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Glama

Custom MCP Server for Cursor

by Feustey

MCP - 带有 RAG 的问答系统

MCP 是一种先进的问答系统,使用检索增强生成 (RAG) 技术根据文档集提供准确且符合上下文的答案。

特征

  • 🔍 文档中的语义搜索
  • 💾 使用 Redis 进行智能缓存
  • 📊 使用 MongoDB 进行持久存储
  • 🤖 与 OpenAI 集成,用于嵌入和文本生成
  • 📈 系统监控和指标
  • 🔄 异步操作管理

先决条件

  • Python 3.9+
  • MongoDB 社区版
  • Redis
  • OpenAI API 密钥

设施

  1. 克隆存储库:
git clone https://github.com/votre-username/mcp.git cd mcp
  1. 安装系统依赖项:
# MongoDB brew tap mongodb/brew brew install mongodb-community brew services start mongodb-community # Redis brew install redis brew services start redis
  1. 配置Python环境:
python -m venv .venv source .venv/bin/activate # Sur Unix/macOS pip install -r requirements.txt
  1. 配置环境变量:
cp .env.example .env # Éditer .env avec vos configurations

快速使用

from src.rag import RAGWorkflow # Initialisation rag = RAGWorkflow() # Ingestion de documents await rag.ingest_documents("chemin/vers/documents") # Interrogation response = await rag.query("Votre question ici ?")

文档

测试

python -m pytest tests/ -v

项目结构

mcp/ ├── src/ │ ├── __init__.py │ ├── rag.py # Workflow RAG principal │ ├── models.py # Modèles de données │ ├── mongo_operations.py # Opérations MongoDB │ ├── redis_operations.py # Opérations Redis │ └── database.py # Configuration de la base de données ├── tests/ │ ├── __init__.py │ ├── test_mcp.py │ └── test_mongo_integration.py ├── prompts/ │ ├── system_prompt.txt │ ├── query_prompt.txt │ └── response_prompt.txt ├── docs/ │ ├── installation.md │ ├── usage.md │ ├── architecture.md │ └── api.md ├── requirements.txt ├── .env.example └── README.md

贡献

  1. 分叉项目
  2. 为你的功能创建一个分支( git checkout -b feature/AmazingFeature
  3. 提交您的更改( git commit -m 'Add some AmazingFeature'
  4. 推送到分支( git push origin feature/AmazingFeature
  5. 打开拉取请求

执照

该项目已获得 MIT 许可。有关详细信息,请参阅LICENSE文件。

接触

您的姓名 - @your_twitter

项目链接: https://github.com/your-username/mcp

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

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

连接到 Cursor 并使用 LlamaIndex 通过 Linkup 和 RAG 功能实现深度网络搜索。

  1. 特征
    1. 先决条件
      1. 设施
        1. 快速使用
          1. 文档
            1. 测试
              1. 项目结构
                1. 贡献
                  1. 执照
                    1. 接触

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