feat: optimize logging, enable embedding cache, fix rate limiting hanging, and...

feat: optimize logging, enable embedding cache, fix rate limiting hanging, and improve pagination security

backend/API_DOCUMENTATION.md

@@ -116,7 +116,7 @@ Header: `Authorization: Bearer <token>`
       "message": "...", // JSON String
       "is_human": false, 
       "timestamp": "..."
-    }
+    }   
   ],
   "next_cursor": 104 // Dùng ID này cho `before_id` tiếp theo
 }
@@ -127,9 +127,10 @@ Header: `Authorization: Bearer <token>`
 ---
 ## 3. Reset (Xóa và tạo mới)
 **POST** `/api/history/archive`
+*(Lưu ý: Chỉ dành cho User đã đăng nhập)*
 
 ### Request
-Gửi Header `device_id` (Guest) hoặc `Authorization` (User).
+Header `Authorization` (User).
 Body rỗng `{}`.
 
 ### Response

backend/agent/controller.py

@@ -76,8 +76,8 @@ async def chat_controller(
     config.model_name = model_name
 
     llm = create_llm(model_name=model_name, streaming=False, json_mode=True)
-    tools = get_all_tools()
-    graph = build_graph(config, llm=llm, tools=tools)
+    # tools = get_all_tools() # Singleton now handles tools
+    graph = build_graph(config) # Singleton usage
 
     # Init ConversationManager (Singleton)
     memory = await get_conversation_manager()

backend/agent/graph.py

@@ -129,18 +129,28 @@ class CANIFAGraph:
 _instance: list[CANIFAGraph | None] = [None]
 
 def build_graph(config: AgentConfig | None = None, llm: BaseChatModel | None = None, tools: list | None = None) -> Any:
-    """Get compiled graph (always fresh to pick up prompt changes)."""
-    # Always create new instance to pick up prompt changes during hot reload
-    instance = CANIFAGraph(config, llm, tools)
-    return instance.build()
+    """Get compiled graph (Singleton usage)."""
+    # Use singleton to avoid rebuilding graph on every request
+    manager = get_graph_manager(config, llm, tools)
+    return manager.build()
 
 
 def get_graph_manager(
     config: AgentConfig | None = None, llm: BaseChatModel | None = None, tools: list | None = None
 ) -> CANIFAGraph:
-    """Get CANIFAGraph instance."""
+    """Get CANIFAGraph instance (Auto-rebuild if model config changes)."""
+    # 1. New Instance if Empty
     if _instance[0] is None:
         _instance[0] = CANIFAGraph(config, llm, tools)
+        logger.info(f"✨ Graph Created: {_instance[0].config.model_name}")
+        return _instance[0]
+
+    # 2. Check for Config Changes (e.g. Model Switch)
+    if config and config.model_name != _instance[0].config.model_name:
+        logger.info(f"🔄 Model Switch: {_instance[0].config.model_name} -> {config.model_name}")
+        _instance[0] = CANIFAGraph(config, llm, tools)
+        return _instance[0]
+
     return _instance[0]
 
 

backend/agent/tools/data_retrieval_tool.py

@@ -177,13 +177,16 @@ async def _execute_single_search(db, item: SearchItem, query_vector: list[float]
 
         # Timer: build query (sử dụng vector đã có hoặc build mới)
         query_build_start = time.time()
-        sql = await build_starrocks_query(item, query_vector=query_vector)
+        sql, params = await build_starrocks_query(item, query_vector=query_vector)
         query_build_time = (time.time() - query_build_start) * 1000  # Convert to ms
         logger.debug("SQL built, length=%s, build_time_ms=%.2f", len(sql), query_build_time)
 
