Version 1.4.40

Author: quantumfusion

Diff for: abacusai/__init__.py

@@ -31,7 +31,7 @@
 from .chatllm_project import ChatllmProject
 from .chatllm_referral_invite import ChatllmReferralInvite
 from .chatllm_task import ChatllmTask
-from .client import AgentResponse, ApiClient, ApiException, ClientOptions, ReadOnlyClient, _request_context
+from .client import AgentResponse, ApiClient, ApiException, ClientOptions, ReadOnlyClient, ToolResponse, _request_context
 from .code_agent_response import CodeAgentResponse
 from .code_autocomplete_response import CodeAutocompleteResponse
 from .code_bot import CodeBot
@@ -127,6 +127,7 @@
 from .hosted_app import HostedApp
 from .hosted_app_container import HostedAppContainer
 from .hosted_app_file_read import HostedAppFileRead
+from .hosted_artifact import HostedArtifact
 from .hosted_model_token import HostedModelToken
 from .hume_voice import HumeVoice
 from .image_gen_settings import ImageGenSettings
@@ -245,4 +246,4 @@
 from .workflow_node_template import WorkflowNodeTemplate
 
 
-__version__ = "1.4.39"
+__version__ = "1.4.40"

Diff for: abacusai/api_class/ai_agents.py

@@ -488,6 +488,18 @@ def from_template(cls, template_name: str, name: str, configs: dict = None, inpu
 
     @classmethod
     def from_tool(cls, tool_name: str, name: str, configs: dict = None, input_mappings: Union[Dict[str, WorkflowNodeInputMapping], List[WorkflowNodeInputMapping]] = None, input_schema: Union[List[str], WorkflowNodeInputSchema] = None, output_schema: Union[List[str], WorkflowNodeOutputSchema] = None):
+        """
+        Creates and returns a WorkflowGraphNode based on an available user created tool.
+        Note: DO NOT specify the output mapping for the tool; it will be inferred automatically. Doing so will raise an error.
+
+        Args:
+            tool_name: The name of the tool. There should already be a tool created in the platform with tool_name.
+            name: The name to assign to the WorkflowGraphNode instance.
+            configs (optional): The configuration state of the tool to use (if necessary). If not specified, will use the tool's default configuration.
+            input_mappings (optional): The WorkflowNodeInputMappings for this node.
+            input_schema (optional): The WorkflowNodeInputSchema for this node.
+            output_schema (optional): The WorkflowNodeOutputSchema for this node.
+        """
         node = cls.from_template(
             template_name=tool_name,
             name=name,
@@ -501,6 +513,18 @@ def from_tool(cls, tool_name: str, name: str, configs: dict = None, input_mappin
 
     @classmethod
     def from_system_tool(cls, tool_name: str, name: str, configs: dict = None, input_mappings: Union[Dict[str, WorkflowNodeInputMapping], List[WorkflowNodeInputMapping]] = None, input_schema: Union[List[str], WorkflowNodeInputSchema] = None, output_schema: Union[List[str], WorkflowNodeOutputSchema] = None):
+        """
+        Creates and returns a WorkflowGraphNode based on the name of an available system tool.
+        Note: DO NOT specify the output mapping for the tool; it will be inferred automatically. Doing so will raise an error.
+
+        Args:
+            tool_name: The name of the tool. There should already be a tool created in the platform with tool_name.
+            name: The name to assign to the WorkflowGraphNode instance.
+            configs (optional): The configuration state of the tool to use (if necessary). If not specified, will use the tool's default configuration.
+            input_mappings (optional): The WorkflowNodeInputMappings for this node.
+            input_schema (optional): The WorkflowNodeInputSchema for this node.
+            output_schema (optional): The WorkflowNodeOutputSchema for this node.
+        """
         node = cls.from_tool(tool_name, name, configs, input_mappings, input_schema, output_schema)
         node.template_metadata['is_system_tool'] = True
         return node

