src/qa.py

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

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__)

# ==========================================================
# 🧱 Permanent Embeddings Cache Directory
# ==========================================================
CACHE_EMB_DIR = os.path.join(os.path.dirname(__file__), "embed_cache")
os.makedirs(CACHE_EMB_DIR, exist_ok=True)

try:
    test_file = os.path.join(CACHE_EMB_DIR, "test_write.tmp")
    with open(test_file, "w") as f:
        f.write("ok")
    os.remove(test_file)
    print(f"✅ Cache directory ready and writable: {CACHE_EMB_DIR}")
except Exception as e:
    print(f"⚠️ Cache directory not writable ({CACHE_EMB_DIR}): {e}")
    CACHE_EMB_DIR = "/tmp/embed_cache"
    os.makedirs(CACHE_EMB_DIR, exist_ok=True)
    print(f"🔄 Fallback to temporary cache: {CACHE_EMB_DIR}")

# ==========================================================
# 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 (Multilingual E5 — supports Hindi + English)
# ==========================================================
try:
    # 🆕 Switched to multilingual model (same 384-dim dimension, so FAISS stays compatible)
    _query_model = SentenceTransformer(
        "intfloat/multilingual-e5-small",
        cache_folder=CACHE_DIR
    )
    print("✅ Loaded embedding model: intfloat/multilingual-e5-small (multilingual mode)")
except Exception as e:
    print(f"⚠️ Embedding load failed ({e}), attempting English-only fallback...")
    try:
        _query_model = SentenceTransformer("intfloat/e5-small-v2", cache_folder=CACHE_DIR)
        print("🔁 Fallback: intfloat/e5-small-v2 loaded successfully.")
    except Exception:
        _query_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", cache_folder=CACHE_DIR)
        print("🔁 Final fallback: all-MiniLM-L6-v2 loaded.")

# ==========================================================
# 3️⃣ GPT-4o via SAP Gen AI Hub — Lazy Initialization
# ==========================================================
CRED_PATH = os.path.join(os.path.dirname(__file__), "GEN AI HUB PROXY.json")
_chat_llm = None

def get_chat_llm(model_name: str = "gpt-4o", temperature: float = 0.3, max_tokens: int = 1500):
    global _chat_llm
    if _chat_llm is not None:
        return _chat_llm

    try:
        if os.path.exists(CRED_PATH):
            with open(CRED_PATH, "r") as key_file:
                svcKey = json.load(key_file)
            os.environ.update({
                "AICORE_AUTH_URL": svcKey.get("url", ""),
                "AICORE_CLIENT_ID": svcKey.get("clientid", ""),
                "AICORE_CLIENT_SECRET": svcKey.get("clientsecret", ""),
                "AICORE_BASE_URL": svcKey.get("serviceurls", {}).get("AI_API_URL", ""),
            })

        proxy_client = get_proxy_client("gen-ai-hub")
        _chat_llm = ChatOpenAI(
            proxy_model_name=model_name,
            proxy_client=proxy_client,
            temperature=temperature,
            max_tokens=max_tokens,
        )
        print(f"✅ GPT-4o (via Gen AI Hub) initialized lazily for model: {model_name}")
        return _chat_llm

    except Exception as e:
        print(f"⚠️ Gen AI Hub lazy init failed: {e}")
        _chat_llm = None
        raise


# ==========================================================
# 4️⃣ Embedding Generator (Batch-Optimized)
# ==========================================================
def embed_chunks(chunks, batch_size: int = 32):
    if not chunks:
        return np.array([])

    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 Embedding Cache Manager
# ==========================================================
def _hash_name(file_name: str, chunk_size: int, overlap: int, num_chunks: int):
    combo = f"{file_name}_{chunk_size}_{overlap}_{num_chunks}"
    return hashlib.md5(combo.encode()).hexdigest()[:8]

def _clean_old_caches(base_name: str, keep_latest: int = 5):
    files = [
        (os.path.getmtime(os.path.join(CACHE_EMB_DIR, f)), f)
        for f in os.listdir(CACHE_EMB_DIR)
        if f.startswith(base_name)
    ]
    if len(files) > keep_latest:
        files.sort(reverse=True)
        for _, old_file in files[keep_latest:]:
            try:
                os.remove(os.path.join(CACHE_EMB_DIR, old_file))
                print(f"🧹 Removed old cache: {old_file}")
            except Exception:
                pass

def cache_embeddings(file_name: str, chunks, embed_func, chunk_size: int = None, overlap: int = None):
    cache_key = _hash_name(file_name, chunk_size or 1000, overlap or 100, len(chunks))
    cache_file = f"{os.path.basename(file_name)}_cs{chunk_size}_ov{overlap}_{cache_key}.pkl"
    cache_path = os.path.join(CACHE_EMB_DIR, cache_file)
    base_name = os.path.basename(file_name)

