Add Docker stage/prod, entrypoint auto-scaling, mock APIs, comment log functions

Dockerfile.k6

-FROM grafana/k6:latest
-
-# Copy K6 test script
-COPY backend/hehe/k6-chatbot-test.js /scripts/chatbot-test.js
-
-# Default command - chạy K6 test
-CMD ["run", "--out", "json=/tmp/results.json", "/scripts/chatbot-test.js"]

backend/Dockerfile.prod

+# ============================================================
+# DOCKERFILE.PROD - Production (Multi-Worker Gunicorn)
+# ============================================================
+# Multi-stage build để optimize dung lượng image
+FROM python:3.11-slim as builder
+
+WORKDIR /app
+
+# Copy requirements.txt
+COPY requirements.txt .
+
+# Install dependencies to a local directory
+RUN pip install --user --no-cache-dir -r requirements.txt
+
+# ============================================================
+# Final stage - Production image (tối giản)
+FROM python:3.11-slim
+
+WORKDIR /app
+
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV ENV=production
+
+# Copy Python packages từ builder stage
+COPY --from=builder /root/.local /root/.local
+ENV PATH=/root/.local/bin:$PATH
+
+# Copy source code
+COPY . .
+
+# Copy entrypoint script
+COPY entrypoint.sh /app/entrypoint.sh
+RUN chmod +x /app/entrypoint.sh
+
+# Expose port 5000
+EXPOSE 5000
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD python -c "import requests; requests.get('http://localhost:5000/health')" || exit 1
+
+ENTRYPOINT ["/app/entrypoint.sh"]

backend/Dockerfile → backend/Dockerfile.stage

+# ============================================================
+# DOCKERFILE.STAGE - Development/Staging (Hot Reload)
+# ============================================================
 # Sử dụng Python 3.11 Slim để tối ưu dung lượng
 FROM python:3.11-slim
 
@@ -7,6 +10,7 @@ WORKDIR /app
 # Thiết lập biến môi trường để log in ra ngay lập tức
 ENV PYTHONUNBUFFERED=1
 ENV PYTHONDONTWRITEBYTECODE=1
+ENV ENV=development
 
 # Copy requirements.txt trước để tận dụng Docker cache
 COPY requirements.txt .
@@ -21,5 +25,6 @@ COPY . .
 # Expose port 5000 (Port chạy server)
 EXPOSE 5000
 
-# Lệnh chạy server dùng uvicorn
-CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "5000"]
+# Lệnh chạy server dùng uvicorn với hot reload
+# ⚡ Hot reload - tự động restart khi code thay đổi (dùng cho dev/stage)
+CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "5000", "--reload"]

