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    Class AIAccessor (2.15.0) 

      AIAccessor 
 ( 
 df 
 , 
 base_bqml 
 = 
 None 
 )

    API documentation for AIAccessor class.

    Methods

    classify

      classify 
 ( 
 instruction 
 : 
 str 
 , 
 model 
 , 
 labels 
 : 
 typing 
 . 
 Sequence 
 [ 
 str 
 ], 
 output_column 
 : 
 str 
 = 
 "result" 
 , 
 ground_with_google_search 
 : 
 bool 
 = 
 False 
 , 
 )

    Classifies the rows of dataframes based on user instruction into the provided labels.

    Examples:

     >>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> bpd.options.experiments.ai_operators = True
>>> bpd.options.compute.ai_ops_confirmation_threshold = 25

>>> import bigframes.ml.llm as llm
>>> model = llm.GeminiTextGenerator(model_name="gemini-2.0-flash-001")

>>> df = bpd.DataFrame({
...     "feedback_text": [
...         "The product is amazing, but the shipping was slow.",
...         "I had an issue with my recent bill.",
...         "The user interface is very intuitive."
...     ],
... })
>>> df.ai.classify("{feedback_text}", model=model, labels=["Shipping", "Billing", "UI"])
                                       feedback_text     result
0  The product is amazing, but the shipping was s...   Shipping
1                I had an issue with my recent bill.    Billing
2              The user interface is very intuitive.         UI
<BLANKLINE>
[3 rows x 2 columns]
    Parameters
    Name
    Description
    instruction
    str

    An instruction on how to classify the data. This value must contain column references by name, which should be wrapped in a pair of braces. For example, if you have a column "feedback", you can refer to this column with"{food}".

    model
    bigframes.ml.llm.GeminiTextGenerator

    A GeminiTextGenerator provided by Bigframes ML package.

    labels
    Sequence[str]

    A collection of labels (categories). It must contain at least two and at most 20 elements. Labels are case sensitive. Duplicated labels are not allowed.

    output_column
    str, default "result"

    The name of column for the output.

    ground_with_google_search
    bool, default False

    Enables Grounding with Google Search for the GeminiTextGenerator model. When set to True, the model incorporates relevant information from Google Search results into its responses, enhancing their accuracy and factualness. Note: Using this feature may impact billing costs. Refer to the pricing page for details: https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models The default is False .

    Exceptions
    Type
    Description
    NotImplementedError
    when the AI operator experiment is off.
    ValueError
    when the instruction refers to a non-existing column, when no columns are referred to, or when the count of labels does not meet the requirement.
    Returns
    Type
    Description
    bigframes.pandas.DataFrame
    DataFrame with classification result.

    filter

      filter 
 ( 
 instruction 
 : 
 str 
 , 
 model 
 , 
 ground_with_google_search 
 : 
 bool 
 = 
 False 
 )

    Filters the DataFrame with the semantics of the user instruction.

    Examples:

     >>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> bpd.options.experiments.ai_operators = True
>>> bpd.options.compute.ai_ops_confirmation_threshold = 25

>>> import bigframes.ml.llm as llm
>>> model = llm.GeminiTextGenerator(model_name="gemini-2.0-flash-001")

>>> df = bpd.DataFrame({"country": ["USA", "Germany"], "city": ["Seattle", "Berlin"]})
>>> df.ai.filter("{city} is the capital of {country}", model)
   country    city
1  Germany  Berlin
<BLANKLINE>
[1 rows x 2 columns]
    Parameters
    Name
    Description
    instruction
    str

    An instruction on how to filter the data. This value must contain column references by name, which should be wrapped in a pair of braces. For example, if you have a column "food", you can refer to this column in the instructions like: "The {food} is healthy."

    model
    bigframes.ml.llm.GeminiTextGenerator

    A GeminiTextGenerator provided by Bigframes ML package.

    ground_with_google_search
    bool, default False

    Enables Grounding with Google Search for the GeminiTextGenerator model. When set to True, the model incorporates relevant information from Google Search results into its responses, enhancing their accuracy and factualness. Note: Using this feature may impact billing costs. Refer to the pricing page for details: https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models The default is False .

    Exceptions
    Type
    Description
    NotImplementedError
    when the AI operator experiment is off.
    ValueError
    when the instruction refers to a non-existing column, or when no columns are referred to.
    Returns
    Type
    Description
    bigframes.pandas.DataFrame
    DataFrame filtered by the instruction.

    forecast

      forecast 
 ( 
 timestamp_column 
 : 
 str 
 , 
 data_column 
 : 
 str 
 , 
 * 
 , 
 model 
 : 
 str 
 = 
 "TimesFM 2.0" 
 , 
 id_columns 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 Iterable 
 [ 
 str 
 ]] 
 = 
 None 
 , 
 horizon 
 : 
 int 
 = 
 10 
 , 
 confidence_level 
 : 
 float 
 = 
 0.95 
 )

    Forecast time series at future horizon. Using Google Research's open source TimesFM( https://github.com/google-research/timesfm ) model.

