Vector databases (beyond the hype)

Practical AI

There’s so much talk (and hype) these days about vector databases. We thought it would be timely and practical to have someone on the show that has been hands on with the various options and actually tried to build applications leveraging vector search. Prashanth Rao is a real practitioner that has spent and huge amount of time exploring the expanding set of vector database offerings. After introducing vector database and giving us a mental model of how they fit in with other datastores, Prashanth digs into the trade offs as related to indices, hosting options, embedding vs. query optimization, and more.

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Featuring:

• Prashanth Rao – Website, GitHub, LinkedIn, X
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

Vector databases blog posts from Prashanth:

• (Part 1): What makes each one different?
• (Part 2): Understanding their internals
• (Part 3): Not all indexes are created equal

Something missing or broken? PRs welcome!

챕터

1. Welcome to Practical AI (00:00:00)

2. Prashanth Rao (00:00:43)

3. What are vector DBs (00:01:40)

4. Semantics and vectors (00:03:21)

5. How vectors are utilized (00:04:20)

6. Evolution of NoSQL (00:09:53)

7. Sponsor: Changelog News (00:15:52)

https://changelog.com/news

8. How do we use them (00:17:53)

9. Use cases for vector DBs (00:20:37)

10. Over-optimizing (00:23:58)

11. Tradeoffs (00:26:14)

12. Mature vendors (00:31:00)

13. Speedrunning the blog points (00:33:04)

14. In-memory vs on-disk (00:34:48)

15. Environments (00:40:43)

16. Exciting things in the space (00:44:21)

17. Outro (00:50:45)