Version 1.4.64

Author: gyanendra8

abacusai/__init__.py

@@ -279,4 +279,4 @@
 from .workflow_node_template import WorkflowNodeTemplate
 
 
-__version__ = "1.4.63"
+__version__ = "1.4.64"

abacusai/api_class/model.py

@@ -490,6 +490,7 @@ class ChatLLMTrainingConfig(TrainingConfig):
         search_score_cutoff (float): Minimum search score to consider a document as a valid search result.
         include_bm25_retrieval (bool): Combine BM25 search score with vector search using reciprocal rank fusion.
         database_connector_id (str): Database connector ID to use for connecting external database that gives access to structured data to the LLM.
+        database_connector_ids (List[str]): List of database connector IDs to use for connecting external databases that give access to structured data to the LLM.
         database_connector_tables (List[str]): List of tables to use from the database connector for the ChatLLM.
         enable_code_execution (bool): Enable python code execution in the ChatLLM. This equips the LLM with a python kernel in which all its code is executed.
         enable_response_caching (bool): Enable caching of LLM responses to speed up response times and improve reproducibility.
@@ -527,7 +528,8 @@ class ChatLLMTrainingConfig(TrainingConfig):
     data_columns_to_ignore: List[str] = dataclasses.field(default=None)
     search_score_cutoff: float = dataclasses.field(default=None)
     include_bm25_retrieval: bool = dataclasses.field(default=None)
-    database_connector_id: str = dataclasses.field(default=None)
+    database_connector_id: str = dataclasses.field(default=None)  # deprecated
+    database_connector_ids: List[str] = dataclasses.field(default=None)
     database_connector_tables: List[str] = dataclasses.field(default=None)
     enable_code_execution: bool = dataclasses.field(default=None)
     metadata_columns: list = dataclasses.field(default=None, metadata={'deprecated': True})

abacusai/application_connector.py

@@ -9,25 +9,27 @@ class ApplicationConnector(AbstractApiClass):
             client (ApiClient): An authenticated API Client instance
             applicationConnectorId (str): The unique ID for the connection.
             service (str): The service this connection connects to
+            serviceName (str): For OAuth services, the specific provider name (e.g., 'spotify')
             name (str): A user-friendly name for the service
             createdAt (str): When the API key was created
             status (str): The status of the Application Connector
             auth (dict): Non-secret connection information for this connector
     """
 
-    def __init__(self, client, applicationConnectorId=None, service=None, name=None, createdAt=None, status=None, auth=None):
+    def __init__(self, client, applicationConnectorId=None, service=None, serviceName=None, name=None, createdAt=None, status=None, auth=None):
         super().__init__(client, applicationConnectorId)
         self.application_connector_id = applicationConnectorId
         self.service = service
+        self.service_name = serviceName
         self.name = name
         self.created_at = createdAt
         self.status = status
         self.auth = auth
         self.deprecated_keys = {}
 
     def __repr__(self):
-        repr_dict = {f'application_connector_id': repr(self.application_connector_id), f'service': repr(self.service), f'name': repr(
-            self.name), f'created_at': repr(self.created_at), f'status': repr(self.status), f'auth': repr(self.auth)}
+        repr_dict = {f'application_connector_id': repr(self.application_connector_id), f'service': repr(self.service), f'service_name': repr(
+            self.service_name), f'name': repr(self.name), f'created_at': repr(self.created_at), f'status': repr(self.status), f'auth': repr(self.auth)}
         class_name = "ApplicationConnector"
         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])
@@ -40,7 +42,7 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'application_connector_id': self.application_connector_id, 'service': self.service,
+        resp = {'application_connector_id': self.application_connector_id, 'service': self.service, 'service_name': self.service_name,
                 'name': self.name, 'created_at': self.created_at, 'status': self.status, 'auth': self.auth}
         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 = {'explanations', 'global_prediction_args'}
+        self.deprecated_keys = {'global_prediction_args', 'explanations'}
 
