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llm_complete Function

The llm_complete function generates text completions using specified models and prompts for dynamic data generation.

1. Basic Usage Examples​

1.1 Inline Prompt​

SELECT llm_complete(
    {'model_name': 'gpt-4'},
    {'prompt': 'Explain the purpose of FlockMTL.'}
) AS flockmtl_purpose;

Description: This example uses an inline prompt to generate a text completion with the gpt-4 model. The prompt asks the model to explain the purpose of FlockMTL. The function returns a completion for each row based on the provided prompt.

1.2 Named Prompt​

SELECT llm_complete(
    {'model_name': 'summarizer', 'secret_name': 'summarizer_secret'},
    {'prompt_name': 'description-generation', 'version': 1},
    {'product_name': product_name}
) AS product_description
FROM products;

Description: In this example, a named prompt description-generation is used with the summarizer model. The function generates product descriptions using data from the product_name column for each row in the products table.

2. Advanced Example​

WITH enhanced_products AS (
    SELECT
        product_id,
        product_name,
        llm_complete(
            {'model_name': 'reduce-model'},
            {'prompt_name': 'summarize-content', 'version': 2},
            {'product_name': product_name}
        ) AS enhanced_description
    FROM products
)
SELECT product_id, product_name, enhanced_description
FROM enhanced_products
WHERE LENGTH(enhanced_description) > 50;

Description: This advanced example demonstrates the use of a pre-configured prompt summarize-content with version 2 and the reduce-model. It processes the product_name column and generates a summarized description. The query then filters out rows where the generated description is shorter than 50 characters.

3. Input Parameters​

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

3.1 Model Configuration​

  • Parameter: model_name and secret_name

3.1.1 Model Selection​

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

3.1.2 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' }

3.2 Prompt Configuration​

  • Parameter: prompt or prompt_name

    3.2.1 Inline Prompt​

    Directly provides the prompt.

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

    3.2.2 Named Prompt​

    References a pre-configured prompt.

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

    3.2.3 Named Prompt with Version​

    References a specific version of a prompt.

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

3.3 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}

4. Output​

The function generates a text completion for each row based on the provided prompt and input data.

  • Type: Text (string)
  • Behavior: Maps over each row and generates a response per tuple.