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MCP Rag Server

mcp-rag-server is a service based on the Model Context Protocol (MCP) that supports Retrieval Augmented Generation (RAG) and can index documents and provide relevant context for large language models.
2.5 points
29

What is the MCP RAG Server?

The MCP RAG Server is an intelligent document processing system that can automatically analyze the content of your documents, extract key information, and provide the most relevant context when a large language model needs it. It significantly improves the accuracy and relevance of AI conversations through advanced Retrieval Augmented Generation (RAG) technology.

How to use the MCP RAG Server?

Simply install the server and configure your document path, and the system will automatically process all documents. Then you can obtain the most relevant document fragments for any question through simple queries.

Use cases

It is particularly suitable for scenarios that require precise context support, such as knowledge base Q&A, intelligent document retrieval, and customer service system enhancement. Whether it's technical documents, product manuals, or customer information, it can effectively improve the understanding ability of AI.

Main features

Multi-format supportAutomatically process various document formats such as .txt, .md, .json, .jsonl, and .csv without additional conversion.
Smart chunkingConfigurable text chunk size to ensure information integrity while optimizing retrieval efficiency.
Local vector storageUse SQLite to store document vectors. The data is completely under your control and does not rely on cloud services.
Multi-embedding model supportCompatible with various embedding models such as OpenAI, Ollama, Granite, and Nomic to flexibly meet different needs.

Advantages and limitations

Advantages
Fully local operation to ensure data privacy and security
Lightweight design with low resource consumption
Simple installation and configuration process
Seamless integration with mainstream large language models
Limitations
Indexing a large number of documents for the first time may take a long time
Requires running Ollama or other embedding model services locally
Currently does not support real-time document update monitoring

How to use

Install the server
Install globally via npm or run directly using npx
Configure environment variables
Set necessary environment variables, such as the embedding model API address and vector storage path
Index documents
Specify the directory path containing documents to start the indexing process
Query documents
Obtain the most relevant document fragments for your question through queries

Usage examples

Technical document Q&ACreate an intelligent document assistant for the development team to quickly answer API usage questions
Product knowledge baseBuild a product knowledge base to help customer service staff quickly find product information

Frequently Asked Questions

How to check the document indexing progress?
Which embedding model is recommended?
Will the indexed documents be stored in the cloud?

Related resources

MCP Protocol Documentation
Learn more about the Model Context Protocol specification
Ollama Official Website
Get the recommended embedding model
LangChain Documentation
Understand the underlying vector storage technology used
Installation
Copy the following command to your Client for configuration
{
  "mcpServers": {
    "rag": {
      "command": "npx",
      "args": ["-y", "mcp-rag-server"],
      "env": {
        "BASE_LLM_API": "http://localhost:11434/v1",
        "EMBEDDING_MODEL": "nomic-embed-text",
        "VECTOR_STORE_PATH": "./vector_store",
        "CHUNK_SIZE": "500"
      }
    }
  }
}
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