backend/agent/mock_controller.py

+"""
+Fashion Q&A Agent Controller
+Langfuse will auto-trace via LangChain integration (no code changes needed).
+"""
+
+import json
+import logging
+import uuid
+
+from fastapi import BackgroundTasks
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+from langchain_core.runnables import RunnableConfig
+
+from common.conversation_manager import ConversationManager, get_conversation_manager
+from common.langfuse_client import get_callback_handler, langfuse_trace_context
+from common.llm_factory import create_llm
+from config import DEFAULT_MODEL
+
+from .graph import build_graph
+from .models import AgentState, get_config
+from .tools.get_tools import get_all_tools
+
+logger = logging.getLogger(__name__)
+
+
+async def chat_controller(
+    query: str,
+    user_id: str,
+    background_tasks: BackgroundTasks,
+    model_name: str = DEFAULT_MODEL,
+    images: list[str] | None = None,
+) -> dict:
+    """
+    Controller main logic for non-streaming chat requests.
+    Langfuse will automatically trace all LangChain operations.
+    """
+    logger.info(f"▶️ Starting chat_controller with model: {model_name} for user: {user_id}")
+
+    config = get_config()
+    config.model_name = model_name
+
+    # Enable JSON mode to ensure structured output
+    llm = create_llm(model_name=model_name, streaming=False, json_mode=True)
+    tools = get_all_tools()
+
+    graph = build_graph(config, llm=llm, tools=tools)
+
+    # Init ConversationManager (Singleton)
+    memory = await get_conversation_manager()
+
+    # LOAD HISTORY & Prepare State (Optimize: history logic remains solid)
+    history_dicts = await memory.get_chat_history(user_id, limit=20)
+
+    history = []
+    for h in reversed(history_dicts):
+        msg_cls = HumanMessage if h["is_human"] else AIMessage
+        history.append(msg_cls(content=h["message"]))
+
+    initial_state, exec_config = _prepare_execution_context(
+        query=query, user_id=user_id, history=history, images=images
+    )
+
+    try:
+        # 🔥 Wrap graph execution với langfuse_trace_context để set user_id cho tất cả observations
+        with langfuse_trace_context(user_id=user_id, session_id=user_id):
+            # TỐI ƯU: Chạy Graph
+            result = await graph.ainvoke(initial_state, config=exec_config)
+
+            # TỐI ƯU: Extract IDs từ Tool Messages một lần duy nhất
+            all_product_ids = _extract_product_ids(result.get("messages", []))
+
+            # TỐI ƯU: Xử lý AI Response
+            ai_raw_content = result.get("ai_response").content if result.get("ai_response") else ""
+            logger.info(f"💾 [RAW AI OUTPUT]:\n{ai_raw_content}")
+
+            # Parse JSON để lấy text response và product_ids từ AI
+            ai_text_response = ai_raw_content
+            try:
+                # Vì json_mode=True, OpenAI sẽ nhả raw JSON
+                ai_json = json.loads(ai_raw_content)
+
+                # Extract text response từ JSON
+                ai_text_response = ai_json.get("ai_response", ai_raw_content)
+
+                # Merge product_ids từ AI JSON (nếu có) - KHÔNG dùng set() vì dict unhashable
+                explicit_ids = ai_json.get("product_ids", [])
+                if explicit_ids and isinstance(explicit_ids, list):
+                    # Merge và deduplicate by SKU
+                    seen_skus = {p["sku"] for p in all_product_ids if "sku" in p}
+                    for product in explicit_ids:
+                        if isinstance(product, dict) and product.get("sku") not in seen_skus:
+                            all_product_ids.append(product)
+                            seen_skus.add(product.get("sku"))
+            except (json.JSONDecodeError, Exception) as e:
+                # Nếu AI trả về text thường (hiếm khi xảy ra trong JSON mode) thì ignore
+                logger.warning(f"Could not parse AI response as JSON: {e}")
+                pass
+
+            # BACKGROUND TASK: Lưu history nhanh gọn
