ShopAgent

An autonomous multi-modal fashion shopping agent powered by LangGraph and ReAct pattern.

Demo

📄 demo.pdf

Overview

ShopAgent autonomously decides which tools to call, maintains conversation state, and determines when to respond. Built with LangGraph, it uses agentic patterns for intelligent product discovery.

Key Features:

• Autonomous tool selection and execution
• Text search via Search API
• Conversational context awareness
• Real-time visual analysis (style extraction from images)

Tech Stack

Component	Technology
Agent Framework	LangGraph
LLM	any LLM supported by LangChain
Search	Search API (HTTP)
Frontend	Streamlit
Dataset	Kaggle Fashion Products

Architecture

Agent Flow:

graph LR
    START --> Agent
    Agent -->|Has tool_calls| Tools
    Agent -->|No tool_calls| END
    Tools --> Agent

    subgraph "Agent Node"
        A[Receive Messages] --> B[LLM Reasoning]
        B --> C{Need Tools?}
        C -->|Yes| D[Generate tool_calls]
        C -->|No| E[Generate Response]
    end

    subgraph "Tool Node"
        F[Execute Tools] --> G[Return ToolMessage]
    end

Available Tools:

• search_products(query) - Text-based product search via Search API
• analyze_image_style(image_path) - VLM style analysis

Examples

Text Search:

User: "winter coats for women"
Agent: search_products("winter coats women") → Returns 5 products

Style Analysis + Search:

User: [uploads vintage jacket] "what style is this? find matching pants"
Agent: analyze_image_style(path) → "Vintage denim bomber..."
       search_products("vintage pants casual") → Returns matching items

Multi-turn Context:

Turn 1: "show me red dresses"
Agent: search_products("red dresses") → Results

Turn 2: "make them formal"
Agent: [remembers context] → search_products("red formal dresses") → Results

Complex Reasoning:

User: [uploads office outfit] "I like the shirt but need something more casual"
Agent: analyze_image_style(path) → Extracts shirt details
       search_products("casual shirt [color] [style]") → Returns casual alternatives

**Note:** For image uploads "find similar", use analyze_image_style first to extract attributes, then search_products with the description.

Installation

Prerequisites:

• Python 3.12+ (LangChain 1.x 要求 Python 3.10+)
• OpenAI API Key

1. Setup Environment

# Clone and install dependencies
git clone <repository-url>
cd ShopAgent
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY

2. (Optional) Download Dataset

For image style analysis, you may download the Fashion Product Images Dataset from Kaggle:

python scripts/download_dataset.py

3. Launch Application

# 使用启动脚本（推荐）
./scripts/start.sh

# 或直接运行
streamlit run app.py

Product search uses the external Search API. Configure SEARCH_API_BASE_URL and SEARCH_API_TENANT_ID in .env if needed.

Opens at http://localhost:8501

CentOS 8 部署

详见 docs/DEPLOY_CENTOS8.md