# ShopAgent 项目技术实现报告

  
  ## 一、项目概述
  

  ShopAgent 是一个基于 **LangGraph** 和 **ReAct 模式** 的自主多模态时尚购物智能体。系统能够自主决定调用哪些工具、维护对话状态、判断何时回复，实现智能化的商品发现与推荐。

  
  ### 核心特性
  
  - **自主工具选择与执行**：Agent 根据用户意图自主选择并调用工具

  - **文本搜索**：通过 Search API 进行商品搜索

  - **对话上下文感知**：多轮对话中保持上下文记忆
  - **实时视觉分析**：基于 VLM 的图片风格分析
  
  ---
  
  ## 二、技术栈
  
  | 组件 | 技术选型 |
  |------|----------|
  | 运行环境 | Python 3.12 |
  | Agent 框架 | LangGraph 1.x |
  | LLM 框架 | LangChain 1.x（支持任意 LLM，默认 gpt-4o-mini） |

  | 搜索服务 | Search API (HTTP) |

  | 前端 | Streamlit |
  | 数据集 | Kaggle Fashion Products |
  
  ---
  
  ## 三、系统架构
  
  ### 3.1 整体架构图
  
  ```
  ┌─────────────────────────────────────────────────────────────────┐
  │                     Streamlit 前端 (app.py)                       │
  └─────────────────────────────────────────────────────────────────┘
                                      │
                                      ▼
  ┌─────────────────────────────────────────────────────────────────┐
  │              ShoppingAgent (shopping_agent.py)                    │
  │  ┌───────────────────────────────────────────────────────────┐  │
  │  │  LangGraph StateGraph + ReAct Pattern                      │  │
  │  │  START → Agent → [Has tool_calls?] → Tools → Agent → END   │  │
  │  └───────────────────────────────────────────────────────────┘  │
  └─────────────────────────────────────────────────────────────────┘

          │                    │
          ▼                    ▼
  ┌──────────────┐   ┌─────────────────────┐
  │ search_      │   │ analyze_image_style  │
  │ products     │   │ (OpenAI Vision)      │
  └──────┬───────┘   └──────────┬──────────┘
         │                      │
         ▼                      │
  ┌──────────────────┐          │
  │   Search API     │          │
  │ (HTTP POST)      │          │
  └──────────────────┘          │
                                 ▼

  ┌─────────────────────────────────────────────────────────────────┐

  │           OpenAI API (VLM 风格分析)                               │

  └─────────────────────────────────────────────────────────────────┘
  ```
  
  ### 3.2 Agent 流程图（LangGraph）
  
  ```mermaid
  graph LR
      START --> Agent
      Agent -->|Has tool_calls| Tools
      Agent -->|No tool_calls| END
      Tools --> Agent
  ```
  
  ---
  
  ## 四、关键代码实现
  
  ### 4.1 Agent 核心实现（shopping_agent.py）
  
  #### 4.1.1 状态定义
  
  ```python
  from typing_extensions import Annotated, TypedDict
  from langgraph.graph.message import add_messages
  
  class AgentState(TypedDict):
      """State for the shopping agent with message accumulation"""
      messages: Annotated[Sequence[BaseMessage], add_messages]
      current_image_path: Optional[str]  # Track uploaded image
  ```
  
  - `messages` 使用 `add_messages` 实现消息累加，支持多轮对话
  - `current_image_path` 存储当前上传的图片路径供工具使用
  
  #### 4.1.2 LangGraph 图构建
  
  ```python
  def _build_graph(self):
      """Build the LangGraph StateGraph"""
      
      def agent_node(state: AgentState):
          """Agent decision node - decides which tools to call or when to respond"""
          messages = state["messages"]
          if not any(isinstance(m, SystemMessage) for m in messages):
              messages = [SystemMessage(content=system_prompt)] + list(messages)
          response = self.llm_with_tools.invoke(messages)
          return {"messages": [response]}
  
      tool_node = ToolNode(self.tools)
  
      def should_continue(state: AgentState):
          """Determine if agent should continue or end"""
          last_message = state["messages"][-1]
          if hasattr(last_message, "tool_calls") and last_message.tool_calls:
              return "tools"
          return END
  
      workflow = StateGraph(AgentState)
      workflow.add_node("agent", agent_node)
      workflow.add_node("tools", tool_node)
      workflow.add_edge(START, "agent")
      workflow.add_conditional_edges("agent", should_continue, ["tools", END])
      workflow.add_edge("tools", "agent")
  
      checkpointer = MemorySaver()
      return workflow.compile(checkpointer=checkpointer)
  ```
  
