"Semantic search, RAG, and memory systems" matching MCP servers:

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

Indexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.

Local semantic recall MCP server for citation-integrity workflows. Enables semantic search over a text corpus, returning file paths and page numbers for verification, with 100% local processing using ONNX embeddings and LanceDB.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.

Provides semantic code search over codebases using local embeddings with natural language queries. Supports hybrid search, file watching, and respects .gitignore.

Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.

Enables natural language search of local photo archives using AI-powered semantic understanding, with integration into Claude Desktop via the Model Context Protocol.

Enables persistent project memory for Hermes Agent by storing and retrieving wiki, code, and learning layers locally, with tools to recall, learn, retire, and ingest information beyond the context window.

Enables AI agents to manage system updates, application installations, and remote host orchestration through MCP tools.

Serves UltraRAG memory to agents over stdio MCP, providing project-local and per-user global memory. It lets agents read and append standing memory and daily dialogue rounds in UltraRAG's format, with an optional browser view.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

Enables agents to interact with a governed semantic layer for querying and authoring metrics, providing tools for discovery, planning, validation, and execution of analytics queries.

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.

Exposes a governed semantic layer to AI agents so they select metrics and dimensions from a closed, engineer-declared vocabulary instead of writing SQL. Validates every typed call against the contract, rejects out-of-contract requests with structured errors and suggestions, and returns query rows together with the compiled SQL and dbt lineage for auditing.

Enables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

MCP server for querying the Semantic Scholar Academic Graph API, enabling paper search, author lookup, citation tracking, and paper recommendations.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Enables local semantic and Japanese keyword search over Outlook Classic mail through MCP, including indexing connected mail folders and reading cached messages. It supports OpenAI-compatible embeddings, incremental sync, and metadata-filtered search via MCP tools.

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.

A read-only PostgreSQL MCP server for AI coding agents that exposes database schema and sample data as tools, with LLM-powered semantic search enriched by a user-authored semantic layer.

A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.

Enables Claude Desktop and Claude Code to perform local hybrid semantic and BM25 search over full-text PDFs in a Zotero library, with filters, context retrieval, and citation-backed answers.

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

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

Enables AI assistants to maintain a persistent, local memory using SQLite with ranked full-text search, German morphological matching, and two-axis filing, all without external services.

MCP file cache for AI coding agents. Re-reading an unchanged file costs ~40 tokens instead of the whole file; a changed file comes back as a diff. Adds grep, BM25 search and glob over cached files, atomic edits with dry_run, and token-budgeted batch reads. One local daemon serves every client. No API keys, no GPU. Works with Claude Code, Cursor, VS Code, Codex and OpenCode.

MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.