Changelog Master Feed cover image

Changelog Master Feed

Open source, on-disk vector search with LanceDB (Practical AI #250)

Dec 19, 2023
Chang She, Co-founder and CEO of LanceDB, discusses their open source, on-disk, embedded vector search offering. They talk about the unique columnar database structure that enables serverless deployments and drastic savings without performance hits at scale. They also explore the potential applications and benefits of autonomous vehicles and edge computing technology, as well as exciting developments in the practical AI space.
41:53

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • LanceDB's unique columnar database structure enables serverless deployments and significant cost savings at scale.
  • LanceDB's separation of compute and storage, facilitated by its columnar format and disk-based vector indices, allows for efficient scaling and fast query performance.

Deep dives

Lance DB: An Overview and Evolution

Lance DB started as a company focused on serving computer vision projects, aiming to build better data infrastructure for managing unstructured data. The motivation came from the complexity and challenges experienced when working with multimodal data for AI. They developed a storage layer called Lance Columnar Format to manage tabular and unstructured data more effectively. Eventually, Lance DB introduced a vector index for deduplication and finding relevant samples for training, which led to its recognition as a vector database. It offers ease of use, hyperscalability, cost-effectiveness, and the ability to manage metadata, raw assets, and vectors together. With applications in generative AI, e-commerce, search engines, and computer vision, Lance DB is well-positioned to handle large datasets and support various use cases.

Remember Everything You Learn from Podcasts

Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
App store bannerPlay store banner
Get the app