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Shared Knowledge RAG
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Shared Knowledge RAG

The Shared Knowledge MCP Server is a middleware that provides unified knowledge base services for multiple AI assistants, enabling efficient information retrieval and sharing through RAG technology.
Knowledge management and memoryDeveloper tools#Knowledge sharing#RAG retrieval#AI middleware#Vector storage.JavaScript
2.5 points
6.7K
Open Site

Overview

Installation

Content Details

Alternatives

What is the Shared Knowledge MCP Server?

The Shared Knowledge MCP Server is a tool for managing and sharing a knowledge base. It enables efficient document search and analysis through Retrieval Augmented Generation (RAG) technology. No matter which AI assistant you use, you can access a unified knowledge base.

How to use the Shared Knowledge MCP Server?

You can start using it in just a few steps: install the server, configure environment variables, and start the service. After that, you can easily query the required information within any supported AI assistant.

Applicable scenarios

It is suitable for enterprise teams that need cross - platform collaboration, developer communities, and individual users who want to integrate multi - source information.

Main features

Compatibility with multiple AI assistants
Supports multiple popular AI assistants such as CLINE, Cursor, Claude Desktop, etc.
High - performance retrieval
Based on RAG technology, quickly find the most relevant document fragments.
Scalable storage engine
Supports multiple vector databases (such as HNSWLib, Chroma, Pinecone, etc.) to meet different needs.
Advantages
Unified knowledge base management reduces duplicate work
Supports powerful retrieval algorithms to improve efficiency
Limitations
High computing resources may be required for the initial deployment
Some advanced features depend on third - party services (such as Pinecone)

How to use

Clone the repository
Use Git to clone the Shared Knowledge MCP Server code to your local machine.
Install dependencies
After entering the project directory, run npm to install the dependencies.
Configure environment variables
Set the necessary environment variables such as OPENAI_API_KEY and KNOWLEDGE_BASE_PATH.
Start the server
Use the npm command to start the MCP server.

Usage examples

Find project contribution guidelines
When new members want to know how to participate in project development, they can query the contribution guidelines.
Solve technical problems
When encountering technical difficulties, you can search for historical solutions by keywords.

Frequently Asked Questions

How to change the default vector storage type?
What if no relevant documents are found?

Related resources

Official documentation
Detailed installation guides and technical documentation.
GitHub repository
Source code and Issue tracking.
Video tutorial
Quick - start video.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "shared-knowledge-base": {
      "command": "node",
      "args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
      "env": {
        "KNOWLEDGE_BASE_PATH": "/path/to/your/rules",
        "OPENAI_API_KEY": "your-openai-api-key",
        "SIMILARITY_THRESHOLD": "0.7",
        "CHUNK_SIZE": "1000",
        "CHUNK_OVERLAP": "200",
        "VECTOR_STORE_TYPE": "hnswlib"
      }
    }
  }
}

{
  "mcpServers": {
    "shared-knowledge-base": {
      "command": "node",
      "args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
      "env": {
        "KNOWLEDGE_BASE_PATH": "/path/to/your/rules",
        "OPENAI_API_KEY": "your-openai-api-key",
        "VECTOR_STORE_TYPE": "pinecone",
        "VECTOR_STORE_CONFIG": "{\"apiKey\":\"your-pinecone-api-key\",\"environment\":\"your-environment\",\"index\":\"your-index-name\"}"
      }
    }
  }
}

{
  "mcpServers": {
    "shared-knowledge-base": {
      "command": "node",
      "args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
      "env": {
        "KNOWLEDGE_BASE_PATH": "/path/to/your/docs",
        "OPENAI_API_KEY": "your-openai-api-key",
        "SIMILARITY_THRESHOLD": "0.7",
        "CHUNK_SIZE": "1000",
        "CHUNK_OVERLAP": "200",
        "VECTOR_STORE_TYPE": "hnswlib",
        "VECTOR_STORE_CONFIG": "{}"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

{
  "mcpServers": {
    "shared-knowledge-base": {
      "command": "node",
      "args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
      "env": {
        "KNOWLEDGE_BASE_PATH": "/path/to/your/docs",
        "OPENAI_API_KEY": "your-openai-api-key",
        "SIMILARITY_THRESHOLD": "0.7",
        "CHUNK_SIZE": "1000",
        "CHUNK_OVERLAP": "200",
        "VECTOR_STORE_TYPE": "weaviate",
        "VECTOR_STORE_CONFIG": "{\"url\":\"http://localhost:8080\",\"className\":\"Document\",\"textKey\":\"content\"}"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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