Class Claude3TextGenerator (1.19.0)

Claude3TextGenerator(
    *,
    model_name: typing.Literal[
        "claude-3-sonnet", "claude-3-haiku", "claude-3-5-sonnet", "claude-3-opus"
    ] = "claude-3-sonnet",
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None
)

Claude3 text generator LLM model.

Go to Google Cloud Console -> Vertex AI -> Model Garden page to enabe the models before use. Must have the Consumer Procurement Entitlement Manager Identity and Access Management (IAM) role to enable the models. https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-partner-models#grant-permissions

The models only availabe in specific regions. Check https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#regions for details.

Parameters
Name	Description
`model_name`	`str, Default to "claude-3-sonnet"` The model for natural language tasks. Possible values are "claude-3-sonnet", "claude-3-haiku", "claude-3-5-sonnet" and "claude-3-opus". "claude-3-sonnet" is Anthropic's dependable combination of skills and speed. It is engineered to be dependable for scaled AI deployments across a variety of use cases. "claude-3-haiku" is Anthropic's fastest, most compact vision and text model for near-instant responses to simple queries, meant for seamless AI experiences mimicking human interactions. "claude-3-5-sonnet" is Anthropic's most powerful AI model and maintains the speed and cost of Claude 3 Sonnet, which is a mid-tier model. "claude-3-opus" is Anthropic's second-most powerful AI model, with strong performance on highly complex tasks. https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#available-claude-models Default to "claude-3-sonnet".
`session`	`bigframes.Session or None` BQ session to create the model. If None, use the global default session.
`connection_name`	`str or None` Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.

Methods

repr

__repr__()

Print the estimator's constructor with all non-default parameter values.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
`deep`	`bool, default True` Default `True`. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
`Dictionary`	A dictionary of parameter names mapped to their values.

predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    max_output_tokens: int = 128,
    top_k: int = 40,
    top_p: float = 0.95
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

Parameters
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series` Input DataFrame or Series, which contains only one column of prompts. Prompts can include preamble, questions, suggestions, instructions, or examples.
`max_output_tokens`	`int, default 128` Maximum number of tokens that can be generated in the response. Specify a lower value for shorter responses and a higher value for longer responses. A token may be smaller than a word. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. Default 128. Possible values are in the range [1, 4096].
`top_k`	`int, default 40` Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Default 40. Possible values [1, 40].
`top_p`	`float, default 0.95` Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and not consider C at all. Specify a lower value for less random responses and a higher value for more random responses. Default 0.95. Possible values [0.0, 1.0].

Returns
Type	Description
`bigframes.dataframe.DataFrame`	DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values.

to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.llm.Claude3TextGenerator

Save the model to BigQuery.

Parameters
Name	Description
`model_name`	`str` The name of the model.
`replace`	`bool, default False` Determine whether to replace if the model already exists. Default to False.

Returns
Type	Description
`Claude3TextGenerator`	Saved model.