| """ |
| Enterprise Knowledge Assistant - Core RAG Pipeline (v2) |
| Improved hallucination guard: checks both original and rewritten query distances. |
| """ |
|
|
| def retrieve_relevant_chunks(query, embedding_model, index, all_chunks, top_k=5): |
| query_embedding = embedding_model.encode([query], convert_to_numpy=True) |
| distances, indices = index.search(query_embedding.astype('float32'), top_k) |
| results = [all_chunks[idx] for idx in indices[0]] |
| return results, distances[0][0] |
|
|
|
|
| def generate_with_groq(prompt, groq_client, model="openai/gpt-oss-120b", max_tokens=150): |
| response = groq_client.chat.completions.create( |
| model=model, |
| messages=[{"role": "user", "content": prompt}], |
| reasoning_effort="low", |
| max_tokens=max_tokens, |
| temperature=0, |
| ) |
| return response.choices[0].message.content |
|
|
|
|
| def ask_chatbot_v4(query, embedding_model, index, all_chunks, groq_client, |
| conversation_history, top_k=5, similarity_threshold=0.88): |
| |
| greetings = { |
| "hi", "hello", "hey", |
| "hi eva", "hello eva", "hey eva", |
| "good morning", "good afternoon", "good evening" |
| } |
|
|
| if query.lower().strip() in greetings: |
| return ( |
| "Hello! I'm EVA, your Enterprise Knowledge Assistant. " |
| "How can I help you with HR, Legal, Finance, or IT queries?", |
| query, |
| 1.0 |
| ) |
|
|
|
|
| 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 clearer and more explicit for a document search system, resolving any references to earlier parts of the conversation. |
| Only output the rewritten question, nothing else. |
| |
| New question: {query}""" |
|
|
| rewritten = generate_with_groq(rewrite_prompt, groq_client).strip() |
|
|
| original_embedding = embedding_model.encode([query], convert_to_numpy=True) |
| rewritten_embedding = embedding_model.encode([rewritten], convert_to_numpy=True) |
|
|
| orig_distances, orig_indices = index.search(original_embedding.astype('float32'), top_k) |
| rewrite_distances, rewrite_indices = index.search(rewritten_embedding.astype('float32'), top_k) |
|
|
| if orig_distances[0][0] <= rewrite_distances[0][0]: |
| best_distance = orig_distances[0][0] |
| indices = orig_indices |
| else: |
| best_distance = rewrite_distances[0][0] |
| indices = rewrite_indices |
|
|
| if best_distance > similarity_threshold: |
| answer = "I couldn't find information about this in the available documents. This question may be outside the scope of the current knowledge base." |
| else: |
| relevant_chunks = [all_chunks[idx] for idx in indices[0]] |
| context_text = "\n\n".join([f"[Source: {c['domain']} - {c['title']}]\n{c['text']}" |
| for c in relevant_chunks]) |
|
|
| answer_prompt = f"""You are an enterprise knowledge assistant having an ongoing conversation. |
| |
| Conversation so far: |
| {history_text} |
| |
| Answer using ONLY the context below. If the context doesn't fully answer the question, say what's missing honestly. |
| |
| Context: |
| {context_text} |
| |
| New question: {query} |
| |
| Answer:""" |
| answer = generate_with_groq(answer_prompt, groq_client) |
|
|
| conversation_history.append({'question': query, 'answer': answer}) |
| return answer, rewritten, best_distance |
|
|