+            background_tasks.add_task(
+                _handle_post_chat_async,
+                memory=memory,
+                user_id=user_id,
+                human_query=query,
+                ai_msg=AIMessage(content=ai_text_response),
+            )
+
+            return {
+                "ai_response": ai_text_response,  # CHỈ text, không phải JSON
+                "product_ids": all_product_ids,  # Array of product objects
+            }
+
+    except Exception as e:
+        logger.error(f"💥 Chat error for user {user_id}: {e}", exc_info=True)
+        raise
+
+
+def _extract_product_ids(messages: list) -> list[dict]:
+    """
+    Extract full product info from tool messages (data_retrieval_tool results).
+    Returns list of product objects with: sku, name, price, sale_price, url, thumbnail_image_url.
+    """
+    products = []
+    seen_skus = set()
+
+    for msg in messages:
+        if isinstance(msg, ToolMessage):
+            try:
+                # Tool result is JSON string
+                tool_result = json.loads(msg.content)
+
+                # Check if tool returned products
+                if tool_result.get("status") == "success" and "products" in tool_result:
+                    for product in tool_result["products"]:
+                        sku = product.get("internal_ref_code")
+                        if sku and sku not in seen_skus:
+                            seen_skus.add(sku)
+
+                            # Extract full product info
+                            product_obj = {
+                                "sku": sku,
+                                "name": product.get("magento_product_name", ""),
+                                "price": product.get("price_vnd", 0),
+                                "sale_price": product.get("sale_price_vnd"),  # null nếu không sale
+                                "url": product.get("magento_url_key", ""),
+                                "thumbnail_image_url": product.get("thumbnail_image_url", ""),
+                            }
+                            products.append(product_obj)
+            except (json.JSONDecodeError, KeyError, TypeError) as e:
+                logger.debug(f"Could not parse tool message for products: {e}")
+                continue
+
+    return products
+
+
+def _prepare_execution_context(query: str, user_id: str, history: list, images: list | None):
+    """Prepare initial state and execution config for the graph run."""
+    initial_state: AgentState = {
+        "user_query": HumanMessage(content=query),
+        "messages": [HumanMessage(content=query)],
+        "history": history,
+        "user_id": user_id,
+        "images_embedding": [],
+        "ai_response": None,
+    }
+    run_id = str(uuid.uuid4())
+
+    # Metadata for LangChain (tags for logging/filtering)
+    metadata = {
+        "run_id": run_id,
+        "tags": "chatbot,production",
+    }
+
+    # 🔥 CallbackHandler - sẽ được wrap trong langfuse_trace_context để set user_id
+    # Per Langfuse docs: propagate_attributes() handles user_id propagation
+    langfuse_handler = get_callback_handler()
+
+    exec_config = RunnableConfig(
+        configurable={
+            "user_id": user_id,
+            "transient_images": images or [],
+            "run_id": run_id,
+        },
+        run_id=run_id,
+        metadata=metadata,
+        callbacks=[langfuse_handler] if langfuse_handler else [],
+    )
+    return initial_state, exec_config
+
+
+async def _handle_post_chat_async(
+    memory: ConversationManager, user_id: str, human_query: str, ai_msg: AIMessage | None
+):
+    """Save chat history in background task after response is sent."""
+    if ai_msg:
+        try:
+            await memory.save_conversation_turn(user_id, human_query, ai_msg.content)
+            logger.debug(f"Saved conversation for user {user_id}")
+        except Exception as e:
+            logger.error(f"Failed to save conversation for user {user_id}: {e}", exc_info=True)

