abacusai.api_class

Attributes

`DocumentRetrieverConfig`
`Segment`

Classes

`ApiClass`	Helper class that provides a standard way to create an ABC using
`FieldDescriptor`	Configs for vector store indexing.
`JSONSchema`
`WorkflowNodeInputMapping`	Represents a mapping of inputs to a workflow node.
`WorkflowNodeInputSchema`	A schema conformant to react-jsonschema-form for workflow node input.
`WorkflowNodeOutputMapping`	Represents a mapping of output from a workflow node.
`WorkflowNodeOutputSchema`	A schema conformant to react-jsonschema-form for a workflow node output.
`WorkflowGraphNode`	Represents a node in an Agent workflow graph.
`WorkflowGraphEdge`	Represents an edge in an Agent workflow graph.
`WorkflowGraph`	Represents an Agent workflow graph.
`AgentConversationMessage`	Message format for agent conversation
`WorkflowNodeTemplateConfig`	Represents a WorkflowNode template config.
`WorkflowNodeTemplateInput`	Represents an input to the workflow node generated using template.
`WorkflowNodeTemplateOutput`	Represents an output returned by the workflow node generated using template.
`ApiClass`	Helper class that provides a standard way to create an ABC using
`HotkeyPrompt`	A config class for a Data Science Co-Pilot Hotkey
`ApiClass`	Helper class that provides a standard way to create an ABC using
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`BatchPredictionArgs`	An abstract class for Batch Prediction args specific to problem type.
`ForecastingBatchPredictionArgs`	Batch Prediction Config for the FORECASTING problem type
`NamedEntityExtractionBatchPredictionArgs`	Batch Prediction Config for the NAMED_ENTITY_EXTRACTION problem type
`PersonalizationBatchPredictionArgs`	Batch Prediction Config for the PERSONALIZATION problem type
`PredictiveModelingBatchPredictionArgs`	Batch Prediction Config for the PREDICTIVE_MODELING problem type
`PretrainedModelsBatchPredictionArgs`	Batch Prediction Config for the PRETRAINED_MODELS problem type
`SentenceBoundaryDetectionBatchPredictionArgs`	Batch Prediction Config for the SENTENCE_BOUNDARY_DETECTION problem type
`ThemeAnalysisBatchPredictionArgs`	Batch Prediction Config for the THEME_ANALYSIS problem type
`ChatLLMBatchPredictionArgs`	Batch Prediction Config for the ChatLLM problem type
`TrainablePlugAndPlayBatchPredictionArgs`	Batch Prediction Config for the TrainablePlugAndPlay problem type
`AIAgentBatchPredictionArgs`	Batch Prediction Config for the AIAgents problem type
`_BatchPredictionArgsFactory`	Helper class that provides a standard way to create an ABC using
`ApiClass`	Helper class that provides a standard way to create an ABC using
`Blob`	An object for storing and passing file data.
`BlobInput`	An object for storing and passing file data.
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`DatasetConfig`	An abstract class for dataset configs
`StreamingConnectorDatasetConfig`	An abstract class for dataset configs specific to streaming connectors.
`KafkaDatasetConfig`	Dataset config for Kafka Streaming Connector
`_StreamingConnectorDatasetConfigFactory`	Helper class that provides a standard way to create an ABC using
`ApiClass`	Helper class that provides a standard way to create an ABC using
`DocumentType`	Generic enumeration.
`OcrMode`	Generic enumeration.
`DatasetConfig`	An abstract class for dataset configs
`ParsingConfig`	Custom config for dataset parsing.
`DocumentProcessingConfig`	Document processing configuration.
`DatasetDocumentProcessingConfig`	Document processing configuration for dataset imports.
`IncrementalDatabaseConnectorConfig`	Config information for incremental datasets from database connectors
`AttachmentParsingConfig`	Config information for parsing attachments
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`DatasetConfig`	An abstract class for dataset configs
`DatasetDocumentProcessingConfig`	Document processing configuration for dataset imports.
`ApplicationConnectorDatasetConfig`	An abstract class for dataset configs specific to application connectors.
`ConfluenceDatasetConfig`	Dataset config for Confluence Application Connector
`GoogleAnalyticsDatasetConfig`	Dataset config for Google Analytics Application Connector
`GoogleDriveDatasetConfig`	Dataset config for Google Drive Application Connector
`JiraDatasetConfig`	Dataset config for Jira Application Connector
`OneDriveDatasetConfig`	Dataset config for OneDrive Application Connector
`SharepointDatasetConfig`	Dataset config for Sharepoint Application Connector
`ZendeskDatasetConfig`	Dataset config for Zendesk Application Connector
`AbacusUsageMetricsDatasetConfig`	Dataset config for Abacus Usage Metrics Application Connector
`TeamsScraperDatasetConfig`	Dataset config for Teams Scraper Application Connector
`FreshserviceDatasetConfig`	Dataset config for Freshservice Application Connector
`_ApplicationConnectorDatasetConfigFactory`	Helper class that provides a standard way to create an ABC using
`ApiClass`	Helper class that provides a standard way to create an ABC using
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`PredictionArguments`	An abstract class for prediction arguments specific to problem type.
`OptimizationPredictionArguments`	Prediction arguments for the OPTIMIZATION problem type
`TimeseriesAnomalyPredictionArguments`	Prediction arguments for the TS_ANOMALY problem type
`ChatLLMPredictionArguments`	Prediction arguments for the CHAT_LLM problem type
`RegressionPredictionArguments`	Prediction arguments for the PREDICTIVE_MODELING problem type
`ForecastingPredictionArguments`	Prediction arguments for the FORECASTING problem type
`CumulativeForecastingPredictionArguments`	Prediction arguments for the CUMULATIVE_FORECASTING problem type
`NaturalLanguageSearchPredictionArguments`	Prediction arguments for the NATURAL_LANGUAGE_SEARCH problem type
`FeatureStorePredictionArguments`	Prediction arguments for the FEATURE_STORE problem type
`_PredictionArgumentsFactory`	Helper class that provides a standard way to create an ABC using
`ApiClass`	Helper class that provides a standard way to create an ABC using
`VectorStoreTextEncoder`	Generic enumeration.
`VectorStoreConfig`	Config for indexing options of a document retriever. Default values of optional arguments are heuristically selected by the Abacus.AI platform based on the underlying data.
`ApiEnum`	Generic enumeration.
`ProblemType`	Generic enumeration.
`RegressionObjective`	Generic enumeration.
`RegressionTreeHPOMode`	Generic enumeration.
`PartialDependenceAnalysis`	Generic enumeration.
`RegressionAugmentationStrategy`	Generic enumeration.
`RegressionTargetTransform`	Generic enumeration.
`RegressionTypeOfSplit`	Generic enumeration.
`RegressionTimeSplitMethod`	Generic enumeration.
`RegressionLossFunction`	Generic enumeration.
`ExplainerType`	Generic enumeration.
`SamplingMethodType`	Generic enumeration.
`MergeMode`	Generic enumeration.
`OperatorType`	Generic enumeration.
`MarkdownOperatorInputType`	Generic enumeration.
`FillLogic`	Generic enumeration.
`BatchSize`	Generic enumeration.
`HolidayCalendars`	Generic enumeration.
`FileFormat`	Generic enumeration.
`ExperimentationMode`	Generic enumeration.
`PersonalizationTrainingMode`	Generic enumeration.
`PersonalizationObjective`	Generic enumeration.
`ForecastingObjective`	Generic enumeration.
`ForecastingFrequency`	Generic enumeration.
`ForecastingDataSplitType`	Generic enumeration.
`ForecastingLossFunction`	Generic enumeration.
`ForecastingLocalScaling`	Generic enumeration.
`ForecastingFillMethod`	Generic enumeration.
`ForecastingQuanitlesExtensionMethod`	Generic enumeration.
`TimeseriesAnomalyDataSplitType`	Generic enumeration.
`TimeseriesAnomalyTypeOfAnomaly`	Generic enumeration.
`TimeseriesAnomalyUseHeuristic`	Generic enumeration.
`NERObjective`	Generic enumeration.
`NERModelType`	Generic enumeration.
`NLPDocumentFormat`	Generic enumeration.
`SentimentType`	Generic enumeration.
`ClusteringImputationMethod`	Generic enumeration.
`ConnectorType`	Generic enumeration.
`ApplicationConnectorType`	Generic enumeration.
`StreamingConnectorType`	Generic enumeration.
`PythonFunctionArgumentType`	Generic enumeration.
`PythonFunctionOutputArgumentType`	Generic enumeration.
`VectorStoreTextEncoder`	Generic enumeration.
`LLMName`	Generic enumeration.
`MonitorAlertType`	Generic enumeration.
`FeatureDriftType`	Generic enumeration.
`DataIntegrityViolationType`	Generic enumeration.
`BiasType`	Generic enumeration.
`AlertActionType`	Generic enumeration.
`PythonFunctionType`	Generic enumeration.
`EvalArtifactType`	Generic enumeration.
`FieldDescriptorType`	Generic enumeration.
`WorkflowNodeInputType`	Generic enumeration.
`WorkflowNodeOutputType`	Generic enumeration.
`OcrMode`	Generic enumeration.
`DocumentType`	Generic enumeration.
`StdDevThresholdType`	Generic enumeration.
`DataType`	Generic enumeration.
`AgentInterface`	Generic enumeration.
`WorkflowNodeTemplateType`	Generic enumeration.
`ProjectConfigType`	Generic enumeration.
`CPUSize`	Generic enumeration.
`MemorySize`	Generic enumeration.
`ResponseSectionType`	Generic enumeration.
`CodeLanguage`	Generic enumeration.
`DeploymentConversationType`	Generic enumeration.
`ApiClass`	Helper class that provides a standard way to create an ABC using
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`DocumentProcessingConfig`	Document processing configuration.
`SamplingConfig`	An abstract class for the sampling config of a feature group
`NSamplingConfig`	The number of distinct values of the key columns to include in the sample, or number of rows if key columns not specified.
`PercentSamplingConfig`	The fraction of distinct values of the feature group to include in the sample.
`_SamplingConfigFactory`	Helper class that provides a standard way to create an ABC using
`MergeConfig`	An abstract class for the merge config of a feature group
`LastNMergeConfig`	Merge LAST N chunks/versions of an incremental dataset.
`TimeWindowMergeConfig`	Merge rows within a given timewindow of the most recent timestamp
`_MergeConfigFactory`	Helper class that provides a standard way to create an ABC using
`OperatorConfig`	Configuration for a template Feature Group Operation
`UnpivotConfig`	Unpivot Columns in a FeatureGroup.
`MarkdownConfig`	Transform a input column to a markdown column.
`CrawlerTransformConfig`	Transform a input column of urls to html text
`ExtractDocumentDataConfig`	Extracts data from documents.
`DataGenerationConfig`	Generate synthetic data using a model for finetuning an LLM.
`UnionTransformConfig`	Takes Union of current feature group with 1 or more selected feature groups of same type.
`_OperatorConfigFactory`	A class to select and return the the correct type of Operator Config based on a serialized OperatorConfig instance.
`ApiClass`	Helper class that provides a standard way to create an ABC using
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`TrainingConfig`	An abstract class for the training config options used to train the model.
`PersonalizationTrainingConfig`	Training config for the PERSONALIZATION problem type
`RegressionTrainingConfig`	Training config for the PREDICTIVE_MODELING problem type
`ForecastingTrainingConfig`	Training config for the FORECASTING problem type
`NamedEntityExtractionTrainingConfig`	Training config for the NAMED_ENTITY_EXTRACTION problem type
`NaturalLanguageSearchTrainingConfig`	Training config for the NATURAL_LANGUAGE_SEARCH problem type
`ChatLLMTrainingConfig`	Training config for the CHAT_LLM problem type
`SentenceBoundaryDetectionTrainingConfig`	Training config for the SENTENCE_BOUNDARY_DETECTION problem type
`SentimentDetectionTrainingConfig`	Training config for the SENTIMENT_DETECTION problem type
`DocumentClassificationTrainingConfig`	Training config for the DOCUMENT_CLASSIFICATION problem type
`DocumentSummarizationTrainingConfig`	Training config for the DOCUMENT_SUMMARIZATION problem type
`DocumentVisualizationTrainingConfig`	Training config for the DOCUMENT_VISUALIZATION problem type
`ClusteringTrainingConfig`	Training config for the CLUSTERING problem type
`ClusteringTimeseriesTrainingConfig`	Training config for the CLUSTERING_TIMESERIES problem type
`EventAnomalyTrainingConfig`	Training config for the EVENT_ANOMALY problem type
`TimeseriesAnomalyTrainingConfig`	Training config for the TS_ANOMALY problem type
`CumulativeForecastingTrainingConfig`	Training config for the CUMULATIVE_FORECASTING problem type
`ThemeAnalysisTrainingConfig`	Training config for the THEME ANALYSIS problem type
`AIAgentTrainingConfig`	Training config for the AI_AGENT problem type
`CustomTrainedModelTrainingConfig`	Training config for the CUSTOM_TRAINED_MODEL problem type
`CustomAlgorithmTrainingConfig`	Training config for the CUSTOM_ALGORITHM problem type
`OptimizationTrainingConfig`	Training config for the OPTIMIZATION problem type
`_TrainingConfigFactory`	Helper class that provides a standard way to create an ABC using
`DeployableAlgorithm`	Algorithm that can be deployed to a model.
`ApiClass`	Helper class that provides a standard way to create an ABC using
`StdDevThresholdType`	Generic enumeration.
`TimeWindowConfig`	Time Window Configuration
`ForecastingMonitorConfig`	Forecasting Monitor Configuration
`StdDevThreshold`	Std Dev Threshold types
`ItemAttributesStdDevThreshold`	Item Attributes Std Dev Threshold for Monitor Alerts
`RestrictFeatureMappings`	Restrict Feature Mappings for Monitor Filtering
`MonitorFilteringConfig`	Monitor Filtering Configuration
`ApiClass`	Helper class that provides a standard way to create an ABC using
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`AlertConditionConfig`	An abstract class for alert condition configs
`AccuracyBelowThresholdConditionConfig`	Accuracy Below Threshold Condition Config for Monitor Alerts
`FeatureDriftConditionConfig`	Feature Drift Condition Config for Monitor Alerts
`TargetDriftConditionConfig`	Target Drift Condition Config for Monitor Alerts
`HistoryLengthDriftConditionConfig`	History Length Drift Condition Config for Monitor Alerts
`DataIntegrityViolationConditionConfig`	Data Integrity Violation Condition Config for Monitor Alerts
`BiasViolationConditionConfig`	Bias Violation Condition Config for Monitor Alerts
`PredictionCountConditionConfig`	Deployment Prediction Condition Config for Deployment Alerts. By default we monitor if predictions made over a time window has reduced significantly.
`_AlertConditionConfigFactory`	Helper class that provides a standard way to create an ABC using
`AlertActionConfig`	An abstract class for alert action configs
`EmailActionConfig`	Email Action Config for Monitor Alerts
`_AlertActionConfigFactory`	Helper class that provides a standard way to create an ABC using
`MonitorThresholdConfig`	Monitor Threshold Config for Monitor Alerts
`ApiClass`	Helper class that provides a standard way to create an ABC using
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`FeatureMappingConfig`	Feature mapping configuration for a feature group type.
`ProjectFeatureGroupTypeMappingsConfig`	Project feature group type mappings.
`ConstraintConfig`	Constraint configuration.
`ProjectFeatureGroupConfig`	An abstract class for project feature group configuration.
`ConstraintProjectFeatureGroupConfig`	Constraint project feature group configuration.
`ReviewModeProjectFeatureGroupConfig`	Review mode project feature group configuration.
`_ProjectFeatureGroupConfigFactory`	Helper class that provides a standard way to create an ABC using
`ApiClass`	Helper class that provides a standard way to create an ABC using
`PythonFunctionArgument`	A config class for python function arguments
`OutputVariableMapping`	A config class for python function arguments
`ApiClass`	Helper class that provides a standard way to create an ABC using
`_ApiClassFactory`	Helper class that provides a standard way to create an ABC using
`FeatureGroupExportConfig`	An abstract class for feature group exports.
`FileConnectorExportConfig`	File connector export config for feature groups
`DatabaseConnectorExportConfig`	Database connector export config for feature groups
`_FeatureGroupExportConfigFactory`	Helper class that provides a standard way to create an ABC using
`ApiClass`	Helper class that provides a standard way to create an ABC using
`ResponseSection`	A response section that an agent can return to render specific UI elements.
`AgentFlowButtonResponseSection`	A response section that an AI Agent can return to render a button.
`ImageUrlResponseSection`	A response section that an agent can return to render an image.
`TextResponseSection`	A response section that an agent can return to render text.
`RuntimeSchemaResponseSection`	A segment that an agent can return to render json and ui schema in react-jsonschema-form format for workflow nodes.
`CodeResponseSection`	A response section that an agent can return to render code.
`Base64ImageResponseSection`	A response section that an agent can return to render a base64 image.
`CollapseResponseSection`	A response section that an agent can return to render a collapsible component.
`ListResponseSection`	A response section that an agent can return to render a list.
`ChartResponseSection`	A response section that an agent can return to render a chart.
`DataframeResponseSection`	A response section that an agent can return to render a pandas dataframe.

