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MCP RAG Server

@karaage0703

代码仓库主页

基本信息

分类

记忆与知识

许可证

MIT

传输方式

stdio

作者

karaage0703

关于 MCP RAG Server

暂无概览

MCP RAG Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-rag-server": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

MCP RAG Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP RAG Server?

MCP RAG Server is a Python server that provides Retrieval-Augmented Generation (RAG) functionality compliant with the Model Context Protocol (MCP). It indexes documents in formats such as Markdown, text, PowerPoint, Word, and PDF using the multilingual-e5-large embedding model, and retrieves relevant information via vector search with PostgreSQL and pgvector. It is designed for developers building MCP-based RAG applications.

How to use MCP RAG Server?

Install dependencies with uv sync, set up PostgreSQL 14+ with the pgvector extension, and configure environment variables in .env. Start the MCP server with uv run python -m src.main or use CLI commands like python -m src.cli index to index documents. MCP hosts (e.g., Claude Desktop, Cline, Cursor) can integrate the server via a JSON configuration entry specifying command and args.

Key features of MCP RAG Server

  • Supports multiple document formats (Markdown, text, PowerPoint, Word, PDF)
  • Embedding model configurable (default: multilingual-e5-large)
  • Vector search with PostgreSQL and pgvector
  • Differential indexing (processes only new or modified files)
  • Context chunk and full document retrieval
  • CLI tools for index management and document count

Use cases of MCP RAG Server

  • Searching document repositories with natural language queries
  • Building MCP-compatible RAG applications
  • Indexing and querying slides, PDFs, and text documents
  • Incrementally updating a document index without reprocessing unchanged files

常见问题

1

What are the system requirements?

Python 3.10 or higher and PostgreSQL 14 or higher with the pgvector extension are required.

2

How do I change the embedding model?

Set the `EMBEDDING_MODEL`, `EMBEDDING_DIM`, and prefix environment variables in `.env`, then clear and reindex using `python -m src.cli clear` and `python -m src.cli index`.

3

How can I backup and restore the indexed data?

Backup the PostgreSQL database with `pg_dump` and optionally the processed document directory. To restore, set up a new PostgreSQL instance with pgvector, create the database, restore the dump, and copy processed files if needed.

4

What transport does the server use?

The server uses JSON-RPC over stdio.

5

How is authentication handled?

Authentication is not mentioned in the README.

评论

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