marvin.ai.text

Core LLM tools for working with text and structured data.

`Model` ¶

A Pydantic model that can be instantiated from a natural language string, in addition to keyword arguments.

`from_text_async` `async` `classmethod` ¶

Class method to create an instance of the model from a natural language string.

Parameters:

Name	Type	Description	Default
`text`	`str`	The natural language string to convert into an instance of the model.	required
`instructions`	`str`	Specific instructions for the conversion. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`Model`	`Model`	An instance of the model.

Example

from marvin.ai.text import Model
class Location(Model):
    '''A location'''
    city: str
    state: str
    country: str

await Location.from_text_async("big apple, ny, usa")

`caption` ¶

Generates a caption for an image using a language model synchronously.

Parameters:

Name	Type	Description	Default
`data`	`Union[Image, List[Image]]`	The image or images to caption.	required
`instructions`	`str`	Instructions for the caption generation.	`None`
`model_kwargs`	`dict`	Additional arguments for the language model.	`None`

Returns:

Name	Type	Description
`str`	`str`	Generated caption.

`caption_async` `async` ¶

Generates a caption for a set of images using a language model.

Parameters:

Name	Type	Description	Default
`data`	`Union[Image, List[Image]]`	The image or images to caption.	required
`instructions`	`str`	Instructions for the caption generation.	`None`
`model_kwargs`	`dict`	Additional arguments for the language model.	`None`

Returns:

Name	Type	Description
`str`	`str`	Generated caption.

`cast` ¶

Converts the input data into the specified type.

This function uses a language model to convert the input data into a specified type. The conversion process can be guided by specific instructions. The function also supports additional arguments for the language model.

Parameters:

Name	Type	Description	Default
`data`	`FN_INPUT_TYPES`	Union[str, Image, list[Union[str, Image]]]: the data to which the function will be applied.	required
`target`	`type`	The type to convert the data into. If none is provided but instructions are provided, `str` is assumed.	`None`
`instructions`	`str`	Specific instructions for the conversion. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`T`	`T`	The converted data of the specified type.

`cast_async` `async` ¶

Converts the input data into the specified type.

This function uses a language model to convert the input data into a specified type. The conversion process can be guided by specific instructions. The function also supports additional arguments for the language model.

Parameters:

Name	Type	Description	Default
`data`	`FN_INPUT_TYPES`	Union[str, Image, list[Union[str, Image]]]: the data to which the function will be applied.	required
`target`	`type`	The type to convert the data into. If none is provided but instructions are provided, `str` is assumed.	`None`
`instructions`	`str`	Specific instructions for the conversion. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`T`	`T`	The converted data of the specified type.

`classifier` ¶

Class decorator that modifies the behavior of an Enum class to classify a string.

This decorator modifies the call method of the Enum class to use the marvin.classify function instead of the default Enum behavior. This allows the Enum class to classify a string based on its members.

Parameters:

Name	Type	Description	Default
`cls`	`Enum`	The Enum class to be decorated.	`None`
`instructions`	`str`	Instructions for the AI on how to perform the classification.	`None`
`model_kwargs`	`dict`	Additional keyword arguments to pass to the model.	`None`

Returns:

Name	Type	Description
`Enum`		The decorated Enum class with modified call method.

Raises:

Type	Description
`AssertionError`	If the decorated class is not a subclass of Enum.

`classify` ¶

Classifies the provided data based on the provided labels.

This function uses a language model with a logit bias to classify the input data. The logit bias constrains the language model's response to a single token, making this function highly efficient for classification tasks. The function will always return one of the provided labels.

Parameters:

Name	Type	Description	Default
`data`	`FN_INPUT_TYPES`	Union[str, Image, list[Union[str, Image]]]: the data to which the function will be applied.	required
`labels`	`Union[Enum, list[T], type]`	The labels to classify the data into.	required
`instructions`	`str`	Specific instructions for the classification. Defaults to None.	`None`
`return_index`	`bool`	Whether to return the index of the label instead of the label itself.	`False`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Type	Description
`Union[T, int]`	Union[T, int]: The label or index that the data was classified into.

`classify_async` `async` ¶

Classifies the provided data based on the provided labels.

This function uses a language model with a logit bias to classify the input data. The logit bias constrains the language model's response to a single token, making this function highly efficient for classification tasks. The function will always return one of the provided labels.

Parameters:

Name	Type	Description	Default
`data`	`FN_INPUT_TYPES`	Union[str, Image, list[Union[str, Image]]]: the data to which the function will be applied.	required
`labels`	`Union[Enum, list[T], type]`	The labels to classify the data into.	required
`instructions`	`str`	Specific instructions for the classification. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Type	Description
`Union[T, int]`	Union[T, int]: The label or index that the data was classified into.

`extract` ¶

Extracts entities of a specific type from the provided data.

This function uses a language model to identify and extract entities of the specified type from the input data. The extracted entities are returned as a list.

Note that either a target type or instructions must be provided (or both). If only instructions are provided, the target type is assumed to be a string.

Parameters:

Name	Type	Description	Default
`data`	`FN_INPUT_TYPES`	Union[str, Image, list[Union[str, Image]]]: the data to which the function will be applied.	required
`target`	`type`	The type of entities to extract.	`None`
`instructions`	`str`	Specific instructions for the extraction. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`list`	`list[T]`	A list of extracted entities of the specified type.

