mcp-md-vector-search
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mcp-md-vector-search is a lightweight MCP server implementation designed for efficient similarity searches on local Markdown documents.
mcp-md-vector-search is a specialized server implementation that utilizes the Model Context Protocol (MCP) to perform similarity searches on Markdown documents. By leveraging PGLite and pgvector, it provides a robust solution for indexing and searching through text data efficiently. This server is particularly useful for applications that require quick retrieval of contextually similar documents, making it ideal for content management systems, knowledge bases, and other text-heavy applications. The integration of PGLite ensures that the server remains lightweight and easy to deploy, while pgvector enhances its capability to handle vector-based searches, which are crucial for modern natural language processing tasks.
Features
- Efficient Similarity Search: Utilizes pgvector for fast and accurate similarity searches on Markdown documents.
- Lightweight Implementation: Built on PGLite, ensuring minimal resource usage and easy deployment.
- Markdown Support: Specifically designed to handle and process Markdown documents.
- MCP Integration: Fully compatible with the Model Context Protocol for seamless integration with other MCP-based tools.
- Scalable Architecture: Capable of handling large datasets with ease, making it suitable for enterprise applications.