backend/agent/tools/data_retrieval_tool.save.py

-"""
-CANIFA Data Retrieval Tool - Tối giản cho Agentic Workflow.
-Hỗ trợ Hybrid Search: Semantic (Vector) + Metadata Filter.
-"""
-
-import asyncio
-import json
-import logging
-import time
-from decimal import Decimal
-
-from langchain_core.tools import tool
-from pydantic import BaseModel, Field
-
-from agent.tools.product_search_helpers import build_starrocks_query
-from common.starrocks_connection import StarRocksConnection
-
-# from langsmith import traceable
-
-logger = logging.getLogger(__name__)
-
-
-class DecimalEncoder(json.JSONEncoder):
-    """Xử lý kiểu Decimal từ Database khi convert sang JSON."""
-
-    def default(self, obj):
-        if isinstance(obj, Decimal):
-            return float(obj)
-        return super().default(obj)
-
-
-class SearchItem(BaseModel):
-    """Cấu trúc một mục tìm kiếm đơn lẻ trong Multi-Search."""
-
-    query: str = Field(
-        ...,
-        description="Câu hỏi/mục đích tự do của user (đi chơi, dự tiệc, phỏng vấn,...) - dùng cho Semantic Search",
-    )
-    keywords: str | None = Field(
-        ..., description="Từ khóa sản phẩm cụ thể (áo polo, quần jean,...) - dùng cho LIKE search"
-    )
-    magento_ref_code: str | None = Field(
-        ..., description="Mã sản phẩm hoặc mã màu/SKU (Ví dụ: 8TS24W001 hoặc 8TS24W001-SK010)."
-    )
-    product_line_vn: str | None = Field(..., description="Dòng sản phẩm (Áo phông, Quần short,...)")
-    gender_by_product: str | None = Field(..., description="Giới tính: male, female")
-    age_by_product: str | None = Field(..., description="Độ tuổi: adult, kids, baby, others")
-    master_color: str | None = Field(..., description="Màu sắc chính (Đen/ Black, Trắng/ White,...)")
-    material_group: str | None = Field(
-        ...,
-        description="Nhóm chất liệu. BẮT BUỘC dùng đúng: 'Yarn - Sợi', 'Knit - Dệt Kim', 'Woven - Dệt Thoi', 'Knit/Woven - Dệt Kim/Dệt Thoi'.",
-    )
-    season: str | None = Field(..., description="Mùa (Spring Summer, Autumn Winter)")
-    style: str | None = Field(..., description="Phong cách (Basic Update, Fashion,...)")
-    fitting: str | None = Field(..., description="Form dáng (Regular, Slim, Loose,...)")
-    form_neckline: str | None = Field(..., description="Kiểu cổ (Crew Neck, V-neck,...)")
-    form_sleeve: str | None = Field(..., description="Kiểu tay (Short Sleeve, Long Sleeve,...)")
-    price_min: float | None = Field(..., description="Giá thấp nhất")
-    price_max: float | None = Field(..., description="Giá cao nhất")
-    action: str = Field(..., description="Hành động: 'search' (tìm kiếm) hoặc 'visual_search' (phân tích ảnh)")
-
-
-class MultiSearchParams(BaseModel):
-    """Tham số cho Parallel Multi-Search."""
-
-    searches: list[SearchItem] = Field(..., description="Danh sách các truy vấn tìm kiếm chạy song song")
-
-
-@tool(args_schema=MultiSearchParams)
-# @traceable(run_type="tool", name="data_retrieval_tool")
-async def data_retrieval_tool(searches: list[SearchItem]) -> str:
-    """
-    Siêu công cụ tìm kiếm sản phẩm CANIFA - Hỗ trợ Parallel Multi-Search (Chạy song song nhiều query).
-
-    💡 ĐIỂM ĐẶC BIỆT:
-    Công cụ này cho phép thực hiện NHIỀU truy vấn tìm kiếm CÙNG LÚC.
-    Hãy dùng nó khi cần SO SÁNH sản phẩm hoặc tìm trọn bộ OUTFIT (mix & match).
-
-    ⚠️ QUAN TRỌNG - KHI NÀO DÙNG GÌ:
-
-    1️⃣ DÙNG 'query' (Semantic Search - BUỘC PHẢI CÓ):
-       - Áp dụng cho mọi lượt search để cung cấp bối cảnh (context).
-       - Ví dụ: "áo thun nam đi biển", "quần tây công sở", "đồ cho bé màu xanh"...
-
-    2️⃣ DÙNG METADATA FILTERS (Exact/Partial Match):
-       - Khi khách nói rõ THUỘC TÍNH: Màu sắc, giá, giới tính, độ tuổi, mã sản phẩm.
-       - **QUY TẮC MÃ SẢN PHẨM:** Mọi loại mã (VD: `8TS...` hoặc `8TS...-SK...`) → Điền vào `magento_ref_code`.
-       - **QUY TẮC CHẤT LIÊU (material_group):** Chỉ dùng: `Yarn - Sợi`, `Knit - Dệt Kim`, `Woven - Dệt Thoi`, `Knit/Woven - Dệt Kim/Dệt Thoi`.
-
-    📝 VÍ DỤ CHI TIẾT (Single Search):
-       - Example 1: searches=[{"query": "áo polo nam giá dưới 400k", "keywords": "áo polo", "gender_by_product": "male", "price_max": 400000}]
-       - Example 2: searches=[{"query": "sản phẩm mã 8TS24W001", "magento_ref_code": "8TS24W001"}]
-
-    🚀 VÍ DỤ CẤP CAO (Multi-Search Parallel):
-       - Example 3 - So sánh: "So sánh áo thun nam đen và áo sơ mi trắng dưới 500k"
-         Tool Call: searches=[
-             {"query": "áo thun nam màu đen dưới 500k", "keywords": "áo thun", "master_color": "Đen", "gender_by_product": "male", "price_max": 500000},
