🛍️ AI Shopping Assistant

An intelligent, conversational shopping assistant powered by the Groq AI model and Model Context Protocol (MCP) for smart web-based product discovery and decision-making.

✨ Overview

The AI Shopping Assistant is an interactive, AI-powered chatbot that helps users make smarter shopping decisions. Backed by xAI’s Groq LLM and the Model Context Protocol (MCP), it can:

• 🧠 Understand natural language queries
• 🔎 Conduct real-time searches on shopping platforms
• 🛒 Compare products, services, and features
• 💸 Provide price guidance and recommendations

Whether you're choosing between phones, comparing streaming services, or searching for the best air purifier under a budget—this assistant is your ultimate shopping buddy.

🧩 Features

🗂️ Project Structure

ai-shopping-assistant/
├── shopping_assistant.py      # Main assistant logic
├── requirements.txt           # Python dependencies
├── .env                       # Environment variables
├── browser_mcp.json           # MCP config for search engines
└── README.md                  # You're reading it!

🔮 Future Enhancements

• 🛒 Real-time price scraping from major e-retailers
• 🧬 Personalized recommendations via user profiles
• 🖥️ Web-based UI for a seamless UX
• 🛠️ Enhanced MCP integration with more shopping portals

🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository

2. Create your feature branch

   Copy

   git checkout -b feature/your-feature

3. Commit your changes

   Copy

   git commit -m "Add your feature"

4. Push and open a PR

   Copy

   git push origin feature/your-feature

Images:

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🧠 AI + Shopping = Smarter Choices Start your intelligent shopping journey now with the AI Shopping Assistant.

🚀 Installation

1. Clone the Repository

git clone <repository-url>
cd ai-shopping-assistant

2. Set Up a Virtual Environment (Optional)

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install Python Dependencies

pip install -r requirements.txt

4. Install MCP Node.js Dependencies

Make sure Node.js and npm are installed:

npm install -g @playwright/mcp @openbnb/mcp-server-airbnb duckduckgo-mcp-server

5. Environment Setup

Create a .env file in the root directory:

echo "GROQ_API_KEY=your-api-key-here" > .env

6. MCP Configuration

Ensure you have a valid browser_mcp.json in your MCP directory (e.g., D:\mcp\mcpdemo\):

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    },
    "airbnb": {
      "command": "npx",
      "args": ["-y", "@openbnb/mcp-server-airbnb"]
    },
    "duckduckgo-search": {
      "command": "npx",
      "args": ["-y", "duckduckgo-mcp-server"]
    }
  }
}

In shopping_assistant.py, set:

self.config_file = r"path/to/your/browser_mcp.json"

🧠 Usage

Start the Assistant:

python shopping_assistant.py

Example Queries:

🛒 You: Best laptop for programming under $1000  
🤖 Assistant: 🔍 Searching... (attempt 1/3)  
✅ Successfully retrieved current information  
[Laptop recommendations with specs and prices]

Commands You Can Use:

• exit or quit – End the session
• clear – Reset chat history
• context – View recent conversation summary
• status – Check last search time and rate limits

📦 Requirements

Add the following to your requirements.txt:

langchain-grok==0.1.0
python-dotenv==1.0.0
requests==2.31.0
mcp-use==<latest-version>

⚙️ Configuration Details

⚠️ Limitations

• Requires internet connection for API and MCP search
• Prices may vary—verify with retailers
• Only predefined categories supported
• MCP setup requires proper Node.js configuration
• Offline fallbacks may offer limited depth