Create an IVFFLAT index

This page describes how to use stored embeddings to generate indexes and query embeddings using an IVFFlatindex with AlloyDB for PostgreSQL. For more information about storing embedding, see Store vector embeddings.

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;

Create an IVFFlat index

Stock pgvector also provides a version of the IVF index named IVFFlat that provides faster build time and has a smaller memory footprint as compared to the hnsw index.

To create an IVFFlat index, complete the following steps:

CREATE INDEX INDEX_NAME ON TABLE
  USING ivfflat (EMBEDDING_COLUMN DISTANCE_FUNCTION)
  WITH (lists = LIST_COUNT);

Replace the following:

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

  • 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: vector_l2_ops

    • Inner product: vector_ip_ops

    • Cosine distance: vector_cosine_ops

  • LIST_COUNT: the number of lists to use with this index. For more information about how to decide this value, see Tune an IVFFlat 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 ivfflat (CAST(EMBEDDING_COLUMN AS vector(DIMENSIONS)))'}} DISTANCE_FUNCTION)
  WITH (lists = LIST_COUNT);

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.

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

Run a query

After you store and index the embeddings in your database, you can start querying using the pgvector query functionality.

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 also use the embedding() function to translate the text into a vector. You apply the vector to one of the pgvector nearest-neighbor operators, <-> 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.

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