RAG Isn't One-Size-Fits-All: Here's How to Tune It for Your Use Case
Great RAG comes from a tight iteration loop. Learn how to systematically improve each layer of your RAG system using Kiln and LanceDB.
Great RAG comes from a tight iteration loop. Learn how to systematically improve each layer of your RAG system using Kiln and LanceDB.
Our November newsletter highlights Lance community governance, a deep dive on Lance and Iceberg, a demo of Netflix's multimodal search, previous talk recordings, and the latest product and community updates.
Our October newsletter highlights Semantic.Art, Lance File 2.1, RaBitQ Quantization, upcoming events, latest product and community updates.
SemanticDotArt turns art discovery into a multimodal search experience, matching feelings, phrases, and images with LanceDB's hybrid retrieval.
Our September newsletter welcomes new Lancelot members, highlights TwelveLabs semantic video recommendations, Cognee’s AI memory layer, and shares the latest product and community updates.
The 2.1 file version is now stable, learn what that means for you and what's coming next.
A lightweight open source web UI for exploring Lance datasets, viewing schemas, and browsing table data with vector visualization support.
How Cognee uses LanceDB to deliver durable, isolated, and low-ops AI memory from local development to managed production.
Introducing RaBitQ quantization in LanceDB for higher compression, faster indexing, and better recall on high‑dimensional embeddings.
Build semantic video recommendations using TwelveLabs embeddings, LanceDB storage, and Geneva pipelines with Ray.
