MCP RAG Server

Name: MCP RAG Server
Author: karaage0703

@karaage0703

代码仓库主页

关于 MCP RAG Server

暂无概览

基本信息

分类

记忆与知识

许可证

MIT

运行时

python

传输方式

stdio

发布者

karaage0703

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"
      ]
    }
  }
}

工具

未检测到工具

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

FAQ from MCP RAG Server

What are the system requirements?

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

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.

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.

What transport does the server use?

The server uses JSON-RPC over stdio.

How is authentication handled?

Authentication is not mentioned in the README.

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