Upload src/eva_chatbot.py with huggingface_hub

src/eva_chatbot.py

@@ -3,8 +3,22 @@ EVA - Enterprise Virtual Assistant
 Complete chatbot: greeting handling + router + memory + hybrid retrieval + extraction
 """
 
+# Fast, instant-match greetings — checked BEFORE any LLM call.
+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",
+}
+
+
 def is_small_talk(query, groq_client, generate_with_groq_fn):
-    prompt = f"""Is this message small talk/greeting/casual conversation (like "hi", "hello", "how are you", "thanks", "bye") 
+    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}"""
@@ -14,7 +28,7 @@ Message: {query}"""
 
 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's a greeting, introduce yourself briefly and invite them to ask a question.
+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}
 
@@ -24,42 +38,42 @@ Response:"""
 
 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, 
+            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, 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}
-    
+        conversation_history.append({"question": query, "answer": answer})
+        return {"answer": answer, "exact_quote": None, "source": None, "domain": None}
+
     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']}" 
+        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 
+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, domain, embedding_model, domain_indices, domain_chunks_map, 
+
+    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:
-        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?"
-        conversation_history.append({'question': query, 'answer': answer})
-        return {'answer': answer, 'exact_quote': None, 'source': None, 'domain': domain}
-    
-    context_text = "\n\n".join([f"[Source: {c['domain']} - {c['title']}]\n{c['text']}" for dist, c in results])
+        answer = f"I am {bot_name}, and I could not find information about this in my current knowledge base. Could you rephrase, or ask about HR, Legal, Finance, or IT topics?"
+        conversation_history.append({"question": query, "answer": answer})
+        return {"answer": answer, "exact_quote": 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 {bot_name}, an enterprise knowledge assistant having an ongoing conversation.
 
 Conversation so far:
@@ -74,8 +88,8 @@ New question: {query}
 
 Answer:"""
     answer = generate_with_groq_fn(prompt, groq_client)
-    
-    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': 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": domain}