| """ |
| EVA - Enterprise Virtual Assistant |
| Complete chatbot: greeting handling + router + memory + hybrid retrieval + extraction |
| """ |
|
|
| import re |
|
|
| GREETINGS = { |
| "hi", "hello", "hey", "yo", "sup", |
| "hi eva", "hello eva", "hey eva", |
| "good morning", "good afternoon", "good evening", |
| "thanks", "thank you", "thanks eva", "thank you eva", |
| "bye", "goodbye", "see you", "ok", "okay", "cool", |
| } |
|
|
| NOT_FOUND_MARKER = "NOT_FOUND_IN_KB" |
|
|
|
|
| def is_small_talk(query, groq_client, generate_with_groq_fn): |
| normalized = query.lower().strip().strip("!?.,") |
| if normalized in GREETINGS: |
| return True |
| prompt = f"""Is this message small talk/greeting/casual conversation (like "hi", "hello", "how are you", "thanks", "bye") |
| rather than an actual question needing information lookup? Answer ONLY "yes" or "no". |
| |
| Message: {query}""" |
| response = generate_with_groq_fn(prompt, groq_client).strip().lower() |
| return "yes" in response |
|
|
|
|
| def handle_small_talk(query, groq_client, generate_with_groq_fn, bot_name="EVA"): |
| prompt = f"""You are {bot_name}, a friendly enterprise knowledge assistant chatbot for HR, Legal, Finance, and IT questions. |
| Respond naturally and briefly to this casual message. If it is a greeting, introduce yourself briefly and invite them to ask a question. |
| |
| Message: {query} |
| |
| Response:""" |
| return generate_with_groq_fn(prompt, groq_client) |
|
|
|
|
| def _extract_company_names(context_text): |
| """Pull company/source names out of the [Source: domain - title] tags so we can verify against them.""" |
| titles = re.findall(r"(?:\[Source:\s*[^\-]+\s*-\s*([^\]]+)\])", context_text) |
| names = set() |
| for t in titles: |
| |
| words = t.strip().split() |
| names.add(t.strip().lower()) |
| if words: |
| names.add(words[0].strip(",.").lower()) |
| return names |
|
|
|
|
| def _verify_answer_grounded(answer, context_text, generate_with_groq_fn, groq_client): |
| """Ask the model itself to fact-check the draft answer against the context, sentence by sentence.""" |
| check_prompt = f"""You will check a draft answer against a set of source excerpts. |
| |
| Source excerpts: |
| {context_text} |
| |
| Draft answer: |
| {answer} |
| |
| Does the draft answer mention ANY company name, country, number, or specific fact that does NOT appear anywhere in the source excerpts above? Be strict — a fact must appear word-for-word or as a clear paraphrase of something actually in the excerpts, not just be plausible. |
| |
| Answer ONLY "yes" (something was added that isn't in the sources) or "no" (everything in the draft is grounded in the sources).""" |
| verdict = generate_with_groq_fn(check_prompt, groq_client, max_tokens=10).strip().lower() |
| return "yes" in verdict |
|
|
|
|
| def ask_eva(query, conversation_history, 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=0.85, bot_name="EVA"): |
|
|
| if is_small_talk(query, groq_client, generate_with_groq_fn): |
| answer = handle_small_talk(query, groq_client, generate_with_groq_fn, bot_name) |
| conversation_history.append({'question': query, 'answer': answer}) |
| return {'answer': answer, 'exact_quote': None, 'source': None, 'domain': None} |
|
|
| router_domain, confidence_or_probs = classify_query_fn(query, router_model, tokenizer, label_encoder) |
|
|
| history_text = "" |
| if conversation_history: |
| history_text = "\n".join([f"User: {h['question']}\nAssistant: {h['answer']}" |
| for h in conversation_history[-3:]]) |
|
|
| rewrite_prompt = f"""Given this conversation history: |
| {history_text} |
| |
| Rewrite the NEW question to be a clear, standalone, explicit search query — resolving any references |
| to earlier parts of the conversation. Keep it short. Only output the rewritten question. |
| |
| New question: {query} |
| |
| Rewritten question:""" |
| rewritten = generate_with_groq_fn(rewrite_prompt, groq_client).strip() |
|
|
| results = retrieve_hybrid_fn(rewritten, router_domain, embedding_model, domain_indices, domain_chunks_map, |
| index, all_chunks, top_k=top_k) |
|
|
| best_distance = results[0][0] |
| fallback_answer = f"I'm {bot_name}, and I couldn't find information about this in my current knowledge base. Could you rephrase, or ask about HR, Legal, Finance, or IT topics?" |
|
|
| if best_distance > hallucination_threshold: |
| conversation_history.append({'question': query, 'answer': fallback_answer}) |
| return {'answer': fallback_answer, 'exact_quote': None, 'source': None, 'domain': router_domain} |
|
|
| actual_domain = results[0][1].get('domain', router_domain) |
|
|
| context_text = "\n\n".join([f"[Source: {c['domain']} - {c['title']}]\n{c['text']}" for dist, c in results]) |
| prompt = f"""You are {bot_name}, answering a colleague's question directly and conversationally — like a knowledgeable coworker, not a report generator. |
| |
| STRICT RULES: |
| 1. Use ONLY facts, numbers, company names, and country names that appear WORD-FOR-WORD in the context below. Never add outside facts, even ones you're confident are true. |
| 2. Does the context below actually answer the question — not just share a word, but genuinely address what's asked? If not, respond with EXACTLY: {NOT_FOUND_MARKER} |
| 3. Otherwise, answer normally: 1-2 sentence direct lead-in, then 2-4 short bullets only using facts/companies present in the context. No source names, no citations, no headers. |
| |
| Context: |
| {context_text} |
| |
| Question: {query} |
| |
| Answer:""" |
| answer = generate_with_groq_fn(prompt, groq_client, max_tokens=350) |
|
|
| if NOT_FOUND_MARKER in answer: |
| conversation_history.append({'question': query, 'answer': fallback_answer}) |
| return {'answer': fallback_answer, 'exact_quote': None, 'source': None, 'domain': actual_domain} |
|
|
| |
| if _verify_answer_grounded(answer, context_text, generate_with_groq_fn, groq_client): |
| conversation_history.append({'question': query, 'answer': fallback_answer}) |
| return {'answer': fallback_answer, 'exact_quote': None, 'source': None, 'domain': actual_domain} |
|
|
| exact_quote, _ = extract_exact_answer_fn(rewritten, results[0][1]['text'], qa_model, qa_tokenizer) |
|
|
| conversation_history.append({'question': query, 'answer': answer}) |
| return {'answer': answer, 'exact_quote': exact_quote, 'source': results[0][1]['title'], 'domain': actual_domain} |
|
|
