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canifa_tool_test_recommend
Commit 446d8f46 authored by Vũ Hoàng Anh's avatar Vũ Hoàng Anh
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feat: add hybrid search with keyword filtering, update port to 5003

parent c9a437ef
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      backend/Dockerfile
      View file @ 446d8f46
      # Sử dụng Python 3.11 Slim để tối ưu dung lượng
      # Lấy Python 3.11 slim (ít file, nhẹ)
      FROM python:3.11-slim
      # Thiết lập thư mục làm việc
      # Thư mục làm việc
      WORKDIR /app
      # Cài đặt các dependencies hệ thống cần thiết (nếu có, ví dụ build tools)
      # RUN apt-get update && apt-get install -y gcc libpq-dev && rm -rf /var/lib/apt/lists/*
      ENV PYTHONUNBUFFERED=1 PYTHONDONTWRITEBYTECODE=1
      # Copy requirements.txt trước để tận dụng Docker cache
      # Copy requirements rồi cài package
      COPY requirements.txt .
      RUN pip install -r requirements.txt
      # Cài đặt thư viện Python
      RUN pip install --no-cache-dir -r requirements.txt
      # Copy toàn bộ source code vào image
      # Copy code
      COPY . .
      # Expose port 5000 (Port chạy server)
      EXPOSE 5000
      # Copy entrypoint script (nếu có)
      COPY entrypoint.sh /app/entrypoint.sh
      RUN chmod +x /app/entrypoint.sh
      # Mở port 5003
      EXPOSE 5003
      # Lệnh chạy server
      CMD ["python", "server.py"]
      # Chạy server
      CMD ["/app/entrypoint.sh"]
      \ No newline at end of file
      backend/agent/__init__.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Fashion Q&A Agent Package
      """
      from .graph import build_graph
      from .models import AgentConfig, AgentState, get_config
      __all__ = [
      "AgentConfig",
      "AgentState",
      "build_graph",
      "get_config",
      ]
      backend/agent/controller.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Fashion Q&A Agent Controller
      Điều phối luồng chạy của Agent, tích hợp ConversationManager (Postgres Memory).
      Switched to LangSmith for tracing (configured via environment variables).
      """
      import json
      import logging
      import uuid
      from collections.abc import AsyncGenerator
      from langchain_core.messages import AIMessage, HumanMessage
      from langchain_core.runnables import RunnableConfig
      from common.llm_factory import create_llm
      from common.conversation_manager import get_conversation_manager, ConversationManager
      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, model_name: str = DEFAULT_MODEL, conversation_id: str | None = None, images: list[str] | None = None
      ) -> AsyncGenerator[str, None]:
      # 1. Khởi tạo & Chuẩn bị (Dependency Injection)
      logger.info(f"▶️ Starting chat_controller with model: {model_name} for user: {user_id}")
      config = get_config()
      config.model_name = model_name
      # Khởi tạo resources - Factory sẽ tự động chọn provider dựa trên tên model
      llm = create_llm(model_name=model_name, streaming=True)
      tools = get_all_tools()
      graph = build_graph(config, llm=llm, tools=tools)
      # Init ConversationManager (Singleton)
      memory = get_conversation_manager()
      actual_conv_id = conversation_id or str(uuid.uuid4())
      # LOAD HISTORY & Prepare State
      # Get history from Postgres (returns list of dicts)
      history_dicts = memory.get_chat_history(user_id, limit=10)
      # Convert to BaseMessage objects
      history = []
      for h in reversed(history_dicts): # API returns desc, we want chronological for context
      if h['is_human']:
      history.append(HumanMessage(content=h['message']))
      else:
      history.append(AIMessage(content=h['message']))
      current_human_msg = HumanMessage(content=query)
      initial_state, exec_config = _prepare_execution_context(
      query=query, user_id=user_id, actual_conv_id=actual_conv_id, history=history, images=images
      )
      final_ai_message = None
      # 3. Stream Engine
      try:
      async for event in graph.astream(initial_state, config=exec_config, stream_mode="values"):
      final_ai_message = _extract_last_ai_message(event) or final_ai_message
      # Serialize messages to dicts to avoid "content='...'" string representation
      if "messages" in event:
      event["messages"] = [
      m.dict() if hasattr(m, "dict") else m
      for m in event["messages"]
      ]
      yield f"data: {json.dumps(event, default=str, ensure_ascii=False)}\n\n"
      # 4. Hậu xử lý (Lưu DB)
      _handle_post_chat(
      memory=memory,
      user_id=user_id,
      human_query=query,
      ai_msg=final_ai_message,
      )
      yield "data: [DONE]\n\n"
      except Exception as e:
      logger.error(f"💥 Stream error: {e}", exc_info=True)
      yield f"data: {json.dumps({'error': str(e)})}\n\n"
      finally:
      logger.info(f"✅ Request completed for conversation {actual_conv_id}")
      def _prepare_execution_context(query: str, user_id: str, actual_conv_id: str, history: list, images: list | None):
      """Tách logic chuẩn bị state và config để giảm độ phức tạp."""
      initial_state: AgentState = {
      "messages": [HumanMessage(content=query)],
      "history": history,
      "user_id": user_id,
      "images": [],
      "thread_id": actual_conv_id,
      "image_analysis": None,
      }
      run_id = str(uuid.uuid4())
      # Metadata for LangSmith
      metadata = {
      "conversation_id": actual_conv_id,
      "user_id": user_id,
      "run_id": run_id
      }
      exec_config = RunnableConfig(
      configurable={
      "conversation_id": actual_conv_id,
      "user_id": user_id,
      "transient_images": images or [],
      "run_id": run_id,
      },
      run_id=run_id,
      metadata=metadata, # Attach metadata for LangSmith
      )
      return initial_state, exec_config
      def _extract_last_ai_message(event: dict) -> AIMessage | None:
      """Trích xuất tin nhắn AI cuối cùng từ event stream."""
      if event.get("messages"):
      last_msg = event["messages"][-1]
      if isinstance(last_msg, AIMessage):
      return last_msg
      return None
      def _handle_post_chat(memory: ConversationManager, user_id: str, human_query: str, ai_msg: AIMessage | None):
      """Xử lý lưu history sau khi kết thúc stream. LangSmith tự động trace nên không cần update thủ công."""
      if ai_msg:
      # Save User Message
      memory.save_message(user_id, human_query, True)
      # Save AI Message
      memory.save_message(user_id, ai_msg.content, False)
      logger.info(f"💾 Saved conversation for user {user_id} to Postgres")
      backend/agent/controller_save.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Fashion Q&A Agent Controller
      Điều phối luồng chạy của Agent, tích hợp ConversationManager (Postgres Memory).
      Using Langfuse @observe() decorator for automatic trace creation.
      """
      import json
      import logging
      import uuid
      from collections.abc import AsyncGenerator
      from langchain_core.messages import AIMessage, HumanMessage
      from langchain_core.runnables import RunnableConfig
      from langfuse import get_client, observe, propagate_attributes
      from langfuse.langchain import CallbackHandler
      from common.llm_factory import create_llm
      from common.conversation_manager import get_conversation_manager, ConversationManager
      from .graph import build_graph
      from .models import AgentState, get_config
      from .tools.get_tools import get_all_tools
      logger = logging.getLogger(__name__)
      @observe(capture_input=False, capture_output=False)
      async def chat_controller(
      query: str, user_id: str, model_name: str, conversation_id: str | None = None, images: list[str] | None = None
      ) -> AsyncGenerator[str, None]:
      # 1. Khởi tạo & Chuẩn bị (Dependency Injection)
      logger.info(f"▶️ Starting chat_controller for user: {user_id}")
      config = get_config()
      config.model_name = model_name
      # Khởi tạo resources bên ngoài để dễ test/mock
      llm = create_llm(model_name=model_name, api_key=config.openai_api_key, streaming=True)
      tools = get_all_tools()
      graph = build_graph(config, llm=llm, tools=tools)
      # Init ConversationManager (Singleton)
      memory = get_conversation_manager()
      actual_conv_id = conversation_id or str(uuid.uuid4())
      # 2. Chạy logic chính trong trace context
      with propagate_attributes(session_id=actual_conv_id, user_id=user_id, tags=["canifa", "chatbot"]):
      # LOAD HISTORY & Prepare State
      # Get history from Postgres (returns list of dicts)
      history_dicts = memory.get_chat_history(user_id, limit=10)
      # Convert to BaseMessage objects
      history = []
      for h in reversed(history_dicts): # API returns desc, we want chronological for context
      if h['is_human']:
      history.append(HumanMessage(content=h['message']))
      else:
      history.append(AIMessage(content=h['message']))
      current_human_msg = HumanMessage(content=query)
      initial_state, exec_config = _prepare_execution_context(
      query=query, user_id=user_id, actual_conv_id=actual_conv_id, history=history, images=images
      )
      final_ai_message = None
      # 3. Stream Engine
      try:
      async for event in graph.astream(initial_state, config=exec_config, stream_mode="values"):
      final_ai_message = _extract_last_ai_message(event) or final_ai_message
      # Serialize messages to dicts to avoid "content='...'" string representation
      if "messages" in event:
      event["messages"] = [
      m.dict() if hasattr(m, "dict") else m
      for m in event["messages"]
      ]
      yield f"data: {json.dumps(event, default=str, ensure_ascii=False)}\n\n"
      # 4. Hậu xử lý (Lưu DB & Trace)
      _handle_post_chat(
      memory=memory,
      user_id=user_id,
      human_query=query,
      ai_msg=final_ai_message,
      query=query,
      images_count=len(images) if images else 0,
      )
      yield "data: [DONE]\n\n"
      except Exception as e:
      logger.error(f"💥 Stream error: {e}", exc_info=True)
      yield f"data: {json.dumps({'error': str(e)})}\n\n"
      finally:
      logger.info(f"✅ Request completed for conversation {actual_conv_id}")
      def _prepare_execution_context(query: str, user_id: str, actual_conv_id: str, history: list, images: list | None):
      """Tách logic chuẩn bị state và config để giảm độ phức tạp."""
      initial_state: AgentState = {
      "messages": [HumanMessage(content=query)],
      "history": history,
      "user_id": user_id,
      "images": [],
      "thread_id": actual_conv_id,
      "image_analysis": None,
      }
      run_id = str(uuid.uuid4())
      exec_config = RunnableConfig(
      configurable={
      "conversation_id": actual_conv_id,
      "user_id": user_id,
      "transient_images": images or [],
      "run_id": run_id,
      },
      run_id=run_id,
      callbacks=[CallbackHandler()],
      )
      return initial_state, exec_config
      def _extract_last_ai_message(event: dict) -> AIMessage | None:
      """Trích xuất tin nhắn AI cuối cùng từ event stream."""
      if event.get("messages"):
      last_msg = event["messages"][-1]
      if isinstance(last_msg, AIMessage):
      return last_msg
      return None
      def _handle_post_chat(memory: ConversationManager, user_id: str, human_query: str, ai_msg: AIMessage | None, query: str, images_count: int):
      """Xử lý lưu history và update trace sau khi kết thúc stream."""
      if ai_msg:
      # Save User Message
      memory.save_message(user_id, human_query, True)
      # Save AI Message
      memory.save_message(user_id, ai_msg.content, False)
      logger.info(f"💾 Saved conversation for user {user_id} to Postgres")
      # Update trace
      try:
      langfuse = get_client()
      langfuse.update_current_trace(
      name="canifa-chatbot-query",
      input={"query": query, "images_count": images_count},
      output={"response": ai_msg.content if ai_msg else None},
      )
      except Exception as e:
      logger.warning(f"Failed to update trace: {e}")
      backend/agent/graph.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Fashion Q&A Agent Graph
      LangGraph workflow với clean architecture.
      Tất cả resources (LLM, Tools) khởi tạo trong __init__.
      Sử dụng ConversationManager (Postgres) để lưu history thay vì checkpoint.
      """
      import logging
      from typing import Any
      from langchain_core.language_models import BaseChatModel
      from langchain_core.messages import HumanMessage
      from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
      from langchain_core.runnables import RunnableConfig
      from langgraph.cache.memory import InMemoryCache
      from langgraph.graph import END, StateGraph
      from langgraph.prebuilt import ToolNode
      from langgraph.types import CachePolicy
      from common.llm_factory import create_llm
      from .models import AgentConfig, AgentState, get_config
      from .prompt import get_system_prompt
      from .tools.get_tools import get_all_tools, get_collection_tools
      logger = logging.getLogger(__name__)
      class CANIFAGraph:
      """
      Fashion Q&A Agent Graph Manager.
      Khởi tạo tất cả resources trong __init__, các node dùng self.xxx.
      """
      def __init__(
      self,
      config: AgentConfig | None = None,
      llm: BaseChatModel | None = None,
      tools: list | None = None,
      ):
      self.config = config or get_config()
      self._compiled_graph: Any | None = None
      # Dependency Injection: Ưu tiên dùng llm/tools được truyền vào
      self.llm: BaseChatModel = llm or create_llm(
      model_name=self.config.model_name, api_key=self.config.openai_api_key, streaming=True
      )
      # Phân loại tools
      self.all_tools = tools or get_all_tools()
      self.collection_tools = get_collection_tools() # Vẫn lấy list name để routing
      # Retrieval tools are logically all tools minus collection tools (conceptually, or specific list)
      # For simplicity and robust tool usage, we can bind all tools to retrieval node if needed,
      # or separate them. The user code snippet uses `self.retrieval_tools` but passed `all_tools`.
      # Reviewing user snippet: `workflow.add_node("retrieve_tools", ToolNode(self.retrieval_tools)...`
      # But `retrieval_tools` wasn't defined in __init__ in the user snippet, likely implied.
      # I'll define retrieval_tools as all tools for now or filter if strictly needed.
      # Assuming all_tools are retrieval compatible except collection ones?
      # Let's use all_tools for the ToolNode to be safe unless distinct behavior is needed.
      self.retrieval_tools = self.all_tools
      self.llm_with_tools = self.llm.bind_tools(self.all_tools)
      self.system_prompt = get_system_prompt()
      self.cache = InMemoryCache()
      async def _agent_node(self, state: AgentState, config: RunnableConfig) -> dict:
      """Agent node - LLM reasoning với tools và history sạch."""
      messages = state.get("messages", [])
      history = state.get("history", [])
      prompt = ChatPromptTemplate.from_messages(
      [
      ("system", self.system_prompt),
      MessagesPlaceholder(variable_name="history"), # Long-term clean history
      MessagesPlaceholder(variable_name="messages"), # Current turn technical messages
      ]
      )
      # 2. Xử lý Image hint (Lấy từ Config của lượt chạy này)
      transient_images = config.get("configurable", {}).get("transient_images", [])
      if transient_images and messages:
      # Removed image processing logic as requested
      pass
      # Invoke LLM
      chain = prompt | self.llm_with_tools
      response = await chain.ainvoke({"messages": messages, "history": history})
      return {"messages": [response]}
      def _should_continue(self, state: AgentState) -> str:
      """Routing: tool nodes hoặc end."""
      last_message = state["messages"][-1]
      if not hasattr(last_message, "tool_calls") or not last_message.tool_calls:
      logger.info("🏁 Agent finished")
      return "end"
      tool_names = [tc["name"] for tc in last_message.tool_calls]
      collection_names = [t.name for t in self.collection_tools]
      if any(name in collection_names for name in tool_names):
      logger.info(f"🔄 → collect_tools: {tool_names}")
      return "collect_tools"
      logger.info(f"🔄 → retrieve_tools: {tool_names}")
      return "retrieve_tools"
      def build(self) -> Any:
      """Build và compile LangGraph workflow (Không dùng Checkpointer)."""
      if self._compiled_graph is not None:
      return self._compiled_graph
      logger.info("🔨 Building LangGraph workflow (No Checkpointer)...")
      workflow = StateGraph(AgentState)
      # Nodes
      workflow.add_node("agent", self._agent_node)
      workflow.add_node("retrieve_tools", ToolNode(self.retrieval_tools), cache_policy=CachePolicy(ttl=3600))
      workflow.add_node("collect_tools", ToolNode(self.collection_tools))
      # Edges
      workflow.set_entry_point("agent")
      workflow.add_conditional_edges(
      "agent",
      self._should_continue,
      {"retrieve_tools": "retrieve_tools", "collect_tools": "collect_tools", "end": END},
      )
      workflow.add_edge("retrieve_tools", "agent")
      workflow.add_edge("collect_tools", "agent")
      # Compile WITHOUT checkpointer
      self._compiled_graph = workflow.compile(cache=self.cache)
      # ❌ KHÔNG ATTACH Langfuse callback vào compiled graph
      # ✅ Sẽ pass callback vào runtime config của mỗi lượt chạy
      logger.info("✅ Graph compiled (Langfuse callback will be per-run)")
      return self._compiled_graph
      @property
      def graph(self) -> Any:
      return self.build()
      # --- Singleton & Public API ---
      _instance: list[CANIFAGraph | None] = [None]
      def build_graph(config: AgentConfig | None = None, llm: BaseChatModel | None = None, tools: list | None = None) -> Any:
      """Get compiled graph (singleton)."""
      if _instance[0] is None:
      _instance[0] = CANIFAGraph(config, llm, tools)
      return _instance[0].build()
      def get_graph_manager(
      config: AgentConfig | None = None, llm: BaseChatModel | None = None, tools: list | None = None
      ) -> CANIFAGraph:
      """Get CANIFAGraph instance."""
      if _instance[0] is None:
      _instance[0] = CANIFAGraph(config, llm, tools)
      return _instance[0]
      def reset_graph() -> None:
      """Reset singleton for testing."""
      _instance[0] = None
      backend/agent/memory.py deleted 100644 → 0
      View file @ c9a437ef
      # """
      # Simple Memory Manager for Fashion Q&A Agent
      # Tối giản hóa: Chỉ sử dụng 1 Collection 'conversations' trong MongoDB
      # """
      # logger = logging.getLogger(__name__)
      class SimpleMemoryManager:
      """
      Quản lý bộ nhớ tối giản: Lưu/Load message từ 1 collection duy nhất.
      Sử dụng conversation_id làm định danh chính.
      TẠM THỜI COMMENT LOGIC ĐỂ TEST CHAY
      """
      def __init__(self):
      # self.client = get_mongo_client()
      # self.db = self.client[MONGODB_DB_NAME or "ai_law"]
      # self.collection = self.db["conversations"] # Tên bảng tối giản
      # self._indexes_created = False
      pass
      async def _ensure_indexes(self):
      # if not self._indexes_created:
      # # Index theo ID cuộc hội thoại và thời gian cập nhật
      # await self.collection.create_index("_id")
      # await self.collection.create_index("updated_at")
      # self._indexes_created = True
      pass
      async def save_messages(
      self,
      conversation_id: str,
      messages: list, # List[BaseMessage],
      user_id: str | None = None, # Optional[str] = None
      ):
      """Lưu toàn bộ danh sách tin nhắn vào cuộc hội thoại."""
      # try:
      # await self._ensure_indexes()
      # messages_dict = [self._message_to_dict(msg) for msg in messages]
      # await self.collection.update_one(
      # {"_id": conversation_id},
      # {
      # "$set": {
      # "user_id": user_id,
      # "messages": messages_dict,
      # "updated_at": datetime.utcnow(),
      # },
      # "$setOnInsert": {
      # "created_at": datetime.utcnow(),
      # }
      # },
      # upsert=True
      # )
      # except Exception as e:
      # # logger.error(f"❌ Save memory error: {e}")
      # raise
      pass
      async def load_messages(self, conversation_id: str, limit: int = 20) -> list: # List[BaseMessage]:
      """Load N tin nhắn gần nhất của cuộc hội thoại."""