Functions

`get_clean_function_source_code_for_agent`(func)
`validate_constructor_arg_types`([friendly_class_name])
`validate_input_dict_param`(dict_object, friendly_class_name)
`deprecated_enums`(*enum_values)

Package Contents

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

abacusai.api_class.get_clean_function_source_code_for_agent(func)

Parameters:: func (Callable)

abacusai.api_class.validate_constructor_arg_types(friendly_class_name=None)

abacusai.api_class.validate_input_dict_param(dict_object, friendly_class_name, must_contain=[])

class abacusai.api_class.FieldDescriptor

Bases: abacusai.api_class.abstract.ApiClass

Configs for vector store indexing.

Parameters:

field (str) – The field to be extracted. This will be used as the key in the response.
description (str) – The description of this field. If not included, the response_field will be used.
example_extraction (Union[str, int, bool, float]) – An example of this extracted field.
type (FieldDescriptorType) – The type of this field. If not provided, the default type is STRING.

field: str

description: str

example_extraction: str | int | bool | float | list | dict

type: abacusai.api_class.enums.FieldDescriptorType

class abacusai.api_class.JSONSchema

classmethod from_fields_list(fields_list)

Parameters:: fields_list (List[str])

classmethod to_fields_list(json_schema)

Return type:: List[str]

class abacusai.api_class.WorkflowNodeInputMapping

Bases: abacusai.api_class.abstract.ApiClass

Represents a mapping of inputs to a workflow node.

Parameters:

name (str) – The name of the input variable of the node function.
variable_type (WorkflowNodeInputType) – The type of the input.
variable_source (str) – The name of the node this variable is sourced from. If the type is WORKFLOW_VARIABLE, the value given by the source node will be directly used. If the type is USER_INPUT, the value given by the source node will be used as the default initial value before the user edits it. Set to None if the type is USER_INPUT and the variable doesn’t need a pre-filled initial value.
is_required (bool) – Indicates whether the input is required. Defaults to True.

name: str

variable_type: abacusai.api_class.enums.WorkflowNodeInputType

variable_source: str

source_prop: str

is_required: bool

default_value: Any

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(mapping)

Parameters:: mapping (dict)

class abacusai.api_class.WorkflowNodeInputSchema

Bases: abacusai.api_class.abstract.ApiClass, JSONSchema

A schema conformant to react-jsonschema-form for workflow node input.

To initialize a WorkflowNodeInputSchema dependent on another node’s output, use the from_workflow_node method.

Parameters:

json_schema (dict) – The JSON schema for the input, conformant to react-jsonschema-form specification. Must define keys like “title”, “type”, and “properties”. Supported elements include Checkbox, Radio Button, Dropdown, Textarea, Number, Date, and file upload. Nested elements, arrays, and other complex types are not supported.
ui_schema (dict) – The UI schema for the input, conformant to react-jsonschema-form specification.

json_schema: dict

ui_schema: dict

schema_source: str

schema_prop: str

runtime_schema: bool

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(schema)

Parameters:: schema (dict)

classmethod from_workflow_node(schema_source, schema_prop)

Creates a WorkflowNodeInputSchema instance which references the schema generated by a WorkflowGraphNode.

Parameters:

schema_source (str) – The name of the source WorkflowGraphNode.
schema_prop (str) – The name of the input schema parameter which source node outputs.

class abacusai.api_class.WorkflowNodeOutputMapping

Bases: abacusai.api_class.abstract.ApiClass

Represents a mapping of output from a workflow node.

Parameters:

name (str) – The name of the output.
variable_type (Union[WorkflowNodeOutputType, str]) – The type of the output in the form of an enum or a string.

name: str

variable_type: abacusai.api_class.enums.WorkflowNodeOutputType | str

__post_init__()

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(mapping)

Parameters:: mapping (dict)

class abacusai.api_class.WorkflowNodeOutputSchema

Bases: abacusai.api_class.abstract.ApiClass, JSONSchema

A schema conformant to react-jsonschema-form for a workflow node output.

Parameters:: json_schema (dict) – The JSON schema for the output, conformant to react-jsonschema-form specification.

json_schema: dict

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(schema)

Parameters:: schema (dict)

class abacusai.api_class.WorkflowGraphNode(name, input_mappings=None, output_mappings=None, function=None, function_name=None, source_code=None, input_schema=None, output_schema=None, template_metadata=None)

Bases: abacusai.api_class.abstract.ApiClass

Represents a node in an Agent workflow graph.

Parameters:

name (str) – A unique name for the workflow node.
input_mappings (List[WorkflowNodeInputMapping]) – List of input mappings for the node. Each arg/kwarg of the node function should have a corresponding input mapping.
output_mappings (List[WorkflowNodeOutputMapping]) – List of output mappings for the node. Each field in the returned dict/AgentResponse must have a corresponding output mapping.
function (callable) – The callable node function reference.
input_schema (WorkflowNodeInputSchema) – The react json schema for the user input variables.
output_schema (WorkflowNodeOutputSchema) – The react json schema for the output to be shown on UI.
function_name (str)
source_code (str)
template_metadata (dict)

Additional Attributes:: function_name (str): The name of the function. source_code (str): The source code of the function.

template_metadata

classmethod _raw_init(name, input_mappings=None, output_mappings=None, function=None, function_name=None, source_code=None, input_schema=None, output_schema=None, template_metadata=None)

Parameters:

name (str)
input_mappings (List[WorkflowNodeInputMapping])
output_mappings (List[WorkflowNodeOutputMapping])
function (callable)
function_name (str)
source_code (str)
input_schema (WorkflowNodeInputSchema)
output_schema (WorkflowNodeOutputSchema)
template_metadata (dict)

classmethod from_template(template_name, name, configs=None, input_mappings=None, input_schema=None, output_schema=None, sleep_time=None)

Parameters:

template_name (str)
name (str)
configs (dict)
input_mappings (Union[Dict[str, WorkflowNodeInputMapping], List[WorkflowNodeInputMapping]])
input_schema (Union[List[str], WorkflowNodeInputSchema])
output_schema (Union[List[str], WorkflowNodeOutputSchema])
sleep_time (int)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(node)

Parameters:: node (dict)

__setattr__(name, value)

__getattribute__(name)

class Outputs(node)

Parameters:: node (WorkflowGraphNode)

node

__getattr__(name)

property outputs

class abacusai.api_class.WorkflowGraphEdge(source, target, details=None)

Bases: abacusai.api_class.abstract.ApiClass

Represents an edge in an Agent workflow graph.

To make an edge conditional, provide {‘EXECUTION_CONDITION’: ‘<condition>’} key-value in the details dictionary. The condition should be a Pythonic expression string that evaluates to a boolean value and only depends on the outputs of the source node of the edge.

Parameters:

source (str) – The name of the source node of the edge.
target (str) – The name of the target node of the edge.
details (dict) – Additional details about the edge. Like the condition for edge execution.

source: str | WorkflowGraphNode

target: str | WorkflowGraphNode

details: dict

to_nx_edge()

class abacusai.api_class.WorkflowGraph

Bases: abacusai.api_class.abstract.ApiClass

Represents an Agent workflow graph.

The edges define the node invocation order.

Parameters:

nodes (List[WorkflowGraphNode]) – A list of nodes in the workflow graph.
edges (List[WorkflowGraphEdge]) – A list of edges in the workflow graph, where each edge is a tuple of source, target, and details.
primary_start_node (Union[str, WorkflowGraphNode]) – The primary node to start the workflow from.

nodes: List[WorkflowGraphNode]

edges: List[WorkflowGraphEdge]

primary_start_node: str | WorkflowGraphNode

common_source_code: str

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(graph)

Parameters:: graph (dict)

class abacusai.api_class.AgentConversationMessage

Bases: abacusai.api_class.abstract.ApiClass

Message format for agent conversation

Parameters:

is_user (bool) – Whether the message is from the user.
text (str) – The message’s text.
document_contents (dict) – Dict of document name to document text in case of any document present.

is_user: bool

text: str

document_contents: dict

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.WorkflowNodeTemplateConfig

Bases: abacusai.api_class.abstract.ApiClass

Represents a WorkflowNode template config.

Parameters:

name (str) – A unique name of the config.
description (str) – The description of this config.
default_value (str) – Default value of the config to be used if value is not provided during node initialization.
is_required (bool) – Indicates whether the config is required. Defaults to False.

name: str

description: str

default_value: str

is_required: bool

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(mapping)

Parameters:: mapping (dict)

class abacusai.api_class.WorkflowNodeTemplateInput

Bases: abacusai.api_class.abstract.ApiClass

Represents an input to the workflow node generated using template.

Parameters:

name (str) – A unique name of the input.
is_required (bool) – Indicates whether the input is required. Defaults to False.
description (str) – The description of this input.

name: str

is_required: bool

description: str

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(mapping)

Parameters:: mapping (dict)

class abacusai.api_class.WorkflowNodeTemplateOutput

Bases: abacusai.api_class.abstract.ApiClass

Represents an output returned by the workflow node generated using template.

Parameters:

name (str) – The name of the output.
variable_type (WorkflowNodeOutputType) – The type of the output.
description (str) – The description of this output.

name: str

variable_type: abacusai.api_class.enums.WorkflowNodeOutputType

description: str

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(mapping)

Parameters:: mapping (dict)

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.HotkeyPrompt

Bases: abacusai.api_class.abstract.ApiClass

A config class for a Data Science Co-Pilot Hotkey

Parameters:

prompt (str) – The prompt to send to Data Science Co-Pilot
title (str) – A short, descriptive title for the prompt. If not provided, one will be automatically generated.

prompt: str

title: str

disable_problem_type_context: bool

ignore_history: bool

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.BatchPredictionArgs

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for Batch Prediction args specific to problem type.

_support_kwargs: bool

kwargs: dict

problem_type: abacusai.api_class.enums.ProblemType

classmethod _get_builder()

class abacusai.api_class.ForecastingBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the FORECASTING problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation
predictions_start_date (str) – The start date for predictions. Accepts timestamp integers and strings in many standard formats such as YYYY-MM-DD, YYYY-MM-DD HH:MM:SS, or YYYY-MM-DDTHH:MM:SS. If not specified, the prediction start date will be automatically defined.
use_prediction_offset (bool) – If True, use prediction offset.
start_date_offset (int) – Sets prediction start date as this offset relative to the prediction start date.
forecasting_horizon (int) – The number of timestamps to predict in the future. Range: [1, 1000].
item_attributes_to_include_in_the_result (list) – List of columns to include in the prediction output.
explain_predictions (bool) – If True, calculates explanations for the forecasted values along with predictions.
create_monitor (bool) – Controls whether to automatically create a monitor to calculate the drift each time the batch prediction is run. Defaults to true if not specified.

for_eval: bool

predictions_start_date: str

use_prediction_offset: bool

start_date_offset: int

forecasting_horizon: int

item_attributes_to_include_in_the_result: list

explain_predictions: bool

create_monitor: bool

__post_init__()

class abacusai.api_class.NamedEntityExtractionBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the NAMED_ENTITY_EXTRACTION problem type

Parameters:: for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

for_eval: bool

__post_init__()

class abacusai.api_class.PersonalizationBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the PERSONALIZATION problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.
number_of_items (int) – Number of items to recommend.
item_attributes_to_include_in_the_result (list) – List of columns to include in the prediction output.
score_field (str) – If specified, relative item scores will be returned using a field with this name

for_eval: bool

number_of_items: int

item_attributes_to_include_in_the_result: list

score_field: str

__post_init__()

class abacusai.api_class.PredictiveModelingBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the PREDICTIVE_MODELING problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.
explainer_type (enums.ExplainerType) – The type of explainer to use to generate explanations on the batch prediction.
number_of_samples_to_use_for_explainer (int) – Number Of Samples To Use For Kernel Explainer.
include_multi_class_explanations (bool) – If True, Includes explanations for all classes in multi-class classification.
features_considered_constant_for_explanations (str) – Comma separate list of fields to treat as constant in SHAP explanations.
importance_of_records_in_nested_columns (str) – Returns importance of each index in the specified nested column instead of SHAP column explanations.
explanation_filter_lower_bound (float) – If set explanations will be limited to predictions above this value, Range: [0, 1].
explanation_filter_upper_bound (float) – If set explanations will be limited to predictions below this value, Range: [0, 1].
explanation_filter_label (str) – For classification problems specifies the label to which the explanation bounds are applied.
output_columns (list) – A list of column names to include in the prediction result.
explain_predictions (bool) – If True, calculates explanations for the predicted values along with predictions.
create_monitor (bool) – Controls whether to automatically create a monitor to calculate the drift each time the batch prediction is run. Defaults to true if not specified.

for_eval: bool

explainer_type: abacusai.api_class.enums.ExplainerType

number_of_samples_to_use_for_explainer: int

include_multi_class_explanations: bool

features_considered_constant_for_explanations: str

importance_of_records_in_nested_columns: str

explanation_filter_lower_bound: float

explanation_filter_upper_bound: float

explanation_filter_label: str

output_columns: list

explain_predictions: bool

create_monitor: bool

__post_init__()

class abacusai.api_class.PretrainedModelsBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the PRETRAINED_MODELS problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.
files_output_location_prefix (str) – The output location prefix for the files.
channel_id_to_label_map (str) – JSON string for the map from channel ids to their labels.

for_eval: bool

files_output_location_prefix: str

channel_id_to_label_map: str

__post_init__()

class abacusai.api_class.SentenceBoundaryDetectionBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the SENTENCE_BOUNDARY_DETECTION problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation
explode_output (bool) – Explode data so there is one sentence per row.

for_eval: bool

explode_output: bool

__post_init__()

class abacusai.api_class.ThemeAnalysisBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the THEME_ANALYSIS problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.
analysis_frequency (str) – The length of each analysis interval.
start_date (str) – The end point for predictions.
analysis_days (int) – How many days to analyze.

for_eval: bool

analysis_frequency: str

start_date: str

analysis_days: int

__post_init__()

class abacusai.api_class.ChatLLMBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the ChatLLM problem type

Parameters:: for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

for_eval: bool

__post_init__()

class abacusai.api_class.TrainablePlugAndPlayBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the TrainablePlugAndPlay problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.
create_monitor (bool) – Controls whether to automatically create a monitor to calculate the drift each time the batch prediction is run. Defaults to true if not specified.

for_eval: bool

create_monitor: bool

__post_init__()

class abacusai.api_class.AIAgentBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the AIAgents problem type

__post_init__()

class abacusai.api_class._BatchPredictionArgsFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'problem_type'

config_class_map

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.Blob(contents, mime_type=None, filename=None, size=None)

Bases: abacusai.api_class.abstract.ApiClass

An object for storing and passing file data. In AI Agents, if a function accepts file upload as an argument, the uploaded file is passed as a Blob object. If a function returns a Blob object, it will be rendered as a file download.

Parameters:

contents (bytes) – The binary contents of the blob.
mime_type (str) – The mime type of the blob.
filename (str) – The original filename of the blob.
size (int) – The size of the blob in bytes.

filename: str

contents: bytes

mime_type: str

size: int

classmethod from_local_file(file_path)

Parameters:: file_path (str)
Return type:: Blob

classmethod from_contents(contents, filename=None, mime_type=None)

Parameters:

contents (bytes)
filename (str)
mime_type (str)

Return type:

Blob

class abacusai.api_class.BlobInput(filename=None, contents=None, mime_type=None, size=None)

Bases: Blob

An object for storing and passing file data. In AI Agents, if a function accepts file upload as an argument, the uploaded file is passed as a BlobInput object.

