LANCEDB

1. Build production RAG, agentic, semantic-search, and recommendation systems with vector, full-text, and hybrid search combined with SQL filters, horizontally scalable to 100K+ QPS. 2. Manage and filter petabyte-scale multimodal datasets (images, video, point clouds). Deduplicate billions of rows, identify edge cases for labeling, and explore data distributions without copying data. 3. Add new columns and features and embeddings at scale with minimal I/O overhead. Declarative pipelines, automatic versioning, schema evolution, and the ability to branch and roll back experiments without rewriting tables. 4. High-performance random access and low-overhead shuffles for training and fine-tuning. Achieve up to 70% Model FLOPS Utilization with no egress bottleneck from the same table used for exploration.