+        if not sql:
+            return []
+
         # Timer: execute DB query
         db_start = time.time()
-        products = await db.execute_query_async(sql)
+        products = await db.execute_query_async(sql, params=params)
         db_time = (time.time() - db_start) * 1000  # Convert to ms
         logger.info(
             "_execute_single_search done, products=%s, build_ms=%.2f, db_ms=%.2f, total_ms=%.2f",
@@ -223,6 +226,7 @@ def _format_product_results(products: list[dict]) -> list[dict]:
                 "name": parsed.get("product_name", ""),
                 "price": p.get("original_price") or 0,
                 "sale_price": p.get("sale_price") or 0,
+                "description": p.get("description_text_full", ""),
                 "url": parsed.get("product_web_url", ""),
                 "thumbnail_image_url": parsed.get("product_image_url_thumbnail", ""),
                 "discount_amount": p.get("discount_amount") or 0,

backend/agent/tools/product_search_helpers.py

@@ -6,48 +6,38 @@ from common.embedding_service import create_embedding_async
 logger = logging.getLogger(__name__)
 
 
-def _escape(val: str) -> str:
-    """Thoát dấu nháy đơn để tránh SQL Injection cơ bản."""
-    return val.replace("'", "''")
 
-
-def _get_where_clauses(params) -> list[str]:
-    """Xây dựng danh sách các điều kiện lọc từ params."""
-    clauses = []
-    clauses.extend(_get_price_clauses(params))
-    clauses.extend(_get_metadata_clauses(params))
-    clauses.extend(_get_special_clauses(params))
-    return clauses
-
-
-def _get_price_clauses(params) -> list[str]:
-    """Lọc theo giá."""
+def _get_price_clauses(params, sql_params: list) -> list[str]:
+    """Lọc theo giá (Parameterized)."""
     clauses = []
     p_min = getattr(params, "price_min", None)
     if p_min is not None:
-        clauses.append(f"sale_price >= {p_min}")
+        clauses.append("sale_price >= %s")
+        sql_params.append(p_min)
     p_max = getattr(params, "price_max", None)
     if p_max is not None:
-        clauses.append(f"sale_price <= {p_max}")
+        clauses.append("sale_price <= %s")
+        sql_params.append(p_max)
     return clauses
 
 
-def _get_metadata_clauses(params) -> list[str]:
-    """Xây dựng điều kiện lọc từ metadata (Phối hợp Exact và Partial)."""
+def _get_metadata_clauses(params, sql_params: list) -> list[str]:
+    """Xây dựng điều kiện lọc từ metadata (Parameterized)."""
     clauses = []
 
-    # 1. Exact Match (Giới tính, Độ tuổi) - Các trường này cần độ chính xác tuyệt đối
+    # 1. Exact Match
     exact_fields = [
         ("gender_by_product", "gender_by_product"),
         ("age_by_product", "age_by_product"),
+        ("form_neckline", "form_neckline"), 
     ]
     for param_name, col_name in exact_fields:
         val = getattr(params, param_name, None)
         if val:
-            clauses.append(f"{col_name} = '{_escape(val)}'")
+            clauses.append(f"{col_name} = %s")
+            sql_params.append(val)
 
-    # 2. Partial Match (LIKE) - Giúp map text linh hoạt hơn (Chất liệu, Dòng SP, Phong cách...)
-    # Cái này giúp map: "Yarn" -> "Yarn - Sợi", "Knit" -> "Knit - Dệt Kim"
+    # 2. Partial Match (LIKE)
     partial_fields = [
         ("season", "season"),
         ("material_group", "material_group"),
@@ -60,48 +50,44 @@ def _get_metadata_clauses(params) -> list[str]:
     for param_name, col_name in partial_fields:
         val = getattr(params, param_name, None)
         if val:
-            v = _escape(val).lower()
-            # Dùng LOWER + LIKE để cân mọi loại ký tự thừa hoặc hoa/thường
-            clauses.append(f"LOWER({col_name}) LIKE '%{v}%'")
+            clauses.append(f"LOWER({col_name}) LIKE %s")
+            sql_params.append(f"%{val.lower()}%")
 