-             {"query": "áo sơ mi nam trắng dưới 500k", "keywords": "áo sơ mi", "master_color": "Trắng", "gender_by_product": "male", "price_max": 500000}
-         ]
-       - Example 4 - Phối đồ: "Tìm cho mình một cái quần jean và một cái áo khoác để đi chơi"
-         Tool Call: searches=[
-             {"query": "quần jean đi chơi năng động", "keywords": "quần jean"},
-             {"query": "áo khoác đi chơi năng động", "keywords": "áo khoác"}
-         ]
-       - Example 5 - Cả gia đình: "Tìm áo phông màu xanh cho bố, mẹ và bé trai"
-         Tool Call: searches=[
-             {"query": "áo phông nam người lớn màu xanh", "keywords": "áo phông", "master_color": "Xanh", "gender_by_product": "male", "age_by_product": "adult"},
-             {"query": "áo phông nữ người lớn màu xanh", "keywords": "áo phông", "master_color": "Xanh", "gender_by_product": "female", "age_by_product": "adult"},
-             {"query": "áo phông bé trai màu xanh", "keywords": "áo phông", "master_color": "Xanh", "gender_by_product": "male", "age_by_product": "others"}
-         ]
-    """
-    logger.info("🔧 [DEBUG] data_retrieval_tool STARTED")
-    try:
-        logger.info("🔧 [DEBUG] Creating StarRocksConnection instance")
-        db = StarRocksConnection()
-        logger.info("🔧 [DEBUG] StarRocksConnection created successfully")
-
-        # 0. Log input parameters (Đúng ý bro)
-        logger.info(f"📥 [Tool Input] data_retrieval_tool received {len(searches)} items:")
-        for idx, item in enumerate(searches):
-            logger.info(f"   🔹 Item [{idx}]: {item.dict(exclude_none=True)}")
-
-        # 1. Tạo tasks chạy song song (Parallel)
-        logger.info("🔧 [DEBUG] Creating parallel tasks")
-        tasks = []
-        for item in searches:
-            tasks.append(_execute_single_search(db, item))
-
-        logger.info(f"🚀 [Parallel Search] Executing {len(searches)} queries simultaneously...")
-        logger.info("🔧 [DEBUG] About to call asyncio.gather()")
-        results = await asyncio.gather(*tasks)
-        logger.info(f"🔧 [DEBUG] asyncio.gather() completed with {len(results)} results")
-
-        # 2. Tổng hợp kết quả
-        combined_results = []
-        for i, products in enumerate(results):
-            combined_results.append(
-                {
-                    "search_index": i,
-                    "search_criteria": searches[i].dict(exclude_none=True),
-                    "count": len(products),
-                    "products": products,
-                }
-            )
-
-        return json.dumps({"status": "success", "results": combined_results}, ensure_ascii=False, cls=DecimalEncoder)
-
-    except Exception as e:
-        logger.error(f"Error in Multi-Search data_retrieval_tool: {e}")
-        return json.dumps({"status": "error", "message": str(e)})
-
-
-async def _execute_single_search(db: StarRocksConnection, item: SearchItem) -> list[dict]:
-    """Thực thi một search query đơn lẻ (Async)."""
-    try:
-        logger.info(f"🔧 [DEBUG] _execute_single_search STARTED for query: {item.query[:50] if item.query else 'None'}")
-
-        # ⏱️ Timer: Build query (bao gồm embedding nếu có)
-        query_build_start = time.time()
-        logger.info("🔧 [DEBUG] Calling build_starrocks_query()")
-        sql = await build_starrocks_query(item)
-        query_build_time = (time.time() - query_build_start) * 1000  # Convert to ms
-        logger.info(f"🔧 [DEBUG] SQL query built, length: {len(sql)}")
-        logger.info(f"⏱️ [TIMER] Query Build Time (bao gồm embedding): {query_build_time:.2f}ms")
-
-        # ⏱️ Timer: Execute DB query
-        db_start = time.time()
-        logger.info("🔧 [DEBUG] Calling db.execute_query_async()")
-        products = await db.execute_query_async(sql)
-        db_time = (time.time() - db_start) * 1000  # Convert to ms
-        logger.info(f"🔧 [DEBUG] Query executed, got {len(products)} products")
-        logger.info(f"⏱️ [TIMER] DB Query Execution Time: {db_time:.2f}ms")
-        logger.info(f"⏱️ [TIMER] Total Time (Build + DB): {query_build_time + db_time:.2f}ms")
-
-        return _format_product_results(products)
-    except Exception as e:
-        logger.error(f"Single search error for item {item}: {e}")
-        return []
-
-
-def _format_product_results(products: list[dict]) -> list[dict]:
-    """Lọc và format kết quả trả về cho Agent."""
-    allowed_fields = {
-        "internal_ref_code",
-        "description_text_full",
-    }
-    return [{k: v for k, v in p.items() if k in allowed_fields} for p in products[:5]]

