Class Claude3TextGenerator (1.32.0) 

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 available 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,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    *,
    max_output_tokens: int = 128,
    top_k: int = 40,
    top_p: float = 0.95,
    max_retries: int = 0
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

Parameters
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series

Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, it must contain a "prompt" column for prediction. 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].
max_retries int, default 0

Max number of retries if the prediction for any rows failed. Each try needs to make progress (i.e. has successfully predicted rows) to continue the retry. Each retry will append newly succeeded rows. When the max retries are reached, the remaining rows (the ones without successful predictions) will be appended to the end of the result.
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.