Version 1.2.4

Author: Austin Zielman

abacusai/__init__.py

@@ -4,4 +4,4 @@
 from .streaming_client import StreamingClient
 
 
-__version__ = "1.2.2"
+__version__ = "1.2.4"

abacusai/agent.py

@@ -23,6 +23,7 @@ class Agent(AbstractApiClass):
             agentExecutionConfig (dict): The config for arguments used to execute the agent.
             latestAgentVersion (AgentVersion): The latest agent version.
             codeSource (CodeSource): If a python model, information on the source code
+            workflowGraph (WorkflowGraph): The workflow graph for the agent.
     """
 
     def __init__(self, client, name=None, agentId=None, createdAt=None, projectId=None, notebookId=None, predictFunctionName=None, sourceCode=None, agentConfig=None, memory=None, trainingRequired=None, agentExecutionConfig=None, codeSource={}, latestAgentVersion={}, workflowGraph={}):

abacusai/agent_data_upload_result.py

@@ -1,4 +1,3 @@
-from .agent_data_document_info import AgentDataDocumentInfo
 from .return_class import AbstractApiClass
 
 
@@ -8,12 +7,12 @@ class AgentDataUploadResult(AbstractApiClass):
 
         Args:
             client (ApiClient): An authenticated API Client instance
-            docInfos (AgentDataDocumentInfo): A list of dict for information on the documents uploaded to agent.
+            docInfos (list[agentdatadocumentinfo]): A list of dict for information on the documents uploaded to agent.
     """
 
-    def __init__(self, client, docInfos={}):
+    def __init__(self, client, docInfos=None):
         super().__init__(client, None)
-        self.doc_infos = client._build_class(AgentDataDocumentInfo, docInfos)
+        self.doc_infos = docInfos
         self.deprecated_keys = {}
 
     def __repr__(self):
@@ -30,5 +29,5 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'doc_infos': self._get_attribute_as_dict(self.doc_infos)}
+        resp = {'doc_infos': self.doc_infos}
         return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}

abacusai/agent_version.py

@@ -20,6 +20,7 @@ class AgentVersion(AbstractApiClass):
             error (str): Relevant error if the status is FAILED.
             agentExecutionConfig (dict): The config for arguments used to execute the agent.
             codeSource (CodeSource): If a python model, information on where the source code is located.
+            workflowGraph (WorkflowGraph): The workflow graph for the agent.
     """
 
     def __init__(self, client, agentVersion=None, status=None, agentId=None, agentConfig=None, publishingStartedAt=None, publishingCompletedAt=None, pendingDeploymentIds=None, failedDeploymentIds=None, error=None, agentExecutionConfig=None, codeSource={}, workflowGraph={}):

abacusai/api_class/abstract.py

@@ -4,7 +4,8 @@
 import re
 from abc import ABC
 from copy import deepcopy
-from typing import Any
+from textwrap import dedent
+from typing import Any, Callable, get_origin, get_type_hints
 
