"Understanding Document Embeddings, Knowledge Graphs, and Vector Representations" matching MCP servers:

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

Integrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.

Enables self-hosted ingestion of arbitrary documents with local chunking and embedding, plus explainable hybrid vector and lexical search over REST and MCP. Provides read-only MCP tools for searching documents, retrieving chunks, and listing collections.

Enables semantic and full-text search over markdown notes, with tools to create, edit, move, and delete notes, all through MCP. Runs entirely locally using Ollama for embeddings and SQLite for storage, working with Obsidian or standalone.

Enables agents to turn documents into vectors, perform approximate nearest neighbor search with HNSW, and filter by metadata via MCP tools for RAG workflows.

Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

An MCP server that guides AI assistants to navigate documentation using their built-in tools (grep, file reading) instead of traditional RAG.

A self-hosted, MIT-licensed MCP server that securely connects private documents and markdown files to LLMs. It provides a lightweight, local-first RAG infrastructure compatible with any MCP-enabled AI client or framework.

Vendor-neutral MCP server for knowledge retrieval across repositories, offering stable tools for searching, getting, and listing documents with hybrid lexical and optional semantic search.

A local document evidence layer for MCP clients that ingests documents (PDF, Office, images) with optional OCR, indexes them in SQLite FTS, and provides retrieval tools with source coordinates.

Provides AI agents with multi-format document indexing, hybrid dense and sparse search with reranking, and relational SQL querying over extracted tables.

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

An MCP server that acts as a knowledge engine for software projects, delivering relevant context at the start of a task and accumulating knowledge at its end through tools like start_task, context, finish_task, remember, and search, with a file-based source of truth and optional semantic retrieval via Graphiti/Neo4j.

An MCP-native evidence retrieval platform that ingests source material, builds lexical and vector indexes, performs hybrid retrieval, and returns structured evidence packages for AI assistants.

Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.

Enables AI agents and MCP clients to search, retrieve, and read documents across unified knowledge sources including ShowDoc, GitHub, Gitee, ZenTao, Evernote, and Tencent Docs. It also supports connecting external MCP servers as additional knowledge sources.

Turns your Readwise library into a blazing-fast semantic search engine with a streaming MCP server for LLM clients, supporting vector search, nightly syncs, and deployment on serverless platforms like Vercel.