Haiku RAG is an intelligent retrieval - augmented generation system built on LanceDB, Pydantic AI, and Docling. It supports hybrid search, re - ranking, Q&A agents, multi - agent research processes, and provides local - first document processing and MCP server integration.

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 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.

The Storm MCP server is an open protocol that implements the Anthropic Model Context Protocol, supports the integration of LLM and RAG data sources, and provides functions such as context sharing, tool system, file management, and API integration.

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.

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 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.

A high-performance local RAG system based on Rust, integrated with Claude Desktop via the MCP protocol to achieve local processing, semantic search, and privacy protection of PDF documents.

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.

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 server based on the Model Context Protocol (MCP) that provides access to clinical guidelines from the National Comprehensive Cancer Network (NCCN). This system ensures the accuracy and reliability of medical guidance by directly reading the PDF content of the guidelines instead of using RAG technology.

Apple RAG MCP is a retrieval-augmented generation system that provides Apple development expertise for AI agents. It integrates official Swift documentation, design guides, and Apple Developer YouTube content, and provides accurate technical answers through AI-driven hybrid search technology.

This project implements an MCP server based on the Gemini API, providing access to various technical documents. With a large context window of 2M tokens, it directly processes complete documents without the need for chunking or retrieval steps of traditional RAG systems. It supports functions such as document query, code specification check, and problem - solving hints, and is suitable for the Roo/Cline environment.

A lightweight MCP server focused on crawling document websites and performing RAG indexing using Jina AI and Supabase, supporting multi - project management, intelligent URL discovery, and hybrid content extraction.

Agentic is an intelligent business service system based on the agent architecture. It coordinates the RAG service and monitoring service through the central application proxy to provide comprehensive solutions.

The Prem MCP Server is a Model Context Protocol server designed for Prem AI, supporting seamless integration with MCP - compatible clients such as Claude, and providing functions such as chat completion, RAG support, document management, and template system.

An MCP server project that connects to the Dust.tt AI agent platform, implementing multi-cloud service provider interfaces via HTTP calls and integrating systems thinking agents and RAG functions.

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.

The Storm MCP server is an open protocol that enables seamless integration of LLM applications with RAG data sources and tools, supports custom embedded models and vector database connections, and provides functions such as context sharing, tool systems, and file management.

A RAG system built with open-source embedding models, vector databases, and the Gemini large language model, supporting local document processing and dynamic index update.

A custom MCP server that connects to the Dust.tt AI agent platform, realizing multi - cloud service integration through HTTP calls, providing systems thinking agent, RAG support, and web navigation capabilities.

This project showcases a lightweight multi-agent AI system that combines the Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) for business analysis. The system provides statistical analysis of business data and knowledge retrieval functions for natural language queries by coordinating multiple dedicated tool servers. It features a modular design that facilitates expansion and the switching of LLM backends.

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.

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 aims to enhance the management experience of Pi-hole devices through FastAPI and the RAG system, providing interaction functions based on natural language processing.