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This page shows you how to install AlloyDB Omni and integrate
AlloyDB AI.
AlloyDB AI
is a suite of features included with AlloyDB Omni that let you
build enterprise generative AI applications. For more information about the
AI/ML functionality of AlloyDB, see
Build generative AI applications.
AlloyDB Omni with AlloyDB AI lets you query remote ML models to work with online predictions and text embeddings generated from ML models. AlloyDB Omni with AlloyDB AI can also process vector embeddings from other content such as an image, for example, if you use the google_ml.predict_row interface and do the translation yourself in the query.
Based on where you want to install AlloyDB Omni with AlloyDB AI, select one of the following options:
Verify AlloyDB Omni with AlloyDB AI installation
To verify your installation is successful and uses model prediction, enter the following:
In the previous query, the embedding() call generates embeddings for the input text AlloyDB AI.
array_dims returns the dimensions of the array returned by embedding().
Since the text-embedding-005 model returns an output with 768 dimensions, the output is [768].
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-04-12 UTC."],[[["AlloyDB Omni with AlloyDB AI enables users to build enterprise generative AI applications and query remote ML models for online predictions and text embeddings."],["Installation of AlloyDB Omni with AlloyDB AI can be done through a single-server Kubernetes GDC air-gapped environment."],["Users can verify the successful installation and use of model prediction by creating the `google_ml_integration` extension and using the `embedding()` function to generate text embeddings."],["The `embedding()` function, demonstrated with the `text-embedding-005` model, returns an output with 768 dimensions, as shown in the verification example."],["Vertex AI model support and stability are subject to Vertex AI's model versioning and lifecycle guidelines."]]],[]]