RAG Docs
A document semantic search service based on the Qdrant vector database, supporting URL and local file imports and providing natural language query functions.
3 points
8.5K

What is MCP-Ragdocs?

MCP-Ragdocs is a Model Context Protocol (MCP) server that enables semantic search and retrieval of documents through a vector database (Qdrant). It can add documents from URLs or local files and supports queries in natural language.

How to use MCP-Ragdocs?

First, install the server and start the relevant services. Then, set the environment variables through the configuration file. Finally, use the client to query documents.

Applicable Scenarios

Suitable for enterprises, developer teams, and individual users who need to quickly retrieve a large number of documents, especially in fields such as API documentation and product manuals.

Main Features

Add Documents
Add documents to the RAG database from URLs or local files.
Semantic Search
Use natural language queries to quickly locate the required documents.
List of Document Sources
List all currently stored document sources.
Advantages
Supports multiple document formats and sources.
Based on a vector database, with high search efficiency.
Compatible with multiple embedding models and flexible configuration.
Free and open - source, easy to deploy and expand.
Limitations
Requires a certain technical foundation to complete the initial setup.
May require higher hardware resources for ultra - large - scale documents.
Depends on external services such as Qdrant and Ollama, and network connection interruptions will affect performance.

How to Use

Install the Server
Globally install the MCP - Ragdocs server: npm install -g @qpd - v/mcp - server - ragdocs.
Start Qdrant
Run the Qdrant container through Docker: docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant.
Configure Environment Variables
Edit the configuration file and set the necessary environment variables.
Test Run
Ensure that the Qdrant and Ollama services are working properly.

Usage Examples

Add Documentation
Add a certain API documentation to the system.
Search for Documentation
Find relevant information about authentication.
List Documentation Sources
View all currently stored documentation sources.

Frequently Asked Questions

How to install MCP - Ragdocs?
What if the embedding model cannot be found?
Does it support multi - language documents?
Is there a graphical interface?

Related Resources

Official Documentation
Project homepage and complete documentation.
Qdrant Official Website
Vector database solution.
Ollama Documentation
Tool for generating embedding models.

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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