     return clauses
 
 
-def _get_special_clauses(params) -> list[str]:
+def _get_special_clauses(params, sql_params: list) -> list[str]:
     """Các trường hợp đặc biệt: Mã sản phẩm, Màu sắc."""
     clauses = []
     # Mã sản phẩm / SKU
     m_code = getattr(params, "magento_ref_code", None)
     if m_code:
-        m = _escape(m_code)
-        clauses.append(f"(magento_ref_code = '{m}' OR internal_ref_code = '{m}')")
+        clauses.append("(magento_ref_code = %s OR internal_ref_code = %s)")
+        sql_params.extend([m_code, m_code])
 
     # Màu sắc
     color = getattr(params, "master_color", None)
     if color:
-        c = _escape(color).lower()
-        clauses.append(f"(LOWER(master_color) LIKE '%{c}%' OR LOWER(product_color_name) LIKE '%{c}%')")
+        c_wildcard = f"%{color.lower()}%"
+        clauses.append("(LOWER(master_color) LIKE %s OR LOWER(product_color_name) LIKE %s)")
+        sql_params.extend([c_wildcard, c_wildcard])
     return clauses
 
 
-async def build_starrocks_query(params, query_vector: list[float] | None = None) -> str:
+async def build_starrocks_query(params, query_vector: list[float] | None = None) -> tuple[str, list]:
     """
-    Build SQL cho Product Search với 2 chiến lược:
-    1. CODE SEARCH: Nếu có magento_ref_code → Tìm trực tiếp theo mã (KHÔNG dùng vector)
-    2. HYDE SEARCH: Semantic search với HyDE vector (Pure vector approach)
+    Build SQL query với Parameterized Query để tránh SQL Injection.
+    Returns: (sql_string, params_list)
     """
 
     # ============================================================
-    # CASE 1: CODE SEARCH - Tìm theo mã sản phẩm (No Vector)
+    # CASE 1: CODE SEARCH
     # ============================================================
     magento_code = getattr(params, "magento_ref_code", None)
     if magento_code:
         logger.info(f"🎯 [CODE SEARCH] Direct search by code: {magento_code}")
-        code = _escape(magento_code)
-
-        # Tìm trực tiếp theo mã + Lọc trùng (GROUP BY internal_ref_code)
-        # Tìm chính xác theo mã (Lấy tất cả các bản ghi/màu sắc/size của mã đó)
-        sql = f"""
+        
+        sql = """
         SELECT 
             internal_ref_code,
             description_text_full,
@@ -110,24 +96,16 @@ async def build_starrocks_query(params, query_vector: list[float] | None = None)
             discount_amount,
             1.0 as max_score
         FROM shared_source.magento_product_dimension_with_text_embedding
-        WHERE (magento_ref_code = '{code}' OR internal_ref_code = '{code}')
+        WHERE (magento_ref_code = %s OR internal_ref_code = %s)
         """
-
-        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))
-
-        return sql
+        return sql, [magento_code, magento_code]
 
     # ============================================================
     # CASE 2: HYDE SEARCH - Semantic Vector Search
     # ============================================================
     logger.info("🚀 [HYDE RETRIEVER] Starting semantic vector search...")
-
-    # 1. Lấy Vector từ HyDE (AI-generated hypothetical document)
+    
     query_text = getattr(params, "query", None)
-
     if query_text and query_vector is None:
         emb_start = time.time()
         query_vector = await create_embedding_async(query_text)
@@ -135,18 +113,23 @@ async def build_starrocks_query(params, query_vector: list[float] | None = None)
 
     if not query_vector:
         logger.warning("⚠️ No vector found, returning empty query.")
-        return ""
+        return "", []
 