    Parameters
    Name
    Description
    timestamp_column
    str

    A str value that specified the name of the time points column. The time points column provides the time points used to generate the forecast. The time points column must use one of the following data types: TIMESTAMP, DATE and DATETIME

    data_column
    str

    A str value that specifies the name of the data column. The data column contains the data to forecast. The data column must use one of the following data types: INT64, NUMERIC and FLOAT64

    model
    str, default "TimesFM 2.0"

    A str value that specifies the name of the model. TimesFM 2.0 is the only supported value, and is the default value.

    id_columns
    Iterable[str] or None, default None

    An iterable of str value that specifies the names of one or more ID columns. Each ID identifies a unique time series to forecast. Specify one or more values for this argument in order to forecast multiple time series using a single query. The columns that you specify must use one of the following data types: STRING, INT64, ARRAY

    horizon
    int, default 10

    An int value that specifies the number of time points to forecast. The default value is 10. The valid input range is [1, 10,000].

    confidence_level
    float, default 0.95

    A FLOAT64 value that specifies the percentage of the future values that fall in the prediction interval. The default value is 0.95. The valid input range is [0, 1).

    Exceptions
    Type
    Description
    ValueError
    when referring to a non-existing column.
    Returns
    Type
    Description
    DataFrame
    The forecast dataframe matches that of the BigQuery AI.FORECAST function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast

    join

      join 
 ( 
 other 
 , 
 instruction 
 : 
 str 
 , 
 model 
 , 
 ground_with_google_search 
 : 
 bool 
 = 
 False 
 )

    Joines two dataframes by applying the instruction over each pair of rows from the left and right table.

    Examples:

     >>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> bpd.options.experiments.ai_operators = True
>>> bpd.options.compute.ai_ops_confirmation_threshold = 25

>>> import bigframes.ml.llm as llm
>>> model = llm.GeminiTextGenerator(model_name="gemini-2.0-flash-001")

>>> cities = bpd.DataFrame({'city': ['Seattle', 'Ottawa', 'Berlin', 'Shanghai', 'New Delhi']})
>>> continents = bpd.DataFrame({'continent': ['North America', 'Africa', 'Asia']})

>>> cities.ai.join(continents, "{city} is in {continent}", model)
        city      continent
0    Seattle  North America
1     Ottawa  North America
2   Shanghai           Asia
3  New Delhi           Asia
<BLANKLINE>
[4 rows x 2 columns]
    Parameters
    Name
    Description
    other
    bigframes.pandas.DataFrame

    The other dataframe.

    instruction
    str

    An instruction on how left and right rows can be joined. This value must contain column references by name. which should be wrapped in a pair of braces. For example: "The {city} belongs to the {country}". For column names that are shared between two dataframes, you need to add "left." and "right." prefix for differentiation. This is especially important when you do self joins. For example: "The {left.employee_name} reports to {right.employee_name}" For unique column names, this prefix is optional.

    model
    bigframes.ml.llm.GeminiTextGenerator

    A GeminiTextGenerator provided by Bigframes ML package.

    ground_with_google_search
    bool, default False

    Enables Grounding with Google Search for the GeminiTextGenerator model. When set to True, the model incorporates relevant information from Google Search results into its responses, enhancing their accuracy and factualness. Note: Using this feature may impact billing costs. Refer to the pricing page for details: https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models The default is False .

    Exceptions
    Type
    Description
    ValueErro
    if the amount of data that will be sent for LLM processing is larger than max_rows.:
    Returns
    Type
    Description
    bigframes.pandas.DataFrame
    The joined dataframe.

    map

      map 
 ( 
 instruction 
 : 
 str 
 , 
 model 
 , 
 output_schema 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 Dict 
 [ 
 str 
 , 
 str 
 ]] 
 = 
 None 
 , 
 ground_with_google_search 
 : 
 bool 
 = 
 False 
 , 
 )

    Maps the DataFrame with the semantics of the user instruction. The name of the keys in the output_schema parameter carry semantic meaning, and can be used for information extraction.

    Examples:

     >>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> bpd.options.experiments.ai_operators = True
>>> bpd.options.compute.ai_ops_confirmation_threshold = 25

>>> import bigframes.ml.llm as llm
>>> model = llm.GeminiTextGenerator(model_name="gemini-2.0-flash-001")

>>> df = bpd.DataFrame({"ingredient_1": ["Burger Bun", "Soy Bean"], "ingredient_2": ["Beef Patty", "Bittern"]})
>>> df.ai.map("What is the food made from {ingredient_1} and {ingredient_2}? One word only.", model=model, output_schema={"food": "string"})
  ingredient_1 ingredient_2      food
0   Burger Bun   Beef Patty  Burger
<BLANKLINE>
1     Soy Bean      Bittern    Tofu
<BLANKLINE>
<BLANKLINE>
[2 rows x 3 columns]


>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> bpd.options.experiments.ai_operators = True
>>> bpd.options.compute.ai_ops_confirmation_threshold = 25

>>> import bigframes.ml.llm as llm
>>> model = llm.GeminiTextGenerator(model_name="gemini-2.0-flash-001")

