Create qa.py

src/qa.py

@@ -0,0 +1,44 @@
+# 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()