Create an IVF index

This page describes how to use stored embeddings to generate indexes and query embeddings using an IVF index 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 IVF index

Stock pgvector supports approximate nearest-neighbor searching through indexing. AlloyDB adds to this support with a scalar quantization feature that you can specify when you create an index. When enabled, scalar quantization can significantly speed up queries that have larger dimensional vectors, and lets you store vectors with up to 8,000 dimensions.

To enable scalar quantization on a pgvector -based index, specify IVF as the index method, and SQ8 as the quantizer:

  CREATE 
  
 INDEX 
  
  INDEX_NAME 
 
  
 ON 
  
  TABLE 
 
  
 USING 
  
 ivf 
  
 ( 
  EMBEDDING_COLUMN 
 
  
  DISTANCE_FUNCTION 
 
 ) 
  
 WITH 
  
 ( 
 lists 
  
 = 
  
  LIST_COUNT 
 
 , 
  
 quantizer 
  
 = 
  
 ' QUANTIZER 
' 
 ); 
 

Replace the following:

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

• QUANTIZER : the type of quantizer that you want to use.

  Set to either of the following:

  • SQ8 : Recommended. Faster query response but results in some recall loss, which doesn't impact production scenarios.
  • FLAT : slower query response and higher memory usage, but can attain negligible recall loss.

  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 
  
 ivf 
  
 ( 
 CAST 
 ( 
  EMBEDDING_COLUMN 
 
  
 AS 
  
 vector 
 ( 
  DIMENSIONS 
 
 ))) 
 '}} DISTANCE_FUNCTION 
) 
 WITH (lists = LIST_COUNT 
, quantizer = ' 
 SQ8 
 ' 
 ); 
 

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 postings phase indicates that the index creation is nearing completion.

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

What's next

• Create a ScaNN index
• Run vector similarity searches
• Tune vector query performance
• Vector index metrics
• Learn how to build a smart shopping assistant with AlloyDB, pgvector, and model endpoint management .