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llm_filter Function

The llm_filter function evaluates a condition based on a given prompt and returns a boolean value (TRUE or FALSE). This function mostly used in the workload of WHERE clause of a query.

1. Basic Usage Examples​

1.1 Using an Inline Prompt​

SELECT * 
FROM products
WHERE llm_filter(
    {'model_name': 'gpt-4'}, 
    {'prompt': 'Is this product description eco-friendly?'}, 
    {'description': product_description}
);

Description: This example uses an inline prompt to filter rows based on whether the product description is considered eco-friendly by the gpt-4 model. If the model returns TRUE, the row is included in the result.

1.2 Using a Named Prompt​

SELECT * 
FROM products
WHERE llm_filter(
    {'model_name': 'gpt-4'}, 
    {'prompt_name': 'eco-friendly-check'}, 
    {'description': product_description}
);

Description: In this example, a named prompt (eco-friendly-check) is used to determine if the product description is eco-friendly. This allows for reusing pre-configured prompts for similar filtering tasks.

1.3 Combining with Other SQL Logic​

WITH filtered_products AS (
    SELECT product_id, product_name, product_description
    FROM products
    WHERE llm_filter(
        {'model_name': 'gpt-4', 'secret_name': 'openai_key'}, 
        {'prompt': 'Is this product description eco-friendly?'}, 
        {'description': product_description}
    )
)
SELECT * FROM filtered_products;

Description: This example demonstrates how to combine llm_filter with other SQL logic. It filters the products based on the eco-friendliness of their descriptions and processes the result in a subquery for further use.

1.4 Advanced Example​

WITH relevant_reviews AS (
    SELECT review_id, review_content
    FROM reviews
    WHERE llm_filter(
        {'model_name': 'gpt-4'}, 
        {'prompt': 'Does this review content contain a positive sentiment?'}, 
        {'content': review_content}
    )
)
SELECT * FROM relevant_reviews
WHERE LENGTH(review_content) > 50;

Description: This advanced example uses llm_filter to filter reviews based on positive sentiment. It then further filters the results to only include reviews with content longer than 50 characters.

2. Input Parameters​

The llm_filter function accepts three structured inputs: model configuration, prompt configuration, and input data columns.

2.1 Model Configuration​

  • Parameter: model_name and secret_name

2.1.1 Model Selection​

  • Description: Specifies the model used for text generation.
  • Example: 
    { 'model_name': 'gpt-4' }

2.1.2 Model Selection with Secret​

  • Description: Specifies the model along with the secret name to be used for authentication when accessing the model.
  • Example: 
    { 'model_name': 'gpt-4', 'secret_name': 'your_secret_name' }

2.2 Prompt Configuration​

  • Parameter: prompt or prompt_name

    2.2.1 Inline Prompt​

    Directly provides the prompt.

    • Example: 
      { 'prompt': 'Summarize the content of the article.' }

    2.2.2 Named Prompt​

    References a pre-configured prompt.

    • Example: 
      { 'prompt_name': 'summarize-content' }

    2.2.3 Named Prompt with Version​

    References a specific version of a prompt.

    • Example: 
      { 'prompt_name': 'summarize-content', 'version': 2 }

2.3 Input Data Columns (OPTIONAL)​

  • Parameter: Column mappings
  • Description: Specifies the columns from the table to be passed to the model as input.
  • Example: 
    {'product_name': product_name, 'product_description': product_description}

3. Output​

The function returns a BOOLEAN value (TRUE or FALSE), indicating whether the row satisfies the condition specified in the prompt.

Example Output:
For a prompt like "Is this product description eco-friendly?":

  • Input Row:
    • product_description: "Made from 100% recyclable materials, this product is perfect for eco-conscious buyers."
  • Output:
    • TRUE