"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:

Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.

Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

Keyless local RAG over your own documents (ChromaDB) with traceable citations, exposed as MCP tools and a FastAPI endpoint.

Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.

A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.

A modular RAG server with hybrid search and LLM rerank, exposing 3 MCP tools for AI clients like Claude Desktop and Cursor.

A Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. It provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities.

MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.

An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.

Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.

Enables agents to retrieve relevant context from local documents via MCP tools, supporting ingestion, semantic search, metadata filtering, and evidence inspection entirely on-device.

A Retrieval Augmented Generation MCP server that ingests documents into a local vector database and enables semantic search queries.

Local MCP server that provides a search_documents tool to query a RAG pipeline built with Ollama embeddings and ChromaDB, enabling Claude Desktop to retrieve relevant document chunks.

MCP server that enables semantic search over PDF documents using RAG with ChromaDB and OpenAI embeddings.

A CLI tool and MCP server that turns markdown documentation into a searchable, queryable knowledge base.