generate

marvin.fns.generate

Functions

generate

def generate(target: TargetType[T] | None = None, n: int = 1, instructions: str | None = None, agent: Agent | None = None, thread: Thread | str | None = None, context: dict[str, Any] | None = None, handlers: list[Handler | AsyncHandler] | None = None, prompt: str | None = None) -> list[T]

Generates examples of a specific type or matching a description.

This function uses a language model to generate high-quality, random examples that match the specified type or description. The examples are returned as a list.

Args: target: The type of entities to generate. n: The number of examples to generate. Defaults to 1. instructions: Optional instructions describing what to generate. Used to provide specific guidance about what kinds of examples to create. agent: Optional custom agent to use for generation. If not provided, the default agent will be used. thread: Optional thread for maintaining conversation context. Can be either a Thread object or a string thread ID. context: Optional dictionary of additional context to include in the task. handlers: Optional list of handlers to use for the task. prompt: Optional prompt to use for the task. If not provided, the default prompt will be used. Returns: A list of n generated entities of type T.

generate_async

async def generate_async(target: TargetType[T] | None = None, n: int = 1, instructions: str | None = None, agent: Agent | None = None, thread: Thread | str | None = None, context: dict[str, Any] | None = None, handlers: list[Handler | AsyncHandler] | None = None, prompt: str | None = None) -> list[T]

Generates examples of a specific type or matching a description asynchronously.

This function uses a language model to generate high-quality, random examples that match the specified type or description. The examples are returned as a list.

Args: target: The type of entities to generate. n: The number of examples to generate. Defaults to 1. instructions: Optional instructions describing what to generate. Used to provide specific guidance about what kinds of examples to create. agent: Optional custom agent to use for generation. If not provided, the default agent will be used. thread: Optional thread for maintaining conversation context. Can be either a Thread object or a string thread ID. context: Optional dictionary of additional context to include in the task. handlers: Optional list of handlers to use for the task. prompt: Optional prompt to use for the task. If not provided, the default prompt will be used. Returns: A list of n generated entities of type T.

generate_schema

def generate_schema(instructions: str, base_schema: JSONSchema | None = None, agent: Agent | None = None, thread: Thread | str | None = None, context: dict[str, Any] | None = None, handlers: list[Handler | AsyncHandler] | None = None, prompt: str | None = None) -> JSONSchema

Generates a JSON schema from a description.

generate_schema_async

async def generate_schema_async(instructions: str, base_schema: JSONSchema | None = None, agent: Agent | None = None, thread: Thread | str | None = None, context: dict[str, Any] | None = None, handlers: list[Handler | AsyncHandler] | None = None, prompt: str | None = None) -> JSONSchema

Generates a JSON schema from a description.

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Parent Module: fns