      # try:
      # await self._ensure_indexes()
      # doc = await self.collection.find_one({"_id": conversation_id})
      # if not doc or "messages" not in doc:
      # return []
      # msgs_dict = doc["messages"]
      # # Chỉ lấy số lượng tin nhắn giới hạn để tiết kiệm token
      # if limit:
      # msgs_dict = msgs_dict[-limit:]
      # return [self._dict_to_message(m) for m in msgs_dict]
      # except Exception as e:
      # # logger.error(f"❌ Load memory error: {e}")
      # return []
      return []
      # def _message_to_dict(self, msg: BaseMessage) -> dict:
      # return {
      # "type": msg.__class__.__name__,
      # "content": msg.content,
      # "timestamp": datetime.utcnow().isoformat(),
      # }
      # def _dict_to_message(self, msg_dict: dict) -> BaseMessage:
      # m_type = msg_dict.get("type", "HumanMessage")
      # content = msg_dict.get("content", "")
      # if m_type == "AIMessage": return AIMessage(content=content)
      # if m_type == "SystemMessage": return SystemMessage(content=content)
      # return HumanMessage(content=content)
      # Singleton
      _memory_manager = None
      def get_memory_manager():
      global _memory_manager
      if _memory_manager is None:
      _memory_manager = SimpleMemoryManager()
      return _memory_manager
      backend/agent/models.py deleted 100644 → 0
      View file @ c9a437ef
      from typing import Annotated, Any, TypedDict
      from langchain_core.messages import BaseMessage
      from langgraph.graph.message import add_messages
      from pydantic import BaseModel
      import config as global_config
      class QueryRequest(BaseModel):
      """API Request model cho Fashion Q&A Chat"""
      query: str
      history: list[BaseMessage] | None = None
      model_name: str = global_config.DEFAULT_MODEL
      user_id: str | None = None
      images: list[str] | None = None
      image_analysis: dict[str, Any] | None = None
      conversation_id: str | None = None
      class AgentState(TypedDict):
      """Trạng thái của Agent trong LangGraph."""
      messages: Annotated[list[BaseMessage], add_messages]
      history: list[BaseMessage] # Conversation history sạch (Human + AI)
      user_id: str | None
      images: list[str] | None
      image_analysis: dict[str, Any] | None
      thread_id: str | None
      class AgentConfig:
      """Class chứa cấu hình runtime cho Agent."""
      def __init__(self, **kwargs):
      self.model_name = kwargs.get("model_name") or global_config.DEFAULT_MODEL
      self.openai_api_key = kwargs.get("openai_api_key")
      self.google_api_key = kwargs.get("google_api_key")
      self.groq_api_key = kwargs.get("groq_api_key")
      self.supabase_url = kwargs.get("supabase_url")
      self.supabase_key = kwargs.get("supabase_key")
      self.langfuse_public_key = kwargs.get("langfuse_public_key")
      self.langfuse_secret_key = kwargs.get("langfuse_secret_key")
      self.langfuse_base_url = kwargs.get("langfuse_base_url")
      def get_config() -> AgentConfig:
      """Khởi tạo cấu hình Agent từ các biến môi trường."""
      return AgentConfig(
      model_name=global_config.DEFAULT_MODEL,
      openai_api_key=global_config.OPENAI_API_KEY,
      google_api_key=global_config.GOOGLE_API_KEY,
      groq_api_key=global_config.GROQ_API_KEY,
      supabase_url=global_config.AI_SUPABASE_URL,
      supabase_key=global_config.AI_SUPABASE_KEY,
      langfuse_public_key=global_config.LANGFUSE_PUBLIC_KEY,
      langfuse_secret_key=global_config.LANGFUSE_SECRET_KEY,
      langfuse_base_url=global_config.LANGFUSE_BASE_URL,
      )
      backend/agent/nodes/__init__.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Agent Nodes Package
      """
      from .agent import agent_node
      __all__ = ["agent_node"]
      backend/agent/prompt.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Fashion Q&A Agent System Prompts - CiCi Fashion Consultant
      Professional and engaging style guide for CANIFA product recommendations
      """
      from datetime import datetime
      def get_system_prompt() -> str:
      """
      Get system prompt for Fashion Q&A Agent
      Returns:
      str: System prompt with current date and professional persona
      """
      now = datetime.now()
      date_str = now.strftime("%B %d, %Y")
      return f"""You are **CiCi**, a professional fashion consultant for CANIFA. Today is {date_str}.
      ## CORE BEHAVIOR
      **Personality:**
      - Enthusiastic, warm, and helpful
      - Expert in fashion recommendations
      - Genuine and trustworthy (never oversell)
      - Responsive and action-oriented
      **Communication Style:**
      - **ALWAYS respond in the customer's language** (Vietnamese or English)
      - If customer speaks Vietnamese → respond in Vietnamese with warm, persuasive tone ("thảo mai")
      - If customer speaks English → respond in English
      - Use appropriate emojis sparingly (😍, ✨, 💖, 👗)
      - Be concise and scannable (use bullet points, short paragraphs)
      - End responses with a clear next action or question
      ## WHEN TO CALL TOOLS
      **USE data_retrieval_tool IMMEDIATELY when customer:**
      - Asks to find/search products: "Find me...", "Show me...", "I'm looking for..."
      - Describes what they want to wear: "I need a white t-shirt for summer"
      - Mentions specific attributes: color, price, style, fit, season, age group, gender
      - Asks for alternatives: "What other options do you have?"
      - Says they want something specific: "Do you have X in Y color?"
      **EXAMPLES that TRIGGER tool call:**
      ✅ "Show me blue dresses under 300k"
      ✅ "I need casual wear for men"
      ✅ "Áo đi chơi mùa hè màu xanh dưới 300k"
      ✅ "Do you have this in red?"
      ✅ "What other colors available?"
      **DO NOT call tools for:**
      ❌ Out of scope: "How do I track my order?" → Redirect to customer service
      ❌ Non-product: "Tell me fashion trends" → General advice, no tool needed
      ❌ Already answered: Customer just asking follow-up → Use previous results
      **Tool docstring** already defines all parameters - extract values from customer request and map to tool parameters. Only pass non-null values.
      ## TOOL RESULT HANDLING
      **🚨 CRITICAL: After receiving tool results, YOU MUST:**
      1. **STOP and present results immediately** if tool returns products (status: "success")
      - Don't call tool again with different parameters
      - Present the products you got to the customer
      - Let customer ask for refinements if they want
      2. **Maximum 1 retry** if tool returns no results (status: "no_results")
      - First attempt failed? Try ONE more time with broader parameters (remove 1-2 filters)
      - After 2nd attempt, STOP and suggest alternatives to customer
      - NEVER call tool more than 2 times for same customer request
      3. **Stop immediately** if tool returns error (status: "error")
      - Don't retry on errors
      - Apologize and ask customer to try different search
      **Decision Tree After Tool Call:**
      ```
      Tool Result Received
      ↓
      Has products (count > 0)?
      YES → ✅ PRESENT products to customer NOW (don't call tool again)
      NO → Is this 1st attempt?
      YES → ⚠️ Try ONE more time with broader filters
      NO → ❌ STOP, suggest alternatives to customer
      ```
      **Examples:**
      ❌ **WRONG - Don't do this:**
      ```
      1. Call tool(keywords="áo polo", master_color="Xanh", price_max=300000)
      2. Get 3 products
      3. Call tool again(keywords="áo", price_max=500000) ← WHY? Already have results!
      4. Get more products
      5. Call tool again... ← INFINITE LOOP!
      ```
      ✅ **CORRECT - Do this:**
      ```
      1. Call tool(keywords="áo polo", master_color="Xanh", price_max=300000)
      2. Get 3 products
      3. STOP → Present those 3 products to customer ✅
      4. Wait for customer feedback
      ```
      ✅ **CORRECT - No results retry:**
      ```
      1. Call tool(keywords="áo polo", master_color="Hồng fuxia", price_max=200000)
      2. Get 0 products (no_results)
      3. Try ONCE more: tool(keywords="áo polo", master_color="Hồng", price_max=250000)
      4. Get 2 products → STOP → Present ✅
      OR Get 0 products → STOP → Suggest alternatives ✅
      ```
      ## RESPONSE FORMAT FOR PRODUCT RESULTS
      **CRITICAL: ALWAYS include these fields when presenting products:**
      - Product Name
      - Product Code (internal_ref_code)
      - Price (sale_price with comma formatting)
      - Available Colors (list all from available_colors field)
      - Material/Fabric (material or material_group)
      - Product URL (product_web_url) - Clickable link
      - Image (use product_image_url or product_image_url_thumbnail)
      When presenting products from tool response:
      ```
      Great! I found these perfect options for you:
      ✨ **[Product Name]** (Mã: [internal_ref_code])
      - Giá: [PRICE] VNĐ (hoặc Price: [PRICE] VND)
      - Màu sắc: [list all available colors]
      - Chất liệu: [material_group or material]
      - Mô tả: [brief description_text if available]
      🔗 Xem chi tiết: [product_web_url]
      📸 Ảnh: [product_image_url]
      → Perfect for: [use case based on season/style/fitting]
      [Repeat for additional products, max 5]
      Which one catches your eye? I can help you find alternatives if you'd like! 💖
      ```
      **Formatting Rules:**
      - **ALWAYS show product code** (internal_ref_code) in format: (Mã: XXX)
      - **ALWAYS include clickable URL** from product_web_url field
      - Group same product (same internal_ref_code) with different colors into ONE block
      - List colors as: "Màu sắc: [color1], [color2], [color3]" or "Available in: [color1], [color2]"
      - Always format prices with comma (e.g., 299,000 VNĐ or 299,000 VND)
      - Include material info from material_group or material field
      - Use description_text for product details when available
      - Highlight why each product is special based on metadata (season, style, fitting)
      - Create a "hard to choose because all are great" feeling
      ## HANDLING EDGE CASES
      **No Results:**
      - Acknowledge customer's preference
      - Suggest nearby alternatives (different color/style/price)
      - Offer to refine search with different parameters
      - Example: "This exact color is sold out, but we have a beautiful Pastel Purple that's trending right now. Want me to show you alternatives?"
      **Uncertain/Ambiguous Requests:**
      - Ask clarifying questions to extract proper parameters
      - Suggest what you think they might want
      - Example: "When you say 'casual summer outfit for a date', are you thinking shorts, a light dress, or both?"
      **Out of Scope Questions:**
      - Orders, shipping, returns → "Please contact our website or customer service"
      - Personal data access → "I can only help with product recommendations"
      - Non-CANIFA brands → "I specialize in CANIFA products only"
      ## GOLDEN RULES
      1. ✅ Use data_retrieval_tool IMMEDIATELY (don't say "let me check")
      2. ✅ Respond as if you already know the products
      3. ✅ **STOP after getting tool results** - present products immediately, don't call again
      4. ✅ **Maximum 2 tool calls per customer request** (1 attempt + 1 retry if no results)
      5. ✅ Group duplicate products by color/variant
      6. ✅ Always be concise - longer isn't better
      7. ✅ Genuine recommendations > aggressive selling
      8. ❌ Never call tool multiple times if you already have products
      9. ❌ Never list same product with different colors as separate items
      10. ❌ Never use phrases like "let me check", "please wait", "I'll look into it"
      11. ❌ Don't repeat information multiple times
      12. ❌ **NEVER call tool more than twice for same request**
      ## EXAMPLES
      **Customer:** "I need a white t-shirt for men under 200k"
      **CiCi (Internal):** Call data_retrieval_tool(keywords="t-shirt", master_color="White", gender_by_product="male", price_max=200000)
      **CiCi (Response):** "Perfect! White t-shirts are a classic choice. Here are my top picks:
      ✨ **Basic Cotton T-Shirt**
      - Price: 149,000 VND
      - Colors: White, Light Gray
      - Material: 100% Cotton - breathable and comfortable all day
      → Our best-seller! Perfect for everyday wear.
      [+ 1-2 more options if available]
      Ready to pick one? 😊"
      ## VIETNAMESE EXAMPLES (Ví dụ tiếng Việt)
      **Example 1 - Greeting:**
      Customer: "Chào em"
      CiCi (Response): "Chào anh/chị ạ! Em là CiCi - chuyên gia tư vấn thời trang CANIFA ✨
      Hôm nay anh/chị cần em tìm gì ạ? Em có thể giúp:
      - Tìm kiếm sản phẩm theo nhu cầu (áo, quần, váy...)
      - Tư vấn phối đồ theo dịp (đi chơi, đi làm, dự tiệc...)
      - Lọc theo màu sắc, giá cả, size, phong cách
      Cứ thoải mái nói với em nhé! 💖"
      **Example 2 - Positive Results:**
      Customer: "Tìm cho tôi áo đi chơi mùa hè, màu xanh, dưới 300k"
      CiCi (Response): "Ôi, bạn có gu thật đấy! Áo đi chơi mùa hè màu xanh là xu hướng cực hot năm nay! CiCi tìm được mấy 'siêu phẩm' giá hạt dẻ này cho bạn nè:
      ✨ **Áo Phông Xanh Mint Premium** (Mã: 1TS23S012)
      - Giá: 249,000 VNĐ (giảm từ 399,000 VNĐ - rẻ không tưởng!)
      - Màu sắc: Xanh mint, Xanh navy
      - Chất liệu: Knit - Dệt Kim, 100% Cotton mát mẻ
      - Mô tả: Áo phông cotton 100%, cổ tròn tay cộc
      🔗 Xem chi tiết: https://canifa.com/ao-phong-1ts23s012
      → Perfect for: Đi biển, dạo phố, picnic mùa hè
      ✨ **Áo Linen Xanh Navy** (Mã: 1AL24S089)
      - Giá: 299,000 VNĐ (sale cực sốc!)
      - Màu sắc: Xanh navy, Xanh cobalt
      - Chất liệu: Woven - Dệt Thoi, Linen mềm thoáng khí
      - Mô tả: Áo linen cao cấp, form regular fit
      🔗 Xem chi tiết: https://canifa.com/ao-linen-1al24s089
      → Perfect for: Đi tiệc casual, gặp bạn, văn phòng
      Bạn thích em nào? Mặc lên là sang chảnh hết nấc luôn! 💖"
      **Example 3 - Ambiguous Request:**
      Customer: "Tìm đồ cho bé gái mùa hè"
      CiCi (Response): "Ôi, bé gái mùa hè chắc là xinh lắm! Mẹ muốn tìm áo, quần, hay váy bé? Và bé bao nhiêu tuổi để em tìm size phù hợp nhé?
      Cứ nói với em, em sẽ tìm những bộ đẹp nhất cho con yêu của mẹ! 💖"
      **Example 4 - No Results Handling:**
      Customer: "Có quần áo màu hồng fuxia không?"
      CiCi (Response): "Tiếc quá, mẫu màu hồng fuxia đang cháy hàng lắm bạn ơi! 😭 Nhưng không sao, em có mấy màu hồng tương tự cực xinh:
      - **Hồng Pastel** - nhẹ nhàng, tôn da
      - **Hồng Coral** - sáng sủa, trendy
      - **Hồng Đào** - kinh điển, sang chảnh
      Bạn muốn em tìm chi tiết mẫu nào không? Đảm bảo bạn sẽ mê ngay từ cái nhìn đầu tiên! ✨"
      ---"""
      backend/agent/tools/__init__.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Tools Package
      Export tool và factory function
      """
      from .data_retrieval_tool import data_retrieval_tool
      from .get_tools import get_all_tools
      __all__ = ["data_retrieval_tool", "get_all_tools"]
      backend/agent/tools/customer_info_tool.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Tool thu thập thông tin khách hàng (Tên, Số điện thoại, Email)
      Dùng để đẩy data về CRM hoặc hệ thống lưu trữ khách hàng.
      """
      import json
      import logging
      from langchain_core.tools import tool
      logger = logging.getLogger(__name__)
      @tool
      async def collect_customer_info(name: str, phone: str, email: str | None = None) -> str:
      """
      Sử dụng tool này để ghi lại thông tin khách hàng khi họ muốn tư vấn sâu hơn,
      nhận khuyến mãi hoặc đăng ký mua hàng.
      Args:
      name: Tên của khách hàng
      phone: Số điện thoại của khách hàng
      email: Email của khách hàng (không bắt buộc)
      """
      try:
      print(f"\n[TOOL] --- 📝 Thu thập thông tin khách hàng: {name} - {phone} ---")
      logger.info(f"📝 Collecting customer info: {name}, {phone}, {email}")
      # Giả lập việc đẩy data đi (CRM/Sheet)
      # Trong thực tế, bạn sẽ gọi một API ở đây
      db_record = {
      "customer_name": name,
      "phone_number": phone,
      "email_address": email,
      "status": "pending_consultation",
      }
      # Trả về kết quả thành công
      return json.dumps(
      {
      "status": "success",
      "message": (
      f"Cảm ơn anh/chị {name}. CiCi đã ghi nhận thông tin và sẽ có nhân viên "
      f"liên hệ tư vấn qua số điện thoại {phone} sớm nhất ạ!"
      ),
      "data_captured": db_record,
      },
      ensure_ascii=False,
      )
      except Exception as e:
      logger.error(f"❌ Lỗi khi thu thập thông tin: {e}")
      return json.dumps(
      {
      "status": "error",
      "message": f"Xin lỗi, CiCi gặp sự cố khi lưu thông tin. Anh/chị vui lòng thử lại sau ạ. Lỗi: {e!s}",
      },
      ensure_ascii=False,
      )
      backend/agent/tools/data_retrieval_tool.py deleted 100644 → 0
      View file @ c9a437ef
      """
      CANIFA Data Retrieval Tool - Tối giản cho Agentic Workflow.
      Hỗ trợ Hybrid Search: Semantic (Vector) + Metadata Filter.
      """
      import json
      import logging
      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
      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 SearchParams(BaseModel):
      """Cấu trúc tham số tìm kiếm mà Agent phải cung cấp, map trực tiếp với Database."""
      query: str | None = Field(
      None,
      description="Câu hỏi/mục đích tự do của user (đi chơi, dự tiệc, phỏng vấn,...) - dùng cho Semantic Search",
      )
      keywords: str | None = Field(
      None, description="Từ khóa kỹ thuật cụ thể (áo polo, quần jean,...) - dùng cho LIKE search"
      )
      internal_ref_code: str | None = Field(None, description="Mã sản phẩm (ví dụ: 1TS23S012)")
      product_color_code: str | None = Field(None, description="Mã màu sản phẩm (ví dụ: 1TS23S012-SK010)")
      product_line_vn: str | None = Field(None, description="Dòng sản phẩm (Áo phông, Quần short,...)")
      gender_by_product: str | None = Field(None, description="Giới tính: male, female")
      age_by_product: str | None = Field(None, description="Độ tuổi: adult, kids, baby, others")
      master_color: str | None = Field(None, description="Màu sắc chính (Đen/ Black, Trắng/ White,...)")
      material_group: str | None = Field(None, description="Nhóm chất liệu (Knit - Dệt Kim, Woven - Dệt Thoi,...)")
      season: str | None = Field(None, description="Mùa (Spring Summer, Autumn Winter)")
      style: str | None = Field(None, description="Phong cách (Basic Update, Fashion,...)")
      fitting: str | None = Field(None, description="Form dáng (Regular, Slim, Loose,...)")
      form_neckline: str | None = Field(None, description="Kiểu cổ (Crew Neck, V-neck,...)")
      form_sleeve: str | None = Field(None, description="Kiểu tay (Short Sleeve, Long Sleeve,...)")
      price_min: float | None = Field(None, description="Giá thấp nhất")
      price_max: float | None = Field(None, description="Giá cao nhất")
      action: str = Field("search", description="Hành động: 'search' (tìm kiếm) hoặc 'visual_search' (phân tích ảnh)")
      from langsmith import traceable
      @tool(args_schema=SearchParams)
      @traceable(run_type="tool", name="data_retrieval_tool")
      async def data_retrieval_tool(
      action: str = "search",
      query: str | None = None,
      keywords: str | None = None,
      internal_ref_code: str | None = None,
      product_color_code: str | None = None,
      product_line_vn: str | None = None,
      gender_by_product: str | None = None,
      age_by_product: str | None = None,
      master_color: str | None = None,
      material_group: str | None = None,
      season: str | None = None,
      style: str | None = None,
      fitting: str | None = None,
      form_neckline: str | None = None,
      form_sleeve: str | None = None,
      price_min: float | None = None,
      price_max: float | None = None,
      ) -> str:
      """
      Tìm kiếm sản phẩm trong database của CANIFA sử dụng tìm kiếm ngữ nghĩa (Semantic), từ khóa (Keywords) hoặc các bộ lọc thuộc tính.
      Cơ chế hoạt động (Hybrid Search):
      - Nếu có 'query': Hệ thống sẽ tạo vector embedding và tìm kiếm theo độ tương đồng ngữ nghĩa.
      - Nếu có 'keywords' hoặc các thuộc tính khác: Hệ thống sẽ tạo các câu lệnh SQL WHERE để lọc chính xác kết quả.