Parameters:

filename (str) – The original filename of the blob.
contents (bytes) – The binary contents of the blob.
mime_type (str) – The mime type of the blob.
size (int) – The size of the blob in bytes.

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.DatasetConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for dataset configs

Parameters:: is_documentset (bool) – Whether the dataset is a document set

is_documentset: bool

class abacusai.api_class.StreamingConnectorDatasetConfig

Bases: abacusai.api_class.dataset.DatasetConfig

An abstract class for dataset configs specific to streaming connectors.

Parameters:: streaming_connector_type (StreamingConnectorType) – The type of streaming connector

streaming_connector_type: abacusai.api_class.enums.StreamingConnectorType

classmethod _get_builder()

class abacusai.api_class.KafkaDatasetConfig

Bases: StreamingConnectorDatasetConfig

Dataset config for Kafka Streaming Connector

Parameters:: topic (str) – The kafka topic to consume

topic: str

__post_init__()

class abacusai.api_class._StreamingConnectorDatasetConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'streaming_connector_type'

config_class_map

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.DocumentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

SIMPLE_TEXT = 'SIMPLE_TEXT'

TEXT = 'TEXT'

TABLES_AND_FORMS = 'TABLES_AND_FORMS'

EMBEDDED_IMAGES = 'EMBEDDED_IMAGES'

SCANNED_TEXT = 'SCANNED_TEXT'

classmethod is_ocr_forced(document_type)

Parameters:: document_type (DocumentType)

class abacusai.api_class.OcrMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'AUTO'

DEFAULT = 'DEFAULT'

LAYOUT = 'LAYOUT'

SCANNED = 'SCANNED'

COMPREHENSIVE = 'COMPREHENSIVE'

COMPREHENSIVE_V2 = 'COMPREHENSIVE_V2'

COMPREHENSIVE_TABLE_MD = 'COMPREHENSIVE_TABLE_MD'

COMPREHENSIVE_FORM_MD = 'COMPREHENSIVE_FORM_MD'

COMPREHENSIVE_FORM_AND_TABLE_MD = 'COMPREHENSIVE_FORM_AND_TABLE_MD'

TESSERACT_FAST = 'TESSERACT_FAST'

LLM = 'LLM'

AUGMENTED_LLM = 'AUGMENTED_LLM'

classmethod aws_ocr_modes()

class abacusai.api_class.DatasetConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for dataset configs

Parameters:: is_documentset (bool) – Whether the dataset is a document set

is_documentset: bool

class abacusai.api_class.ParsingConfig

Bases: abacusai.api_class.abstract.ApiClass

Custom config for dataset parsing.

Parameters:

escape (str) – Escape character for CSV files. Defaults to ‘”’.
csv_delimiter (str) – Delimiter for CSV files. Defaults to None.
file_path_with_schema (str) – Path to the file with schema. Defaults to None.

escape: str

csv_delimiter: str

file_path_with_schema: str

class abacusai.api_class.DocumentProcessingConfig

Bases: abacusai.api_class.abstract.ApiClass

Document processing configuration.

Parameters:

document_type (DocumentType) – Type of document. Can be one of Text, Tables and Forms, Embedded Images, etc. If not specified, type will be decided automatically.
highlight_relevant_text (bool) – Whether to extract bounding boxes and highlight relevant text in search results. Defaults to False.
extract_bounding_boxes (bool) – Whether to perform OCR and extract bounding boxes. If False, no OCR will be done but only the embedded text from digital documents will be extracted. Defaults to False.
ocr_mode (OcrMode) – OCR mode. There are different OCR modes available for different kinds of documents and use cases. This option only takes effect when extract_bounding_boxes is True.
use_full_ocr (bool) – Whether to perform full OCR. If True, OCR will be performed on the full page. If False, OCR will be performed on the non-text regions only. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
remove_header_footer (bool) – Whether to remove headers and footers. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
remove_watermarks (bool) – Whether to remove watermarks. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
convert_to_markdown (bool) – Whether to convert extracted text to markdown. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
mask_pii (bool) – Whether to mask personally identifiable information (PII) in the document text/tokens. Defaults to False.

document_type: abacusai.api_class.enums.DocumentType = None

highlight_relevant_text: bool = None

extract_bounding_boxes: bool = False

ocr_mode: abacusai.api_class.enums.OcrMode

use_full_ocr: bool = None

remove_header_footer: bool = False

remove_watermarks: bool = True

convert_to_markdown: bool = False

mask_pii: bool = False

__post_init__()

_detect_ocr_mode()

classmethod _get_filtered_dict(config)

Filters out default values from the config

Parameters:: config (dict)

class abacusai.api_class.DatasetDocumentProcessingConfig

Bases: DocumentProcessingConfig

Document processing configuration for dataset imports.

Parameters:

extract_bounding_boxes (bool) – Whether to perform OCR and extract bounding boxes. If False, no OCR will be done but only the embedded text from digital documents will be extracted. Defaults to False.
ocr_mode (OcrMode) – OCR mode. There are different OCR modes available for different kinds of documents and use cases. This option only takes effect when extract_bounding_boxes is True.
use_full_ocr (bool) – Whether to perform full OCR. If True, OCR will be performed on the full page. If False, OCR will be performed on the non-text regions only. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
remove_header_footer (bool) – Whether to remove headers and footers. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
remove_watermarks (bool) – Whether to remove watermarks. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
convert_to_markdown (bool) – Whether to convert extracted text to markdown. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
page_text_column (str) – Name of the output column which contains the extracted text for each page. If not provided, no column will be created.

page_text_column: str = None

class abacusai.api_class.IncrementalDatabaseConnectorConfig

Bases: abacusai.api_class.abstract.ApiClass

Config information for incremental datasets from database connectors

Parameters:: timestamp_column (str) – If dataset is incremental, this is the column name of the required column in the dataset. This column must contain timestamps in descending order which are used to determine the increments of the incremental dataset.

timestamp_column: str

class abacusai.api_class.AttachmentParsingConfig

Bases: abacusai.api_class.abstract.ApiClass

Config information for parsing attachments

Parameters:

feature_group_name (str) – feature group name
column_name (str) – column name
urls (str) – list of urls

feature_group_name: str

column_name: str

urls: str

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.DatasetConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for dataset configs

Parameters:: is_documentset (bool) – Whether the dataset is a document set

is_documentset: bool

class abacusai.api_class.DatasetDocumentProcessingConfig

Bases: DocumentProcessingConfig

Document processing configuration for dataset imports.

Parameters:

extract_bounding_boxes (bool) – Whether to perform OCR and extract bounding boxes. If False, no OCR will be done but only the embedded text from digital documents will be extracted. Defaults to False.
ocr_mode (OcrMode) – OCR mode. There are different OCR modes available for different kinds of documents and use cases. This option only takes effect when extract_bounding_boxes is True.
use_full_ocr (bool) – Whether to perform full OCR. If True, OCR will be performed on the full page. If False, OCR will be performed on the non-text regions only. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
remove_header_footer (bool) – Whether to remove headers and footers. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
remove_watermarks (bool) – Whether to remove watermarks. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
convert_to_markdown (bool) – Whether to convert extracted text to markdown. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
page_text_column (str) – Name of the output column which contains the extracted text for each page. If not provided, no column will be created.

page_text_column: str = None

class abacusai.api_class.ApplicationConnectorDatasetConfig

Bases: abacusai.api_class.dataset.DatasetConfig

An abstract class for dataset configs specific to application connectors.

Parameters:

application_connector_type (enums.ApplicationConnectorType) – The type of application connector
application_connector_id (str) – The ID of the application connector
document_processing_config (DatasetDocumentProcessingConfig) – The document processing configuration. Only valid if is_documentset is True for the dataset.

application_connector_type: abacusai.api_class.enums.ApplicationConnectorType

application_connector_id: str

document_processing_config: abacusai.api_class.dataset.DatasetDocumentProcessingConfig

classmethod _get_builder()

class abacusai.api_class.ConfluenceDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Confluence Application Connector :param location: The location of the pages to fetch :type location: str :param space_key: The space key of the space from which we fetch pages :type space_key: str :param pull_attachments: Whether to pull attachments for each page :type pull_attachments: bool :param extract_bounding_boxes: Whether to extract bounding boxes from the documents :type extract_bounding_boxes: bool

location: str

space_key: str

pull_attachments: bool

extract_bounding_boxes: bool

__post_init__()

class abacusai.api_class.GoogleAnalyticsDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Google Analytics Application Connector

Parameters:

location (str) – The view id of the report in the connector to fetch
start_timestamp (int) – Unix timestamp of the start of the period that will be queried
end_timestamp (int) – Unix timestamp of the end of the period that will be queried

location: str

start_timestamp: int

end_timestamp: int

__post_init__()

class abacusai.api_class.GoogleDriveDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Google Drive Application Connector

Parameters:

location (str) – The regex location of the files to fetch
csv_delimiter (str) – If the file format is CSV, use a specific csv delimiter
extract_bounding_boxes (bool) – Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True
merge_file_schemas (bool) – Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True

location: str

csv_delimiter: str

extract_bounding_boxes: bool

merge_file_schemas: bool

__post_init__()

class abacusai.api_class.JiraDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Jira Application Connector

Parameters:

jql (str) – The JQL query for fetching issues
custom_fields (list) – A list of custom fields to include in the dataset
include_comments (bool) – Fetch comments for each issue
include_watchers (bool) – Fetch watchers for each issue

jql: str

custom_fields: list

include_comments: bool

include_watchers: bool

__post_init__()

class abacusai.api_class.OneDriveDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for OneDrive Application Connector

Parameters:

location (str) – The regex location of the files to fetch
csv_delimiter (str) – If the file format is CSV, use a specific csv delimiter
extract_bounding_boxes (bool) – Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True
merge_file_schemas (bool) – Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True

location: str

csv_delimiter: str

extract_bounding_boxes: bool

merge_file_schemas: bool

__post_init__()

class abacusai.api_class.SharepointDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Sharepoint Application Connector

Parameters:

location (str) – The regex location of the files to fetch
csv_delimiter (str) – If the file format is CSV, use a specific csv delimiter
extract_bounding_boxes (bool) – Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True
merge_file_schemas (bool) – Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True

location: str

csv_delimiter: str

extract_bounding_boxes: bool

merge_file_schemas: bool

__post_init__()

class abacusai.api_class.ZendeskDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Zendesk Application Connector

Parameters:: location (str) – The regex location of the files to fetch

location: str

__post_init__()

class abacusai.api_class.AbacusUsageMetricsDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Abacus Usage Metrics Application Connector

Parameters:

include_entire_conversation_history (bool) – Whether to show the entire history for this deployment conversation
include_all_feedback (bool) – Whether to include all feedback for this deployment conversation

include_entire_conversation_history: bool

include_all_feedback: bool

__post_init__()

class abacusai.api_class.TeamsScraperDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Teams Scraper Application Connector

Parameters:

pull_chat_messages (bool) – Whether to pull teams chat messages
pull_channel_posts (bool) – Whether to pull posts for each channel
pull_transcripts (bool) – Whether to pull transcripts for calendar meetings

pull_chat_messages: bool

pull_channel_posts: bool

pull_transcripts: bool

__post_init__()

class abacusai.api_class.FreshserviceDatasetConfig

Bases: ApplicationConnectorDatasetConfig

Dataset config for Freshservice Application Connector

__post_init__()

class abacusai.api_class._ApplicationConnectorDatasetConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'application_connector_type'

config_class_map

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.PredictionArguments

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for prediction arguments specific to problem type.

_support_kwargs: bool

kwargs: dict

problem_type: abacusai.api_class.enums.ProblemType

classmethod _get_builder()

class abacusai.api_class.OptimizationPredictionArguments

Bases: PredictionArguments

Prediction arguments for the OPTIMIZATION problem type

Parameters:

forced_assignments (dict) – Set of assignments to force and resolve before returning query results.
solve_time_limit_seconds (float) – Maximum time in seconds to spend solving the query.
include_all_assignments (bool) – If True, will return all assignments, including assignments with value 0. Default is False.

forced_assignments: dict

solve_time_limit_seconds: float

include_all_assignments: bool

__post_init__()

class abacusai.api_class.TimeseriesAnomalyPredictionArguments

Bases: PredictionArguments

Prediction arguments for the TS_ANOMALY problem type

Parameters:

start_timestamp (str) – Timestamp from which anomalies have to be detected in the training data
end_timestamp (str) – Timestamp to which anomalies have to be detected in the training data
get_all_item_data (bool) – If True, anomaly detection has to be performed on all the data related to input ids

start_timestamp: str

end_timestamp: str

get_all_item_data: bool

__post_init__()

class abacusai.api_class.ChatLLMPredictionArguments

Bases: PredictionArguments

Prediction arguments for the CHAT_LLM problem type

Parameters:

llm_name (str) – Name of the specific LLM backend to use to power the chat experience.
num_completion_tokens (int) – Default for maximum number of tokens for chat answers.
system_message (str) – The generative LLM system message.
temperature (float) – The generative LLM temperature.
search_score_cutoff (float) – Cutoff for the document retriever score. Matching search results below this score will be ignored.
ignore_documents (bool) – If True, will ignore any documents and search results, and only use the messages to generate a response.

llm_name: str

num_completion_tokens: int

system_message: str

temperature: float

search_score_cutoff: float

ignore_documents: bool

__post_init__()

class abacusai.api_class.RegressionPredictionArguments

Bases: PredictionArguments

Prediction arguments for the PREDICTIVE_MODELING problem type

Parameters:

explain_predictions (bool) – If true, will explain predictions.
explainer_type (str) – Type of explainer to use for explanations.

explain_predictions: bool

explainer_type: str

__post_init__()

class abacusai.api_class.ForecastingPredictionArguments

Bases: PredictionArguments

Prediction arguments for the FORECASTING problem type

Parameters:

num_predictions (int) – The number of timestamps to predict in the future.
prediction_start (str) – The start date for predictions (e.g., “2015-08-01T00:00:00” as input for mid-night of 2015-08-01).
explain_predictions (bool) – If True, explain predictions for forecasting.
explainer_type (str) – Type of explainer to use for explanations.
get_item_data (bool) – If True, will return the data corresponding to items as well.

num_predictions: int

prediction_start: str

explain_predictions: bool

explainer_type: str

get_item_data: bool

__post_init__()

class abacusai.api_class.CumulativeForecastingPredictionArguments

Bases: PredictionArguments

Prediction arguments for the CUMULATIVE_FORECASTING problem type

Parameters:

num_predictions (int) – The number of timestamps to predict in the future.
prediction_start (str) – The start date for predictions (e.g., “2015-08-01T00:00:00” as input for mid-night of 2015-08-01).
explain_predictions (bool) – If True, explain predictions for forecasting.
explainer_type (str) – Type of explainer to use for explanations.
get_item_data (bool) – If True, will return the data corresponding to items as well.

num_predictions: int

prediction_start: str

explain_predictions: bool

explainer_type: str

get_item_data: bool

__post_init__()

class abacusai.api_class.NaturalLanguageSearchPredictionArguments

Bases: PredictionArguments

Prediction arguments for the NATURAL_LANGUAGE_SEARCH problem type

Parameters:

llm_name (str) – Name of the specific LLM backend to use to power the chat experience.
num_completion_tokens (int) – Default for maximum number of tokens for chat answers.
system_message (str) – The generative LLM system message.
temperature (float) – The generative LLM temperature.
search_score_cutoff (float) – Cutoff for the document retriever score. Matching search results below this score will be ignored.
ignore_documents (bool) – If True, will ignore any documents and search results, and only use the messages to generate a response.

llm_name: str

num_completion_tokens: int

system_message: str

temperature: float

search_score_cutoff: float

ignore_documents: bool

__post_init__()

class abacusai.api_class.FeatureStorePredictionArguments

Bases: PredictionArguments

Prediction arguments for the FEATURE_STORE problem type

Parameters:: limit_results (int) – If provided, will limit the number of results to the value specified.

limit_results: int

__post_init__()

class abacusai.api_class._PredictionArgumentsFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'problem_type'

config_class_map

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.VectorStoreTextEncoder

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

E5 = 'E5'

OPENAI = 'OPENAI'

OPENAI_COMPACT = 'OPENAI_COMPACT'

OPENAI_LARGE = 'OPENAI_LARGE'

SENTENCE_BERT = 'SENTENCE_BERT'

E5_SMALL = 'E5_SMALL'

CODE_BERT = 'CODE_BERT'

class abacusai.api_class.VectorStoreConfig

Bases: abacusai.api_class.abstract.ApiClass

Config for indexing options of a document retriever. Default values of optional arguments are heuristically selected by the Abacus.AI platform based on the underlying data.

