Version 1.4.54

Author: gyanendra8

abacusai/__init__.py

@@ -20,6 +20,7 @@
 from .app_user_group_sign_in_token import AppUserGroupSignInToken
 from .application_connector import ApplicationConnector
 from .audio_gen_settings import AudioGenSettings
+from .audio_url_result import AudioUrlResult
 from .batch_prediction import BatchPrediction
 from .batch_prediction_version import BatchPredictionVersion
 from .batch_prediction_version_logs import BatchPredictionVersionLogs
@@ -272,4 +273,4 @@
 from .workflow_node_template import WorkflowNodeTemplate
 
 
-__version__ = "1.4.53"
+__version__ = "1.4.54"

abacusai/api_class/enums.py

@@ -416,6 +416,8 @@ class ApplicationConnectorType(ApiEnum):
     BOX = 'BOX'
     SFTPAPPLICATION = 'SFTPAPPLICATION'
     OAUTH = 'OAUTH'
+    SALESFORCE = 'SALESFORCE'
+    TWITTER = 'TWITTER'
 
     @classmethod
     def user_connectors(cls):
@@ -425,9 +427,15 @@ def user_connectors(cls):
             cls.GMAILUSER,
             cls.SLACK,
             cls.JIRA,
-            cls.ONEDRIVE
+            cls.ONEDRIVE,
+            cls.SALESFORCE,
+            cls.TWITTER
         ]
 
+    @classmethod
+    def database_connectors(cls):
+        return [cls.SALESFORCE]
+
 
 class StreamingConnectorType(ApiEnum):
     KAFKA = 'KAFKA'

abacusai/api_class/model.py

@@ -450,7 +450,7 @@ def __post_init__(self):
 @dataclasses.dataclass
 class SystemConnectorTool(ApiClass):
     """
-    System connector tool
+    System connector tool used to integrate chatbots with external services.
 
     Args:
         value (str): The name of the tool.
@@ -501,6 +501,7 @@ class ChatLLMTrainingConfig(TrainingConfig):
         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.
         builtin_tools (List[SystemConnectorTool]): List of builtin system connector tools to use in the ChatLLM. Using builtin tools does not require enabling tool bar (enable_tool_bar flag).
+        mcp_server_configs (str): JSON string of MCP servers configs to use in the ChatLLM model. This should not be used with document_retrievers.
     """
     document_retrievers: List[str] = dataclasses.field(default=None)
     num_completion_tokens: int = dataclasses.field(default=None)
@@ -539,6 +540,7 @@ class ChatLLMTrainingConfig(TrainingConfig):
     json_response_schema: str = dataclasses.field(default=None)
     mask_pii: bool = dataclasses.field(default=None)
     builtin_tools: List[SystemConnectorTool] = dataclasses.field(default=None)
+    mcp_server_configs: str = dataclasses.field(default=None)
 
     def __post_init__(self):
         self.problem_type = enums.ProblemType.CHAT_LLM

abacusai/audio_url_result.py

@@ -0,0 +1,36 @@
+from .return_class import AbstractApiClass
+
+
+class AudioUrlResult(AbstractApiClass):
+    """
+        TTS result
+
+        Args:
+            client (ApiClient): An authenticated API Client instance
+            audioUrl (str): The audio url.
+            creditsUsed (float): The credits used.
+    """
+
+    def __init__(self, client, audioUrl=None, creditsUsed=None):
+        super().__init__(client, None)
+        self.audio_url = audioUrl
+        self.credits_used = creditsUsed
+        self.deprecated_keys = {}
+
+    def __repr__(self):
+        repr_dict = {f'audio_url': repr(
+            self.audio_url), f'credits_used': repr(self.credits_used)}
+        class_name = "AudioUrlResult"
+        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 = {'audio_url': self.audio_url, 'credits_used': self.credits_used}
+        return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}

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

@@ -173,6 +173,7 @@
 from .streaming_auth_token import StreamingAuthToken
 from .streaming_connector import StreamingConnector
 from .training_config_options import TrainingConfigOptions
+from .unified_connector import UnifiedConnector
 from .upload import Upload
 from .upload_part import UploadPart
 from .use_case import UseCase
@@ -661,7 +662,7 @@ 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 = '1.4.53'
+    client_version = '1.4.54'
 
     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
@@ -1587,7 +1588,7 @@ def list_application_connector_objects(self, application_connector_id: str) -> l
             application_connector_id (str): Unique string identifier for the application connector."""
         return self._call_api('listApplicationConnectorObjects', 'GET', query_params={'applicationConnectorId': application_connector_id})
 
-    def get_connector_auth(self, service: Union[ApplicationConnectorType, str] = None, application_connector_id: str = None, scopes: List = None) -> ApplicationConnector:
+    def get_connector_auth(self, service: Union[ApplicationConnectorType, str] = None, application_connector_id: str = None, scopes: List = None) -> UnifiedConnector:
         """Get the authentication details for a given connector. For user level connectors, the service is required. For org level connectors, the application_connector_id is required.
 
