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| """ | |
| Enterprise Knowledge Assistant - FINAL Complete Chatbot Function (v2) | |
| Fixed: query rewriting now preserves intent instead of drifting/elaborating. | |
| """ | |
| def ask_chatbot_final_v2(query, embedding_model, index, all_chunks, groq_client, | |
| router_model, tokenizer, label_encoder, | |
| domain_indices, domain_chunks_map, | |
| qa_model, qa_tokenizer, | |
| classify_query_fn, retrieve_hybrid_fn, generate_with_groq_fn, extract_exact_answer_fn, | |
| top_k=5, hallucination_threshold=0.95): | |
| domain, confidence_or_probs = classify_query_fn(query, router_model, tokenizer, label_encoder) | |
| router_confidence = float(confidence_or_probs.max()) if hasattr(confidence_or_probs, 'shape') else float(confidence_or_probs) | |
| rewrite_prompt = f"""Rewrite this question to be clearer for a document search system. | |
| Keep it SHORT and preserve the EXACT original meaning. Do not add new concepts, legal terms, or expand the scope. | |
| If the question uses casual phrasing (e.g. "become a mother"), just convert it to the standard term (e.g. "maternity leave"), nothing more. | |
| Original question: {query} | |
| Rewritten question (short, same meaning):""" | |
| rewritten = generate_with_groq_fn(rewrite_prompt, groq_client).strip() | |
| results = retrieve_hybrid_fn(rewritten, domain, embedding_model, domain_indices, domain_chunks_map, | |
| index, all_chunks, top_k=top_k) | |
| best_distance = results[0][0] | |
| if best_distance > hallucination_threshold: | |
| return {'narrated_answer': "I couldn't find information about this in the available documents.", | |
| 'exact_quote': None, 'quote_confidence': None, 'source': None, 'domain': domain} | |
| 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. | |
| Directly explain WHAT the policy/content actually says, in clear detail. Cite sources after explaining substance. | |
| Context: | |
| {context_text} | |
| Question: {query} | |
| Answer:""" | |
| generative_answer = generate_with_groq_fn(prompt, groq_client) | |
| best_chunk_text = results[0][1]['text'] | |
| exact_answer, extract_confidence = extract_exact_answer_fn(rewritten, best_chunk_text, qa_model, qa_tokenizer) | |
| return {'narrated_answer': generative_answer, 'exact_quote': exact_answer, | |
| 'quote_confidence': extract_confidence, 'source': results[0][1]['title'], 'domain': domain} | |