MCP Qdrant Codebase Embeddings

Uses Qdrant vector embeddings to understand semantic relationships in codebases.

An MCP server for the Chroma embedding database, providing persistent, searchable working memory for AI-assisted development with features like automated context recall and codebase indexing.

Embeddings, vector search, document storage, and full-text search with the open-source AI application database

Implement semantic memory layer on top of the Qdrant vector search engine

A vector database server powered by Chroma, enabling semantic document search, metadata filtering, and document management.

An MCP server for vector storage and retrieval using ChromaDB.

Interact with on-disk documents using agentic RAG and hybrid search via LanceDB.

A production-ready persistent memory system for AI agents, offering searchable memory across sessions with semantic search and support for multiple database backends.

About Local FAISS vector store as an MCP server – drop-in local RAG for Claude / Copilot / Agents.

Provides persistent memory for Claude using ChromaDB for semantic search and storage.

Provides semantic search capabilities for PostgreSQL databases using the pgvector extension, with support for multiple embedding providers.