27.3.9.8 Convenience Methods

The GenAI API provides the convenience methods described in this section under the ml namespace. These methods act as wrappers for the LLM methods; rather than being invoked as LLM instance methods, they take the model ID as one of the options passed to them. ml.generate() and ml.rag() return only the text portions of the values returned by their LLM counterparts.

• ml.generate()

• ml.embed()

• ml.load()

• ml.rag()

ml.generate()

This method is a wrapper for LLM.generate(). It loads the model specified by the model_id specified as one of the options, generates a response based on the prompt using this model, and returns the response. The default model_id ("cohere.command") is used if one is not specified. Like the LLM method, ml.generate() supports two variants, one for a single invocation, and one for batch processing.

Signature (single job)

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  String ml.generate(
    String prompt,
    Object options
  )

Arguments

• prompt (String) (default "cohere.command"): The desired prompt

• options (Object) (default {}): The options employed for generation; these follow the same rules as the options used with LLM.generate()

Return type

• String: The text of the response

Usage

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  //  Both invocations use "cohere.command" as the model_id
  
  let response = ml.generate("What is Mysql?", {max_tokens: 10})
  
  let response = ml.generate("What is Mysql?", {model_id: "cohere.command", max_tokens: 10})

Signature (batch processing)

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  undefined ml.generate(
    Table inputTable, 
    String inputColumn, 
    String outputColumn, 
    Object options
  )

Arguments

• inputTable (Table): Table to use for operations

• inputColumn (String): Name of column from inputTable to be embedded

• outputColumn (String): Name of column in which to store embeddings; this can be either a fully-qualified name of a column or the name of the column only; in the latter case, the input table and its schema are used to construct the fully-qualified name

• options (Object) (optional; default {}): An object containing the options used for embedding; see the description of ML_EMBED_ROW for available options

Return type

• undefined

Usage

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  let schema = session.getSchema("mlcorpus")
  let table = schema.getTable("genai_table")
  
  ml.generate(table, "input", "mlcorpus.predictions.response", {max_tokens: 10})

ml.embed()

This method is a wrapper for LLM.embed(). Like the LLM method, it supports two variants, one for a single invocation, and one for batch processing.

Signature (single job)

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  Float32Array ml.embed(
    String query,
    Object options
  )

Arguments

• query (String): Text of a natural-language query

• options (Object) (default {}): The options employed for generation; these follow the same rules as the options used with LLM.embed(); the default model_id is "all_minilm_l12_v2"

Return type

• Float32Array (MySQL VECTOR): The embedding

Usage

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  //  These produce the same result
  
  let embedding = ml.embed("What is Mysql?", {model_id: "all_minilm_l12_v2"})
  
  let embedding = ml.embed("What is Mysql?", {})

Signature (batch processing)

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  undefined ml.embed(
    Table inputTable,
    String inputColumn,
    String outputColumn,
    Object options
  )

Arguments

• inputTable (Table): Table to use for operations

• inputColumn (String): Name of column from inputTable to be embedded

• outputColumn (String): Name of column in which to store embeddings; this can be either a fully-qualified name of a column or the name of the column only; in the latter case, the input table and its schema are used to construct the fully-qualified name

• options (Object) (optional; default {}): An object containing the options used for embedding; see the description of ML_EMBED_ROW for available options

Return type

• undefined

Usage

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  let schema = session.getSchema("mlcorpus")
  let table = schema.getTable("genai_table")
  
  ml.embed(table, "input", "mlcorpus.predictions.response", 
          {model_id: "all_minilm_l12_v2"})

ml.load()

This static method loads an existing (and already trained) HeatWave AutoML model having the name specified. An error is thrown if model with the given name does not exist.

Signature

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  Object ml.load(
    String name
  )

Arguments

• name (String): The name of the model.

Return type

• Object: Any of Classifier, Regressor, Forecaster, AnomalyDetector, or Recommender, depending on the type of model loaded.

ml.load() was added in MySQL 9.2.0. For more information, see ML_MODEL_LOAD.

ml.rag()

This is a wrapper for LLM.rag(). Like the LLM method, it supports two variants, one for a single invocation, and one for batch processing.

Signature (single job)

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  String ml.rag(
    String query,
    Object options
  )

Arguments

• query (String): Text of a natural-language query

• options (Object) (default {}): The options employed for generation; these follow the same rules as the options used with LLM.rag(); the default model_id is "mistral-7b-instruct-v1"

Return type

• String: Response text

Usage

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  //  These produce the same result
  
  let result = ml.rag("What is MySql?", {schema: ["vector_store"], n_citations: 1, model_options: {model_id: "mistral-7b-instruct-v1"}})
  
  let result = ml.rag("What is MySql?", {schema: ["vector_store"], n_citations: 1})

Signature (batch processing)

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  undefined ml.rag(
    Table inputTable,
    String inputColumn,
    String outputColumn,
    Object options
  )

Arguments

• inputTable (Table): Table to use for operations

• inputColumn (String): Name of column from inputTable to be embedded

• outputColumn (String): Name of column in which to store embeddings; this can be either a fully-qualified name of a column or the name of the column only; in the latter case, the input table and its schema are used to construct the fully-qualified name

• options (Object) (optional; default {}): An object containing the options used for embedding; see the description of ML_EMBED_ROW for available options

Return type

• undefined

Usage

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  let schema = session.getSchema("mlcorpus")
  let table = schema.getTable("genai_table")
  
  ml.rag(table, "input", "mlcorpus.predictions.response", 
        {schema: ["vector_store"], n_citations: 1, model_options: 
        {model_id: "mistral-7b-instruct-v1"}});