Provides semantic search capabilities by leveraging a local Qdrant vector database and OpenAI embeddings.
Sponsored
This tool operates as a Model Context Protocol (MCP) server, offering robust semantic search functionalities. It allows users to perform natural language searches across their document collections, powered by a local Qdrant vector database for complete data privacy and OpenAI's embedding models for automatic text-to-vector conversion. Integrated seamlessly with MCP clients like Claude Code, it simplifies collection and document management while ensuring intelligent request throttling for OpenAI API calls.
주요 기능
01Semantic Search: Natural language search across document collections
02Local Vector Database: Runs Qdrant locally via Docker for data privacy
03Automatic Embeddings: Uses OpenAI's models to convert text to vectors
04MCP Integration: Works seamlessly with Claude Code and other MCP clients
05Collection Management: Create, list, delete, and manage vector collections
062 GitHub stars
사용 사례
01Managing document collections with advanced metadata filtering capabilities
02Integrating semantic search into AI assistants like Claude Code
03Building local, private knowledge bases for natural language querying
