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.

Claude Context is an MCP plugin that provides in - depth context of the entire codebase for AI programming assistants through semantic code search. It supports multiple embedding models and vector databases to achieve efficient code retrieval.

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.

PageIndex MCP is an inference-based vectorless RAG system. Through the MCP protocol, it exposes the tree-like index of documents to LLMs, enabling platforms such as Claude to retrieve information from PDF documents through structural reasoning like human experts, without the need for a vector database.

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

A remote ChromaDB server based on the Streamable HTTP MCP protocol, providing AI assistants such as Claude with the ability to remotely access the vector database, supporting cross - platform shared memory and semantic search.

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.

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

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

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.

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.

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.

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 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.

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

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.

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.

SQLite MCP Server is an enterprise-level SQLite database enhancement tool that provides 73 dedicated tools, supporting advanced analysis, JSON operations, text processing, vector search, geospatial operations, and intelligent workflow automation. It has AI-native JSON operations and enhanced security.

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.

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

Context Lens is a local semantic search tool that can convert any content into a searchable knowledge base, enabling AI assistants to understand the meaning rather than just match keywords. It uses a built - in LanceDB vector database, supports local files, GitHub repositories, and URL content, does not require an API key or cloud service, and processes data completely locally.

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.

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

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

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.

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.

MCP service integrating knowledge graph and vector database based on SQLite

The MCP Document Indexer is a Python - based local document indexing and search server that uses the LanceDB vector database and a local LLM (through Ollama) to implement real - time monitoring, multi - format document processing, and semantic search, and provides tools for AI assistants such as Claude through the Model Context Protocol (MCP).

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.

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.

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.

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

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

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