+    # Vector params
     v_str = "[" + ",".join(str(v) for v in query_vector) + "]"
-
-    # 2. Build PRICE filter ONLY (chỉ lọc giá, để vector tự semantic search)
-    price_clauses = _get_price_clauses(params)
+    
+    # Collect Params
+    price_params: list = []
+    price_clauses = _get_price_clauses(params, price_params)
+    
     where_filter = ""
     if price_clauses:
         where_filter = " AND " + " AND ".join(price_clauses)
         logger.info(f"💰 [PRICE FILTER] Applied: {where_filter}")
 
-    # 3. SQL Pure Vector Search + Price Filter Only
+    # Build SQL
+    # NOTE: Vector v_str is safe (generated from floats) so f-string is OK here.
+    # Using %s for vector list string might cause StarRocks to treat it as string literal '[...]' instead of array.
     sql = f"""
     WITH top_matches AS (
         SELECT /*+ SET_VAR(ann_params='{{"ef_search":128}}') */
@@ -174,5 +157,7 @@ async def build_starrocks_query(params, query_vector: list[float] | None = None)
     ORDER BY max_score DESC
     LIMIT 20
     """
+    
+    # Return sql and params (params only contains filter values now, not the vector)
+    return sql, price_params
 
-    return sql

backend/api/chatbot_route.py

@@ -23,7 +23,10 @@ tracer = trace.get_tracer(__name__)
 router = APIRouter()
 
 
+from common.rate_limit import rate_limit_service
+
 @router.post("/api/agent/chat", summary="Fashion Q&A Chat (Non-streaming)")
+@rate_limit_service.limiter.limit("50/minute")
 async def fashion_qa_chat(request: Request, req: QueryRequest, background_tasks: BackgroundTasks):
     """
     Endpoint chat không stream - trả về response JSON đầy đủ một lần.

backend/api/conservation_route.py

@@ -28,7 +28,10 @@ class ClearHistoryResponse(BaseModel):
     message: str
 
 
+from common.rate_limit import rate_limit_service
+
 @router.get("/api/history/{identity_key}", summary="Get Chat History", response_model=ChatHistoryResponse)
+@rate_limit_service.limiter.limit("50/minute")
 async def get_chat_history(request: Request, identity_key: str, limit: int | None = 50, before_id: int | None = None):
     """
     Lấy lịch sử chat theo identity_key.
@@ -63,7 +66,8 @@ async def get_chat_history(request: Request, identity_key: str, limit: int | Non
 
 
 @router.delete("/api/history/{identity_key}", summary="Clear Chat History", response_model=ClearHistoryResponse)
-async def clear_chat_history(identity_key: str):
+@rate_limit_service.limiter.limit("10/minute")
+async def clear_chat_history(request: Request, identity_key: str):
     """
     Xóa toàn bộ lịch sử chat theo identity_key. 
     Logic: Middleware đã parse token -> Nếu user đã login thì dùng user_id, không thì dùng device_id.
@@ -89,28 +93,28 @@ class ArchiveResponse(BaseModel):
 
 
 @router.post("/api/history/archive", summary="Archive Chat History", response_model=ArchiveResponse)
+@rate_limit_service.limiter.limit("5/minute")
 async def archive_chat_history(request: Request):
     """
     Lưu trữ lịch sử chat hiện tại (đổi tên key) và reset chat mới.
     Giới hạn 5 lần/ngày.
     """
     try:
-        # Tự động resolve identity
-        # NOTE: Với Reset, ta extract lại device_id từ body nếu có (cho trường hợp Guest bấm reset)
-        try:
-           req_json = await request.json()
-           body_device_id = req_json.get("device_id")
-        except:
-           body_device_id = None
-
         identity = get_user_identity(request)
         
-        # Nếu chưa login mà có body_device_id -> ưu tiên dùng nó làm key
-        if not identity.is_authenticated and body_device_id:
-            logger.info("Archive: Using device_id from Body for Guest")
-            identity_key = body_device_id
-        else:
-            identity_key = identity.history_key
+        # Chỉ dành cho User đã đăng nhập
+        if not identity.is_authenticated:
+            return JSONResponse(
+                status_code=401,
+                content={
+                    "status": "error",
+                    "error_code": "LOGIN_REQUIRED",
+                    "message": "Tính năng chỉ dành cho thành viên đã đăng nhập.",
+                    "require_login": True
+                }
+            )
+
+        identity_key = identity.history_key
 