     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 = {'explanations', 'global_prediction_args'}
+        self.deprecated_keys = {'global_prediction_args', 'explanations'}
 
     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

@@ -663,7 +663,7 @@ 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 = '1.4.63'
+    client_version = '1.4.64'
 
     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
@@ -4652,7 +4652,7 @@ def create_model_version_from_local_files(self, model_id: str, optional_artifact
             ModelUpload: Collection of upload IDs to upload the model artifacts."""
         return self._call_api('createModelVersionFromLocalFiles', 'POST', query_params={}, body={'modelId': model_id, 'optionalArtifacts': optional_artifacts}, parse_type=ModelUpload)
 
-    def get_streaming_chat_response(self, deployment_token: str, deployment_id: str, messages: list, 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, ignore_documents: bool = False, include_search_results: bool = False):
+    def get_streaming_chat_response(self, deployment_token: str, deployment_id: str, messages: list, 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, ignore_documents: bool = False, include_search_results: bool = False, user_info: dict = None):
         """Return an asynchronous generator which continues the conversation based on the input messages and search results.
 
         Args:
@@ -4667,7 +4667,8 @@ def get_streaming_chat_response(self, deployment_token: str, deployment_id: str,
             search_score_cutoff (float): Cutoff for the document retriever score. Matching search results below this score will be ignored.
             chat_config (dict): A dictionary specifying the query chat config override.
             ignore_documents (bool): If True, will ignore any documents and search results, and only use the messages to generate a response.
-            include_search_results (bool): If True, will also return search results, if relevant. """
+            include_search_results (bool): If True, will also return search results, if relevant.
+            user_info (dict): The information of the user to act on behalf of for user restricted data sources. """
         headers = {'APIKEY': self.api_key}
         body = {
             'deploymentToken': deployment_token,
@@ -4681,15 +4682,16 @@ def get_streaming_chat_response(self, deployment_token: str, deployment_id: str,
             'searchScoreCutoff': search_score_cutoff,
             'chatConfig': chat_config,
             'ignoreDocuments': ignore_documents,
-            'includeSearchResults': include_search_results
+            'includeSearchResults': include_search_results,
+            'userInfo': user_info
         }
         endpoint = self._get_proxy_endpoint(deployment_id, deployment_token)
         if endpoint is None:
             raise Exception(
                 'API not supported, Please contact Abacus.ai support')
         return sse_asynchronous_generator(f'{endpoint}/api/getStreamingChatResponse', headers, body)
 
-    def get_streaming_conversation_response(self, deployment_token: str, deployment_id: 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, ignore_documents: bool = False, include_search_results: bool = False):
+    def get_streaming_conversation_response(self, deployment_token: str, deployment_id: 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, ignore_documents: bool = False, include_search_results: bool = False, user_info: dict = None):
         """Return an asynchronous generator which continues the conversation based on the input messages and search results.
 
         Args:
@@ -4706,7 +4708,8 @@ def get_streaming_conversation_response(self, deployment_token: str, deployment_
             search_score_cutoff (float): Cutoff for the document retriever score. Matching search results below this score will be ignored.
             chat_config (dict): A dictionary specifying the query chat config override.
             ignore_documents (bool): If True, will ignore any documents and search results, and only use the messages to generate a response.
-            include_search_results (bool): If True, will also return search results, if relevant. """
+            include_search_results (bool): If True, will also return search results, if relevant.
+            user_info (dict): The information of the user to act on behalf of for user restricted data sources. """
         headers = {'APIKEY': self.api_key}
         body = {
             'deploymentToken': deployment_token,
@@ -4722,7 +4725,8 @@ def get_streaming_conversation_response(self, deployment_token: str, deployment_
             'searchScoreCutoff': search_score_cutoff,
             'chatConfig': chat_config,
             'ignoreDocuments': ignore_documents,
-            'includeSearchResults': include_search_results
+            'includeSearchResults': include_search_results,
+            'userInfo': user_info
         }
         endpoint = self._get_proxy_endpoint(deployment_id, deployment_token)
         if endpoint is None:
@@ -7902,17 +7906,6 @@ def transfer_style(self, deployment_token: str, deployment_id: str, source_image
             deployment_id, deployment_token) if deployment_token else None
         return self._call_api('transferStyle', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, data={}, files={'sourceImage': source_image, 'styleImage': style_image}, streamable_response=True, server_override=prediction_url)
 
