Perform a vector search

This tutorial describes how to set up and perform a vector search in AlloyDB for PostgreSQL using the Google Cloud console. Examples are included to show vector search capabilities, and they're intended for demonstration purposes only.

For information about how to use filtered vector search to refine your similarity searches, see Filtered vector search in AlloyDB for PostgreSQL .

To learn how to perform a vector search with Vertex AI embeddings, see Getting started with Vector Embeddings with AlloyDB AI .

Objectives

Create an AlloyDB cluster and primary instance.
Connect to your database and install required extensions.
Create a product and product inventory table.
Insert data to the product and product inventory tables and perform a basic vector search.
Create a ScaNN index on the products table.
Perform a simple vector search.
Perform a complex vector search with a filter and a join.

Costs

In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator .

New Google Cloud users might be eligible for a free trial .

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up .

Before you begin

Enable billing and required APIs

In the Google Cloud console, go to the Clusterspage.

Go to project selector
Make sure that billing is enabled for your Google Cloud project.
Enable the Cloud APIs necessary to create and connect to AlloyDB for PostgreSQL.

Enable the APIs
1. In the Confirm project step, click Next to confirm the name of the project you are going to make changes to.
2. In the Enable APIs step, click Enable to enable the following:
  - AlloyDB API
  - Compute Engine API
  - Service Networking API
  - Vertex AI API

Create an AlloyDB cluster and primary instance

In the Google Cloud console, go to the Clusterspage.

Go to Clusters
Click Create cluster.
In Cluster ID, enter my-cluster .
Enter a password. Take note of this password because you use it in this tutorial.
Select a region—for example, us-central1 (Iowa) .
Select the default network.

If you have a private access connection, continue to the next step. Otherwise, click Set up connectionand follow these steps:
1. In Allocate an IP range, click Use an automatically allocated IP range.
2. Click Continueand then click Create connection.
In Zonal availability, select Single zone.
Select the 2 vCPU,16 GB machine type.
In Connectivity, select Enable public IP.
Click Create cluster. It might take several minutes for AlloyDB to create the cluster and display it on the primary cluster Overviewpage.
In Instances in your cluster, expand the Connectivitypane. Take note of the Connection URIbecause you use it in this tutorial.

The connection URI is in the projects/<var>PROJECT_ID</var>/locations/<var>REGION_ID</var>/clusters/my-cluster/instances/my-cluster-primary format.

Grant Vertex AI user permission to AlloyDB service agent

To enable AlloyDB to use Vertex AI text embedding models, you must add Vertex AI user permissions to the AlloyDB service agent for the project where your cluster and instance is located.

For more information about how to add the permissions, see Grant Vertex AI user permission to AlloyDB service agent .

Connect to your database using a web browser

In the Google Cloud console, go to the Clusterspage.

Go to Clusters
In the Resource namecolumn, click the name of your cluster, my-cluster .
In the navigation pane, click AlloyDB Studio.
In the Sign in to AlloyDB Studiopage, follow these steps:
1. Select the postgres database.
2. Select the postgres user.
3. Enter the password you created in Create a cluster and its primary instance .
4. Click Authenticate. The Explorerpane displays a list of the objects in the postgres database.
Open a new tab by clicking + New SQL editor tabor + New tab.

Install required extensions

Run the following query to install the vector and alloydb_scann extensions:

   
 CREATE 
  
 EXTENSION 
  
 IF 
  
 NOT 
  
 EXISTS 
  
 vector 
 ; 
  
 CREATE 
  
 EXTENSION 
  
 IF 
  
 NOT 
  
 EXISTS 
  
 alloydb_scann 
 ;

Insert product and product inventory data and perform a basic vector search

Run the following statement to create a product table that does the following:

Stores basic product information.
Includes an embedding vector column that computes and stores an embedding vector for a product description of each product.

   
 CREATE 
  
 TABLE 
  
 product 
  
 ( 
  
 id 
  
 INT 
  
 PRIMARY 
  
 KEY 
 , 
  
 name 
  
 VARCHAR 
 ( 
 255 
 ) 
  
 NOT 
  
 NULL 
 , 
  
 description 
  
 TEXT 
 , 
  
 category 
  
 VARCHAR 
 ( 
 255 
 ), 
  
 color 
  
 VARCHAR 
 ( 
 255 
 ), 
  
 embedding 
  
 vector 
 ( 
 768 
 ) 
  
 GENERATED 
  
 ALWAYS 
  
 AS 
  
 ( 
 embedding 
 ( 
 'text-embedding-005' 
 , 
  
 description 
 )) 
  
 STORED 
  
 );

If needed, you can use the Logs Explorer to view logs and troubleshoot errors.

