"Understanding RAG (Retrieval-Augmented Generation or related topics)" matching MCP servers:

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

Enables natural-language semantic search over a fully local knowledge base, keeping embeddings, storage, and retrieval on the machine.

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 semantic search over Obsidian markdown notes using BAAI/bge-m3 embeddings and ChromaDB, accessible via the Model Context Protocol.

MCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.

Enables semantic search and reading of Obsidian Markdown notes through read-only MCP tools, allowing Claude to retrieve relevant passages from a local vault.

Enables semantic and keyword search over Obsidian-style Markdown knowledge bases by registering any folder as a vault, with support for incremental indexing, raw source reads, and cache-backed embeddings over MCP stdio.

MCP server for hybrid retrieval over markdown vaults, combining sqlite-vec embeddings and FTS5 keywords with reciprocal rank fusion, plus optional cross-encoder reranking.

Enables semantic search over an Obsidian vault using natural language, retrieving relevant notes and extracted conclusions.

Answers questions about a folder of markdown notes using hybrid retrieval that fuses FTS5 keyword search and vector search, then reranks results with a cross-encoder.

Provides local hybrid search over an Obsidian vault via the Model Context Protocol. It enables Claude Code to retrieve relevant notes with citations, using on-device embeddings and BM25 rank fusion.

Enables semantic search and content retrieval from local Obsidian vaults through the Model Context Protocol. It allows AI assistants to query notes by meaning, filter by tags, and access full note content for enhanced knowledge integration.

Enables natural-language and keyword search across Obsidian notes, returning reranked passages with provenance, and supports reading notes and incremental indexing.

Enables users to connect their own account to AI clients to list notebooks and sources, fetch complete indexed source text and summaries, and ask grounded questions that return citation-to-source mappings, without creating, renaming, uploading, sharing, or deleting content.

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.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

Enables MCP clients to search a folder of markdown notes and read individual notes through two read-only tools, using hybrid dense-vector and BM25 retrieval.

Enables notes management, weather and news search, document ingestion and RAG-based semantic search using GroundX, with OpenAI GPT integration for summarization and completions.

Enables MCP-capable clients to search a folder of Markdown notes with hybrid full-text and embedding retrieval, plus read, write, append, and reindex operations that keep a single shared SQLite index in sync with the files.

MCP server for local-first RAG over Obsidian vaults, enabling AI agents to search and ask questions about notes with grounded citations.

Enables semantic search over personal markdown notes by indexing them into a vector database and exposing search, reindex, and status tools via MCP.

Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.

Transforms Obsidian vaults into AI-powered knowledge bases using multi-layer RAG with advanced knowledge graph integration, enabling semantic search, entity extraction, and relationship mapping across personal notes.

Enables semantic search over an Obsidian vault using ChromaDB, allowing users to search, read, and list notes via natural language.

Enables MCP clients to give AI agents a shared, persistent Graph-RAG memory backed by an Obsidian-native vault, with hybrid search, token-budgeted recall, and memory updates.

Reads the local SQLite cache of the Notion desktop app to enable fast, offline search and retrieval of Notion pages from Claude.

Enables deterministic, token-minimal multi-hop retrieval from a vault of [[wikilink]] notes via the Model Context Protocol, allowing LLMs to answer questions by following authored links without vector databases or LLM calls during retrieval.

A lightweight MCP server that connects AI agents to a local Zotero library for paper management and metadata retrieval. It enables users to search titles and abstracts, browse collections, and automatically ingest papers via arXiv ID or DOI with PDF attachments.

Integrates with Ninetn, an AI-powered memo application, enabling AI-assisted memo creation, retrieval, search, and listing through natural language interactions.

Enables LLMs to search and retrieve personal journal entries using RAG, and add new entries with date filtering and multiple vector stores.