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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']
}
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