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MCP Server for ML Model Integration

by nicknochnack
GitHub
Python
13
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构建 MCP 服务器

有关如何构建 MCP 服务器以服务于经过训练的随机森林模型并将其与 Bee Framework 集成以实现 ReAct 交互的完整演练。

亲眼见证它的精彩表现📺

启动 MCP 服务器🚀

  1. 克隆此 repo git clone https://github.com/nicknochnack/BuildMCPServer
  2. 运行 MCP 服务器
    cd BuildMCPServer
    uv venv
    source .venv/bin/activate
    uv add .
    uv add ".[dev]"
    uv run mcp dev server.py
  3. 要运行代理，请在单独的终端中运行：
    source .venv/bin/activate
    uv run singleflowagent.py

启动 FastAPI 托管的 ML 服务器

git clone https://github.com/nicknochnack/CodeThat-FastML
cd CodeThat-FastML
pip install -r requirements.txt
uvicorn mlapi:app --reload
关于如何构建它的详细说明也可以在这里找到

其他参考

  • 构建 MCP 客户端（用于单流代理）
  • 我构建机器学习服务器的原始视频

谁、何时、为什么？

👨🏾‍💻 作者：Nick Renotte 📅 版本：1.x 📜 许可证：本项目采用 MIT 许可证

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

将经过训练的随机森林模型与 Bee Framework 相集成的服务器，使 AI 工具和代理能够与 ReAct 进行交互。

  1. 亲眼见证它的精彩表现📺
    1. 启动 MCP 服务器🚀
      1. 启动 FastAPI 托管的 ML 服务器
        1. 其他参考
          1. 谁、何时、为什么？

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