rag-memory-mcp

rag-memory-mcp

An advanced MCP server for RAG-enabled memory through a knowledge graph with vector search capabilities. This server extends the basic memory concepts with semantic search, document processing, and hybrid retrieval for more intelligent memory management.

Inspired by: Knowledge Graph Memory Server from the Model Context Protocol project.

Note: This server is designed to run locally alongside MCP clients (e.g., Claude Desktop, VS Code) and requires local file system access for database storage.

✨ Key Features

• 🧠 Knowledge Graph Memory: Persistent entities, relationships, and observations
• 🔍 Vector Search: Semantic similarity search using sentence transformers
• 📄 Document Processing: RAG-enabled document chunking and embedding
• 🔗 Hybrid Search: Combines vector similarity with graph traversal
• ⚡ SQLite Backend: Fast local storage with sqlite-vec for vector operations
• 🎯 Entity Extraction: Automatic term extraction from documents

Tools

This server provides comprehensive memory management through the Model Context Protocol (MCP):

📚 Document Management

• storeDocument: Store documents with metadata for processing
• chunkDocument: Create text chunks with configurable parameters
• embedChunks: Generate vector embeddings for semantic search
• extractTerms: Extract potential entity terms from documents
• linkEntitiesToDocument: Create explicit entity-document associations
• deleteDocuments: Remove documents and associated data
• listDocuments: View all stored documents with metadata

🧠 Knowledge Graph

• createEntities: Create new entities with observations and types
• createRelations: Establish relationships between entities
• addObservations: Add contextual information to existing entities
• deleteEntities: Remove entities and their relationships
• deleteRelations: Remove specific relationships
• deleteObservations: Remove specific observations from entities

🔍 Search & Retrieval

• hybridSearch: Advanced search combining vector similarity and graph traversal
• searchNodes: Find entities by name, type, or observation content
• openNodes: Retrieve specific entities and their relationships
• readGraph: Get complete knowledge graph structure

📊 Analytics

• getKnowledgeGraphStats: Comprehensive statistics about the knowledge base

Usage Scenarios

This server is ideal for scenarios requiring intelligent memory and document understanding:

• Research and Documentation: Store, process, and intelligently retrieve research papers
• Knowledge Base Construction: Build interconnected knowledge from documents
• Conversational Memory: Remember context across chat sessions with semantic understanding
• Content Analysis: Extract and relate concepts from large document collections
• Intelligent Assistance: Provide contextually aware responses based on stored knowledge

Client Configuration

This section explains how to configure MCP clients to use the rag-memory-mcp server.

Usage with Claude Desktop / Cursor

Add the following configuration to your claude_desktop_config.json (Claude Desktop) or mcp.json (Cursor):

{
  "mcpServers": {
    "rag-memory": {
      "command": "npx",
      "args": ["-y", "rag-memory-mcp"]
    }
  }
}

With specific version:

{
  "mcpServers": {
    "rag-memory": {
      "command": "npx",
      "args": ["-y", "[email protected]"]
    }
  }
}

With custom database path:

{
  "mcpServers": {
    "rag-memory": {
      "command": "npx",
      "args": ["-y", "rag-memory-mcp"],
      "env": {
        "MEMORY_DB_PATH": "/path/to/custom/memory.db"
      }
    }
  }
}

Usage with VS Code

Add the following configuration to your User Settings (JSON) file or .vscode/mcp.json:

{
  "mcp": {
    "servers": {
      "rag-memory-mcp": {
        "command": "npx",
        "args": ["-y", "rag-memory-mcp"]
      }
    }
  }
}

Core Concepts

Entities

Entities are the primary nodes in the knowledge graph. Each entity has:

• A unique name (identifier)
• An entity type (e.g., "PERSON", "CONCEPT", "TECHNOLOGY")
• A list of observations (contextual information)

Example:

{
  "name": "Machine Learning",
  "entityType": "CONCEPT",
  "observations": [
    "Subset of artificial intelligence",
    "Focuses on learning from data",
    "Used in recommendation systems"
  ]
}

Relations

Relations define directed connections between entities, describing how they interact:

Example:

{
  "from": "React",
  "to": "JavaScript",
  "relationType": "BUILT_WITH"
}

Observations

Observations are discrete pieces of information about entities:

• Stored as strings
• Attached to specific entities
• Can be added or removed independently
• Should be atomic (one fact per observation)

Documents & Vector Search

Documents are processed through:

1. Storage: Raw text with metadata
2. Chunking: Split into manageable pieces
3. Embedding: Convert to vector representations
4. Linking: Associate with relevant entities

This enables hybrid search that combines:

• Vector similarity (semantic matching)
• Graph traversal (conceptual relationships)

Environment Variables

• MEMORY_DB_PATH: Path to the SQLite database file (default: memory.db in the server directory)

Development Setup

This section is for developers looking to modify or contribute to the server.

Prerequisites

• Node.js: Check package.json for version compatibility
• npm: Used for package management

Installation (Developers)

1. Clone the repository:

git clone https://github.com/ttommyth/rag-memory-mcp.git
cd rag-memory-mcp

2. Install dependencies:

npm install

Building

npm run build

Running (Development)

npm run watch  # For development with auto-rebuild

Development Commands

• Build: npm run build
• Watch: npm run watch
• Prepare: npm run prepare

Usage Example

Here's a typical workflow for building and querying a knowledge base:

// 1. Store a document
await storeDocument({
  id: "ml_intro",
  content: "Machine learning is a subset of AI...",
  metadata: { type: "educational", topic: "ML" }
});

// 2. Process the document
await chunkDocument({ documentId: "ml_intro" });
await embedChunks({ documentId: "ml_intro" });

// 3. Extract and create entities
const terms = await extractTerms({ documentId: "ml_intro" });
await createEntities({
  entities: [
    {
      name: "Machine Learning",
      entityType: "CONCEPT",
      observations: ["Subset of artificial intelligence", "Learns from data"]
    }
  ]
});

// 4. Search with hybrid approach
const results = await hybridSearch({
  query: "artificial intelligence applications",
  limit: 10,
  useGraph: true
});

System Prompt Suggestions

For optimal memory utilization, consider using this system prompt:

You have access to a RAG-enabled memory system with knowledge graph capabilities. Follow these guidelines:

1. **Information Storage**:
   - Store important documents using the document management tools
   - Create entities for people, concepts, organizations, and technologies
   - Build relationships between related concepts

2. **Information Retrieval**:
   - Use hybrid search for comprehensive information retrieval
   - Leverage both semantic similarity and graph relationships
   - Search entities before creating duplicates

3. **Memory Maintenance**:
   - Add observations to enrich entity context
   - Link documents to relevant entities for better discoverability
   - Use statistics to monitor knowledge base growth

4. **Processing Workflow**:
   - Store → Chunk → Embed → Extract → Link
   - Always process documents completely for best search results

Contributing

Contributions are welcome! Please follow standard development practices and ensure all tests pass before submitting pull requests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Built with: TypeScript, SQLite, sqlite-vec, Hugging Face Transformers, Model Context Protocol SDK