src/extractive_qa.py

"""
Enterprise Knowledge Assistant - Extractive QA Layer
Combines generative narration with exact-quote extraction for verifiable answers.
"""

import torch

def extract_exact_answer(question, context, qa_model, qa_tokenizer):
    inputs = qa_tokenizer(question, context, return_tensors="pt", truncation=True, max_length=384)
    
    with torch.no_grad():
        outputs = qa_model(**inputs)
    
    answer_start = torch.argmax(outputs.start_logits)
    answer_end = torch.argmax(outputs.end_logits) + 1
    
    answer_tokens = inputs['input_ids'][0][answer_start:answer_end]
    answer_text = qa_tokenizer.decode(answer_tokens, skip_special_tokens=True)
    
    confidence = torch.softmax(outputs.start_logits, dim=1).max().item()
    
    return answer_text, confidence


def ask_with_extraction(query, domain, embedding_model, domain_indices, domain_chunks_map,
                          index, all_chunks, groq_client, qa_model, qa_tokenizer, 
                          retrieve_hybrid_fn, generate_with_groq_fn, top_k=5):
    
    results = retrieve_hybrid_fn(query, domain, embedding_model, domain_indices, domain_chunks_map, 
                                   index, all_chunks, top_k=top_k)
    
    context_text = "\n\n".join([f"[Source: {c['domain']} - {c['title']}]\n{c['text']}" for dist, c in results])
    
    prompt = f"""You are an enterprise knowledge assistant. Answer using ONLY the context below.

IMPORTANT: Do not just tell the user "refer to source X" or "see document Y." Instead, directly explain 
WHAT the clause/policy/fact actually says, in your own words, synthesizing the actual content. 
Only mention source names as supporting citations after explaining the substance.

Context:
{context_text}

Question: {query}

Answer (explain the actual content directly, then cite sources):"""
    
    generative_answer = generate_with_groq_fn(prompt, groq_client)
    
    best_chunk_text = results[0][1]['text']
    exact_answer, confidence = extract_exact_answer(query, best_chunk_text, qa_model, qa_tokenizer)
    
    return {
        'narrated_answer': generative_answer,
        'exact_quote': exact_answer,
        'quote_confidence': confidence,
        'source': results[0][1]['title']
    }