How Cognee Builds AI Memory Layers with LanceDB
How Cognee uses LanceDB to deliver durable, isolated, and low-ops AI memory from local development to managed production.
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 August 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.
How CodeRabbit leverages LanceDB-powered context engineering turns every review into a quality breakthrough.
How Netflix built a Media Data Lake powered by LanceDB and the Multimodal Lakehouse to unify petabytes of media assets for machine learning pipelines.
Learn how to build scalable feature engineering pipelines with Geneva and LanceDB. This demo transforms image data into rich features including captions, embeddings, and metadata using distributed Ray clusters.