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llm_last Function

The llm_last function is used to extract the least relevant result from a set of rows based on a model's prompt and input columns. It operates over a set of rows, generally combined with a GROUP BY clause, to return the least relevant row for each group.

1. Usage Examples​

1.1. Example without GROUP BY​

Retrieve the least relevant product feature across all rows:

SELECT llm_last(
    {'model_name': 'gpt-4'},
    {'prompt': 'What is the least relevant detail for these products, based on their names and descriptions?'},
    {'product_name': product_name, 'product_description': product_description}
) AS last_product_feature
FROM products;

This query will return the least relevant feature from all product descriptions and product names.

1.2. Example with GROUP BY​

Retrieve the least relevant product feature for each product category:

SELECT category,
       llm_last(
           {'model_name': 'gpt-4'},
           {'prompt': 'What is the least relevant detail for these products, based on their names and descriptions?'},
           {'product_name': product_name, 'product_description': product_description}
       ) AS last_product_feature
FROM products
GROUP BY category;

In this case, the query groups products by category and returns the least relevant feature for each category.

1.3. Using a Named Prompt with GROUP BY​

Use a reusable prompt such as "least-relevant-detail" to extract the least relevant feature for each product category:

SELECT category,
       llm_last(
           {'model_name': 'gpt-4', 'secret_name': 'my_key'},
           {'prompt_name': 'least-relevant-detail', 'version': 1},
           {'product_name': product_name, 'product_description': product_description}
       ) AS last_product_feature
FROM products
GROUP BY category;

If the version parameter is omitted, the system will use the latest version of the least-relevant-detail prompt by default.

1.4. Advanced Example with Multiple Columns and GROUP BY​

Retrieve the least relevant feature for products grouped by category, using both the product name and description:

WITH product_info AS (
    SELECT category, product_name, product_description
    FROM products
    WHERE category = 'Electronics'
)
SELECT category,
       llm_last(
           {'model_name': 'gpt-4'},
           {'prompt': 'What is the least relevant detail for these products, based on their names and descriptions?'},
           {'product_name': product_name, 'product_description': product_description}
       ) AS last_product_feature
FROM product_info
GROUP BY category;

This example will extract the least relevant feature from both the product name and description for each product category.

2. Input Parameters​

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​

Two types of prompts can be used:

  1. Inline Prompt

    • Directly provides the prompt in the query.
    • Example: 
      {'prompt': 'What is the least relevant detail for these products, based on their names and descriptions?'}

  2. Named Prompt

    • Refers to a pre-configured prompt by name.
    • Example: 
      {'prompt_name': 'least-relevant-detail'}

  3. Named Prompt with Version

    • Refers to a specific version of a pre-configured prompt.
    • Example: 
      {'prompt_name': 'least-relevant-detail', 'version': 1}

2.3. Column Mappings (Optional)​

  • Key: Column mappings.
  • Purpose: Maps table columns to prompt variables for input.
  • Example: 
    {'product_name': product_name, 'product_description': product_description}

3. Output​

  • Type: JSON object.
  • Behavior: The function returns a JSON object containing the values of the columns you provided in the input. The structure of the returned JSON will mirror the input columns' values.

Output Example:
For a query that extracts the least relevant feature, the result could look like:

  • Input Rows:

    • product_name: "Wireless Headphones"
    • product_description: "High-quality wireless headphones with noise cancellation."

  • Output JSON:

    {
      "product_name": "Wireless Headphones",
      "product_description": "High-quality wireless headphones with noise cancellation."
    }

The output contains the values for product_name and product_description from the least relevant row based on the prompt.