Key strengths
Multimodal data curation and deduplicationUnified vector, full-text, and hybrid search with SQL filtersFeature engineering with Python UDFs and automatic updatesTraining directly from curated data with up to 70% MFUScalable vector search up to 10 billion vectors
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LanceDB — Multimodal Lakehouse for AI
LanceDB is a multimodal lakehouse purpose-built for AI/ML engineers and researchers constructing multimodal data pipelines, vector search systems, and model training infrastructure. It unifies vector search, full-text search, and hybrid search under a single SQL-filterable query layer, scaling to up to 10 billion vectors. LanceDB supports feature engineering via Python UDFs with automatic updates, multimodal data curation and deduplication, and enables training directly from curated datasets with up to 70% MFU (Model FLOPs Utilization). Native integrations include DuckDB, LlamaIndex, CrewAI, Spark, and Hermes Agent.