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.
In May, we announced 3 new members to join our Lancelot …
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.
Our September newsletter highlights LanceDB powering Netflix's Media Data Lake, a case study on CodeRabbit's AI-powered code reviews, and updates on Lance Namespace and Spark integration.
Learn how to build real-time multimodal AI analytics by integrating Apache Fluss streaming storage with Lance's AI-optimized lakehouse. This guide demonstrates streaming multimodal data processing for RAG systems and ML workflows.
Learn how to productionalize AI workloads with Lance Namespace's enterprise stack integration and the scalability of LanceDB and Ray for end-to-end ML pipelines.