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

    print(f"💡 No cache found for {base_name} ({chunk_size}/{overlap}). Generating new embeddings...")
    embeddings = embed_func(chunks)
    with open(cache_path, "wb") as f:
        pickle.dump(embeddings, f)
    print(f"💾 Cached embeddings saved as {cache_file}")
    _clean_old_caches(base_name, keep_latest=5)
    return embeddings


# ==========================================================
# 6️⃣ Prompt Templates (Original Strict)
# ==========================================================
STRICT_PROMPT = (
    "You are an enterprise documentation assistant.\n"
    "Use all relevant information from the CONTEXT below.\n"
    "When multiple causes, steps, or key points are discussed, present them as short, well-structured bullet points.\n"
    "When the answer focuses on a single concept, definition, or explanation, write it as a clear and compact paragraph.\n"
    "Keep the tone professional and concise. Do not invent facts outside the provided content.\n"
    "Do not mention or refer to internal elements such as 'chunks', 'chunk numbers', 'passages', or 'sections of the document'.\n"
    "If the answer cannot be found directly but there are partial clues, summarize those clues briefly starting with 'Based on the available information,'.\n"
    "If nothing at all in the CONTEXT relates to the question, reply 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"
    "Do not mention or refer to internal elements such as 'chunks', 'chunk numbers', or 'sections of the document'.\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:"
)


# ==========================================================
# 7️⃣ 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 = 7,
                    min_similarity: float = 0.6, candidate_multiplier: int = 3,
                    embeddings: list = None):
    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]

        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 []

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

        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):
                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]
        if not filtered:
            print(f"⚠️ No chunks ≥ {min_similarity:.2f} — using top {top_k} ranked chunks instead.")
            filtered = [idx for idx, sim in ranked[:top_k]]

        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]
        avg_sim = np.mean([s for _, s in ranked[:top_k]])
        print(f"✅ Retrieved {len(final_chunks)} chunks | avg_sim={avg_sim:.3f} | threshold={min_similarity:.2f}")
        return final_chunks

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


# ==========================================================
# 8️⃣ Answer Generation
# ==========================================================
# ==========================================================
# 8️⃣ Answer Generation (Lazy GPT-4o Initialization + Language-Aware)
# ==========================================================
def generate_answer(query: str, retrieved_chunks: list, reasoning_mode: bool = False, doc_lang: str = "en"):
    """
    Generates an answer using GPT-4o (SAP Gen AI Hub proxy).
    Now supports Hindi or English response formatting automatically.
    """
    if not retrieved_chunks:
        return "Sorry, I couldn’t find relevant information in the document."

    # Try lazy initialization
    try:
        chat_llm_local = get_chat_llm()
    except Exception:
        return "⚠️ GPT-4o not initialized. Check credentials or rebuild the Space."

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

    # 🌐 Language-specific prompt logic
    if doc_lang == "hi":
        # Hindi-language response
        prompt = (
            f"आप एक दस्तावेज़ सहायक हैं जो दिए गए अंशों के आधार पर सटीक उत्तर देता है। "
            f"कृपया नीचे दिए गए संदर्भ का उपयोग करते हुए प्रश्न का उत्तर हिंदी में दें। "
            f"यदि उत्तर स्पष्ट रूप से दस्तावेज़ में नहीं है, तो कहें — "
            f"'मुझे इस दस्तावेज़ के आधार पर उत्तर ज्ञात नहीं है।'\n\n"
            f"संदर्भ:\n{context}\n\nप्रश्न: {query}\nउत्तर:"
        )
    else:
        # Default English prompts
        prompt = (REASONING_PROMPT if reasoning_mode else STRICT_PROMPT).format(context=context, query=query)

    # System role
    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},
    ]

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



# ==========================================================
# 9️⃣ Generic Text Generation Helper
# ==========================================================
def genai_generate(prompt: str) -> str:
    try:
        chat_llm_local = get_chat_llm()
    except Exception:
        raise RuntimeError("⚠️ GPT-4o not initialized. Check credentials or rebuild the Space.")

    messages = [
        {"role": "system", "content": "You are a concise, intelligent text generator."},
        {"role": "user", "content": prompt.strip()},
    ]

    try:
        response = chat_llm_local.invoke(messages)
        return response.content.strip()
    except Exception as e:
        print(f"⚠️ genai_generate() failed: {e}")
        return "⚠️ Unable to generate response."


# ==========================================================
# 🔟 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))