Delete rag_utils_unsloth.py

rag_utils_unsloth.py

@@ -1,42 +0,0 @@
-import faiss
-import pickle
-import numpy as np
-import torch
-import os
-
-from sentence_transformers import SentenceTransformer
-from unsloth import FastLanguageModel
-
-def load_faiss_index(index_path="faiss_index/faiss_index.faiss", doc_path="faiss_index/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():
-    return SentenceTransformer("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
-
-def query_index(question, index, documents, model, k=3):
-    question_embedding = model.encode([question])
-    _, indices = index.search(np.array(question_embedding).astype("float32"), k)
-    results = [documents[i] for i in indices[0]]
-    return results
-
-def generate_answer(question, context):
-    model_id = "unsloth/mistral-7b-instruct-v0.1-bnb-4bit"
-
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name=model_id,
-        max_seq_length=4096,
-        dtype="float32",            # pour CPU uniquement
-        load_in_4bit=True,
-        device_map="auto"
-    )
-
-    tokenizer.pad_token = tokenizer.eos_token
-
-    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)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)