Delete edu_pilot_gradio_space_final/rag_utils.py

edu_pilot_gradio_space_final/rag_utils.py

@@ -1,46 +0,0 @@
-import faiss
-import pickle
-import numpy as np
-import re
-from sentence_transformers import SentenceTransformer
-from huggingface_hub import hf_hub_download
-from llama_cpp import Llama
-
-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)
-    return [documents[i] for i in indices[0]]
-
-def nettoyer_context(context):
-    context = re.sub(r"\[\'(.*?)\'\]", r"\1", context)
-    context = context.replace("None", "")
-    return context
-
-
-
-import os
-from huggingface_hub import InferenceClient
-
-client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1", token=os.environ.get("edup"))
-
-def generate_answer(question, context):
-    prompt = f"""Voici des informations sur des établissements et formations :
-
-{context}
-
-Formule ta réponse comme un conseiller d’orientation bienveillant, de manière fluide et naturelle.
-
-Question : {question}
-Réponse :"""
-
-    response = client.text_generation(prompt, max_new_tokens=300)
-    return response