Version 1.1.0

abacusai/__init__.py

@@ -4,4 +4,4 @@
 from .streaming_client import StreamingClient
 
 
-__version__ = "1.0.5"
+__version__ = "1.1.0"

abacusai/api_class/__init__.py

@@ -1,4 +1,5 @@
 from .ai_agents import *
+from .ai_chat import *
 from .batch_prediction import *
 from .blob_input import *
 from .dataset import *

abacusai/api_class/abstract.py

@@ -156,12 +156,12 @@ class _ApiClassFactory(ABC):
     @classmethod
     def from_dict(cls, config: dict) -> ApiClass:
         support_kwargs = cls.config_abstract_class and cls.config_abstract_class._support_kwargs
-        config_class_key = cls.config_class_key if (cls.config_abstract_class and not cls.config_abstract_class._upper_snake_case_keys) else camel_case(cls.config_class_key)
+        not_upper_snake_case_keys = cls.config_abstract_class and not cls.config_abstract_class._upper_snake_case_keys
+        config_class_key = cls.config_class_key if (not_upper_snake_case_keys) else camel_case(cls.config_class_key)
+        if not_upper_snake_case_keys and config_class_key not in config and camel_case(config_class_key) in config:
+            config_class_key = camel_case(config_class_key)
         if not support_kwargs and config_class_key not in (config or {}):
-            if camel_case(config_class_key) in (config or {}):
-                config_class_key = camel_case(config_class_key)
-            else:
-                raise KeyError(f'Could not find {config_class_key} in {config}')
+            raise KeyError(f'Could not find {config_class_key} in {config}')
         config_class_type = config.get(config_class_key, None)
         if isinstance(config_class_type, str):
             config_class_type = config_class_type.upper()
@@ -186,4 +186,5 @@ def from_dict(cls, config: dict) -> ApiClass:
             raise ValueError(f'Invalid type {config_class_type}')
         if not config_class._upper_snake_case_keys:
             config = {snake_case(k): v for k, v in config.items()}
+        config.pop(snake_case(config_class_key), None)
         return config_class(**config)

abacusai/api_class/ai_chat.py

@@ -0,0 +1,16 @@
+import dataclasses
+
+from .abstract import ApiClass
+
+
+@dataclasses.dataclass
+class HotkeyPrompt(ApiClass):
+    """
+    A config class for a Data Science Co-Pilot Hotkey
+
+    Args:
+        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 = dataclasses.field(default=None)

abacusai/api_class/batch_prediction.py

@@ -9,7 +9,7 @@ class BatchPredictionArgs(ApiClass):
     _support_kwargs: bool = dataclasses.field(default=True, repr=False, init=False)
 
     kwargs: dict = dataclasses.field(default_factory=dict)
-    problem_type: enums.ProblemType = dataclasses.field(default=None)
+    problem_type: enums.ProblemType = dataclasses.field(default=None, repr=False, init=False)
 
     @classmethod
     def _get_builder(cls):
@@ -223,7 +223,7 @@ def __post_init__(self):
 @dataclasses.dataclass
 class _BatchPredictionArgsFactory(_ApiClassFactory):
     config_abstract_class = BatchPredictionArgs
-    config_class_key = 'problemType'
+    config_class_key = 'problem_type'
     config_class_map = {
         enums.ProblemType.ANOMALY_DETECTION: AnomalyDetectionBatchPredictionArgs,
         enums.ProblemType.ANOMALY_OUTLIERS: AnomalyOutliersBatchPredictionArgs,

abacusai/api_class/dataset_application_connector.py

@@ -6,7 +6,7 @@
 
 @dataclasses.dataclass
 class DatasetConfig(ApiClass):
-    application_connector_type: enums.ApplicationConnectorType = dataclasses.field(default=None, repr=False)
+    application_connector_type: enums.ApplicationConnectorType = dataclasses.field(default=None, repr=False, init=False)
 