Run the following query to create a product_inventory table that stores information about available inventory and corresponding prices. The product_inventory and product tables are used in this tutorial to run complex vector search queries.

  CREATE 
  
 TABLE 
  
 product_inventory 
  
 ( 
  
 id 
  
 INT 
  
 PRIMARY 
  
 KEY 
 , 
  
 product_id 
  
 INT 
  
 REFERENCES 
  
 product 
 ( 
 id 
 ), 
  
 inventory 
  
 INT 
 , 
  
 price 
  
 DECIMAL 
 ( 
 10 
 , 
 2 
 ) 
 );

Run the following query to insert product data into the product table:

  INSERT 
  
 INTO 
  
 product 
  
 ( 
 id 
 , 
  
 name 
 , 
  
 description 
 , 
 category 
 , 
  
 color 
 ) 
  
 VALUES 
 ( 
 1 
 , 
  
 'Stuffed Elephant' 
 , 
  
 'Soft plush elephant with floppy ears.' 
 , 
  
 'Plush Toys' 
 , 
  
 'Gray' 
 ), 
 ( 
 2 
 , 
  
 'Remote Control Airplane' 
 , 
  
 'Easy-to-fly remote control airplane.' 
 , 
  
 'Vehicles' 
 , 
  
 'Red' 
 ), 
 ( 
 3 
 , 
  
 'Wooden Train Set' 
 , 
  
 'Classic wooden train set with tracks and trains.' 
 , 
  
 'Vehicles' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 4 
 , 
  
 'Kids Tool Set' 
 , 
  
 'Toy tool set with realistic tools.' 
 , 
  
 'Pretend Play' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 5 
 , 
  
 'Play Food Set' 
 , 
  
 'Set of realistic play food items.' 
 , 
  
 'Pretend Play' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 6 
 , 
  
 'Magnetic Tiles' 
 , 
  
 'Set of colorful magnetic tiles for building.' 
 , 
  
 'Construction Toys' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 7 
 , 
  
 'Kids Microscope' 
 , 
  
 'Microscope for kids with different magnification levels.' 
 , 
  
 'Educational Toys' 
 , 
  
 'White' 
 ), 
 ( 
 8 
 , 
  
 'Telescope for Kids' 
 , 
  
 'Telescope designed for kids to explore the night sky.' 
 , 
  
 'Educational Toys' 
 , 
  
 'Blue' 
 ), 
 ( 
 9 
 , 
  
 'Coding Robot' 
 , 
  
 'Robot that teaches kids basic coding concepts.' 
 , 
  
 'Educational Toys' 
 , 
  
 'White' 
 ), 
 ( 
 10 
 , 
  
 'Kids Camera' 
 , 
  
 'Durable camera for kids to take pictures and videos.' 
 , 
  
 'Electronics' 
 , 
  
 'Pink' 
 ), 
 ( 
 11 
 , 
  
 'Walkie Talkies' 
 , 
  
 'Set of walkie talkies for kids to communicate.' 
 , 
  
 'Electronics' 
 , 
  
 'Blue' 
 ), 
 ( 
 12 
 , 
  
 'Karaoke Machine' 
 , 
  
 'Karaoke machine with built-in microphone and speaker.' 
 , 
  
 'Electronics' 
 , 
  
 'Black' 
 ), 
 ( 
 13 
 , 
  
 'Kids Drum Set' 
 , 
  
 'Drum set designed for kids with adjustable height.' 
 , 
  
 'Musical Instruments' 
 , 
  
 'Blue' 
 ), 
 ( 
 14 
 , 
  
 'Kids Guitar' 
 , 
  
 'Acoustic guitar for kids with nylon strings.' 
 , 
  
 'Musical Instruments' 
 , 
  
 'Brown' 
 ), 
 ( 
 15 
 , 
  
 'Kids Keyboard' 
 , 
  
 'Electronic keyboard with different instrument sounds.' 
 , 
  
 'Musical Instruments' 
 , 
  
 'Black' 
 ), 
 ( 
 16 
 , 
  
 'Art Easel' 
 , 
  
 'Double-sided art easel with chalkboard and whiteboard.' 
 , 
  