      - Kết hợp cả hai để mang lại kết quả tối ưu nhất.
      Ví dụ sử dụng (Examples):
      1. Tìm kiếm theo ý định chung:
      User: "Tìm cho mình một bộ đồ đi biển mát mẻ"
      Tool call: data_retrieval_tool(query="bộ đồ đi biển mát mẻ", gender_by_product="Female")
      2. Tìm chính xác theo loại sản phẩm và giá:
      User: "Áo polo nam dưới 400k"
      Tool call: data_retrieval_tool(keywords="áo polo", gender_by_product="Male", price_max=400000)
      3. Tìm theo mã sản phẩm cụ thể:
      User: "Check sản phẩm 8TS24W001"
      Tool call: data_retrieval_tool(internal_ref_code="8TS24W001")
      4. Kết hợp tìm kiếm sâu:
      User: "Áo khoác len mùa đông cho bé trai từ 200k đến 500k"
      Tool call: data_retrieval_tool(query="áo khoác len ấm áp", material_group="Len", age_by_product="Kids", price_min=200000, price_max=500000)
      """
      try:
      # 1. Log & Prepare Params
      # 1. Log & Prepare Params
      params = SearchParams(
      action=action,
      query=query,
      keywords=keywords,
      internal_ref_code=internal_ref_code,
      product_color_code=product_color_code,
      product_line_vn=product_line_vn,
      gender_by_product=gender_by_product,
      age_by_product=age_by_product,
      master_color=master_color,
      material_group=material_group,
      season=season,
      style=style,
      fitting=fitting,
      form_neckline=form_neckline,
      form_sleeve=form_sleeve,
      price_min=price_min,
      price_max=price_max,
      )
      _log_agent_call(params)
      # 2. Prepare Vector (Async) if needed
      query_vector = None
      if query:
      from common.embedding_service import create_embedding_async
      query_vector = await create_embedding_async(query)
      # 3. Execute Search (Async)
      sql = build_starrocks_query(params, query_vector=query_vector)
      db = StarRocksConnection()
      products = await db.execute_query_async(sql)
      if not products:
      return _handle_no_results(query, keywords)
      # 4. Format Results
      clean_products = _format_product_results(products)
      return json.dumps(
      {"status": "success", "count": len(clean_products), "products": clean_products},
      ensure_ascii=False,
      cls=DecimalEncoder,
      )
      except Exception as e:
      logger.error(f"Error in data_retrieval_tool: {e}")
      return json.dumps({"status": "error", "message": str(e)})
      def _log_agent_call(params: SearchParams):
      """Log parameters for debugging."""
      filtered_params = {k: v for k, v in params.dict().items() if v is not None}
      logger.info(f"📋 Agent Tool Call - data_retrieval_tool: {json.dumps(filtered_params, ensure_ascii=False)}")
      def _handle_no_results(query: str | None, keywords: str | None) -> str:
      """Return standardized no-results message."""
      logger.warning(f"No products found for search: query={query}, keywords={keywords}")
      return json.dumps(
      {
      "status": "no_results",
      "message": "Không tìm thấy sản phẩm nào phù hợp với yêu cầu. Vui lòng thử lại với từ khóa hoặc bộ lọc khác.",
      },
      ensure_ascii=False,
      )
      def _format_product_results(products: list[dict]) -> list[dict]:
      """Filter and format product fields for the agent."""
      allowed_fields = {
      "internal_ref_code",
      "magento_ref_code",
      "product_color_code",
      "product_name",
      "color_code",
      "master_color",
      "product_color_name",
      "season_sale",
      "season",
      "style",
      "fitting",
      "size_scale",
      "graphic",
      "pattern",
      "weaving",
      "shape_detail",
      "form_neckline",
      "form_sleeve",
      "form_length",
      "form_waistline",
      "form_shoulderline",
      "material",
      "product_group",
      "product_line_vn",
      "unit_of_measure",
      "sale_price",
      "original_price",
      "material_group",
      "product_line_en",
      "age_by_product",
      "gender_by_product",
      "product_image_url",
      "description_text",
      "product_image_url_thumbnail",
      "product_web_url",
      "product_web_material",
      }
      return [{k: v for k, v in p.items() if k in allowed_fields} for p in products[:5]]
      backend/agent/tools/get_tools.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Tools Factory
      Chỉ return 1 tool duy nhất: data_retrieval_tool
      """
      from langchain_core.tools import Tool
      from .customer_info_tool import collect_customer_info
      from .data_retrieval_tool import data_retrieval_tool
      def get_retrieval_tools() -> list[Tool]:
      """Các tool chỉ dùng để đọc/truy vấn dữ liệu (Có thể cache)"""
      return [data_retrieval_tool]
      def get_collection_tools() -> list[Tool]:
      """Các tool dùng để ghi/thu thập dữ liệu (KHÔNG cache)"""
      return [collect_customer_info]
      def get_all_tools() -> list[Tool]:
      """Return toàn bộ list tools cho Agent"""
      return get_retrieval_tools() + get_collection_tools()
      backend/agent/tools/product_search_helpers.py deleted 100644 → 0
      View file @ c9a437ef
      import logging
      from common.embedding_service import create_embedding
      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_exact_match_clauses(params))
      clauses.extend(_get_special_clauses(params))
      return clauses
      def _get_price_clauses(params) -> list[str]:
      """Lọc theo giá."""
      clauses = []
      p_min = getattr(params, "price_min", None)
      if p_min is not None:
      clauses.append(f"sale_price >= {p_min}")
      p_max = getattr(params, "price_max", None)
      if p_max is not None:
      clauses.append(f"sale_price <= {p_max}")
      return clauses
      def _get_exact_match_clauses(params) -> list[str]:
      """Các trường lọc chính xác (Exact match)."""
      clauses = []
      exact_filters = [
      ("gender_by_product", "gender_by_product"),
      ("age_by_product", "age_by_product"),
      ("season", "season"),
      ("material_group", "material_group"),
      ("product_line_vn", "product_line_vn"),
      ("style", "style"),
      ("fitting", "fitting"),
      ("form_neckline", "form_neckline"),
      ("form_sleeve", "form_sleeve"),
      ]
      for param_name, col_name in exact_filters:
      val = getattr(params, param_name, None)
      if val:
      clauses.append(f"{col_name} = '{_escape(val)}'")
      return clauses
      def _get_special_clauses(params) -> list[str]:
      """Các trường hợp đặc biệt: Mã sản phẩm, Màu sắc."""
      clauses = []
      # Mã sản phẩm
      ref_code = getattr(params, "internal_ref_code", None)
      if ref_code:
      r = _escape(ref_code)
      clauses.append(f"(internal_ref_code = '{r}' OR magento_ref_code = '{r}')")
      # 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}%')")
      return clauses
      def build_starrocks_query(params, query_vector: list[float] | None = None) -> str:
      """
      Build SQL Hybrid tối ưu:
      1. Pre-filtering (Metadata)
      2. Vector Search (HNSW Index)
      3. Grouping (Gom màu theo style)
      """
      # --- Process vector in query field ---
      query_text = getattr(params, "query", None)
      if query_text and query_vector is None:
      query_vector = create_embedding(query_text)
      # --- Build filter clauses ---
      where_clauses = _get_where_clauses(params)
      where_sql = " AND ".join(where_clauses) if where_clauses else "1=1"
      # --- Build SQL ---
      if query_vector and len(query_vector) > 0:
      v_str = "[" + ",".join(str(v) for v in query_vector) + "]"
      # OPTIMIZED: Only SELECT necessary fields in CTE, not SELECT *
      sql = f"""
      WITH top_sku_candidates AS (
      SELECT
      internal_ref_code,
      product_name,
      sale_price,
      original_price,
      master_color,
      product_image_url,
      product_image_url_thumbnail,
      product_web_url,
      description_text,
      material,
      material_group,
      gender_by_product,
      age_by_product,
      season,
      style,
      fitting,
      form_neckline,
      form_sleeve,
      product_line_vn,
      product_color_name,
      cosine_similarity(vector, {v_str}) as similarity_score
      FROM shared_source.magento_product_dimension_with_text_embedding
      WHERE {where_sql} AND vector IS NOT NULL
      ORDER BY similarity_score DESC
      LIMIT 50
      )
      SELECT
      internal_ref_code,
      ANY_VALUE(product_name) as product_name,
      ANY_VALUE(sale_price) as sale_price,
      ANY_VALUE(original_price) as original_price,
      GROUP_CONCAT(DISTINCT master_color ORDER BY master_color SEPARATOR ', ') as available_colors,
      ANY_VALUE(product_image_url) as product_image_url,
      ANY_VALUE(product_image_url_thumbnail) as product_image_url_thumbnail,
      ANY_VALUE(product_web_url) as product_web_url,
      ANY_VALUE(description_text) as description_text,
      ANY_VALUE(material) as material,
      ANY_VALUE(material_group) as material_group,
      ANY_VALUE(gender_by_product) as gender_by_product,
      ANY_VALUE(age_by_product) as age_by_product,
      ANY_VALUE(season) as season,
      ANY_VALUE(style) as style,
      ANY_VALUE(fitting) as fitting,
      ANY_VALUE(form_neckline) as form_neckline,
      ANY_VALUE(form_sleeve) as form_sleeve,
      ANY_VALUE(product_line_vn) as product_line_vn,
      MAX(similarity_score) as max_score
      FROM top_sku_candidates
      GROUP BY internal_ref_code
      ORDER BY max_score DESC
      LIMIT 5
      """ # noqa: S608
      else:
      # FALLBACK: Keyword search - MAXIMALLY OPTIMIZED (No CTE overhead)
      keywords = getattr(params, "keywords", None)
      keyword_filter = ""
      if keywords:
      k = _escape(keywords).lower()
      keyword_filter = f" AND LOWER(product_name) LIKE '%{k}%'"
      # Direct query - No CTE needed, StarRocks optimizes GROUP BY internally
      sql = f"""
      SELECT
      internal_ref_code,
      ANY_VALUE(product_name) as product_name,
      ANY_VALUE(sale_price) as sale_price,
      ANY_VALUE(original_price) as original_price,
      GROUP_CONCAT(DISTINCT master_color ORDER BY master_color SEPARATOR ', ') as available_colors,
      ANY_VALUE(product_image_url) as product_image_url,
      ANY_VALUE(product_image_url_thumbnail) as product_image_url_thumbnail,
      ANY_VALUE(product_web_url) as product_web_url,
      ANY_VALUE(description_text) as description_text,
      ANY_VALUE(material) as material,
      ANY_VALUE(material_group) as material_group,
      ANY_VALUE(gender_by_product) as gender_by_product,
      ANY_VALUE(age_by_product) as age_by_product,
      ANY_VALUE(season) as season,
      ANY_VALUE(style) as style,
      ANY_VALUE(fitting) as fitting,
      ANY_VALUE(form_neckline) as form_neckline,
      ANY_VALUE(form_sleeve) as form_sleeve,
      ANY_VALUE(product_line_vn) as product_line_vn
      FROM shared_source.magento_product_dimension_with_text_embedding
      WHERE {where_sql} {keyword_filter}
      GROUP BY internal_ref_code
      HAVING COUNT(*) > 0
      ORDER BY sale_price ASC
      LIMIT 5
      """ # noqa: S608
      logger.info(f"📊 Query Mode: {'Vector' if query_vector else 'Keyword'}")
      return sql
      backend/api/chatbot_route.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Fashion Q&A Agent Router
      FastAPI endpoints cho Fashion Q&A Agent service.
      Router chỉ chứa định nghĩa API, logic nằm ở controller.
      """
      import json
      import logging
      from collections.abc import AsyncGenerator
      from fastapi import APIRouter, HTTPException, Request
      from fastapi.responses import StreamingResponse
      from agent.controller import chat_controller
      from agent.models import QueryRequest
      from config import DEFAULT_MODEL
      logger = logging.getLogger(__name__)
      router = APIRouter()
      @router.post("/chat", summary="Fashion Q&A Chat (Non-streaming)")
      async def fashion_qa_chat(req: QueryRequest, request: Request):
      """
      Endpoint chat không stream - trả về response JSON đầy đủ một lần.
      """
      # Trích xuất user_id từ request (auth middleware)
      user_id = getattr(request.state, "user_id", None) or req.user_id or "default_user"
      logger.info(f"📥 [Incoming Query - NonStream] User: {user_id} | Query: {req.query}")
      try:
      # Gọi controller để xử lý logic và nhận generator stream
      # Note: Vì chat_controller có decorator @observe(), cần await để unwrap
      generator: AsyncGenerator[str, None] = chat_controller(
      query=req.query,
      user_id=user_id,
      model_name=DEFAULT_MODEL,
      conversation_id=req.conversation_id,
      images=req.images,
      )
      # Collect toàn bộ events từ generator
      final_response = None
      async for chunk in generator:
      # Parse SSE data format
      if chunk.startswith("data: "):
      data_str = chunk[6:].strip()
      if data_str != "[DONE]":
      final_response = json.loads(data_str)
      # Trả về response cuối cùng
      if final_response and "messages" in final_response:
      last_message = final_response["messages"][-1]
      response_text = last_message.get("content", "") if isinstance(last_message, dict) else str(last_message)
      logger.info(f"📤 [Outgoing Response - NonStream] User: {user_id} | Response: {response_text}")
      return {
      "status": "success",
      "response": response_text,
      "conversation_id": req.conversation_id,
      }
      return {"status": "error", "message": "No response generated"}
      except Exception as e:
      logger.error(f"Error in fashion_qa_chat: {e}", exc_info=True)
      raise HTTPException(status_code=500, detail=str(e)) from e
      # ====================== FASHION Q&A CHAT API ======================
      @router.post("/stream/chat", summary="Fashion Q&A Chat with Streaming Response")
      async def fashion_qa_chat_stream(req: QueryRequest, request: Request):
      """
      Endpoint duy nhất cho việc chat với Fashion Agent.
      """
      # Trích xuất user_id từ request (auth middleware)
      user_id = getattr(request.state, "user_id", None) or req.user_id or "default_user"
      logger.info(f"📥 [Incoming Query] User: {user_id} | Query: {req.query}")
      try:
      # Gọi controller để xử lý logic và nhận generator stream
      # Note: Vì chat_controller có decorator @observe(), cần await để unwrap
      generator: AsyncGenerator[str, None] = chat_controller(
      query=req.query,
      user_id=user_id,
      model_name=DEFAULT_MODEL,
      conversation_id=req.conversation_id,
      images=req.images,
      )
      async def logging_generator(gen: AsyncGenerator[str, None]):
      full_response_log = ""
      first_chunk = True
      try:
      async for chunk in gen:
      if first_chunk:
      logger.info("🚀 [Stream Started] First chunk received")
      first_chunk = False
      full_response_log += chunk
      yield chunk
      except Exception as e:
      logger.error(f"❌ [Stream Error] {e}")
      yield f"data: {json.dumps({'error': str(e)})}\n\n"
      logger.info(f"📤 [Outgoing Response Stream Finished] Total Chunks Length: {len(full_response_log)}")
      return StreamingResponse(
      logging_generator(generator),
      media_type="text/event-stream",
      headers={
      "Cache-Control": "no-cache",
      "Connection": "keep-alive",
      "X-Accel-Buffering": "no",
      },
      )
      except Exception as e:
      logger.error(f"Error in fashion_qa_chat: {e}", exc_info=True)
      raise HTTPException(status_code=500, detail=str(e)) from e
      backend/api/conservation_route.py deleted 100644 → 0
      View file @ c9a437ef
      from fastapi import APIRouter, HTTPException
      from typing import List, Dict, Any, Optional
      from pydantic import BaseModel
      import logging
      from common.conversation_manager import get_conversation_manager
      router = APIRouter(tags=["Conservation"])
      logger = logging.getLogger(__name__)
      class ChatMessage(BaseModel):
      id: int
      user_id: str | None = None # Optional usually not needed in list but good for consistency
      message: str
      is_human: bool
      timestamp: Any
      class ChatHistoryResponse(BaseModel):
      data: List[Dict[str, Any]]
      next_cursor: Optional[int] = None
      @router.get("/history/{user_id}", summary="Get Chat History by User ID", response_model=ChatHistoryResponse)
      async def get_chat_history(user_id: str, limit: Optional[int] = 20, before_id: Optional[int] = None):
      """
      Lấy lịch sử chat của user từ Postgres database.
      Trả về object chứa `data` (list messages) và `next_cursor` để dùng cho trang tiếp theo.
      """
      try:
      # Sử dụng ConversationManager Singleton
      manager = get_conversation_manager()
      # Lấy history từ DB với pagination
      history = manager.get_chat_history(user_id, limit=limit, before_id=before_id)
      next_cursor = None
      if history and len(history) > 0:
      # Cursor cho trang tiếp theo chính là ID của tin nhắn cuối cùng (cũ nhất trong batch này)
      next_cursor = history[-1]['id']
      return {
      "data": history,
      "next_cursor": next_cursor
      }
      except Exception as e:
      logger.error(f"Error fetching chat history for user {user_id}: {e}")
      raise HTTPException(status_code=500, detail=str(e))
      backend/api/recommend_image.py 0 → 100644
      View file @ 446d8f46
      import os
      import sys
      import io
      import time
      import numpy as np
      from fastapi import APIRouter, UploadFile, File, Form, HTTPException
      from fastapi.responses import JSONResponse
      from PIL import Image
      from loguru import logger
      # Đường dẫn đến module image_process
      BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
      IMAGE_PROCESS_DIR = os.path.join(BASE_DIR, "image_process")
      if IMAGE_PROCESS_DIR not in sys.path:
      sys.path.append(IMAGE_PROCESS_DIR)
      from core.encoder import ImageEncoder
      from common.starrocks_connection import StarRocksConnection
      router = APIRouter(prefix="/api/recommend", tags=["Recommendation StarRocks"])
      # Singleton
      _encoder = None
      _db = StarRocksConnection()
      def get_encoder():
      global _encoder
      if _encoder is None:
      _encoder = ImageEncoder()
      return _encoder
      @router.post("/image")
      async def recommend_by_image(
      file: UploadFile = File(...),
      ):
      """
      API tìm kiếm sản phẩm bằng ảnh - Hardcoded StarRocks SQL logic
      """
      start_time = time.time()
      encoder = get_encoder()
      try:
      # 1. Xử lý ảnh và lấy Vector
      contents = await file.read()
      img = Image.open(io.BytesIO(contents)).convert("RGB")
      # Trích xuất vector 768-dim
      vector = encoder.encode_image(img)
      # Chuyển vector sang định dạng string [v1, v2, ...] để gắn vào SQL
      v_str = "[" + ",".join(map(str, vector.tolist())) + "]"
      # 2. Hardcoded SQL cho StarRocks (CTE + Grouping)
      # Bảng đích: magento_product_dimension_with_image_embedding
      target_table = "shared_source.magento_product_dimension_with_image_embedding"
      sql = f"""
      WITH top_sku_candidates AS (
      SELECT
      internal_ref_code,
      product_name,
      sale_price,
      original_price,
      master_color,
      product_image_url,
      product_image_url_thumbnail,
      product_web_url,
      description_text,
      material,
      material_group,
      gender_by_product,
      age_by_product,
      season,
      style,
      fitting,
      form_neckline,
      form_sleeve,
      product_line_vn,
      product_color_name,
      cosine_similarity(vector, {v_str}) as similarity_score
      FROM {target_table}
      WHERE vector IS NOT NULL
      ORDER BY similarity_score DESC
      LIMIT 50
      )
      SELECT
      internal_ref_code,
      MAX_BY(product_name, similarity_score) as product_name,
      MAX_BY(sale_price, similarity_score) as sale_price,
      MAX_BY(original_price, similarity_score) as original_price,
      GROUP_CONCAT(DISTINCT master_color ORDER BY master_color SEPARATOR ', ') as available_colors,
      MAX_BY(product_image_url, similarity_score) as product_image_url,
      MAX_BY(product_image_url_thumbnail, similarity_score) as product_image_url_thumbnail,
      MAX_BY(product_web_url, similarity_score) as product_web_url,
      MAX_BY(description_text, similarity_score) as description_text,
      MAX_BY(material, similarity_score) as material,
      MAX_BY(material_group, similarity_score) as material_group,
      MAX_BY(gender_by_product, similarity_score) as gender_by_product,
      MAX_BY(age_by_product, similarity_score) as age_by_product,
      MAX_BY(season, similarity_score) as season,
      MAX_BY(style, similarity_score) as style,
      MAX_BY(fitting, similarity_score) as fitting,
      MAX_BY(form_neckline, similarity_score) as form_neckline,
      MAX_BY(form_sleeve, similarity_score) as form_sleeve,
      MAX_BY(product_line_vn, similarity_score) as product_line_vn,
      MAX(similarity_score) as similarity_score
      FROM top_sku_candidates
      GROUP BY internal_ref_code
      ORDER BY similarity_score DESC
      LIMIT 10
      """
      # 3. Thực thi query
      results = await _db.execute_query_async(sql)
      process_time = time.time() - start_time
      logger.info(f"Visual search done in {process_time:.3f}s. Found {len(results)} groups.")
