bigframes.bigquery.ai.generate_table#

Generates a table using a BigQuery ML model.

See the AI.GENERATE_TABLE function syntax for additional reference.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> # The user is responsible for constructing a DataFrame that contains
>>> # the necessary columns for the model's prompt. For example, a
>>> # DataFrame with a 'prompt' column for text classification.
>>> df = bpd.DataFrame({'prompt': ["some text to classify"]})
>>> result = bbq.ai.generate_table(
...     "project.dataset.model_name",
...     data=df,
...     output_schema="category STRING"
... )

Parameters:

model (bigframes.ml.base.BaseEstimator or str) – The model to use for table generation.
data (bigframes.pandas.DataFrame or bigframes.pandas.Series) – The data to generate table for. If a Series is provided, it is treated as the ‘prompt’ column. If a DataFrame is provided, it must contain a ‘prompt’ column, or you must rename the column you wish to generate table to ‘prompt’.
output_schema (str) – A string defining the output schema (e.g., “col1 STRING, col2 INT64”).
temperature (float, optional) – A FLOAT64 value that is used for sampling promiscuity. The value must be in the range [0.0, 1.0].
top_p (float, optional) – A FLOAT64 value that changes how the model selects tokens for output.
max_output_tokens (int, optional) – An INT64 value that sets the maximum number of tokens in the generated table.
stop_sequences (List[str], optional) – An ARRAY<STRING> value that contains the stop sequences for the model.
request_type (str, optional) – A STRING value that contains the request type for the model.

Returns:

The generated table.

Return type:

bigframes.pandas.DataFrame

bigframes.bigquery.ai.generate_table#

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