Diff for: abacusai/api_class/enums.py

@@ -475,6 +475,8 @@ class LLMName(ApiEnum):
     OPENAI_GPT4O = 'OPENAI_GPT4O'
     OPENAI_GPT4O_MINI = 'OPENAI_GPT4O_MINI'
     OPENAI_O1_MINI = 'OPENAI_O1_MINI'
+    OPENAI_GPT4_1 = 'OPENAI_GPT4_1'
+    OPENAI_GPT4_1_MINI = 'OPENAI_GPT4_1_MINI'
     OPENAI_GPT3_5 = 'OPENAI_GPT3_5'
     OPENAI_GPT3_5_TEXT = 'OPENAI_GPT3_5_TEXT'
     LLAMA3_1_405B = 'LLAMA3_1_405B'
@@ -500,6 +502,8 @@ class LLMName(ApiEnum):
     QWQ_32B = 'QWQ_32B'
     GEMINI_1_5_FLASH = 'GEMINI_1_5_FLASH'
     XAI_GROK = 'XAI_GROK'
+    XAI_GROK_3 = 'XAI_GROK_3'
+    XAI_GROK_3_MINI = 'XAI_GROK_3_MINI'
     DEEPSEEK_V3 = 'DEEPSEEK_V3'
     DEEPSEEK_R1 = 'DEEPSEEK_R1'
 

Diff for: abacusai/client.py

@@ -320,6 +320,15 @@ def __getattr__(self, item):
             f"'{self.__class__.__name__}' object has no attribute '{item}'")
 
 
+class ToolResponse(AgentResponse):
+    """
+    Response object for tool to support non-text response sections
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
 class ClientOptions:
     """
     Options for configuring the ApiClient
@@ -652,7 +661,7 @@ 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 = '1.4.39'
+    client_version = '1.4.40'
 
     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
@@ -2962,7 +2971,7 @@ def create_dataset_from_pandas(self, feature_group_table_name: str, df: pd.DataF
             table_name=feature_group_table_name, file_format='PARQUET')
         return self._upload_from_pandas(upload, df)
 
-    def get_assignments_online_with_new_inputs(self, deployment_token: str, deployment_id: str, assignments_df: pd.DataFrame = None, constraints_df: pd.DataFrame = None, constraint_equations_df: pd.DataFrame = None, feature_mapping_dict: dict = None, solve_time_limit_seconds: float = None):
+    def get_assignments_online_with_new_inputs(self, deployment_token: str, deployment_id: str, assignments_df: pd.DataFrame = None, constraints_df: pd.DataFrame = None, constraint_equations_df: pd.DataFrame = None, feature_mapping_dict: dict = None, solve_time_limit_seconds: float = None, optimality_gap_limit: float = None):
         """
         Get alternative positive assignments for given query. Optimal assignments are ignored and the alternative assignments are returned instead.
 
@@ -2974,6 +2983,7 @@ def get_assignments_online_with_new_inputs(self, deployment_token: str, deployme
             constraint_equations_df (pd.DataFrame): A dataframe which tells us about the operator / constant / penalty etc of a constraint
                                                     This gives us some data which is needed to make sense of the constraints_df.
             solve_time_limit_seconds (float): Maximum time in seconds to spend solving the query.
+            optimality_gap_limit (float): Optimality gap we want to come within, after which we accept the solution as valid. (0 means we only want an optimal solution). it is abs(best_solution_found - best_bound) / abs(best_solution_found)
 
         Returns:
             OptimizationAssignment: The assignments for a given query.
@@ -3012,7 +3022,7 @@ def _serialize_df_with_dtypes(df):
                       'feature_mapping_dict': feature_mapping_dict}
 
         result = self.get_assignments_online_with_new_serialized_inputs(
-            deployment_token=deployment_token, deployment_id=deployment_id, query_data=query_data, solve_time_limit_seconds=solve_time_limit_seconds)
+            deployment_token=deployment_token, deployment_id=deployment_id, query_data=query_data, solve_time_limit_seconds=solve_time_limit_seconds, optimality_gap_limit=optimality_gap_limit)
         return result
 
     def create_dataset_version_from_pandas(self, table_name_or_id: str, df: pd.DataFrame, clean_column_names: bool = False) -> Dataset:
@@ -7570,17 +7580,18 @@ def get_alternative_assignments(self, deployment_token: str, deployment_id: str,
             deployment_id, deployment_token) if deployment_token else None
         return self._call_api('getAlternativeAssignments', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, body={'queryData': query_data, 'addConstraints': add_constraints, 'solveTimeLimitSeconds': solve_time_limit_seconds, 'bestAlternateOnly': best_alternate_only}, server_override=prediction_url)
 