Parameters:

chunk_size (int) – The size of text chunks in the vector store.
chunk_overlap_fraction (float) – The fraction of overlap between chunks.
text_encoder (VectorStoreTextEncoder) – Encoder used to index texts from the documents.
chunk_size_factors (list) – Chunking data with multiple sizes. The specified list of factors are used to calculate more sizes, in addition to chunk_size.
score_multiplier_column (str) – If provided, will use the values in this metadata column to modify the relevance score of returned chunks for all queries.
prune_vectors (bool) – Transform vectors using SVD so that the average component of vectors in the corpus are removed.
index_metadata_columns (bool) – If True, metadata columns of the FG will also be used for indexing and querying.
use_document_summary (bool) – If True, uses the summary of the document in addition to chunks of the document for indexing and querying.
summary_instructions (str) – Instructions for the LLM to generate the document summary.

chunk_size: int

chunk_overlap_fraction: float

text_encoder: abacusai.api_class.enums.VectorStoreTextEncoder

chunk_size_factors: list

score_multiplier_column: str

prune_vectors: bool

index_metadata_columns: bool

use_document_summary: bool

summary_instructions: str

abacusai.api_class.DocumentRetrieverConfig

abacusai.api_class.deprecated_enums(*enum_values)

class abacusai.api_class.ApiEnum

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

__deprecated_values__ = []

is_deprecated()

__eq__(other)

__hash__()

class abacusai.api_class.ProblemType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AI_AGENT = 'ai_agent'

EVENT_ANOMALY = 'event_anomaly'

CLUSTERING = 'clustering'

CLUSTERING_TIMESERIES = 'clustering_timeseries'

CUMULATIVE_FORECASTING = 'cumulative_forecasting'

NAMED_ENTITY_EXTRACTION = 'nlp_ner'

NATURAL_LANGUAGE_SEARCH = 'nlp_search'

CHAT_LLM = 'chat_llm'

SENTENCE_BOUNDARY_DETECTION = 'nlp_sentence_boundary_detection'

SENTIMENT_DETECTION = 'nlp_sentiment'

DOCUMENT_CLASSIFICATION = 'nlp_classification'

DOCUMENT_SUMMARIZATION = 'nlp_summarization'

DOCUMENT_VISUALIZATION = 'nlp_document_visualization'

PERSONALIZATION = 'personalization'

PREDICTIVE_MODELING = 'regression'

FINETUNED_LLM = 'finetuned_llm'

FORECASTING = 'forecasting'

CUSTOM_TRAINED_MODEL = 'plug_and_play'

CUSTOM_ALGORITHM = 'trainable_plug_and_play'

FEATURE_STORE = 'feature_store'

IMAGE_CLASSIFICATION = 'vision_classification'

OBJECT_DETECTION = 'vision_object_detection'

IMAGE_VALUE_PREDICTION = 'vision_regression'

MODEL_MONITORING = 'model_monitoring'

LANGUAGE_DETECTION = 'language_detection'

OPTIMIZATION = 'optimization'

PRETRAINED_MODELS = 'pretrained'

THEME_ANALYSIS = 'theme_analysis'

TS_ANOMALY = 'ts_anomaly'

class abacusai.api_class.RegressionObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUC = 'auc'

ACCURACY = 'acc'

LOG_LOSS = 'log_loss'

PRECISION = 'precision'

RECALL = 'recall'

F1_SCORE = 'fscore'

MAE = 'mae'

MAPE = 'mape'

WAPE = 'wape'

RMSE = 'rmse'

R_SQUARED_COEFFICIENT_OF_DETERMINATION = 'r^2'

class abacusai.api_class.RegressionTreeHPOMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RAPID = 'rapid'

THOROUGH = 'thorough'

class abacusai.api_class.PartialDependenceAnalysis

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RAPID = 'rapid'

THOROUGH = 'thorough'

class abacusai.api_class.RegressionAugmentationStrategy

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

SMOTE = 'smote'

RESAMPLE = 'resample'

class abacusai.api_class.RegressionTargetTransform

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

LOG = 'log'

QUANTILE = 'quantile'

YEO_JOHNSON = 'yeo-johnson'

BOX_COX = 'box-cox'

class abacusai.api_class.RegressionTypeOfSplit

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RANDOM = 'Random Sampling'

TIMESTAMP_BASED = 'Timestamp Based'

ROW_INDICATOR_BASED = 'Row Indicator Based'

STRATIFIED_RANDOM_SAMPLING = 'Stratified Random Sampling'

class abacusai.api_class.RegressionTimeSplitMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

TEST_SPLIT_PERCENTAGE_BASED = 'Test Split Percentage Based'

TEST_START_TIMESTAMP_BASED = 'Test Start Timestamp Based'

class abacusai.api_class.RegressionLossFunction

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

HUBER = 'Huber'

MSE = 'Mean Squared Error'

MAE = 'Mean Absolute Error'

MAPE = 'Mean Absolute Percentage Error'

MSLE = 'Mean Squared Logarithmic Error'

TWEEDIE = 'Tweedie'

CROSS_ENTROPY = 'Cross Entropy'

FOCAL_CROSS_ENTROPY = 'Focal Cross Entropy'

AUTOMATIC = 'Automatic'

CUSTOM = 'Custom'

class abacusai.api_class.ExplainerType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

KERNEL_EXPLAINER = 'KERNEL_EXPLAINER'

LIME_EXPLAINER = 'LIME_EXPLAINER'

TREE_EXPLAINER = 'TREE_EXPLAINER'

EBM_EXPLAINER = 'EBM_EXPLAINER'

class abacusai.api_class.SamplingMethodType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

N_SAMPLING = 'N_SAMPLING'

PERCENT_SAMPLING = 'PERCENT_SAMPLING'

class abacusai.api_class.MergeMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

LAST_N = 'LAST_N'

TIME_WINDOW = 'TIME_WINDOW'

class abacusai.api_class.OperatorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

UNPIVOT = 'UNPIVOT'

MARKDOWN = 'MARKDOWN'

CRAWLER = 'CRAWLER'

EXTRACT_DOCUMENT_DATA = 'EXTRACT_DOCUMENT_DATA'

DATA_GENERATION = 'DATA_GENERATION'

UNION = 'UNION'

class abacusai.api_class.MarkdownOperatorInputType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

HTML = 'HTML'

class abacusai.api_class.FillLogic

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AVERAGE = 'average'

MAX = 'max'

MEDIAN = 'median'

MIN = 'min'

CUSTOM = 'custom'

BACKFILL = 'bfill'

FORWARDFILL = 'ffill'

LINEAR = 'linear'

NEAREST = 'nearest'

class abacusai.api_class.BatchSize

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

BATCH_8 = 8

BATCH_16 = 16

BATCH_32 = 32

BATCH_64 = 64

BATCH_128 = 128

BATCH_256 = 256

BATCH_384 = 384

BATCH_512 = 512

BATCH_740 = 740

BATCH_1024 = 1024

class abacusai.api_class.HolidayCalendars

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AU = 'AU'

UK = 'UK'

US = 'US'

class abacusai.api_class.FileFormat

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AVRO = 'AVRO'

PARQUET = 'PARQUET'

TFRECORD = 'TFRECORD'

TSV = 'TSV'

CSV = 'CSV'

ORC = 'ORC'

JSON = 'JSON'

ODS = 'ODS'

XLS = 'XLS'

GZ = 'GZ'

ZIP = 'ZIP'

TAR = 'TAR'

DOCX = 'DOCX'

PDF = 'PDF'

MD = 'md'

RAR = 'RAR'

JPEG = 'JPG'

PNG = 'PNG'

TIF = 'TIFF'

NUMBERS = 'NUMBERS'

PPTX = 'PPTX'

PPT = 'PPT'

HTML = 'HTML'

TXT = 'txt'

EML = 'eml'

MP3 = 'MP3'

MP4 = 'MP4'

FLV = 'flv'

MOV = 'mov'

MPG = 'mpg'

MPEG = 'mpeg'

WEBM = 'webm'

WMV = 'wmv'

MSG = 'msg'

class abacusai.api_class.ExperimentationMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RAPID = 'rapid'

THOROUGH = 'thorough'

class abacusai.api_class.PersonalizationTrainingMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

EXPERIMENTAL = 'EXP'

PRODUCTION = 'PROD'

class abacusai.api_class.PersonalizationObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

NDCG = 'ndcg'

NDCG_5 = 'ndcg@5'

NDCG_10 = 'ndcg@10'

MAP = 'map'

MAP_5 = 'map@5'

MAP_10 = 'map@10'

MRR = 'mrr'

PERSONALIZATION = 'personalization@10'

COVERAGE = 'coverage'

class abacusai.api_class.ForecastingObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ACCURACY = 'w_c_accuracy'

WAPE = 'wape'

MAPE = 'mape'

CMAPE = 'cmape'

RMSE = 'rmse'

CV = 'coefficient_of_variation'

BIAS = 'bias'

SRMSE = 'srmse'

class abacusai.api_class.ForecastingFrequency

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

HOURLY = '1H'

DAILY = '1D'

WEEKLY_SUNDAY_START = '1W'

WEEKLY_MONDAY_START = 'W-MON'

WEEKLY_SATURDAY_START = 'W-SAT'

MONTH_START = 'MS'

MONTH_END = '1M'

QUARTER_START = 'QS'

QUARTER_END = '1Q'

YEARLY = '1Y'

class abacusai.api_class.ForecastingDataSplitType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'Automatic Time Based'

TIMESTAMP = 'Timestamp Based'

ITEM = 'Item Based'

PREDICTION_LENGTH = 'Force Prediction Length'

L_SHAPED_AUTO = 'L-shaped Split - Automatic Time Based'

L_SHAPED_TIMESTAMP = 'L-shaped Split - Timestamp Based'

class abacusai.api_class.ForecastingLossFunction

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

CUSTOM = 'Custom'

MEAN_ABSOLUTE_ERROR = 'mae'

NORMALIZED_MEAN_ABSOLUTE_ERROR = 'nmae'

PEAKS_MEAN_ABSOLUTE_ERROR = 'peaks_mae'

MEAN_ABSOLUTE_PERCENTAGE_ERROR = 'stable_mape'

POINTWISE_ACCURACY = 'accuracy'

ROOT_MEAN_SQUARE_ERROR = 'rmse'

NORMALIZED_ROOT_MEAN_SQUARE_ERROR = 'nrmse'

ASYMMETRIC_MEAN_ABSOLUTE_PERCENTAGE_ERROR = 'asymmetric_mape'

STABLE_STANDARDIZED_MEAN_ABSOLUTE_PERCENTAGE_ERROR = 'stable_standardized_mape_with_cmape'

GAUSSIAN = 'mle_gaussian_local'

GAUSSIAN_FULL_COVARIANCE = 'mle_gaussfullcov'

GUASSIAN_EXPONENTIAL = 'mle_gaussexp'

MIX_GAUSSIANS = 'mle_gaussmix'

WEIBULL = 'mle_weibull'

NEGATIVE_BINOMIAL = 'mle_negbinom'

LOG_ROOT_MEAN_SQUARE_ERROR = 'log_rmse'

class abacusai.api_class.ForecastingLocalScaling

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ZSCORE = 'zscore'

SLIDING_ZSCORE = 'sliding_zscore'

LAST_POINT = 'lastpoint'

MIN_MAX = 'minmax'

MIN_STD = 'minstd'

ROBUST = 'robust'

ITEM = 'item'

class abacusai.api_class.ForecastingFillMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

BACK = 'BACK'

MIDDLE = 'MIDDLE'

FUTURE = 'FUTURE'

class abacusai.api_class.ForecastingQuanitlesExtensionMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

DIRECT = 'direct'

QUADRATIC = 'quadratic'

ANCESTRAL_SIMULATION = 'simulation'

class abacusai.api_class.TimeseriesAnomalyDataSplitType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'Automatic Time Based'

TIMESTAMP = 'Fixed Timestamp Based'

class abacusai.api_class.TimeseriesAnomalyTypeOfAnomaly

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

HIGH_PEAK = 'high_peak'

LOW_PEAK = 'low_peak'

class abacusai.api_class.TimeseriesAnomalyUseHeuristic

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ENABLE = 'enable'

DISABLE = 'disable'

AUTOMATIC = 'automatic'

class abacusai.api_class.NERObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

LOG_LOSS = 'log_loss'

AUC = 'auc'

PRECISION = 'precision'

RECALL = 'recall'

ANNOTATIONS_PRECISION = 'annotations_precision'

ANNOTATIONS_RECALL = 'annotations_recall'

class abacusai.api_class.NERModelType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

PRETRAINED_BERT = 'pretrained_bert'

PRETRAINED_ROBERTA_27 = 'pretrained_roberta_27'

PRETRAINED_ROBERTA_43 = 'pretrained_roberta_43'

PRETRAINED_MULTILINGUAL = 'pretrained_multilingual'

LEARNED = 'learned'

class abacusai.api_class.NLPDocumentFormat

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'auto'

TEXT = 'text'

DOC = 'doc'

TOKENS = 'tokens'

class abacusai.api_class.SentimentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

VALENCE = 'valence'

EMOTION = 'emotion'

class abacusai.api_class.ClusteringImputationMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTOMATIC = 'Automatic'

ZEROS = 'Zeros'

INTERPOLATE = 'Interpolate'

class abacusai.api_class.ConnectorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FILE = 'FILE'

DATABASE = 'DATABASE'

STREAMING = 'STREAMING'

APPLICATION = 'APPLICATION'

class abacusai.api_class.ApplicationConnectorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

GOOGLEANALYTICS = 'GOOGLEANALYTICS'

GOOGLEDRIVE = 'GOOGLEDRIVE'

GIT = 'GIT'

CONFLUENCE = 'CONFLUENCE'

JIRA = 'JIRA'

ONEDRIVE = 'ONEDRIVE'

ZENDESK = 'ZENDESK'

SLACK = 'SLACK'

SHAREPOINT = 'SHAREPOINT'

TEAMS = 'TEAMS'

ABACUSUSAGEMETRICS = 'ABACUSUSAGEMETRICS'

MICROSOFTAUTH = 'MICROSOFTAUTH'

FRESHSERVICE = 'FRESHSERVICE'

ZENDESKSUNSHINEMESSAGING = 'ZENDESKSUNSHINEMESSAGING'

GOOGLEDRIVEUSER = 'GOOGLEDRIVEUSER'

GOOGLEWORKSPACEUSER = 'GOOGLEWORKSPACEUSER'

GMAILUSER = 'GMAILUSER'

GOOGLECALENDAR = 'GOOGLECALENDAR'

GOOGLESHEETS = 'GOOGLESHEETS'

GOOGLEDOCS = 'GOOGLEDOCS'

ONEDRIVEUSER = 'ONEDRIVEUSER'

TEAMSSCRAPER = 'TEAMSSCRAPER'

GITHUBUSER = 'GITHUBUSER'

OKTASAML = 'OKTASAML'

class abacusai.api_class.StreamingConnectorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

KAFKA = 'KAFKA'

class abacusai.api_class.PythonFunctionArgumentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FEATURE_GROUP = 'FEATURE_GROUP'

INTEGER = 'INTEGER'

STRING = 'STRING'

BOOLEAN = 'BOOLEAN'

FLOAT = 'FLOAT'

JSON = 'JSON'

LIST = 'LIST'

DATASET_ID = 'DATASET_ID'

MODEL_ID = 'MODEL_ID'

FEATURE_GROUP_ID = 'FEATURE_GROUP_ID'

MONITOR_ID = 'MONITOR_ID'

BATCH_PREDICTION_ID = 'BATCH_PREDICTION_ID'

DEPLOYMENT_ID = 'DEPLOYMENT_ID'

class abacusai.api_class.PythonFunctionOutputArgumentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

NTEGER = 'INTEGER'

STRING = 'STRING'

BOOLEAN = 'BOOLEAN'

FLOAT = 'FLOAT'

JSON = 'JSON'

LIST = 'LIST'

DATASET_ID = 'DATASET_ID'

MODEL_ID = 'MODEL_ID'

FEATURE_GROUP_ID = 'FEATURE_GROUP_ID'

MONITOR_ID = 'MONITOR_ID'

