Version 1.4.83

Author: gyanendra8

abacusai/__init__.py

@@ -281,6 +281,8 @@
 from .user import User
 from .user_exception import UserException
 from .user_group_object_permission import UserGroupObjectPermission
+from .vertical import Vertical
+from .vertical_files_result import VerticalFilesResult
 from .video_gen_costs import VideoGenCosts
 from .video_gen_model import VideoGenModel
 from .video_gen_model_options import VideoGenModelOptions
@@ -301,4 +303,4 @@
 from .workflow_node_template import WorkflowNodeTemplate
 
 
-__version__ = "1.4.82"
+__version__ = "1.4.83"

abacusai/api_class/ai_agents.py

@@ -1055,17 +1055,24 @@ class WebhookTriggerConfig(TriggerConfig):
     Args:
         test_webhook_url (str): The webhook URL for testing. Populated by the API.
         production_webhook_url (str): The webhook URL for production. Populated by the API.
+        response_config (dict): Optional configuration for the webhook HTTP response. Supports:
+            - status_code (int): HTTP status code to return. Defaults to 200.
+            - headers (dict): Custom response headers (key-value pairs).
+            - body (dict|str): Custom response body. Can be a dict (returned as JSON) or a string.
+            - auto_verify (bool): Automatically handle service-specific verification protocols
+              (e.g., Slack challenge, GitHub ping). Defaults to True.
     """
     test_webhook_url: str = dataclasses.field(default=None)
     production_webhook_url: str = dataclasses.field(default=None)
+    response_config: dict = dataclasses.field(default=None)
 
     def to_dict(self):
-        return {'sleep_time': None, 'test_webhook_url': self.test_webhook_url, 'production_webhook_url': self.production_webhook_url}
+        return {'sleep_time': None, 'test_webhook_url': self.test_webhook_url, 'production_webhook_url': self.production_webhook_url, 'response_config': self.response_config}
 
     @classmethod
     def from_dict(cls, configs: dict):
         validate_input_dict_param(configs, friendly_class_name='webhook_trigger_config')
-        return cls(test_webhook_url=configs.get('test_webhook_url'), production_webhook_url=configs.get('production_webhook_url'))
+        return cls(test_webhook_url=configs.get('test_webhook_url'), production_webhook_url=configs.get('production_webhook_url'), response_config=configs.get('response_config'))
 
 
 @validate_constructor_arg_types('workflow_graph_edge')

abacusai/api_class/document_retriever.py

@@ -20,6 +20,7 @@ class VectorStoreConfig(ApiClass):
         use_document_summary (bool): If True, uses the summary of the document in addition to chunks of the document for indexing and querying.
         summary_instructions (str): Instructions for the LLM to generate the document summary.
         standalone_deployment (bool): If True, the document retriever will be deployed as a standalone deployment.
+        page_level (bool): If True, stores each page separately for paginated documents instead of chunking across pages. Default is False.
     """
     chunk_size: int = dataclasses.field(default=None)
     chunk_overlap_fraction: float = dataclasses.field(default=None)
@@ -31,6 +32,7 @@ class VectorStoreConfig(ApiClass):
     use_document_summary: bool = dataclasses.field(default=None)
     summary_instructions: str = dataclasses.field(default=None)
     standalone_deployment: bool = dataclasses.field(default=False)
+    page_level: bool = dataclasses.field(default=False)
 
 
 DocumentRetrieverConfig = VectorStoreConfig

abacusai/api_class/enums.py

@@ -526,7 +526,8 @@ class VectorStoreTextEncoder(ApiEnum):
                   'OPENAI_O1_MINI',
                   'GEMINI_1_5_PRO', 'GEMINI_1_5_FLASH', 'GEMINI_2_PRO', 'GEMINI_2_FLASH_THINKING',
                   'XAI_GROK',
-                  'CLAUDE_V3_OPUS', 'CLAUDE_V3_HAIKU',
+                  'CLAUDE_V3_HAIKU', 'CLAUDE_V3_OPUS', 'CLAUDE_V3_5_HAIKU', 'CLAUDE_V3_5_SONNET', 'CLAUDE_V3_7_SONNET',
+                  'CLAUDE_V4_SONNET', 'CLAUDE_V4_OPUS',
                   'LLAMA3_LARGE_CHAT', 'QWEN_2_5_32B_BASE')
 class LLMName(ApiEnum):
     OPENAI_GPT3_5 = 'OPENAI_GPT3_5'
@@ -561,6 +562,8 @@ class LLMName(ApiEnum):
     CLAUDE_V4_SONNET = 'CLAUDE_V4_SONNET'
     CLAUDE_V4_OPUS = 'CLAUDE_V4_OPUS'
     CLAUDE_V4_5_SONNET = 'CLAUDE_V4_5_SONNET'
+    CLAUDE_V4_5_OPUS = 'CLAUDE_V4_5_OPUS'
+    CLAUDE_V4_6_OPUS = 'CLAUDE_V4_6_OPUS'
     GEMINI_1_5_PRO = 'GEMINI_1_5_PRO'
     GEMINI_1_5_FLASH = 'GEMINI_1_5_FLASH'
     GEMINI_2_PRO = 'GEMINI_2_PRO'

