Generative AI overview
This document describes the generative artificial intelligence (AI) features that BigQuery ML supports. These features let you perform AI tasks in BigQuery ML by using pre-trained Vertex AI foundation models. Supported tasks include the following:
You access a Vertex AI model to perform one of these functions by creating a remote model in BigQuery ML that represents the Vertex AI model's endpoint. Once you have created a remote model over the Vertex AI model that you want to use, you access that model's capabilities by running a BigQuery ML function against the remote model.
This approach lets you use the capabilities of these Vertex AI models to analyze BigQuery data by using SQL.
Workflow
You can use remote models over Vertex AI models and remote models over Cloud AI services together with BigQuery ML functions in order to accomplish complex data analysis and generative AI tasks.
The following diagram shows some typical workflows where you might use these capabilities together:
Generative AI
You can use large language models (LLMs) to perform tasks such as text summarization and generation. For example, you could summarize a long report, or analyze sentiment in customer feedback. You can use vision language models (VLMs) to analyze visual content such as images and videos for tasks like visual captioning and visual Q&A. You can use multimodal models to perform the same tasks as LLMs and VLMs, plus additional tasks like audio transcription and document analysis.
To perform generative AI tasks, you can create
a reference to a pre-trained Vertex AI
model
by creating a remote model and specifying the model name
for the ENDPOINT
value. The following Vertex AI models
are supported:
gemini-1.5-flash
(Preview)gemini-1.5-pro
(Preview)gemini-1.0-pro
gemini-1.0-pro-vision
(Preview)text-bison
text-bison-32k
text-unicorn
To provide feedback or request support for the models in preview, send an email to bqml-feedback@google.com.
When you create a remote model that references either the gemini-1.0-pro-002
model or any version of the text-bison
model, you can
optionally choose to configure
supervised tuning
(Preview) at the same time.
After you create the model, you can use the
ML.GENERATE_TEXT
function
to interact with that model:
- For remote models based on
gemini-1.0-pro
,text-bison
,text-bison-32k
, ortext-unicorn
models, you can use theML.GENERATE_TEXT
function with a prompt you provide in a query or from a column in a standard table. - For remote models based on the
gemini-1.0-pro-vision
model, you can use theML.GENERATE_TEXT
function to analyze image or video content from an object table with a prompt you provide as a function argument. - For remote models based on the
gemini-1.5-flash
orgemini-1.5-pro
models, you can use theML.GENERATE_TEXT
function to analyze text, image, audio, video, or PDF content from an object table with a prompt you provide as a function argument, or you can generate text from a prompt you provide in a query or from a column in a standard table.
You can use
grounding
and
safety attributes
when you use Gemini models with the ML.GENERATE_TEXT
function, provided that
you are using a standard table for input. Grounding lets the
Gemini model use additional information from the internet to
generate more specific and factual responses. Safety attributes let the
Gemini model filter the responses it returns based on the
attributes you specify.
All inference occurs in Vertex AI. The results are stored in BigQuery.
To learn more, try
generating text with the ML.GENERATE_TEXT
function.
Embedding
You can use embedding to identify semantically similar items. For example, you can use text embedding to identify how similar two pieces of text are. If the pieces of text are semantically similar, then their respective embeddings are located near each other in the embedding vector space.
You can use BigQuery ML models to create the following types of embeddings:
- To create text embeddings, you can create a reference to one of the
Vertex AI
text-embedding
ortext-multilingual-embedding
embedding models by creating a remote model and specifying the LLM name for theENDPOINT
value. - To create multimodal embeddings, which can embed text, images, and videos into
the same semantic space, you can create a reference to the Vertex AI
multimodalembedding
LLM by creating a remote model and specifying the LLM name for theENDPOINT
value. This feature is in preview. - To create embeddings for structured independent and identically distributed random variables (IID) data, you can use a Principal component analysis (PCA) model or an Autoencoder model. This feature is in preview.
- To create embeddings for user or item data, you can use a Matrix factorization model. This feature is in preview.
After you create the model, you can use the
ML.GENERATE_EMBEDDING
function
to interact with it. For all types of supported models, ML.GENERATE_EMBEDDING
works with data in
standard tables. For multimodal
embedding models, ML.GENERATE_EMBEDDING
also works with visual
content in object tables. For remote
models, all inference occurs in Vertex AI. For other model types,
all inference occurs in BigQuery. The results are stored in
BigQuery.
To learn more, try creating
text embeddings,
image embeddings
and video embeddings
with the ML.GENERATE_EMBEDDING
function.
For a smaller, lightweight text embedding, try using a pretrained TensorFlow model, such as NNLM, SWIVEL, or BERT.
For information about choosing the best model for your embedding use case, see Choosing a text embedding model.
What's next
- Generate text by using a
text-bison
model and theML.GENERATE_TEXT
function. - Generate text by using a
Gemini
model and theML.GENERATE_TEXT
function. - Generate text by using the
ML.GENERATE_TEXT
function with your data. - Tune a model using your data.
- Analyze images with a Gemini vision model.
- For more information about performing inference over machine learning models, see Model inference overview.