BATCH_PREDICTION_ID = 'BATCH_PREDICTION_ID'

DEPLOYMENT_ID = 'DEPLOYMENT_ID'

ANY = 'ANY'

class abacusai.api_class.VectorStoreTextEncoder

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

E5 = 'E5'

OPENAI = 'OPENAI'

OPENAI_COMPACT = 'OPENAI_COMPACT'

OPENAI_LARGE = 'OPENAI_LARGE'

SENTENCE_BERT = 'SENTENCE_BERT'

E5_SMALL = 'E5_SMALL'

CODE_BERT = 'CODE_BERT'

class abacusai.api_class.LLMName

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

OPENAI_GPT4 = 'OPENAI_GPT4'

OPENAI_GPT4_32K = 'OPENAI_GPT4_32K'

OPENAI_GPT4_128K = 'OPENAI_GPT4_128K'

OPENAI_GPT4_128K_LATEST = 'OPENAI_GPT4_128K_LATEST'

OPENAI_GPT4O = 'OPENAI_GPT4O'

OPENAI_GPT4O_MINI = 'OPENAI_GPT4O_MINI'

OPENAI_GPT3_5 = 'OPENAI_GPT3_5'

OPENAI_GPT3_5_TEXT = 'OPENAI_GPT3_5_TEXT'

LLAMA3_1_405B = 'LLAMA3_1_405B'

LLAMA3_1_70B = 'LLAMA3_1_70B'

LLAMA3_1_8B = 'LLAMA3_1_8B'

LLAMA3_LARGE_CHAT = 'LLAMA3_LARGE_CHAT'

CLAUDE_V3_OPUS = 'CLAUDE_V3_OPUS'

CLAUDE_V3_SONNET = 'CLAUDE_V3_SONNET'

CLAUDE_V3_HAIKU = 'CLAUDE_V3_HAIKU'

CLAUDE_V3_5_SONNET = 'CLAUDE_V3_5_SONNET'

CLAUDE_V3_5_HAIKU = 'CLAUDE_V3_5_HAIKU'

GEMINI_1_5_PRO = 'GEMINI_1_5_PRO'

ABACUS_SMAUG3 = 'ABACUS_SMAUG3'

ABACUS_DRACARYS = 'ABACUS_DRACARYS'

QWEN_2_5_32B = 'QWEN_2_5_32B'

GEMINI_1_5_FLASH = 'GEMINI_1_5_FLASH'

class abacusai.api_class.MonitorAlertType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ACCURACY_BELOW_THRESHOLD = 'AccuracyBelowThreshold'

FEATURE_DRIFT = 'FeatureDrift'

DATA_INTEGRITY_VIOLATIONS = 'DataIntegrityViolations'

BIAS_VIOLATIONS = 'BiasViolations'

HISTORY_LENGTH_DRIFT = 'HistoryLengthDrift'

TARGET_DRIFT = 'TargetDrift'

PREDICTION_COUNT = 'PredictionCount'

class abacusai.api_class.FeatureDriftType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

KL = 'kl'

KS = 'ks'

WS = 'ws'

JS = 'js'

PSI = 'psi'

CHI_SQUARE = 'chi_square'

CSI = 'csi'

class abacusai.api_class.DataIntegrityViolationType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

NULL_VIOLATIONS = 'null_violations'

RANGE_VIOLATIONS = 'range_violations'

CATEGORICAL_RANGE_VIOLATION = 'categorical_range_violations'

TOTAL_VIOLATIONS = 'total_violations'

class abacusai.api_class.BiasType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

DEMOGRAPHIC_PARITY = 'demographic_parity'

EQUAL_OPPORTUNITY = 'equal_opportunity'

GROUP_BENEFIT_EQUALITY = 'group_benefit'

TOTAL = 'total'

class abacusai.api_class.AlertActionType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

EMAIL = 'Email'

class abacusai.api_class.PythonFunctionType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FEATURE_GROUP = 'FEATURE_GROUP'

PLOTLY_FIG = 'PLOTLY_FIG'

STEP_FUNCTION = 'STEP_FUNCTION'

class abacusai.api_class.EvalArtifactType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FORECASTING_ACCURACY = 'bar_chart'

FORECASTING_VOLUME = 'bar_chart_volume'

FORECASTING_HISTORY_LENGTH_ACCURACY = 'bar_chart_accuracy_by_history'

class abacusai.api_class.FieldDescriptorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

STRING = 'STRING'

INTEGER = 'INTEGER'

FLOAT = 'FLOAT'

BOOLEAN = 'BOOLEAN'

DATETIME = 'DATETIME'

DATE = 'DATE'

class abacusai.api_class.WorkflowNodeInputType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

USER_INPUT = 'USER_INPUT'

WORKFLOW_VARIABLE = 'WORKFLOW_VARIABLE'

class abacusai.api_class.WorkflowNodeOutputType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ATTACHMENT = 'ATTACHMENT'

BOOLEAN = 'BOOLEAN'

FLOAT = 'FLOAT'

INTEGER = 'INTEGER'

DICT = 'DICT'

LIST = 'LIST'

STRING = 'STRING'

RUNTIME_SCHEMA = 'RUNTIME_SCHEMA'

ANY = 'ANY'

classmethod normalize_type(python_type)

Parameters:: python_type (Union[str, type, None, WorkflowNodeOutputType])
Return type:: WorkflowNodeOutputType

class abacusai.api_class.OcrMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'AUTO'

DEFAULT = 'DEFAULT'

LAYOUT = 'LAYOUT'

SCANNED = 'SCANNED'

COMPREHENSIVE = 'COMPREHENSIVE'

COMPREHENSIVE_V2 = 'COMPREHENSIVE_V2'

COMPREHENSIVE_TABLE_MD = 'COMPREHENSIVE_TABLE_MD'

COMPREHENSIVE_FORM_MD = 'COMPREHENSIVE_FORM_MD'

COMPREHENSIVE_FORM_AND_TABLE_MD = 'COMPREHENSIVE_FORM_AND_TABLE_MD'

TESSERACT_FAST = 'TESSERACT_FAST'

LLM = 'LLM'

AUGMENTED_LLM = 'AUGMENTED_LLM'

classmethod aws_ocr_modes()

class abacusai.api_class.DocumentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

SIMPLE_TEXT = 'SIMPLE_TEXT'

TEXT = 'TEXT'

TABLES_AND_FORMS = 'TABLES_AND_FORMS'

EMBEDDED_IMAGES = 'EMBEDDED_IMAGES'

SCANNED_TEXT = 'SCANNED_TEXT'

classmethod is_ocr_forced(document_type)

Parameters:: document_type (DocumentType)

class abacusai.api_class.StdDevThresholdType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ABSOLUTE = 'ABSOLUTE'

PERCENTILE = 'PERCENTILE'

STDDEV = 'STDDEV'

class abacusai.api_class.DataType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

INTEGER = 'integer'

FLOAT = 'float'

STRING = 'string'

DATE = 'date'

DATETIME = 'datetime'

BOOLEAN = 'boolean'

LIST = 'list'

STRUCT = 'struct'

NULL = 'null'

BINARY = 'binary'

class abacusai.api_class.AgentInterface

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

DEFAULT = 'DEFAULT'

CHAT = 'CHAT'

MATRIX = 'MATRIX'

AUTONOMOUS = 'AUTONOMOUS'

class abacusai.api_class.WorkflowNodeTemplateType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

TRIGGER = 'trigger'

DEFAULT = 'default'

class abacusai.api_class.ProjectConfigType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

CONSTRAINTS = 'CONSTRAINTS'

CHAT_FEEDBACK = 'CHAT_FEEDBACK'

REVIEW_MODE = 'REVIEW_MODE'

class abacusai.api_class.CPUSize

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

SMALL = 'small'

MEDIUM = 'medium'

LARGE = 'large'

class abacusai.api_class.MemorySize

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

SMALL = 16

MEDIUM = 32

LARGE = 64

XLARGE = 128

classmethod from_value(value)

class abacusai.api_class.ResponseSectionType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AGENT_FLOW_BUTTON = 'agent_flow_button'

ATTACHMENTS = 'attachments'

BASE64_IMAGE = 'base64_image'

CHART = 'chart'

CODE = 'code'

COLLAPSIBLE_COMPONENT = 'collapsible_component'

IMAGE_URL = 'image_url'

RUNTIME_SCHEMA = 'runtime_schema'

LIST = 'list'

TABLE = 'table'

TEXT = 'text'

class abacusai.api_class.CodeLanguage

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

PYTHON = 'python'

SQL = 'sql'

class abacusai.api_class.DeploymentConversationType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

CHAT_LLM = 'CHATLLM'

SIMPLE_AGENT = 'SIMPLE_AGENT'

COMPLEX_AGENT = 'COMPLEX_AGENT'

WORKFLOW_AGENT = 'WORKFLOW_AGENT'

COPILOT = 'COPILOT'

AGENT_CONTROLLER = 'AGENT_CONTROLLER'

CODE_LLM = 'CODE_LLM'

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.DocumentProcessingConfig

Bases: abacusai.api_class.abstract.ApiClass

Document processing configuration.

Parameters:

document_type (DocumentType) – Type of document. Can be one of Text, Tables and Forms, Embedded Images, etc. If not specified, type will be decided automatically.
highlight_relevant_text (bool) – Whether to extract bounding boxes and highlight relevant text in search results. Defaults to False.
extract_bounding_boxes (bool) – Whether to perform OCR and extract bounding boxes. If False, no OCR will be done but only the embedded text from digital documents will be extracted. Defaults to False.
ocr_mode (OcrMode) – OCR mode. There are different OCR modes available for different kinds of documents and use cases. This option only takes effect when extract_bounding_boxes is True.
use_full_ocr (bool) – Whether to perform full OCR. If True, OCR will be performed on the full page. If False, OCR will be performed on the non-text regions only. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
remove_header_footer (bool) – Whether to remove headers and footers. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
remove_watermarks (bool) – Whether to remove watermarks. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.
convert_to_markdown (bool) – Whether to convert extracted text to markdown. Defaults to False. This option only takes effect when extract_bounding_boxes is True.
mask_pii (bool) – Whether to mask personally identifiable information (PII) in the document text/tokens. Defaults to False.

document_type: abacusai.api_class.enums.DocumentType = None

highlight_relevant_text: bool = None

extract_bounding_boxes: bool = False

ocr_mode: abacusai.api_class.enums.OcrMode

use_full_ocr: bool = None

remove_header_footer: bool = False

remove_watermarks: bool = True

convert_to_markdown: bool = False

mask_pii: bool = False

__post_init__()

_detect_ocr_mode()

classmethod _get_filtered_dict(config)

Filters out default values from the config

Parameters:: config (dict)

class abacusai.api_class.SamplingConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for the sampling config of a feature group

sampling_method: abacusai.api_class.enums.SamplingMethodType

classmethod _get_builder()

__post_init__()

class abacusai.api_class.NSamplingConfig

Bases: SamplingConfig

The number of distinct values of the key columns to include in the sample, or number of rows if key columns not specified.

Parameters:

sample_count (int) – The number of rows to include in the sample
key_columns (List[str]) – The feature(s) to use as the key(s) when sampling

sample_count: int

key_columns: List[str]

__post_init__()

class abacusai.api_class.PercentSamplingConfig

Bases: SamplingConfig

The fraction of distinct values of the feature group to include in the sample.

Parameters:

sample_percent (float) – The percentage of the rows to sample
key_columns (List[str]) – The feature(s) to use as the key(s) when sampling

sample_percent: float

key_columns: List[str]

__post_init__()

class abacusai.api_class._SamplingConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_class_key = 'sampling_method'

config_abstract_class

config_class_map

class abacusai.api_class.MergeConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for the merge config of a feature group

merge_mode: abacusai.api_class.enums.MergeMode

classmethod _get_builder()

__post_init__()

class abacusai.api_class.LastNMergeConfig

Bases: MergeConfig

Merge LAST N chunks/versions of an incremental dataset.

Parameters:

num_versions (int) – The number of versions to merge. num_versions == 0 means merge all versions.
include_version_timestamp_column (bool) – If set, include a column with the creation timestamp of source FG versions.

num_versions: int

include_version_timestamp_column: bool

__post_init__()

class abacusai.api_class.TimeWindowMergeConfig

Bases: MergeConfig

Merge rows within a given timewindow of the most recent timestamp

Parameters:

feature_name (str) – Time based column to index on
time_window_size_ms (int) – Range of merged rows will be [MAX_TIME - time_window_size_ms, MAX_TIME]
include_version_timestamp_column (bool) – If set, include a column with the creation timestamp of source FG versions.

feature_name: str

time_window_size_ms: int

include_version_timestamp_column: bool

__post_init__()

class abacusai.api_class._MergeConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_class_key = 'merge_mode'

config_abstract_class

config_class_map

class abacusai.api_class.OperatorConfig

Bases: abacusai.api_class.abstract.ApiClass

Configuration for a template Feature Group Operation

operator_type: abacusai.api_class.enums.OperatorType

classmethod _get_builder()

__post_init__()

class abacusai.api_class.UnpivotConfig

Bases: OperatorConfig

Unpivot Columns in a FeatureGroup.

Parameters:

columns (List[str]) – Which columns to unpivot.
index_column (str) – Name of new column containing the unpivoted column names as its values
value_column (str) – Name of new column containing the row values that were unpivoted.
exclude (bool) – If True, the unpivoted columns are all the columns EXCEPT the ones in the columns argument. Default is False.

columns: List[str]

index_column: str

value_column: str

exclude: bool

__post_init__()

class abacusai.api_class.MarkdownConfig

Bases: OperatorConfig

Transform a input column to a markdown column.

Parameters:

input_column (str) – Name of input column to transform.
output_column (str) – Name of output column to store transformed data.
input_column_type (MarkdownOperatorInputType) – Type of input column to transform.

input_column: str

output_column: str

input_column_type: abacusai.api_class.enums.MarkdownOperatorInputType

__post_init__()

class abacusai.api_class.CrawlerTransformConfig

Bases: OperatorConfig

Transform a input column of urls to html text

Parameters:

input_column (str) – Name of input column to transform.
output_column (str) – Name of output column to store transformed data.
depth_column (str) – Increasing depth explores more links, capturing more content
disable_host_restriction (bool) – If True, will not restrict crawling to the same host.
honour_website_rules (bool) – If True, will respect robots.txt rules.
user_agent (str) – If provided, will use this user agent instead of randomly selecting one.

input_column: str

output_column: str

depth_column: str

input_column_type: str

crawl_depth: int

disable_host_restriction: bool

honour_website_rules: bool

user_agent: str

__post_init__()

class abacusai.api_class.ExtractDocumentDataConfig

Bases: OperatorConfig

Extracts data from documents.

Parameters:

doc_id_column (str) – Name of input document ID column.
document_column (str) – Name of the input document column which contains the page infos. This column will be transformed to include the document processing config in the output feature group.
document_processing_config (DocumentProcessingConfig) – Document processing configuration.

doc_id_column: str

document_column: str

document_processing_config: abacusai.api_class.dataset.DocumentProcessingConfig

__post_init__()

class abacusai.api_class.DataGenerationConfig

Bases: OperatorConfig

Generate synthetic data using a model for finetuning an LLM.

Parameters:

prompt_col (str) – Name of the input prompt column.
completion_col (str) – Name of the output completion column.
description_col (str) – Name of the description column.
id_col (str) – Name of the identifier column.
generation_instructions (str) – Instructions for the data generation model.
temperature (float) – Sampling temperature for the model.
fewshot_examples (int) – Number of fewshot examples used to prompt the model.
concurrency (int) – Number of concurrent processes.
examples_per_target (int) – Number of examples per target.
subset_size (Optional[int]) – Size of the subset to use for generation.
verify_response (bool) – Whether to verify the response.
token_budget (int) – Token budget for generation.
oversample (bool) – Whether to oversample the data.
documentation_char_limit (int) – Character limit for documentation.
frequency_penalty (float) – Penalty for frequency of token appearance.
model (str) – Model to use for data generation.
seed (Optional[int]) – Seed for random number generation.

prompt_col: str

completion_col: str

description_col: str

id_col: str

generation_instructions: str

temperature: float

fewshot_examples: int

concurrency: int

examples_per_target: int

subset_size: int

verify_response: bool

token_budget: int

oversample: bool

documentation_char_limit: int

frequency_penalty: float

model: str

seed: int

__post_init__()

class abacusai.api_class.UnionTransformConfig

Bases: OperatorConfig

Takes Union of current feature group with 1 or more selected feature groups of same type.

