src/qa.py

"""
qa.py — GPT-4o (SAP Gen AI Hub) + ReRank Retrieval
--------------------------------------------------
✅ Semantic retrieval (FAISS + cosine re-rank + neighbor fill)
✅ Bullet-aware similarity boost for procedural chunks
✅ Embedding caching (per PDF)
✅ Smart factual mode (fast)
✅ Deep reasoning mode (ChatGPT-like)
"""

import os
import re
import json
import pickle
import hashlib
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from gen_ai_hub.proxy.core.proxy_clients import get_proxy_client
from gen_ai_hub.proxy.langchain.openai import ChatOpenAI

print("✅ qa.py (GPT-4o via Gen AI Hub + Bullet-Aware Retrieval + Cache) 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 Embedding Model (E5-small-v2)
# ==========================================================
try:
    _query_model = SentenceTransformer(
        "intfloat/e5-small-v2",  # ⚡ Faster, 384-dim embeddings
        cache_folder=CACHE_DIR
    )
    print("✅ Loaded embedding model: intfloat/e5-small-v2 (fast mode)")
except Exception as e:
    print(f"⚠️ Embedding load failed ({e}), using MiniLM fallback")
    _query_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", cache_folder=CACHE_DIR)

# ==========================================================
# 3️⃣ GPT-4o via SAP Gen AI Hub
# ==========================================================
print("✅ Loading GPT-4o via SAP Gen AI Hub...")
CRED_PATH = os.path.join(os.path.dirname(__file__), "GEN AI HUB PROXY.json")

try:
    with open(CRED_PATH, "r") as key_file:
        svcKey = json.load(key_file)

    os.environ.update({
        "AICORE_AUTH_URL": svcKey["url"],
        "AICORE_CLIENT_ID": svcKey["clientid"],
        "AICORE_CLIENT_SECRET": svcKey["clientsecret"],
        "AICORE_RESOURCE_GROUP": "default",
        "AICORE_BASE_URL": svcKey["serviceurls"]["AI_API_URL"]
    })

    proxy_client = get_proxy_client("gen-ai-hub")
    chat_llm = ChatOpenAI(
        proxy_model_name="gpt-4o",
        proxy_client=proxy_client,
        temperature=0.3,
        max_tokens=1500
    )
    print("✅ GPT-4o (via Gen AI Hub) ready for generation.")
except Exception as e:
    print(f"⚠️ Gen AI Hub setup failed: {e}")
    chat_llm = None

# ==========================================================
# 4️⃣ Embedding Cache Manager
# ==========================================================
CACHE_EMB_DIR = "/tmp/embed_cache"
os.makedirs(CACHE_EMB_DIR, exist_ok=True)

def _hash_name(file_name: str):
    """Generate unique hash for PDF file name."""
    return hashlib.md5(file_name.encode()).hexdigest()

def cache_embeddings(file_name: str, chunks, embed_func):
    """
    Checks if cached embeddings exist for a PDF; if not, compute and save.
    """
    cache_path = os.path.join(CACHE_EMB_DIR, f"{_hash_name(file_name)}.pkl")

    if os.path.exists(cache_path):
        print(f"🧠 Loaded cached embeddings for {file_name}")
        with open(cache_path, "rb") as f:
            return pickle.load(f)

    print(f"💡 No cache found for {file_name}. Generating embeddings...")
    embeddings = embed_func(chunks)
    with open(cache_path, "wb") as f:
        pickle.dump(embeddings, f)
    print(f"💾 Cached embeddings saved for {file_name}")
    return embeddings

def embed_chunks(chunks, batch_size=32):
    """
    Batch-encode text chunks for speed.
    """
    all_embeddings = []
    for i in range(0, len(chunks), batch_size):
        batch = [f"passage: {c}" for c in chunks[i:i+batch_size]]
        batch_embs = _query_model.encode(
            batch,
            convert_to_numpy=True,
            normalize_embeddings=True,
            show_progress_bar=False
        )
        all_embeddings.extend(batch_embs)
    print(f"⚡ Embedded {len(all_embeddings)} chunks in batches of {batch_size}")
    return np.array(all_embeddings)

# ==========================================================
# 5️⃣ Prompt Templates
# ==========================================================
STRICT_PROMPT = (
    "You are an enterprise documentation assistant.\n"
    "Use all relevant information from the CONTEXT below.\n"
    "If multiple related points appear across chunks, combine them logically into one clear answer.\n"
    "Keep the answer concise but complete. Do not invent facts outside the provided content.\n"
    "If the answer cannot be found even after considering all chunks, say exactly:\n"
    "'I don't know based on the provided document.'\n\n"
    "Context:\n{context}\n\nQuestion: {query}\nAnswer:"
)

