiducarmel/SupDeVinci_M1_MachineLearning_DeepLearning/releases/download/datas/CONVENTION_SYNTEC.pdf",
+            dest,
+        )
         print("OK.")
-
-    # Verification
     size_kb = os.path.getsize(dest) / 1024
-    print(f"\\n✅ {dest} ({size_kb:.0f} Ko) pret dans ./documents/")
-
-telecharger_documents()
-
-# ----- Pipeline RAG (identique au notebook) -----
-all_docs = []
-for f in sorted(os.listdir(DOCS_DIR)):
-    if f.endswith('.pdf'):
-        all_docs.extend(PyPDFLoader(os.path.join(DOCS_DIR, f)).load())
-chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100).split_documents(all_docs)
-
-# La clé NVIDIA est lue dans la variable d'environnement NVIDIA_API_KEY (secret du Space)
-embeddings = NVIDIAEmbeddings(model="nvidia/llama-nemotron-embed-1b-v2", truncate="NONE")
-vector_store = Chroma.from_documents(chunks, embeddings)
-retriever = vector_store.as_retriever(search_kwargs={"k": 3})
-llm = ChatNVIDIA(model="openai/gpt-oss-120b", temperature=0.2, max_completion_tokens=2048)
-
-prompt = ChatPromptTemplate.from_template(
-    "Tu es un assistant RH expert de la convention collective Syntec. "
-    "Réponds à la QUESTION en t'appuyant UNIQUEMENT sur le CONTEXTE ci-dessous.\\n"
-    "Si l'information n'y figure pas, réponds exactement : « Je ne sais pas ».\\n"
-    "Sois concis et cite la source (document et numéro de page).\\n\\n"
-    "CONTEXTE :\\n{context}\\n\\nQUESTION : {input}")
+    print(f"\n✅ {dest} ({size_kb:.0f} Ko) pret dans ./documents/")
+
 
 def extract_answer(response):
     text = (response.content or "").strip()
@@ -61,27 +38,67 @@ def extract_answer(response):
         text = (response.additional_kwargs.get("reasoning_content", "") or "").strip()
     return text
 
+
 def format_docs(docs):
-    return "\\n\\n".join(f"[{d.metadata.get('source', '?').split('/')[-1]} — page {d.metadata.get('page')}] {d.page_content}" for d in docs)
+    return "\n\n".join(
+        f"[{d.metadata.get('source', '?').split('/')[-1]} — page {d.metadata.get('page')}] {d.page_content}"
+        for d in docs
+    )
 
-generation = prompt | llm | extract_answer
 
-def rag_answer(question):
-    docs = retriever.invoke(question)
-    answer = generation.invoke({"context": format_docs(docs), "input": question})
-    return {"answer": answer, "context": docs}
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    telecharger_documents()
+
+    all_docs = []
+    for f in sorted(os.listdir(DOCS_DIR)):
+        if f.endswith(".pdf"):
+            all_docs.extend(PyPDFLoader(os.path.join(DOCS_DIR, f)).load())
+    chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100).split_documents(all_docs)
+
+    embeddings = NVIDIAEmbeddings(model="nvidia/llama-nemotron-embed-1b-v2", truncate="NONE")
+    vector_store = Chroma.from_documents(chunks, embeddings)
+
+    prompt = ChatPromptTemplate.from_template(
+        "Tu es un assistant RH expert de la convention collective Syntec. "
+        "Réponds à la QUESTION en t'appuyant UNIQUEMENT sur le CONTEXTE ci-dessous.\n"
+        "Si l'information n'y figure pas, réponds exactement : « Je ne sais pas ».\n"
+        "Sois concis et cite la source (document et numéro de page).\n\n"
+        "CONTEXTE :\n{context}\n\nQUESTION : {input}"
+    )
+    llm = ChatNVIDIA(model="openai/gpt-oss-120b", temperature=0.2, max_completion_tokens=2048)
+
+    rag_state["retriever"] = vector_store.as_retriever(search_kwargs={"k": 3})
+    rag_state["generation"] = prompt | llm | extract_answer
+
+    yield
+
+    rag_state.clear()
+
+
+app = FastAPI(title="Assistant RH — Convention Syntec — API RAG", lifespan=lifespan)
 
-app = FastAPI(title="Assistant RH — Convention Syntec — API RAG")
 
 class QuestionIn(BaseModel):
     question: str
 
+
+def rag_answer(question):
+    docs = rag_state["retriever"].invoke(question)
+    answer = rag_state["generation"].invoke({"context": format_docs(docs), "input": question})
+    return {"answer": answer, "context": docs}
+
+
 @app.get("/")
 def health():
     return {"status": "ok"}
 
+
 @app.post("/ask")
 def ask(payload: QuestionIn):
     result = rag_answer(payload.question)
-    sources = [{"document": d.metadata.get("source", "?").split("/")[-1], "page": d.metadata.get("page")} for d in result["context"]]
-    return {"answer": result["answer"], "sources": sources}
+    sources = [
+        {"document": d.metadata.get("source", "?").split("/")[-1], "page": d.metadata.get("page")}
+        for d in result["context"]
+    ]
+    return {"answer": result["answer"], "sources": sources}

requierment.txt → requirements.txt

@@ -5,4 +5,5 @@ langchain-community
 langchain-chroma
 langchain-nvidia-ai-endpoints
 langchain-text-splitters
-pypdf
+pypdf
+python-dotenv