 'Arts & Crafts' 
 , 
  
 'White' 
 ), 
 ( 
 17 
 , 
  
 'Finger Paints' 
 , 
  
 'Set of non-toxic finger paints for kids.' 
 , 
  
 'Arts & Crafts' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 18 
 , 
  
 'Modeling Clay' 
 , 
  
 'Set of colorful modeling clay.' 
 , 
  
 'Arts & Crafts' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 19 
 , 
  
 'Watercolor Paint Set' 
 , 
  
 'Watercolor paint set with brushes and palette.' 
 , 
  
 'Arts & Crafts' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 20 
 , 
  
 'Beading Kit' 
 , 
  
 'Kit for making bracelets and necklaces with beads.' 
 , 
  
 'Arts & Crafts' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 21 
 , 
  
 '3D Puzzle' 
 , 
  
 '3D puzzle of a famous landmark.' 
 , 
  
 'Puzzles' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 22 
 , 
  
 'Race Car Track Set' 
 , 
  
 'Race car track set with cars and accessories.' 
 , 
  
 'Vehicles' 
 , 
  
 'Multicolor' 
 ), 
 ( 
 23 
 , 
  
 'RC Monster Truck' 
 , 
  
 'Remote control monster truck with oversized tires.' 
 , 
  
 'Vehicles' 
 , 
  
 'Green' 
 ), 
 ( 
 24 
 , 
  
 'Train Track Expansion Set' 
 , 
  
 'Expansion set for wooden train tracks.' 
 , 
  
 'Vehicles' 
 , 
  
 'Multicolor' 
 );

Optional: Run the following query to verify that the data is inserted in the product table:
```
  SELECT 
  
 * 
  
 FROM 
  
 product 
 ; 
 
```

Run the following query to insert inventory data into the product_inventory table:

  INSERT 
  
 INTO 
  
 product_inventory 
  
 ( 
 id 
 , 
  
 product_id 
 , 
  
 inventory 
 , 
  
 price 
 ) 
  
 VALUES 
 ( 
 1 
 , 
  
 1 
 , 
  
 9 
 , 
  
 13 
 . 
 09 
 ), 
 ( 
 2 
 , 
  
 2 
 , 
  
 40 
 , 
  
 79 
 . 
 82 
 ), 
 ( 
 3 
 , 
  
 3 
 , 
  
 34 
 , 
  
 52 
 . 
 49 
 ), 
 ( 
 4 
 , 
  
 4 
 , 
  
 9 
 , 
  
 12 
 . 
 03 
 ), 
 ( 
 5 
 , 
  
 5 
 , 
  
 36 
 , 
  
 71 
 . 
 29 
 ), 
 ( 
 6 
 , 
  
 6 
 , 
  
 10 
 , 
  
 51 
 . 
 49 
 ), 
 ( 
 7 
 , 
  
 7 
 , 
  
 7 
 , 
  
 37 
 . 
 35 
 ), 
 ( 
 8 
 , 
  
 8 
 , 
  
 6 
 , 
  
 10 
 . 
 87 
 ), 
 ( 
 9 
 , 
  
 9 
 , 
  
 7 
 , 
  
 42 
 . 
 47 
 ), 
 ( 
 10 
 , 
  
 10 
 , 
  
 3 
 , 
  
 24 
 . 
 35 
 ), 
 ( 
 11 
 , 
  
 11 
 , 
  
 4 
 , 
  
 10 
 . 
 20 
 ), 
 ( 
 12 
 , 
  
 12 
 , 
  
 47 
 , 
  
 74 
 . 
 57 
 ), 
 ( 
 13 
 , 
  
 13 
 , 
  
 5 
 , 
  
 28 
 . 
 54 
 ), 
 ( 
 14 
 , 
  
 14 
 , 
  
 11 
 , 
  
 25 
 . 
 58 
 ), 
 ( 
 15 
 , 
  
 15 
 , 
  
 21 
 , 
  
 69 
 . 
 84 
 ), 
 ( 
 16 
 , 
  
 16 
 , 
  
 6 
 , 
  
 47 
 . 
 73 
 ), 
 ( 
 17 
 , 
  
 17 
 , 
  
 26 
 , 
  
 81 
 . 
 00 
 ), 
 ( 
 18 
 , 
  
 18 
 , 
  
 11 
 , 
  
 91 
 . 
 60 
 ), 
 ( 
 19 
 , 
  
 19 
 , 
  
 8 
 , 
  
 78 
 . 
 53 
 ), 
 ( 
 20 
 , 
  
 20 
 , 
  
 43 
 , 
  
 84 
 . 
 33 
 ), 
 ( 
 21 
 , 
  
 21 
 , 
  
 46 
 , 
  
 90 
 . 
 01 
 ), 
 ( 
 22 
 , 
  
 22 
 , 
  
 6 
 , 
  
 49 
 . 
 82 
 ), 
 ( 
 23 
 , 
  
 23 
 , 
  
 37 
 , 
  
 50 
 . 
 20 
 ), 
 ( 
 24 
 , 
  
 24 
 , 
  
 27 
 , 
  
 99 
 . 
 27 
 );

