Version 1.4.32

Author: arvindsun

abacusai/__init__.py

@@ -40,6 +40,7 @@
 from .code_source import CodeSource
 from .compute_point_info import ComputePointInfo
 from .concatenation_config import ConcatenationConfig
+from .constants_autocomplete_response import ConstantsAutocompleteResponse
 from .cpu_gpu_memory_specs import CpuGpuMemorySpecs
 from .custom_chat_instructions import CustomChatInstructions
 from .custom_loss_function import CustomLossFunction
@@ -236,4 +237,4 @@
 from .workflow_node_template import WorkflowNodeTemplate
 
 
-__version__ = "1.4.31"
+__version__ = "1.4.32"

abacusai/api_class/ai_agents.py

@@ -302,10 +302,10 @@ class WorkflowGraphNode(ApiClass):
     Args:
         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.
+        output_mappings (List[str]): List of outputs for the node. Each field in the returned dict/AgentResponse must have a corresponding output in the list.
         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.
+        output_schema (List[str]): The list of outputs to be shown on UI. Each output corresponds to a field in the output mappings of the node.
 
     Additional Attributes:
         function_name (str): The name of the function.
@@ -595,16 +595,13 @@ class WorkflowGraph(ApiClass):
     """
     Represents an Agent workflow graph.
 
-    The edges define the node invocation order.
-
     Args:
         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.
         common_source_code (str): Common source code that can be used across all nodes.
     """
     nodes: List[WorkflowGraphNode] = dataclasses.field(default_factory=list)
-    edges: List[Union[WorkflowGraphEdge, Tuple[WorkflowGraphNode, WorkflowGraphNode, dict], Tuple[str, str, dict]]] = dataclasses.field(default_factory=list)
+    edges: List[Union[WorkflowGraphEdge, Tuple[WorkflowGraphNode, WorkflowGraphNode, dict], Tuple[str, str, dict]]] = dataclasses.field(default_factory=list, metadata={'deprecated': True})
     primary_start_node: Union[str, WorkflowGraphNode] = dataclasses.field(default=None)
     common_source_code: str = dataclasses.field(default=None)
     specification_type: str = dataclasses.field(default='data_flow')

abacusai/api_class/enums.py

@@ -722,3 +722,7 @@ class DeploymentConversationType(ApiEnum):
     COPILOT = 'COPILOT'
     AGENT_CONTROLLER = 'AGENT_CONTROLLER'
     CODE_LLM = 'CODE_LLM'
+    CODE_LLM_AGENT = 'CODE_LLM_AGENT'
+    CHAT_LLM_TASK = 'CHAT_LLM_TASK'
+    COMPUTER_AGENT = 'COMPUTER_AGENT'
+    SEARCH_LLM = 'SEARCH_LLM'

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/chatllm_task.py

@@ -16,9 +16,11 @@ class ChatllmTask(AbstractApiClass):
             deploymentConversationId (str): The deployment conversation id associated with the chatllm task.
             enableEmailAlerts (bool): Whether email alerts are enabled for the chatllm task.
             email (str): The email to send alerts to.
+            computePointsUsed (int): The compute points used for the chatllm task.
+            wasSeen (bool): Whether the chatllm task has been seen.
     """
 
-    def __init__(self, client, chatllmTaskId=None, name=None, instructions=None, lifecycle=None, scheduleInfo=None, externalApplicationId=None, deploymentConversationId=None, enableEmailAlerts=None, email=None):
+    def __init__(self, client, chatllmTaskId=None, name=None, instructions=None, lifecycle=None, scheduleInfo=None, externalApplicationId=None, deploymentConversationId=None, enableEmailAlerts=None, email=None, computePointsUsed=None, wasSeen=None):
         super().__init__(client, chatllmTaskId)
         self.chatllm_task_id = chatllmTaskId
         self.name = name
@@ -29,11 +31,13 @@ def __init__(self, client, chatllmTaskId=None, name=None, instructions=None, lif
         self.deployment_conversation_id = deploymentConversationId
         self.enable_email_alerts = enableEmailAlerts
         self.email = email
+        self.compute_points_used = computePointsUsed
+        self.was_seen = wasSeen
         self.deprecated_keys = {}
 