      return {
      "status": "success",
      "process_time": f"{process_time:.3f}s",
      "results": results
      }
      except Exception as e:
      logger.error(f"Recommend Error: {e}")
      return JSONResponse(
      status_code=500,
      content={"status": "error", "message": str(e)}
      )
      backend/api/recommend_text.py 0 → 100644
      View file @ 446d8f46
      """
      RECOMMEND TEXT API
      ═══════════════════
      API endpoint cho Text-based Product Recommendation
      Sử dụng Hybrid Search: Semantic (Vector) Search → Keyword Filtering
      """
      import logging
      from fastapi import APIRouter
      from pydantic import BaseModel, Field
      from typing import Optional
      from search.hybrid_search import hybrid_search
      logger = logging.getLogger(__name__)
      router = APIRouter(prefix="/api/recommend", tags=["Recommendation - Text"])
      class SearchParams(BaseModel):
      """Request body cho search API"""
      query: str = Field(..., description="Search query (VD: 'áo mùa đông cho bé gái')")
      limit: int = Field(default=20, ge=1, le=100, description="Số kết quả trả về (1-100)")
      price_min: Optional[float] = Field(default=None, ge=0, description="Giá tối thiểu")
      price_max: Optional[float] = Field(default=None, ge=0, description="Giá tối đa")
      @router.post("/text")
      async def recommend_by_text(params: SearchParams):
      """
      🔍 HYBRID SEARCH: Semantic Search → Keyword Filtering
      Flow:
      1. Thêm prefix "description_text: " vào query
      2. Embed prefixed query → Vector
      3. Vector search (HNSW) → TOP 200 candidates
      4. Parse keywords → Filter (season, gender, age, product_type, color...)
      5. Return TOP N results
      Example queries:
      - "áo mùa đông cho bé gái"
      - "quần jean nam"
      - "váy đỏ người lớn"
      - "áo khoác phao trẻ em"
      """
      logger.info(f"Hybrid search: query='{params.query}', limit={params.limit}")
      result = await hybrid_search(
      query=params.query,
      limit=params.limit,
      price_min=params.price_min,
      price_max=params.price_max,
      )
      return result
      backend/check_db.py 0 → 100644
      View file @ 446d8f46
      import pymysql
      conn = pymysql.connect(
      host='172.16.2.100',
      port=9030,
      user='anhvh',
      password='v0WYGeyLRCckXotT',
      database='shared_source',
      cursorclass=pymysql.cursors.DictCursor
      )
      try:
      with conn.cursor() as cursor:
      # Check product có gender=male nhưng tên có "nữ"
      print("=== Products với gender='male' và product_line LIKE '%Áo%' ===\n")
      cursor.execute("""
      SELECT product_name, gender_by_product, product_line_vn
      FROM magento_product_dimension_with_text_embedding
      WHERE gender_by_product = 'male' AND product_line_vn LIKE '%Áo%'
      LIMIT 10
      """)
      for row in cursor.fetchall():
      print(f" {row['product_name']}")
      print(f" → gender: {row['gender_by_product']}, line: {row['product_line_vn']}\n")
      print("\n=== Check products có tên 'Áo body nữ' ===\n")
      cursor.execute("""
      SELECT product_name, gender_by_product, product_line_vn
      FROM magento_product_dimension_with_text_embedding
      WHERE product_name LIKE '%Áo body nữ%'
      LIMIT 5
      """)
      for row in cursor.fetchall():
      print(f" {row['product_name']}")
      print(f" → gender: {row['gender_by_product']}, line: {row['product_line_vn']}\n")
      finally:
      conn.close()
      backend/datadb/text.txt 0 → 100644
      View file @ 446d8f46
      product_name: Pack 3 đôi tất bé gái cổ thấp. master_color: Xanh da trời/ Blue. product_image_url: https://2885371169.e.cdneverest.net/pub/media/catalog/product/1/a/1ax24a002-pb449-7-9-ghep-u.jpg. product_image_url_thumbnail: https://2885371169.e.cdneverest.net/pub/media/catalog/product/1/a/1ax24a002-pb449-7-9-ghep-u.jpg. product_web_url: https://canifa.com/tat-be-gai-1ax24a002?color=PB449&utm_source=chatbot&utm_medium=rsa&utm_campaign=testver25.9. description_text: Pack 3 đôi tất cổ thấp, họa tiết basic.
      Chất liệu cotton pha mềm mại, co giãn tốt, màu sắc đơn giản dễ phối mix đồ.. material: None. material_group: Yarn - Sợi. gender_by_product: female. age_by_product: others. season: Year. style: Feminine. fitting: Slim. size_scale: 4/6. form_neckline: None. form_sleeve: None. product_line_vn: Tất. product_color_name: Blue Strip 449
      \ No newline at end of file
      backend/docker-compose.yml
      View file @ 446d8f46
      version: "3.8"
      services:
      # --- Backend Service ---
      backend:
      build: .
      container_name: canifa_backend
      env_file: .env
      ports:
      - "5000:5000"
      - "5003:5003"
      volumes:
      - .:/app # Mount code để hot-reload khi dev (tuỳ chọn)
      - .:/app
      environment:
      - CHECKPOINT_POSTGRES_URL=postgresql://postgres:password@postgres_db:5432/canifa_chat
      # Các biến môi trường khác bro có thể thêm vào đây hoặc dùng file .env
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - LANGFUSE_PUBLIC_KEY=${LANGFUSE_PUBLIC_KEY}
      - LANGFUSE_SECRET_KEY=${LANGFUSE_SECRET_KEY}
      - LANGFUSE_HOST=${LANGFUSE_HOST}
      - STARROCKS_HOST=${STARROCKS_HOST}
      - STARROCKS_port=${STARROCKS_port}
      - STARROCKS_USER=${STARROCKS_USER}
      - STARROCKS_PASSWORD=${STARROCKS_PASSWORD}
      - STARROCKS_DB=${STARROCKS_DB}
      depends_on:
      - postgres_db
      - PORT=5003
      restart: unless-stopped
      # --- Database Service (Postgres) ---
      postgres_db:
      image: postgres:15-alpine
      container_name: canifa_postgres
      environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: password
      POSTGRES_DB: canifa_chat
      ports:
      - "5433:5432" # Map ra host port 5433 để tránh đụng độ nếu máy bro đang chạy Postgres khác
      volumes:
      - postgres_data:/var/lib/postgresql/data
      restart: unless-stopped
      volumes:
      postgres_data:
      deploy:
      resources:
      limits:
      memory: 8g
      logging:
      driver: "json-file"
      options:
      tag: "{{.Name}}"
      backend/entrypoint.sh 0 → 100644
      View file @ 446d8f46
      #!/bin/bash
      NUM_CORES=$(nproc)
      WORKERS=$((2 * NUM_CORES + 1))
      echo "🔧 [STARTUP] CPU cores: $NUM_CORES"
      echo "🔧 [STARTUP] Gunicorn workers: $WORKERS"
      exec gunicorn \
      server:app \
      --workers "$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
      \ No newline at end of file
      backend/locust/api_server_perf.py deleted 100644 → 0
      View file @ c9a437ef
      """
      Performance Test API Server
      Server giả lập để test tải với Locust - KHÔNG gọi OpenAI, KHÔNG gọi Postgres.
      """
      import logging
      import sys
      import time
      from pathlib import Path
      import uvicorn
      from fastapi import FastAPI, HTTPException
      from pydantic import BaseModel
      # Setup path to import backend modules
      sys.path.insert(0, str(Path(__file__).parent.parent))
      # Setup Logging
      logging.basicConfig(level=logging.INFO)
      logger = logging.getLogger(__name__)
      # ============================================================
      # 1. MOCK LLM - Trả lời siêu tốc, không gọi OpenAI
      # ============================================================
      from typing import Any
      from langchain_core.language_models import BaseChatModel
      from langchain_core.messages import AIMessage
      from langchain_core.outputs import ChatGeneration, ChatResult
      class MockHighSpeedLLM(BaseChatModel):
      """
      Mock LLM siêu tốc cho Performance Testing.
      Không gọi OpenAI - trả về response ngay lập tức.
      """
      def _generate(
      self, messages: list[Any], stop: list[str] | None = None, run_manager: Any | None = None, **kwargs: Any
      ) -> ChatResult:
      return ChatResult(
      generations=[
      ChatGeneration(message=AIMessage(content="[MOCK] Xin chào! Đây là bot test, không phải OpenAI thật."))
      ]
      )
      @property
      def _llm_type(self) -> str:
      return "mock-high-speed"
      def bind_tools(self, tools: Any, **kwargs: Any) -> Any:
      """Bypass tool binding - trả về self."""
      return self
      # ============================================================
      # 2. PATCH create_llm TRƯỚC KHI IMPORT GRAPH
      # ============================================================
      def mock_create_llm(*args, **kwargs):
      """Factory function giả - trả về MockLLM."""
      logger.info("🎭 MockLLM được gọi thay vì OpenAI!")
      return MockHighSpeedLLM()
      # Patch TRƯỚC khi import agent.graph
      import common.llm_factory
      common.llm_factory.create_llm = mock_create_llm
      logger.info("🎭 PATCHED common.llm_factory.create_llm")
      # ============================================================
      # 3. PATCH Checkpointer - Dùng MemorySaver thay Postgres
      # ============================================================
      from langgraph.checkpoint.memory import MemorySaver
      # Tạo 1 instance MemorySaver dùng chung
      _shared_memory_saver = MemorySaver()
      def mock_get_checkpointer():
      """Trả về MemorySaver thay vì Postgres."""
      logger.info("🧠 Using MemorySaver (RAM) instead of Postgres")
      return _shared_memory_saver
      import agent.checkpointer
      # Patch function
      agent.checkpointer.get_checkpointer = mock_get_checkpointer
      # QUAN TRỌNG: Reset singleton để nó gọi lại mock function
      agent.checkpointer._checkpointer_manager._checkpointer = _shared_memory_saver
      logger.info("🔧 PATCHED agent.checkpointer (Reset singleton)")
      # ============================================================
      # 4. PATCH Langfuse - Tắt để tránh Rate Limit
      # ============================================================
      def mock_get_callback_handler():
      """Trả về None - không gửi trace."""
      return
      import common.langfuse_client
      common.langfuse_client.get_callback_handler = mock_get_callback_handler
      logger.info("🔇 PATCHED common.langfuse_client.get_callback_handler")
      # ============================================================
      # 5. GIỜ MỚI IMPORT GRAPH (Sau khi patch xong)
      # ============================================================
      from agent.config import get_config
      from langchain_core.messages import HumanMessage
      from agent.graph import build_graph
      from agent.tools.product_search_helpers import build_starrocks_query
      # ============================================================
      # 6. IMPORT CHO DB TEST
      # ============================================================
      from common.starrocks_connection import StarRocksConnection
      # ============================================================
      # FASTAPI APP
      # ============================================================
      app = FastAPI(title="Performance Test API")
      # Request Models
      class SearchRequest(BaseModel):
      query: str
      limit: int = 10
      class MockParams(BaseModel):
      query_text: str = ""
      limit: int = 10
      sku: str = None
      gender: str = None
      season: str = None
      color: str = None
      product_line: str = None
      price_min: float = None
      price_max: float = None
      # Global variables
      mock_graph = None
      @app.on_event("startup")
      async def startup_event():
      global mock_graph
      logger.info("🚀 Performance Test API Server Starting...")
      # 1. Pre-warm DB connection
      conn = StarRocksConnection()
      conn.connect()
      logger.info("✅ StarRocks Connection initialized")
      # 2. Build Mock Graph (LLM đã bị patch từ đầu file)
      config = get_config()
      mock_graph = build_graph(config)
      logger.info("✅ Mock Graph built successfully (No OpenAI, No Postgres, No Langfuse)")
      # ============================================================
      # ENDPOINTS
      # ============================================================
      @app.post("/test/db-search")
      async def test_db_search(request: SearchRequest):
      """
      Test StarRocks DB Search.
      KHÔNG GỌI OPENAI - Chỉ test DB.
      """
      start_time = time.time()
      try:
      params = MockParams(query_text=request.query, limit=request.limit)
      sql = build_starrocks_query(params)
      db = StarRocksConnection()
      products = await db.execute_query_async(sql)
      # Filter fields
      limited_products = products[:5]
      ALLOWED_FIELDS = {
      "product_name",
      "sale_price",
      "original_price",
      "product_image_url_thumbnail",
      "product_web_url",
      "master_color",
      "product_color_name",
      "material",
      "internal_ref_code",
      }
      clean_products = [{k: v for k, v in p.items() if k in ALLOWED_FIELDS} for p in limited_products]
      process_time = time.time() - start_time
      return {
      "status": "success",
      "count": len(clean_products),
      "process_time_seconds": round(process_time, 4),
      "products": clean_products,
      }
      except Exception as e:
      logger.error(f"DB Search Error: {e}")
      raise HTTPException(status_code=500, detail=str(e))
      @app.post("/test/db-ping")
      async def test_db_ping():
      """
      Test kết nối DB thuần túy (SELECT 1).
      """
      start_time = time.time()
      try:
      db = StarRocksConnection()
      await db.execute_query_async("SELECT 1")
      process_time = time.time() - start_time
      return {"status": "success", "process_time_seconds": round(process_time, 4)}
      except Exception as e:
      raise HTTPException(status_code=500, detail=str(e))
      @app.post("/test/graph-mock-chat")
      async def test_graph_mock_chat(request: SearchRequest):
      """
      Test toàn bộ Flow Graph với MockLLM.
      KHÔNG GỌI OPENAI - Test logic code Python + LangGraph.
      """
      if not mock_graph:
      raise HTTPException(500, "Mock Graph not initialized")
      start_time = time.time()
      try:
      # Tạo thread_id unique cho mỗi request
      thread_id = f"perf_test_{int(time.time() * 1000)}"
      input_state = {
      "messages": [HumanMessage(content=request.query)],
      "user_id": "perf_user",
      }
      config_runnable = {"configurable": {"thread_id": thread_id}}
      # Chạy Graph
      async for _event in mock_graph.astream(input_state, config=config_runnable):
      pass # Chỉ cần chạy hết flow
      process_time = time.time() - start_time
      return {
      "status": "success",
      "mode": "mock_llm",
      "process_time_seconds": round(process_time, 4),
      "message": "Graph Flow executed successfully",
      }
      except Exception as e:
      logger.error(f"Graph Error: {e}")
      raise HTTPException(status_code=500, detail=str(e))
      @app.get("/")
      async def root():
      return {"message": "Performance Test API is running!", "mode": "MOCK (No OpenAI)"}
      # ============================================================
      # MAIN
      # ============================================================
      if __name__ == "__main__":
      print("=" * 60)
      print("🚀 PERFORMANCE TEST SERVER")
      print("=" * 60)
      print("🎭 LLM: MockHighSpeedLLM (No OpenAI)")
      print("🧠 Checkpointer: MemorySaver (No Postgres)")
      print("🔇 Langfuse: Disabled")
      print("=" * 60)
      uvicorn.run(app, host="0.0.0.0", port=8000)
      backend/locust/locustfile.py deleted 100644 → 0
      View file @ c9a437ef
      import random
      from locust import HttpUser, between, task
      # Danh sách từ khóa để random
      PRODUCTS = [
      "áo phông",
      "áo khoác",
      "áo len",
      "áo polo",
      "áo sơ mi",
      "quần jean",
      "quần âu",
      "quần short",
      "váy liền",
      "chân váy",
      ]
      COLORS = ["", "màu đen", "màu trắng", "màu đỏ", "màu xanh", "màu vàng", "màu be"]
      GENDERS = ["", "nam", "nữ", "bé trai", "bé gái"]
      class FashionUser(HttpUser):
      # Thời gian nghỉ giữa các lần request (mô phỏng người thật: 1-3s)
      wait_time = between(1, 3)
      # --- DB TESTS (ĐÃ TEST XONG, TẠM TẮT) ---
      # @task(3)
      # def search_random_product(self):
      # """
      # Giả lập user tìm kiếm sản phẩm bất kỳ.
      # Ghép random: [Sản phẩm] + [Giới tính] + [Màu]
      # Ví dụ: "áo phông nam màu đen"
      # """
      # # Random query builder
      # p = random.choice(PRODUCTS)
      # g = random.choice(GENDERS)
      # c = random.choice(COLORS)
      # query = f"{p} {g} {c}".strip()
      # # Loại bỏ khoảng trắng thừa
      # query = " ".join(query.split())
      # self.client.post("/test/db-search", json={"query": query, "limit": 10}, name="/test/db-search (Dynamic)")
      # @task(1)
      # def search_hot_items(self):
      # """
      # Giả lập các món Hot trend nhiều người tìm giống nhau (Cache test)
      # """
      # hot_item = "áo giữ nhiệt"
      # self.client.post("/test/db-search", json={"query": hot_item, "limit": 10}, name="/test/db-search (Hero Item)")
      # @task(3)
      # def fast_ping_check(self):
      # """
      # Kiểm tra tốc độ thuần túy của việc lấy Connection + SELECT 1.
      # Nếu cái này < 100ms mà Search > 3000ms --> Do câu Query nặng.
      # Nếu cái này cũng > 3000ms --> Do Pool bị nghẽn (Server quá tải).
      # """
      # self.client.post("/test/db-ping", json={}, name="/test/db-ping (Isolate Latency)")
      # --- LOGIC GRAPH TEST (BẬT) ---
      @task(1)
      def check_graph_overhead(self):
      """
      Test Logic Flow (Mock LLM + MemorySaver).
      ⚠️ Chạy với 300-500 users thôi, không nên quá 1000.
