MCP Server for Qdrant
by MCP-Mirror
The MCP Server for Qdrant is a server that stores and retrieves information from a Qdrant vector database. It allows you to store text information with optional metadata and perform semantic searches.
Last updated: N/A
What is MCP Server for Qdrant?
This is a Machine Control Protocol (MCP) server designed to interact with a Qdrant vector database. It provides tools to store, manage, and retrieve textual information along with associated metadata using semantic search capabilities.
How to use MCP Server for Qdrant?
First, install the server using pip or from source. Configure the server by setting environment variables in a .env file, specifying the Qdrant URL, API key, collection name, and embedding provider. Run the server locally using python -m mcp_server_qdrant.main or make run, or deploy it using Docker with docker-compose up. Use the qdrant-store tool to store information and qdrant-find to perform semantic searches.
Key features of MCP Server for Qdrant
Store text information with optional metadata in Qdrant
Semantic search for stored information
FastEmbed integration for text embeddings
Environment-based configuration
Docker support
Use cases of MCP Server for Qdrant
Storing and retrieving knowledge base articles
Building a semantic search engine for documents
Creating a chatbot with memory
Implementing a recommendation system based on text similarity
FAQ from MCP Server for Qdrant
What is Qdrant?
What is Qdrant?
Qdrant is a vector similarity search engine.
What is FastEmbed?
What is FastEmbed?
FastEmbed is a library for generating text embeddings.
How do I configure the Qdrant connection?
How do I configure the Qdrant connection?
Set the QDRANT_URL and QDRANT_API_KEY environment variables.
How do I store information in the database?
How do I store information in the database?
Use the qdrant-store tool with the text and optional metadata.
How do I search for information?
How do I search for information?
Use the qdrant-find tool with your search query.