-    def generate_image(self, deployment_token: str, deployment_id: str, query_data: dict) -> io.BytesIO:
-        """Generate an image from text prompt.
-
-        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 is safe to embed this model within an application or website.
-            deployment_id (str): A unique identifier to a deployment created under the project.
-            query_data (dict): Specifies the text prompt. For example, {'prompt': 'a cat'}"""
-        prediction_url = self._get_prediction_endpoint(
-            deployment_id, deployment_token) if deployment_token else None
-        return self._call_api('generateImage', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, body={'queryData': query_data}, streamable_response=True, server_override=prediction_url)
-
     def execute_agent(self, deployment_token: str, deployment_id: str, arguments: list = None, keyword_arguments: dict = None) -> Dict:
         """Executes a deployed AI agent function using the arguments as keyword arguments to the agent execute function.
 

abacusai/prediction_client.py

@@ -672,17 +672,6 @@ def transfer_style(self, deployment_token: str, deployment_id: str, source_image
             deployment_id, deployment_token) if deployment_token else None
         return self._call_api('transferStyle', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, data={}, files={'sourceImage': source_image, 'styleImage': style_image}, streamable_response=True, server_override=prediction_url)
 
-    def generate_image(self, deployment_token: str, deployment_id: str, query_data: dict) -> io.BytesIO:
-        """Generate an image from text prompt.
-
-        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 is safe to embed this model within an application or website.
-            deployment_id (str): A unique identifier to a deployment created under the project.
-            query_data (dict): Specifies the text prompt. For example, {'prompt': 'a cat'}"""
-        prediction_url = self._get_prediction_endpoint(
-            deployment_id, deployment_token) if deployment_token else None
-        return self._call_api('generateImage', 'POST', query_params={'deploymentToken': deployment_token, 'deploymentId': deployment_id}, body={'queryData': query_data}, streamable_response=True, server_override=prediction_url)
-
     def get_matrix_agent_schema(self, deployment_token: str, deployment_id: str, query: str, doc_infos: list = None, deployment_conversation_id: str = None, external_session_id: str = None) -> Dict:
         """Executes a deployed AI agent function using the arguments as keyword arguments to the agent execute function.
 

abacusai/presentation_export_result.py

@@ -8,15 +8,18 @@ class PresentationExportResult(AbstractApiClass):
         Args:
             client (ApiClient): An authenticated API Client instance
             filePath (str): The path to the exported presentation
+            webViewLink (str): The web view link to the exported presentation (if applicable)
     """
 
-    def __init__(self, client, filePath=None):
+    def __init__(self, client, filePath=None, webViewLink=None):
         super().__init__(client, None)
         self.file_path = filePath
+        self.web_view_link = webViewLink
         self.deprecated_keys = {}
 
     def __repr__(self):
-        repr_dict = {f'file_path': repr(self.file_path)}
+        repr_dict = {f'file_path': repr(
+            self.file_path), f'web_view_link': repr(self.web_view_link)}
         class_name = "PresentationExportResult"
         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])
@@ -29,5 +32,6 @@ def to_dict(self):
         Returns:
             dict: The dict value representation of the class parameters
         """
-        resp = {'file_path': self.file_path}
+        resp = {'file_path': self.file_path,
+                'web_view_link': self.web_view_link}
         return {key: value for key, value in resp.items() if value is not None and key not in self.deprecated_keys}