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sourangshupal

MCP-RAG Server

by sourangshupal
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MCP-RAG：带有 RAG 的模型上下文协议🚀

使用 GroundX 和 OpenAI 实现的强大而高效的 RAG（检索增强生成），采用现代上下文处理 (MCP) 构建。

🌟 功能

  • 高级 RAG 实现：利用 GroundX 进行高精度文档检索

  • 模型上下文协议：与 MCP 无缝集成，增强上下文处理

  • 类型安全：使用 Pydantic 构建，可进行强大的类型检查和验证

  • 灵活的配置：通过环境变量轻松定制设置

  • 文档提取：支持 PDF 文档提取和处理

  • 智能搜索：具有评分的语义搜索功能

Related MCP server: Chalee MCP RAG

🛠️ 先决条件

  • Python 3.12 或更高版本

  • OpenAI API 密钥

  • GroundX API 密钥

  • MCP CLI 工具

📦安装

  1. 克隆存储库：

git clone <repository-url>
cd mcp-rag

  1. 创建并激活虚拟环境：

uv sync
source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`

⚙️ 配置

  1. 复制示例环境文件：

cp .env.example .env

  1. 在.env中配置环境变量：

GROUNDX_API_KEY="your-groundx-api-key"
OPENAI_API_KEY="your-openai-api-key"
BUCKET_ID="your-bucket-id"

🚀 使用方法

启动服务器

使用以下命令运行检查服务器：

mcp dev server.py

文档摄取

要获取新文档：

from server import ingest_documents

result = ingest_documents("path/to/your/document.pdf")
print(result)

执行搜索

基本搜索查询：

from server import process_search_query

response = process_search_query("your search query here")
print(f"Query: {response.query}")
print(f"Score: {response.score}")
print(f"Result: {response.result}")

使用自定义配置：

from server import process_search_query, SearchConfig

config = SearchConfig(
    completion_model="gpt-4",
    bucket_id="custom-bucket-id"
)
response = process_search_query("your query", config)

📚 依赖项

  • groundx （≥2.3.0）：核心 RAG 功能

  • openai (≥1.75.0)：OpenAI API 集成

  • mcp[cli] (≥1.6.0)：现代上下文处理工具

  • ipykernel （≥6.29.5）：Jupyter 笔记本支持

🔒 安全

  • 切勿提交包含 API 密钥的.env文件

  • 对所有敏感信息使用环境变量

  • 定期轮换您的 API 密钥

  • 监控 API 使用情况，防止任何未经授权的访问

🤝 贡献

  1. 分叉存储库

  2. 创建你的功能分支（ git checkout -b feature/amazing-feature ）

  3. 提交您的更改（ git commit -m 'Add some amazing feature' ）

  4. 推送到分支（ git push origin feature/amazing-feature ）

  5. 打开拉取请求

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