         Args:
@@ -1596,19 +1597,19 @@ def get_connector_auth(self, service: Union[ApplicationConnectorType, str] = Non
             scopes (List): The scopes to request for the connector.
 
         Returns:
-            ApplicationConnector: The application connector with the authentication details."""
-        return self._call_api('getConnectorAuth', 'GET', query_params={'service': service, 'applicationConnectorId': application_connector_id, 'scopes': scopes}, parse_type=ApplicationConnector)
+            UnifiedConnector: The application connector with the authentication details."""
+        return self._call_api('getConnectorAuth', 'GET', query_params={'service': service, 'applicationConnectorId': application_connector_id, 'scopes': scopes}, parse_type=UnifiedConnector)
 
-    def get_user_connector_auth(self, service: Union[ApplicationConnectorType, str], scopes: List = None) -> ApplicationConnector:
+    def get_user_connector_auth(self, service: Union[ApplicationConnectorType, str], scopes: List = None) -> UnifiedConnector:
         """Get the authentication details for a given user level connector.
 
         Args:
             service (ApplicationConnectorType): The service name.
             scopes (List): The scopes to request for the connector.
 
         Returns:
-            ApplicationConnector: The application connector with the authentication details."""
-        return self._call_api('getUserConnectorAuth', 'GET', query_params={'service': service, 'scopes': scopes}, parse_type=ApplicationConnector)
+            UnifiedConnector: The application connector with the authentication details."""
+        return self._call_api('getUserConnectorAuth', 'GET', query_params={'service': service, 'scopes': scopes}, parse_type=UnifiedConnector)
 
     def list_streaming_connectors(self) -> List[StreamingConnector]:
         """Retrieves a list of all streaming connectors along with their corresponding attributes.
@@ -7550,7 +7551,7 @@ def get_chat_response_with_binary_data(self, deployment_token: str, deployment_i
             deployment_id, deployment_token) if deployment_token else None
         return self._call_api('getChatResponseWithBinaryData', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, data={'messages': json.dumps(messages) if (messages is not None and not isinstance(messages, str)) else messages, 'llmName': json.dumps(llm_name) if (llm_name is not None and not isinstance(llm_name, str)) else llm_name, 'numCompletionTokens': json.dumps(num_completion_tokens) if (num_completion_tokens is not None and not isinstance(num_completion_tokens, str)) else num_completion_tokens, 'systemMessage': json.dumps(system_message) if (system_message is not None and not isinstance(system_message, str)) else system_message, 'temperature': json.dumps(temperature) if (temperature is not None and not isinstance(temperature, str)) else temperature, 'filterKeyValues': json.dumps(filter_key_values) if (filter_key_values is not None and not isinstance(filter_key_values, str)) else filter_key_values, 'searchScoreCutoff': json.dumps(search_score_cutoff) if (search_score_cutoff is not None and not isinstance(search_score_cutoff, str)) else search_score_cutoff, 'chatConfig': json.dumps(chat_config) if (chat_config is not None and not isinstance(chat_config, str)) else chat_config}, files=attachments, server_override=prediction_url)
 
-    def get_conversation_response(self, deployment_id: str, message: str, deployment_token: str, deployment_conversation_id: str = None, external_session_id: str = None, llm_name: str = None, num_completion_tokens: int = None, system_message: str = None, temperature: float = 0.0, filter_key_values: dict = None, search_score_cutoff: float = None, chat_config: dict = None, doc_infos: list = None, user_info: dict = None) -> Dict:
+    def get_conversation_response(self, deployment_id: str, message: str, deployment_token: str, deployment_conversation_id: str = None, external_session_id: str = None, llm_name: str = None, num_completion_tokens: int = None, system_message: str = None, temperature: float = 0.0, filter_key_values: dict = None, search_score_cutoff: float = None, chat_config: dict = None, doc_infos: list = None, user_info: dict = None, execute_usercode_tool: bool = False) -> Dict:
         """Return a conversation response which continues the conversation based on the input message and deployment conversation id (if exists).
 
