ragdocs

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.

• 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

• 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)

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")

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)

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")

Delete a document from the RAG system.

Parameters:

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

npm install -g @mcpservers/ragdocs

{
  "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"
      }
    }
  }
}

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

• 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"

Apache License 2.0