R

Ragdocs (Vector Documentation Search)

An MCP service for document retrieval based on vector search, providing relevant document context to enhance the response ability of AI assistants
2 points
7

What is the RAG Document MCP Server?

The RAG Document MCP Server is a document retrieval and processing tool based on vector search. It can quickly find the most relevant fragments to the user's query from a large number of documents. With this tool, you can easily build a knowledge base with context awareness, improving the accuracy and efficiency of the question - answering system.

How to use the RAG Document MCP Server?

First, you need to configure environment variables and start the server. Then, perform operations such as searching, adding, or deleting documents through the provided API interfaces. See the 'How to Use' section below for specific steps.

Applicable Scenarios

It is suitable for scenarios such as building knowledge bases, enhancing customer service systems, developing intelligent assistants, and implementing semantic document search.

Main Features

Document SearchSupports natural - language queries and returns the most relevant document fragments to the query.
List Stored Document SourcesDisplays all indexed documents and their related information in the current system.
URL ExtractionAutomatically crawls hyperlinks on web pages and can optionally add them to the queue for subsequent processing.
Remove Document SourcesPermanently deletes unwanted documents based on the specified URL.
Process Task QueueProcesses all pending documents in the queue in order.
Clear Task QueueImmediately clears the unprocessed tasks in the queue.

Advantages and Limitations

Advantages
Powerful vector search ability to improve retrieval accuracy.
Supports multiple document sources, with high flexibility.
Easy to integrate into existing systems.
Real - time context enhancement to improve dialogue quality.
Limitations
It may take a long time to index large - scale document sets.
Relies on external services such as OpenAI and Qdrant, with high costs.
Requires manual maintenance of the document queue status.

How to Use

Installation and Configuration
Ensure that Node.js and related dependencies are installed, and set environment variables (OPENAI_API_KEY, QDRANT_URL, QDRANT_API_KEY).
Start the Server
Run the command to start the MCP server.
Perform Basic Operations
Try using the search_documentation or list_sources commands to test the functions.

Usage Examples

Search for a Specific TopicWhen a user asks about the specific configuration of the RAG server, relevant information can be obtained through search_documentation.
Add New DocumentsCrawl documents from an external website and add them to the queue through extract_urls.

Frequently Asked Questions

How to install the RAG Document MCP Server?
Does it support custom embedding models?
How to clear the task queue?

Related Resources

Official Documentation
Project source code and detailed documentation.
Qdrant Official Website
The official platform for vector databases.
Claude Desktop Configuration Guide
How to integrate the RAG Document MCP Server in Claude Desktop.
Installation
Copy the following command to your Client for configuration
{
  "mcpServers": {
    "rag-docs": {
      "command": "npx",
      "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
      ],
      "env": {
        "OPENAI_API_KEY": "",
        "QDRANT_URL": "",
        "QDRANT_API_KEY": ""
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.
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