"""
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):
    # Handle greetings without sending them through RAG
    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