 from .enums import ApiEnum
 
@@ -34,6 +35,16 @@ def snake_case(value):
     return value
 
 
+def get_clean_function_source_code(func: Callable):
+    sample_lambda = (lambda: 0)
+    if isinstance(func, type(sample_lambda)) and func.__name__ == sample_lambda.__name__:
+        raise ValueError('Lambda function not allowed.')
+    source_code = inspect.getsource(func)
+    # If function source code has some initial indentation, remove it (Ex - can happen if the functor was defined inside a function)
+    source_code = dedent(source_code)
+    return source_code
+
+
 @dataclasses.dataclass
 class ApiClass(ABC):
     _upper_snake_case_keys: bool = dataclasses.field(default=False, repr=False, init=False)
@@ -111,12 +122,12 @@ def to_dict_helper(api_class_obj):
             res = {}
             api_class_dict = vars(api_class_obj)
             if self._support_kwargs:
-                kwargs = api_class_dict.pop('kwargs', None)
+                kwargs = api_class_dict.get('kwargs', None)
                 api_class_dict.update(kwargs or {})
             for k, v in api_class_dict.items():
-                if not k.startswith('__'):
-                    k = upper_snake_case(k) if self._upper_snake_case_keys else camel_case(k)
-                if v is not None:
+                if v is not None and k != 'kwargs':
+                    if not k.startswith('__'):
+                        k = upper_snake_case(k) if self._upper_snake_case_keys else camel_case(k)
                     if isinstance(v, ApiClass):
                         res[k] = to_dict_helper(v)
                     elif isinstance(v, list):
@@ -125,17 +136,19 @@ def to_dict_helper(api_class_obj):
                         res[k] = {key: to_dict_helper(val) if isinstance(val, ApiClass) else val for key, val in v.items()}
                     elif isinstance(v, datetime.datetime) or isinstance(v, datetime.date):
                         res[k] = v.isoformat() if v else v
+                    elif isinstance(v, ApiEnum):
+                        res[k] = v.value
                     else:
-                        if isinstance(v, ApiEnum):
-                            res[k] = v.value
-                        else:
-                            res[k] = v
+                        res[k] = v
             return res
 
         return to_dict_helper(self)
 
     @classmethod
     def from_dict(cls, input_dict: dict):
+        if input_dict is None:
+            return None
+        obj = None
         if input_dict:
             if builder := cls._get_builder():
                 config_class_key = None
@@ -152,14 +165,28 @@ def from_dict(cls, input_dict: dict):
                         if config_class_key not in input_dict_with_config_key and camel_case(config_class_key) not in input_dict_with_config_key:
                             input_dict_with_config_key[config_class_key] = value
 
-                return builder.from_dict(input_dict_with_config_key)
+                obj = builder.from_dict(input_dict_with_config_key)
+
             if not cls._upper_snake_case_keys:
                 input_dict = {snake_case(k): v for k, v in input_dict.items()}
             if not cls._support_kwargs:
                 # only use keys that are valid fields in the ApiClass
                 field_names = set((field.name) for field in dataclasses.fields(cls))
                 input_dict = {k: v for k, v in input_dict.items() if k in field_names}
-        return cls(**input_dict)
+        if obj is None:
+            obj = cls(**input_dict)
+
+        for attr_name, attr_type in get_type_hints(cls).items():
+            if attr_name in input_dict and inspect.isclass(attr_type) and issubclass(attr_type, ApiClass):
+                setattr(obj, attr_name, attr_type.from_dict(input_dict[attr_name]))
+            elif attr_name in input_dict and get_origin(attr_type) is list and attr_type.__args__ and inspect.isclass(attr_type.__args__[0]) and issubclass(attr_type.__args__[0], ApiClass):
+                class_type = attr_type.__args__[0]
+                if isinstance(input_dict[attr_name], list):
+                    setattr(obj, attr_name, [class_type.from_dict(item) for item in input_dict[attr_name]])
+                else:
+                    raise ValueError(f'Expected list for {attr_name} but got {type(input_dict[attr_name])}')
+
+        return obj
 
 
 @dataclasses.dataclass

abacusai/api_class/ai_agents.py

@@ -2,7 +2,7 @@
 from typing import List, Union
 
 from . import enums
-from .abstract import ApiClass
+from .abstract import ApiClass, get_clean_function_source_code
 
 
 @dataclasses.dataclass
@@ -14,7 +14,7 @@ class FieldDescriptor(ApiClass):
         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 (enums.FieldDescriptorType): The type of this field. If not provided, the default type is STRING.
+        type (FieldDescriptorType): The type of this field. If not provided, the default type is STRING.
     """
     field: str = dataclasses.field()
     description: str = dataclasses.field(default=None)
@@ -29,20 +29,23 @@ class WorkflowNodeInputMapping(ApiClass):
 
     Args:
         name (str): The name of the input.
-        variable_type (str): The type of the input.
-        workflow_variable_source (str): The workflow source stage of the input.
+        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 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): Whether the input is required.
     """
     name: str
     variable_type: enums.WorkflowNodeInputType
-    workflow_variable_source: str = dataclasses.field(default=None)
+    variable_source: str = dataclasses.field(default=None)
     is_required: bool = dataclasses.field(default=True)
 
     def to_dict(self):
         return {
             'name': self.name,
             'variable_type': self.variable_type,
-            'workflow_variable_source': self.workflow_variable_source,
+            'variable_source': self.variable_source,
             'is_required': self.is_required
         }
 
