working rag + filters + fallbacks

.gitignore

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+.env
+faiss_db
+data/
+__pycache__/

Dockerfile

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+FROM python:3.11-slim
+WORKDIR /code
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["gunicorn", "app:app", "-b", "0.0.0.0:7860"]

app.py

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+# app.py
+from flask import Flask, request, jsonify
+from rag import ask_question
+
+app = Flask(__name__)
+
+@app.route("/webhook", methods=["POST"])
+def webhook():
+    payload = request.get_json(force=True)
+    phone = payload["phone"]
+    question = payload["question"]
+    answer, docs = ask_question(phone, question)
+    return jsonify({"answer": answer, "docs": len(docs)})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

rag.py

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+# rag.py  (v2 – with filters + fallbacks)
+from __future__ import annotations
+import os, uuid, tempfile, requests, shutil, re
+from pathlib import Path
+from functools import lru_cache
+from typing import List, Tuple
+from datasets import load_dataset
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain_huggingface import HuggingFaceEndpoint
+from supabase import create_client
+
+# ---------- config ----------
+HF_DS = "NimrodDev/LD_Events2"
+EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+LLM_MODEL = "microsoft/DialoGPT-medium"
+FAISS_PATH = Path("faiss_db")
+SUPABASE_URL = os.getenv("SUPABASE_URL")
+SUPABASE_KEY = os.getenv("SUPABASE_KEY")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+# ---------- keyword filters ----------
+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)\b", re.I)
+COMPLAIN_RE = re.compile(r"\b(complain|bad|terrible|awful|disappointed|angry|slow|rude)\b", re.I)
+
+# ---------- company-specific fallbacks ----------
+FALLBACKS = {
+    "LD Events": {
+        "greeting": "Hello! 👋 Welcome to LD Events – your trusted partner for weddings, graduations and corporate events.",
+        "money": "Our pricing depends on venue, guest count and package. Please share a few details so we can give you a tailored quote.",
+        "complain": "We’re sorry to hear this. A senior agent will contact you within 30 minutes to resolve the issue.",
+        "thanks": "You’re welcome! If you need anything else, just text back.",
+        "bye": "Thanks for chatting with LD Events. Have a lovely day!",
+        "default": "I’m not sure about that, but a human agent will follow up shortly."
+    },
+    "Lamaki Designs": {
+        "greeting": "Karibu! 🏗️ Lamaki Designs here – quality construction, architectural plans and project management.",
+        "money": "Cost varies by project size and materials. Kindly share your plot size / plan so we can estimate for you.",
+        "complain": "We apologise for the inconvenience. Our site manager will call you within 30 minutes to sort it out.",
+        "thanks": "Asante! Feel free to text any time.",
+        "bye": "Good-bye and stay safe!",
+        "default": "Let me get back to you on that."
+    }
+}
+
+# ---------- helpers ----------
+def _company_from_text(text: str) -> str:
+    t = text.lower()
+    if any(k in t for k in ("ld events", "event", "wedding", "venue", "graduation")):
+        return "LD Events"
+    if any(k in t for k in ("lamaki", "construction", "build", "site", "bungalow", "architect")):
+        return "Lamaki Designs"
+    return "LD Events"  # default
+
+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"
+    return "normal"
+
+def _fallback_answer(company: str, intent: str) -> str:
+    return FALLBACKS[company].get(intent, FALLBACKS[company]["default"])
+
+# ---------- pdf loader ----------
+def download_pdfs() -> List[Path]:
+    data_dir = Path("data")
+    data_dir.mkdir(exist_ok=True)
+    ds = load_dataset(HF_DS, split="train", streaming=True)
+    paths = []
+    for row in ds:
+        url = row["pdf_url"]
+        name = row.get("name", uuid.uuid4().hex) + ".pdf"
+        dest = data_dir / name
+        if not dest.exists():
+            r = requests.get(url, stream=True, timeout=30)
+            r.raise_for_status()
+            with open(dest, "wb") as f:
+                shutil.copyfileobj(r.raw, f)
+        paths.append(dest)
+    return paths
+
+# ---------- vector store ----------
+@lru_cache(maxsize=1)
+def get_vectorstore() -> FAISS:
+    if FAISS_PATH.exists():
+        return FAISS.load_local(str(FAISS_PATH), HuggingFaceEmbeddings(model_name=EMBED_MODEL),
+                                allow_dangerous_deserialization=True)
+    docs = []
+    splitter = RecursiveCharacterTextSplitter(chunk_size=600, chunk_overlap=50)
+    for pdf in download_pdfs():
+        text = Path(pdf).read_text(encoding="utf-8", errors="ignore")
+        docs.extend(splitter.create_documents([text], metadatas=[{"source": pdf.name}]))
+    vs = FAISS.from_documents(docs, HuggingFaceEmbeddings(model_name=EMBED_MODEL))
+    vs.save_local(str(FAISS_PATH))
+    return vs
+
+# ---------- llm ----------
+@lru_cache(maxsize=1)
+def get_llm():
+    return HuggingFaceEndpoint(
+        repo_id=LLM_MODEL,
+        temperature=0.1,
+        max_new_tokens=150,
+        huggingfacehub_api_token=HF_TOKEN
+    )
+
+PROMPT = PromptTemplate.from_template("""You are Amina, assistant for {company}.
+Use only the context below. If unsure, say: “A human agent will follow up.”
+Context: {context}
+Question: {question}
+Answer:""")
+
+# ---------- main entry ----------
+def ask_question(phone: str, question: str) -> Tuple[str, List]:
+    intent = _detect_intent(question)
+    company = _company_from_text(question)
+
+    # short-circuit greetings/thanks/bye – no LLM, no context needed
+    if intent in ("greeting", "thanks", "bye"):
+        answer = _fallback_answer(company, intent)
+        _save_chat(phone, question, answer)
+        return answer, []
+
+    # money or complaints: fallback if no docs
+    vs = get_vectorstore()
+    docs = vs.similarity_search(question, k=3)
+    if not docs:
+        answer = _fallback_answer(company, intent if intent in ("money", "complain") else "default")
+        _save_chat(phone, question, answer)
+        return answer, []
+
+    # normal RAG
+    qa = RetrievalQA.from_chain_type(
+        llm=get_llm(),
+        retriever=vs.as_retriever(search_kwargs={"k": 3}),
+        return_source_documents=True,
+        chain_type_kwargs={"prompt": PROMPT}
+    )
+    result = qa({"query": question, "company": company})
+    answer = result["result"].strip()
+    _save_chat(phone, question, answer)
+    return answer, result.get("source_documents", [])
+
+def _save_chat(phone: str, q: str, a: str) -> None:
+    supabase.table("chat_memory").insert({"user_phone": phone, "role": "user", "message": q}).execute()
+    supabase.table("chat_memory").insert({"user_phone": phone, "role": "assistant", "message": a}).execute()

requirements.txt

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+flask==3.0.2
+langchain==0.3.0
+langchain-community==0.3.0
+langchain-huggingface==0.3.0
+sentence-transformers==3.0.0
+faiss-cpu==1.8.0
+datasets==2.20.0
+supabase==2.6.1
+python-dotenv==1.0.1
+gunicorn==22.0.0