Run the following vector search query that tries to find products that are similar to the word music . This means that even though the word music isn't explicitly mentioned in the product description, the result shows products that are relevant to the query:
```
  SELECT 
  
 * 
  
 FROM 
  
 product 
 ORDER 
  
 BY 
  
 embedding 
  
< = 
>  
 embedding 
 ( 
 'text-embedding-005' 
 , 
  
 'music' 
 ):: 
 vector 
 LIMIT 
  
 3 
 ; 
 
```
The result of the query is as follows:

Performing a basic vector search without creating an index uses exact nearest neighbor search (KNN), which provides efficient recall. At scale, using KNN might impact performance. For a better query performance, we recommend that you use the ScaNN index for approximate nearest neighbor (ANN) search, which provides high recall with low latencies.

Without creating an index, AlloyDB defaults to using exact nearest-neighbor search (KNN).

To learn more about using ScaNN at scale, see Getting started with Vector Embeddings with AlloyDB AI .

Create a ScaNN index on products table

Run the following query to create a product_index ScaNN index on the product table:

   
 CREATE 
  
 INDEX 
  
 product_index 
  
 ON 
  
 product 
  
 USING 
  
 scann 
  
 ( 
 embedding 
  
 cosine 
 ) 
  
 WITH 
  
 ( 
 num_leaves 
 = 
 5 
 );

The num_leaves parameter indicates the number of leaf nodes that the tree-based index builds the index with. For more information on how to tune this parameter, see Tune vector query performance .

Perform a vector search

Run the following vector search query that tries to find products that are similar to the natural language query music . Even though the word music isn't included in the product description, the result shows products that are relevant to the query:

  SET 
  
 LOCAL 
  
 scann 
 . 
 num_leaves_to_search 
  
 = 
  
 2 
 ; 
 SELECT 
  
 * 
  
 FROM 
  
 product 
 ORDER 
  
 BY 
  
 embedding 
  
< = 
>  
 embedding 
 ( 
 'text-embedding-005' 
 , 
  
 'music' 
 ):: 
 vector 
  
 LIMIT 
  
 3 
 ;

The query results are as follows:

The scann.num_leaves_to_search query parameter controls the number of leaf nodes that are searched during a similarity search. The num_leaves and scann.num_leaves_to_search parameter values help to achieve a balance of performance and recall.

Perform a vector search that uses a filter and a join

You can run filtered vector search queries efficiently even when you use the ScaNN index. Run the following complex vector search query, which returns relevant results that satisfy the query conditions, even with filters:

  SET 
  
 LOCAL 
  
 scann 
 . 
 num_leaves_to_search 
  
 = 
  
 2 
 ; 
 SELECT 
  
 * 
  
 FROM 
  
 product 
  
 p 
 JOIN 
  
 product_inventory 
  
 pi 
  
 ON 
  
 p 
 . 
 id 
  
 = 
  
 pi 
 . 
 product_id 
 WHERE 
  
 pi 
 . 
 price 
 < 
 80 
 . 
 00 
 ORDER 
  
 BY 
  
 embedding 
  
< = 
>  
 embedding 
 ( 
 'text-embedding-005' 
 , 
  
 'music' 
 ):: 
 vector 
 LIMIT 
  
 3 
 ;

Accelerate your filtered vector search

You can use the columnar engine to improve the performance of vector similarity searches, specifically K-Nearest Neighbor (KNN) searches, when combined with highly selective predicate filtering —for example, using LIKE — in databases. In this section, you use the vector extension and the AlloyDB google_columnar_engine extension .

Performance improvements come from the columnar engine's built-in efficiency in scanning large datasets and applying filters —such as LIKE predicates— coupled with its ability, using vector support, to pre-filter rows. This functionality reduces the number of data subsets required for subsequent KNN vector distance calculations, and it helps to optimize complex analytical queries involving standard filtering and vector search.

To compare the execution time of a KNN vector search filtered by a LIKE predicate before and after you enable the columnar engine, follow these steps:

Enable the vector extension to support vector data types and operations. Run the following statements to create an example table (items) with an ID, a text description, and a 512-dimension vector embedding column.

  CREATE 
  
 EXTENSION 
  
 IF 
  
 NOT 
  
 EXISTS 
  
 vector 
 ; 
 CREATE 
  
 TABLE 
  
 items 
  
 ( 
  
 id 
  
 SERIAL 
  
 PRIMARY 
  
 KEY 
 , 
  
 description 
  
 TEXT 
 , 
  
 embedding 
  
 VECTOR 
 ( 
 512 
 ) 
 );

Populate the data by running the following statements to insert 1 million rows into the example items table.