@@ -54,7 +57,7 @@ class WorkflowNodeOutputMapping(ApiClass):
 
     Args:
         name (str): The name of the output.
-        variable_type (str): The type of the output.
+        variable_type (WorkflowNodeOutputType): The type of the output.
     """
     name: str
     variable_type: enums.WorkflowNodeOutputType = dataclasses.field(default=enums.WorkflowNodeOutputType.STRING)
@@ -72,22 +75,19 @@ class WorkflowGraphNode(ApiClass):
     A node in an Agent workflow graph.
 
     Args:
-        name (str): Display name of the worflow node.
+        name (str): A unique name for the workflow node.
         input_mappings (List[WorkflowNodeInputMapping]): List of input mappings for the node.
         output_mappings (List[WorkflowNodeOutputMapping]): List of output mappings for the node.
-        function (callable): The callable node function reference if available.
-        function_name (str): The name of the function if available.
-        source_code (str): The source code of the function if available.
+        function (callable): The callable node function reference.
         input_schema (dict): The react json schema for the input form if applicable.
         output_schema (dict): The react json schema for the output if applicable.
         package_requirements (list): List of package requirements for the node.
     """
 
     def __init__(self, name: str, input_mappings: List[WorkflowNodeInputMapping], output_mappings: List[WorkflowNodeOutputMapping], function: callable = None, function_name: str = None, source_code: str = None, input_schema: dict = None, output_schema: dict = None, package_requirements: list = None):
         if function:
-            import inspect
             self.function_name = function.__name__
-            self.source_code = inspect.getsource(function)
+            self.source_code = get_clean_function_source_code(function)
         elif function_name and source_code:
             self.function_name = function_name
             self.source_code = source_code
@@ -133,8 +133,8 @@ class WorkflowGraphEdge(ApiClass):
     An edge in an Agent workflow graph.
 
     Args:
-        source (str): The source node of the edge.
-        target (str): The target node of the edge.
+        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.
     """
     source: str

abacusai/api_class/batch_prediction.py

@@ -32,7 +32,7 @@ class ForecastingBatchPredictionArgs(BatchPredictionArgs):
        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.
-       automate_monitoring (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.
+       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 = dataclasses.field(default=None)
     predictions_start_date: str = dataclasses.field(default=None)
@@ -41,7 +41,7 @@ class ForecastingBatchPredictionArgs(BatchPredictionArgs):
     forecasting_horizon: int = dataclasses.field(default=None)
     item_attributes_to_include_in_the_result: list = dataclasses.field(default=None)
     explain_predictions: bool = dataclasses.field(default=None)
-    automate_monitoring: bool = dataclasses.field(default=None)
+    create_monitor: bool = dataclasses.field(default=None)
 
     def __post_init__(self):
         self.problem_type = enums.ProblemType.FORECASTING
@@ -100,7 +100,7 @@ class PredictiveModelingBatchPredictionArgs(BatchPredictionArgs):
        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.
-       automate_monitoring (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.
+       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 = dataclasses.field(default=None)
     explainer_type: enums.ExplainerType = dataclasses.field(default=None)
@@ -113,7 +113,7 @@ class PredictiveModelingBatchPredictionArgs(BatchPredictionArgs):
     explanation_filter_label: str = dataclasses.field(default=None)
     output_columns: list = dataclasses.field(default=None)
     explain_predictions: bool = dataclasses.field(default=None)
-    automate_monitoring: bool = dataclasses.field(default=None)
+    create_monitor: bool = dataclasses.field(default=None)
 
     def __post_init__(self):
         self.problem_type = enums.ProblemType.PREDICTIVE_MODELING
@@ -194,10 +194,10 @@ class TrainablePlugAndPlayBatchPredictionArgs(BatchPredictionArgs):
 