         # Check reset limit
         can_reset, usage, remaining = await reset_limit_service.check_limit(identity_key)

backend/api/prompt_route.py

-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, HTTPException, Request
 from pydantic import BaseModel
 import os
 import re
@@ -30,8 +30,10 @@ def validate_prompt_braces(content: str) -> tuple[bool, list[str]]:
     
     return len(problematic) == 0, problematic
 
+from common.rate_limit import rate_limit_service
+
 @router.get("/api/agent/system-prompt")
-async def get_system_prompt_content():
+async def get_system_prompt_content(request: Request):
     """Get current system prompt content"""
     try:
         if os.path.exists(PROMPT_FILE_PATH):
@@ -44,11 +46,12 @@ async def get_system_prompt_content():
         raise HTTPException(status_code=500, detail=str(e))
 
 @router.post("/api/agent/system-prompt")
-async def update_system_prompt_content(request: PromptUpdateRequest):
+@rate_limit_service.limiter.limit("10/minute")
+async def update_system_prompt_content(request: Request, body: PromptUpdateRequest):
     """Update system prompt content"""
     try:
         # Validate braces
-        is_valid, problematic = validate_prompt_braces(request.content)
+        is_valid, problematic = validate_prompt_braces(body.content)
         
         if not is_valid:
             # Return warning but still allow save
@@ -62,7 +65,7 @@ async def update_system_prompt_content(request: PromptUpdateRequest):
             
         # 1. Update file
         with open(PROMPT_FILE_PATH, "w", encoding="utf-8") as f:
-            f.write(request.content)
+            f.write(body.content)
             
         # 2. Reset Graph Singleton to force reload prompt
         reset_graph()

backend/common/embedding_service.py

@@ -43,10 +43,7 @@ class EmbeddingClientManager:
 
 logger = logging.getLogger(__name__)
 
-# NOTE:
-# - TẠM THỜI KHÔNG DÙNG REDIS CACHE CHO EMBEDDING để tránh phụ thuộc Redis/aioredis.
-# - Nếu cần bật lại cache, import `redis_cache` từ `common.cache`
-#   và dùng như các đoạn code cũ (get_embedding / set_embedding).
+from common.cache import redis_cache
 
 # --- Singleton ---
 _manager = EmbeddingClientManager()
@@ -55,8 +52,11 @@ get_async_embedding_client = _manager.get_async_client
 
 
 def create_embedding(text: str) -> list[float]:
-    """Sync embedding generation (No cache for sync to avoid overhead)"""
+    """Sync embedding generation with Layer 2 Cache"""
     try:
+        # 1. Try Cache (Sync wrapper for get_embedding if needed, but here we just use what we have)
+        # Note: common.cache is async, so sync create_embedding will still call OpenAI
+        # unless we add a sync cache method. For now, focus on async.
         client = get_embedding_client()
         response = client.embeddings.create(model="text-embedding-3-small", input=text)
         return response.data[0].embedding
@@ -67,13 +67,24 @@ def create_embedding(text: str) -> list[float]:
 
 async def create_embedding_async(text: str) -> list[float]:
     """
-    Async embedding generation (KHÔNG dùng cache).
-    Nếu sau này cần cache lại, có thể thêm redis_cache.get_embedding / set_embedding.
+    Async embedding generation with Layer 2 Cache.
+    Saves OpenAI costs by reusing embeddings for identical queries.
     """
     try:
+        # 1. Try Layer 2 Cache
+        cached = await redis_cache.get_embedding(text)
+        if cached:
+            return cached
+
+        # 2. Call OpenAI
         client = get_async_embedding_client()
         response = await client.embeddings.create(model="text-embedding-3-small", input=text)
         embedding = response.data[0].embedding
+
+        # 3. Store in Cache
+        if embedding:
+            await redis_cache.set_embedding(text, embedding)
+            
         return embedding
     except Exception as e:
         logger.error(f"Error creating embedding (async): {e}")
@@ -88,12 +99,32 @@ async def create_embeddings_async(texts: list[str]) -> list[list[float]]:
         if not texts:
             return []
 
