Qdrant
Manages vectors, performs similarity searches, and provides automatic text-to-vector embedding for the Qdrant vector database.
About
This server provides an interface for interacting with Qdrant, a vector database, by leveraging the MCP framework. It automates text-to-vector embedding using FastEmbed, enabling efficient storage and retrieval of text content through vector search. Configuration is simplified with environment variables for default settings. It includes tools for text similarity search, vector management, and point operations, with support for local and Docker deployments, and a comprehensive test suite.
Key Features
- Automatic text-to-vector embedding using FastEmbed.
- Includes tools for vector management and point operations.
- Stores and retrieves text content with vector search.
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- Enables text similarity search by content.
- Supports default collection configuration through environment variables.
Use Cases
- Creating recommendation systems based on text similarity.
- Building semantic search applications.
- Enhancing existing applications with vector-based search capabilities.
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