MCP Memory Server with Qdrant Persistence
by delorenj
This MCP server provides a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database. It allows you to create, manage, and search entities and relations within a knowledge graph.
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What is MCP Memory Server with Qdrant Persistence?
This is an MCP server that implements a knowledge graph with semantic search capabilities. It uses Qdrant vector database for persistence and semantic similarity calculations, and OpenAI embeddings for generating vector representations of entities and relations.
How to use MCP Memory Server with Qdrant Persistence?
To use this server, you need to set up the required environment variables (OpenAI API key, Qdrant URL, Qdrant API key, and Qdrant collection name). You can then either run the server locally using npm install and npm run build, or deploy it using Docker. Finally, add the server to your MCP settings.
Key features of MCP Memory Server with Qdrant Persistence
Graph-based knowledge representation with entities and relations
Semantic search using Qdrant vector database
OpenAI embeddings for semantic similarity
HTTPS support with reverse proxy compatibility
Docker support for easy deployment
Use cases of MCP Memory Server with Qdrant Persistence
Building and querying knowledge graphs
Semantic search for similar concepts
Entity and relation management
Integrating knowledge graphs into MCP workflows
FAQ from MCP Memory Server with Qdrant Persistence
What is Qdrant?
What is Qdrant?
Qdrant is a vector similarity search engine and vector database.
Why use OpenAI embeddings?
Why use OpenAI embeddings?
OpenAI embeddings provide a way to represent text semantically, allowing for similarity search.
How do I configure HTTPS?
How do I configure HTTPS?
You can configure HTTPS by setting up a reverse proxy like Nginx or Apache and updating the QDRANT_URL environment variable.
What are the required environment variables?
What are the required environment variables?
The required environment variables are OPENAI_API_KEY, QDRANT_URL, QDRANT_API_KEY, and QDRANT_COLLECTION_NAME.
How do I contribute to this project?
How do I contribute to this project?
Fork the repository, create a feature branch, make your changes, and submit a pull request.