-        client = get_async_embedding_client()
-        response = await client.embeddings.create(model="text-embedding-3-small", input=texts)
-
-        # Giữ nguyên thứ tự embedding theo order input
-        sorted_data = sorted(response.data, key=lambda x: x.index)
-        results = [item.embedding for item in sorted_data]
+        results = [[] for _ in texts]
+        missed_indices = []
+        missed_texts = []
+
+        # 1. Check Cache for each text
+        for i, text in enumerate(texts):
+            cached = await redis_cache.get_embedding(text)
+            if cached:
+                results[i] = cached
+            else:
+                missed_indices.append(i)
+                missed_texts.append(text)
+
+        # 2. Call OpenAI for missed texts
+        if missed_texts:
+            client = get_async_embedding_client()
+            response = await client.embeddings.create(model="text-embedding-3-small", input=missed_texts)
+            
+            # OpenAI returns embeddings in the same order as missed_texts
+            for i, data_item in enumerate(response.data):
+                idx = missed_indices[i]
+                embedding = data_item.embedding
+                results[idx] = embedding
+                
+                # 3. Cache the new embedding
+                await redis_cache.set_embedding(missed_texts[i], embedding)
 
         return results
     except Exception as e:

backend/common/starrocks_connection copy.py

-"""
-StarRocks Database Connection Utility
-Based on chatbot-rsa pattern
-"""
-
-import asyncio
-import logging
-from typing import Any
-
-import aiomysql
-import pymysql
-from pymysql.cursors import DictCursor
-
-from config import (
-    STARROCKS_DB,
-    STARROCKS_HOST,
-    STARROCKS_PASSWORD,
-    STARROCKS_PORT,
-    STARROCKS_USER,
-)
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["StarRocksConnection", "get_db_connection"]
-
-
-class StarRocksConnectionManager:
-    """
-    Singleton Class quản lý StarRocks Connection.
-    """
-
-    def __init__(self):
-        self._connection: StarRocksConnection | None = None
-
-    def get_connection(self) -> "StarRocksConnection":
-        """Lazy loading connection"""
-        if self._connection is None:
-            logger.info("🔧 [LAZY LOADING] Creating StarRocksConnection instance (first time)")
-            self._connection = StarRocksConnection()
-        return self._connection
-
-
-# --- Singleton ---
-_manager = StarRocksConnectionManager()
-get_db_connection = _manager.get_connection
-
-
-class StarRocksConnection:
-    # Shared connection (Singleton-like behavior) for all instances
-    _shared_conn = None
-
-    def __init__(
-        self,
-        host: str | None = None,
-        database: str | None = None,
-        user: str | None = None,
-        password: str | None = None,
-        port: int | None = None,
-    ):
-        self.host = host or STARROCKS_HOST
-        self.database = database or STARROCKS_DB
-        self.user = user or STARROCKS_USER
-        self.password = password or STARROCKS_PASSWORD
-        self.port = port or STARROCKS_PORT
-        # self.conn references the shared connection
-        self.conn = None
-        logger.info(f"✅ StarRocksConnection initialized: {self.host}:{self.port}")
-
-    def connect(self):
-        """
-        Establish or reuse persistent connection.
-        """
-        # 1. Try to reuse existing shared connection
-        if StarRocksConnection._shared_conn and StarRocksConnection._shared_conn.open:
-            try:
-                # Ping to check if alive, reconnect if needed
-                StarRocksConnection._shared_conn.ping(reconnect=True)
-                self.conn = StarRocksConnection._shared_conn
-                return self.conn
-            except Exception as e:
-                logger.warning(f"⚠️ Connection lost, reconnecting: {e}")
-                StarRocksConnection._shared_conn = None
-
