Q&A Copilot#

Q&A Copilot is the intelligent question-answering component of the InteRecipe system, a professional Data-Juicer AI assistant built on the AgentScope framework.

Core Components#

  • Juicy Agent: Intelligent Q&A agent based on ReActAgent

  • MCP Integration: Code analysis capabilities through Serena MCP server

  • Redis Storage: Supports session history and feedback data persistence

  • Web API: Provides RESTful interfaces for frontend integration

Quick Start#

Prerequisites#

  • Python >= 3.10

  • Redis server

  • DashScope API Key (for large language model calls)

Installation#

  1. Install dependencies

    cd ..
uv pip install .[qa]
cd qa-copilot

  2. Install and start Redis

    # Ubuntu/Debian
sudo apt-get install redis-server
redis-server --daemonize yes

# macOS
brew install redis
brew services start redis

Configuration#

  1. Set environment variables

    export DASHSCOPE_API_KEY="your_dashscope_api_key"

  2. Configure Data-Juicer path

    Edit the setup_server.sh file and replace DATA_JUICER_PATH with the absolute path to your local data-juicer repository:

    export DATA_JUICER_PATH="/path/to/your/data-juicer"

  3. Start the service

    bash setup_server.sh

Usage#

Web API Interfaces#

After starting the service, the system provides the following API interfaces:

1. Q&A Conversation#

POST /process
Content-Type: application/json

{
  "input": [
    {
      "role": "user", 
      "content": [{"type": "text", "text": "How to use Data-Juicer for data cleaning?"}]
    }
  ],
  "session_id": "your_session_id"
}

2. Get Session History#

POST /memory
Content-Type: application/json

{
  "session_id": "your_session_id"
}

3. Clear Session History#

POST /clear
Content-Type: application/json

{
  "session_id": "your_session_id"
}

4. Submit Feedback#

POST /feedback
Content-Type: application/json

{
  "message_id": "msg_id",
  "feedback": "like",  // "like" or "dislike"
  "session_id": "your_session_id",
  "user_id": "user_id"
}

WebUI#

you can simply run the following command in your terminal:

npx @agentscope-ai/chat agentscope-runtime-webui --url http://localhost:8080/process

Refer to AgentScope Runtime WebUI for more information.

Configuration Details#

Model Configuration#

In app_deploy.py, you can configure the language model to use:

model=DashScopeChatModel(
    "qwen-max",  # Model name
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    stream=True,  # Enable streaming response
)

MCP Service Configuration#

The system uses Serena MCP server to provide code analysis capabilities:

serena_command = [
    "uvx", "--with", "pyright[nodejs]",
    "--from", "git+https://github.com/oraios/serena",
    "serena", "start-mcp-server",
    "--project", DATA_JUICER_PATH,
    "--mode", "planning",
]

Troubleshooting#

Common Issues#

  1. Redis connection failure

    • Ensure Redis service is running: redis-cli ping

    • Check if Redis port is occupied: netstat -tlnp | grep 6379

  2. MCP service startup failure

    • Ensure DATA_JUICER_PATH is correct and exists

    • Check if Node.js is installed (Serena MCP dependency)

  3. API Key error

    • Verify DASHSCOPE_API_KEY environment variable is correctly set

    • Confirm API Key is valid and has sufficient quota

License#

This project uses the same license as the main project. For details, please refer to the LICENSE file.