The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.
High-performance vector database for Node.js with automatic native/WASM fallback
High-performance vector database with HNSW indexing - 50k+ inserts/sec, built in Rust for AI/ML similarity search and semantic search applications
A local-first Model Context Protocol (MCP) server that provides semantic search capabilities for codebases
Qdrant-compatible Node.js/TypeScript API that stores/searches embeddings in YDB using a global one-table layout with exact and approximate KNN search over serialized vectors.