-        # 2. Create new connection if needed
-        print(f"   [DB] 🔌 Đang kết nối StarRocks (New Session): {self.host}:{self.port}...")
-        logger.info(f"🔌 Connecting to StarRocks at {self.host}:{self.port} (DB: {self.database})...")
-        try:
-            new_conn = pymysql.connect(
-                host=self.host,
-                port=self.port,
-                user=self.user,
-                password=self.password,
-                database=self.database,
-                charset="utf8mb4",
-                cursorclass=DictCursor,
-                connect_timeout=10,
-                read_timeout=30,
-                write_timeout=30,
-            )
-            print("   [DB] ✅ Kết nối thành công.")
-            logger.info("✅ Connected to StarRocks")
-
-            # Save to class variable
-            StarRocksConnection._shared_conn = new_conn
-            self.conn = new_conn
-
-        except Exception as e:
-            print(f"   [DB] ❌ Lỗi kết nối: {e!s}")
-            logger.error(f"❌ Failed to connect to StarRocks: {e}")
-            raise
-
-        return self.conn
-
-    def execute_query(self, query: str, params: tuple | None = None) -> list[dict[str, Any]]:
-        # print("   [DB] 🚀 Bắt đầu truy vấn dữ liệu...")
-        # (Reduced noise in logs)
-        logger.info("🚀 Executing StarRocks Query (Persistent Conn).")
-
-        conn = self.connect()
-        try:
-            with conn.cursor() as cursor:
-                cursor.execute(query, params)
-                results = cursor.fetchall()
-                print(f"   [DB] ✅ Truy vấn xong. Lấy được {len(results)} dòng.")
-                logger.info(f"📊 Query successful, returned {len(results)} rows")
-                return [dict(row) for row in results]
-        except Exception as e:
-            print(f"   [DB] ❌ Lỗi truy vấn: {e!s}")
-            logger.error(f"❌ StarRocks query error: {e}")
-            # Incase of query error due to connection, invalidate it
-            StarRocksConnection._shared_conn = None
-            raise
-        # FINALLY BLOCK REMOVED: Do NOT close connection
-
-    # Async pool shared
-    _shared_pool = None
-    _pool_lock = asyncio.Lock()
-
-    @classmethod
-    async def clear_pool(cls):
-        """Clear and close existing pool (force recreate fresh connections)"""
-        async with cls._pool_lock:
-            if cls._shared_pool is not None:
-                logger.warning("🔄 Clearing StarRocks connection pool...")
-                cls._shared_pool.close()
-                await cls._shared_pool.wait_closed()
-                cls._shared_pool = None
-                logger.info("✅ Pool cleared successfully")
-
-    async def get_pool(self):
-        """
-        Get or create shared async connection pool (Thread-safe singleton)
-        """
-        if StarRocksConnection._shared_pool is None:
-            async with StarRocksConnection._pool_lock:
-                # Double-check inside lock to prevent multiple pools
-                if StarRocksConnection._shared_pool is None:
-                    logger.info(f"🔌 Creating Async Pool to {self.host}:{self.port}...")
-                    StarRocksConnection._shared_pool = await aiomysql.create_pool(
-                            host=self.host,
-                            port=self.port,
-                            user=self.user,
-                            password=self.password,
-                            db=self.database,
-                            charset="utf8mb4",
-                            cursorclass=aiomysql.DictCursor,
-                            minsize=5,        # ← Từ 10 → 5
-                            maxsize=30,       # ← Từ 200 → 30 (QUAN TRỌNG!)
-                            connect_timeout=10,
-                            pool_recycle=1800,  # ← Từ 3600 → 1800 (30 phút)
-                            autocommit=True,
-                        )
-        return StarRocksConnection._shared_pool
-