      """
      query = random.choice(["hello", "tìm áo", "giá bao nhiêu", "có màu gì"])
      self.client.post("/test/graph-mock-chat", json={"query": query}, name="/test/graph Logic (Mock LLM)")
      backend/locust/run.txt deleted 100644 → 0
      View file @ c9a437ef
      locust -f locustfile.py --host=http://localhost:8000
      \ No newline at end of file
      backend/run.txt
      View file @ 446d8f46
      .\.venv\Scripts\activate
      \ No newline at end of file
      .\.venv\Scripts\
      .\.venv\Scripts\activate
      uvicorn server:app --host 0.0.0.0 --port 8000 --reload
      uvicorn server:app --host 0.0.0.0 --port 5000
      docker restart chatbot-backend
      docker restart chatbot-backend && docker logs -f chatbot-backend
      docker logs -f chatbot-backend
      docker restart chatbot-backend
      \ No newline at end of file
      backend/scripts/check_gender_data.py 0 → 100644
      View file @ 446d8f46
      """
      Script: Check products với data gender bị sai
      Tìm các sản phẩm có tên chứa "nữ" nhưng gender_by_product = 'male' (và ngược lại)
      """
      import pymysql
      conn = pymysql.connect(
      host='172.16.2.100',
      port=9030,
      user='anhvh',
      password='v0WYGeyLRCckXotT',
      database='shared_source',
      cursorclass=pymysql.cursors.DictCursor
      )
      try:
      with conn.cursor() as cursor:
      # Products tên có "nữ" nhưng gender = 'male'
      print("=" * 80)
      print("PRODUCTS TÊN CÓ 'NỮ' NHƯNG gender = 'male' (DATA SAI!)")
      print("=" * 80)
      cursor.execute("""
      SELECT
      magento_ref_code,
      internal_ref_code,
      product_name,
      gender_by_product,
      product_line_vn
      FROM magento_product_dimension_with_text_embedding
      WHERE gender_by_product = 'male'
      AND (product_name LIKE '%nữ%' OR product_name LIKE '%Nữ%')
      LIMIT 50
      """)
      rows = cursor.fetchall()
      print(f"\nTìm thấy {len(rows)} sản phẩm:\n")
      for row in rows:
      print(f"magento_ref_code: {row['magento_ref_code']}")
      print(f" internal_ref_code: {row['internal_ref_code']}")
      print(f" product_name: {row['product_name']}")
      print(f" gender: {row['gender_by_product']} (SAI! Phải là female)")
      print(f" product_line: {row['product_line_vn']}")
      print()
      # Products tên có "nam/trai" nhưng gender = 'female'
      print("\n" + "=" * 80)
      print("PRODUCTS TÊN CÓ 'NAM/TRAI' NHƯNG gender = 'female' (DATA SAI!)")
      print("=" * 80)
      cursor.execute("""
      SELECT
      magento_ref_code,
      internal_ref_code,
      product_name,
      gender_by_product,
      product_line_vn
      FROM magento_product_dimension_with_text_embedding
      WHERE gender_by_product = 'female'
      AND (product_name LIKE '%nam%' OR product_name LIKE '%trai%'
      OR product_name LIKE '%Nam%' OR product_name LIKE '%Trai%')
      LIMIT 50
      """)
      rows = cursor.fetchall()
      print(f"\nTìm thấy {len(rows)} sản phẩm:\n")
      for row in rows:
      print(f"magento_ref_code: {row['magento_ref_code']}")
      print(f" internal_ref_code: {row['internal_ref_code']}")
      print(f" product_name: {row['product_name']}")
      print(f" gender: {row['gender_by_product']} (có thể sai)")
      print(f" product_line: {row['product_line_vn']}")
      print()
      finally:
      conn.close()
      backend/search/__init__.py 0 → 100644
      View file @ 446d8f46
      # Search module for Hybrid Search
      # Combines Semantic (Vector) Search + Keyword Filtering
      from .hybrid_search import hybrid_search
      from .query_parser import parse_query, build_where_clause
      from .keyword_mappings import KEYWORD_MAPPINGS
      __all__ = [
      "hybrid_search",
      "parse_query",
      "build_where_clause",
      "KEYWORD_MAPPINGS",
      ]
      backend/search/hybrid_search.py 0 → 100644
      View file @ 446d8f46
      """
      HYBRID SEARCH
      ═════════════
      Kết hợp Semantic (Vector) Search + Keyword Filtering
      Flow:
      1. Thêm prefix "description_text: " vào query (match format với DB)
      2. Embed prefixed query → Vector
      3. Vector search trong StarRocks (HNSW Index) → TOP N candidates
      4. Parse keywords từ query gốc → Build filters
      5. Filter candidates → Return kết quả cuối
      Usage:
      results = await hybrid_search("áo mùa đông cho bé gái", limit=20)
      """
      import logging
      import time
      from typing import Optional
      from common.embedding_service import create_embedding_async
      from common.starrocks_connection import StarRocksConnection
      from .query_parser import parse_query, build_where_clause, get_matched_keywords
      logger = logging.getLogger(__name__)
      # Prefix để match format với data đã embed trong DB
      QUERY_PREFIX = "description_text: "
      # Số lượng candidates lấy từ vector search (trước khi filter)
      DEFAULT_CANDIDATE_LIMIT = 200
      # StarRocks connection
      _db = StarRocksConnection()
      async def hybrid_search(
      query: str,
      limit: int = 20,
      candidate_limit: int = DEFAULT_CANDIDATE_LIMIT,
      price_min: Optional[float] = None,
      price_max: Optional[float] = None,
      ef_search: int = 128,
      ) -> dict:
      """
      Hybrid Search: Vector Search + Keyword Filter
      Args:
      query: User query string (VD: "áo mùa đông cho bé gái")
      limit: Số kết quả trả về (default: 20)
      candidate_limit: Số candidates từ vector search (default: 200)
      price_min: Giá tối thiểu (optional)
      price_max: Giá tối đa (optional)
      ef_search: HNSW ef_search parameter (default: 128)
      Returns:
      dict: {
      "status": "success",
      "query": original query,
      "prefixed_query": query with prefix,
      "matched_keywords": list of matched keywords,
      "filters_applied": dict of filters,
      "process_time": time in seconds,
      "results": list of products
      }
      """
      start_time = time.time()
      try:
      # ═══════════════════════════════════════════════════════════════════
      # STEP 1: Prefix query để match format với DB
      # ═══════════════════════════════════════════════════════════════════
      prefixed_query = QUERY_PREFIX + query.strip()
      logger.info(f"Prefixed query: '{prefixed_query}'")
      # ═══════════════════════════════════════════════════════════════════
      # STEP 2: Embed prefixed query
      # ═══════════════════════════════════════════════════════════════════
      embed_start = time.time()
      query_vector = await create_embedding_async(prefixed_query)
      if not query_vector:
      logger.error("Failed to create embedding for query")
      return {
      "status": "error",
      "message": "Failed to create embedding",
      "results": []
      }
      v_str = "[" + ",".join(map(str, query_vector)) + "]"
      logger.info(f"Embedding took {time.time() - embed_start:.3f}s")
      # ═══════════════════════════════════════════════════════════════════
      # STEP 3: Parse keywords từ query GỐC (không có prefix)
      # ═══════════════════════════════════════════════════════════════════
      filters = parse_query(query)
      where_clause = build_where_clause(filters)
      matched_keywords = get_matched_keywords(query)
      logger.info(f"Matched keywords: {matched_keywords}")
      logger.info(f"Filters: {filters}")
      # ═══════════════════════════════════════════════════════════════════
      # STEP 4: Add price filter nếu có
      # ═══════════════════════════════════════════════════════════════════
      price_conditions = []
      if price_min is not None and price_min > 0:
      price_conditions.append(f"sale_price >= {int(price_min)}")
      if price_max is not None and price_max > 0:
      price_conditions.append(f"sale_price <= {int(price_max)}")
      # Combine với keyword filters
      if price_conditions:
      if where_clause:
      where_clause += " AND " + " AND ".join(price_conditions)
      else:
      where_clause = "WHERE " + " AND ".join(price_conditions)
      # ═══════════════════════════════════════════════════════════════════
      # STEP 5: Build SQL Query
      # Vector search TRƯỚC (trong CTE) → Keyword filter SAU (outer query)
      # ═══════════════════════════════════════════════════════════════════
      sql = f"""
      WITH semantic_candidates AS (
      SELECT
      /*+ SET_VAR(ann_params='{{"ef_search":{ef_search}}}') */
      internal_ref_code,
      product_name,
      description_text,
      product_image_url,
      product_web_url,
      sale_price,
      original_price,
      discount_amount,
      master_color,
      season,
      gender_by_product,
      age_by_product,
      product_line_vn,
      style,
      approx_cosine_similarity(vector, {v_str}) as similarity_score
      FROM shared_source.magento_product_dimension_with_text_embedding
      ORDER BY similarity_score DESC
      LIMIT {candidate_limit}
      )
      SELECT
      internal_ref_code,
      MAX_BY(product_name, similarity_score) as product_name,
      MAX_BY(description_text, similarity_score) as description_text,
      MAX_BY(product_image_url, similarity_score) as product_image_url,
      MAX_BY(product_web_url, similarity_score) as product_web_url,
      MAX_BY(sale_price, similarity_score) as sale_price,
      MAX_BY(original_price, similarity_score) as original_price,
      MAX_BY(discount_amount, similarity_score) as discount_amount,
      GROUP_CONCAT(DISTINCT master_color ORDER BY master_color SEPARATOR ', ') as available_colors,
      MAX_BY(season, similarity_score) as season,
      MAX_BY(gender_by_product, similarity_score) as gender_by_product,
      MAX_BY(age_by_product, similarity_score) as age_by_product,
      MAX_BY(product_line_vn, similarity_score) as product_line_vn,
      MAX_BY(style, similarity_score) as style,
      MAX(similarity_score) as similarity_score
      FROM semantic_candidates
      {where_clause}
      GROUP BY internal_ref_code
      ORDER BY similarity_score DESC
      LIMIT {limit}
      """
      logger.info(f"SQL Query:\n{sql}")
      # ═══════════════════════════════════════════════════════════════════
      # STEP 6: Execute query
      # ═══════════════════════════════════════════════════════════════════
      db_start = time.time()
      results = await _db.execute_query_async(sql)
      logger.info(f"DB query took {time.time() - db_start:.3f}s, returned {len(results)} results")
      process_time = time.time() - start_time
      return {
      "status": "success",
      "query": query,
      "prefixed_query": prefixed_query,
      "matched_keywords": matched_keywords,
      "filters_applied": filters,
      "candidate_limit": candidate_limit,
      "result_count": len(results),
      "process_time": f"{process_time:.3f}s",
      "results": results
      }
      except Exception as e:
      logger.error(f"Hybrid search error: {e}", exc_info=True)
      return {
      "status": "error",
      "message": str(e),
      "results": []
      }
      async def semantic_only_search(
      query: str,
      limit: int = 20,
      ef_search: int = 128,
      ) -> dict:
      """
      Semantic search only (không có keyword filter)
      Dùng khi muốn test pure vector search
      """
      start_time = time.time()
      prefixed_query = QUERY_PREFIX + query.strip()
      query_vector = await create_embedding_async(prefixed_query)
      if not query_vector:
      return {"status": "error", "message": "Failed to create embedding", "results": []}
      v_str = "[" + ",".join(map(str, query_vector)) + "]"
      sql = f"""
      SELECT
      /*+ SET_VAR(ann_params='{{"ef_search":{ef_search}}}') */
      internal_ref_code,
      product_name,
      description_text,
      product_image_url,
      sale_price,
      master_color,
      season,
      gender_by_product,
      age_by_product,
      product_line_vn,
      approx_cosine_similarity(vector, {v_str}) as similarity_score
      FROM shared_source.magento_product_dimension_with_text_embedding
      ORDER BY similarity_score DESC
      LIMIT {limit}
      """
      results = await _db.execute_query_async(sql)
      return {
      "status": "success",
      "query": query,
      "prefixed_query": prefixed_query,
      "search_type": "semantic_only",
      "process_time": f"{time.time() - start_time:.3f}s",
      "results": results
      }
      backend/search/keyword_mappings.py 0 → 100644
      View file @ 446d8f46
      """
      KEYWORD MAPPINGS TABLE
      ══════════════════════
      Bảng mapping từ keywords tiếng Việt/Anh → DB field values
      Dựa trên data thực tế từ StarRocks
      Cấu trúc mỗi entry:
      "keyword": {
      "field": tên cột trong DB,
      "values": list giá trị có thể match,
      "op": "=" hoặc "IN" hoặc "LIKE"
      }
      """
      KEYWORD_MAPPINGS = {
      # ═══════════════════════════════════════════════════════════════════════
      # SEASON (Mùa) → field: season
      # DB values: 'Collection', 'Fall Winter', 'Spring Summer', 'Year'
      # ═══════════════════════════════════════════════════════════════════════
      "mùa đông": {"field": "season", "values": ["Fall Winter"], "op": "IN"},
      "thu đông": {"field": "season", "values": ["Fall Winter"], "op": "IN"},
      "mùa hè": {"field": "season", "values": ["Spring Summer"], "op": "IN"},
      "xuân hè": {"field": "season", "values": ["Spring Summer"], "op": "IN"},
      "hè": {"field": "season", "values": ["Spring Summer"], "op": "IN"},
      "đông": {"field": "season", "values": ["Fall Winter"], "op": "IN"},
      "winter": {"field": "season", "values": ["Fall Winter"], "op": "IN"},
      "fall winter": {"field": "season", "values": ["Fall Winter"], "op": "IN"},
      "summer": {"field": "season", "values": ["Spring Summer"], "op": "IN"},
      "spring summer": {"field": "season", "values": ["Spring Summer"], "op": "IN"},
      "cả năm": {"field": "season", "values": ["Year"], "op": "IN"},
      # ═══════════════════════════════════════════════════════════════════════
      # GENDER (Giới tính) → field: gender_by_product
      # DB values: 'male', 'female', 'others'
      # ═══════════════════════════════════════════════════════════════════════
      "bé gái": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "bé trai": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "con gái": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "con trai": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "nam giới": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "nữ giới": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "phụ nữ": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "đàn ông": {"field": "gender_by_product", "values": ["male"], "op": "="},
      # Bỏ "nam", "nữ" vì dễ nhầm với "việt nam", "nữ tính", etc.
      # Dùng: "áo nam", "quần nam", "áo nữ", "quần nữ" thay thế
      "áo nam": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "quần nam": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "đồ nam": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "áo nữ": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "quần nữ": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "đồ nữ": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "men": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "women": {"field": "gender_by_product", "values": ["female"], "op": "="},
      "boy": {"field": "gender_by_product", "values": ["male"], "op": "="},
      "girl": {"field": "gender_by_product", "values": ["female"], "op": "="},
      # ═══════════════════════════════════════════════════════════════════════
      # AGE (Độ tuổi) → field: age_by_product
      # DB values: 'adult', 'others'
      # ═══════════════════════════════════════════════════════════════════════
      "trẻ em": {"field": "age_by_product", "values": ["others"], "op": "="},
      "em bé": {"field": "age_by_product", "values": ["others"], "op": "="},
      "người lớn": {"field": "age_by_product", "values": ["adult"], "op": "="},
      "bé": {"field": "age_by_product", "values": ["others"], "op": "="},
      "kids": {"field": "age_by_product", "values": ["others"], "op": "="},
      "adult": {"field": "age_by_product", "values": ["adult"], "op": "="},
      # ═══════════════════════════════════════════════════════════════════════
      # PRODUCT TYPE (Loại sản phẩm) → field: product_line_vn
      # Dùng LIKE operator vì cần match partial
      # ═══════════════════════════════════════════════════════════════════════
      # Áo các loại
      "áo khoác lông vũ": {"field": "product_line_vn", "values": ["Áo khoác lông vũ"], "op": "LIKE"},
      "áo khoác gió": {"field": "product_line_vn", "values": ["Áo khoác gió"], "op": "LIKE"},
      "áo khoác chần bông": {"field": "product_line_vn", "values": ["Áo khoác chần bông"], "op": "LIKE"},
      "áo khoác chống nắng": {"field": "product_line_vn", "values": ["Áo khoác chống nắng"], "op": "LIKE"},
      "áo khoác nỉ": {"field": "product_line_vn", "values": ["Áo khoác nỉ có mũ", "Áo khoác nỉ không mũ"], "op": "LIKE"},
      "áo khoác dạ": {"field": "product_line_vn", "values": ["Áo khoác dạ"], "op": "LIKE"},
      "áo khoác gilet": {"field": "product_line_vn", "values": ["Áo khoác gilet", "Áo khoác gilet chần bông"], "op": "LIKE"},
      "áo khoác": {"field": "product_line_vn", "values": ["Áo khoác"], "op": "LIKE"},
      "áo phao": {"field": "product_line_vn", "values": ["Áo khoác lông vũ", "Áo khoác chần bông"], "op": "LIKE"},
      "áo len": {"field": "product_line_vn", "values": ["Áo len"], "op": "LIKE"},
      "áo nỉ": {"field": "product_line_vn", "values": ["Áo nỉ", "Áo nỉ có mũ"], "op": "LIKE"},
      "áo thun": {"field": "product_line_vn", "values": ["Áo phông"], "op": "LIKE"},
      "áo phông": {"field": "product_line_vn", "values": ["Áo phông"], "op": "LIKE"},
      "áo sơ mi": {"field": "product_line_vn", "values": ["Áo Sơ mi"], "op": "LIKE"},
      "áo polo": {"field": "product_line_vn", "values": ["Áo Polo"], "op": "LIKE"},
      "áo ba lỗ": {"field": "product_line_vn", "values": ["Áo ba lỗ"], "op": "LIKE"},
      "áo giữ nhiệt": {"field": "product_line_vn", "values": ["Áo giữ nhiệt"], "op": "LIKE"},
      "áo kiểu": {"field": "product_line_vn", "values": ["Áo kiểu"], "op": "LIKE"},
      "áo body": {"field": "product_line_vn", "values": ["Áo Body"], "op": "LIKE"},
      "áo lót": {"field": "product_line_vn", "values": ["Áo lót"], "op": "LIKE"},
      "cardigan": {"field": "product_line_vn", "values": ["Cardigan"], "op": "LIKE"},
      "blazer": {"field": "product_line_vn", "values": ["Blazer"], "op": "LIKE"},
      "hoodie": {"field": "product_line_vn", "values": ["Áo nỉ có mũ", "Áo khoác nỉ có mũ"], "op": "LIKE"},
      "áo": {"field": "product_line_vn", "values": ["Áo"], "op": "LIKE"},
      # Quần các loại
      "quần jean": {"field": "product_line_vn", "values": ["Quần jean"], "op": "LIKE"},
      "quần jeans": {"field": "product_line_vn", "values": ["Quần jean"], "op": "LIKE"},
      "quần soóc": {"field": "product_line_vn", "values": ["Quần soóc"], "op": "LIKE"},
      "quần short": {"field": "product_line_vn", "values": ["Quần soóc"], "op": "LIKE"},
      "quần dài": {"field": "product_line_vn", "values": ["Quần dài"], "op": "LIKE"},
      "quần nỉ": {"field": "product_line_vn", "values": ["Quần nỉ"], "op": "LIKE"},
      "quần khaki": {"field": "product_line_vn", "values": ["Quần Khaki"], "op": "LIKE"},
      "quần leggings": {"field": "product_line_vn", "values": ["Quần leggings"], "op": "LIKE"},
      "leggings": {"field": "product_line_vn", "values": ["Quần leggings"], "op": "LIKE"},
      "quần lót": {"field": "product_line_vn", "values": ["Quần lót", "Quần lót đùi", "Quần lót tam giác"], "op": "LIKE"},
      "quần": {"field": "product_line_vn", "values": ["Quần"], "op": "LIKE"},
      # Váy/Đầm
      "váy liền": {"field": "product_line_vn", "values": ["Váy liền"], "op": "LIKE"},
      "chân váy": {"field": "product_line_vn", "values": ["Chân váy"], "op": "LIKE"},
      "quần váy": {"field": "product_line_vn", "values": ["Quần váy"], "op": "LIKE"},
      "đầm": {"field": "product_line_vn", "values": ["Váy liền"], "op": "LIKE"},
      "váy": {"field": "product_line_vn", "values": ["Váy"], "op": "LIKE"},
      # Đồ bộ
      "bộ mặc nhà": {"field": "product_line_vn", "values": ["Bộ mặc nhà"], "op": "LIKE"},
      "đồ bộ": {"field": "product_line_vn", "values": ["Bộ mặc nhà", "Bộ quần áo"], "op": "LIKE"},
      "pyjama": {"field": "product_line_vn", "values": ["Pyjama", "Bộ mặc nhà"], "op": "LIKE"},
      "đồ ngủ": {"field": "product_line_vn", "values": ["Pyjama", "Bộ mặc nhà"], "op": "LIKE"},
      "bộ thể thao": {"field": "product_line_vn", "values": ["Bộ thể thao"], "op": "LIKE"},
      # Phụ kiện
      "tất": {"field": "product_line_vn", "values": ["Tất"], "op": "LIKE"},
      "mũ": {"field": "product_line_vn", "values": ["Mũ"], "op": "LIKE"},
      "khăn": {"field": "product_line_vn", "values": ["Khăn"], "op": "LIKE"},
      "túi xách": {"field": "product_line_vn", "values": ["Túi xách"], "op": "LIKE"},
      "túi": {"field": "product_line_vn", "values": ["Túi xách"], "op": "LIKE"},
      # ═══════════════════════════════════════════════════════════════════════
      # STYLE (Phong cách) → field: style
      # DB values: 'Utility', 'Athleisure', 'Feminine', 'Essential', 'Basic',
      # 'Smart Casual', 'Basic Update', 'Dynamic', 'Trend'
      # ═══════════════════════════════════════════════════════════════════════
      "thể thao": {"field": "style", "values": ["Athleisure", "Dynamic"], "op": "IN"},
      "năng động": {"field": "style", "values": ["Dynamic", "Athleisure"], "op": "IN"},
      "sport": {"field": "style", "values": ["Athleisure"], "op": "IN"},
      "thanh lịch": {"field": "style", "values": ["Feminine", "Smart Casual"], "op": "IN"},
      "nữ tính": {"field": "style", "values": ["Feminine"], "op": "IN"},
      "basic": {"field": "style", "values": ["Basic", "Basic Update"], "op": "IN"},
      "cơ bản": {"field": "style", "values": ["Basic", "Basic Update", "Essential"], "op": "IN"},