REASONING_PROMPT = (
    "You are an expert enterprise assistant capable of reasoning.\n"
    "Think step by step and synthesize information even if scattered across chunks.\n"
    "Base your answer primarily on the CONTEXT, but if multiple partial clues exist, combine them logically.\n"
    "You may fill reasonable gaps with general knowledge to form a complete answer.\n"
    "If absolutely nothing in the document relates, say exactly:\n"
    "'I don't know based on the provided document.'\n\n"
    "Context:\n{context}\n\nQuestion: {query}\nLet's reason step-by-step:\nAnswer:"
)

# ==========================================================
# 6️⃣ Retrieval — FAISS + Bullet-Aware Re-rank + Neighbor Fill
# ==========================================================
from vectorstore import build_faiss_index

def retrieve_chunks(query: str, index, chunks: list, top_k: int = 5,
                    min_similarity: float = 0.6, candidate_multiplier: int = 3,
                    embeddings: list = None):
    """
    Retrieves top relevant chunks and preserves context continuity.
    Adds small similarity boost for procedural (bullet or numbered) chunks.
    """

    if not index or not chunks:
        print("⚠️ No FAISS index or chunks provided — returning empty result.")
        return []

    try:
        q_emb = _query_model.encode(
            [f"query: {query.strip()}"],
            convert_to_numpy=True,
            normalize_embeddings=True
        )[0]

        # ✅ Dimension sanity check
        if hasattr(index, "d") and q_emb.shape[0] != index.d:
            print(f"⚠️ FAISS dimension mismatch: index={index.d}, query={q_emb.shape[0]}")
            if embeddings:
                print("🔄 Rebuilding FAISS index...")
                index = build_faiss_index(embeddings)
            else:
                return []

        # Step 1️⃣ — Initial FAISS retrieval
        num_candidates = max(top_k * candidate_multiplier, top_k + 2)
        distances, indices = index.search(np.array([q_emb]).astype("float32"), num_candidates)
        candidate_indices = [int(i) for i in indices[0] if i >= 0]
        candidate_indices = list(dict.fromkeys(candidate_indices))

        # Step 2️⃣ — Re-rank with bullet-aware boost
        doc_embs = _query_model.encode(
            [f"passage: {chunks[i]}" for i in candidate_indices],
            convert_to_numpy=True,
            normalize_embeddings=True,
        )
        sims = cosine_similarity([q_emb], doc_embs)[0]
        boosted_sims = []
        for idx, sim in zip(candidate_indices, sims):
            text = chunks[idx].strip()
            if re.match(r"^[-•\d]+[\.\s]", text):  # bullet or step pattern
                sim += 0.05
            boosted_sims.append((idx, sim))

        ranked = sorted(boosted_sims, key=lambda x: x[1], reverse=True)
        filtered = [idx for idx, sim in ranked if sim >= min_similarity][:top_k]

        # Step 3️⃣ — Add neighboring chunks for continuity
        neighbors = set()
        for idx in filtered:
            for n in [idx - 1, idx + 1]:
                if 0 <= n < len(chunks):
                    neighbors.add(n)
        filtered = sorted(set(filtered) | neighbors)

        final_chunks = [chunks[i] for i in filtered]
        print(f"✅ Retrieved {len(final_chunks)} chunks (bullet-aware + continuity).")
        return final_chunks

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

# ==========================================================
# 7️⃣ Answer Generation
# ==========================================================
def generate_answer(query: str, retrieved_chunks: list, reasoning_mode: bool = False):
    if not retrieved_chunks:
        return "Sorry, I couldn’t find relevant information in the document."
    if chat_llm is None:
        return "⚠️ GPT-4o not initialized. Check credentials or rebuild the Space."

    context = "\n".join(f"[Chunk {i+1}] {chunk.strip()}" for i, chunk in enumerate(retrieved_chunks))
    prompt = (REASONING_PROMPT if reasoning_mode else STRICT_PROMPT).format(context=context, query=query)

    messages = [
        {"role": "system", "content":
            "You are an expert enterprise documentation assistant. "
            "When reasoning_mode is off, stay strictly factual and concise. "
            "When reasoning_mode is on, combine insights across chunks logically "
            "and explain briefly. "
            "If the answer is not in the document, reply exactly: "
            "'I don't know based on the provided document.'"},
        {"role": "user", "content": prompt},
    ]

    try:
        response = chat_llm.invoke(messages)
        return response.content.strip()
    except Exception as e:
        print(f"⚠️ GPT-4o generation failed: {e}")
        return "⚠️ Error: Could not generate an answer."

# ==========================================================
# 8️⃣ Local Test
# ==========================================================
if __name__ == "__main__":
    from vectorstore import build_faiss_index

    dummy_chunks = [
        "- Step 1: Enable order confirmation capability.",
        "- Step 2: Configure supplier email.",
        "Setup instructions and configuration details.",
        "Prerequisites for automation are described here."
    ]

    embeddings = embed_chunks(dummy_chunks)
    index = build_faiss_index(embeddings)

    query = "What are the prerequisites for commerce automation?"
    retrieved = retrieve_chunks(query, index, dummy_chunks)
    print("🔍 Retrieved:", retrieved)
    print("💬 Answer:", generate_answer(query, retrieved, reasoning_mode=False))