     Args:
         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.
-        automate_monitoring (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.
+        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 = dataclasses.field(default=None)
-    automate_monitoring: bool = dataclasses.field(default=None)
+    create_monitor: bool = dataclasses.field(default=None)
 
     def __post_init__(self):
         self.problem_type = enums.ProblemType.CUSTOM_ALGORITHM

abacusai/api_class/dataset_application_connector.py

@@ -8,8 +8,13 @@
 class DatasetConfig(ApiClass):
     """
     An abstract class for dataset configs specific to application connectors.
+
+    Args:
+        application_connector_type(enums.ApplicationConnectorType): The type of application connector
+        is_documentset (bool): Whether the dataset is a document set
     """
     application_connector_type: enums.ApplicationConnectorType = dataclasses.field(default=None, repr=False, init=False)
+    is_documentset: bool = dataclasses.field(default=None)
 
     @classmethod
     def _get_builder(cls):
@@ -21,12 +26,16 @@ class ConfluenceDatasetConfig(DatasetConfig):
     """
     Dataset config for Confluence Application Connector
     Args:
+        location (str): The location of the pages to fetch
         pull_attachments (bool, optional): Whether to pull attachments for each page
         space_key (str, optional): The space key to fetch pages from
+        extract_bounding_boxes (bool, optional): Whether to extract bounding boxes from the documents
 
     """
+    location: str = dataclasses.field(default=None)
     pull_attachments: bool = dataclasses.field(default=False)
     space_key: str = dataclasses.field(default=None)
+    extract_bounding_boxes: bool = dataclasses.field(default=False)
 
     def __post_init__(self):
         self.application_connector_type = enums.ApplicationConnectorType.CONFLUENCE
@@ -57,13 +66,11 @@ class GoogleDriveDatasetConfig(DatasetConfig):
 
     Args:
         location (str): The regex location of the files to fetch
-        is_documentset (bool): Whether the dataset is a document set
         csv_delimiter (str, optional): If the file format is CSV, use a specific csv delimiter
         extract_bounding_boxes (bool, optional): Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True
         merge_file_schemas (bool, optional): Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True
     """
     location: str = dataclasses.field(default=None)
-    is_documentset: bool = dataclasses.field(default=None)
     csv_delimiter: str = dataclasses.field(default=None)
     extract_bounding_boxes: bool = dataclasses.field(default=False)
     merge_file_schemas: bool = dataclasses.field(default=False)
@@ -99,13 +106,11 @@ class OneDriveDatasetConfig(DatasetConfig):
 
     Args:
         location (str): The regex location of the files to fetch
-        is_documentset (bool): Whether the dataset is a document set
         csv_delimiter (str, optional): If the file format is CSV, use a specific csv delimiter
         extract_bounding_boxes (bool, optional): Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True
         merge_file_schemas (bool, optional): Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True
     """
     location: str = dataclasses.field(default=None)
-    is_documentset: bool = dataclasses.field(default=None)
     csv_delimiter: str = dataclasses.field(default=None)
     extract_bounding_boxes: bool = dataclasses.field(default=False)
     merge_file_schemas: bool = dataclasses.field(default=False)
@@ -127,7 +132,6 @@ class SharepointDatasetConfig(DatasetConfig):
         merge_file_schemas (bool, optional): Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True
     """
     location: str = dataclasses.field(default=None)
-    is_documentset: bool = dataclasses.field(default=None)
     csv_delimiter: str = dataclasses.field(default=None)
     extract_bounding_boxes: bool = dataclasses.field(default=False)
     merge_file_schemas: bool = dataclasses.field(default=False)
@@ -152,11 +156,14 @@ class AbacusUsageMetricsDatasetConfig(DatasetConfig):
 
     Args:
         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 = dataclasses.field(default=False)
+    include_all_feedback: bool = dataclasses.field(default=False)
 
     def __post_init__(self):
         self.application_connector_type = enums.ApplicationConnectorType.ABACUSUSAGEMETRICS
+        self.is_documentset = False
 
 
 @dataclasses.dataclass