src/qa.py

"""
qa.py — Retrieval + Generation Layer
-------------------------------------
Handles:
• Query embedding (SentenceTransformer / E5-compatible)
• Chunk retrieval (FAISS with neighborhood merging + re-ranking)
• Answer generation (OpenAI GPT-4o-mini → FLAN-T5 fallback)
Optimized for Hugging Face Spaces & Streamlit.
"""

import os
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from vectorstore import search_faiss

print("✅ qa.py loaded from:", __file__)

# ==========================================================
# 1️⃣ Hugging Face Cache Setup
# ==========================================================
CACHE_DIR = "/tmp/hf_cache"
os.makedirs(CACHE_DIR, exist_ok=True)
os.environ.update({
    "HF_HOME": CACHE_DIR,
    "TRANSFORMERS_CACHE": CACHE_DIR,
    "HF_DATASETS_CACHE": CACHE_DIR,
    "HF_MODULES_CACHE": CACHE_DIR
})

# ==========================================================
# 2️⃣ Query Embedding Model
# ==========================================================
try:
    _query_model = SentenceTransformer("intfloat/e5-small-v2", cache_folder=CACHE_DIR)
    print("✅ Loaded query model: intfloat/e5-small-v2")
except Exception as e:
    print(f"⚠️ Query model load failed ({e}), falling back to MiniLM.")
    _query_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", cache_folder=CACHE_DIR)

# ==========================================================
# 3️⃣ LLM Setup: OpenAI (primary) + FLAN (fallback)
# ==========================================================
USE_OPENAI = bool(os.getenv("OPENAI_API_KEY"))
_answer_model = None  # ensures it's always defined

if USE_OPENAI:
    try:
        from openai import OpenAI
        client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        print("✅ Using OpenAI GPT-4o-mini for answer generation")
    except Exception as e:
        print(f"⚠️ Failed to initialize OpenAI client: {e}")
        USE_OPENAI = False

# Always prepare fallback safely
try:
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
    MODEL_NAME = "google/flan-t5-base"
    _tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, cache_dir=CACHE_DIR)
    _model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME, cache_dir=CACHE_DIR)
    _answer_model = pipeline("text2text-generation", model=_model, tokenizer=_tokenizer, device=-1)
    print("💡 Fallback FLAN-T5 ready.")
except Exception as e:
    print(f"⚠️ Could not initialize FLAN fallback: {e}")

# ==========================================================
# 4️⃣ Prompt Template
# ==========================================================
PROMPT_TEMPLATE = """
You are an enterprise knowledge assistant.
Use ONLY the CONTEXT below to answer the QUESTION clearly, completely, and factually.
If the context doesn’t contain the answer, reply exactly:
"I don't know based on the provided document."

---
Context:
{context}
---
Question:
{query}
---
Answer:
"""

# ==========================================================
# 5️⃣ Chunk Retrieval Function
# ==========================================================
def retrieve_chunks(query: str, index, chunks: list, top_k: int = 5):
    """Retrieve top-K relevant chunks, merge nearby ones, and re-rank by semantic similarity."""
    if not index or not chunks:
        return []

    try:
        # Step 1: Encode the query
        query_emb = _query_model.encode(
            [f"query: {query.strip()}"],
            convert_to_numpy=True,
            normalize_embeddings=True
        )[0]

        # Step 2: Initial FAISS retrieval
        distances, indices = index.search(np.array([query_emb]).astype("float32"), top_k * 2)

        # Step 3: Merge neighboring chunks
        merged_chunks = []
        for idx in indices[0]:
            neighbors = [chunks[i] for i in range(max(0, idx - 1), min(len(chunks), idx + 2))]
            merged_chunks.append(" ".join(neighbors))

        # Step 4: Re-rank using cosine similarity
        chunk_vecs = np.array([
            _query_model.encode([c], convert_to_numpy=True, normalize_embeddings=True)[0]
            for c in merged_chunks
        ])
        scores = cosine_similarity(np.array([query_emb]), chunk_vecs)[0]
        sorted_indices = np.argsort(scores)[::-1]

        # Step 5: Return top-ranked merged chunks
        return [merged_chunks[i] for i in sorted_indices[:top_k]]

    except Exception as e:
        print(f"⚠️ Retrieval error: {e}")
        return []


# ==========================================================
# 6️⃣ Answer Generation Function
# ==========================================================
def generate_answer(query: str, retrieved_chunks: list):
    """Generate factual, context-grounded answers using OpenAI or fallback FLAN-T5."""
    if not retrieved_chunks:
        return "Sorry, I couldn’t find relevant information in the document."

    # Build full context
    context = "\n\n".join([
        f"[Chunk {i+1}]: {chunk.strip()}"
        for i, chunk in enumerate(retrieved_chunks)
    ])
    prompt = PROMPT_TEMPLATE.format(context=context, query=query)

    # --- Try OpenAI first ---
    if USE_OPENAI:
        try:
            response = client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[
                    {"role": "system", "content": "You are a precise enterprise document assistant."},
                    {"role": "user", "content": prompt},
                ],
                temperature=0.4,
                max_tokens=800,
            )
            return response.choices[0].message.content.strip()

        except Exception as e:
            print(f"⚠️ OpenAI generation failed: {e}. Switching to fallback...")

    # --- Fallback to FLAN-T5 ---
    try:
        if _answer_model:
            result = _answer_model(
                prompt,
                max_new_tokens=600,
                do_sample=False,
                temperature=0.3
            )
            return result[0]["generated_text"].strip()
        else:
            return "⚠️ Error: Fallback model not available."
    except Exception as e:
        print(f"⚠️ Fallback model failed: {e}")
        return "⚠️ Error: Both OpenAI and fallback generation failed."


# ==========================================================
# 7️⃣ Local Test
# ==========================================================
if __name__ == "__main__":
    dummy_chunks = [
        "Step 1: Open the dashboard and navigate to reports.",
        "Step 2: Click 'Export' to download a CSV summary.",
        "Step 3: Review the generated report in your downloads folder."
    ]
    from vectorstore import build_faiss_index

    index = build_faiss_index([
        _query_model.encode(
            [f"passage: {chunk}"],
            convert_to_numpy=True,
            normalize_embeddings=True
        )[0]
        for chunk in dummy_chunks
    ])

    query = "What are the steps to export a report?"
    retrieved = retrieve_chunks(query, index, dummy_chunks)
    print("🔍 Retrieved:", retrieved)
    print("💬 Answer:", generate_answer(query, retrieved))