Create indexes and query vectors

This document shows you how to use stored embeddings to generate indexes and query embeddings. For more information about storing embedding, see Store vector embeddings.

You can create ScaNN, IVF, IVFFlat, and HNSW indexes with AlloyDB.

Caution: The postgres_ann extension is renamed to alloydb_scann. Before you upgrade to AlloyDB Omni, 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.

Before you begin

Before you can start creating indexes, you must complete the following prerequisites.

• Embedding vectors are added to a table in your AlloyDB database.

• The vector extension version 0.5.0 or later that is based on pgvector, extended by Google for AlloyDB is installed.

  CREATE EXTENSION IF NOT EXISTS vector;
  

• To generate ScaNN indexes, install the alloydb_scann extension in addition to the vector extension.

  CREATE EXTENSION IF NOT EXISTS alloydb_scann;
  

Note: The alloydb_scann extension can be used with PostgreSQL 14 and 15 compatible databases.

Create an index

You can create one of the following index types for tables in your database.

Note: As the index names are shared across the database, create an index name unique to each table in your database.

ScaNN IVF IVFFlat HNSW

Create a ScaNN index

AlloyDB alloydb_scann, a PostgreSQL extension developed by Google that implements a highly efficient nearest-neighbor index powered by the ScaNN algorithm.

The ScaNN index is a tree-based quantization index for approximate nearest neighbor search. It provides lower index building time and smaller memory footprint as compared to HNSW. In addition, it provides faster QPS in comparison to HNSW based on the workload.

Note: Before you create an index, ensure that you add embedding vectors to a table in your AlloyDB database. If you try to generate a ScaNN index on an empty or partitioned table, then you might encounter some issues. For more information about the errors generated, see Troubleshoot ScaNN index errors.

Two-level tree ScaNN index

To apply a two-level tree index using the ScaNN algorithm to a column containing stored vector embeddings, run the following DDL query:

CREATE INDEX INDEX_NAME ON TABLE
  USING scann (EMBEDDING_COLUMN DISTANCE_FUNCTION)
  WITH (num_leaves=NUM_LEAVES_VALUE);

Replace the following:

• INDEX_NAME: the name of the index you want to create—for example, my-scann-index. The index names are shared across your database. Ensure that each index name is unique to each table in your database.

• TABLE: the table to add the index to.

• EMBEDDING_COLUMN: a column that stores vector data.

• DISTANCE_FUNCTION: the distance function to use with this index. Choose one of the following:

  • L2 distance: l2

  • Dot product: dot_product

  • Cosine distance: cosine

• NUM_LEAVES_VALUE: the number of partitions to apply to this index. Set to any value between 1 to 1048576. For more information about how to decide this value, see Tune a ScaNN index.

Three-level tree ScaNN index

To create a three-level tree index using the ScaNN algorithm to a column containing stored vector embeddings, run the following DDL query:

CREATE INDEX INDEX_NAME ON TABLE
  USING scann (EMBEDDING_COLUMN DISTANCE_FUNCTION)
  WITH (num_leaves=NUM_LEAVES_VALUE, max_num_levels = MAX_NUM_LEVELS);

Replace the following:

• MAX_NUM_LEVELS: the maximum number of levels of the K-means clustering tree. Set to 1(default) for two-level tree-based quantization and to 2 for three-level tree-based quantization.

After you create the index, you can run nearest-neighbor search queries that make use of the index by following the instructions in Make a nearest-neighbor query with given text.

The index parameters must be set to strike a right balance between QPS and recall. For more information about tuning the ScaNN index, see Tune a ScaNN index.

To create this index on an embedding column that uses the real[] data type instead of vector, cast the column into the vector data type:

CREATE INDEX INDEX_NAME ON TABLE
  USING scann (CAST(EMBEDDING_COLUMN AS vector(DIMENSIONS)) DISTANCE_FUNCTION)
  WITH (num_leaves=NUM_LEAVES_VALUE, max_num_levels = MAX_NUM_LEVELS);

Replace DIMENSIONS with the dimensional width of the embedding column. For more information about how to find the dimensions, see the vector_dims function in Vector functions.

To view the indexing progress, use the pg_stat_progress_create_index view:

SELECT * FROM pg_stat_progress_create_index;

The phase column shows the current state of your index creation, and the building index: tree training phase disappears after the index is created.

To tune your index for a target recall and QPS balance, see Tune a ScaNN index.

Run a query

After you have stored and indexed embeddings in your database, you can start querying using the pgvector query functionality. You cannot run bulk search queries using the alloydb_scann extension.

To find the nearest semantic neighbors for an embedding vector, you can run the following example query, where you set the same distance function that you used during the index creation.

  SELECT * FROM TABLE
    ORDER BY EMBEDDING_COLUMN DISTANCE_FUNCTION_QUERY ['EMBEDDING']
    LIMIT ROW_COUNT

Replace the following:

• TABLE: the table containing the embedding to compare the text to.

• INDEX_NAME: the name of the index you want to use—for example, my-scann-index.

• EMBEDDING_COLUMN: the column containing the stored embeddings.

• DISTANCE_FUNCTION_QUERY: the distance function to use with this query. Choose one of the following based on the distance function used while creating the index:

  • L2 distance: <->

  • Inner product: <#>

  • Cosine distance: <=>

• EMBEDDING: the embedding vector you want to find the nearest stored semantic neighbors of.

• ROW_COUNT: the number of rows to return.

  Specify 1 if you want only the single best match.

For more information about other query examples, see Querying.

You can use also use the embedding() function to translate the text into a vector. You apply the vector to one of the pgvector nearest-neighbor operator, <-> for L2 distance, to find the database rows with the most semantically similar embeddings.

Because embedding() returns a real array, you must explicitly cast the embedding() call to vector in order to use these values with pgvector operators.

  CREATE EXTENSION google_ml_integration;
  CREATE EXTENSION IF NOT EXISTS vector;

  SELECT * FROM TABLE
    ORDER BY EMBEDDING_COLUMN::vector
    <-> embedding('MODEL_IDVERSION_TAG', 'TEXT')
    LIMIT ROW_COUNT

Replace the following:

• MODEL_ID: the ID of the model to query.

  If you are using the Vertex AI Model Garden, then specify textembedding-gecko@003 as the model ID. These are the cloud-based models that AlloyDB can use for text embeddings. For more information, see Text embeddings.

• Optional: VERSION_TAG: the version tag of the model to query. Prepend the tag with @.

  If you are using one of the textembedding-gecko English models with Vertex AI, then specify one of the version tags—for example, textembedding-gecko@003, listed in Model versions.

  Google strongly recommends that you always specify the version tag. If you don't specify the version tag, then AlloyDB always uses the latest model version, which might lead to unexpected results.

• TEXT: the text to translate into a vector embedding.

What's next

• An example embedding workflow
• Tune vector query performance
• Vector index metrics