abacusai/api_class/model.py

@@ -506,6 +506,7 @@ class ChatLLMTrainingConfig(TrainingConfig):
         config_connectors (List[str]): List of names of config connectors to use in the ChatLLM. This should not be used with document_retrievers.
         mcp_servers (List[str]): List of names of MCP servers to use in the ChatLLM. This should not be used with document_retrievers.
         agentic_loop_mode (bool): Enables use of agentic loop that uses a series of tool calls when needed to respond. If set to False, the agentic loop will not be used. If not set or set to Auto, the agentic loop will be automatically used based on certain conditions like presence of tools in the model.
+        max_page_images (int): Maximum number of page images to extract and send to the LLM for visual analysis from RAG search results. Defaults to 5.
     """
     document_retrievers: List[str] = dataclasses.field(default=None)
     num_completion_tokens: int = dataclasses.field(default=None)
@@ -521,6 +522,7 @@ class ChatLLMTrainingConfig(TrainingConfig):
     keyword_requirement_instructions: str = dataclasses.field(default=None)
     query_rewrite_instructions: str = dataclasses.field(default=None)
     max_search_results: int = dataclasses.field(default=None)
+    max_page_images: int = dataclasses.field(default=5)
     data_feature_group_ids: List[str] = dataclasses.field(default=None)
     data_prompt_context: str = dataclasses.field(default=None)
     data_prompt_table_context: Dict[str, str] = dataclasses.field(default=None)
@@ -769,10 +771,12 @@ class AIAgentTrainingConfig(TrainingConfig):
         description (str): Description of the agent function.
         agent_interface (AgentInterface): The interface that the agent will be deployed with.
         agent_connectors: (List[enums.ApplicationConnectorType]): The connectors needed for the agent to function.
+        autonomous_trigger_type (AutonomousTriggerType): The type of trigger for autonomous agents. SCHEDULE for periodic execution, WEBHOOK for event-driven execution. Only applicable when agent_interface is AUTONOMOUS.
     """
     description: str = dataclasses.field(default=None)
     agent_interface: enums.AgentInterface = dataclasses.field(default=None)
     agent_connectors: List[enums.ApplicationConnectorType] = dataclasses.field(default=None)
+    autonomous_trigger_type: enums.AutonomousTriggerType = dataclasses.field(default=None)
     enable_binary_input: bool = dataclasses.field(default=None, metadata={'deprecated': True})
     agent_input_schema: dict = dataclasses.field(default=None, metadata={'deprecated': True})
     agent_output_schema: dict = dataclasses.field(default=None, metadata={'deprecated': True})

abacusai/chatllm_project.py

@@ -13,21 +13,25 @@ class ChatllmProject(AbstractApiClass):
             customInstructions (str): The custom instructions of the chatllm project.
             createdAt (str): The creation time of the chatllm project.
             updatedAt (str): The update time of the chatllm project.
+            verticalType (str): The vertical type (HEALTH or null for regular projects).
+            preferences (dict): The preferences JSON for vertical projects.
     """
 
-    def __init__(self, client, chatllmProjectId=None, name=None, description=None, customInstructions=None, createdAt=None, updatedAt=None):
+    def __init__(self, client, chatllmProjectId=None, name=None, description=None, customInstructions=None, createdAt=None, updatedAt=None, verticalType=None, preferences=None):
         super().__init__(client, chatllmProjectId)
         self.chatllm_project_id = chatllmProjectId
         self.name = name
         self.description = description
         self.custom_instructions = customInstructions
         self.created_at = createdAt
         self.updated_at = updatedAt
+        self.vertical_type = verticalType
+        self.preferences = preferences
         self.deprecated_keys = {}
 
     def __repr__(self):
-        repr_dict = {f'chatllm_project_id': repr(self.chatllm_project_id), f'name': repr(self.name), f'description': repr(
-            self.description), f'custom_instructions': repr(self.custom_instructions), f'created_at': repr(self.created_at), f'updated_at': repr(self.updated_at)}
+        repr_dict = {f'chatllm_project_id': repr(self.chatllm_project_id), f'name': repr(self.name), f'description': repr(self.description), f'custom_instructions': repr(
+            self.custom_instructions), f'created_at': repr(self.created_at), f'updated_at': repr(self.updated_at), f'vertical_type': repr(self.vertical_type), f'preferences': repr(self.preferences)}
         class_name = "ChatllmProject"
         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])
@@ -40,6 +44,6 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'chatllm_project_id': self.chatllm_project_id, 'name': self.name, 'description': self.description,
-                'custom_instructions': self.custom_instructions, 'created_at': self.created_at, 'updated_at': self.updated_at}
+        resp = {'chatllm_project_id': self.chatllm_project_id, 'name': self.name, 'description': self.description, 'custom_instructions': self.custom_instructions,
+                'created_at': self.created_at, 'updated_at': self.updated_at, 'vertical_type': self.vertical_type, 'preferences': self.preferences}
         return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}