  -- Simplified example of inserting matching (~0.1%) and non-matching data 
 INSERT 
  
 INTO 
  
 items 
  
 ( 
 description 
 , 
  
 embedding 
 ) 
 SELECT 
  
 CASE 
  
 WHEN 
  
 g 
  
 % 
  
 1000 
  
 = 
  
 0 
  
 THEN 
  
 'product_' 
  
 || 
  
 md5 
 ( 
 random 
 ():: 
 text 
 ) 
  
 || 
  
 '_common' 
  
 -- ~0.1% match 
  
 ELSE 
  
 'generic_item_' 
  
 || 
  
 g 
  
 || 
  
 '_' 
  
 || 
  
 md5 
 ( 
 random 
 ():: 
 text 
 ) 
  
 -- ~99.9% don't match 
  
 END 
 , 
  
 random_vector 
 ( 
 512 
 ) 
  
 -- Assumes random_vector function exists 
 FROM 
  
 generate_series 
 ( 
 1 
 , 
  
 999999 
 ) 
  
 g 
 ;

Measure the baseline performance of the vector similarity search without the columnar engine.

  SELECT 
  
 id 
 , 
  
 description 
 , 
  
 embedding 
  
< - 
>  
 '[...]' 
  
 AS 
  
 distance 
 FROM 
  
 items 
 WHERE 
  
 description 
  
 LIKE 
  
 '%product_%_common%' 
 ORDER 
  
 BY 
  
 embedding 
  
< - 
>  
 '[...]' 
 LIMIT 
  
 100 
 ;

Enable columnar engine and vector support by running the following command in the Google Cloud CLI. To use the gcloud CLI, you can install and initialize the gcloud CLI.

 gcloud  
beta  
alloydb  
instances  
update  
INSTANCE_ID  
 \ 
  
--cluster = 
CLUSTER_ID  
 \ 
  
--region = 
REGION_ID  
 \ 
  
--project = 
PROJECT_ID  
 \ 
  
--database-flags = 
google_columnar_engine.enabled = 
on,google_columnar_engine.enable_vector_support = 
on

Add the items table to the columnar engine:

  SELECT 
  
 google_columnar_engine_add 
 ( 
 'items' 
 );

Measure the performance of the vector similarity search using the columnar engine. You re-run the query that you previously ran to measure baseline performance.

  SELECT 
  
 id 
 , 
  
 description 
 , 
  
 embedding 
  
< - 
>  
 '[...]' 
  
 AS 
  
 distance 
 FROM 
  
 items 
 WHERE 
  
 description 
  
 LIKE 
  
 '%product_%_common%' 
 ORDER 
  
 BY 
  
 embedding 
  
< - 
>  
 '[...]' 
 LIMIT 
  
 100 
 ;

To check whether the query ran with the columnar engine, run the following command:

  explain 
  
 ( 
 analyze 
 ) 
  
 SELECT 
  
 id 
 , 
  
 description 
 , 
  
 embedding 
  
< - 
>  
 '[...]' 
  
 AS 
  
 distance 
 FROM 
  
 items 
 WHERE 
  
 description 
  
 LIKE 
  
 '%product_%_common%' 
 ORDER 
  
 BY 
  
 embedding 
  
< - 
>  
 '[...]' 
 LIMIT 
  
 100 
 ;

Clean up

In the Google Cloud console, go to the Clusterspage.

Go to Clusters
Click the name of your cluster, my-cluster , in the Resource namecolumn.
Click Delete cluster.
In Delete cluster my-cluster, enter my-cluster to confirm you want to delete your cluster.
Click Delete.
If you created a private connection when you created a cluster , go to the Google Cloud console Networking page and click Delete VPC network.

What's next

Learn real-world use cases for vector search .
Get started with vector embeddings using AlloyDB AI .
Learn how to build generative AI applications using AlloyDB AI .
Create a ScaNN index .
Tune your ScaNN indexes .
Learn how to build a smart shopping assistant with AlloyDB, pgvector, and model endpoint management .
Troubleshoot errors using the Logs Explorer .

Perform a vector search Stay organized with collections Save and categorize content based on your preferences.

Objectives

Costs

Before you begin

Enable billing and required APIs

Create an AlloyDB cluster and primary instance

Grant Vertex AI user permission to AlloyDB service agent

Connect to your database using a web browser

Install required extensions

Insert product and product inventory data and perform a basic vector search

Create a ScaNN index on products table

Perform a vector search

Perform a vector search that uses a filter and a join

Accelerate your filtered vector search

Clean up

What's next

Perform a vector search