      "smart casual": {"field": "style", "values": ["Smart Casual"], "op": "IN"},
      "công sở": {"field": "style", "values": ["Smart Casual"], "op": "IN"},
      "trendy": {"field": "style", "values": ["Trend"], "op": "IN"},
      "thời trang": {"field": "style", "values": ["Trend"], "op": "IN"},
      # ═══════════════════════════════════════════════════════════════════════
      # COLOR (Màu sắc) → field: master_color
      # DB values: 'Đen/ Black', 'Trắng/ White', 'Xanh da trời/ Blue', etc.
      # Dùng LIKE vì format phức tạp
      # ═══════════════════════════════════════════════════════════════════════
      "màu đen": {"field": "master_color", "values": ["Đen"], "op": "LIKE"},
      "màu trắng": {"field": "master_color", "values": ["Trắng"], "op": "LIKE"},
      "màu đỏ": {"field": "master_color", "values": ["Đỏ"], "op": "LIKE"},
      "màu xanh": {"field": "master_color", "values": ["Xanh"], "op": "LIKE"},
      "màu hồng": {"field": "master_color", "values": ["Hồng"], "op": "LIKE"},
      "màu vàng": {"field": "master_color", "values": ["Vàng"], "op": "LIKE"},
      "màu nâu": {"field": "master_color", "values": ["Nâu"], "op": "LIKE"},
      "màu xám": {"field": "master_color", "values": ["Xám"], "op": "LIKE"},
      "màu be": {"field": "master_color", "values": ["Be"], "op": "LIKE"},
      "màu cam": {"field": "master_color", "values": ["Cam"], "op": "LIKE"},
      "màu tím": {"field": "master_color", "values": ["Tím"], "op": "LIKE"},
      "đen": {"field": "master_color", "values": ["Đen"], "op": "LIKE"},
      "trắng": {"field": "master_color", "values": ["Trắng"], "op": "LIKE"},
      "đỏ": {"field": "master_color", "values": ["Đỏ"], "op": "LIKE"},
      "xanh": {"field": "master_color", "values": ["Xanh"], "op": "LIKE"},
      "hồng": {"field": "master_color", "values": ["Hồng"], "op": "LIKE"},
      "vàng": {"field": "master_color", "values": ["Vàng"], "op": "LIKE"},
      "nâu": {"field": "master_color", "values": ["Nâu"], "op": "LIKE"},
      "xám": {"field": "master_color", "values": ["Xám"], "op": "LIKE"},
      "be": {"field": "master_color", "values": ["Be"], "op": "LIKE"},
      "cam": {"field": "master_color", "values": ["Cam"], "op": "LIKE"},
      "tím": {"field": "master_color", "values": ["Tím"], "op": "LIKE"},
      # ═══════════════════════════════════════════════════════════════════════
      # FORM_SLEEVE (Kiểu tay) → field: form_sleeve
      # DB values: 'Full length Sleeve', 'Short Sleeve', 'Sleeveless', etc.
      # ═══════════════════════════════════════════════════════════════════════
      "dài tay": {"field": "form_sleeve", "values": ["Full length Sleeve"], "op": "IN"},
      "tay dài": {"field": "form_sleeve", "values": ["Full length Sleeve"], "op": "IN"},
      "cộc tay": {"field": "form_sleeve", "values": ["Short Sleeve"], "op": "IN"},
      "tay ngắn": {"field": "form_sleeve", "values": ["Short Sleeve"], "op": "IN"},
      "ngắn tay": {"field": "form_sleeve", "values": ["Short Sleeve"], "op": "IN"},
      "sát nách": {"field": "form_sleeve", "values": ["Sleeveless"], "op": "IN"},
      "không tay": {"field": "form_sleeve", "values": ["Sleeveless"], "op": "IN"},
      # ═══════════════════════════════════════════════════════════════════════
      # FITTING (Dáng) → field: fitting
      # DB values: 'Skinny', 'Slim', 'Slimfit', 'Relax', 'Oversize', 'Boxy', 'Regular'
      # ═══════════════════════════════════════════════════════════════════════
      "ôm": {"field": "fitting", "values": ["Skinny", "Slim", "Slimfit"], "op": "IN"},
      "slim": {"field": "fitting", "values": ["Slim", "Slimfit"], "op": "IN"},
      "skinny": {"field": "fitting", "values": ["Skinny"], "op": "IN"},
      "rộng": {"field": "fitting", "values": ["Oversize", "Relax", "Boxy"], "op": "IN"},
      "oversize": {"field": "fitting", "values": ["Oversize"], "op": "IN"},
      "regular": {"field": "fitting", "values": ["Regular"], "op": "IN"},
      "thoải mái": {"field": "fitting", "values": ["Relax", "Regular"], "op": "IN"},
      # ═══════════════════════════════════════════════════════════════════════
      # PRODUCT_GROUP (Nhóm sản phẩm) → field: product_group
      # DB values: 'Set', 'Top', 'Outerwear', 'Accessories', 'Dress', 'Bottom', 'Knitwear', 'Underwear'
      # ═══════════════════════════════════════════════════════════════════════
      "đồ lót": {"field": "product_group", "values": ["Underwear"], "op": "IN"},
      "nội y": {"field": "product_group", "values": ["Underwear"], "op": "IN"},
      "phụ kiện": {"field": "product_group", "values": ["Accessories"], "op": "IN"},
      "outerwear": {"field": "product_group", "values": ["Outerwear"], "op": "IN"},
      "knitwear": {"field": "product_group", "values": ["Knitwear"], "op": "IN"},
      }
      backend/search/query_parser.py 0 → 100644
      View file @ 446d8f46
      """
      QUERY PARSER
      ════════════
      Parse user query để extract keywords và build SQL WHERE clause
      Functions:
      - parse_query(): Extract filters từ query text
      - build_where_clause(): Convert filters thành SQL WHERE
      """
      import re
      import logging
      from .keyword_mappings import KEYWORD_MAPPINGS
      logger = logging.getLogger(__name__)
      def is_word_match(keyword: str, query: str) -> bool:
      """
      Check keyword có phải là từ riêng biệt trong query không
      Tránh case "việt nam" match "nam"
      VD:
      - "nam" trong "áo nam" → True (từ riêng biệt)
      - "nam" trong "việt nam" → False (substring của từ khác)
      - "bé gái" trong "áo bé gái" → True
      """
      # Pattern: keyword phải có word boundary (space, start, end) ở 2 đầu
      # Dùng \b cho word boundary với tiếng Việt cần xử lý đặc biệt
      # Escape special regex characters trong keyword
      escaped_keyword = re.escape(keyword)
      # Pattern: từ phải đứng riêng (có space hoặc đầu/cuối string)
      pattern = r'(?:^|[\s,\.!?])' + escaped_keyword + r'(?:[\s,\.!?]|$)'
      return bool(re.search(pattern, query))
      def parse_query(query: str) -> dict:
      """
      Parse query text và extract filters dựa trên KEYWORD_MAPPINGS
      Args:
      query: User query string (VD: "áo mùa đông cho bé gái màu hồng")
      Returns:
      dict: Filters grouped by field
      {
      "season": {"values": ["Fall Winter"], "op": "IN"},
      "gender_by_product": {"values": ["female"], "op": "="},
      ...
      }
      Note:
      - Nếu không có keyword nào match → trả về {} → không filter gì cả (lấy hết)
      - Keywords phải là từ riêng biệt (word boundary), tránh "việt nam" → "nam"
      """
      query_lower = query.lower().strip()
      filters = {}
      matched_keywords = set()
      # Sort keywords by length (dài trước) để match chính xác hơn
      # VD: "áo khoác" phải match trước "áo"
      # VD: "bé gái" phải match trước "bé"
      sorted_keywords = sorted(KEYWORD_MAPPINGS.keys(), key=len, reverse=True)
      for keyword in sorted_keywords:
      # Check keyword có xuất hiện như từ riêng biệt không
      if not is_word_match(keyword, query_lower):
      continue
      # Kiểm tra keyword này có phải là substring của keyword đã match không
      # VD: đã match "bé gái" thì không match "bé" nữa
      is_substring_of_matched = False
      for matched in matched_keywords:
      if keyword in matched and keyword != matched:
      is_substring_of_matched = True
      break
      if is_substring_of_matched:
      continue
      mapping = KEYWORD_MAPPINGS[keyword]
      field = mapping["field"]
      # Chỉ lấy field đầu tiên match (không override)
      if field not in filters:
      filters[field] = {
      "values": mapping["values"],
      "op": mapping["op"]
      }
      matched_keywords.add(keyword)
      logger.debug(f"Matched keyword: '{keyword}' → {field} = {mapping['values']}")
      logger.info(f"Query '{query}' → Extracted filters: {filters}")
      return filters
      def build_where_clause(filters: dict) -> str:
      """
      Build SQL WHERE clause từ extracted filters
      Args:
      filters: Dict of filters from parse_query()
      Returns:
      str: SQL WHERE clause (VD: "WHERE season IN ('Fall Winter') AND ...")
      Returns empty string if no filters
      """
      if not filters:
      return ""
      clauses = []
      for field, config in filters.items():
      values = config["values"]
      op = config["op"]
      if op == "=":
      # Single value equality
      # VD: gender_by_product = 'female'
      clauses.append(f"{field} = '{values[0]}'")
      elif op == "IN":
      # Multiple values
      # VD: season IN ('Fall Winter')
      vals_str = ", ".join([f"'{v}'" for v in values])
      clauses.append(f"{field} IN ({vals_str})")
      elif op == "LIKE":
      # Text search với LIKE
      # VD: (product_line_vn LIKE '%Áo%' OR product_line_vn LIKE '%Áo khoác%')
      like_parts = [f"{field} LIKE '%{v}%'" for v in values]
      if len(like_parts) == 1:
      clauses.append(like_parts[0])
      else:
      clauses.append(f"({' OR '.join(like_parts)})")
      where_clause = "WHERE " + " AND ".join(clauses)
      logger.debug(f"Built WHERE clause: {where_clause}")
      return where_clause
      def get_matched_keywords(query: str) -> list[str]:
      """
      Get list of keywords that were matched in the query
      Useful for debugging/logging
      """
      query_lower = query.lower().strip()
      matched = []
      sorted_keywords = sorted(KEYWORD_MAPPINGS.keys(), key=len, reverse=True)
      for keyword in sorted_keywords:
      # Check word boundary
      if not is_word_match(keyword, query_lower):
      continue
      # Check not substring of already matched
      is_sub = any(keyword in m and keyword != m for m in matched)
      if not is_sub:
      matched.append(keyword)
      return matched
      backend/server.py
      View file @ 446d8f46
      ......@@ -36,9 +36,8 @@ from fastapi import FastAPI
      from fastapi.middleware.cors import CORSMiddleware
      from fastapi.staticfiles import StaticFiles # Import StaticFiles
      # Updated APIs
      from api.chatbot_route import router as chatbot_router
      from api.conservation_route import router as conservation_router
      # from api.recommend_image import router as recommend_image_router
      from api.recommend_text import router as recommend_text_router
      from common.middleware import ClerkAuthMiddleware
      from config import PORT
      ......@@ -49,17 +48,6 @@ app = FastAPI(
      version="1.0.0",
      )
      # Clerk Auth (comment out if testing locally without valid token might be needed,
      # but user environment has it enabled. Static files usually should be public or protected.
      # For now, we mount static files BEFORE auth if we want them public, or just let auth handle it.
      # Usually static files for testing are convenient to be public
      # or we just need to authenticate via header in fetch.
      # But for simple HTML test page, often it is easier if it is open.
      # However, app.add_middleware applies to everything.
      # Let's assume ClerkAuthMiddleware allows public paths or we just login.
      # IMPORTANT: If ClerkAuthMiddleware blocks everything, the static page won't load easily without token.
      # But user just asked to mount it.
      # app.add_middleware(ClerkAuthMiddleware)
      print("✅ Clerk Authentication middleware DISABLED (for testing)")
      # Add CORS middleware
      ......@@ -71,15 +59,9 @@ app.add_middleware(
      allow_headers=["*"],
      )
      # ========== REST API Routes ==========
      # Conversation History (Mới)
      app.include_router(conservation_router)
      # app.include_router(recommend_image_router)
      app.include_router(recommend_text_router)
      # Chatbot Agent (Mới)
      app.include_router(chatbot_router, prefix="/api/agent")
      # Mount Static Files
      # Mount this LAST to avoid conflicts with API routes
      try:
      static_dir = os.path.join(os.path.dirname(__file__), "static")
      if not os.path.exists(static_dir):
      ......
      backend/static/index.html
      View file @ 446d8f46
      <!DOCTYPE html>
      <html lang="en">
      <html lang="vi">
      <head>
      <meta charset="UTF-8">
      <meta name="viewport" content="width=device-width, initial-scale=1.0">
      <title>Canifa Chatbot Test</title>
      <title>Canifa AI Search</title>
      <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
      <style>
      body {
      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
      max-width: 900px;
      margin: 0 auto;
      padding: 20px;
      background-color: #1e1e1e;
      color: #e0e0e0;
      :root {
      --primary-color: #D71920;
      --text-dark: #1F2937;
      --text-gray: #6B7280;
      --card-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
      }
      .container {
      background: #2d2d2d;
      padding: 20px;
      border-radius: 12px;
      box-shadow: 0 4px 15px rgba(0, 0, 0, 0.5);
      * {
      box-sizing: border-box;
      font-family: 'Inter', sans-serif;
      }
      body {
      background-color: #111827;
      /* Dark Mode Background */
      color: #F3F4F6;
      margin: 0;
      padding: 2rem;
      display: flex;
      flex-direction: column;
      height: 90vh;
      border: 1px solid #444;
      align-items: center;
      }
      /* --- Header & Search Bar --- */
      .header {
      display: flex;
      justify-content: space-between;
      align-items: center;
      margin-bottom: 20px;
      padding-bottom: 10px;
      border-bottom: 1px solid #444;
      text-align: center;
      margin-bottom: 2rem;
      width: 100%;
      max-width: 800px;
      }
      .header h2 {
      margin: 0;
      color: #fff;
      .title {
      font-size: 2.5rem;
      font-weight: 800;
      margin-bottom: 0.5rem;
      }
      .title span {
      color: var(--primary-color);
      }
      .config-area {
      .subtitle {
      color: var(--text-gray);
      margin-bottom: 2rem;
      }
      .search-box {
      background: #1F2937;
      padding: 0.5rem;
      border-radius: 50px;
      display: flex;
      gap: 10px;
      align-items: center;
      box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.3);
      border: 1px solid #374151;
      }
      input[type="text"] {
      padding: 8px 12px;
      border: 1px solid #555;
      border-radius: 6px;
      background: #3d3d3d;
      color: #fff;
      flex: 1;
      background: transparent;
      border: none;
      color: white;
      font-size: 1.1rem;
      padding: 0.8rem 1.5rem;
      outline: none;
      }
      button {
      padding: 8px 16px;
      background: #007acc;
      button.search-btn {
      background: var(--primary-color);
      color: white;
      border: none;
      border-radius: 6px;
      padding: 0.8rem 2rem;
      border-radius: 40px;
      font-weight: 600;
      cursor: pointer;
      transition: opacity 0.2s;
      font-weight: 500;
      }
      button:hover {
      opacity: 0.9;
      transition: 0.2s;
      }
      button:disabled {
      background: #555;
      cursor: not-allowed;
      button.search-btn:hover {
      background: #b9151b;
      }
      .chat-box {
      flex: 1;
      overflow-y: auto;
      padding: 20px;
      border: 1px solid #444;
      border-radius: 8px;
      margin-bottom: 20px;
      background: #252526;
      display: flex;
      flex-direction: column;
      gap: 10px;
      }
      .message-container {
      /* --- Filter Section (Price Slider) --- */
      .filter-section {
      margin-top: 1.5rem;
      background: #1F2937;
      padding: 1rem 2rem;
      border-radius: 12px;
      display: flex;
      flex-direction: column;
      max-width: 80%;
      align-items: center;
      gap: 1.5rem;
      border: 1px solid #374151;
      }
      .message-container.user {
      align-self: flex-end;
      align-items: flex-end;
      .filter-label {
      font-weight: 500;
      font-size: 0.95rem;
      }
      .message-container.bot {
      align-self: flex-start;
      align-items: flex-start;
      input[type="range"] {
      -webkit-appearance: none;
      width: 200px;
      height: 6px;
      background: #4B5563;
      border-radius: 5px;
      outline: none;
      cursor: pointer;
      }
      .sender-name {
      font-size: 0.8em;
      margin-bottom: 4px;
      color: #aaa;
      margin-left: 4px;
      margin-right: 4px;
      input[type="range"]::-webkit-slider-thumb {
      -webkit-appearance: none;
      width: 20px;
      height: 20px;
      background: var(--primary-color);
      border-radius: 50%;
      cursor: pointer;
      }
      .message {
      padding: 12px 16px;
      border-radius: 12px;
      line-height: 1.5;
      word-wrap: break-word;
      position: relative;
      .price-display {
      font-weight: 700;
      color: var(--primary-color);
      min-width: 120px;
      }
      .message.user {
      background: #007acc;
      color: white;
      border-bottom-right-radius: 2px;
      /* --- Stats & Grid --- */
      .stats {
      margin: 2rem 0 1rem;
      color: var(--text-gray);
      font-size: 0.9rem;
      width: 100%;
      max-width: 1400px;
      }
      .message.bot {
      background: #3e3e42;
      color: #e0e0e0;
      border-bottom-left-radius: 2px;
      border: 1px solid #555;
      .grid {
      display: grid;
      grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));
      gap: 1.5rem;
      width: 100%;
      max-width: 1400px;
      }
      .message.system {
      background: #3d2d2d;
      color: #ff6b6b;
      align-self: center;
      font-size: 0.9em;
      max-width: 90%;
      border: 1px solid #552b2b;
      .card {
      background: #1F2937;
      border-radius: 12px;
      overflow: hidden;
      border: 1px solid #374151;
      transition: transform 0.2s;
      cursor: pointer;
      position: relative;
      }
      .timestamp {
      font-size: 0.7em;
      opacity: 0.7;
      margin-top: 6px;
      display: block;
      text-align: right;
      .card:hover {
      transform: translateY(-5px);
      border-color: #4B5563;
      }
      .input-area {
      display: flex;
      gap: 10px;
      .card-img-wrap {
      width: 100%;
      padding-top: 133%;
      /* 3:4 aspect ratio */
      position: relative;
      background: #111;
      }
      .input-area input {
      flex: 1;
      padding: 12px;
      border: 1px solid #555;
      border-radius: 8px;
      font-size: 16px;
      background: #3d3d3d;
      color: #fff;
      .card-img {
      position: absolute;
      top: 0;
      left: 0;
      width: 100%;
      height: 100%;
      object-fit: cover;
      }
      .input-area input:focus {
      outline: 2px solid #007acc;
      border-color: transparent;
      .match-badge {
      position: absolute;
      top: 10px;
      right: 10px;
      background: rgba(0, 0, 0, 0.8);
      color: #F87171;
      padding: 4px 8px;
      border-radius: 4px;
      font-size: 0.75rem;
      font-weight: 700;
      border: 1px solid #F87171;
      }
      .load-more {
      text-align: center;
      margin-bottom: 10px;
      .card-body {
      padding: 1rem;
      }
      .load-more button {
      background: #3e3e42;
      color: #ccc;
      font-size: 0.85em;
      width: 100%;
      border: 1px dashed #555;
      .price-row {
      display: flex;
      gap: 8px;
      align-items: baseline;
      margin-bottom: 6px;
      }
      .load-more button:hover {
      background: #4e4e52;
      color: #fff;
      .curr-price {
      color: #F87171;
      font-weight: 700;
      font-size: 1.1rem;
      }
      .typing-indicator {
      font-style: italic;
      color: #888;
      font-size: 0.9em;
      margin-bottom: 10px;
      display: none;
      margin-left: 10px;
      .old-price {
      color: #6B7280;
      text-decoration: line-through;
      font-size: 0.9rem;
      }
      /* Scrollbar styling */
      ::-webkit-scrollbar {
      width: 8px;
      .discount {
      background: #fee2e2;
      color: #DC2626;
      font-size: 0.7rem;
      padding: 2px 4px;
      border-radius: 3px;
      font-weight: 700;
      }
      ::-webkit-scrollbar-track {
      background: #2d2d2d;
      h3 {
      font-size: 0.95rem;
      margin: 0 0 0.5rem;
      line-height: 1.4;
      color: #E5E7EB;
      display: -webkit-box;
      -webkit-line-clamp: 2;
      -webkit-box-orient: vertical;
      overflow: hidden;
      }
      ::-webkit-scrollbar-thumb {
      background: #555;
      border-radius: 4px;
      .meta {
      font-size: 0.8rem;
      color: #9CA3AF;
      display: flex;
      align-items: center;
      gap: 6px;
      }
      ::-webkit-scrollbar-thumb:hover {
      background: #666;
      .color-dot {
      width: 10px;
      height: 10px;
      border-radius: 50%;
      background: #ccc;
      display: inline-block;
      }
      </style>
      </head>
      <body>
      <div class="container">
      <div class="header">
      <h2>🤖 Canifa AI Chat</h2>
      <div class="config-area">