     def __repr__(self):
-        repr_dict = {f'chatllm_task_id': repr(self.chatllm_task_id), f'name': repr(self.name), f'instructions': repr(self.instructions), f'lifecycle': repr(self.lifecycle), f'schedule_info': repr(
-            self.schedule_info), f'external_application_id': repr(self.external_application_id), f'deployment_conversation_id': repr(self.deployment_conversation_id), f'enable_email_alerts': repr(self.enable_email_alerts), f'email': repr(self.email)}
+        repr_dict = {f'chatllm_task_id': repr(self.chatllm_task_id), f'name': repr(self.name), f'instructions': repr(self.instructions), f'lifecycle': repr(self.lifecycle), f'schedule_info': repr(self.schedule_info), f'external_application_id': repr(
+            self.external_application_id), f'deployment_conversation_id': repr(self.deployment_conversation_id), f'enable_email_alerts': repr(self.enable_email_alerts), f'email': repr(self.email), f'compute_points_used': repr(self.compute_points_used), f'was_seen': repr(self.was_seen)}
         class_name = "ChatllmTask"
         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])
@@ -46,6 +50,6 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'chatllm_task_id': self.chatllm_task_id, 'name': self.name, 'instructions': self.instructions, 'lifecycle': self.lifecycle, 'schedule_info': self.schedule_info,
-                'external_application_id': self.external_application_id, 'deployment_conversation_id': self.deployment_conversation_id, 'enable_email_alerts': self.enable_email_alerts, 'email': self.email}
+        resp = {'chatllm_task_id': self.chatllm_task_id, 'name': self.name, 'instructions': self.instructions, 'lifecycle': self.lifecycle, 'schedule_info': self.schedule_info, 'external_application_id': self.external_application_id,
+                'deployment_conversation_id': self.deployment_conversation_id, 'enable_email_alerts': self.enable_email_alerts, 'email': self.email, 'compute_points_used': self.compute_points_used, 'was_seen': self.was_seen}
         return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}

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 = '1.4.31'
+    client_version = '1.4.32'
 
     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
@@ -3935,9 +3935,9 @@ def get_agent_context_user_info(self):
             raise ValueError(
                 'User information not available. Please use UI interface for this agent to work.')
 
-    def get_agent_runtime_config(self, key: str):
+    def get_runtime_config(self, key: str):
         """
-        Gets the deployment level runtime config for the agent
+        Gets the deployment level runtime config value given the key
 
         Args:
             key(str): Key for which the config value is to be fetched

abacusai/constants_autocomplete_response.py

@@ -0,0 +1,39 @@
+from .return_class import AbstractApiClass
+
+
+class ConstantsAutocompleteResponse(AbstractApiClass):
+    """
+        A dictionary of constants to be used in the autocomplete.
+
+        Args:
+            client (ApiClient): An authenticated API Client instance
+            maxPendingRequests (int): The maximum number of pending requests.
+            acceptanceDelay (int): The acceptance delay.
+            debounceDelay (int): The debounce delay.
+    """
+
+    def __init__(self, client, maxPendingRequests=None, acceptanceDelay=None, debounceDelay=None):
+        super().__init__(client, None)
+        self.max_pending_requests = maxPendingRequests
+        self.acceptance_delay = acceptanceDelay
+        self.debounce_delay = debounceDelay
+        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)}
+        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])
+        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 = {'max_pending_requests': self.max_pending_requests,
+                'acceptance_delay': self.acceptance_delay, 'debounce_delay': self.debounce_delay}
+        return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}

docs/_sources/autoapi/abacusai/api_class/ai_agents/index.rst.txt

@@ -357,14 +357,14 @@ Module Contents
    :type name: str
    :param input_mappings: List of input mappings for the node. Each arg/kwarg of the node function should have a corresponding input mapping.
    :type input_mappings: List[WorkflowNodeInputMapping]
-   :param output_mappings: List of output mappings for the node. Each field in the returned dict/AgentResponse must have a corresponding output mapping.
-   :type output_mappings: List[WorkflowNodeOutputMapping]
+   :param output_mappings: List of outputs for the node. Each field in the returned dict/AgentResponse must have a corresponding output in the list.
+   :type output_mappings: List[str]
    :param function: The callable node function reference.
    :type function: callable
    :param input_schema: The react json schema for the user input variables.
    :type input_schema: WorkflowNodeInputSchema
-   :param output_schema: The react json schema for the output to be shown on UI.
-   :type output_schema: WorkflowNodeOutputSchema
+   :param output_schema: The list of outputs to be shown on UI. Each output corresponds to a field in the output mappings of the node.
+   :type output_schema: List[str]
 
    Additional Attributes:
        function_name (str): The name of the function.
@@ -469,12 +469,8 @@ Module Contents
 
    Represents an Agent workflow graph.
 
-   The edges define the node invocation order.
-
    :param nodes: A list of nodes in the workflow graph.
    :type nodes: List[WorkflowGraphNode]
-   :param edges: A list of edges in the workflow graph, where each edge is a tuple of source, target, and details.
-   :type edges: List[WorkflowGraphEdge]
    :param primary_start_node: The primary node to start the workflow from.
    :type primary_start_node: Union[str, WorkflowGraphNode]
    :param common_source_code: Common source code that can be used across all nodes.

docs/_sources/autoapi/abacusai/api_class/enums/index.rst.txt

@@ -2999,3 +2999,23 @@ Module Contents
 
 
 
+   .. py:attribute:: CODE_LLM_AGENT
+      :value: 'CODE_LLM_AGENT'
+
+
+
+   .. py:attribute:: CHAT_LLM_TASK
+      :value: 'CHAT_LLM_TASK'
+
+
+
+   .. py:attribute:: COMPUTER_AGENT
+      :value: 'COMPUTER_AGENT'
+
+
+
+   .. py:attribute:: SEARCH_LLM
+      :value: 'SEARCH_LLM'
+
+
+