Update rag_utils.py

rag_utils.py

@@ -1,5 +1,3 @@
-
-import os
 import faiss
 import pickle
 import numpy as np
@@ -7,15 +5,13 @@ import torch
 from sentence_transformers import SentenceTransformer
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-def load_faiss_index(index_path="faiss_index/faiss_index.faiss", doc_path="faiss_index/documents.pkl"):
+def load_faiss_index(index_path="faiss_index.faiss", doc_path="documents.pkl"):
     index = faiss.read_index(index_path)
     with open(doc_path, "rb") as f:
         documents = pickle.load(f)
     return index, documents
 
 def get_embedding_model():
-    # Pas besoin de token ici, modèle public
-    print("✅ Chargement de l'encodeur multi-qa-MiniLM-L6-cos-v1")
     return SentenceTransformer("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
 
 def query_index(question, index, documents, model, k=3):
@@ -24,23 +20,16 @@ def query_index(question, index, documents, model, k=3):
     return [documents[i] for i in indices[0]]
 
 def generate_answer(question, context):
-    token = os.getenv("HUGGINGFACE")  # requis pour Mistral
-    model_id = "tiiuae/falcon-rw-1b"
+    model_id = "Salesforce/codegen-350M-mono"
 
-    tokenizer = AutoTokenizer.from_pretrained(model_id, token=token)
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
     tokenizer.pad_token = tokenizer.eos_token
 
-    model = AutoModelForCausalLM.from_pretrained(
-        model_id,
-        token=token,
-        device_map="auto",
-        torch_dtype=torch.float16
-    )
+    model = AutoModelForCausalLM.from_pretrained(model_id)
 
     prompt = f"Voici un contexte :\n{context}\n\nQuestion : {question}\nRéponse :"
-    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(model.device)
-    outputs = model.generate(**inputs, max_new_tokens=256, pad_token_id=tokenizer.eos_token_id)
-    print("🔍 Contexte utilisé pour la génération :")
-    print(context[:500])
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(**inputs, max_new_tokens=128, pad_token_id=tokenizer.eos_token_id)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
+