Find information in vector databases using semantic search with natural language queries. Retrieve relevant matches from stored embeddings based on similarity.
Search Meilisearch indexes using vector embeddings to find semantically similar content, supporting hybrid text-vector searches and customizable filtering.
Perform vector similarity searches in Baidu Vector Database by combining vector matching and scalar attribute filtering for precise data retrieval. Specify table, vector, and optional filters to get relevant results efficiently.
PG-MCP is an HTTP server implementation that enables AI systems to interact with PostgreSQL databases via MCP, providing tools for querying, connecting to multiple databases, and exploring schema resources. The system enriches context by extracting table/column description from database catalogs.
Provides comprehensive management of OpenAI Vector Stores, allowing AI assistants to upload files, manage vector databases, and handle batch operations via the OpenAI API. It supports multiple deployment methods, including Cloudflare Workers and local NPM installation, for seamless integration with MCP-compatible clients.