backend/agent/tools/product_search_helpers.py

-import asyncio
 import time
 
 from common.embedding_service import create_embedding_async
@@ -114,7 +113,7 @@ async def build_starrocks_query(params, query_vector: list[float] | None = None)
         print("✅ [CODE SEARCH] Query built - No vector search needed!")
 
         # Ghi log debug query FULL vào Background Task (Không làm chậm Request)
-        asyncio.create_task(save_query_to_log(sql))
+        # asyncio.create_task(save_query_to_log(sql))
 
         return sql
 
@@ -173,60 +172,62 @@ async def build_starrocks_query(params, query_vector: list[float] | None = None)
     LIMIT 10
     """
 
-    # Ghi log debug query FULL vào Background Task (Không làm chậm Request)
-    asyncio.create_task(save_query_to_log(sql))
-
     return sql
 
 
-async def save_query_to_log(sql: str):
-    """Lưu query full vào file hyde_pure_query.txt."""
-    import os
-    log_path = r"D:\cnf\chatbot_canifa\backend\logs\hyde_pure_query.txt"
-    try:
-        log_dir = os.path.dirname(log_path)
-        if not os.path.exists(log_dir):
-            os.makedirs(log_dir)
-        with open(log_path, "w", encoding="utf-8") as f:
-            f.write(sql)
-        print(f"💾 Full Query saved to: {log_path}")
-    except Exception as e:
-        print(f"Save query log failed: {e}")
-
-
-async def save_preview_to_log(search_query: str, products: list[dict]):
-    """Lưu kết quả DB trả về vào db_preview.txt (Format đẹp cho AI)."""
-    import json
-    import os
-    preview_path = r"D:\cnf\chatbot_canifa\backend\logs\db_preview.txt"
-    try:
-        log_dir = os.path.dirname(preview_path)
-        if not os.path.exists(log_dir):
-            os.makedirs(log_dir)
-
-        with open(preview_path, "a", encoding="utf-8") as f:
-            f.write(f"\n{'='*60}\n")
-            f.write(f"⏰ TIME: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
-            f.write(f"🔍 SEARCH: {search_query}\n")
-            f.write(f"📊 RESULTS COUNT: {len(products)}\n")
-            f.write(f"{'-'*60}\n")
-            
-            if not products:
-                f.write("❌ NO PRODUCTS FOUND\n")
-            else:
-                for idx, p in enumerate(products[:5], 1):
-                    code = p.get("internal_ref_code", "N/A")
-                    sale = p.get("sale_price", "N/A")
-                    orig = p.get("original_price", "N/A")
-                    disc = p.get("discount_amount", "0")
-                    score = p.get("max_score", p.get("similarity_score", "N/A"))
-                    desc = p.get("description_text_full", "No Description")
-                    
-                    f.write(f"{idx}. [{code}] Score: {score}\n")
-                    f.write(f"   💰 Price: {sale} (Orig: {orig}, Disc: {disc}%)\n")
-                    f.write(f"   📝 Desc: {desc}\n")
-            
-            f.write(f"{'='*60}\n")
-        print(f"💾 DB Preview (Results) saved to: {preview_path}")
-    except Exception as e:
-        print(f"Save preview log failed: {e}")
+# ============================================================
+# TEMPORARILY COMMENTED OUT - save_query_to_log
+# ============================================================
+# async def save_query_to_log(sql: str):
+#     """Lưu query full vào file hyde_pure_query.txt."""
+#     import os
+#     log_path = r"D:\cnf\chatbot_canifa\backend\logs\hyde_pure_query.txt"
+#     try:
+#         log_dir = os.path.dirname(log_path)
+#         if not os.path.exists(log_dir):
+#             os.makedirs(log_dir)
+#         with open(log_path, "w", encoding="utf-8") as f:
+#             f.write(sql)
+#         print(f"💾 Full Query saved to: {log_path}")
+#     except Exception as e:
+#         print(f"Save query log failed: {e}")
+
+
+# ============================================================
+# TEMPORARILY COMMENTED OUT - save_preview_to_log
+# ============================================================
+# async def save_preview_to_log(search_query: str, products: list[dict]):
+#     """Lưu kết quả DB trả về vào db_preview.txt (Format đẹp cho AI)."""
+#     import os
+#     preview_path = r"D:\cnf\chatbot_canifa\backend\logs\db_preview.txt"
+#     try:
+#         log_dir = os.path.dirname(preview_path)
+#         if not os.path.exists(log_dir):
+#             os.makedirs(log_dir)
+#
+#         with open(preview_path, "a", encoding="utf-8") as f:
+#             f.write(f"\n{'='*60}\n")
+#             f.write(f"⏰ TIME: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
+#             f.write(f"🔍 SEARCH: {search_query}\n")
+#             f.write(f"📊 RESULTS COUNT: {len(products)}\n")
+#             f.write(f"{'-'*60}\n")
+#
+#             if not products:
+#                 f.write("❌ NO PRODUCTS FOUND\n")
+#             else:
+#                 for idx, p in enumerate(products[:5], 1):
+#                     code = p.get("internal_ref_code", "N/A")
+#                     sale = p.get("sale_price", "N/A")
+#                     orig = p.get("original_price", "N/A")
+#                     disc = p.get("discount_amount", "0")
+#                     score = p.get("max_score", p.get("similarity_score", "N/A"))
+#                     desc = p.get("description_text_full", "No Description")
+#
+#                     f.write(f"{idx}. [{code}] Score: {score}\n")
+#                     f.write(f"   💰 Price: {sale} (Orig: {orig}, Disc: {disc}%)\n")
+#                     f.write(f"   📝 Desc: {desc}\n")
+#
+#             f.write(f"{'='*60}\n")
+#         print(f"💾 DB Preview (Results) saved to: {preview_path}")
+#     except Exception as e:
+#         print(f"Save preview log failed: {e}")

