"Kotlin RAG (Retrieval-Augmented Generation) implementation resources" matching MCP servers:

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

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 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.

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

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