  关键点：
  - **agent_node**：将消息传入 LLM，由 LLM 决定是否调用工具
  - **should_continue**：若有 `tool_calls` 则进入工具节点，否则结束
  - **MemorySaver**：按 `thread_id` 持久化对话状态
  
  #### 4.1.3 System Prompt 设计
  
  ```python
  system_prompt = """You are an intelligent fashion shopping assistant. You can:
  1. Search for products by text description (use search_products)

  2. Analyze image style and attributes (use analyze_image_style)

  
  When a user asks about products:
  - For text queries: use search_products directly

  - For image uploads: use analyze_image_style first to understand the product, then use search_products with the extracted description

  - You can call multiple tools in sequence if needed
  - Always provide helpful, friendly responses
  
  CRITICAL FORMATTING RULES:
  When presenting product results, you MUST use this EXACT format for EACH product:
  1. [Product Name]
     ID: [Product ID Number]
     Category: [Category]
     Color: [Color]
     Gender: [Gender]
     (Include Season, Usage, Relevance if available)
  ..."""
  ```
  
  通过 system prompt 约束工具使用和输出格式，保证前端可正确解析产品信息。
  
  #### 4.1.4 对话入口与流式处理
  
  ```python
  def chat(self, query: str, image_path: Optional[str] = None) -> dict:
      # Build input message
      message_content = query
      if image_path:
          message_content = f"{query}\n[User uploaded image: {image_path}]"
  
      config = {"configurable": {"thread_id": self.session_id}}
      input_state = {
          "messages": [HumanMessage(content=message_content)],
          "current_image_path": image_path,
      }
  
      tool_calls = []
      for event in self.graph.stream(input_state, config=config):
          if "agent" in event:
              for msg in event["agent"].get("messages", []):
                  if hasattr(msg, "tool_calls") and msg.tool_calls:
                      for tc in msg.tool_calls:
                          tool_calls.append({"name": tc["name"], "args": tc.get("args", {})})
          if "tools" in event:
              # 记录工具执行结果
              ...
  
      final_state = self.graph.get_state(config)
      response_text = final_state.values["messages"][-1].content
  
      return {"response": response_text, "tool_calls": tool_calls, "error": False}
  ```
  
  ---
  
  ### 4.2 搜索工具实现（search_tools.py）
  

  #### 4.2.1 文本搜索（Search API）

  
  ```python
  @tool
  def search_products(query: str, limit: int = 5) -> str:
      """Search for fashion products using natural language descriptions."""
      try:

          url = f"{settings.search_api_base_url.rstrip('/')}/search/"
          headers = {
              "Content-Type": "application/json",
              "X-Tenant-ID": settings.search_api_tenant_id,
          }
          payload = {
              "query": query,
              "size": min(limit, 20),
              "from": 0,
              "language": "zh",
          }
  
          response = requests.post(url, json=payload, headers=headers, timeout=60)
          data = response.json()
          results = data.get("results", [])

  
          if not results:
              return "No products found matching your search."
  
          output = f"Found {len(results)} product(s):\n\n"
          for idx, product in enumerate(results, 1):

              output += f"{idx}. {product.get('title', 'Unknown Product')}\n"
              output += f"   ID: {product.get('spu_id', 'N/A')}\n"
              output += f"   Category: {product.get('category_path', 'N/A')}\n"
              output += f"   Price: {product.get('price')}\n"

              output += "\n"
  
          return output.strip()
      except Exception as e:
          return f"Error searching products: {str(e)}"
  ```
  

  #### 4.2.2 视觉分析（VLM）

  
  ```python
  @tool
  def analyze_image_style(image_path: str) -> str:
      """Analyze a fashion product image using AI vision to extract detailed style information."""
      with open(img_path, "rb") as image_file:
          image_data = base64.b64encode(image_file.read()).decode("utf-8")
  
      prompt = """Analyze this fashion product image and provide a detailed description.
  Include:
  - Product type (e.g., shirt, dress, shoes, pants, bag)
  - Primary colors
  - Style/design (e.g., casual, formal, sporty, vintage, modern)
  - Pattern or texture (e.g., plain, striped, checked, floral)
  - Key features (e.g., collar type, sleeve length, fit)
  - Material appearance (if obvious, e.g., denim, cotton, leather)
  - Suitable occasion (e.g., office wear, party, casual, sports)
  Provide a comprehensive yet concise description (3-4 sentences)."""
  
      client = get_openai_client()
      response = client.chat.completions.create(
          model="gpt-4o-mini",
          messages=[{
              "role": "user",
              "content": [
                  {"type": "text", "text": prompt},
                  {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}", "detail": "high"}},
              ],
          }],
          max_tokens=500,
          temperature=0.3,
      )
  
      return response.choices[0].message.content.strip()
  ```
  
