Use Generative AI to get personalized recommendations in an ecommerce application

Objective

In this tutorial, you learn how to:

Use Google provided Vertex AI Generative AI models in a Spanner database.
Use Generative AI to provide personalized product recommendations in a sample ecommerce application.

Costs

This tutorial uses billable components of Google Cloud, including:

Spanner
Vertex AI

For more information about Spanner costs, see the Spanner pricing page.

For more information about Vertex AI costs, see the Vertex AI pricing page.

Create the ecommerce website schema

For this tutorial, we use the following schema and data:

CREATE TABLE Products (
  id INT64,
  name STRING(MAX),
  description STRING(MAX),
  category_id INT64,
) PRIMARY KEY(id);

CREATE TABLE Categories (
  id INT64,
  name STRING(MAX)
) PRIMARY KEY(id);

CREATE TABLE Users (
  id INT64,
  age INT64,
  likes STRING(MAX)
) PRIMARY KEY(id);

INSERT INTO Categories (id, name) VALUES
    (1, "Toys"),
    (2, "Tools");

INSERT INTO Products (id, name, description, category_id) VALUES
    (1, "Plush Bear", "Really fluffy. Safe for infants.", 1),
    (2, "Bike", "Bike for teenagers.", 1),
    (3, "Drill", "Cordless.", 2);

INSERT INTO Users (id, age, likes) VALUES
    (1, 30, "DIY"),
    (2, 14, "Toys");

Register a Generative AI model in a Spanner schema

In this tutorial, we use the Vertex AI text-bison model to provide personalized product recommendations to end customers. To register this model in a Spanner database, execute the following DDL statement:

CREATE MODEL TextBison
INPUT (prompt STRING(MAX))
OUTPUT (content STRING(MAX))
REMOTE
OPTIONS (
  endpoint = '//aiplatform.googleapis.com/projects/PROJECT/locations/LOCATION/publishers/google/models/text-bison'
);

Replace the following:

PROJECT: the project ID
LOCATION: the region where you are using Vertex AI

Schema discovery and validation isn't available for Generative AI models. Therefore, you must provide INPUT and OUTPUT clauses that match the model's schema. You can find the full schema of the text-bison model on the Vertex AI Model API reference page.

As long as both the database and endpoints are in the same project, Spanner should grant appropriate permissions automatically. Otherwise, review the model endpoint access control section of the CREATE MODEL reference page.

To verify the model was registered correctly, query it with the ML.PREDICT function. The model expects a single STRING column named prompt. You can use a Spanner subquery to generate the prompt column. The TextBison model requires you to specify a maxOutputTokens model parameter. Other parameters are optional. The Vertex AI text-bison model doesn't support batching, so you must use the @{remote_udf_max_rows_per_rpc=1} parameter to set the batch size to 1.

SELECT content
FROM ML.PREDICT(
  MODEL TextBison,
  (SELECT "Is 13 prime?" AS prompt),
  STRUCT(256 AS maxOutputTokens, 0.2 AS temperature, 40 as topK, 0.95 AS topP)
) @{remote_udf_max_rows_per_rpc=1};

+--------------------+
| content            |
+--------------------+
| "Yes, 13 is prime" |
+--------------------+

Use the `TextBison` Model to answer customer questions

Generative AI text models can solve a wide array of problems. For example, a user on an ecommerce website might be browsing for products that are safe for infants. With a single query, we can pass their question to the TextBison model. All we need to do is provide relevant context for the question by fetching product details from the database.

NOTE: Some model answers were edited for brevity.

