What is Ragdocs

RagDocs is an MCP server designed for RAG (Retrieval-Augmented Generation) based document search and management, utilizing the Qdrant vector database and Ollama/OpenAI embeddings for semantic search capabilities.

Use cases

Use cases for RagDocs include managing documentation for projects, conducting semantic searches for information retrieval, organizing resources by domain, and facilitating collaboration through shared document management.

How to use

To use RagDocs, install it via npm, configure the MCP server settings with the appropriate Qdrant URL and embedding provider, and utilize the available tools to add, search, list, and delete documents.

Key features

Key features include adding documentation with metadata, semantic search through documents, listing and organizing documents, deleting documents, support for both Ollama and OpenAI embeddings, automatic text chunking and embedding generation, and vector storage with Qdrant.

Where to use

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RagDocs MCP Server

A Model Context Protocol (MCP) server that provides RAG (Retrieval-Augmented Generation) capabilities using Qdrant vector database and Ollama/OpenAI embeddings. This server enables semantic search and management of documentation through vector similarity.

Features

• Add documentation with metadata
• Semantic search through documents
• List and organize documentation
• Delete documents
• Support for both Ollama (free) and OpenAI (paid) embeddings
• Automatic text chunking and embedding generation
• Vector storage with Qdrant

Prerequisites

• Node.js 16 or higher
• One of the following Qdrant setups:
  • Local instance using Docker (free)
  • Qdrant Cloud account with API key (managed service)
• One of the following for embeddings:
  • Ollama running locally (default, free)
  • OpenAI API key (optional, paid)

Available Tools

1. add_document

Add a document to the RAG system.

Parameters:

• url (required): Document URL/identifier
• content (required): Document content
• metadata (optional): Document metadata
  • title: Document title
  • contentType: Content type (e.g., “text/markdown”)

2. search_documents

Search through stored documents using semantic similarity.

Parameters:

• query (required): Natural language search query
• options (optional):
  • limit: Maximum number of results (1-20, default: 5)
  • scoreThreshold: Minimum similarity score (0-1, default: 0.7)
  • filters:
    • domain: Filter by domain
    • hasCode: Filter for documents containing code
    • after: Filter for documents after date (ISO format)
    • before: Filter for documents before date (ISO format)

3. list_documents

List all stored documents with pagination and grouping options.

Parameters (all optional):

• page: Page number (default: 1)
• pageSize: Number of documents per page (1-100, default: 20)
• groupByDomain: Group documents by domain (default: false)
• sortBy: Sort field (“timestamp”, “title”, or “domain”)
• sortOrder: Sort order (“asc” or “desc”)

4. delete_document

Delete a document from the RAG system.

Parameters:

• url (required): URL of the document to delete

Installation

npm install -g @mcpservers/ragdocs

MCP Server Configuration

{
  "mcpServers": {
    "ragdocs": {
      "command": "node",
      "args": [
        "@mcpservers/ragdocs"
      ],
      "env": {
        "QDRANT_URL": "http://127.0.0.1:6333",
        "EMBEDDING_PROVIDER": "ollama"
      }
    }
  }
}

Using Qdrant Cloud:

{
  "mcpServers": {
    "ragdocs": {
      "command": "node",
      "args": [
        "@mcpservers/ragdocs"
      ],
      "env": {
        "QDRANT_URL": "https://your-cluster-url.qdrant.tech",
        "QDRANT_API_KEY": "your-qdrant-api-key",
        "EMBEDDING_PROVIDER": "ollama"
      }
    }
  }
}

Using OpenAI:

{
  "mcpServers": {
    "ragdocs": {
      "command": "node",
      "args": [
        "@mcpservers/ragdocs"
      ],
      "env": {
        "QDRANT_URL": "http://127.0.0.1:6333",
        "EMBEDDING_PROVIDER": "openai",
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

Local Qdrant with Docker

docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant

Environment Variables

• QDRANT_URL: URL of your Qdrant instance
  • For local: “http://127.0.0.1:6333” (default)
  • For cloud: “https://your-cluster-url.qdrant.tech”
• QDRANT_API_KEY: API key for Qdrant Cloud (required when using cloud instance)
• EMBEDDING_PROVIDER: Choice of embedding provider (“ollama” or “openai”, default: “ollama”)
• OPENAI_API_KEY: OpenAI API key (required if using OpenAI)
• EMBEDDING_MODEL: Model to use for embeddings
  • For Ollama: defaults to “nomic-embed-text”
  • For OpenAI: defaults to “text-embedding-3-small”

License

Apache License 2.0