File size: 1,504 Bytes
01ae746 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
# app/qa.py
from sentence_transformers import SentenceTransformer
from vectorstore import search_faiss
from transformers import pipeline
# Load embedding model (for query encoding)
_query_model = SentenceTransformer("all-MiniLM-L6-v2")
# Load Hugging Face generative model (FLAN-T5-small is free & CPU friendly)
_answer_model = pipeline("text2text-generation", model="google/flan-t5-small")
def retrieve_chunks(query: str, index, chunks: list, top_k: int = 3):
"""Encode query and return top_k matching chunks from FAISS index."""
query_embedding = _query_model.encode([query], convert_to_numpy=True)[0]
return search_faiss(query_embedding, index, chunks, top_k)
def generate_answer(query: str, retrieved_chunks: list) -> str:
"""Generate a natural language answer using Hugging Face model."""
if not retrieved_chunks:
return "Sorry, I could not find relevant information in the document."
# Combine retrieved chunks into a single context
context = " ".join(retrieved_chunks)
# ✅ Stronger prompt to guide the model
prompt = f"""
You are a helpful assistant.
Read the context carefully and answer the question with clear, step-by-step instructions.
Do not just repeat the context; explain in your own words.
Context:
{context}
Question:
{query}
Answer (in numbered steps):
"""
# Run the Hugging Face pipeline
result = _answer_model(prompt, max_length=300, clean_up_tokenization_spaces=True)
return result[0]["generated_text"].strip() |