  ---
  

  ### 4.3 Streamlit 前端（app.py）

  #### 4.3.1 会话与 Agent 初始化

  
  ```python
  def initialize_session():
      if "session_id" not in st.session_state:
          st.session_state.session_id = str(uuid.uuid4())
      if "shopping_agent" not in st.session_state:
          st.session_state.shopping_agent = ShoppingAgent(session_id=st.session_state.session_id)
      if "messages" not in st.session_state:
          st.session_state.messages = []
      if "uploaded_image" not in st.session_state:
          st.session_state.uploaded_image = None
  ```
  

  #### 4.3.2 产品信息解析

  
  ```python
  def extract_products_from_response(response: str) -> list:
      """从 Agent 回复中解析产品信息"""
      products = []
      for line in response.split("\n"):
          if re.match(r"^\*?\*?\d+\.\s+", line):
              if current_product:
                  products.append(current_product)
              current_product = {"name": re.sub(r"^\*?\*?\d+\.\s+", "", line).replace("**", "").strip()}
          elif "ID:" in line:
              id_match = re.search(r"(?:ID|id):\s*(\d+)", line)
              if id_match:
                  current_product["id"] = id_match.group(1)
          elif "Category:" in line:
              cat_match = re.search(r"Category:\s*(.+?)(?:\n|$)", line)
              if cat_match:
                  current_product["category"] = cat_match.group(1).strip()
          # ... Color, Gender, Season, Usage, Similarity/Relevance
      return products
  ```
  

  #### 4.3.3 多轮对话中的图片引用

  
  ```python
  # 用户输入 "make them formal" 时，若上一条消息有图片，则引用该图片
  if any(ref in query_lower for ref in ["this", "that", "the image", "it"]):
      for msg in reversed(st.session_state.messages):
          if msg.get("role") == "user" and msg.get("image_path"):
              image_path = msg["image_path"]
              break
  ```
  
  ---
  

  ### 4.4 配置管理（config.py）

  
  ```python
  class Settings(BaseSettings):
      openai_api_key: str
      openai_model: str = "gpt-4o-mini"

      search_api_base_url: str = "http://120.76.41.98:6002"
      search_api_tenant_id: str = "162"

  
      class Config:
          env_file = ".env"
  ```
  
  ---
  
  ## 五、部署与运行
  
  ### 5.1 依赖服务
  

  - **Search API**：外部搜索服务（HTTP）
  - **OpenAI API**：LLM 与 VLM 图像分析

  
  ### 5.2 启动流程
  
  ```bash
  # 1. 环境
  pip install -r requirements.txt

  cp .env.example .env  # 配置 OPENAI_API_KEY、SEARCH_API_* 等

  # 2. （可选）下载数据

  python scripts/download_dataset.py  # Kaggle Fashion Product Images Dataset
  

  # 3. 启动应用

  streamlit run app.py

  # 或 ./scripts/start.sh

  ```
  
  ---
  
  ## 六、典型交互流程
  
  | 场景 | 用户输入 | Agent 行为 | 工具调用 |
  |------|----------|------------|----------|
  | 文本搜索 | "winter coats for women" | 直接文本搜索 | `search_products("winter coats women")` |

  | 风格分析+搜索 | [上传复古夹克] "what style? find matching pants" | 先分析风格再搜索 | `analyze_image_style(path)` → `search_products("vintage pants casual")` |
  | 多轮上下文 | [第1轮] "show me red dresses"<br>[第2轮] "make them formal" | 结合上下文 | `search_products("red formal dresses")` |
  
  ---
  
  ## 七、设计要点总结
  
  1. **ReAct 模式**：Agent 自主决定何时调用工具、调用哪些工具、是否继续调用。
  2. **LangGraph 状态图**：`START → Agent → [条件] → Tools → Agent → END`，支持多轮工具调用。

  3. **搜索与风格分析**：Search API 文本搜索 + VLM 图像风格分析。
  4. **会话持久化**：`MemorySaver` + `thread_id` 实现多轮对话记忆。
  5. **格式约束**：System prompt 严格限制产品输出格式，便于前端解析和展示。

  
  ---
  
  ## 八、附录：项目结构
  
  ```

  ShopAgent/

  ├── app/
  │   ├── agents/
  │   │   └── shopping_agent.py
  │   ├── config.py
  │   ├── services/

  │   └── tools/
  │       └── search_tools.py
  ├── scripts/
  │   ├── download_dataset.py
  │   └── index_data.py
  ├── app.py
  ├── docker-compose.yml
  └── requirements.txt
  ```