      <input type="text" id="userId" placeholder="User ID" value="test_user_001">
      <button onclick="loadHistory(true)">↻ History</button>
      <button onclick="clearUI()" style="background: #d32f2f;">✗ Clear UI</button>
      </div>
      </div>
      <div class="chat-box" id="chatBox">
      <div class="load-more" id="loadMoreBtn" style="display: none;">
      <button onclick="loadHistory(false)">Load Older Messages ⬆️</button>
      </div>
      <div id="messagesArea" style="display: flex; flex-direction: column; gap: 15px;"></div>
      </div>
      <div class="header">
      <h1 class="title">Canifa <span>AI Search</span></h1>
      <p class="subtitle">Tìm kiếm sản phẩm theo ý bạn - AI Power Recommendation</p>
      <div class="typing-indicator" id="typingIndicator">AI is typing...</div>
      <!-- Search Bar -->
      <div class="search-box">
      <input type="text" id="inp-query" placeholder="Ví dụ: áo phông mùa đông...">
      <button class="search-btn" onclick="search()">Tìm Kiếm</button>
      </div>
      <div class="input-area">
      <input type="text" id="userInput" placeholder="Type your message..." onkeypress="handleKeyPress(event)"
      autocomplete="off">
      <button onclick="sendMessage()" id="sendBtn">➤ Send</button>
      <!-- Price Filter -->
      <div class="filter-section">
      <span class="filter-label">💰 Giá tối đa:</span>
      <input type="range" id="inp-price" min="0" max="2000000" step="50000" value="2000000"
      oninput="updatePriceLabel(this.value)">
      <span class="price-display" id="lbl-price">2.000.000 đ</span>
      </div>
      </div>
      <script>
      let currentCursor = null;
      let isTyping = false;
      async function loadHistory(isRefresh) {
      const userId = document.getElementById('userId').value;
      const messagesArea = document.getElementById('messagesArea');
      const loadMoreBtn = document.getElementById('loadMoreBtn');
      if (!userId) {
      alert('Please enter a User ID');
      return;
      }
      if (isRefresh) {
      messagesArea.innerHTML = '';
      currentCursor = null;
      }
      const url = `/history/${userId}?limit=20${currentCursor ? `&before_id=${currentCursor}` : ''}`;
      try {
      const response = await fetch(url);
      const data = await response.json();
      const messages = data.data || data;
      const cursor = data.next_cursor || null;
      if (Array.isArray(messages) && messages.length > 0) {
      currentCursor = cursor;
      const batch = [...messages].reverse();
      if (isRefresh) {
      batch.forEach(msg => appendMessage(msg, 'bottom'));
      setTimeout(() => {
      const chatBox = document.getElementById('chatBox');
      chatBox.scrollTop = chatBox.scrollHeight;
      }, 100);
      } else {
      // Keep scroll position relative to bottom content
      const chatBox = document.getElementById('chatBox');
      const oldHeight = chatBox.scrollHeight;
      batch.forEach(msg => appendMessage(msg, 'top'));
      // Adjust scroll to keep view stable
      chatBox.scrollTop = chatBox.scrollHeight - oldHeight;
      }
      loadMoreBtn.style.display = currentCursor ? 'block' : 'none';
      } else {
      if (isRefresh) {
      messagesArea.innerHTML = '<div class="message system">No history found. Start chatting!</div>';
      }
      loadMoreBtn.style.display = 'none';
      }
      } catch (error) {
      console.error('Error loading history:', error);
      alert('Failed to load history');
      }
      }
      function appendMessage(msg, position = 'bottom') {
      const messagesArea = document.getElementById('messagesArea');
      // Container wrapper for alignment
      const container = document.createElement('div');
      container.className = `message-container ${msg.is_human ? 'user' : 'bot'}`;
      // Sender Name Label
      const sender = document.createElement('div');
      sender.className = 'sender-name';
      sender.innerText = msg.is_human ? 'You' : 'Canifa AI';
      container.appendChild(sender);
      <!-- Results -->
      <div class="stats" id="stats-area"></div>
      <div class="grid" id="grid-area"></div>
      // Message Bubble
      const div = document.createElement('div');
      div.className = `message ${msg.is_human ? 'user' : 'bot'}`;
      div.innerText = msg.message;
      // Timestamp inside bubble
      const time = document.createElement('span');
      time.className = 'timestamp';
      time.innerText = new Date(msg.timestamp).toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' });
      div.appendChild(time);
      // Debug ID (optional)
      // const meta = document.createElement('div');
      // meta.style.fontSize = '9px';
      // meta.style.opacity = '0.3';
      // meta.innerText = `id: ${msg.id}`;
      // div.appendChild(meta);
      container.appendChild(div);
      <script>
      const formatMoney = (amount) => new Intl.NumberFormat('vi-VN', { style: 'currency', currency: 'VND' }).format(amount);
      if (position === 'top') {
      messagesArea.insertBefore(container, messagesArea.firstChild);
      } else {
      messagesArea.appendChild(container); // Corrected to append container
      }
      function updatePriceLabel(val) {
      document.getElementById('lbl-price').innerText = formatMoney(val);
      }
      async function sendMessage() {
      const input = document.getElementById('userInput');
      const userId = document.getElementById('userId').value;
      const text = input.value.trim();
      const sendBtn = document.getElementById('sendBtn');
      const typingIndicator = document.getElementById('typingIndicator');
      const chatBox = document.getElementById('chatBox');
      async function search() {
      const query = document.getElementById('inp-query').value.trim();
      const maxPrice = document.getElementById('inp-price').value;
      const grid = document.getElementById('grid-area');
      const stats = document.getElementById('stats-area');
      if (!text || !userId) return;
      if (!query) return alert("Nhập nội dung tìm kiếm đã bro!");
      // Disable input
      input.disabled = true;
      sendBtn.disabled = true;
      typingIndicator.style.display = 'block';
      // Add user message immediately
      appendMessage({
      message: text,
      is_human: true,
      timestamp: new Date().toISOString(),
      id: 'pending'
      });
      input.value = '';
      chatBox.scrollTop = chatBox.scrollHeight;
      grid.innerHTML = '<p style="text-align:center; width:100%; grid-column:1/-1;">Đang tìm kiếm...</p>';
      stats.innerText = '';
      try {
      // SWITCH TO NON-STREAMING ENDPOINT
      const response = await fetch('/api/agent/chat', {
      const res = await fetch('/api/recommend/text', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
      query: text,
      user_id: userId,
      model_name: "gpt-4o-mini"
      query: query,
      price_max: parseInt(maxPrice)
      })
      });
      if (!response.ok) throw new Error('Network response was not ok');
      const data = await response.json();
      // Create bot message placeholder
      const messagesArea = document.getElementById('messagesArea');
      const data = await res.json();
      const container = document.createElement('div');
      container.className = 'message-container bot';
      const sender = document.createElement('div');
      sender.className = 'sender-name';
      sender.innerText = 'Canifa AI';
      container.appendChild(sender);
      if (data.status !== 'success') {
      alert('Lỗi rồi bro: ' + data.message);
      return;
      }
      const botMsgDiv = document.createElement('div');
      botMsgDiv.className = 'message bot';
      const products = data.results || [];
      stats.innerText = `⚡ Tìm thấy ${products.length} kết quả trong ${data.process_time}`;
      if (data.status === 'success') {
      botMsgDiv.innerText = data.response;
      } else {
      botMsgDiv.innerText = "Error: " + (data.message || "Unknown error");
      botMsgDiv.style.color = 'red';
      if (products.length === 0) {
      grid.innerHTML = '<p style="text-align:center; width:100%; grid-column:1/-1;">Không tìm thấy sản phẩm nào trong tầm giá này.</p>';
      return;
      }
      container.appendChild(botMsgDiv);
      messagesArea.appendChild(container);
      chatBox.scrollTop = chatBox.scrollHeight;
      } catch (error) {
      console.error('Error sending message:', error);
      appendMessage({
      message: `Error: ${error.message}`,
      is_human: false,
      timestamp: new Date().toISOString(),
      id: 'error'
      });
      } finally {
      input.disabled = false;
      sendBtn.disabled = false;
      typingIndicator.style.display = 'none';
      input.focus();
      chatBox.scrollTop = chatBox.scrollHeight;
      grid.innerHTML = products.map(p => {
      const discount = p.discount_amount > 0 ? `-${Math.round(p.discount_amount)}%` : '';
      const oldPrice = p.original_price > p.sale_price ? formatMoney(p.original_price) : '';
      const match = (p.similarity_score * 100).toFixed(0);
      const img = p.product_image_url || 'https://via.placeholder.com/300x400?text=No+Img';
      const colors = p.available_colors ? p.available_colors.split(',').length + ' màu' : '1 màu';
      return `
      <div class="card" onclick="window.open('${p.product_web_url}', '_blank')">
      <div class="card-img-wrap">
      <img src="${img}" class="card-img" loading="lazy">
      <div class="match-badge">★ ${match}% Match</div>
      </div>
      <div class="card-body">
      <div class="price-row">
      <span class="curr-price">${formatMoney(p.sale_price)}</span>
      ${oldPrice ? `<span class="old-price">${oldPrice}</span>` : ''}
      ${discount ? `<span class="discount">${discount}</span>` : ''}
      </div>
      <h3>${p.product_name}</h3>
      <div class="meta">
      <span>🎨 ${colors}</span>
      <span>🆔 ${p.internal_ref_code}</span>
      </div>
      </div>
      </div>
      `;
      }).join('');
      } catch (e) {
      console.error(e);
      alert('Lỗi kết nối server!');
      }
      }
      function handleKeyPress(event) {
      if (event.key === 'Enter') {
      sendMessage();
      }
      }
      function clearUI() {
      document.getElementById('messagesArea').innerHTML = '';
      }
      </script>
      </body>
      ......
      backend/static/recommend.html 0 → 100644
      View file @ 446d8f46
      <!DOCTYPE html>
      <html lang="en">
      <head>
      <meta charset="UTF-8">
      <meta name="viewport" content="width=device-width, initial-scale=1.0">
      <title>Canifa StarRocks Visual Search</title>
      <style>
      :root {
      --primary: #d32f2f;
      --dark: #121212;
      --card: #1e1e1e;
      --text: #e0e0e0;
      --accent: #2196f3;
      }
      body {
      font-family: 'Inter', -apple-system, sans-serif;
      background-color: var(--dark);
      color: var(--text);
      margin: 0;
      padding: 20px;
      display: flex;
      flex-direction: column;
      align-items: center;
      }
      .container {
      width: 100%;
      max-width: 1100px;
      }
      .header {
      text-align: center;
      margin-bottom: 30px;
      }
      .header h1 {
      color: var(--primary);
      margin-bottom: 5px;
      }
      .upload-section {
      background: var(--card);
      border: 2px dashed #444;
      border-radius: 15px;
      padding: 30px;
      text-align: center;
      cursor: pointer;
      transition: all 0.3s;
      }
      .upload-section:hover {
      border-color: var(--primary);
      background: #252525;
      }
      #imagePreview {
      max-width: 200px;
      border-radius: 8px;
      margin-top: 15px;
      display: none;
      box-shadow: 0 4px 15px rgba(0, 0, 0, 0.5);
      }
      .results-grid {
      display: grid;
      grid-template-columns: repeat(auto-fill, minmax(260px, 1fr));
      gap: 20px;
      margin-top: 30px;
      }
      .product-card {
      background: var(--card);
      border-radius: 12px;
      overflow: hidden;
      border: 1px solid #333;
      transition: transform 0.2s;
      position: relative;
      }
      .product-card:hover {
      transform: translateY(-5px);
      border-color: var(--accent);
      }
      .match-score {
      position: absolute;
      top: 10px;
      right: 10px;
      background: rgba(33, 150, 243, 0.9);
      color: white;
      padding: 4px 10px;
      border-radius: 15px;
      font-size: 0.85em;
      font-weight: bold;
      }
      .product-image {
      width: 100%;
      aspect-ratio: 2/3;
      object-fit: cover;
      background: #252525;
      }
      .product-info {
      padding: 15px;
      }
      .product-name {
      font-weight: bold;
      font-size: 1em;
      margin-bottom: 8px;
      height: 2.4em;
      overflow: hidden;
      display: -webkit-box;
      -webkit-line-clamp: 2;
      -webkit-box-orient: vertical;
      }
      .price-row {
      display: flex;
      align-items: center;
      gap: 10px;
      margin-bottom: 8px;
      }
      .sale-price {
      color: var(--primary);
      font-weight: bold;
      font-size: 1.1em;
      }
      .original-price {
      text-decoration: line-through;
      color: #888;
      font-size: 0.9em;
      }
      .sku {
      font-size: 0.8em;
      color: #666;
      margin-top: 10px;
      }
      .btn-view {
      display: block;
      width: 100%;
      padding: 8px;
      background: var(--accent);
      color: white;
      text-align: center;
      text-decoration: none;
      border-radius: 5px;
      margin-top: 10px;
      font-size: 0.9em;
      }
      .loader {
      display: none;
      width: 30px;
      height: 30px;
      border: 3px solid #333;
      border-top: 3px solid var(--primary);
      border-radius: 50%;
      animation: spin 1s linear infinite;
      margin: 20px auto;
      }
      @keyframes spin {
      100% {
      transform: rotate(360deg);
      }
      }
      </style>
      </head>
      <body>
      <div class="container">
      <div class="header">
      <h1>🚀 StarRocks Visual Recommend</h1>
      <p>Tìm kiếm sản phẩm bằng ảnh trực tiếp từ database StarRocks</p>
      </div>
      <div class="upload-section" onclick="document.getElementById('fileInput').click()">
      <p>Nhấn để chọn ảnh sản phẩm</p>
      <input type="file" id="fileInput" hidden accept="image/*" onchange="handleFile(this)">
      <img id="imagePreview" alt="Preview">
      </div>
      <div id="loader" class="loader"></div>
      <div id="resultsArea" class="results-grid"></div>
      </div>
      <script>
      async function handleFile(input) {
      const file = input.files[0];
      if (!file) return;
      // Preview
      const reader = new FileReader();
      reader.onload = (e) => {
      document.getElementById('imagePreview').src = e.target.result;
      document.getElementById('imagePreview').style.display = 'inline-block';
      };
      reader.readAsDataURL(file);
      // Upload
      const loader = document.getElementById('loader');
      const resultsArea = document.getElementById('resultsArea');
      loader.style.display = 'block';
      resultsArea.innerHTML = '';
      const formData = new FormData();
      formData.append('file', file);
      try {
      const response = await fetch('/api/recommend/image', {
      method: 'POST',
      body: formData
      });
      const data = await response.json();
      if (data.status === 'success') {
      renderResults(data.results);
      } else {
      resultsArea.innerHTML = `<p style="color:red">Lỗi: ${data.message}</p>`;
      }
      } catch (err) {
      resultsArea.innerHTML = `<p style="color:red">Lỗi: ${err.message}</p>`;
      } finally {
      loader.style.display = 'none';
      }
      }
      function renderResults(results) {
      const resultsArea = document.getElementById('resultsArea');
      if (results.length === 0) {
      resultsArea.innerHTML = '<p>Không tìm thấy sản phẩm nào phù hợp.</p>';
      return;
      }
      results.forEach(item => {
      const score = (item.similarity_score * 100).toFixed(1);
      const card = document.createElement('div');
      card.className = 'product-card';
      card.innerHTML = `
      <div class="match-score">${score}%</div>
      <img src="${item.product_image_url_thumbnail || item.product_image_url}" class="product-image" onerror="this.src='https://placehold.co/400x600?text=Canifa'">
      <div class="product-info">
      <div class="product-name">${item.product_name}</div>
      <div class="price-row">
      <span class="sale-price">${item.sale_price.toLocaleString()}đ</span>
      ${item.original_price > item.sale_price ? `<span class="original-price">${item.original_price.toLocaleString()}đ</span>` : ''}
      </div>
      <div style="font-size:0.85em; color:#aaa">Màu sắc: ${item.available_colors}</div>
      <div class="sku">#${item.internal_ref_code}</div>
      <a href="${item.product_web_url}" target="_blank" class="btn-view">Xem trên Canifa.com</a>
      </div>
      `;
      resultsArea.appendChild(card);
      });
      }
      </script>
      </body>
      </html>
      \ No newline at end of file
      data/magento_product_dimension_with_image_embedding_202512241158.json 0 → 100644
      View file @ 446d8f46
      This source diff could not be displayed because it is too large. You can view the blob instead.
      image_process/config.py 0 → 100644
      View file @ 446d8f46
      import os
      # Model Paths
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      MODELS_DIR = os.path.join(BASE_DIR, "models")
      YOLO_MODEL_PATH = os.path.join(MODELS_DIR, "yolow-l_0_05_nms_0_3_v2.onnx")
      SIGLIP_MODEL_NAME = "hf-hub:Marqo/marqo-fashionSigLIP"
      # FAISS config
      INDEX_PATH = os.path.join(MODELS_DIR, "vector_db.index")
      MAPPING_PATH = os.path.join(MODELS_DIR, "sku_mapping.pkl")
      # Thresholds
      DEFAULT_CONFIDENCE_THRESHOLD = 0.5
      MARGIN_HIGH_CONFIDENCE = 0.1
      # Device
      DEVICE = "cpu"
      image_process/core/detector.py 0 → 100644
      View file @ 446d8f46
      import onnxruntime as ort
      import numpy as np
      import cv2
      from PIL import Image
      from loguru import logger
      class ClothingDetector:
      def __init__(self, model_path="models/yolow-l_0_05_nms_0_3_v2.onnx"):
      self.model_path = model_path
      if not os.path.exists(model_path):
      logger.warning(f"YOLO model not found at {model_path}. Please place it there.")
      self.session = None
      else:
      logger.info(f"Loading YOLO model from {model_path}...")
      self.session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
      logger.info("YOLO loaded.")
      def detect_and_crop(self, pil_image: Image.Image, score_threshold=0.2):
      """
      Detect clothing items and return a list of cropped PIL images.
      """
      if self.session is None:
      return [pil_image] # Fallback to original if no model
      # 1. Preprocess
      orig_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
      orig_h, orig_w = orig_image.shape[:2]
      resized_image = cv2.resize(orig_image, (640, 640))
      inference_input = resized_image.astype(np.float32) / 255.0
      inference_input = np.transpose(inference_input, (2, 0, 1))
      inference_input = np.expand_dims(inference_input, axis=0)
      # 2. Inference
      input_name = self.session.get_inputs()[0].name
      output_names = [o.name for o in self.session.get_outputs()]
      outputs = self.session.run(output_names, {input_name: inference_input})
      # [x_min, y_min, x_max, y_max], scores, class_ids
      bboxes = outputs[1][0]
      scores = outputs[2][0]
      crops = []
      for i, score in enumerate(scores):
      if score > score_threshold:
      x1, y1, x2, y2 = bboxes[i]
      # Scale back to original
      x1 = int(x1 * orig_w / 640)
      y1 = int(y1 * orig_h / 640)
      x2 = int(x2 * orig_w / 640)
      y2 = int(y2 * orig_h / 640)
      # Crop
      crop_cv2 = orig_image[y1:y2, x1:x2]
      if crop_cv2.size > 0:
      crop_pil = Image.fromarray(cv2.cvtColor(crop_cv2, cv2.COLOR_BGR2RGB))
      crops.append(crop_pil)
      # If nothing detected, return original image as a single crop
      return crops if crops else [pil_image]
      import os
      image_process/core/encoder.py 0 → 100644
      View file @ 446d8f46
      import torch
      from open_clip import create_model_and_transforms, get_tokenizer
      from PIL import Image
      import numpy as np
      from loguru import logger
      class ImageEncoder:
      def __init__(self, model_name='hf-hub:Marqo/marqo-fashionSigLIP', device='cpu'):
      self.device = device
      logger.info(f"Loading SigLIP model: {model_name} on {device}...")
      self.model, _, self.preprocess = create_model_and_transforms(model_name, device=device)
      self.model.eval()
      logger.info("Model loaded successfully.")
      def encode_image(self, pil_image: Image.Image) -> np.ndarray:
      """
      Extracts the feature vector (embedding) from an image.
