cmc

Dockerfile

@@ -1,6 +1,14 @@
 # ---------- Base ----------
 FROM python:3.11-slim
 
+# ---------- Environment Setup ----------
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV HF_HOME=/code/.cache/huggingface
+ENV TRANSFORMERS_CACHE=/code/.cache/huggingface
+ENV TORCH_HOME=/code/.cache/torch
+
 # ---------- System Dependencies ----------
 RUN apt-get update && apt-get install -y --no-install-recommends \
     build-essential \
@@ -11,13 +19,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 # ---------- Working Directory ----------
 WORKDIR /code
 
-# ---------- Environment Variables ----------
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV HF_HOME=/code/.cache/huggingface
-ENV TRANSFORMERS_CACHE=/code/.cache/huggingface
-ENV TORCH_HOME=/code/.cache/torch
-
 # ---------- Install Python Dependencies ----------
 COPY requirements.txt .
 RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
@@ -38,3 +39,4 @@ EXPOSE 7860
 # ---------- Start the App ----------
 CMD ["gunicorn", "app:app", "-b", "0.0.0.0:7860", "--timeout", "300"]
 
+

app.py

@@ -1,23 +1,42 @@
 from flask import Flask, request, jsonify
+from rag import get_retriever, get_qa_chain  # ✅ import helper functions from rag.py
 
 app = Flask(__name__)
 
+# Initialize global retriever and QA chain once at startup
+retriever = get_retriever()
+qa_chain = get_qa_chain()
+
 @app.route("/webhook", methods=["POST"])
 def webhook():
     data = request.get_json()
     question = data.get("question")
     phone = data.get("phone")
 
-    # retrieve docs from FAISS or Supabase (pseudo)
+    if not question:
+        return jsonify({"error": "Missing question"}), 400
+
+    # Retrieve documents from FAISS/Supabase
     retrieved_docs = retriever.get_relevant_documents(question)
 
     if not retrieved_docs:
-        return jsonify({"answer": "I couldn’t find relevant info on that yet.", "docs": 0})
+        return jsonify({
+            "answer": "I couldn’t find relevant info on that yet.",
+            "docs": 0
+        })
+
+    # Generate an answer using the QA chain
+    answer = qa_chain.invoke({
+        "question": question,
+        "context": retrieved_docs
+    })
 
-    # Generate answer
-    answer = qa_chain.invoke({"question": question, "context": retrieved_docs})
     return jsonify({"answer": answer, "docs": len(retrieved_docs)})
 
+@app.route("/", methods=["GET"])
+def index():
+    return jsonify({"status": "running", "message": "Lamaki RAG backend active!"})
+
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)
 

rag.py

@@ -26,6 +26,7 @@ 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}")
 
@@ -47,8 +48,7 @@ HUMAN_RE    = re.compile(r"\b(agent|human|representative|manager|someone|person)
 # ---------------------------------------------------------------- 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?",
+        "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! 💐",
@@ -57,8 +57,7 @@ FALLBACKS = {
         "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?",
+        "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.",
@@ -115,7 +114,7 @@ def get_vectorstore() -> FAISS:
 
     texts = get_texts()
     if not texts:
-        print("⚠️ No dataset found; building dummy FAISS index.")
+        print("⚠️ No dataset found; using dummy FAISS index.")
         return FAISS.from_texts(["No context available."],
                                 HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2"))
 
@@ -171,12 +170,12 @@ def ask_question(phone: str, question: str) -> Tuple[str, List]:
     qa = RetrievalQA.from_chain_type(
         llm=get_llm(),
         retriever=retriever,
-        chain_type_kwargs={"prompt": PROMPT},
+        chain_type_kwargs={"prompt": PROMPT.partial(company=company)},
         return_source_documents=True,
     )
 
     try:
-        result = qa({"query": question, "company": company})
+        result = qa({"query": question})
         answer = result.get("result", "").strip()
         docs = result.get("source_documents", [])
     except Exception as e:
@@ -192,9 +191,14 @@ def ask_question(phone: str, question: str) -> Tuple[str, List]:
 
 # ---------------------------------------------------------------- SUPABASE LOGGING
 def _save_chat(phone: str, q: str, a: str) -> None:
-    if not supabase: return
+    if not supabase:
+        return
     try:
-        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()
+        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}")
+