     @classmethod
     def _get_builder(cls):
@@ -145,7 +145,7 @@ def __post_init__(self):
 @dataclasses.dataclass
 class _DatasetConfigFactory(_ApiClassFactory):
     config_abstract_class = DatasetConfig
-    config_class_key = 'applicationConnectorType'
+    config_class_key = 'application_connector_type'
     config_class_map = {
         enums.ApplicationConnectorType.CONFLUENCE: ConfluenceDatasetConfig,
         enums.ApplicationConnectorType.GOOGLEANALYTICS: GoogleAnalyticsDatasetConfig,

abacusai/api_class/enums.py

@@ -48,6 +48,7 @@ class ProblemType(ApiEnum):
     OPTIMIZATION = 'optimization'
     PRETRAINED_MODELS = 'pretrained'
     THEME_ANALYSIS = 'theme_analysis'
+    TS_ANOMALY = 'ts_anomaly'
 
 
 class RegressionObjective(ApiEnum):

abacusai/api_class/feature_group.py

@@ -10,6 +10,7 @@ class SamplingConfig(ApiClass):
     """
     An abstract class for the sampling config of a feature group
     """
+    sampling_method: enums.SamplingMethodType = dataclasses.field(default=None, repr=False, init=False)
 
     @classmethod
     def _get_builder(cls):
@@ -32,7 +33,9 @@ class NSamplingConfig(SamplingConfig):
     """
     sample_count: int
     key_columns: List[str] = dataclasses.field(default_factory=list)
-    sampling_method: enums.SamplingMethodType = enums.SamplingMethodType.N_SAMPLING
+
+    def __post_init__(self):
+        self.sampling_method = enums.SamplingMethodType.N_SAMPLING
 
 
 @dataclasses.dataclass
@@ -47,12 +50,15 @@ class PercentSamplingConfig(SamplingConfig):
     """
     sample_percent: float
     key_columns: List[str] = dataclasses.field(default_factory=list)
-    sampling_method: enums.SamplingMethodType = enums.SamplingMethodType.PERCENT_SAMPLING
+
+    def __post_init__(self):
+        self.sampling_method = enums.SamplingMethodType.PERCENT_SAMPLING
 
 
 @dataclasses.dataclass
 class _SamplingConfigFactory(_ApiClassFactory):
     config_class_key = 'sampling_method'
+    config_abstract_class = SamplingConfig
     config_class_map = {
         enums.SamplingMethodType.N_SAMPLING: NSamplingConfig,
         enums.SamplingMethodType.PERCENT_SAMPLING: PercentSamplingConfig,
@@ -64,6 +70,7 @@ class MergeConfig(ApiClass):
     """
     An abstract class for the merge config of a feature group
     """
+    merge_mode: enums.MergeMode = dataclasses.field(default=None, repr=False, init=False)
 
     @classmethod
     def _get_builder(self):
@@ -84,7 +91,9 @@ class LastNMergeConfig(MergeConfig):
         num_versions (int): The number of versions to merge. num_versions == 0 means merge all versions.
     """
     num_versions: int
-    merge_mode: enums.MergeMode = enums.MergeMode.LAST_N
+
+    def __post_init__(self):
+        self.merge_mode = enums.MergeMode.LAST_N
 
 
 @dataclasses.dataclass
@@ -99,12 +108,15 @@ class TimeWindowMergeConfig(MergeConfig):
     """
     feature_name: str
     time_window_size_ms: int
-    merge_mode: enums.MergeMode = enums.MergeMode.TIME_WINDOW
+
+    def __post_init__(self):
+        self.merge_mode = enums.MergeMode.TIME_WINDOW
 