Parameters:

feature_group_ids (List[str]) – List of feature group IDs to union with source FG.
drop_non_intersecting_columns (bool) – If true, will drop columns that are not present in all feature groups. If false fills missing columns with nulls.

feature_group_ids: List[str]

drop_non_intersecting_columns: bool

__post_init__()

class abacusai.api_class._OperatorConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

A class to select and return the the correct type of Operator Config based on a serialized OperatorConfig instance.

config_abstract_class

config_class_key = 'operator_type'

config_class_map

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.TrainingConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for the training config options used to train the model.

_upper_snake_case_keys: bool

_support_kwargs: bool

kwargs: dict

problem_type: abacusai.api_class.enums.ProblemType

algorithm: str

classmethod _get_builder()

class abacusai.api_class.PersonalizationTrainingConfig

Bases: TrainingConfig

Training config for the PERSONALIZATION problem type

Parameters:

objective (PersonalizationObjective) – Ranking scheme used to select final best model.
sort_objective (PersonalizationObjective) – Ranking scheme used to sort models on the metrics page.
training_mode (PersonalizationTrainingMode) – whether to train in production or experimental mode. Defaults to EXP.
target_action_types (List[str]) – List of action types to use as targets for training.
target_action_weights (Dict[str, float]) – Dictionary of action types to weights for training.
session_event_types (List[str]) – List of event types to treat as occurrences of sessions.
test_split (int) – Percent of dataset to use for test data. We support using a range between 6% to 20% of your dataset to use as test data.
recent_days_for_training (int) – Limit training data to a certain latest number of days.
training_start_date (str) – Only consider training interaction data after this date. Specified in the timezone of the dataset.
test_on_user_split (bool) – Use user splits instead of using time splits, when validating and testing the model.
test_split_on_last_k_items (bool) – Use last k items instead of global timestamp splits, when validating and testing the model.
test_last_items_length (int) – Number of items to leave out for each user when using leave k out folds.
test_window_length_hours (int) – Duration (in hours) of most recent time window to use when validating and testing the model.
explicit_time_split (bool) – Sets an explicit time-based test boundary.
test_row_indicator (str) – Column indicating which rows to use for training (TRAIN), validation (VAL) and testing (TEST).
full_data_retraining (bool) – Train models separately with all the data.
sequential_training (bool) – Train a mode sequentially through time.
data_split_feature_group_table_name (str) – Specify the table name of the feature group to export training data with the fold column.
optimized_event_type (str) – The final event type to optimize for and compute metrics on.
dropout_rate (int) – Dropout rate for neural network.
batch_size (BatchSize) – Batch size for neural network.
disable_transformer (bool) – Disable training the transformer algorithm.
disable_gpu (boo) – Disable training on GPU.
filter_history (bool) – Do not recommend items the user has already interacted with.
action_types_exclusion_days (Dict[str, float]) – Mapping from action type to number of days for which we exclude previously interacted items from prediction
session_dedupe_mins (float) – Minimum number of minutes between two sessions for a user.
max_history_length (int) – Maximum length of user-item history to include user in training examples.
compute_rerank_metrics (bool) – Compute metrics based on rerank results.
add_time_features (bool) – Include interaction time as a feature.
disable_timestamp_scalar_features (bool) – Exclude timestamp scalar features.
compute_session_metrics (bool) – Evaluate models based on how well they are able to predict the next session of interactions.
max_user_history_len_percentile (int) – Filter out users with history length above this percentile.
downsample_item_popularity_percentile (float) – Downsample items more popular than this percentile.
use_user_id_feature (bool) – Use user id as a feature in CTR models.
min_item_history (int) – Minimum number of interactions an item must have to be included in training.
query_column (str) – Name of column in the interactions table that represents a natural language query, e.g. ‘blue t-shirt’.
item_query_column (str) – Name of column in the item catalog that will be matched to the query column in the interactions table.
include_item_id_feature (bool) – Add Item-Id to the input features of the model. Applicable for Embedding distance and CTR models.

objective: abacusai.api_class.enums.PersonalizationObjective

sort_objective: abacusai.api_class.enums.PersonalizationObjective

training_mode: abacusai.api_class.enums.PersonalizationTrainingMode

target_action_types: List[str]

target_action_weights: Dict[str, float]

session_event_types: List[str]

test_split: int

recent_days_for_training: int

training_start_date: str

test_on_user_split: bool

test_split_on_last_k_items: bool

test_last_items_length: int

test_window_length_hours: int

explicit_time_split: bool

test_row_indicator: str

full_data_retraining: bool

sequential_training: bool

data_split_feature_group_table_name: str

optimized_event_type: str

dropout_rate: int

batch_size: abacusai.api_class.enums.BatchSize

disable_transformer: bool

disable_gpu: bool

filter_history: bool

action_types_exclusion_days: Dict[str, float]

max_history_length: int

compute_rerank_metrics: bool

add_time_features: bool

disable_timestamp_scalar_features: bool

compute_session_metrics: bool

query_column: str

item_query_column: str

use_user_id_feature: bool

session_dedupe_mins: float

include_item_id_feature: bool

max_user_history_len_percentile: int

downsample_item_popularity_percentile: float

min_item_history: int

__post_init__()

class abacusai.api_class.RegressionTrainingConfig

Bases: TrainingConfig

Training config for the PREDICTIVE_MODELING problem type

Parameters:

objective (RegressionObjective) – Ranking scheme used to select final best model.
sort_objective (RegressionObjective) – Ranking scheme used to sort models on the metrics page.
tree_hpo_mode – (RegressionTreeHPOMode): Turning off Rapid Experimentation will take longer to train.
type_of_split (RegressionTypeOfSplit) – Type of data splitting into train/test (validation also).
test_split (int) – Percent of dataset to use for test data. We support using a range between 5% to 20% of your dataset to use as test data.
disable_test_val_fold (bool) – Do not create a TEST_VAL set. All records which would be part of the TEST_VAL fold otherwise, remain in the TEST fold.
k_fold_cross_validation (bool) – Use this to force k-fold cross validation bagging on or off.
num_cv_folds (int) – Specify the value of k in k-fold cross validation.
timestamp_based_splitting_column (str) – Timestamp column selected for splitting into test and train.
timestamp_based_splitting_method (RegressionTimeSplitMethod) – Method of selecting TEST set, top percentile wise or after a given timestamp.
test_splitting_timestamp (str) – Rows with timestamp greater than this will be considered to be in the test set.
sampling_unit_keys (List[str]) – Constrain train/test separation to partition a column.
test_row_indicator (str) – Column indicating which rows to use for training (TRAIN) and testing (TEST). Validation (VAL) can also be specified.
full_data_retraining (bool) – Train models separately with all the data.
rebalance_classes (bool) – Class weights are computed as the inverse of the class frequency from the training dataset when this option is selected as “Yes”. It is useful when the classes in the dataset are unbalanced. Re-balancing classes generally boosts recall at the cost of precision on rare classes.
rare_class_augmentation_threshold (float) – Augments any rare class whose relative frequency with respect to the most frequent class is less than this threshold. Default = 0.1 for classification problems with rare classes.
augmentation_strategy (RegressionAugmentationStrategy) – Strategy to deal with class imbalance and data augmentation.
training_rows_downsample_ratio (float) – Uses this ratio to train on a sample of the dataset provided.
active_labels_column (str) – Specify a column to use as the active columns in a multi label setting.
min_categorical_count (int) – Minimum threshold to consider a value different from the unknown placeholder.
sample_weight (str) – Specify a column to use as the weight of a sample for training and eval.
numeric_clipping_percentile (float) – Uses this option to clip the top and bottom x percentile of numeric feature columns where x is the value of this option.
target_transform (RegressionTargetTransform) – Specify a transform (e.g. log, quantile) to apply to the target variable.
ignore_datetime_features (bool) – Remove all datetime features from the model. Useful while generalizing to different time periods.
max_text_words (int) – Maximum number of words to use from text fields.
perform_feature_selection (bool) – If enabled, additional algorithms which support feature selection as a pretraining step will be trained separately with the selected subset of features. The details about their selected features can be found in their respective logs.
feature_selection_intensity (int) – This determines the strictness with which features will be filtered out. 1 being very lenient (more features kept), 100 being very strict.
batch_size (BatchSize) – Batch size.
dropout_rate (int) – Dropout percentage rate.
pretrained_model_name (str) – Enable algorithms which process text using pretrained multilingual NLP models.
pretrained_llm_name (str) – Enable algorithms which process text using pretrained large language models.
is_multilingual (bool) – Enable algorithms which process text using pretrained multilingual NLP models.
loss_function (RegressionLossFunction) – Loss function to be used as objective for model training.
loss_parameters (str) – Loss function params in format <key>=<value>;<key>=<value>;…..
target_encode_categoricals (bool) – Use this to turn target encoding on categorical features on or off.
drop_original_categoricals (bool) – This option helps us choose whether to also feed the original label encoded categorical columns to the mdoels along with their target encoded versions.
monotonically_increasing_features (List[str]) – Constrain the model such that it behaves as if the target feature is monotonically increasing with the selected features
monotonically_decreasing_features (List[str]) – Constrain the model such that it behaves as if the target feature is monotonically decreasing with the selected features
data_split_feature_group_table_name (str) – Specify the table name of the feature group to export training data with the fold column.
custom_loss_functions (List[str]) – Registered custom losses available for selection.
custom_metrics (List[str]) – Registered custom metrics available for selection.
partial_dependence_analysis (PartialDependenceAnalysis) – Specify whether to run partial dependence plots for all features or only some features.
do_masked_language_model_pretraining (bool) – Specify whether to run a masked language model unsupervised pretraining step before supervized training in certain supported algorithms which use BERT-like backbones.
max_tokens_in_sentence (int) – Specify the max tokens to be kept in a sentence based on the truncation strategy.
truncation_strategy (str) – What strategy to use to deal with text rows with more than a given number of tokens (if num of tokens is more than “max_tokens_in_sentence”).

objective: abacusai.api_class.enums.RegressionObjective

sort_objective: abacusai.api_class.enums.RegressionObjective

tree_hpo_mode: abacusai.api_class.enums.RegressionTreeHPOMode

partial_dependence_analysis: abacusai.api_class.enums.PartialDependenceAnalysis

type_of_split: abacusai.api_class.enums.RegressionTypeOfSplit

test_split: int

disable_test_val_fold: bool

k_fold_cross_validation: bool

num_cv_folds: int

timestamp_based_splitting_column: str

timestamp_based_splitting_method: abacusai.api_class.enums.RegressionTimeSplitMethod

test_splitting_timestamp: str

sampling_unit_keys: List[str]

test_row_indicator: str

full_data_retraining: bool

rebalance_classes: bool

rare_class_augmentation_threshold: float

augmentation_strategy: abacusai.api_class.enums.RegressionAugmentationStrategy

training_rows_downsample_ratio: float

active_labels_column: str

min_categorical_count: int

sample_weight: str

numeric_clipping_percentile: float

target_transform: abacusai.api_class.enums.RegressionTargetTransform

ignore_datetime_features: bool

max_text_words: int

perform_feature_selection: bool

feature_selection_intensity: int

batch_size: abacusai.api_class.enums.BatchSize

dropout_rate: int

pretrained_model_name: str

pretrained_llm_name: str

is_multilingual: bool

do_masked_language_model_pretraining: bool

max_tokens_in_sentence: int

truncation_strategy: str

loss_function: abacusai.api_class.enums.RegressionLossFunction

loss_parameters: str

target_encode_categoricals: bool

drop_original_categoricals: bool

monotonically_increasing_features: List[str]

monotonically_decreasing_features: List[str]

data_split_feature_group_table_name: str

custom_loss_functions: List[str]

custom_metrics: List[str]

__post_init__()

class abacusai.api_class.ForecastingTrainingConfig

Bases: TrainingConfig

Training config for the FORECASTING problem type

Parameters:

prediction_length (int) – How many timesteps in the future to predict.
objective (ForecastingObjective) – Ranking scheme used to select final best model.
sort_objective (ForecastingObjective) – Ranking scheme used to sort models on the metrics page.
forecast_frequency (ForecastingFrequency) – Forecast frequency.
probability_quantiles (List[float]) – Prediction quantiles.
force_prediction_length (int) – Force length of test window to be the same as prediction length.
filter_items (bool) – Filter items with small history and volume.
enable_feature_selection (bool) – Enable feature selection.
enable_padding (bool) – Pad series to the max_date of the dataset
enable_cold_start (bool) – Enable cold start forecasting by training/predicting for zero history items.
enable_multiple_backtests (bool) – Whether to enable multiple backtesting or not.
num_backtesting_windows (int) – Total backtesting windows to use for the training.
backtesting_window_step_size (int) – Use this step size to shift backtesting windows for model training.
full_data_retraining (bool) – Train models separately with all the data.
additional_forecast_keys – List[str]: List of categoricals in timeseries that can act as multi-identifier.
experimentation_mode (ExperimentationMode) – Selecting Thorough Experimentation will take longer to train.
type_of_split (ForecastingDataSplitType) – Type of data splitting into train/test.
test_by_item (bool) – Partition train/test data by item rather than time if true.
test_start (str) – Limit training data to dates before the given test start.
test_split (int) – Percent of dataset to use for test data. We support using a range between 5% to 20% of your dataset to use as test data.
loss_function (ForecastingLossFunction) – Loss function for training neural network.
underprediction_weight (float) – Weight for underpredictions
disable_networks_without_analytic_quantiles (bool) – Disable neural networks, which quantile functions do not have analytic expressions (e.g, mixture models)
initial_learning_rate (float) – Initial learning rate.
l2_regularization_factor (float) – L2 regularization factor.
dropout_rate (int) – Dropout percentage rate.
recurrent_layers (int) – Number of recurrent layers to stack in network.
recurrent_units (int) – Number of units in each recurrent layer.
convolutional_layers (int) – Number of convolutional layers to stack on top of recurrent layers in network.
convolution_filters (int) – Number of filters in each convolution.
local_scaling_mode (ForecastingLocalScaling) – Options to make NN inputs stationary in high dynamic range datasets.
zero_predictor (bool) – Include subnetwork to classify points where target equals zero.
skip_missing (bool) – Make the RNN ignore missing entries rather instead of processing them.
batch_size (ForecastingBatchSize) – Batch size.
batch_renormalization (bool) – Enable batch renormalization between layers.
history_length (int) – While training, how much history to consider.
prediction_step_size (int) – Number of future periods to include in objective for each training sample.
training_point_overlap (float) – Amount of overlap to allow between training samples.
max_scale_context (int) – Maximum context to use for local scaling.
quantiles_extension_method (ForecastingQuanitlesExtensionMethod) – Quantile extension method
number_of_samples (int) – Number of samples for ancestral simulation
symmetrize_quantiles (bool) – Force symmetric quantiles (like in Gaussian distribution)
use_log_transforms (bool) – Apply logarithmic transformations to input data.
smooth_history (float) – Smooth (low pass filter) the timeseries.
local_scale_target (bool) – Using per training/prediction window target scaling.
use_clipping (bool) – Apply clipping to input data to stabilize the training.
timeseries_weight_column (str) – If set, we use the values in this column from timeseries data to assign time dependent item weights during training and evaluation.
item_attributes_weight_column (str) – If set, we use the values in this column from item attributes data to assign weights to items during training and evaluation.
use_timeseries_weights_in_objective (bool) – If True, we include weights from column set as “TIMESERIES WEIGHT COLUMN” in objective functions.
use_item_weights_in_objective (bool) – If True, we include weights from column set as “ITEM ATTRIBUTES WEIGHT COLUMN” in objective functions.
skip_timeseries_weight_scaling (bool) – If True, we will avoid normalizing the weights.
timeseries_loss_weight_column (str) – Use value in this column to weight the loss while training.
use_item_id (bool) – Include a feature to indicate the item being forecast.
use_all_item_totals (bool) – Include as input total target across items.
handle_zeros_as_missing_values (bool) – If True, handle zero values in demand as missing data.
datetime_holiday_calendars (List[HolidayCalendars]) – Holiday calendars to augment training with.
fill_missing_values (List[List[dict]]) – Strategy for filling in missing values.
enable_clustering (bool) – Enable clustering in forecasting.
data_split_feature_group_table_name (str) – Specify the table name of the feature group to export training data with the fold column.
custom_loss_functions (List[str]) – Registered custom losses available for selection.
custom_metrics (List[str]) – Registered custom metrics available for selection.
return_fractional_forecasts – Use this to return fractional forecast values while prediction
allow_training_with_small_history – Allows training with fewer than 100 rows in the dataset

