rag.py

# rag.py — Dual-company RAG pipeline (LD Events + Lamaki Designs)
from __future__ import annotations
import os, re, json, pickle, tempfile
from typing import List, Tuple
from functools import lru_cache
from supabase import create_client
from datasets import load_dataset
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEndpoint
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_core.prompts import PromptTemplate

# ---------------------------------------------------------------- CONFIG
DATASET_ID  = "NimrodDev/LD_Events2"
LLM_MODEL   = "mistralai/Mistral-7B-Instruct-v0.3"
CACHE_DIR   = os.getenv("HF_HOME", tempfile.gettempdir())
FAISS_PATH  = os.path.join(CACHE_DIR, "faiss_index.pkl")

HF_TOKEN    = os.getenv("HF_TOKEN", os.getenv("HUGGINGFACEHUB_API_TOKEN", ""))
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")

supabase = None
if SUPABASE_URL and SUPABASE_KEY:
    try:
        supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
        print("✅ Supabase client initialized.")
    except Exception as e:
        print(f"⚠️ Supabase init failed: {e}")

os.makedirs(CACHE_DIR, exist_ok=True)
os.environ.update({
    "HF_HOME": CACHE_DIR,
    "HF_HUB_CACHE": CACHE_DIR,
    "TRANSFORMERS_CACHE": CACHE_DIR
})

# ---------------------------------------------------------------- INTENT DETECTION
GREETING_RE = re.compile(r"\b(hi|hello|hey|good morning|good afternoon|good evening)\b", re.I)
THANKS_RE   = re.compile(r"\b(thank|thanks|appreciate)\b", re.I)
BYE_RE      = re.compile(r"\b(bye|goodbye|see you|later)\b", re.I)
MONEY_RE    = re.compile(r"\b(price|cost|budget|cheap|expensive|money|usd|ksh|payment|deposit|fee|quote)\b", re.I)
COMPLAIN_RE = re.compile(r"\b(complain|bad|terrible|awful|disappointed|angry|slow|rude|issue|problem)\b", re.I)
HUMAN_RE    = re.compile(r"\b(agent|human|representative|manager|someone|person)\b", re.I)

# ---------------------------------------------------------------- COMPANY FALLBACKS
FALLBACKS = {
    "LD Events": {
        "greeting": "Hello! 👋 I’m *Amina*, your assistant for **LD Events** (weddings, graduations, corporate events) and **Lamaki Designs** (construction & architecture). How may I help you today?",
        "money": "Our event packages vary depending on venue and number of guests. Could you share a few details so we can estimate a quote?",
        "complain": "I’m sorry to hear that 😔. I’ll alert our support team — expect a call from a senior agent shortly.",
        "thanks": "You’re most welcome! 💐",
        "bye": "Thanks for chatting with LD Events. Have a beautiful day!",
        "handoff": "Sure! I’ll connect you to a human representative now. Please hold on a moment.",
        "default": "Let me get back to you on that. I’ve forwarded your question to a senior planner."
    },
    "Lamaki Designs": {
        "greeting": "Karibu! 🏗️ I’m *Amina*, assistant for **Lamaki Designs** (construction, architectural plans, project management) and **LD Events** (weddings, graduations, corporate events). How may I assist?",
        "money": "Construction costs depend on project scope and materials. Kindly share your plot size or design type for an accurate estimate.",
        "complain": "We’re truly sorry for the inconvenience. Our site supervisor will reach out within 30 minutes to help.",
        "thanks": "Asante! We appreciate your time.",
        "bye": "Goodbye 👋 and thank you for trusting Lamaki Designs.",
        "handoff": "No problem. A Lamaki Designs representative will join the chat soon.",
        "default": "Let me get back to you on that — I’ll forward this to our design team."
    }
}

# ---------------------------------------------------------------- HELPERS
def _company_from_text(text: str) -> str:
    t = text.lower()
    if any(k in t for k in ("lamaki", "construction", "architect", "plan", "bungalow", "site", "building")):
        return "Lamaki Designs"
    if any(k in t for k in ("ld events", "event", "wedding", "graduation", "venue", "party")):
        return "LD Events"
    return "LD Events"

def _detect_intent(text: str) -> str:
    if GREETING_RE.search(text): return "greeting"
    if THANKS_RE.search(text):   return "thanks"
    if BYE_RE.search(text):      return "bye"
    if MONEY_RE.search(text):    return "money"
    if COMPLAIN_RE.search(text): return "complain"
    if HUMAN_RE.search(text):    return "handoff"
    return "normal"

def _fallback_answer(company: str, intent: str) -> str:
    return FALLBACKS[company].get(intent, FALLBACKS[company]["default"])