 
 @dataclasses.dataclass
 class _MergeConfigFactory(_ApiClassFactory):
     config_class_key = 'merge_mode'
+    config_abstract_class = MergeConfig
     config_class_map = {
         enums.MergeMode.LAST_N: LastNMergeConfig,
         enums.MergeMode.TIME_WINDOW: TimeWindowMergeConfig,

abacusai/api_class/model.py

@@ -445,6 +445,7 @@ class ChatLLMTrainingConfig(TrainingConfig):
         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.
         data_prompt_context (str): Prompt context for the data feature group ids.
+        hide_generated_sql (bool): When running data queries, hides the generated SQL in the response and will just return the table.
     """
     document_retrievers: List[str] = None
     num_completion_tokens: int = None
@@ -456,6 +457,7 @@ class ChatLLMTrainingConfig(TrainingConfig):
     max_search_results: int = None
     data_feature_group_ids: List[str] = None
     data_prompt_context: str = None
+    hide_generated_sql: bool = None
 
     def __post_init__(self):
         self.problem_type = enums.ProblemType.CHAT_LLM
@@ -612,6 +614,25 @@ def __post_init__(self):
         self.problem_type = enums.ProblemType.EVENT_ANOMALY
 
 
+@dataclasses.dataclass
+class TimeseriesAnomalyTrainingConfig(TrainingConfig):
+    """
+    Training config for the TS_ANOMALY problem type
+
+    Args:
+    type_of_split: 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.
+
+    """
+    type_of_split: str = dataclasses.field(default=None)
+    test_start: str = dataclasses.field(default=None)
+    test_split: int = dataclasses.field(default=None)
+
+    def __post_init__(self):
+        self.problem_type = enums.ProblemType.TS_ANOMALY
+
+
 @dataclasses.dataclass
 class CumulativeForecastingTrainingConfig(TrainingConfig):
     """
@@ -776,7 +797,8 @@ class _TrainingConfigFactory(_ApiClassFactory):
         enums.ProblemType.SENTIMENT_DETECTION: SentimentDetectionTrainingConfig,
         enums.ProblemType.THEME_ANALYSIS: ThemeAnalysisTrainingConfig,
         enums.ProblemType.CUSTOM_ALGORITHM: CustomAlgorithmTrainingConfig,
-        enums.ProblemType.OPTIMIZATION: OptimizationTrainingConfig
+        enums.ProblemType.OPTIMIZATION: OptimizationTrainingConfig,
+        enums.ProblemType.TS_ANOMALY: TimeseriesAnomalyTrainingConfig,
     }
 
 

abacusai/api_class/monitor_alert.py

@@ -7,7 +7,7 @@
 
 @dataclasses.dataclass
 class AlertConditionConfig(ApiClass):
-    alert_type: enums.MonitorAlertType = dataclasses.field(default=None, init=True)
+    alert_type: enums.MonitorAlertType = dataclasses.field(default=None, repr=False, init=False)
 
     @classmethod
     def _get_builder(cls):
@@ -149,7 +149,7 @@ class _AlertConditionConfigFactory(_ApiClassFactory):
 
 @dataclasses.dataclass
 class AlertActionConfig(ApiClass):
-    action_type: enums.AlertActionType = dataclasses.field(default=None, repr=False, init=True)
+    action_type: enums.AlertActionType = dataclasses.field(default=None, repr=False, init=False)
 
     @classmethod
     def _get_builder(cls):
@@ -179,3 +179,24 @@ class _AlertActionConfigFactory(_ApiClassFactory):
     config_class_map = {
         enums.AlertActionType.EMAIL: EmailActionConfig,
     }
+
+
+@dataclasses.dataclass
+class MonitorThresholdConfig(ApiClass):
+    """
+    Monitor Threshold Config for Monitor Alerts
+
+    Args:
+        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: enums.FeatureDriftType = dataclasses.field(default=None)
+    at_risk_threshold: float = dataclasses.field(default=None)
+    severely_drifting_threshold: float = dataclasses.field(default=None)
+
+    def to_dict(self):
+        return {
+            'drift_type': self.drift_type,
+            'at_risk_threshold': self.at_risk_threshold,
+            'severely_drifting_threshold': self.severely_drifting_threshold,
+        }

abacusai/api_class/refresh.py

@@ -7,7 +7,7 @@
 
 @dataclasses.dataclass
 class FeatureGroupExportConfig(ApiClass):
-    connector_type: enums.ConnectorType = dataclasses.field(default=None)
+    connector_type: enums.ConnectorType = dataclasses.field(default=None, repr=False, init=False)
 