prediction_length: int

objective: abacusai.api_class.enums.ForecastingObjective

sort_objective: abacusai.api_class.enums.ForecastingObjective

forecast_frequency: abacusai.api_class.enums.ForecastingFrequency

probability_quantiles: List[float]

force_prediction_length: bool

filter_items: bool

enable_feature_selection: bool

enable_padding: bool

enable_cold_start: bool

enable_multiple_backtests: bool

num_backtesting_windows: int

backtesting_window_step_size: int

full_data_retraining: bool

additional_forecast_keys: List[str]

experimentation_mode: abacusai.api_class.enums.ExperimentationMode

type_of_split: abacusai.api_class.enums.ForecastingDataSplitType

test_by_item: bool

test_start: str

test_split: int

loss_function: abacusai.api_class.enums.ForecastingLossFunction

underprediction_weight: float

disable_networks_without_analytic_quantiles: bool

initial_learning_rate: float

l2_regularization_factor: float

dropout_rate: int

recurrent_layers: int

recurrent_units: int

convolutional_layers: int

convolution_filters: int

local_scaling_mode: abacusai.api_class.enums.ForecastingLocalScaling

zero_predictor: bool

skip_missing: bool

batch_size: abacusai.api_class.enums.BatchSize

batch_renormalization: bool

history_length: int

prediction_step_size: int

training_point_overlap: float

max_scale_context: int

quantiles_extension_method: abacusai.api_class.enums.ForecastingQuanitlesExtensionMethod

number_of_samples: int

symmetrize_quantiles: bool

use_log_transforms: bool

smooth_history: float

local_scale_target: bool

use_clipping: bool

timeseries_weight_column: str

item_attributes_weight_column: str

use_timeseries_weights_in_objective: bool

use_item_weights_in_objective: bool

skip_timeseries_weight_scaling: bool

timeseries_loss_weight_column: str

use_item_id: bool

use_all_item_totals: bool

handle_zeros_as_missing_values: bool

datetime_holiday_calendars: List[abacusai.api_class.enums.HolidayCalendars]

fill_missing_values: List[List[dict]]

enable_clustering: bool

data_split_feature_group_table_name: str

custom_loss_functions: List[str]

custom_metrics: List[str]

return_fractional_forecasts: bool

allow_training_with_small_history: bool

__post_init__()

class abacusai.api_class.NamedEntityExtractionTrainingConfig

Bases: TrainingConfig

Training config for the NAMED_ENTITY_EXTRACTION problem type

Parameters:

llm_for_ner (NERForLLM) – LLM to use for NER from among available LLM
test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.
test_row_indicator (str) – Column indicating which rows to use for training (TRAIN) and testing (TEST).
active_labels_column (str) – Entities that have been marked in a particular text
document_format (NLPDocumentFormat) – Format of the input documents.
minimum_bounding_box_overlap_ratio (float) – Tokens are considered to belong to annotation if the user bounding box is provided and ratio of (token_bounding_box ∩ annotation_bounding_box) / token_bounding_area is greater than the provided value.
save_predicted_pdf (bool) – Whether to save predicted PDF documents
enhanced_ocr (bool) – Enhanced text extraction from predicted digital documents
additional_extraction_instructions (str) – Additional instructions to guide the LLM in extracting the entities. Only used with LLM algorithms.

llm_for_ner: abacusai.api_class.enums.LLMName = None

test_split: int = None

test_row_indicator: str = None

active_labels_column: str = None

document_format: abacusai.api_class.enums.NLPDocumentFormat = None

minimum_bounding_box_overlap_ratio: float = 0.0

save_predicted_pdf: bool = True

enhanced_ocr: bool = False

additional_extraction_instructions: str = None

__post_init__()

class abacusai.api_class.NaturalLanguageSearchTrainingConfig

Bases: TrainingConfig

Training config for the NATURAL_LANGUAGE_SEARCH problem type

Parameters:

abacus_internal_model (bool) – Use a Abacus.AI LLM to answer questions about your data without using any external APIs
num_completion_tokens (int) – Default for maximum number of tokens for chat answers. Reducing this will get faster responses which are more succinct
larger_embeddings (bool) – Use a higher dimension embedding model.
search_chunk_size (int) – Chunk size for indexing the documents.
chunk_overlap_fraction (float) – Overlap in chunks while indexing the documents.
index_fraction (float) – Fraction of the chunk to use for indexing.

abacus_internal_model: bool

num_completion_tokens: int

larger_embeddings: bool

search_chunk_size: int

index_fraction: float

chunk_overlap_fraction: float

__post_init__()

class abacusai.api_class.ChatLLMTrainingConfig

Bases: TrainingConfig

Training config for the CHAT_LLM problem type

Parameters:

document_retrievers (List[str]) – List of names or IDs of document retrievers to use as vector stores of information for RAG responses.
num_completion_tokens (int) – Default for maximum number of tokens for chat answers. Reducing this will get faster responses which are more succinct.
temperature (float) – The generative LLM temperature.
retrieval_columns (list) – Include the metadata column values in the retrieved search results.
filter_columns (list) – Allow users to filter the document retrievers on these metadata columns.
include_general_knowledge (bool) – Allow the LLM to rely not just on RAG search results, but to fall back on general knowledge. Disabled by default.
enable_web_search (bool) – Allow the LLM to use Web Search Engines to retrieve information for better results.
behavior_instructions (str) – Customize the overall behaviour of the model. This controls things like - when to execute code (if enabled), write sql query, search web (if enabled), etc.
response_instructions (str) – Customized instructions for how the model should respond inlcuding the format, persona and tone of the answers.
enable_llm_rewrite (bool) – If enabled, an LLM will rewrite the RAG queries sent to document retriever. Disabled by default.
column_filtering_instructions (str) – Instructions for a LLM call to automatically generate filter expressions on document metadata to retrieve relevant documents for the conversation.
keyword_requirement_instructions (str) – Instructions for a LLM call to automatically generate keyword requirements to retrieve relevant documents for the conversation.
query_rewrite_instructions (str) – Special instructions for the LLM which rewrites the RAG query.
max_search_results (int) – Maximum number of search results in the retrieval augmentation step. If we know that the questions are likely to have snippets which are easily matched in the documents, then a lower number will help with accuracy.
data_feature_group_ids – (List[str]): List of feature group IDs to use to possibly query for the ChatLLM. The created ChatLLM is commonly referred to as DataLLM.
data_prompt_context (str) – Prompt context for the data feature group IDs.
data_prompt_table_context (Dict[str, str]) – Dict of table name and table context pairs to provide table wise context for each structured data table.
data_prompt_column_context (Dict[str, str]) – Dict of ‘table_name.column_name’ and ‘column_context’ pairs to provide column context for some selected columns in the selected structured data table. This replaces the default auto-generated information about the column data.
hide_sql_and_code (bool) – When running data queries, this will hide the generated SQL and Code in the response.
disable_data_summarization (bool) – After executing a query summarize the reponse and reply back with only the table and query run.
data_columns_to_ignore (List[str]) – Columns to ignore while encoding information about structured data tables in context for the LLM. A list of strings of format “<table_name>.<column_name>”
search_score_cutoff (float) – Minimum search score to consider a document as a valid search result.
include_bm25_retrieval (bool) – Combine BM25 search score with vector search using reciprocal rank fusion.
database_connector_id (str) – Database connector ID to use for connecting external database that gives access to structured data to the LLM.
database_connector_tables (List[str]) – List of tables to use from the database connector for the ChatLLM.
enable_code_execution (bool) – Enable python code execution in the ChatLLM. This equips the LLM with a python kernel in which all its code is executed.
enable_response_caching (bool) – Enable caching of LLM responses to speed up response times and improve reproducibility.
unknown_answer_phrase (str) – Fallback response when the LLM can’t find an answer.
enable_tool_bar (bool) – Enable the tool bar in Enterprise ChatLLM to provide additional functionalities like tool_use, web_search, image_gen, etc.
enable_inline_source_citations (bool) – Enable inline citations of the sources in the response.
response_format – (str): When set to ‘JSON’, the LLM will generate a JSON formatted string.
json_response_instructions (str) – Instructions to be followed while generating the json_response if response_format is set to “JSON”. This can include the schema information if the schema is dynamic and its keys cannot be pre-determined.
json_response_schema (str) – Specifies the JSON schema that the model should adhere to if response_format is set to “JSON”. This should be a json-formatted string where each field of the expected schema is mapped to a dictionary containing the fields ‘type’, ‘required’ and ‘description’. For example - ‘{“sample_field”: {“type”: “integer”, “required”: true, “description”: “Sample Field”}}’

document_retrievers: List[str]

num_completion_tokens: int

temperature: float

retrieval_columns: list

filter_columns: list

include_general_knowledge: bool

enable_web_search: bool

behavior_instructions: str

response_instructions: str

enable_llm_rewrite: bool

column_filtering_instructions: str

keyword_requirement_instructions: str

query_rewrite_instructions: str

max_search_results: int

data_feature_group_ids: List[str]

data_prompt_context: str

data_prompt_table_context: Dict[str, str]

data_prompt_column_context: Dict[str, str]

hide_sql_and_code: bool

disable_data_summarization: bool

data_columns_to_ignore: List[str]

search_score_cutoff: float

include_bm25_retrieval: bool

database_connector_id: str

database_connector_tables: List[str]

enable_code_execution: bool

metadata_columns: list

lookup_rewrite_instructions: str

enable_response_caching: bool

unknown_answer_phrase: str

enable_tool_bar: bool

enable_inline_source_citations: bool

response_format: str

json_response_instructions: str

json_response_schema: str

__post_init__()

class abacusai.api_class.SentenceBoundaryDetectionTrainingConfig

Bases: TrainingConfig

Training config for the SENTENCE_BOUNDARY_DETECTION problem type

Parameters:

test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.
dropout_rate (float) – Dropout rate for neural network.
batch_size (BatchSize) – Batch size for neural network.

test_split: int

dropout_rate: float

batch_size: abacusai.api_class.enums.BatchSize

__post_init__()

class abacusai.api_class.SentimentDetectionTrainingConfig

Bases: TrainingConfig

Training config for the SENTIMENT_DETECTION problem type

Parameters:

sentiment_type (SentimentType) – Type of sentiment to detect.
test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

sentiment_type: abacusai.api_class.enums.SentimentType

test_split: int

__post_init__()

class abacusai.api_class.DocumentClassificationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_CLASSIFICATION problem type

Parameters:

zero_shot_hypotheses (List[str]) – Zero shot hypotheses. Example text: ‘This text is about pricing’.
test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

zero_shot_hypotheses: List[str]

test_split: int

__post_init__()

class abacusai.api_class.DocumentSummarizationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_SUMMARIZATION problem type

Parameters:

test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.
dropout_rate (float) – Dropout rate for neural network.
batch_size (BatchSize) – Batch size for neural network.

test_split: int

dropout_rate: float

batch_size: abacusai.api_class.enums.BatchSize

__post_init__()

class abacusai.api_class.DocumentVisualizationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_VISUALIZATION problem type

Parameters:

test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.
dropout_rate (float) – Dropout rate for neural network.
batch_size (BatchSize) – Batch size for neural network.

test_split: int

dropout_rate: float

batch_size: abacusai.api_class.enums.BatchSize

__post_init__()

class abacusai.api_class.ClusteringTrainingConfig

Bases: TrainingConfig

Training config for the CLUSTERING problem type

Parameters:: num_clusters_selection (int) – Number of clusters. If None, will be selected automatically.

num_clusters_selection: int

__post_init__()

class abacusai.api_class.ClusteringTimeseriesTrainingConfig

Bases: TrainingConfig

Training config for the CLUSTERING_TIMESERIES problem type

Parameters:

num_clusters_selection (int) – Number of clusters. If None, will be selected automatically.
imputation (ClusteringImputationMethod) – Imputation method for missing values.

num_clusters_selection: int

imputation: abacusai.api_class.enums.ClusteringImputationMethod

__post_init__()

class abacusai.api_class.EventAnomalyTrainingConfig

Bases: TrainingConfig

Training config for the EVENT_ANOMALY problem type

Parameters:: anomaly_fraction (float) – The fraction of the dataset to classify as anomalous, between 0 and 0.5

anomaly_fraction: float

__post_init__()

class abacusai.api_class.TimeseriesAnomalyTrainingConfig

Bases: TrainingConfig

Training config for the TS_ANOMALY problem type

Parameters:

type_of_split (TimeseriesAnomalyDataSplitType) – Type of data splitting into train/test.
test_start (str) – Limit training data to dates before the given test start.
test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.
fill_missing_values (List[List[dict]]) – strategies to fill missing values and missing timestamps
handle_zeros_as_missing_values (bool) – If True, handle zero values in numeric columns as missing data
timeseries_frequency (str) – set this to control frequency of filling missing values
min_samples_in_normal_region (int) – Adjust this to fine-tune the number of anomalies to be identified.
anomaly_type (TimeseriesAnomalyTypeOfAnomaly) – select what kind of peaks to detect as anomalies
hyperparameter_calculation_with_heuristics (TimeseriesAnomalyUseHeuristic) – Enable heuristic calculation to get hyperparameters for the model
threshold_score (float) – Threshold score for anomaly detection

type_of_split: abacusai.api_class.enums.TimeseriesAnomalyDataSplitType

test_start: str

test_split: int

fill_missing_values: List[List[dict]]

handle_zeros_as_missing_values: bool

timeseries_frequency: str

min_samples_in_normal_region: int

anomaly_type: abacusai.api_class.enums.TimeseriesAnomalyTypeOfAnomaly

hyperparameter_calculation_with_heuristics: abacusai.api_class.enums.TimeseriesAnomalyUseHeuristic

threshold_score: float

__post_init__()

class abacusai.api_class.CumulativeForecastingTrainingConfig

Bases: TrainingConfig

Training config for the CUMULATIVE_FORECASTING problem type

Parameters:

test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.
historical_frequency (str) – Forecast frequency
cumulative_prediction_lengths (List[int]) – List of Cumulative Prediction Frequencies. Each prediction length must be between 1 and 365.
skip_input_transform (bool) – Avoid doing numeric scaling transformations on the input.
skip_target_transform (bool) – Avoid doing numeric scaling transformations on the target.
predict_residuals (bool) – Predict residuals instead of totals at each prediction step.

test_split: int

historical_frequency: str

cumulative_prediction_lengths: List[int]

skip_input_transform: bool

skip_target_transform: bool

predict_residuals: bool

__post_init__()

class abacusai.api_class.ThemeAnalysisTrainingConfig

Bases: TrainingConfig

Training config for the THEME ANALYSIS problem type

__post_init__()

class abacusai.api_class.AIAgentTrainingConfig

Bases: TrainingConfig

Training config for the AI_AGENT problem type

Parameters:

description (str) – Description of the agent function.
agent_interface (AgentInterface) – The interface that the agent will be deployed with.
agent_connectors – (List[enums.ApplicationConnectorType]): The connectors needed for the agent to function.

description: str

agent_interface: abacusai.api_class.enums.AgentInterface

agent_connectors: List[abacusai.api_class.enums.ApplicationConnectorType]

enable_binary_input: bool

agent_input_schema: dict

agent_output_schema: dict

__post_init__()

class abacusai.api_class.CustomTrainedModelTrainingConfig

Bases: TrainingConfig

Training config for the CUSTOM_TRAINED_MODEL problem type

Parameters:

max_catalog_size (int) – Maximum expected catalog size.
max_dimension (int) – Maximum expected dimension of the catalog.
index_output_path (str) – Fully qualified cloud location (GCS, S3, etc) to export snapshots of the embedding to.
docker_image_uri (str) – Docker image URI.
service_port (int) – Service port.
streaming_embeddings (bool) – Flag to enable streaming embeddings.

max_catalog_size: int

max_dimension: int

index_output_path: str

docker_image_uri: str

service_port: int

streaming_embeddings: bool

__post_init__()

class abacusai.api_class.CustomAlgorithmTrainingConfig

Bases: TrainingConfig

Training config for the CUSTOM_ALGORITHM problem type

Parameters:: timeout_minutes (int) – Timeout for the model training in minutes.

timeout_minutes: int

__post_init__()

class abacusai.api_class.OptimizationTrainingConfig

Bases: TrainingConfig

Training config for the OPTIMIZATION problem type

Parameters:

solve_time_limit (float) – The maximum time in seconds to spend solving the problem. Accepts values between 0 and 86400.
optimality_gap_limit (float) – The stopping optimality gap limit. Optimality gap is fractional difference between the best known solution and the best possible solution. Accepts values between 0 and 1.

solve_time_limit: float

optimality_gap_limit: float

__post_init__()

class abacusai.api_class._TrainingConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'problem_type'

config_class_map

class abacusai.api_class.DeployableAlgorithm

Bases: abacusai.api_class.abstract.ApiClass

Algorithm that can be deployed to a model.

