{"podcast":{"title":"AI Engineering Podcast","slug":"ai-engineering-podcast","podcast_index_feed_id":5875646,"rss_url":"https://serve.podhome.fm/rss/c9abdd38-a5dc-5eb2-96fd-f833f93208a7","website_url":"https://www.aiengineeringpodcast.com","image_url":"https://assets.podhome.fm/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638557211890591941ai_engineering_podcast_logo.jpg","author":"Tobias Macey","episode_count":79,"summary":"This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/ai-engineering-podcast"},"episode":{"title":"Building AI Systems on Postgres: An Inside Look at pgai Vectorizer","slug":"building-ai-systems-on-postgres-an-inside-look-at-pgai-vectorizer","published_at":"2024-11-11T00:42:46+00:00","page_url":"https://stenobird.com/podcast/ai-engineering-podcast/building-ai-systems-on-postgres-an-inside-look-at-pgai-vectorizer","show_page_url":"https://stenobird.com/podcast/ai-engineering-podcast","url":"https://www.aiengineeringpodcast.com/pgai-vectorizer-postgres-vector-embedding-episode-39","audio_url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638665426253011221ed13d1c2-d85b-430b-9058-916a2fe48daev1.mp3","summary":"Summary With the growth of vector data as a core element of any AI application comes the need to keep those vectors up to date. When you go beyond prototypes and into production you will need a way to continue experimenting with new embedding models, chunking strategies, etc. You will also need a way to keep the embeddings up to date as your data changes. The team at Timescale created the pgai Vectorizer toolchain to let you manage that work in your Postgres database. In this episode Avthar Sewrathan explains how it works and how you can start using it today. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Your host is Tobias Macey and today I'm interviewing Avthar Sewrathan about the pgai extension for Postgres and how to run your AI workflows in your database Interview Introduction How did you get involved in machine learning? Can you describe what pgai Vectorizer is and the story behind it? What are the benefits of using the database engine to execute AI workflows? What types of operations does pgai Vectorizer enable? What are some common generative AI patterns that can't be done with pgai? AI applications require a large and complex set of dependencies. How does that work with pgai Vectorizer and the Python runtime in Postgres? What are some of the other challenges or system pressures that are introduced by running these AI workflows in the database context? Can you describe how the pgai extension is implemented? With the rapid pace of change in the AI ecosystem, how has that informed the set of features that make sense in pgai Vectorizer and won't require rebuilding in 6 months? Can you describe the workflow of using pgai Vectorizer to build and maintain a set of em…","meta_description":"Summary With the growth of vector data as a core element of any AI application comes the need to keep those vectors up to date. When you go beyond prototy…","key_points":[],"chapters":[],"topics":[],"duration_seconds":3230,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/building-ai-systems-on-postgres-an-inside-look-at-pgai-vectorizer/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/ai-engineering-podcast/building-ai-systems-on-postgres-an-inside-look-at-pgai-vectorizer.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}