MCP Server Ragdocs
An MCP server for document retrieval and processing based on vector search, providing document enhancement functions for AI assistants
rating : 2.5 points
downloads : 56
What is the RAG Document Assistant?
The RAG Document Assistant is a document enhancement tool based on vector search. It analyzes and stores document content, enabling AI assistants to more accurately understand user needs and provide context - relevant answers.How to use the RAG Document Assistant?
Users can add documents, perform searches, and clear the queue through simple commands. The assistant will automatically process document indexing and return relevant results.Applicable Scenarios
Suitable for enterprises, developer communities, and individual users who need to enhance document search capabilities. Particularly suitable for building knowledge - base - driven applications.Main Features
Document SearchSupports natural language queries to quickly locate the required document fragments.
Document Source ListView all stored documents and their metadata.
URL ExtractionAutomatically extract links from web pages and add them to the processing queue.
Remove DocumentsDelete unnecessary document sources.
Queue ManagementMonitor and manage pending document tasks.
Advantages and Limitations
Advantages
Improve the context - awareness ability of AI assistants
Support integration of multiple document sources
Efficient vector search algorithm
Limitations
Dependent on external services (such as Qdrant)
Initial setup may be complex
How to Use
Install Environment Variables
Configure necessary environment variables, such as Qdrant URL and API key.
Start the Server
Run the MCP server to start processing documents.
Add Documents
Add new documents to the system for indexing.
Usage Examples
Example 1: Search for DocumentsUsers enter query terms to obtain relevant document fragments.
Example 2: Batch Process DocumentsProcess multiple documents at once and generate an index.
Frequently Asked Questions
How to install the Ollama model?
How to clear the document queue?
Related Resources
GitHub Repository
Project source code and documentation
Official Documentation
Detailed usage instructions and API reference
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