Parameters:

algorithm (str) – ID of the algorithm.
name (str) – Name of the algorithm.
only_offline_deployable (bool) – Whether the algorithm can only be deployed offline.
trained_model_types (List[dict]) – List of trained model types.

algorithm: str

name: str

only_offline_deployable: bool

trained_model_types: List[dict]

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.StdDevThresholdType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ABSOLUTE = 'ABSOLUTE'

PERCENTILE = 'PERCENTILE'

STDDEV = 'STDDEV'

class abacusai.api_class.TimeWindowConfig

Bases: abacusai.api_class.abstract.ApiClass

Time Window Configuration

Parameters:

window_duration (int) – The duration of the window.
window_from_start (bool) – Whether the window should be from the start of the time series.

window_duration: int

window_from_start: bool

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ForecastingMonitorConfig

Bases: abacusai.api_class.abstract.ApiClass

Forecasting Monitor Configuration

Parameters:

id_column (str) – The name of the column that contains the unique identifier for the time series.
timestamp_column (str) – The name of the column that contains the timestamp for the time series.
target_column (str) – The name of the column that contains the target value for the time series.
start_time (str) – The start time of the time series data.
end_time (str) – The end time of the time series data.
window_config (TimeWindowConfig) – The windowing configuration for the time series data.

id_column: str

timestamp_column: str

target_column: str

start_time: str

end_time: str

window_config: TimeWindowConfig

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.StdDevThreshold

Bases: abacusai.api_class.abstract.ApiClass

Std Dev Threshold types

Parameters:

threshold_type (StdDevThresholdType) – Type of threshold to apply to the item attributes.
value (float) – Value to use for the threshold.

threshold_type: abacusai.api_class.enums.StdDevThresholdType

value: float

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ItemAttributesStdDevThreshold

Bases: abacusai.api_class.abstract.ApiClass

Item Attributes Std Dev Threshold for Monitor Alerts

Parameters:

lower_bound (StdDevThreshold) – Lower bound for the item attributes.
upper_bound (StdDevThreshold) – Upper bound for the item attributes.

lower_bound: StdDevThreshold

upper_bound: StdDevThreshold

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.RestrictFeatureMappings

Bases: abacusai.api_class.abstract.ApiClass

Restrict Feature Mappings for Monitor Filtering

Parameters:

feature_name (str) – The name of the feature to restrict the monitor to.
restricted_feature_values (list) – The values of the feature to restrict the monitor to if feature is a categorical.
start_time (str) – The start time of the timestamp feature to filter from
end_time (str) – The end time of the timestamp feature to filter until
min_value (float) – Value to filter the numerical feature above
max_value (float) – Filtering the numerical feature to below this value

feature_name: str

restricted_feature_values: list

start_time: str

end_time: str

min_value: float

max_value: float

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.MonitorFilteringConfig

Bases: abacusai.api_class.abstract.ApiClass

Monitor Filtering Configuration

Parameters:

start_time (str) – The start time of the prediction time col
end_time (str) – The end time of the prediction time col
restrict_feature_mappings (RestrictFeatureMappings) – The feature mapping to restrict the monitor to.
target_class (str) – The target class to restrict the monitor to.
train_target_feature (str) – Set the target feature for the training data.
prediction_target_feature (str) – Set the target feature for the prediction data.

start_time: str

end_time: str

restrict_feature_mappings: List[RestrictFeatureMappings]

target_class: str

train_target_feature: str

prediction_target_feature: str

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.AlertConditionConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for alert condition configs

alert_type: abacusai.api_class.enums.MonitorAlertType

classmethod _get_builder()

class abacusai.api_class.AccuracyBelowThresholdConditionConfig

Bases: AlertConditionConfig

Accuracy Below Threshold Condition Config for Monitor Alerts

Parameters:: threshold (float) – Threshold for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

threshold: float

__post_init__()

class abacusai.api_class.FeatureDriftConditionConfig

Bases: AlertConditionConfig

Feature Drift Condition Config for Monitor Alerts

Parameters:

feature_drift_type (FeatureDriftType) – Feature drift type to apply the threshold on to determine whether a column has drifted significantly enough to be a violation.
threshold (float) – Threshold for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.
minimum_violations (int) – Number of columns that must exceed the specified threshold to trigger an alert.
feature_names (List[str]) – List of feature names to monitor for this alert.

feature_drift_type: abacusai.api_class.enums.FeatureDriftType

threshold: float

minimum_violations: int

feature_names: List[str]

__post_init__()

class abacusai.api_class.TargetDriftConditionConfig

Bases: AlertConditionConfig

Target Drift Condition Config for Monitor Alerts

Parameters:

feature_drift_type (FeatureDriftType) – Target drift type to apply the threshold on to determine whether a column has drifted significantly enough to be a violation.
threshold (float) – Threshold for when to consider the target column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

feature_drift_type: abacusai.api_class.enums.FeatureDriftType

threshold: float

__post_init__()

class abacusai.api_class.HistoryLengthDriftConditionConfig

Bases: AlertConditionConfig

History Length Drift Condition Config for Monitor Alerts

Parameters:

feature_drift_type (FeatureDriftType) – History length drift type to apply the threshold on to determine whether the history length has drifted significantly enough to be a violation.
threshold (float) – Threshold for when to consider the history length to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

feature_drift_type: abacusai.api_class.enums.FeatureDriftType

threshold: float

__post_init__()

class abacusai.api_class.DataIntegrityViolationConditionConfig

Bases: AlertConditionConfig

Data Integrity Violation Condition Config for Monitor Alerts

Parameters:

data_integrity_type (DataIntegrityViolationType) – This option selects the data integrity violations to monitor for this alert.
minimum_violations (int) – Number of columns that must exceed the specified threshold to trigger an alert.

data_integrity_type: abacusai.api_class.enums.DataIntegrityViolationType

minimum_violations: int

__post_init__()

class abacusai.api_class.BiasViolationConditionConfig

Bases: AlertConditionConfig

Bias Violation Condition Config for Monitor Alerts

Parameters:

bias_type (BiasType) – This option selects the bias metric to monitor for this alert.
threshold (float) – Threshold for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.
minimum_violations (int) – Number of columns that must exceed the specified threshold to trigger an alert.

bias_type: abacusai.api_class.enums.BiasType

threshold: float

minimum_violations: int

__post_init__()

class abacusai.api_class.PredictionCountConditionConfig

Bases: AlertConditionConfig

Deployment Prediction Condition Config for Deployment Alerts. By default we monitor if predictions made over a time window has reduced significantly. :param threshold: Threshold for when to consider to be a violation. Negative means alert on reduction, positive means alert on increase. :type threshold: float :param aggregation_window: Time window to aggregate the predictions over, e.g. 1h, 10m. Only h(hour), m(minute) and s(second) are supported. :type aggregation_window: str :param aggregation_type: Aggregation type to use for the aggregation window, e.g. sum, avg. :type aggregation_type: str

threshold: float

aggregation_window: str

aggregation_type: str

__post_init__()

class abacusai.api_class._AlertConditionConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'alert_type'

config_class_key_value_camel_case = True

config_class_map

class abacusai.api_class.AlertActionConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for alert action configs

action_type: abacusai.api_class.enums.AlertActionType

classmethod _get_builder()

class abacusai.api_class.EmailActionConfig

Bases: AlertActionConfig

Email Action Config for Monitor Alerts

Parameters:

email_recipients (List[str]) – List of email addresses to send the alert to.
email_body (str) – Body of the email to send.

email_recipients: List[str]

email_body: str

__post_init__()

class abacusai.api_class._AlertActionConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'action_type'

config_class_map

class abacusai.api_class.MonitorThresholdConfig

Bases: abacusai.api_class.abstract.ApiClass

Monitor Threshold Config for Monitor Alerts

Parameters:

drift_type (FeatureDriftType) – Feature drift type to apply the threshold on to determine whether a column has drifted significantly enough to be a violation.
threshold_config (ThresholdConfigs) – Thresholds for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

drift_type: abacusai.api_class.enums.FeatureDriftType

at_risk_threshold: float

severely_drifting_threshold: float

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.FeatureMappingConfig

Bases: abacusai.api_class.abstract.ApiClass

Feature mapping configuration for a feature group type.

Parameters:

feature_name (str) – The name of the feature in the feature group.
feature_mapping (str) – The desired feature mapping for the feature.
nested_feature_name (str) – The name of the nested feature in the feature group.

feature_name: str

feature_mapping: str

nested_feature_name: str

class abacusai.api_class.ProjectFeatureGroupTypeMappingsConfig

Bases: abacusai.api_class.abstract.ApiClass

Project feature group type mappings.

Parameters:

feature_group_id (str) – The unique identifier for the feature group.
feature_group_type (str) – The feature group type.
feature_mappings (List[FeatureMappingConfig]) – The feature mappings for the feature group.

feature_group_id: str

feature_group_type: str

feature_mappings: List[FeatureMappingConfig]

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.ConstraintConfig

Bases: abacusai.api_class.abstract.ApiClass

Constraint configuration.

Parameters:

constant (float) – The constant value for the constraint.
operator (str) – The operator for the constraint. Could be ‘EQ’, ‘LE’, ‘GE’
enforcement (str) – The enforcement for the constraint. Could be ‘HARD’ or ‘SOFT’. Default is ‘HARD’
code (str) – The code for the constraint.
penalty (float) – The penalty for violating the constraint.

constant: float

operator: str

enforcement: str | None

code: str | None

penalty: float | None

class abacusai.api_class.ProjectFeatureGroupConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for project feature group configuration.

type: abacusai.api_class.enums.ProjectConfigType

classmethod _get_builder()

class abacusai.api_class.ConstraintProjectFeatureGroupConfig

Bases: ProjectFeatureGroupConfig

Constraint project feature group configuration.

Parameters:: constraints (List[ConstraintConfig]) – The constraint for the feature group. Should be a list of one ConstraintConfig.

constraints: List[ConstraintConfig]

__post_init__()

class abacusai.api_class.ReviewModeProjectFeatureGroupConfig

Bases: ProjectFeatureGroupConfig

Review mode project feature group configuration.

Parameters:: is_review_mode (bool) – The review mode for the feature group.

is_review_mode: bool

__post_init__()

class abacusai.api_class._ProjectFeatureGroupConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'type'

config_class_map

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.PythonFunctionArgument

Bases: abacusai.api_class.abstract.ApiClass

A config class for python function arguments

Parameters:

variable_type (PythonFunctionArgumentType) – The type of the python function argument
name (str) – The name of the python function variable
is_required (bool) – Whether the argument is required
value (Any) – The value of the argument
pipeline_variable (str) – The name of the pipeline variable to use as the value

variable_type: abacusai.api_class.enums.PythonFunctionArgumentType

name: str

is_required: bool

value: Any

pipeline_variable: str

class abacusai.api_class.OutputVariableMapping

Bases: abacusai.api_class.abstract.ApiClass

A config class for python function arguments

Parameters:

variable_type (PythonFunctionOutputArgumentType) – The type of the python function output argument
name (str) – The name of the python function variable

variable_type: abacusai.api_class.enums.PythonFunctionOutputArgumentType

name: str

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:: config (dict)
Return type:: ApiClass

class abacusai.api_class.FeatureGroupExportConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for feature group exports.

connector_type: abacusai.api_class.enums.ConnectorType

classmethod _get_builder()

class abacusai.api_class.FileConnectorExportConfig

Bases: FeatureGroupExportConfig

File connector export config for feature groups

Parameters:

location (str) – The location to export the feature group to
export_file_format (str) – The file format to export the feature group to

location: str

export_file_format: str

__post_init__()

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.DatabaseConnectorExportConfig

Bases: FeatureGroupExportConfig

Database connector export config for feature groups

Parameters:

database_connector_id (str) – The ID of the database connector to export the feature group to
mode (str) – The mode to export the feature group in
object_name (str) – The name of the object to export the feature group to
id_column (str) – The name of the ID column
additional_id_columns (List[str]) – Additional ID columns
data_columns (Dict[str, str]) – The data columns to export the feature group to

database_connector_id: str

mode: str

object_name: str

id_column: str

additional_id_columns: List[str]

data_columns: Dict[str, str]

__post_init__()

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class._FeatureGroupExportConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class

config_class_key = 'connector_type'

config_class_map

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

_repr_html_()

__getitem__(item)

Parameters:: item (str)

__setitem__(item, value)

Parameters:

item (str)
value (Any)

_unset_item(item)

Parameters:: item (str)

get(item, default=None)

Parameters:

item (str)
default (Any)

pop(item, default=NotImplemented)

Parameters:

item (str)
default (Any)

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:: input_dict (dict)

class abacusai.api_class.ResponseSection

Bases: abacusai.api_class.abstract.ApiClass

A response section that an agent can return to render specific UI elements.

Parameters:

type (ResponseSectionType) – The type of the response.
id (str) – The section key of the segment.

type: abacusai.api_class.enums.ResponseSectionType

id: str

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

abacusai.api_class.Segment

class abacusai.api_class.AgentFlowButtonResponseSection(label, agent_workflow_node_name, section_key=None)

Bases: ResponseSection

A response section that an AI Agent can return to render a button.

Parameters:

label (str) – The label of the button.
agent_workflow_node_name (str) – The workflow start node to be executed when the button is clicked.
section_key (str)

label: str

agent_workflow_node_name: str

class abacusai.api_class.ImageUrlResponseSection(url, height, width, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render an image.

Parameters:

url (str) – The url of the image to be displayed.
height (int) – The height of the image.
width (int) – The width of the image.
section_key (str)

url: str

height: int

width: int

class abacusai.api_class.TextResponseSection(text, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render text.

Parameters:

segment (str) – The text to be displayed.
text (str)
section_key (str)

segment: str

class abacusai.api_class.RuntimeSchemaResponseSection(json_schema, ui_schema=None, schema_prop=None)

Bases: ResponseSection

A segment that an agent can return to render json and ui schema in react-jsonschema-form format for workflow nodes. This is primarily used to generate dynamic forms at runtime. If a node returns a runtime schema variable, the UI will render the form upon node execution.

Parameters:

json_schema (dict) – json schema in RJSF format.
ui_schema (dict) – ui schema in RJSF format.
schema_prop (str)

json_schema: dict

ui_schema: dict

class abacusai.api_class.CodeResponseSection(code, language, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render code.

Parameters:

code (str) – The code to be displayed.
language (CodeLanguage) – The language of the code.
section_key (str)

code: str

language: abacusai.api_class.enums.CodeLanguage

class abacusai.api_class.Base64ImageResponseSection(b64_image, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render a base64 image.

Parameters:

b64_image (str) – The base64 image to be displayed.
section_key (str)

b64_image: str

class abacusai.api_class.CollapseResponseSection(title, content, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render a collapsible component.

Parameters:

title (str) – The title of the collapsible component.
content (ResponseSection) – The response section constituting the content of collapsible component
section_key (str)

title: str

content: ResponseSection

to_dict(): Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ListResponseSection(items, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render a list.

Parameters:

items (List[str]) – The list items to be displayed.
section_key (str)

items: List[str]

class abacusai.api_class.ChartResponseSection(chart, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render a chart.

Parameters:

chart (dict) – The chart to be displayed.
section_key (str)

chart: dict

class abacusai.api_class.DataframeResponseSection(df, header=None, section_key=None)

Bases: ResponseSection

A response section that an agent can return to render a pandas dataframe. :param df: The dataframe to be displayed. :type df: pandas.DataFrame :param header: Heading of the table to be displayed. :type header: str

Parameters:

df (Any)
header (str)
section_key (str)

df: Any

header: str

abacusai.api_class

Submodules

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Package Contents