Version 1.4.39

Author: quantumfusion

abacusai/__init__.py

@@ -40,6 +40,7 @@
 from .code_edits import CodeEdits
 from .code_source import CodeSource
 from .code_suggestion_validation_response import CodeSuggestionValidationResponse
+from .code_summary_response import CodeSummaryResponse
 from .compute_point_info import ComputePointInfo
 from .concatenation_config import ConcatenationConfig
 from .constants_autocomplete_response import ConstantsAutocompleteResponse
@@ -133,6 +134,7 @@
 from .inferred_database_column_to_feature_mappings import InferredDatabaseColumnToFeatureMappings
 from .inferred_feature_mappings import InferredFeatureMappings
 from .item_statistics import ItemStatistics
+from .lip_sync_gen_settings import LipSyncGenSettings
 from .llm_app import LlmApp
 from .llm_code_block import LlmCodeBlock
 from .llm_execution_preview import LlmExecutionPreview
@@ -243,4 +245,4 @@
 from .workflow_node_template import WorkflowNodeTemplate
 
 
-__version__ = "1.4.38"
+__version__ = "1.4.39"

abacusai/api_class/batch_prediction.py

@@ -124,10 +124,12 @@ class PretrainedModelsBatchPredictionArgs(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.
+        files_input_location (str): The input location for the files.
         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 = dataclasses.field(default=None)
+    files_input_location: str = dataclasses.field(default=None)
     files_output_location_prefix: str = dataclasses.field(default=None)
     channel_id_to_label_map: str = dataclasses.field(default=None)
 

abacusai/api_class/enums.py

@@ -741,6 +741,7 @@ class DeploymentConversationType(ApiEnum):
     SEARCH_LLM = 'SEARCH_LLM'
     APP_LLM = 'APP_LLM'
     TEST_AGENT = 'TEST_AGENT'
+    SUPER_AGENT = 'SUPER_AGENT'
 
 
 class AgentClientType(ApiEnum):

abacusai/api_class/model.py

@@ -487,7 +487,6 @@ class ChatLLMTrainingConfig(TrainingConfig):
         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"}}'
         mask_pii (bool): Mask PII in the prompts and uploaded documents before sending it to the LLM.
-        custom_tools (List[str]): List of custom tool names to be used in the chat.
     """
     document_retrievers: List[str] = dataclasses.field(default=None)
     num_completion_tokens: int = dataclasses.field(default=None)
@@ -525,7 +524,6 @@ class ChatLLMTrainingConfig(TrainingConfig):
     json_response_instructions: str = dataclasses.field(default=None)
     json_response_schema: str = dataclasses.field(default=None)
     mask_pii: bool = dataclasses.field(default=None)
-    custom_tools: List[str] = dataclasses.field(default=None)
 
     def __post_init__(self):
         self.problem_type = enums.ProblemType.CHAT_LLM

abacusai/api_client_utils.py

@@ -468,9 +468,22 @@ def get_pandas_pages_df(cls, df, feature_group_version: str, doc_id_column: str,
 
         chunk_size = 10 * 1024 * 1024
 
+        def is_valid_config(x):
+            if not isinstance(x, dict):
+                return False
+            if cls.DOCUMENT_PROCESSING_CONFIG not in x:
+                return False
+            if x[cls.DOCUMENT_PROCESSING_CONFIG] is None:
+                return False
+            if x[cls.DOCUMENT_PROCESSING_CONFIG] == {}:
+                return False
+            # if all keys are None, return False
+            if all(v is None for v in x[cls.DOCUMENT_PROCESSING_CONFIG].values()):
+                return False
+            return True
+
         pages_df_with_config = None
-        df_with_config = df[df[document_column].apply(lambda x: isinstance(
-            x, dict) and x.get(cls.DOCUMENT_PROCESSING_CONFIG) is not None)]
+        df_with_config = df[df[document_column].apply(is_valid_config)]
         df = df[~df[doc_id_column].isin(df_with_config[doc_id_column])]
 
         if len(df_with_config) > 0:

abacusai/batch_prediction.py

@@ -84,7 +84,7 @@ def __init__(self, client, batchPredictionId=None, createdAt=None, name=None, de
             BatchPredictionArgs, globalPredictionArgs)
         self.batch_prediction_args = client._build_class(getattr(
             api_class, batchPredictionArgsType, BatchPredictionArgs) if batchPredictionArgsType else BatchPredictionArgs, batchPredictionArgs)
-        self.deprecated_keys = {'global_prediction_args', 'explanations'}
+        self.deprecated_keys = {'explanations', 'global_prediction_args'}
 
     def __repr__(self):
         repr_dict = {f'batch_prediction_id': repr(self.batch_prediction_id), f'created_at': repr(self.created_at), f'name': repr(self.name), f'deployment_id': repr(self.deployment_id), f'file_connector_output_location': repr(self.file_connector_output_location), f'database_connector_id': repr(self.database_connector_id), f'database_output_configuration': repr(self.database_output_configuration), f'file_output_format': repr(self.file_output_format), f'connector_type': repr(self.connector_type), f'legacy_input_location': repr(self.legacy_input_location), f'output_feature_group_id': repr(self.output_feature_group_id), f'feature_group_table_name': repr(self.feature_group_table_name), f'output_feature_group_table_name': repr(self.output_feature_group_table_name), f'summary_feature_group_table_name': repr(self.summary_feature_group_table_name), f'csv_input_prefix': repr(

abacusai/batch_prediction_version.py

@@ -100,7 +100,7 @@ def __init__(self, client, batchPredictionVersion=None, batchPredictionId=None,
             BatchPredictionArgs, globalPredictionArgs)
         self.batch_prediction_args = client._build_class(getattr(
             api_class, batchPredictionArgsType, BatchPredictionArgs) if batchPredictionArgsType else BatchPredictionArgs, batchPredictionArgs)
-        self.deprecated_keys = {'global_prediction_args', 'explanations'}
+        self.deprecated_keys = {'explanations', 'global_prediction_args'}
 
     def __repr__(self):
         repr_dict = {f'batch_prediction_version': repr(self.batch_prediction_version), f'batch_prediction_id': repr(self.batch_prediction_id), f'status': repr(self.status), f'drift_monitor_status': repr(self.drift_monitor_status), f'deployment_id': repr(self.deployment_id), f'model_id': repr(self.model_id), f'model_version': repr(self.model_version), f'predictions_started_at': repr(self.predictions_started_at), f'predictions_completed_at': repr(self.predictions_completed_at), f'database_output_error': repr(self.database_output_error), f'total_predictions': repr(self.total_predictions), f'failed_predictions': repr(self.failed_predictions), f'database_connector_id': repr(self.database_connector_id), f'database_output_configuration': repr(self.database_output_configuration), f'file_connector_output_location': repr(self.file_connector_output_location), f'file_output_format': repr(self.file_output_format), f'connector_type': repr(self.connector_type), f'legacy_input_location': repr(self.legacy_input_location), f'error': repr(self.error), f'drift_monitor_error': repr(self.drift_monitor_error), f'monitor_warnings': repr(self.monitor_warnings), f'csv_input_prefix': repr(

abacusai/client.py

@@ -652,7 +652,7 @@ class BaseApiClient:
         client_options (ClientOptions): Optional API client configurations
         skip_version_check (bool): If true, will skip checking the server's current API version on initializing the client
     """
-    client_version = '1.4.38'
+    client_version = '1.4.39'
 
     def __init__(self, api_key: str = None, server: str = None, client_options: ClientOptions = None, skip_version_check: bool = False, include_tb: bool = False):
         self.api_key = api_key

