Build generative AI applications using AlloyDB AI

AlloyDB AI is a suite of features included with AlloyDB for PostgreSQL and AlloyDB Omni that let you apply the semantic and predictive power of machine learning (ML) models to your data. This page provides an overview of the ML-powered AI functions that are available through AlloyDB.

Caution: The postgres_ann extension is renamed to alloydb_scann. Before you upgrade to AlloyDB Omni version 15.5.5, you must drop any indexes created using the older postgres_ann version. Next, upgrade AlloyDB Omni, and then create the indexes again using the alloydb_scann extension. This applies only to AlloyDB Omni.

Store, index, and query vectors

The stock pgvector PostgreSQL extension extension is customized for AlloyDB, and referred to as vector. It supports storing generated embeddings in a vector column. The extension also adds support for scalar quantization feature to create IVF indexes. You can also create an IVFFlat index or HSNW index that are available with stock pgvector.

For more information about storing vectors, see Store vectors.

Preview — AlloyDB ScaNN index

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

In addition to the customized vector extension, AlloyDB includes the alloydb_scann extension that implements a highly efficient nearest-neighbor index powered by the ScaNN algorithm.

Note: The alloydb_scann extension can be used with PostgreSQL 15 compatible databases.

For more information about creating indexes and querying vectors, see Create indexes and query vectors.

Tune your vector query performance

You can tune your indexes for a balance between query-per-second (QPS) and recall with your queries. For more information about tuning your indexes, see Tune vector query performance.

Generate embeddings and text predictions

AlloyDB AI extends PostgreSQL syntax with two functions for querying models using the google_ml_integration extension:

• Invoke predictions to call a model using SQL within a transaction.

• Generate embeddings to have an LLM translate text prompts into numerical vectors.

  You can use the embedding() function to query Vertex AI models, while the google_ml.embedding() function can be used to query registered Vertex AI, hosted, and third-party models.

  You can then apply these vector embeddings as input to pgvector functions. This includes methods to compare and sort samples of text according to their relative semantic distance.

Note: To generate embeddings for custom hosted or third-party AI models, see Register and call remote AI models using model endpoint management and Register and call remote AI models in AlloyDB for PostgreSQL. This feature is available in Preview.

Use models in the cloud with Vertex AI

You can configure AlloyDB Omni to work with Vertex AI.

This gives your applications the following benefits:

• Your applications can invoke predictions using any model stored in the Vertex AI Model Garden that they have access to.

• Your applications can generate embeddings using the textembedding-gecko English models LLM.

What's next

• Install AlloyDB Omni with AlloyDB AI

• Create indexes and query vectors