     @classmethod
     def _get_builder(cls):
@@ -16,10 +16,12 @@ def _get_builder(cls):
 
 @dataclasses.dataclass
 class FileConnectorExportConfig(FeatureGroupExportConfig):
-    connector_type: enums.ConnectorType = dataclasses.field(default=enums.ConnectorType.FILE, repr=False)
     location: str = dataclasses.field(default=None)
     export_file_format: str = dataclasses.field(default=None)
 
+    def __post_init__(self):
+        self.connector_type = enums.ConnectorType.FILE
+
     def to_dict(self):
         return {
             'connector_type': self.connector_type,
@@ -38,6 +40,9 @@ class DatabaseConnectorExportConfig(FeatureGroupExportConfig):
     additional_id_columns: List[str] = dataclasses.field(default=None)
     data_columns: Dict[str, str] = dataclasses.field(default=None)
 
+    def __post_init__(self):
+        self.connector_type = enums.ConnectorType.DATABASE
+
     def to_dict(self):
         return {
             'connector_type': self.connector_type,
@@ -53,7 +58,7 @@ def to_dict(self):
 @dataclasses.dataclass
 class _FeatureGroupExportConfigFactory(_ApiClassFactory):
     config_abstract_class = FeatureGroupExportConfig
-    config_class_key = 'connectorType'
+    config_class_key = 'connector_type'
     config_class_map = {
         enums.ConnectorType.FILE: FileConnectorExportConfig,
         enums.ConnectorType.DATABASE: DatabaseConnectorExportConfig,

abacusai/chat_message.py

@@ -13,20 +13,22 @@ class ChatMessage(AbstractApiClass):
             isUseful (bool): Whether this message was marked as useful or not
             feedback (str): The feedback provided for the message
             docIds (list): A list of IDs of the uploaded document if the message has
+            hotkeyTitle (str): The title of the hotkey prompt if the message has one
     """
 
-    def __init__(self, client, role=None, text=None, timestamp=None, isUseful=None, feedback=None, docIds=None):
+    def __init__(self, client, role=None, text=None, timestamp=None, isUseful=None, feedback=None, docIds=None, hotkeyTitle=None):
         super().__init__(client, None)
         self.role = role
         self.text = text
         self.timestamp = timestamp
         self.is_useful = isUseful
         self.feedback = feedback
         self.doc_ids = docIds
+        self.hotkey_title = hotkeyTitle
 
     def __repr__(self):
-        repr_dict = {f'role': repr(self.role), f'text': repr(self.text), f'timestamp': repr(
-            self.timestamp), f'is_useful': repr(self.is_useful), f'feedback': repr(self.feedback), f'doc_ids': repr(self.doc_ids)}
+        repr_dict = {f'role': repr(self.role), f'text': repr(self.text), f'timestamp': repr(self.timestamp), f'is_useful': repr(
+            self.is_useful), f'feedback': repr(self.feedback), f'doc_ids': repr(self.doc_ids), f'hotkey_title': repr(self.hotkey_title)}
         class_name = "ChatMessage"
         repr_str = ',\n  '.join([f'{key}={value}' for key, value in repr_dict.items(
         ) if getattr(self, key, None) is not None])
@@ -39,6 +41,6 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'role': self.role, 'text': self.text, 'timestamp': self.timestamp,
-                'is_useful': self.is_useful, 'feedback': self.feedback, 'doc_ids': self.doc_ids}
+        resp = {'role': self.role, 'text': self.text, 'timestamp': self.timestamp, 'is_useful': self.is_useful,
+                'feedback': self.feedback, 'doc_ids': self.doc_ids, 'hotkey_title': self.hotkey_title}
         return {key: value for key, value in resp.items() if value is not None}