`extract_async` `async` ¶

Extracts entities of a specific type from the provided data.

This function uses a language model to identify and extract entities of the specified type from the input data. The extracted entities are returned as a list.

Note that either a target type or instructions must be provided (or both). If only instructions are provided, the target type is assumed to be a string.

Parameters:

Name	Type	Description	Default
`data`	`FN_INPUT_TYPES`	Union[str, Image, list[Union[str, Image]]]: the data to which the function will be applied.	required
`target`	`type`	The type of entities to extract.	`None`
`instructions`	`str`	Specific instructions for the extraction. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`MarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`list`	`list[T]`	A list of extracted entities of the specified type.

`fn` ¶

Converts a Python function into an AI function using a decorator.

This decorator allows a Python function to be converted into an AI function. The AI function uses a language model to generate its output.

Parameters:

Name	Type	Description	Default
`func`	`Callable`	The function to be converted. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`Callable`	`Callable`	The converted AI function.

Example

@fn
def list_fruit(n:int) -> list[str]:
    '''generates a list of n fruit'''

list_fruit(3) # ['apple', 'banana', 'orange']

`generate` ¶

Generates a list of 'n' items of the provided type or based on instructions.

Either a type or instructions must be provided. If instructions are provided without a type, the type is assumed to be a string. The function generates at least 'n' items.

Parameters:

Name	Type	Description	Default
`target`	`type`	The type of items to generate. Defaults to None.	`None`
`instructions`	`str`	Instructions for the generation. Defaults to None.	`None`
`n`	`int`	The number of items to generate. Defaults to 1.	`1`
`use_cache`	`bool`	If True, the function will cache the last 100 responses for each (target, instructions, and temperature) and use those to avoid repetition on subsequent calls. Defaults to True.	`True`
`temperature`	`float`	The temperature for the generation. Defaults to 1.	`1`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`list`	`list[T]`	A list of generated items.

`generate_async` `async` ¶

Generates a list of 'n' items of the provided type or based on instructions.

Either a type or instructions must be provided. If instructions are provided without a type, the type is assumed to be a string. The function generates at least 'n' items.

Parameters:

Name	Type	Description	Default
`target`	`type`	The type of items to generate. Defaults to None.	`None`
`instructions`	`str`	Instructions for the generation. Defaults to None.	`None`
`n`	`int`	The number of items to generate. Defaults to 1.	`1`
`use_cache`	`bool`	If True, the function will cache the last 100 responses for each (target, instructions, and temperature) and use those to avoid repetition on subsequent calls. Defaults to True.	`True`
`temperature`	`float`	The temperature for the generation. Defaults to 1.	`1`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`
`client`	`AsyncMarvinClient`	The client to use for the AI function.	`None`

Returns:

Name	Type	Description
`list`	`list[T]`	A list of generated items.

`generate_llm_response` `async` ¶

Generates a language model response based on a provided prompt template.

This function uses a language model to generate a response based on a provided prompt template. The function supports additional arguments for the prompt and the language model.

Parameters:

Name	Type	Description	Default
`prompt_template`	`str`	The template for the prompt.	required
`prompt_kwargs`	`dict`	Additional keyword arguments for the prompt. Defaults to None.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`

Returns:

Name	Type	Description
`ChatResponse`	`ChatResponse`	The generated response from the language model.

`model` ¶

Class decorator for instantiating a Pydantic model from a string.

This decorator allows a Pydantic model to be instantiated from a string. It's equivalent to subclassing the Model class.

Parameters:

Name	Type	Description	Default
`type_`	`Union[Type[M], None]`	The type of the Pydantic model. Defaults to None.	`None`
`instructions`	`str`	Specific instructions for the conversion.	`None`
`model_kwargs`	`dict`	Additional keyword arguments for the language model. Defaults to None.	`None`

Returns:

Type	Description
`Union[Type[M], Callable[[Type[M]], Type[M]]]`	Union[Type[M], Callable[[Type[M]], Type[M]]]: The decorated Pydantic model.

`prepare_data` ¶

Prepares the input data for the AI function.

This function prepares the input data for the AI function by converting it into a list of strings. If the input data is a list of strings or images, the function converts the images into strings.

Parameters:

Name	Type	Description	Default
`data`	`Union[str, Image, list[Union[str, Image]]]`	The input data to be prepared.	required

Returns:

Type	Description
`list[str]`	list[str]: The prepared input data.

marvin.ai.text

Model ¶

from_text_async async classmethod ¶

caption ¶

caption_async async ¶

cast ¶

cast_async async ¶

classifier ¶

classify ¶

classify_async async ¶

extract ¶

extract_async async ¶

fn ¶

generate ¶

generate_async async ¶

generate_llm_response async ¶

model ¶

prepare_data ¶

`Model` ¶

`from_text_async` `async` `classmethod` ¶

`caption` ¶

`caption_async` `async` ¶

`cast` ¶

`cast_async` `async` ¶

`classifier` ¶

`classify` ¶

`classify_async` `async` ¶

`extract` ¶

`extract_async` `async` ¶

`fn` ¶

`generate` ¶

`generate_async` `async` ¶

`generate_llm_response` `async` ¶

`model` ¶

`prepare_data` ¶