ai Package

Class that controls function choice behavior.

Attributes: enable_kernel_functions: Enable kernel functions. max_auto_invoke_attempts: The maximum number of auto invoke attempts. filters: Filters for the function choice behavior. Available options are: excluded_plugins,

     included_plugins, excluded_functions, or included_functions.

  >>type_<<: The type of function choice behavior.

Properties: auto_invoke_kernel_functions: Check if the kernel functions should be auto-invoked. Determined as max_auto_invoke_attempts > 0.

Methods: configure: Configures the settings for the function call behavior, the default version in this class, does nothing, use subclasses for different behaviors.

Class methods: Auto: Returns FunctionChoiceBehavior class with auto_invoke enabled, and the desired functions based on either the specified filters or the full qualified names. The model will decide which function to use, if any.

  NoneInvoke: Returns FunctionChoiceBehavior class with auto_invoke disabled, and the desired functions
     based on either the specified filters or the full qualified names. The model does not invoke any functions,
     but can rather describe how it would invoke a function to complete a given task/query.

  Required: Returns FunctionChoiceBehavior class with auto_invoke enabled, and the desired functions
     based on either the specified filters or the full qualified names. The model is required to use one of the
     provided functions to complete a given task/query.

Note: This class is marked as 'experimental' and may change in the future.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Base class for prompt execution settings.

Can be used by itself or as a base class for other prompt execution settings. The methods are used to create specific prompt execution settings objects based on the keys in the extension_data field, this way you can create a generic PromptExecutionSettings object in your application, which gets mapped into the keys of the prompt execution settings that each services returns by using the service.get_prompt_execution_settings() method.

Initialize the prompt execution settings.