"A local vector-based search engine for personal documents" matching MCP servers:

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

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

Enables semantic search across indexed documents using vector embeddings. Index GitHub repositories and URLs to perform natural language queries with AI-enhanced contextual results.

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

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

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

A secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.

Gives multiple AI coding agents a shared, persistent memory by indexing your code and documentation into a vector database with AST-aware chunking and hybrid semantic/keyword search. Agents can search across projects and store, update, or forget decisions, conventions and lessons learned, all self-hosted with local embeddings.

Provides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.

Enables local vector database operations with a Pinecone-compatible API, supporting exact and approximate search, metadata filtering, and semantic text queries over MCP.

Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.

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.

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

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

Long-term persistent memory for AI agents with hybrid search (DiskANN + FTS), bi-temporal knowledge graph, and 81.1% LongMemEval accuracy.

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.

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Provides AI coding agents with persistent, long-term memory through local semantic search and SQLite storage. It enables agents to save and retrieve architectural decisions or project context across different conversation sessions without requiring cloud services.

A personal knowledge MCP server with vector database for the Opencode ecosystem. It enables semantic search, tag organization, and session memory logging, all running locally without external API keys.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.