backend/docker-compose.yml

@@ -6,12 +6,17 @@ services:
     env_file: .env
     ports:
       - "5000:5000"
-    # volumes:
-    #   - .:/app
+    volumes:
+      - .:/app
     environment:
       - PORT=5000
     restart: unless-stopped
+    deploy:
+      resources:
+        limits:
+          memory: 8g
     logging:
       driver: "json-file"
       options:
         tag: "{{.Name}}"
+        

backend/entrypoint.sh

+=
+# Lấy số CPU cores
+NUM_CORES=$(nproc)
+
+WORKERS=$((2 * NUM_CORES + 1))
+
+echo "🔧 [STARTUP] Detected CPU cores: $NUM_CORES"
+echo "🔧 [STARTUP] Gunicorn workers: $WORKERS"
+echo "🔧 [STARTUP] Environment: ${ENV:-production}"
+
+# Chạy Gunicorn với số workers tính toán được
+exec gunicorn \
+    server:app \
+    --workers "$WORKERS" \
+    --worker-class uvicorn.workers.UvicornWorker \
+    --worker-connections 1000 \
+    --max-requests 1000 \
+    --max-requests-jitter 100 \
+    --timeout 30 \
+    --access-logfile - \
+    --error-logfile - \
+    --bind 0.0.0.0:5000 \
+    --log-level info

backend/server.py

@@ -50,10 +50,10 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-app.include_router(conservation_router)
 app.include_router(conservation_router)
 app.include_router(chatbot_router)
 
+
 # --- MOCK API FOR LOAD TESTING ---
 try:
     from api.mock_api_route import router as mock_router

docker-compose.n8n.yml

-version: '3.8'
-
-services:
-  n8n:
-    image: n8nio/n8n:latest
-    container_name: n8n_local
-    ports:
-      - "5678:5678"
-    volumes:
-      - n8n_data:/home/node/.n8n
-    environment:
-      - N8N_HOST=localhost
-      - N8N_PORT=5678
-      - N8N_PROTOCOL=http
-      - WEBHOOK_URL=http://localhost:5678/
-      - GENERIC_TIMEZONE=Asia/Ho_Chi_Minh
-    restart: unless-stopped
-
-volumes:
-  n8n_data: