From recommendation systems to LLM-based applications, vector search is a critical component of the modern AI workflow. Existing vector solutions are complicated to use, hard to maintain, and cost too much. LanceDB is a free open-source vector store that can perform low latency vector search on billion-scale vector datasets on a single node.

LanceDB is powered by Lance format, a modern columnar data format for machine learning and data science. Compatible with pandas/polars/duckdb, Lance format supports vector index, predicate pushdown, and random access performance 2000x faster than parquet.

This talk will:

1. Introduce LanceDB and show some example workflows
2. Outline Lance format design and what makes it so fast
3. Review the Lance roadmap and ecosystem integrations

You can find Lance here: https://github.com/eto-ai/lance