RAG (Retrieval-Augmented Generation) for documents in a local directory
概览
RAG (Retrieval-Augmented Generation) for documents in a local directory This is a TypeScript-based MCP server that implements a RAG system for documents stored in a local directory. It allows users to query documents using LLMs with context from locally stored repositories and text files. - List and access documents via docs:// URIs - Documents can be Git repositories or text files - Plain text mime type for content access - list_documents - List all available documents in the DOCS_PATH directory - Returns a formatted list of all documents - Shows total number of available documents - rag_query - Query documents using RAG - Takes document_id and query as parameters - Returns AI-generated responses with context from documents - add_git_repository - Clone a Git repository to the docs directory with optional sparse checkout - Takes repository_url as parameter - Optional document_name parameter to customize the name of the document (use simple descriptive names without '-docs' suffix)...
README
以下为原文 README 节选，完整内容请查看原文。
mcp-docs-rag MCP Server
RAG (Retrieval-Augmented Generation) for documents in a local directory
This is a TypeScript-based MCP server that implements a RAG system for documents stored in a local directory. It allows users to query documents using LLMs with context from locally stored repositories and text files.
Features
Resources
- List and access documents via
docs://URIs - Documents can be Git repositories or text files
- Plain text mime type for content access
Tools
list_documents- List all available documents in the DOCS_PATH directory- Returns a formatted list of all documents
- Shows total number of available documents
rag_query- Query documents using RAG- Takes document_id and query as parameters
- Returns AI-generated responses with context from documents
add_git_repository- Clone a Git repository to the docs directory with optional sparse checkout- Takes repository_url as parameter
- Optional document_name parameter to customize the name of the document (use simple descriptive names without ‘-docs’ suffix)
- Optional subdirectory parameter for sparse checkout of specific directories
- Automatically pulls latest changes if repository already exists
add_text_file- Download a text file to the docs directory- Takes file_url as parameter
- Uses wget to download file
Prompts
guide_documents_usage- Guide on how to use documents and RAG functionality- Includes list of available documents
- Provides usage hints for RAG functionality
Development
Install dependencies:
npm install
Build the server:
npm run build
For development with auto-rebuild:
npm run watch
Setup
This server requires a local directory for storing documents. By default, it uses ~/docs but you can configure a different location with the DOCS_PATH environment variable.
Document Structure
The documents directory can contain:
- Git repositories (cloned directories)
- Plain text files (with .txt extension)
Each document is indexed separately using llama-index.ts with Google’s Gemini embeddings.
API Keys
This server uses Google’s Gemini API for document indexing and querying. You need to set your Gemini API key as an environment variable:
export GEMINI_API_KEY=your-api-key-here
You can obtain a Gemini API key from the Google AI Studio website. Add this key to your shell profile or include it in the environment configuration for Claude Desktop.
Installation
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
On Linux: ~/.config/Claude/claude_desktop_config.json
{
"mcpServers": {
"docs-rag": {
"command": "npx",
"args": ["-y", "@kazuph/mcp-docs-rag"],
"env": {
"DOCS_PATH": "/Users/username/docs",安装
npx -y @kazuph/mcp-docs-rag配置
{
"mcpServers": {
"docs-rag": {
"command": "npx",
"args": ["-y", "@kazuph/mcp-docs-rag"],
"env": {
"DOCS_PATH": "/Users/username/docs",
"GEMINI_API_KEY": "your-api-key-here"
}
}
}
}