Version 1.4.30

Author: arvindsun

abacusai/__init__.py

@@ -29,7 +29,9 @@
 from .chat_session import ChatSession
 from .chatllm_computer import ChatllmComputer
 from .chatllm_referral_invite import ChatllmReferralInvite
+from .chatllm_task import ChatllmTask
 from .client import AgentResponse, ApiClient, ApiException, ClientOptions, ReadOnlyClient, _request_context
+from .code_agent_response import CodeAgentResponse
 from .code_autocomplete_response import CodeAutocompleteResponse
 from .code_bot import CodeBot
 from .code_edit import CodeEdit
@@ -119,6 +121,7 @@
 from .holdout_analysis import HoldoutAnalysis
 from .holdout_analysis_version import HoldoutAnalysisVersion
 from .hosted_model_token import HostedModelToken
+from .hume_voice import HumeVoice
 from .image_gen_settings import ImageGenSettings
 from .indexing_config import IndexingConfig
 from .inferred_database_column_to_feature_mappings import InferredDatabaseColumnToFeatureMappings
@@ -224,6 +227,7 @@
 from .user_exception import UserException
 from .video_gen_settings import VideoGenSettings
 from .video_search_result import VideoSearchResult
+from .voice_gen_details import VoiceGenDetails
 from .web_page_response import WebPageResponse
 from .web_search_response import WebSearchResponse
 from .web_search_result import WebSearchResult
@@ -232,4 +236,4 @@
 from .workflow_node_template import WorkflowNodeTemplate
 
 
-__version__ = "1.4.28"
+__version__ = "1.4.30"

abacusai/agent.py

@@ -102,6 +102,18 @@ def list_versions(self, limit: int = 100, start_after_version: str = None):
         """
         return self.client.list_agent_versions(self.agent_id, limit, start_after_version)
 
+    def copy(self, project_id: str = None):
+        """
+        Creates a copy of the input agent
+
+        Args:
+            project_id (str): Project id to create the new agent to. By default it picks up the source agent's project id.
+
+        Returns:
+            Agent: The newly generated agent.
+        """
+        return self.client.copy_agent(self.agent_id, project_id)
+
     @property
     def description(self) -> str:
         """

abacusai/api_class/ai_agents.py

@@ -594,27 +594,33 @@ class WorkflowGraph(ApiClass):
         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)
     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')
 
     def __post_init__(self):
         if self.edges:
-            for index, edge in enumerate(self.edges):
-                if isinstance(edge, Tuple):
-                    source = edge[0] if isinstance(edge[0], str) else edge[0].name
-                    target = edge[1] if isinstance(edge[1], str) else edge[1].name
-                    details = edge[2] if len(edge) > 2 and isinstance(edge[2], dict) else None
-                    self.edges[index] = WorkflowGraphEdge(source=source, target=target, details=details)
+            if self.specification_type == 'execution_flow':
+                for index, edge in enumerate(self.edges):
+                    if isinstance(edge, Tuple):
+                        source = edge[0] if isinstance(edge[0], str) else edge[0].name
+                        target = edge[1] if isinstance(edge[1], str) else edge[1].name
+                        details = edge[2] if len(edge) > 2 and isinstance(edge[2], dict) else None
+                        self.edges[index] = WorkflowGraphEdge(source=source, target=target, details=details)
+            else:
+                raise ValueError('workflow_graph', 'Workflow Graph no longer supports explicit edges. They are inferred from data flow dependencies.')
 
     def to_dict(self):
         return {
             'nodes': [node.to_dict() for node in self.nodes],
-            'edges': [edge.to_dict() for edge in self.edges],
+            **({'edges': [edge.to_dict() for edge in self.edges]} if self.specification_type == 'execution_flow' else {}),
             'primary_start_node': self.primary_start_node.name if isinstance(self.primary_start_node, WorkflowGraphNode) else self.primary_start_node,
-            'common_source_code': self.common_source_code
+            'common_source_code': self.common_source_code,
+            'specification_type': self.specification_type
         }
 
     @classmethod
@@ -625,19 +631,45 @@ def from_dict(cls, graph: dict):
                 node['__return_filter'] = True
         nodes = [WorkflowGraphNode.from_dict(node) for node in graph.get('nodes', [])]
         edges = [WorkflowGraphEdge.from_dict(edge) for edge in graph.get('edges', [])]
-        if graph.get('primary_start_node') not in [node.name for node in nodes]:
-            non_primary_nodes = set()
-            for edge in edges:
-                non_primary_nodes.add(edge.target)
-            primary_nodes = set([node.name for node in nodes]) - non_primary_nodes
-            graph['primary_start_node'] = primary_nodes.pop() if primary_nodes else None
+        primary_start_node = graph.get('primary_start_node')
+        non_primary_nodes = set()
+        specification_type = graph.get('specification_type', 'execution_flow')
 
