BigQuery vector search and embedding generation

Discover the power of semantic search! With BigQuery's vector search capabilities, you can analyze unstructured data like text, images, and videos based on their underlying meaning. Explore how machine learning transforms your data into numerical representations called embeddings, making it possible to find connections that traditional keyword searches often miss.

In this video, you'll learn how BigQuery seamlessly generates embeddings from unstructured objects and enables semantic search using familiar SQL functions. See a real-world example as we use these techniques to search a non-labeled product image catalog with text.

Vector search resources:

Embedding generation resources:

[newsverse-irrc]

With its seamless integration with the broader Google Cloud ecosystem, BigQuery empowers organizations to effortlessly ingest, store, and analyze massive amounts of data from a variety of sources.

[ammarfasih]

Can someone confirm if these embeddings are created based on some sort of metadata (in text format) or they are created based on the images analyzed, like done by gemini vision pro?

[_m]

i love googlecloud man i run mc server there

[abubakrabdalla]

i'm facing this error

Invalid table-valued function ML.GENERATE_EMBEDDING ML.GENERATE_EMBEDDING expects the 2nd argument to contain a column named content of type STRING. at [3:8]

[batumanagadze]

how did we get product_names based on that query?

[ammarfasih]

is BigQuery embedding and vector search supports the negation? Like say I'm giving following below statement.

"looking for boys t-shirts and not in yellow"

Here I'm looking for boys t-shirts but particularly don't wanna include the the color yellow. At the moment what I've observed it is unable to handle the negation and returning the results with color yellow.

Is there a way to handle this?

[KEVINCABALLERO-nbuv]

Do anyone faced this issue ? Column must have the same array length, while the minimum length is 0 and the maximum length is 768.