SELECT product_id, product_name, content
FROM ML.PREDICT(
  MODEL TextBison,
  (SELECT
    product.id as product_id,
    product.name as product_name,
    CONCAT("Is this product safe for infants?", "\n",
        "Product Name: ", product.name, "\n",
        "Category Name: ", category.name, "\n",
        "Product Description:", product.description) AS prompt
   FROM
     Products AS product JOIN Categories AS category
       ON product.category_id = category.id),
  STRUCT(100 AS maxOutputTokens)
) @{remote_udf_max_rows_per_rpc=1};

-- The model correctly recommends a Plush Bear as safe for infants.
-- Other products are not safe and the model provides justification why.
+------------+-----------------+--------------------------------------------------------------------------------------------------+
| product_id | product_name    | content                                                                                          |
+------------+-----------------+--------------------------------------------------------------------------------------------------+
|          1 | "Plush Bear"    | "Yes, this product is infant safe. [...] "                                                       |
|            |                 | "The product description says that the product is safe for infants. [...]"                       |
+------------+-----------------+--------------------------------------------------------------------------------------------------+
|          2 | "Bike"          | "No, this product is not infant safe. [...] "                                                    |
|            |                 | "It is not safe for infants because it is too big and heavy for them to use. [...]"              |
+------------+-----------------+--------------------------------------------------------------------------------------------------+
|          3 | "Drill"         | "No, this product is not infant safe. [...]"                                                     |
|            |                 | " If an infant were to grab the drill, they could pull it on themselves and cause injury. [...]" |
+------------+-----------------+--------------------------------------------------------------------------------------------------+

You can replace the question literal with a query parameter, such as @UserQuestion, if you want to directly populate the parameter with a customer question. This gives the customer an AI-powered online shopping experience.

Provide personalized product recommendations to customers

In addition to product details, we can also add information about the customer to the prompt. This lets the model take user preferences into consideration so that it can provide fully personalized product recommendations.

SELECT product_id, product_name, content
FROM ML.PREDICT(
  MODEL TextBison,
  (SELECT
    product.id as product_id,
    product.name as product_name,
    CONCAT(
        "Answer with YES or NO only: Is this a good fit for me?",
        "My age:", CAST(user.age AS STRING), "\n",
        "I like:", user.likes,  "\n",
        "Product name: ", product.name, "\n",
        "Category mame: ", category.name, "\n",
        "Product description:", product.description) AS prompt,
   FROM
     Products AS product
       JOIN Categories AS category ON product.category_id = category.id
       JOIN Users AS user ON user.id = 1),
  STRUCT(256 AS maxOutputTokens)
) @{remote_udf_max_rows_per_rpc=1};

-- The model correctly guessed that the user might be interested in a Drill
-- as they are interested in DIY.
+------------+-----------------+-------------+
| product_id | product_name    | content     |
+------------+-----------------+-------------+
|          1 | "Plush Bear"    | "NO"        |
+------------+-----------------+-------------+
|          2 | "Bike"          | "NO"        |
+------------+-----------------+-------------+
|          3 | "Drill"         | "YES"       |
+------------+-----------------+-------------+

To look for a gift for their child, the user can create a profile for their teenager and see a different list of recommendations:

SELECT product_id, product_name, content
FROM ML.PREDICT(
  MODEL TextBison,
  (SELECT
    product.id as product_id,
    product.name as product_name,
    CONCAT(
        "Answer with YES or NO only: Is this a good fit for me?",
        "\nMy's age:", CAST(user.age AS STRING),
        "\nI like:", user.likes,
        "\nProduct Name: ", product.name,
        "\nCategory Name: ", category.name,
        "\nProduct Description:", product.description) AS prompt,
   FROM
     Products AS product
       JOIN Categories AS category ON product.category_id = category.id
       JOIN Users AS user ON user.id = 2),
  STRUCT(40 AS maxOutputTokens)
) @{remote_udf_max_rows_per_rpc=1};

-- The model correctly guesses that a teenager is interested in a Bike,
-- but not a plush bear for infants or spicy peppers.
+------------+-----------------+---------+
| product_id | product_name    | content |
+------------+-----------------+---------+
|          1 | "Plush Bear"    | "NO"    |
+------------+-----------------+---------+
|          2 | "Bike"          | "YES"   |
+------------+-----------------+---------+
|          3 | "Spicy peppers" | "NO"    |
+------------+-----------------+---------+

You can add purchase history or other relevant details to the prompt to give the customer a more customized experience.

Spanner Vertex AI integration helps you assemble complex prompts containing live data and use them to build AI-enabled applications.