AI_CLASSIFY¶

Region availability¶

The following table shows the regions where you can use the AI_CLASSIFY function for both text and images:

Syntax¶

AI_CLASSIFY( <input> , <list_of_categories> [, <config_object>] )

Arguments¶

Required:

input

The string, image, or prompt object that you’re classifying.

For text classification, the input string is case sensitive. Results may vary based on capitalization.

list_of_categories

An array of categories with at least two unique values. The number of categories is restricted only by the token window, but in practice, exceeding twenty categories might reduce classification accuracy. Categories are case sensitive.

Categories can be simple strings or SQL objects of the same type. If you’re using objects, you can provide a description for one or more categories to improve classification accuracy.

For each category, specify the following:

• label (Required): The name of the category.

• description (Optional): Describes the category in no more than 25 words.

Note

Descriptions count as input tokens, which affects the cost of the classification operation. For more information, see Cost considerations.

Optional:

config_object

Configuration settings specified as key/value pairs. Supported keys:

• task_description: A explanation of the classification task that is 50 words or fewer. This can help the model understand the context of the classification task and improve accuracy.

• output_mode: Set to 'multi' for multi-label classification. Defaults to 'single' for single-label classification.

• examples: A list of example objects for few-shot learning. Each example must include:

  • input: Example text to classify.

  • labels: List of correct categories for the input.

  • explanation: Explanation of why the input maps to those categories.

Returns¶

A serialized object. The object’s labels field is an array that specifies the list of categories to which the input belongs.

For single label classification, the labels array has exactly one element. For multi-label classification, the labels field can have multiple elements.

Access control requirements¶

Users must use a role that has the SNOWFLAKE.CORTEX_USER database role. For more information about this privilege, see Cortex LLM privileges.

Usage notes¶

For best results, follow these guidelines:

• Use plain text in English for the input and list_of_categories.

• Avoid trying to classify non-prose such as code snippets, logs, or non-English text.

• Avoid using code or formatting that is not open source (such as proprietary languages or formats) in the text. The underlying language model is not trained on proprietary formats.

• Don’t use abbreviations, special characters, or jargon in the category labels.

• Use descriptive categories. Avoid using category names such as “Xa4s3” or “category 1”.

• Use mutually exclusive categories.

• Providing a clear task description can improve accuracy when the relationship between the input and categories is unclear or complex.

• Adding label descriptions can improve accuracy, especially when labels are ambiguous or require specific selection criteria. Write descriptions that clearly highlight what distinguishes each label from the others.

• Each label, description, and example increases the number of input tokens for every AI_CLASSIFY call, which affects cost.

• Examples can help to improve accuracy.

Examples¶

The following examples use the AI_CLASSIFY function with only the required arguments.

SELECT AI_CLASSIFY('One day I will see the world', ['travel', 'cooking']);

'{
  "labels": ["travel"]
 }';

SELECT AI_CLASSIFY(
  'One day I will see the world and learn to cook my favorite dishes',
  ['travel', 'cooking', 'reading', 'driving'],
  {'output_mode': 'multi'}
);

'{
  "labels": ["travel", "cooking"]
 }';

SELECT AI_CLASSIFY(
  'One day I will see the world and learn to cook my favorite dishes',
  [
    {'label': 'travel', 'description': 'content related to traveling'},
    {'label': 'cooking'},
    {'label': 'reading'},
    {'label': 'driving'}
  ],
  {
    'task_description': 'Determine topics related to the given text',
    'output_mode': 'multi',
    'examples': [
      {
        'input': 'i love traveling with a good book',
        'labels': ['travel', 'reading'],
        'explanation': 'the text mentions traveling and a good book which relates to reading'
      }
    ]
  });

'{
  "labels": ["travel", "cooking"]
}';

CREATE OR REPLACE TEMPORARY TABLE text_classification_table AS
SELECT 'France' AS input, ['North America', 'Europe', 'Asia'] AS classes
UNION ALL
SELECT 'Singapore', ['North America', 'Europe', 'Asia']
UNION ALL
SELECT 'one day I will see the world', ['travel', 'cooking', 'dancing']
UNION ALL
SELECT 'my lobster bisque is second to none', ['travel', 'cooking', 'dancing'];

SELECT input,
    classes,
    AI_CLASSIFY(input, classes):labels AS classification
FROM text_classification_table;

WITH food_pictures AS (
  SELECT
      TO_FILE(file_url) AS img
  FROM DIRECTORY(@file_stage)
)
SELECT
*,
AI_CLASSIFY(img, ['dessert', 'drink', 'main dish', 'side dish']):labels AS classification
FROM food_pictures;

  WITH food_pictures AS (
  SELECT
      TO_FILE(file_url) AS img
  FROM DIRECTORY(@file_stage)
)
SELECT
*,
AI_CLASSIFY(PROMPT('Please help me classify the food within this image {0}', img),
  ['dessert', 'drink', 'main dish', 'side dish']):labels AS classification
FROM food_pictures;

Limitations¶

• Snowflake AI functions don’t work on FILE objects created from files in the following kinds of stages:

  • Internal stages with encryption mode TYPE = 'SNOWFLAKE_FULL'

  • External stages with any customer-side encrypted mode:

    • TYPE = 'AWS_CSE'

    • TYPE = 'AZURE_CSE'

  • User stage

  • Table stage

  • Stage with double-quoted names