-        return cls(
-            nodes=nodes,
-            edges=edges,
-            primary_start_node=graph.get('primary_start_node', None),
-            common_source_code=graph.get('common_source_code', None)
-        )
+        if specification_type == 'execution_flow':
+            if primary_start_node not in [node.name for node in nodes]:
+                for edge in edges:
+                    non_primary_nodes.add(edge.target)
+                primary_nodes = set([node.name for node in nodes]) - non_primary_nodes
+                graph['primary_start_node'] = primary_nodes.pop() if primary_nodes else None
+
+            return cls(
+                nodes=nodes,
+                edges=edges,
+                primary_start_node=graph.get('primary_start_node', None),
+                common_source_code=graph.get('common_source_code', None),
+                specification_type='execution_flow'
+            )
+        else:
+            if edges:
+                raise ValueError('workflow_graph', 'Workflow Graph no longer supports explicit edges. They are inferred from data flow dependencies.')
+
+            if primary_start_node not in [node.name for node in nodes]:
+                for node in nodes:
+                    is_primary_eligible = True
+                    for mapping in node.input_mappings:
+                        if mapping.variable_type == enums.WorkflowNodeInputType.WORKFLOW_VARIABLE:
+                            is_primary_eligible = False
+                            break
+                    if not is_primary_eligible:
+                        non_primary_nodes.add(node.name)
+                primary_nodes = set([node.name for node in nodes]) - non_primary_nodes
+                primary_start_node = primary_nodes.pop() if primary_nodes else None
+            return cls(
+                nodes=nodes,
+                primary_start_node=primary_start_node,
+                common_source_code=graph.get('common_source_code', None),
+                specification_type='data_flow'
+            )
 
 
 @dataclasses.dataclass

abacusai/api_class/enums.py

@@ -470,6 +470,7 @@ class LLMName(ApiEnum):
     OPENAI_GPT4_128K_LATEST = 'OPENAI_GPT4_128K_LATEST'
     OPENAI_GPT4O = 'OPENAI_GPT4O'
     OPENAI_GPT4O_MINI = 'OPENAI_GPT4O_MINI'
+    OPENAI_O1_MINI = 'OPENAI_O1_MINI'
     OPENAI_GPT3_5 = 'OPENAI_GPT3_5'
     OPENAI_GPT3_5_TEXT = 'OPENAI_GPT3_5_TEXT'
     LLAMA3_1_405B = 'LLAMA3_1_405B'
@@ -485,6 +486,7 @@ class LLMName(ApiEnum):
     GEMINI_1_5_PRO = 'GEMINI_1_5_PRO'
     GEMINI_2_FLASH = 'GEMINI_2_FLASH'
     GEMINI_2_FLASH_THINKING = 'GEMINI_2_FLASH_THINKING'
+    GEMINI_2_PRO = 'GEMINI_2_PRO'
     ABACUS_SMAUG3 = 'ABACUS_SMAUG3'
     ABACUS_DRACARYS = 'ABACUS_DRACARYS'
     QWEN_2_5_32B = 'QWEN_2_5_32B'

abacusai/api_class/segments.py

@@ -1,4 +1,5 @@
 import dataclasses
+import uuid
 from typing import Any, List
 
 from . import enums
@@ -18,8 +19,11 @@ class ResponseSection(ApiClass):
     type: enums.ResponseSectionType
     id: str
 
+    def __post_init__(self):
+        self.message_id = str(uuid.uuid4())
+
     def to_dict(self):
-        return {k: v.value if isinstance(v, enums.Enum) else v for k, v in dataclasses.asdict(self).items()}
+        return {**{k: v.value if isinstance(v, enums.Enum) else v for k, v in dataclasses.asdict(self).items() if not k.startswith('_')}, 'message_id': self.message_id}
 
 
 Segment = ResponseSection

abacusai/api_client_utils.py

@@ -3,6 +3,7 @@
 import os
 import re
 import string
+import uuid
 from enum import Enum
 from itertools import groupby
 from typing import IO, Callable, List
@@ -104,7 +105,7 @@ def process_streaming_data(cls, value, context, section_key, data_type, is_trans
                             break
                     if not entry_exists:
                         context.streamed_section_response.append(
-                            {'id': section_key, 'type': data_type, 'mime_type': 'text/plain', 'contents': value})
+                            {'id': section_key, 'type': data_type, 'mime_type': 'text/plain', 'contents': value, 'message_id': str(uuid.uuid4())})
                 else:
                     context.streamed_response.append(str(value))
             elif data_type == 'segment':

abacusai/api_key.py

@@ -11,7 +11,7 @@ class ApiKey(AbstractApiClass):
             apiKey (str): The unique API key scoped to a specific organization. Value will be partially obscured.
             apiKeySuffix (str): The last 4 characters of the API key.
             tag (str): A user-friendly tag for the API key.
-            type (str): The type of the API key, either 'default' or 'code-llm'.
+            type (str): The type of the API key, either 'default', 'code-llm', or 'computer-use'.
             createdAt (str): The timestamp when the API key was created.
             expiresAt (str): The timestamp when the API key will expire.
             isExpired (bool): Whether the API key has expired.

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

@@ -0,0 +1,51 @@
+from .return_class import AbstractApiClass
+
+
+class ChatllmTask(AbstractApiClass):
+    """
+        A chatllm task
+
+        Args:
+            client (ApiClient): An authenticated API Client instance
+            chatllmTaskId (str): The id of the chatllm task.
+            name (str): The name of the chatllm task.
+            instructions (str): The instructions of the chatllm task.
+            lifecycle (str): The lifecycle of the chatllm task.
+            scheduleInfo (dict): The schedule info of the chatllm task.
+            externalApplicationId (str): The external application id associated with the chatllm task.
+            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.
+    """
+
+    def __init__(self, client, chatllmTaskId=None, name=None, instructions=None, lifecycle=None, scheduleInfo=None, externalApplicationId=None, deploymentConversationId=None, enableEmailAlerts=None, email=None):
+        super().__init__(client, chatllmTaskId)
+        self.chatllm_task_id = chatllmTaskId
+        self.name = name
+        self.instructions = instructions
+        self.lifecycle = lifecycle
+        self.schedule_info = scheduleInfo
+        self.external_application_id = externalApplicationId
+        self.deployment_conversation_id = deploymentConversationId
+        self.enable_email_alerts = enableEmailAlerts
+        self.email = email
+        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)}
+        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])
+        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 = {'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}
+        return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}