A lightweight, graph-based retrieval-augmented generation (RAG) framework that uses a dual-layer architecture combining knowledge graphs and vector embeddings to deliver efficient indexing, low-cost incremental updates, and high-quality contextual retrieval.
An open-source, developer-friendly embedded retrieval library and multimodal AI lakehouse designed for fast, scalable, and production-ready vector search, built on the Lance columnar format.
