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 implements an MCP server and client for building intelligent agent applications based on Retrieval Augmented Generation (RAG). The server provides tools such as entity extraction, query optimization, and relevance checking, and the client demonstrates how to connect to the server and use these tools to enhance the performance of the RAG system.

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.

A diary system based on Retrieval Augmented Generation (RAG), supporting diary organization by date and topic, and providing semantic search functionality, which can be connected to an AI agent to enhance interaction.

This project builds a Retrieval Augmented Generation (RAG) server based on IBM Watsonx.ai, uses ChromaDB for vector indexing, and exposes interfaces through the Model Context Protocol (MCP). The system can process PDF documents and answer questions based on the document content, realizing an intelligent question answering function that combines large language models with specific domain knowledge.

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.

An MCP server based on TypeScript that implements a Retrieval Augmented Generation (RAG) system for local documents, supporting querying and indexing of Git repositories and text files.

A Godot documentation query assistant based on Retrieval Augmented Generation (RAG), achieving intelligent Q&A through vectorization technology and semantic search.

mcp-rag-server is a service based on the Model Context Protocol (MCP) that supports Retrieval Augmented Generation (RAG) and can index documents and provide relevant context for large language models.

A server based on the Model Context Protocol (MCP) that provides Retrieval Augmented Generation (RAG) capabilities, integrates clients such as Cursor IDE and Claude Desktop, and enables domain knowledge Q&A, intelligent retrieval, and context-aware response generation.

MCP Apple Notes is a service based on the Model Context Protocol that can perform semantic search and Retrieval Augmented Generation (RAG) on Apple Notes, enabling AI assistants like Claude to reference users' notes in conversations.

This project demonstrates how to implement a Model Context Protocol (MCP) server supporting Retrieval Augmented Generation (RAG), providing functions for knowledge base interaction, information retrieval, and document management.

An implementation of a Retrieval Augmented Generation (RAG) service based on the AWS knowledge base

The RAG Anything MCP Server is a Model Context Protocol server that provides comprehensive Retrieval Augmented Generation (RAG) capabilities. It supports multi-modal document processing and querying, and has functions such as end-to-end document parsing, batch processing, advanced querying, and persistent storage.

mcp - rag - server is a Retrieval Augmented Generation (RAG) server based on the Model Context Protocol (MCP). It provides relevant context for connected LLMs by indexing project documents. It uses ChromaDB and Ollama for local storage and embedding generation, supports multiple file formats, and can be quickly deployed using Docker.

MiniRAG-MCP is an MCP server wrapper built around the MiniRAG project, aiming to provide efficient and reliable Retrieval Augmented Generation (RAG) services for intelligent agent processes on local devices through client-managed LLM inference sampling.

The R2R MCP Server is a service that integrates the Model Context Protocol (MCP) with the R2R system, providing interaction capabilities with MCP-compatible models such as Claude, and supporting functions such as knowledge base access, context search, and Retrieval Augmented Generation (RAG).

This project builds an AI system based on Nasdanika capabilities, focusing on operating on resource collections (interconnected models). It describes model elements and their relationships from multiple angles through the 'narrator' processor, and uses embeddings and vector storage to implement semantic search and RAG (Retrieval - Augmented Generation). It also supports the chat completion functions of OpenAI and Ollama.

An open - source project researching the integration of Retrieval Augmented Generation (RAG) with Multi - Cloud Processing (MCP) servers, focusing on the application of free models in business software and providing a modular learning path and practical cases.

This project is a RAG (Retrieval Augmented Generation) service based on the AWS serverless architecture. It uses Lambda, OpenSearch Serverless, and S3 for document storage and retrieval, provides an MCP protocol interface through API Gateway, and integrates OpenAI for embedding and generation.