AI Engineering Podcast Building AI Systems on Postgres: An Inside Look at pgai Vectorizer
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Nov 11, 2024 Avthar Sewrathan, Head of AI at Timescale and expert in database infrastructure, shares insights into the innovative pgai Vectorizer toolchain. He reveals how this tool enables seamless management of AI workflows in Postgres, emphasizing the importance of keeping vector data updated. The discussion covers optimizing embedding strategies, the balance between user-friendliness and customization for developers, and the future of AI integration within databases. Avthar also touches on challenges in content moderation and semantic search, highlighting the need for continuous improvement and collaboration in the open-source community.
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Beyond Vector Search
- Vector search is insufficient for building good AI systems.
- Production-ready systems need to handle dynamic data changes and model experimentation.
PGAI Genesis
- Timescale initially supported PG Vector for vector search in their hosted product.
- After talking to customers, they realized vector search alone wasn't enough for robust AI systems, leading to PGAI's development.
Balancing Database Load
- Running resource-intensive AI workflows within the database can negatively impact performance.
- PGAI addresses this by managing embedding creation in SQL but executing it on external workers.