-    def get_assignments_online_with_new_serialized_inputs(self, deployment_token: str, deployment_id: str, query_data: dict = None, solve_time_limit_seconds: float = None) -> Dict:
+    def get_assignments_online_with_new_serialized_inputs(self, deployment_token: str, deployment_id: str, query_data: dict = None, solve_time_limit_seconds: float = None, optimality_gap_limit: float = None) -> Dict:
         """Get assignments for given query, with new inputs
 
         Args:
             deployment_token (str): The deployment token used to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it can be safely embedded in an application or website.
             deployment_id (str): The unique identifier of a deployment created under the project.
             query_data (dict): a dictionary with assignment, constraint and constraint_equations_df
-            solve_time_limit_seconds (float): Maximum time in seconds to spend solving the query."""
+            solve_time_limit_seconds (float): Maximum time in seconds to spend solving the query.
+            optimality_gap_limit (float): Optimality gap we want to come within, after which we accept the solution as valid. (0 means we only want an optimal solution). it is abs(best_solution_found - best_bound) / abs(best_solution_found)"""
         prediction_url = self._get_prediction_endpoint(
             deployment_id, deployment_token) if deployment_token else None
-        return self._call_api('getAssignmentsOnlineWithNewSerializedInputs', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, body={'queryData': query_data, 'solveTimeLimitSeconds': solve_time_limit_seconds}, server_override=prediction_url)
+        return self._call_api('getAssignmentsOnlineWithNewSerializedInputs', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, body={'queryData': query_data, 'solveTimeLimitSeconds': solve_time_limit_seconds, 'optimalityGapLimit': optimality_gap_limit}, server_override=prediction_url)
 
     def check_constraints(self, deployment_token: str, deployment_id: str, query_data: dict) -> Dict:
         """Check for any constraints violated by the overrides.

Diff for: abacusai/deployment_conversation.py

@@ -1,4 +1,5 @@
 from .deployment_conversation_event import DeploymentConversationEvent
+from .hosted_artifact import HostedArtifact
 from .return_class import AbstractApiClass
 
 
@@ -11,6 +12,7 @@ class DeploymentConversation(AbstractApiClass):
             deploymentConversationId (str): The unique identifier of the deployment conversation.
             name (str): The name of the deployment conversation.
             deploymentId (str): The deployment id associated with the deployment conversation.
+            ownerUserId (str): The user id of the owner of the deployment conversation.
             createdAt (str): The timestamp at which the deployment conversation was created.
             lastEventCreatedAt (str): The timestamp at which the most recent corresponding deployment conversation event was created at.
             hasHistory (bool): Whether the deployment conversation has any history.
@@ -27,13 +29,15 @@ class DeploymentConversation(AbstractApiClass):
             searchSuggestions (list): The list of search suggestions for the conversation.
             chatllmTaskId (str): The chatllm task id associated with the deployment conversation.
             history (DeploymentConversationEvent): The history of the deployment conversation.
+            hostedArtifacts (HostedArtifact): Artifacts that have been deployed by this conversation.
     """
 
-    def __init__(self, client, deploymentConversationId=None, name=None, deploymentId=None, createdAt=None, lastEventCreatedAt=None, hasHistory=None, externalSessionId=None, regenerateAttempt=None, externalApplicationId=None, unusedDocumentUploadIds=None, humanizeInstructions=None, conversationWarning=None, conversationType=None, metadata=None, llmDisplayName=None, llmBotIcon=None, searchSuggestions=None, chatllmTaskId=None, history={}):
+    def __init__(self, client, deploymentConversationId=None, name=None, deploymentId=None, ownerUserId=None, createdAt=None, lastEventCreatedAt=None, hasHistory=None, externalSessionId=None, regenerateAttempt=None, externalApplicationId=None, unusedDocumentUploadIds=None, humanizeInstructions=None, conversationWarning=None, conversationType=None, metadata=None, llmDisplayName=None, llmBotIcon=None, searchSuggestions=None, chatllmTaskId=None, history={}, hostedArtifacts={}):
         super().__init__(client, deploymentConversationId)
         self.deployment_conversation_id = deploymentConversationId
         self.name = name
         self.deployment_id = deploymentId
+        self.owner_user_id = ownerUserId
         self.created_at = createdAt
         self.last_event_created_at = lastEventCreatedAt
         self.has_history = hasHistory
@@ -51,11 +55,13 @@ def __init__(self, client, deploymentConversationId=None, name=None, deploymentI
         self.chatllm_task_id = chatllmTaskId
         self.history = client._build_class(
             DeploymentConversationEvent, history)
+        self.hosted_artifacts = client._build_class(
+            HostedArtifact, hostedArtifacts)
         self.deprecated_keys = {}
 