      This is the 'penultimate layer' representation.
      """
      try:
      image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
      with torch.no_grad():
      image_features = self.model.encode_image(image)
      # Normalize the features to unit length for better similarity matching
      image_features /= image_features.norm(dim=-1, keepdim=True)
      return image_features.cpu().numpy().flatten()
      except Exception as e:
      logger.error(f"Error encoding image: {e}")
      raise e
      def get_embedding_dimension(self):
      # SigLIP Marqo usually produces 768 dimensions
      return 768
      if __name__ == "__main__":
      # Quick test
      encoder = ImageEncoder()
      mock_image = Image.new('RGB', (224, 224), color='red')
      embedding = encoder.encode_image(mock_image)
      print(f"Embedding shape: {embedding.shape}")
      print(f"First 5 values: {embedding[:5]}")
      image_process/core/matcher.py 0 → 100644
      View file @ 446d8f46
      from loguru import logger
      class MatchResult:
      def __init__(self, top_k_results: list):
      self.results = top_k_results
      self.confidence_level = "low"
      self.message = ""
      def analyze_confidence(self, score_threshold=0.5, margin_threshold=0.1):
      """
      Logic 'Chốt hạ' dựa trên Score và Margin.
      Note: results contain 'distance' (Euclidean), smaller is better.
      But for UI we often use 'similarity' (1 - distance normalized).
      Let's assume input results are sorted by best match.
      """
      if not self.results:
      self.confidence_level = "none"
      self.message = "No matches found."
      return
      # Simple conversion from Euclidean distance to a 'similarity' score for logic
      # This is high-level, actual metric depends on the embedding space.
      def dist_to_sim(d):
      return max(0, 1 - d)
      s1 = dist_to_sim(self.results[0]['distance'])
      if len(self.results) > 1:
      s2 = dist_to_sim(self.results[1]['distance'])
      margin = s1 - s2
      else:
      margin = 1.0 # Only one result, high margin
      logger.info(f"Analysis: Top1 Similarity: {s1:.3f}, Margin: {margin:.3f}")
      if s1 > score_threshold and margin > margin_threshold:
      self.confidence_level = "high"
      self.message = "Exact product identified."
      # Only keep the best one for high confidence
      self.results = [self.results[0]]
      elif s1 > (score_threshold - 0.1) and margin < margin_threshold:
      self.confidence_level = "medium"
      self.message = "Multiple similar products found, clarification needed."
      # Keep top 2-3
      self.results = self.results[:3]
      else:
      self.confidence_level = "low"
      self.message = "No exact match found. Showing similar items."
      def to_dict(self):
      return {
      "confidence": self.confidence_level,
      "message": self.message,
      "items": self.results
      }
      image_process/db/faiss_mgr.py 0 → 100644
      View file @ 446d8f46
      import faiss
      import numpy as np
      import pickle
      import os
      from loguru import logger
      class FaissManager:
      def __init__(self, dimension=768, index_path="models/vector_db.index", mapping_path="models/sku_mapping.pkl"):
      self.dimension = dimension
      self.index_path = index_path
      self.mapping_path = mapping_path
      self.index = None
      self.sku_mapping = [] # List of SKUs, index matches FAISS ID
      if os.path.exists(index_path):
      self.load()
      else:
      logger.info("Initializing new FAISS index...")
      self.index = faiss.IndexFlatL2(dimension)
      self.sku_mapping = []
      def add_vectors(self, vectors: np.ndarray, skus: list):
      """
      Add image vectors and their corresponding SKUs to the index.
      """
      if vectors.shape[1] != self.dimension:
      raise ValueError(f"Vector dimension mismatch. Expected {self.dimension}, got {vectors.shape[1]}")
      self.index.add(vectors.astype('float32'))
      self.sku_mapping.extend(skus)
      logger.info(f"Added {len(skus)} items. Total items: {self.index.ntotal}")
      def search(self, query_vector: np.ndarray, top_k=5):
      """
      Search for the most similar vectors in the index.
      Returns: distances, results (list of dicts with sku and distance)
      """
      if self.index.ntotal == 0:
      return [], []
      query_vector = query_vector.reshape(1, -1).astype('float32')
      distances, indices = self.index.search(query_vector, top_k)
      results = []
      for dist, idx in zip(distances[0], indices[0]):
      if idx != -1: # -1 means not found
      results.append({
      "sku": self.sku_mapping[idx],
      "distance": float(dist)
      })
      return distances[0], results
      def save(self):
      os.makedirs(os.path.dirname(self.index_path), exist_ok=True)
      faiss.write_index(self.index, self.index_path)
      with open(self.mapping_path, "wb") as f:
      pickle.dump(self.sku_mapping, f)
      logger.info(f"Index saved to {self.index_path}")
      def load(self):
      logger.info(f"Loading FAISS index from {self.index_path}...")
      self.index = faiss.read_index(self.index_path)
      with open(self.mapping_path, "rb") as f:
      self.sku_mapping = pickle.load(f)
      logger.info(f"Loaded {self.index.ntotal} items.")
      if __name__ == "__main__":
      # Quick test
      mgr = FaissManager(dimension=4)
      v = np.random.random((2, 4)).astype('float32')
      mgr.add_vectors(v, ["SKU1", "SKU2"])
      dist, res = mgr.search(v[0], top_k=1)
      print(f"Search result: {res}")
      image_process/ingest_json.py 0 → 100644
      View file @ 446d8f46
      import json
      import numpy as np
      import os
      from loguru import logger
      import ast
      # Import our FaissManager
      from db.faiss_mgr import FaissManager
      from config import INDEX_PATH, MAPPING_PATH
      def ingest_from_json(json_path):
      if not os.path.exists(json_path):
      logger.error(f"JSON file not found at {json_path}")
      return
      logger.info(f"Reading data from {json_path}...")
      with open(json_path, 'r', encoding='utf-8') as f:
      data = json.load(f)
      # Process items
      all_items = []
      if isinstance(data, dict):
      for key in data:
      if isinstance(data[key], list):
      all_items.extend(data[key])
      elif isinstance(data, list):
      all_items = data
      logger.info(f"Found {len(all_items)} products to index.")
      faiss_mgr = FaissManager(dimension=768, index_path=INDEX_PATH, mapping_path=MAPPING_PATH)
      vectors = []
      skus = []
      for item in all_items:
      sku = item.get('internal_ref_code')
      vector_str = item.get('vector')
      if not sku or not vector_str:
      continue
      try:
      # Vector is stored as a string "[0.1, 0.2, ...]"
      vector = np.array(ast.literal_eval(vector_str)).astype('float32')
      # Normalize for similarity
      norm = np.linalg.norm(vector)
      if norm > 0:
      vector = vector / norm
      vectors.append(vector)
      skus.append(sku)
      except Exception as e:
      logger.error(f"Error parsing vector for SKU {sku}: {e}")
      if vectors:
      vectors_np = np.stack(vectors)
      faiss_mgr.add_vectors(vectors_np, skus)
      faiss_mgr.save()
      logger.info(f"Ingestion complete. Total items in FAISS: {faiss_mgr.index.ntotal}")
      else:
      logger.warning("No valid vectors found to index.")
      if __name__ == "__main__":
      target_json = r"d:\cnf\chatbot_canifa_image\data\magento_product_dimension_with_image_embedding_202512241158.json"
      ingest_from_json(target_json)
      image_process/main.py 0 → 100644
      View file @ 446d8f46
      import os
      import time
      import io
      from fastapi import FastAPI, Request, File, UploadFile, Form
      from fastapi.responses import HTMLResponse, JSONResponse
      from fastapi.templating import Jinja2Templates
      from PIL import Image
      from loguru import logger
      from core.detector import ClothingDetector
      from core.encoder import ImageEncoder
      from core.matcher import MatchResult
      from db.faiss_mgr import FaissManager
      app = FastAPI(title="Image Identification System")
      # Initialize components
      templates = Jinja2Templates(directory="templates")
      detector = ClothingDetector()
      encoder = ImageEncoder()
      faiss_mgr = FaissManager()
      @app.get("/", response_class=HTMLResponse)
      async def index(request: Request):
      return templates.TemplateResponse("index.html", {"request": request})
      @app.post("/match")
      async def match_image(
      file: UploadFile = File(...),
      score_threshold: float = Form(0.5)
      ):
      start_all = time.time()
      try:
      # 1. Read Image
      contents = await file.read()
      image = Image.open(io.BytesIO(contents)).convert("RGB")
      # 2. Detector: Remove noise (crop products)
      logger.info("Step 1: Detecting and cropping...")
      crops = detector.detect_and_crop(image)
      logger.info(f"Detected {len(crops)} items.")
      final_response_results = []
      # 3. Process each crop
      for i, crop in enumerate(crops):
      logger.info(f"Processing crop {i+1}...")
      # Encoder: Get vector
      embedding = encoder.encode_image(crop)
      # FAISS: Search in DB
      distances, raw_results = faiss_mgr.search(embedding, top_k=5)
      # Matcher: Analyze confidence
      matcher = MatchResult(raw_results)
      matcher.analyze_confidence(score_threshold=score_threshold)
      result_data = matcher.to_dict()
      final_response_results.append(result_data)
      total_time = time.time() - start_all
      return JSONResponse({
      "status": "success",
      "process_time": f"{total_time:.3f}s",
      "detections": final_response_results
      })
      except Exception as e:
      logger.error(f"Processing failed: {e}")
      return JSONResponse({"status": "error", "message": str(e)}, status_code=500)
      if __name__ == "__main__":
      import uvicorn
      uvicorn.run("main:app", host="0.0.0.0", port=8010, reload=True)
      image_process/run.py 0 → 100644
      View file @ 446d8f46
      import os
      import argparse
      from PIL import Image
      from tqdm import tqdm
      import numpy as np
      from loguru import logger
      from core.detector import ClothingDetector
      from core.encoder import ImageEncoder
      from db.faiss_mgr import FaissManager
      def index_images(image_dir, index_path="models/vector_db.index"):
      # 1. Init components
      detector = ClothingDetector()
      encoder = ImageEncoder()
      faiss_mgr = FaissManager(dimension=768, index_path=index_path)
      # 2. Get file list
      extensions = ('.jpg', '.jpeg', '.png', '.webp')
      image_files = [f for f in os.listdir(image_dir) if f.lower().endswith(extensions)]
      logger.info(f"Found {len(image_files)} images in {image_dir}")
      # 3. Process and Index
      for filename in tqdm(image_files):
      try:
      path = os.path.join(image_dir, filename)
      image = Image.open(path).convert("RGB")
      # Detect and crop (using the first detection as the main product for indexing)
      crops = detector.detect_and_crop(image)
      if not crops:
      continue
      # Embed the crop
      embedding = encoder.encode_image(crops[0])
      # SKU is usually the filename without extension
      sku = os.path.splitext(filename)[0]
      # Add to FAISS
      faiss_mgr.add_vectors(np.array([embedding]), [sku])
      except Exception as e:
      logger.error(f"Error processing {filename}: {e}")
      # 4. Save index
      faiss_mgr.save()
      logger.info("Indexing complete.")
      if __name__ == "__main__":
      parser = argparse.ArgumentParser(description="Index images into FAISS database.")
      parser.add_argument("--dir", type=str, required=True, help="Directory containing images to index.")
      parser.add_argument("--out", type=str, default="models/vector_db.index", help="Output index path.")
      args = parser.parse_args()
      index_images(args.dir, args.out)
      image_process/templates/index.html 0 → 100644
      View file @ 446d8f46
      <!DOCTYPE html>
      <html lang="en">
      <head>
      <meta charset="UTF-8">
      <meta name="viewport" content="width=device-width, initial-scale=1.0">
      <title>Image Identification Lab</title>
      <style>
      body {
      font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
      background-color: #f4f7f6;
      margin: 0;
      padding: 20px;
      color: #333;
      }
      .container {
      max-width: 800px;
      margin: auto;
      background: white;
      padding: 30px;
      border-radius: 12px;
      box-shadow: 0 4px 20px rgba(0, 0, 0, 0.08);
      }
      h1 {
      text-align: center;
      color: #2c3e50;
      margin-bottom: 30px;
      }
      .upload-section {
      border: 2px dashed #3498db;
      padding: 40px;
      text-align: center;
      border-radius: 8px;
      cursor: pointer;
      transition: background 0.3s;
      margin-bottom: 20px;
      }
      .upload-section:hover {
      background: #ebf5fb;
      }
      #preview {
      max-width: 100%;
      max-height: 400px;
      display: none;
      margin: 20px auto;
      border-radius: 8px;
      }
      .btn {
      display: block;
      width: 100%;
      padding: 12px;
      background: #3498db;
      color: white;
      border: none;
      border-radius: 6px;
      font-size: 16px;
      cursor: pointer;
      font-weight: bold;
      }
      .btn:hover {
      background: #2980b9;
      }
      #results {
      margin-top: 30px;
      border-top: 2px solid #eee;
      padding-top: 20px;
      }
      .result-item {
      background: #f9f9f9;
      padding: 15px;
      border-radius: 8px;
      margin-bottom: 10px;
      border-left: 5px solid #2ecc71;
      }
      .match-score {
      float: right;
      font-weight: bold;
      color: #27ae60;
      }
      .loader {
      border: 4px solid #f3f3f3;
      border-top: 4px solid #3498db;
      border-radius: 50%;
      width: 30px;
      height: 30px;
      animation: spin 2s linear infinite;
      display: none;
      margin: 20px auto;
      }
      @keyframes spin {
      0% {
      transform: rotate(0deg);
      }
      100% {
      transform: rotate(360deg);
      }
      }
      </style>
      </head>
      <body>
      <div class="container">
      <h1>🔍 Image Identification Lab</h1>
      <div class="upload-section" onclick="document.getElementById('fileInput').click()">
      <p>Click to Upload or Drag & Drop Product Image</p>
      <input type="file" id="fileInput" hidden accept="image/*" onchange="previewImage(event)">
      </div>
      <img id="preview" alt="Preview">
      <button class="btn" onclick="uploadAndMatch()">IDENTIFY PRODUCT</button>
      <div id="loader" class="loader"></div>
      <div id="results">
      <!-- Results will appear here -->
      </div>
      </div>
      <script>
      function previewImage(event) {
      const preview = document.getElementById('preview');
      const file = event.target.files[0];
      const reader = new FileReader();
      reader.onload = function () {
      preview.src = reader.result;
      preview.style.display = 'block';
      }
      if (file) reader.readAsDataURL(file);
      }
      async function uploadAndMatch() {
      const fileInput = document.getElementById('fileInput');
      if (fileInput.files.length === 0) {
      alert("Please select an image first!");
      return;
      }
      const resultsDiv = document.getElementById('results');
      const loader = document.getElementById('loader');
      resultsDiv.innerHTML = '';
      loader.style.display = 'block';
      const formData = new FormData();
      formData.append('file', fileInput.files[0]);
      try {
      const response = await fetch('/match', {
      method: 'POST',
      body: formData
      });
      const data = await response.json();
      loader.style.display = 'none';
      if (data.status === 'success') {
      resultsDiv.innerHTML = `<h3>Process Time: ${data.process_time}</h3>`;
      data.detections.forEach((detection, detIndex) => {
      const detHtml = `
      <div style="margin-top:20px; padding:15px; background:#f0f3f5; border-radius:8px;">
      <h4 style="margin-top:0;">Detection #${detIndex + 1}: ${detection.message}</h4>
      <div style="color: #2980b9; font-weight:bold; margin-bottom:10px;">Confidence: ${detection.confidence.toUpperCase()}</div>
      <div id="items-${detIndex}"></div>
      </div>
      `;
      resultsDiv.insertAdjacentHTML('beforeend', detHtml);
      const itemsDiv = document.getElementById(`items-${detIndex}`);
      detection.items.forEach(res => {
      // Convert distance to a mock similarity % for display
      const sim = Math.max(0, (1 - res.distance) * 100).toFixed(1);
      itemsDiv.innerHTML += `
      <div class="result-item">
      <span class="match-score">${sim}% Match</span>
      <div><strong>SKU:</strong> ${res.sku}</div>
      </div>
      `;
      });
      });
      } else {
      resultsDiv.innerHTML = `<p style="color:red">Error: ${data.message}</p>`;
      }
      } catch (error) {
      loader.style.display = 'none';
      alert("Upload failed: " + error);
      }
      }
      </script>
      </body>
      </html>
      \ No newline at end of file
      image_process/test_search.py 0 → 100644
      View file @ 446d8f46
      import numpy as np
      from db.faiss_mgr import FaissManager
      from config import INDEX_PATH, MAPPING_PATH
      def test_search():
      mgr = FaissManager(dimension=768, index_path=INDEX_PATH, mapping_path=MAPPING_PATH)
      if mgr.index.ntotal == 0:
      print("Index is empty!")
      return
      print(f"Index total: {mgr.index.ntotal}")
      # Create a random query vector
      query = np.random.random((768,)).astype('float32')
      norm = np.linalg.norm(query)
      query /= norm
      dist, res = mgr.search(query, top_k=3)
      print("Top 3 matches for a random vector:")
      for r in res:
      print(f"SKU: {r['sku']}, Distance: {r['distance']:.4f}")
      if __name__ == "__main__":
      test_search()
      ingest_json.py 0 → 100644
      View file @ 446d8f46
      import json
      import numpy as np
      import os
      from loguru import logger
      import ast
      # Import our FaissManager
      from db.faiss_mgr import FaissManager
      from config import INDEX_PATH, MAPPING_PATH
      def ingest_from_json(json_path):
      if not os.path.exists(json_path):
      logger.error(f"JSON file not found at {json_path}")
      return
      logger.info(f"Reading data from {json_path}...")
      with open(json_path, 'r', encoding='utf-8') as f:
      data = json.load(f)
      # The user provided a structure where the key is the SQL query
      # and the value is a list of results.
      all_items = []
      for key in data:
      if isinstance(data[key], list):
      all_items.extend(data[key])
      logger.info(f"Found {len(all_items)} products to index.")
      faiss_mgr = FaissManager(dimension=768, index_path=INDEX_PATH, mapping_path=MAPPING_PATH)
      vectors = []
      skus = []
      for item in all_items:
      sku = item.get('internal_ref_code')
      vector_str = item.get('vector')
      if not sku or not vector_str:
      continue
      try:
      # Vector is stored as a string "[0.1, 0.2, ...]"
      vector = np.array(ast.literal_eval(vector_str)).astype('float32')
      # Normalize to unit length (standard for cosine similarity in FAISS IndexFlatL2)
      norm = np.linalg.norm(vector)
      if norm > 0:
      vector = vector / norm
      vectors.append(vector)
      skus.append(sku)
      except Exception as e:
      logger.error(f"Error parsing vector for SKU {sku}: {e}")
      if vectors:
      vectors_np = np.stack(vectors)
      faiss_mgr.add_vectors(vectors_np, skus)
      faiss_mgr.save()
      logger.info(f"Ingestion complete. Total items in FAISS: {faiss_mgr.index.ntotal}")
      else:
      logger.warning("No valid vectors found to index.")
      if __name__ == "__main__":
      # Path to the JSON file provided by the user
      target_json = r"d:\cnf\chatbot_canifa_image\data\magento_product_dimension_with_image_embedding_202512241158.json"
      ingest_from_json(target_json)
      cdcdccdc @ f5deb28b
      Subproject commit f5deb28b0f9f83c6c3ea53cdd9e68f5c1a5fcbe7
      chatbot-rsa @ d6b45f42
      Subproject commit d6b45f42c45f8f1c5957894201bff23f140da1a2
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