Efficiency: Batch TensorFlow vector generation and data insertion with pg-promise multiple rows

Learn how to build a movie recommender with TensorFlow, Postgres, PGVector, Javascript & Next.js. This is a series of videos where we build a project together step by step. Check the complete playlist here -    • TensorFlow, Postgres, PGVector & Next.js: ...  .

In this chapter you’ll learn how to use pg-promise in combination with TensorFlow universal-sentence-encoder to create vector search embeddings and store them in PostgreSQL. We’ll divide data into batches for faster processing.

You can find helpful links and code snippets in this accompanying article - https://aiven.io/developer/building-a....

Register with Aiven to host Postgres for free and get extra credits: https://go.aiven.io/get-pgvector.

Chapters:
00:00 Intro
00:51 Using pg-promise to optimise working with Postgres
04:52 Split data into batches for TensorFlow processing
10:28 Testing on limited number of movies
13:47 Generating embeddings for the rest of the movies


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