Genkit is an open-source framework for building AI-driven applications, providing Node.js and Go libraries, supporting the integration of multiple AI models and vector databases, and including development tools and a plugin ecosystem.

Chroma is an open-source vector database that provides memory support for Python and JavaScript LLM applications, supporting multiple client types and document operations.

Weaviate's MCP Server supports quick installation through Smithery and integration with Claude Desktop, providing vector search and storage functions.

This project is a server - side implementation based on the Model Context Protocol (MCP), providing seamless integration with the Milvus vector database and enabling multiple LLM applications such as Claude Desktop and Cursor to access database functions through a standardized protocol.

A document semantic search service based on the Qdrant vector database, supporting URL and local file imports and providing natural language query functions.

An MCP memory server based on the Qdrant vector database, providing knowledge graph and semantic search capabilities.

A RAG system based on Python code analysis. It parses the code structure through AST and stores it in the Weaviate vector database, providing code query, natural language Q&A, and visualization functions, and supporting multi-codebase management and dependency analysis.

rag - mcp is an over - designed retrieval - augmented generation system that provides multiple text search modes (semantic search, question - answer search, style search) through a Python server. It uses PostgreSQL and pgvector to store text embedding vectors, supports interaction with AI agents, and has a complex but scalable architecture.

GraphRAG MCP is a hybrid retrieval system that combines the Neo4j graph database and the Qdrant vector database, providing document retrieval services that combine semantics and graph relationships for large language models.

An MCP server project based on FastAPI, including Qdrant vector database integration and OpenWebUI client support

This project is a server based on the Model Context Protocol (MCP), which realizes seamless integration with the Milvus vector database and provides a standardized data access interface for LLM applications. It supports direct invocation of Milvus's search, query, and management functions in applications such as Claude Desktop and Cursor.

A memory system that enhances AI programming capabilities through multi-type database integration, including vector databases, SQLite, and knowledge graphs, for storing and retrieving code patterns, algorithms, and relationships between programming concepts.

The Chroma MCP Server is a model context protocol server designed for the Chroma vector database, aiming to provide persistent working memory functions for AI-assisted development, supporting cross-session context storage and semantic retrieval.

The MCP server project is used to connect to the Weaviate vector database, providing search and storage functions, and supporting the configuration of the development environment through the Claude desktop application.

A system integrating the Qdrant vector database and MCP server for storing and retrieving code snippets, supporting natural language search and semantic retrieval.

This project implements an MCP server based on the FAISS vector database, supporting the Retrieval Augmented Generation (RAG) function, including a complete workflow such as GitHub file download, document indexing, local query, and LLM integration.

This project is a document processing server based on the Model Context Protocol (MCP) standard. By building a vector database and an MCP interface, it enables AI assistants to access external document resources and break through the knowledge limitations of large language models. The project includes two major components: a document processing pipeline and an MCP server. It supports multiple embedding models and file formats and can be applied to scenarios such as querying the latest technical documents and understanding private code libraries.

An MCP server implementation based on the LanceDB vector database, providing semantic memory storage and retrieval functions.

The MCP application is a service that combines RAG and web search tools, using OpenAI embedding vector storage, PostgreSQL as the database, and PGVector as the vector storage, supporting knowledge retrieval and document addition functions.

Files-DB-MCP is a local vector database project designed for software files, providing a fast MCP interface to support LLM programming agents.

A RAG service based on the Qdrant vector database and Ollama/OpenAI embedding, providing document semantic search and management functions.

MCP service integrating knowledge graph and vector database based on SQLite

The Baidu Cloud Vector Database MCP Server is a server that provides access to the functionality of Baidu Cloud Vector Database, supporting cooperation with multiple large language model applications.

The project provides a Dockerized deployment solution, including the main application and the ChromaDB vector database, supporting environment variable configuration and data persistence.

A semantic search service based on the Qdrant vector database and OpenAI embeddings

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.

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.

The VikingDB MCP Server is an MCP service that provides storage and search functions for the VikingDB vector database, supporting quick access through configuration and data operations using related tools.

A Python-based document processing and Retrieval Augmented Generation (RAG) server that uses the DuckDB database to store embedding vectors, supports multiple file format processing, and provides a Web interface and API.

The MCP server is a multi - functional backend service platform that supports file management, database operations, API integration, and vector search. It provides a Docker deployment solution and examples of integration with Tongyi Qianwen.

This project is an example of an MCP server created for the Alibaba Cloud Lindorm multi - model NoSQL database, providing functions such as vector search, full - text retrieval, and SQL operations.

This is an MCP server project that provides documentation support for the Godot RAG model, including functions such as document download, format conversion, chunk processing, and vector database construction

The VikingDB MCP server provides storage and search services for the VikingDB vector database

Wisdom Forging is a powerful knowledge management system that efficiently stores and retrieves experience, insights, and best practices through the Qdrant vector database.

Semantic search system for code snippets based on Qdrant

An MCP vector database service based on Chroma, providing document storage and semantic search functions

A semantic code search server based on the MCP protocol, supporting two embedding models, OpenAI and Ollama, capable of indexing local projects or Git repositories, and providing an enterprise - level private code search solution.

Weaviate's MCP server for knowledge storage and retrieval of the Claude desktop client

MCP Milvus is a Milvus vector database service based on the Model Context Protocol, providing complete database lifecycle management, high-performance vector search, and intelligent session management functions.

A Machine Control Protocol (MCP) server based on the Qdrant vector database, supporting text storage, semantic search, and integration of the FastEmbed embedding model.