         Args:
@@ -7566,10 +7567,11 @@ def get_conversation_response(self, deployment_id: str, message: str, deployment
             filter_key_values (dict): A dictionary mapping column names to a list of values to restrict the retrived search results.
             search_score_cutoff (float): Cutoff for the document retriever score. Matching search results below this score will be ignored.
             chat_config (dict): A dictionary specifiying the query chat config override.
-            doc_infos (list): An optional list of documents use for the conversation. A keyword 'doc_id' is expected to be present in each document for retrieving contents from docstore."""
+            doc_infos (list): An optional list of documents use for the conversation. A keyword 'doc_id' is expected to be present in each document for retrieving contents from docstore.
+            execute_usercode_tool (bool): If True, will return the tool output in the response."""
         prediction_url = self._get_prediction_endpoint(
             deployment_id, deployment_token) if deployment_token else None
-        return self._call_api('getConversationResponse', 'POST', query_params={'deploymentId': deployment_id, 'deploymentToken': deployment_token}, body={'message': message, 'deploymentConversationId': deployment_conversation_id, 'externalSessionId': external_session_id, 'llmName': llm_name, 'numCompletionTokens': num_completion_tokens, 'systemMessage': system_message, 'temperature': temperature, 'filterKeyValues': filter_key_values, 'searchScoreCutoff': search_score_cutoff, 'chatConfig': chat_config, 'docInfos': doc_infos, 'userInfo': user_info}, server_override=prediction_url)
+        return self._call_api('getConversationResponse', 'POST', query_params={'deploymentId': deployment_id, 'deploymentToken': deployment_token}, body={'message': message, 'deploymentConversationId': deployment_conversation_id, 'externalSessionId': external_session_id, 'llmName': llm_name, 'numCompletionTokens': num_completion_tokens, 'systemMessage': system_message, 'temperature': temperature, 'filterKeyValues': filter_key_values, 'searchScoreCutoff': search_score_cutoff, 'chatConfig': chat_config, 'docInfos': doc_infos, 'userInfo': user_info, 'executeUsercodeTool': execute_usercode_tool}, server_override=prediction_url)
 
     def get_conversation_response_with_binary_data(self, deployment_id: str, deployment_token: str, message: str, deployment_conversation_id: str = None, external_session_id: str = None, llm_name: str = None, num_completion_tokens: int = None, system_message: str = None, temperature: float = 0.0, filter_key_values: dict = None, search_score_cutoff: float = None, chat_config: dict = None, attachments: None = None) -> Dict:
         """Return a conversation response which continues the conversation based on the input message and deployment conversation id (if exists).

abacusai/deployment.py

@@ -345,7 +345,7 @@ def create_realtime_monitor(self, realtime_monitor_schedule: str = None, lookbac
         """
         return self.client.create_realtime_monitor(self.deployment_id, realtime_monitor_schedule, lookback_time)
 
-    def get_conversation_response(self, message: str, deployment_token: str, deployment_conversation_id: str = None, external_session_id: str = None, llm_name: str = None, num_completion_tokens: int = None, system_message: str = None, temperature: float = 0.0, filter_key_values: dict = None, search_score_cutoff: float = None, chat_config: dict = None, doc_infos: list = None, user_info: dict = None):
+    def get_conversation_response(self, message: str, deployment_token: str, deployment_conversation_id: str = None, external_session_id: str = None, llm_name: str = None, num_completion_tokens: int = None, system_message: str = None, temperature: float = 0.0, filter_key_values: dict = None, search_score_cutoff: float = None, chat_config: dict = None, doc_infos: list = None, user_info: dict = None, execute_usercode_tool: bool = False):
         """
         Return a conversation response which continues the conversation based on the input message and deployment conversation id (if exists).
 
@@ -362,8 +362,9 @@ def get_conversation_response(self, message: str, deployment_token: str, deploym
             search_score_cutoff (float): Cutoff for the document retriever score. Matching search results below this score will be ignored.
             chat_config (dict): A dictionary specifiying the query chat config override.
             doc_infos (list): An optional list of documents use for the conversation. A keyword 'doc_id' is expected to be present in each document for retrieving contents from docstore.
+            execute_usercode_tool (bool): If True, will return the tool output in the response.
         """
-        return self.client.get_conversation_response(self.deployment_id, message, deployment_token, deployment_conversation_id, external_session_id, llm_name, num_completion_tokens, system_message, temperature, filter_key_values, search_score_cutoff, chat_config, doc_infos, user_info)
+        return self.client.get_conversation_response(self.deployment_id, message, deployment_token, deployment_conversation_id, external_session_id, llm_name, num_completion_tokens, system_message, temperature, filter_key_values, search_score_cutoff, chat_config, doc_infos, user_info, execute_usercode_tool)
 
     def get_conversation_response_with_binary_data(self, deployment_token: str, message: str, deployment_conversation_id: str = None, external_session_id: str = None, llm_name: str = None, num_completion_tokens: int = None, system_message: str = None, temperature: float = 0.0, filter_key_values: dict = None, search_score_cutoff: float = None, chat_config: dict = None, attachments: None = None):
         """