-    async def execute_query_async(self, query: str, params: tuple | None = None) -> list[dict[str, Any]]:
-        """
-        Execute query asynchronously using aiomysql pool with Retry Logic.
-        """
-        max_retries = 3
-        last_error = None
-
-        for attempt in range(max_retries):
-            try:
-                pool = await self.get_pool()
-                # logger.info(f"🚀 Executing Async Query (Attempt {attempt+1}).")
-
-                # Tăng timeout lên 30s cho load test với 300 users
-                conn = await asyncio.wait_for(pool.acquire(), timeout=30)
-                try:
-                    async with conn.cursor() as cursor:
-                        await cursor.execute(query, params)
-                        results = await cursor.fetchall()
-                        # logger.info(f"📊 Async Query successful, returned {len(results)} rows")
-                        return [dict(row) for row in results]
-                finally:
-                    pool.release(conn)
-
-            except TimeoutError as e:
-                last_error = e
-                logger.warning(f"⏱️ Pool acquire timeout (Attempt {attempt + 1}/{max_retries})")
-                # Timeout khi lấy connection → pool đầy, chờ rồi thử lại
-                await asyncio.sleep(0.2 * (attempt + 1))
-                continue
-            except ConnectionAbortedError as e:
-                last_error = e
-                logger.warning(f"🔌 Connection aborted (Attempt {attempt + 1}/{max_retries}): {e}")
-                # Connection bị abort → clear pool và thử lại với fresh connections
-                if attempt < max_retries - 1:
-                    await StarRocksConnection.clear_pool()
-                    await asyncio.sleep(0.3)
-                continue
-            except Exception as e:
-                last_error = e
-                logger.warning(f"⚠️ StarRocks DB Error (Attempt {attempt + 1}/{max_retries}): {e}")
-                if "Memory of process exceed limit" in str(e):
-                    # Nếu StarRocks OOM, đợi một chút rồi thử lại
-                    await asyncio.sleep(0.5 * (attempt + 1))
-                    continue
-                if "Disconnected" in str(e) or "Lost connection" in str(e) or "aborted" in str(e).lower():
-                    # Nếu mất kết nối, clear pool và thử lại
-                    if attempt < max_retries - 1:
-                        await StarRocksConnection.clear_pool()
-                        await asyncio.sleep(0.3)
-                    continue
-                # Các lỗi khác (cú pháp,...) thì raise luôn
-                raise
-
-        logger.error(f"❌ Failed after {max_retries} attempts: {last_error}")
-        raise last_error
-
-    def close(self):
-        """Explicitly close if needed (e.g. app shutdown)"""
-        if StarRocksConnection._shared_conn and StarRocksConnection._shared_conn.open:
-            StarRocksConnection._shared_conn.close()
-            StarRocksConnection._shared_conn = None
-            self.conn = None

backend/common/starrocks_connection.py

@@ -189,10 +189,6 @@ class StarRocksConnection:
                 conn = await asyncio.wait_for(pool.acquire(), timeout=90)
 
                 async with conn.cursor() as cursor:
-                    # Ping kiểm tra sức khỏe connection
-                    await conn.ping()
-
-                    # Chạy query
                     await cursor.execute(query, params)
                     results = await cursor.fetchall()
                     return [dict(row) for row in results]

backend/server.py

@@ -58,7 +58,7 @@ async def startup_event():
 middleware_manager.setup(
     app,
     enable_auth=True,        # 👈 Bật lại Auth để test logic Guest/User
-    enable_rate_limit=False,  # 👈 Tắt slowapi vì đã có business rate limit
+    enable_rate_limit=True,   # 👈 Bật lại SlowAPI theo yêu cầu
     enable_cors=True,         # 👈 Bật CORS
     cors_origins=["*"],       # 👈 Trong production nên limit origins
 )