Question

A data analytics team stores millions of customer review texts in a BigQuery table. They want to build a semantic search feature that finds reviews similar to a given query, and they prefer to stay entirely within SQL workflows without exporting data or writing Python pipelines. They need to generate text embeddings at scale directly in BigQuery. What is the most appropriate low-code approach?

Accepted Answer

Create a BigQuery ML remote model that references a Vertex AI text embedding foundation model, then call ML.GENERATE_EMBEDDING on the reviews table.

Answer

Train an AutoML text classification model in Vertex AI and use its prediction scores as embedding vectors for similarity search.

Answer

Export the reviews to Cloud Storage and call the Natural Language API's entity analysis to compute similarity between documents.

Answer

Use CREATE MODEL with a BigQuery ML matrix factorization model to produce embeddings from the review text columns.