"Notes or documentation related to 'rag'" matching MCP servers:

Enables semantic search and retrieval over an Obsidian vault using local or API-based embeddings, allowing AI assistants to find notes by meaning, get related content, and pull context during conversations.

Provides SQL-backed semantic search over indexed notes using pgvector, exposing tools to search and list note sources via natural language.

A Python MCP server for note management with RAG capabilities, enabling adding, searching, asking questions, summarizing, and deleting notes, using PostgreSQL and ChromaDB with Google Gemini for embeddings and generation.

Crawl any documentation site and return heading-bounded, LLM-ready chunks with source metadata for Pinecone, Chroma, pgvector, LangChain, or LlamaIndex.

Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.

Enables full-text search and paginated retrieval of indexed documentation (PDFs, Markdown, text) through MCP tools, with filtering by category and file.

Enables searching a knowledge base and asking grounded questions with hybrid retrieval, reranking, and cited answers.

Enables semantic search over personal study notes by exposing a vector search tool that Claude Desktop can call to retrieve relevant note content and synthesize grounded answers.

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.

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

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

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

Enables coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.

Enables AI assistants to search and edit Apple Notes on macOS using semantic search, with safe write operations and full local indexing.

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.

End-to-end-encrypted markdown notes that double as agent memory: on-device semantic search (no API key), scoped and revocable agent keys, a hosted connector for Claude.ai and ChatGPT, and a self-hostable relay that stores ciphertext only.

A personal knowledge management system that transforms daily notes into organized, searchable knowledge using the Model Context Protocol.

A RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.

Enables querying, listing, and summarizing personal knowledge base documents using RAG with hybrid search and LLM.

A local hybrid-search MCP server that enables coding agents to query files and folders using natural language, returning relevant code chunks with exact source paths. Everything runs on-device with no API keys or network calls.

Enables coding agents to retrieve relevant sections from a repository's declared documentation scope using hybrid vector and full-text search, with incremental indexing.

A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.

Local-first document management and semantic search for AI coding agents, with a built-in web dashboard and REST API.

A headless local knowledge library and RAG substrate that enables LLM clients to search, retrieve chunks, and list documentation packs through read-only MCP tools.

Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.

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

Enables retrieval-augmented generation over a local markdown corpus, allowing grounded, cited answers via an MCP tool or CLI.

Enables any MCP-capable LLM client to search self-hosted long-term memory over markdown and PDF documents, combining dense semantic vectors with BM25 keyword retrieval and optional cross-encoder reranking. Exposes a read-only tool surface for querying incidents, runbooks, and other knowledge-base content, while writes happen out-of-band through ingestion jobs or a token-gated internal API.

A Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.