# Vector Search

An embedded vector search server based on the MCP protocol for querying transcribed segments and embedded vectors in the Turso database. It supports searching for relevant segments by question.

An MCP service for semantic search of Star Wars planets based on Couchbase

An MCP server based on the Qdrant vector search engine, providing semantic memory layer functions and supporting information storage and retrieval.

A Go - based GitHub agent chat MCP server that provides natural language interaction with GitHub and vector search capabilities.

A knowledge graph service supporting both FastAPI and FastMCP protocols, providing core operation functions of the Qdrant vector database.

NexusHub is a unified MCP server designed for the Claude AI tool, offering various functions such as file system access, database operations, vector search, and GitHub integration. It supports both HTTP and stdio protocols and includes a modern dashboard interface.

A Node.js-based vector search project that uses the LanceDB database and Ollama embedding model to implement document similarity search functionality

The MCP Memory Server provides long-term memory functions for AI assistants and implements efficient vector similarity search based on PostgreSQL and pgvector.

A Docker Compose solution for quickly deploying the Qdrant vector search engine and MCP protocol server locally

This is an experimental Observe MCP server project that provides API interaction capabilities with the Observe platform, including tools for executing OPAL queries, exporting worksheet data, and managing monitors. It enables semantic document search and troubleshooting manual recommendation through the Pinecone vector database, providing a secure data access bridge for technical LLM models.

The RAG Document MCP Server is a vector search-based document processing tool that provides document retrieval and context enhancement functions for AI assistants.

An MCP server for document retrieval and processing based on vector search, providing document enhancement functions for AI assistants

The Pinecone MCP Server is a model context protocol server designed for Claude Desktop, providing read and write interaction capabilities with Pinecone indexes.

DevContext is a powerful AI development context system that provides developers with project-centric continuous context awareness. It includes four memory types: short-term, long-term, situational, and semantic, supports code structure analysis and vector search, and significantly improves development efficiency.

A GitHub intelligent chat MCP server based on the Go language, providing natural language interaction with GitHub functions and vector search capabilities

A multi - agent AI infrastructure based on the Model Context Protocol (MCP), providing a standardized communication layer, supporting vector databases, tool integration, and multi - agent collaboration, including core components such as an inspector dashboard, Qdrant vector database, and Docker network.

This is an MCP server implementation based on the Qdrant vector search engine, providing semantic memory storage and retrieval functions, and supporting integration with multiple AI development tools.

A Qdrant vector database service based on the MCP framework, providing text vectorization storage and similarity search functions.

Semantic search system for code snippets based on Qdrant

A document retrieval system based on MongoDB Atlas vector search and Voyage AI embedding technology, supporting semantic search and text matching, including document chunking, embedding generation, and storage functions.

A powerful MCP server that provides web search and similar content search functions, specifically designed for Claude Desktop.

A Python server project based on the MCP protocol, which uses OpenAI's vector storage function to build a RAG system, supporting the creation of vector databases from multiple file formats and semantic search.