分類文字

CREATE TABLE IF NOT EXISTS product_reviews (
    id INT PRIMARY KEY,
    review TEXT
);

INSERT INTO product_reviews (id, review) VALUES
(1, 'Fantastic, would buy again!'),
(2, 'Broken on arrival.'),
(3, 'Mediocre, not worth the price.');

SELECT ai.classify(
  input => 'TEXT_CONTENT',
  categories => ARRAY['CATEGORY1', 'CATEGORY2', 'CATEGORY3'],
  examples => 'EXAMPLES', -- Optional
  output_mode => 'OUTPUT_MODE', -- Optional. Can be 'single' or 'multi'.
  model_id => 'MODEL_ID' -- Optional. The default value is gemini-3.5-flash-lite.
);

SELECT ai.classify(
  input => 'This is a sales report for Q1.',
  categories => ARRAY['Report', 'Invoice', 'Memo']
);

Report

SELECT ai.classify(
  input => 'The government announced new regulations on AI development, citing both economic opportunities and potential security risks.',
  categories => ARRAY[
    ARRAY['politics', 'Questions about politics'],
    ARRAY['technology', 'Questions about technology'],
    ARRAY['security', 'Questions about security']
  ],
  output_mode => 'multi'
);

politics,technology,security

SELECT ai.classify(
  input => 'The patient reported mild fever and headache.',
  categories => ARRAY[
    ARRAY['urgent', 'Requires immediate medical attention'],
    ARRAY['non-urgent', 'Routine check-up or minor symptoms'],
    ARRAY['query', 'A question about services or appointments']
  ]
);

non-urgent

SELECT ai.classify(
  input => 'Is this item returnable after 90 days?',
  categories => ARRAY[
    ARRAY['policy', 'Questions about return, warranty, or other policies'],
    ARRAY['shipping', 'Questions about order shipment and delivery'],
    ARRAY['payment', 'Questions about payment methods or issues'],
    ARRAY['other', 'Any other type of question']
  ],
  examples => '[
    {"input": "When will my order arrive?", "output": "shipping"},
    {"input": "What is the warranty on this?", "output": "policy"},
    {"input": "My discount code isn''t working.", "output": "payment"}
  ]'
);

policy

SELECT ai.classify(
  input => 'This new song is a banger!',
  categories => ARRAY[
    ARRAY['positive', 'Expresses approval or enjoyment'],
    ARRAY['negative', 'Expresses disapproval or dislike']
  ],
  model_id => 'MODEL_ID'
);

positive

SET google_ml_integration.enable_ai_function_acceleration = 'on';

SELECT
  id,
  ai.classify(
    input => review,
    categories => ARRAY['positive', 'neutral', 'negative']
  ) AS sentiment
FROM product_reviews
ORDER BY id;

SELECT ai.classify(
  inputs => TEXT[],
  categories => TEXT[][],
  examples => TEXT, -- Optional.
  output_mode => VARCHAR, -- Optional. The default value is single.
  batch_size => INT, -- Optional. The default value is 10.
  model_id => VARCHAR(100) -- Optional. The default value is gemini-3.5-flash-lite.
);

SELECT unnest(
  ai.classify(
    inputs => ARRAY(SELECT review FROM product_reviews ORDER BY id),
    categories => ARRAY[
      ARRAY['positive', 'Good sentiment'],
      ARRAY['neutral',  'Neither good nor bad sentiment'],
      ARRAY['negative', 'Bad sentiment']
    ],
    batch_size => 5
  )
) AS sentiment;

 sentiment
-----------
 positive
 negative
 negative

CREATE OR REPLACE FUNCTION ai.classify(
    input_cursor REFCURSOR,
    categories TEXT[][],
    examples TEXT DEFAULT NULL,
    output_mode VARCHAR DEFAULT NULL,
    batch_size INT DEFAULT NULL,
    model_id VARCHAR(100) DEFAULT NULL)
  RETURNS REFCURSOR

DO $$
DECLARE
  cur REFCURSOR;
  result_cur REFCURSOR;
  input_val TEXT;
  sentiment TEXT;
BEGIN
  OPEN cur FOR SELECT review FROM product_reviews ORDER BY id;

  result_cur := ai.classify(
    input_cursor => cur,
    categories => ARRAY[
      ARRAY['positive', 'Good sentiment'],
      ARRAY['neutral',  'Neither good nor bad sentiment'],
      ARRAY['negative', 'Bad sentiment']
    ],
    batch_size => 20
  );

  LOOP
    FETCH result_cur INTO input_val, sentiment;
    EXIT WHEN NOT FOUND;
    RAISE NOTICE 'Sentiment: %', sentiment;
  END LOOP;

  CLOSE result_cur;
  CLOSE cur;
END $$;