>>> df = bpd.DataFrame({"text": ["Elmo lives at 123 Sesame Street."]})
>>> df.ai.map("{text}", model=model, output_schema={"person": "string", "address": "string"})
                               text person            address
0  Elmo lives at 123 Sesame Street.   Elmo  123 Sesame Street
<BLANKLINE>
[1 rows x 3 columns]
    Parameters
    Name
    Description
    instruction
    str

    An instruction on how to map the data. This value must contain column references by name, which should be wrapped in a pair of braces. For example, if you have a column "food", you can refer to this column in the instructions like: "Get the ingredients of {food}."

    model
    bigframes.ml.llm.GeminiTextGenerator

    A GeminiTextGenerator provided by Bigframes ML package.

    output_schema
    Dict[str, str] or None, default None

    The schema used to generate structured output as a bigframes DataFrame. The schema is a string key-value pair of <column_name>:

    ground_with_google_search
    bool, default False

    Enables Grounding with Google Search for the GeminiTextGenerator model. When set to True, the model incorporates relevant information from Google Search results into its responses, enhancing their accuracy and factualness. Note: Using this feature may impact billing costs. Refer to the pricing page for details: https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models The default is False .

    Exceptions
    Type
    Description
    NotImplementedError
    when the AI operator experiment is off.
    ValueError
    when the instruction refers to a non-existing column, or when no columns are referred to.
    Returns
    Type
    Description
    bigframes.pandas.DataFrame
    DataFrame with attached mapping results.
      search 
 ( 
 search_column 
 : 
 str 
 , 
 query 
 : 
 str 
 , 
 top_k 
 : 
 int 
 , 
 model 
 , 
 score_column 
 : 
 typing 
 . 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 )

    Performs AI semantic search on the DataFrame.

    ** Examples: **

     >>> import bigframes.pandas as bpd
>>> bpd.options. display 
.progress_bar = None

>>> import bigframes 
>>> bigframes 
.options.experiments.ai_operators = True
>>> bpd.options.compute.ai_ops_confirmation_threshold = 25

>>> import bigframes.ml.llm as llm
>>> model = llm 
. TextEmbeddingGenerator 
(model_name="text-embedding-005")

>>> df = bpd.DataFrame({"creatures": ["salmon", "sea urchin", "frog", "chimpanzee"]})
>>> df.ai. search 
("creatures", "monkey", top_k=1, model=model, score_column='distance')
    creatures  distance
3  chimpanzee  0.635844
<BLANKLINE>
[1 rows x 2 columns]
    Parameters
    Name
    Description
    query
    str

    The search query.

    top_k
    int

    The number of nearest neighbors to return.

    model
    TextEmbeddingGenerator

    A TextEmbeddingGenerator provided by Bigframes ML package.

    score_column
    Optional[str], default None

    The name of the the additional column containning the similarity scores. If None, this column won't be attached to the result.

    Exceptions
    Type
    Description
    ValueError
    when the search_column is not found from the the data frame.
    TypeError
    when the provided model is not TextEmbeddingGenerator.
    Returns
    Type
    Description
    DataFrame
    the DataFrame with the search result.

    sim_join

      sim_join 
 ( 
 other 
 , 
 left_on 
 : 
 str 
 , 
 right_on 
 : 
 str 
 , 
 model 
 , 
 top_k 
 : 
 int 
 = 
 3 
 , 
 score_column 
 : 
 typing 
 . 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 max_rows 
 : 
 int 
 = 
 1000 
 , 
 )

    Joins two dataframes based on the similarity of the specified columns.

    This method uses BigQuery's VECTOR_SEARCH function to match rows on the left side with the rows that have nearest embedding vectors on the right. In the worst case scenario, the complexity is around O(M * N * log K). Therefore, this is a potentially expensive operation.

    ** Examples: **

     >>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> bpd.options.experiments.ai_operators = True
>>> bpd.options.compute.ai_ops_confirmation_threshold = 25

>>> import bigframes.ml.llm as llm
>>> model = llm.TextEmbeddingGenerator(model_name="text-embedding-005")

>>> df1 = bpd.DataFrame({'animal': ['monkey', 'spider']})
>>> df2 = bpd.DataFrame({'animal': ['scorpion', 'baboon']})

>>> df1.ai.sim_join(df2, left_on='animal', right_on='animal', model=model, top_k=1)
animal  animal_1
0  monkey    baboon
1  spider  scorpion
<BLANKLINE>
[2 rows x 2 columns]
    Parameters
    Name
    Description
    other
    DataFrame

    The other data frame to join with.

    left_on
    str

    The name of the column on left side for the join.

    right_on
    str

    The name of the column on the right side for the join.

    top_k
    int, default 3

    The number of nearest neighbors to return.

    model
    TextEmbeddingGenerator

    A TextEmbeddingGenerator provided by Bigframes ML package.

    score_column
    Optional[str], default None

    The name of the the additional column containning the similarity scores. If None, this column won't be attached to the result.

    Exceptions
    Type
    Description
    ValueError
    when the amount of data to be processed exceeds the specified max_rows.
    Returns
    Type
    Description
    DataFrame
    the data frame with the join result.
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