# ---------------------------------------------------------------- DATA FETCH
@lru_cache(maxsize=1)
def get_texts() -> List[str]:
    try:
        print("🔍 Loading dataset from Parquet...")
        ds = load_dataset(DATASET_ID, split="train", revision="refs/convert/parquet")
        texts = [str(row["text"]) for row in ds if row.get("text")]
        print(f"✅ Loaded {len(texts)} text chunks from {DATASET_ID}")
        return texts
    except Exception as e:
        print(f"⚠️ Dataset load failed: {e}")
        return []

# ---------------------------------------------------------------- VECTORSTORE
@lru_cache(maxsize=1)
def get_vectorstore() -> FAISS:
    if os.path.exists(FAISS_PATH):
        try:
            with open(FAISS_PATH, "rb") as f:
                print("📦 Using cached FAISS index.")
                return pickle.load(f)
        except Exception as e:
            print(f"⚠️ Failed to load FAISS cache: {e}, rebuilding...")

    texts = get_texts()
    if not texts:
        print("⚠️ No dataset found; using dummy FAISS index.")
        return FAISS.from_texts(["No context available."],
                                HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2"))

    splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=100)
    docs = splitter.create_documents(texts)
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    vs = FAISS.from_documents(docs, embeddings)
    with open(FAISS_PATH, "wb") as f:
        pickle.dump(vs, f)
    print("✅ FAISS index created and cached.")
    return vs

# ---------------------------------------------------------------- LLM
@lru_cache(maxsize=1)
def get_llm():
    if not HF_TOKEN:
        raise ValueError("Hugging Face token missing! Please set HF_TOKEN or HUGGINGFACEHUB_API_TOKEN.")
    return HuggingFaceEndpoint(
        repo_id=LLM_MODEL,
        huggingfacehub_api_token=HF_TOKEN,
        temperature=0.3,
        max_new_tokens=300
    )

PROMPT = PromptTemplate.from_template("""
You are Amina, a friendly virtual assistant for {company}.
Use the context below to answer questions concisely and politely.
If unsure, say: "Let me get back to you on that."

Context:
{context}

Question:
{question}

Answer:
""")

# ---------------------------------------------------------------- MAIN CHAT LOGIC
def ask_question(phone: str, question: str) -> Tuple[str, List]:
    intent = _detect_intent(question)
    company = _company_from_text(question)

    # Fast fallback for simple intents
    if intent in ("greeting", "thanks", "bye", "handoff"):
        answer = _fallback_answer(company, intent)
        _save_chat(phone, question, answer)
        return answer, []

    vs = get_vectorstore()
    retriever = vs.as_retriever(search_kwargs={"k": 4})

    qa = RetrievalQA.from_chain_type(
        llm=get_llm(),
        retriever=retriever,
        chain_type_kwargs={"prompt": PROMPT.partial(company=company)},
        return_source_documents=True,
    )

    try:
        result = qa({"query": question})
        answer = result.get("result", "").strip()
        docs = result.get("source_documents", [])
    except Exception as e:
        print(f"❌ QA pipeline error: {e}")
        answer, docs = "", []

    # Smart fallback (pricing, complaints, or missing)
    if not answer or len(answer.split()) < 4:
        answer = _fallback_answer(company, intent if intent in ("money", "complain") else "default")

    _save_chat(phone, question, answer)
    return answer, docs

# ---------------------------------------------------------------- SUPABASE LOGGING
def _save_chat(phone: str, q: str, a: str) -> None:
    if not supabase:
        return
    try:
        data = [
            {"user_phone": phone, "role": "user", "message": q},
            {"user_phone": phone, "role": "assistant", "message": a}
        ]
        supabase.table("chat_memory").insert(data).execute()
    except Exception as e:
        print(f"⚠️ Chat log failed: {e}")