RAG Memory MCP

# rag-memory-mcp [](https://www.npmjs.com/package/rag-memory-mcp) [](https://www.npmjs.com/package/rag-memory-mcp) [](https://github.com/ttommyth/rag-memory-mcp/blob/main/LICENSE) [](https://github.com/ttommyth/rag-memory-mcp) [](https://github.com/ttommyth/rag-memory-mcp/commits/main) 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](https://github.com/modelcontextprotocol/servers/tree/main/src/memory) 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): ```json { "mcpServers": { "rag-memory": { "command": "npx", "args": ["-y", "rag-memory-mcp"] } } } ``` **With specific version:** ```json { "mcpServers": { "rag-memory": { "command": "npx", "args": ["-y", "rag-memory-mcp@1.0.0"] } } } ``` **With custom database path:** ```json { "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`: ```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: ```json { "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: ```json { "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: ```bash git clone https://github.com/ttommyth/rag-memory-mcp.git cd rag-memory-mcp ``` 2. Install dependencies: ```bash npm install ``` ### Building ```bash npm run build ``` ### Running (Development) ```bash 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: ```javascript // 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](LICENSE) file for details. --- **Built with**: TypeScript, SQLite, sqlite-vec, Hugging Face Transformers, Model Context Protocol SDK