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:
- Introduce LanceDB and show some example workflows
- Outline Lance format design and what makes it so fast
- Review the Lance roadmap and ecosystem integrations
You can find Lance here: https://github.com/eto-ai/lance