abacusai/code_summary_response.py

@@ -0,0 +1,33 @@
+from .return_class import AbstractApiClass
+
+
+class CodeSummaryResponse(AbstractApiClass):
+    """
+        A summary response from an LLM
+
+        Args:
+            client (ApiClient): An authenticated API Client instance
+            summary (str): The summary of the code.
+    """
+
+    def __init__(self, client, summary=None):
+        super().__init__(client, None)
+        self.summary = summary
+        self.deprecated_keys = {}
+
+    def __repr__(self):
+        repr_dict = {f'summary': repr(self.summary)}
+        class_name = "CodeSummaryResponse"
+        repr_str = ',\n  '.join([f'{key}={value}' for key, value in repr_dict.items(
+        ) if getattr(self, key, None) is not None and key not in self.deprecated_keys])
+        return f"{class_name}({repr_str})"
+
+    def to_dict(self):
+        """
+        Get a dict representation of the parameters in this class
+
+        Returns:
+            dict: The dict value representation of the class parameters
+        """
+        resp = {'summary': self.summary}
+        return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}

abacusai/constants_autocomplete_response.py

@@ -28,9 +28,10 @@ class ConstantsAutocompleteResponse(AbstractApiClass):
             diagnosticEachThreshold (int): The max number of characters to show for each diagnostic type.
             numVsCodeSuggestions (int): The number of VS Code suggestions to show.
             minReindexingInterval (int): The minimum interval between reindexes in ms.
+            minRefreshSummaryInterval (int): The minimum interval between refresh summary in ms.
     """
 
-    def __init__(self, client, maxPendingRequests=None, acceptanceDelay=None, debounceDelay=None, recordUserAction=None, validateSuggestion=None, validationLinesThreshold=None, maxTrackedRecentChanges=None, diffThreshold=None, derivativeThreshold=None, defaultSurroundingLines=None, maxTrackedVisitChanges=None, selectionCooldownMs=None, viewingCooldownMs=None, maxLines=None, editCooldownMs=None, scrollDebounceMs=None, lspDeadline=None, diagnosticsThreshold=None, diagnosticEachThreshold=None, numVsCodeSuggestions=None, minReindexingInterval=None):
+    def __init__(self, client, maxPendingRequests=None, acceptanceDelay=None, debounceDelay=None, recordUserAction=None, validateSuggestion=None, validationLinesThreshold=None, maxTrackedRecentChanges=None, diffThreshold=None, derivativeThreshold=None, defaultSurroundingLines=None, maxTrackedVisitChanges=None, selectionCooldownMs=None, viewingCooldownMs=None, maxLines=None, editCooldownMs=None, scrollDebounceMs=None, lspDeadline=None, diagnosticsThreshold=None, diagnosticEachThreshold=None, numVsCodeSuggestions=None, minReindexingInterval=None, minRefreshSummaryInterval=None):
         super().__init__(client, None)
         self.max_pending_requests = maxPendingRequests
         self.acceptance_delay = acceptanceDelay
@@ -53,11 +54,12 @@ def __init__(self, client, maxPendingRequests=None, acceptanceDelay=None, deboun
         self.diagnostic_each_threshold = diagnosticEachThreshold
         self.num_vs_code_suggestions = numVsCodeSuggestions
         self.min_reindexing_interval = minReindexingInterval
+        self.min_refresh_summary_interval = minRefreshSummaryInterval
         self.deprecated_keys = {}
 