     def __repr__(self):
-        repr_dict = {f'deployment_conversation_id': repr(self.deployment_conversation_id), f'name': repr(self.name), f'deployment_id': repr(self.deployment_id), f'created_at': repr(self.created_at), f'last_event_created_at': repr(self.last_event_created_at), f'has_history': repr(self.has_history), f'external_session_id': repr(self.external_session_id), f'regenerate_attempt': repr(self.regenerate_attempt), f'external_application_id': repr(self.external_application_id), f'unused_document_upload_ids': repr(
-            self.unused_document_upload_ids), f'humanize_instructions': repr(self.humanize_instructions), f'conversation_warning': repr(self.conversation_warning), f'conversation_type': repr(self.conversation_type), f'metadata': repr(self.metadata), f'llm_display_name': repr(self.llm_display_name), f'llm_bot_icon': repr(self.llm_bot_icon), f'search_suggestions': repr(self.search_suggestions), f'chatllm_task_id': repr(self.chatllm_task_id), f'history': repr(self.history)}
+        repr_dict = {f'deployment_conversation_id': repr(self.deployment_conversation_id), f'name': repr(self.name), f'deployment_id': repr(self.deployment_id), f'owner_user_id': repr(self.owner_user_id), f'created_at': repr(self.created_at), f'last_event_created_at': repr(self.last_event_created_at), f'has_history': repr(self.has_history), f'external_session_id': repr(self.external_session_id), f'regenerate_attempt': repr(self.regenerate_attempt), f'external_application_id': repr(self.external_application_id), f'unused_document_upload_ids': repr(
+            self.unused_document_upload_ids), f'humanize_instructions': repr(self.humanize_instructions), f'conversation_warning': repr(self.conversation_warning), f'conversation_type': repr(self.conversation_type), f'metadata': repr(self.metadata), f'llm_display_name': repr(self.llm_display_name), f'llm_bot_icon': repr(self.llm_bot_icon), f'search_suggestions': repr(self.search_suggestions), f'chatllm_task_id': repr(self.chatllm_task_id), f'history': repr(self.history), f'hosted_artifacts': repr(self.hosted_artifacts)}
         class_name = "DeploymentConversation"
         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])
@@ -68,8 +74,8 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'deployment_conversation_id': self.deployment_conversation_id, 'name': self.name, 'deployment_id': self.deployment_id, 'created_at': self.created_at, 'last_event_created_at': self.last_event_created_at, 'has_history': self.has_history, 'external_session_id': self.external_session_id, 'regenerate_attempt': self.regenerate_attempt, 'external_application_id': self.external_application_id, 'unused_document_upload_ids':
-                self.unused_document_upload_ids, 'humanize_instructions': self.humanize_instructions, 'conversation_warning': self.conversation_warning, 'conversation_type': self.conversation_type, 'metadata': self.metadata, 'llm_display_name': self.llm_display_name, 'llm_bot_icon': self.llm_bot_icon, 'search_suggestions': self.search_suggestions, 'chatllm_task_id': self.chatllm_task_id, 'history': self._get_attribute_as_dict(self.history)}
+        resp = {'deployment_conversation_id': self.deployment_conversation_id, 'name': self.name, 'deployment_id': self.deployment_id, 'owner_user_id': self.owner_user_id, 'created_at': self.created_at, 'last_event_created_at': self.last_event_created_at, 'has_history': self.has_history, 'external_session_id': self.external_session_id, 'regenerate_attempt': self.regenerate_attempt, 'external_application_id': self.external_application_id, 'unused_document_upload_ids': self.unused_document_upload_ids,
+                'humanize_instructions': self.humanize_instructions, 'conversation_warning': self.conversation_warning, 'conversation_type': self.conversation_type, 'metadata': self.metadata, 'llm_display_name': self.llm_display_name, 'llm_bot_icon': self.llm_bot_icon, 'search_suggestions': self.search_suggestions, 'chatllm_task_id': self.chatllm_task_id, 'history': self._get_attribute_as_dict(self.history), 'hosted_artifacts': self._get_attribute_as_dict(self.hosted_artifacts)}
         return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}
 
     def get(self, external_session_id: str = None, deployment_id: str = None, filter_intermediate_conversation_events: bool = True, get_unused_document_uploads: bool = False):