Qdrant MCP Server
A Model Context Protocol (MCP) server implementation for RAG
What is Qdrant MCP Server for RAG?
Qdrant MCP Server for RAG is a Model Context Protocol (MCP) server implementation designed for Retrieval-Augmented Generation (RAG) using the Qdrant vector database.
How to use Qdrant MCP Server?
To use the Qdrant MCP Server, clone the repository, install dependencies, configure environment variables, and start the server. You can also deploy it using Docker.
Key features of Qdrant MCP Server?
- Vector Search: Perform semantic searches over vector embeddings stored in Qdrant.
- Customizable Parameters: Configure search parameters like limit and score threshold.
- LLM Integration: Ready for integration with Claude Desktop and other MCP-compatible tools.
Use cases of Qdrant MCP Server?
- Conducting semantic searches in large datasets.
- Integrating with language models for enhanced data retrieval.
- Customizing search responses based on specific requirements.
FAQ from Qdrant MCP Server?
- What is the prerequisite for using Qdrant MCP Server?
You need Node.js v16+, a Qdrant instance, and optionally an OpenAI API key for production embedding generation.
- Can I use different vector databases?
Yes! The server supports both Qdrant and Chroma vector databases.
- Is there a Docker deployment option?
Yes! You can build and run the server using Docker.
What is Qdrant MCP Server for RAG?
Qdrant MCP Server for RAG is a Model Context Protocol (MCP) server implementation designed for Retrieval-Augmented Generation (RAG) using the Qdrant vector database.
How to use Qdrant MCP Server?
To use the Qdrant MCP Server, clone the repository, install dependencies, configure environment variables, and start the server. You can also deploy it using Docker.
Key features of Qdrant MCP Server?
- Vector Search: Perform semantic searches over vector embeddings stored in Qdrant.
- Customizable Parameters: Configure search parameters like limit and score threshold.
- LLM Integration: Ready for integration with Claude Desktop and other MCP-compatible tools.
Use cases of Qdrant MCP Server?
- Conducting semantic searches in large datasets.
- Integrating with language models for enhanced data retrieval.
- Customizing search responses based on specific requirements.
FAQ from Qdrant MCP Server?
- What is the prerequisite for using Qdrant MCP Server?
You need Node.js v16+, a Qdrant instance, and optionally an OpenAI API key for production embedding generation.
- Can I use different vector databases?
Yes! The server supports both Qdrant and Chroma vector databases.
- Is there a Docker deployment option?
Yes! You can build and run the server using Docker.