     def __repr__(self):
         repr_dict = {f'max_pending_requests': repr(self.max_pending_requests), f'acceptance_delay': repr(self.acceptance_delay), f'debounce_delay': repr(self.debounce_delay), f'record_user_action': repr(self.record_user_action), f'validate_suggestion': repr(self.validate_suggestion), f'validation_lines_threshold': repr(self.validation_lines_threshold), f'max_tracked_recent_changes': repr(self.max_tracked_recent_changes), f'diff_threshold': repr(self.diff_threshold), f'derivative_threshold': repr(self.derivative_threshold), f'default_surrounding_lines': repr(self.default_surrounding_lines), f'max_tracked_visit_changes': repr(
-            self.max_tracked_visit_changes), f'selection_cooldown_ms': repr(self.selection_cooldown_ms), f'viewing_cooldown_ms': repr(self.viewing_cooldown_ms), f'max_lines': repr(self.max_lines), f'edit_cooldown_ms': repr(self.edit_cooldown_ms), f'scroll_debounce_ms': repr(self.scroll_debounce_ms), f'lsp_deadline': repr(self.lsp_deadline), f'diagnostics_threshold': repr(self.diagnostics_threshold), f'diagnostic_each_threshold': repr(self.diagnostic_each_threshold), f'num_vs_code_suggestions': repr(self.num_vs_code_suggestions), f'min_reindexing_interval': repr(self.min_reindexing_interval)}
+            self.max_tracked_visit_changes), f'selection_cooldown_ms': repr(self.selection_cooldown_ms), f'viewing_cooldown_ms': repr(self.viewing_cooldown_ms), f'max_lines': repr(self.max_lines), f'edit_cooldown_ms': repr(self.edit_cooldown_ms), f'scroll_debounce_ms': repr(self.scroll_debounce_ms), f'lsp_deadline': repr(self.lsp_deadline), f'diagnostics_threshold': repr(self.diagnostics_threshold), f'diagnostic_each_threshold': repr(self.diagnostic_each_threshold), f'num_vs_code_suggestions': repr(self.num_vs_code_suggestions), f'min_reindexing_interval': repr(self.min_reindexing_interval), f'min_refresh_summary_interval': repr(self.min_refresh_summary_interval)}
         class_name = "ConstantsAutocompleteResponse"
         repr_str = ',\n  '.join([f'{key}={value}' for key, value in repr_dict.items(
         ) if getattr(self, key, None) is not None and key not in self.deprecated_keys])
@@ -70,6 +72,6 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'max_pending_requests': self.max_pending_requests, 'acceptance_delay': self.acceptance_delay, 'debounce_delay': self.debounce_delay, 'record_user_action': self.record_user_action, 'validate_suggestion': self.validate_suggestion, 'validation_lines_threshold': self.validation_lines_threshold, 'max_tracked_recent_changes': self.max_tracked_recent_changes, 'diff_threshold': self.diff_threshold, 'derivative_threshold': self.derivative_threshold, 'default_surrounding_lines': self.default_surrounding_lines,
-                'max_tracked_visit_changes': self.max_tracked_visit_changes, 'selection_cooldown_ms': self.selection_cooldown_ms, 'viewing_cooldown_ms': self.viewing_cooldown_ms, 'max_lines': self.max_lines, 'edit_cooldown_ms': self.edit_cooldown_ms, 'scroll_debounce_ms': self.scroll_debounce_ms, 'lsp_deadline': self.lsp_deadline, 'diagnostics_threshold': self.diagnostics_threshold, 'diagnostic_each_threshold': self.diagnostic_each_threshold, 'num_vs_code_suggestions': self.num_vs_code_suggestions, 'min_reindexing_interval': self.min_reindexing_interval}
+        resp = {'max_pending_requests': self.max_pending_requests, 'acceptance_delay': self.acceptance_delay, 'debounce_delay': self.debounce_delay, 'record_user_action': self.record_user_action, 'validate_suggestion': self.validate_suggestion, 'validation_lines_threshold': self.validation_lines_threshold, 'max_tracked_recent_changes': self.max_tracked_recent_changes, 'diff_threshold': self.diff_threshold, 'derivative_threshold': self.derivative_threshold, 'default_surrounding_lines': self.default_surrounding_lines, 'max_tracked_visit_changes': self.max_tracked_visit_changes,
+                'selection_cooldown_ms': self.selection_cooldown_ms, 'viewing_cooldown_ms': self.viewing_cooldown_ms, 'max_lines': self.max_lines, 'edit_cooldown_ms': self.edit_cooldown_ms, 'scroll_debounce_ms': self.scroll_debounce_ms, 'lsp_deadline': self.lsp_deadline, 'diagnostics_threshold': self.diagnostics_threshold, 'diagnostic_each_threshold': self.diagnostic_each_threshold, 'num_vs_code_suggestions': self.num_vs_code_suggestions, 'min_reindexing_interval': self.min_reindexing_interval, 'min_refresh_summary_interval': self.min_refresh_summary_interval}
         return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}