From df831662711f2e3912f035e543a403e2872a91fa Mon Sep 17 00:00:00 2001 From: SDKAuto Date: Thu, 8 May 2025 02:53:11 +0000 Subject: [PATCH] CodeGen from PR 33602 in Azure/azure-rest-api-specs Merge b5c6fa6155d1b9c19274b9581c055bb4fa5b841b into 121c5e84647a9cdb1767d5146affdfe2af91d776 --- sdk/ai/azure-ai-assistants/CHANGELOG.md | 5 + sdk/ai/azure-ai-assistants/LICENSE | 21 + sdk/ai/azure-ai-assistants/MANIFEST.in | 7 + sdk/ai/azure-ai-assistants/README.md | 43 + sdk/ai/azure-ai-assistants/_meta.json | 6 + .../apiview-properties.json | 286 + sdk/ai/azure-ai-assistants/azure/__init__.py | 1 + .../azure-ai-assistants/azure/ai/__init__.py | 1 + .../azure/ai/assistants/__init__.py | 32 + .../azure/ai/assistants/_client.py | 103 + .../azure/ai/assistants/_configuration.py | 73 + .../ai/assistants/_operations/__init__.py | 25 + .../ai/assistants/_operations/_operations.py | 6002 ++++++++++++++ .../azure/ai/assistants/_operations/_patch.py | 21 + .../azure/ai/assistants/_patch.py | 21 + .../azure/ai/assistants/_types.py | 24 + .../azure/ai/assistants/_utils/__init__.py | 6 + .../azure/ai/assistants/_utils/model_base.py | 1232 +++ .../ai/assistants/_utils/serialization.py | 2032 +++++ .../azure/ai/assistants/_utils/utils.py | 67 + .../azure/ai/assistants/_version.py | 9 + .../azure/ai/assistants/aio/__init__.py | 29 + .../azure/ai/assistants/aio/_client.py | 107 + .../azure/ai/assistants/aio/_configuration.py | 75 + .../ai/assistants/aio/_operations/__init__.py | 25 + .../assistants/aio/_operations/_operations.py | 4866 ++++++++++++ .../ai/assistants/aio/_operations/_patch.py | 21 + .../azure/ai/assistants/aio/_patch.py | 21 + .../azure/ai/assistants/models/__init__.py | 434 + .../azure/ai/assistants/models/_enums.py | 546 ++ .../azure/ai/assistants/models/_models.py | 7002 +++++++++++++++++ .../azure/ai/assistants/models/_patch.py | 21 + .../azure/ai/assistants/py.typed | 1 + .../azure-ai-assistants/dev_requirements.txt | 3 + sdk/ai/azure-ai-assistants/sdk_packaging.toml | 2 + sdk/ai/azure-ai-assistants/setup.py | 70 + sdk/ai/azure-ai-assistants/tsp-location.yaml | 4 + sdk/ai/ci.yml | 2 + 38 files changed, 23246 insertions(+) create mode 100644 sdk/ai/azure-ai-assistants/CHANGELOG.md create mode 100644 sdk/ai/azure-ai-assistants/LICENSE create mode 100644 sdk/ai/azure-ai-assistants/MANIFEST.in create mode 100644 sdk/ai/azure-ai-assistants/README.md create mode 100644 sdk/ai/azure-ai-assistants/_meta.json create mode 100644 sdk/ai/azure-ai-assistants/apiview-properties.json create mode 100644 sdk/ai/azure-ai-assistants/azure/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_client.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_configuration.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_operations.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_patch.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_patch.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_types.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/model_base.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/serialization.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/utils.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/_version.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_client.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_configuration.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_operations.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_patch.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_patch.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/models/__init__.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_enums.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_models.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_patch.py create mode 100644 sdk/ai/azure-ai-assistants/azure/ai/assistants/py.typed create mode 100644 sdk/ai/azure-ai-assistants/dev_requirements.txt create mode 100644 sdk/ai/azure-ai-assistants/sdk_packaging.toml create mode 100644 sdk/ai/azure-ai-assistants/setup.py create mode 100644 sdk/ai/azure-ai-assistants/tsp-location.yaml diff --git a/sdk/ai/azure-ai-assistants/CHANGELOG.md b/sdk/ai/azure-ai-assistants/CHANGELOG.md new file mode 100644 index 000000000000..628743d283a9 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/CHANGELOG.md @@ -0,0 +1,5 @@ +# Release History + +## 1.0.0b1 (1970-01-01) + +- Initial version diff --git a/sdk/ai/azure-ai-assistants/LICENSE b/sdk/ai/azure-ai-assistants/LICENSE new file mode 100644 index 000000000000..63447fd8bbbf --- /dev/null +++ b/sdk/ai/azure-ai-assistants/LICENSE @@ -0,0 +1,21 @@ +Copyright (c) Microsoft Corporation. + +MIT License + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/sdk/ai/azure-ai-assistants/MANIFEST.in b/sdk/ai/azure-ai-assistants/MANIFEST.in new file mode 100644 index 000000000000..c50d503e6ce9 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/MANIFEST.in @@ -0,0 +1,7 @@ +include *.md +include LICENSE +include azure/ai/assistants/py.typed +recursive-include tests *.py +recursive-include samples *.py *.md +include azure/__init__.py +include azure/ai/__init__.py diff --git a/sdk/ai/azure-ai-assistants/README.md b/sdk/ai/azure-ai-assistants/README.md new file mode 100644 index 000000000000..bebc9b5d52e4 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/README.md @@ -0,0 +1,43 @@ +# Azure Ai Assistants client library for Python + + +## Getting started + +### Install the package + +```bash +python -m pip install azure-ai-assistants +``` + +#### Prequisites + +- Python 3.9 or later is required to use this package. +- You need an [Azure subscription][azure_sub] to use this package. +- An existing Azure Ai Assistants instance. + + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require +you to agree to a Contributor License Agreement (CLA) declaring that you have +the right to, and actually do, grant us the rights to use your contribution. +For details, visit https://cla.microsoft.com. + +When you submit a pull request, a CLA-bot will automatically determine whether +you need to provide a CLA and decorate the PR appropriately (e.g., label, +comment). Simply follow the instructions provided by the bot. You will only +need to do this once across all repos using our CLA. + +This project has adopted the +[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information, +see the Code of Conduct FAQ or contact opencode@microsoft.com with any +additional questions or comments. + + +[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/ +[authenticate_with_token]: https://docs.microsoft.com/azure/cognitive-services/authentication?tabs=powershell#authenticate-with-an-authentication-token +[azure_identity_credentials]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials +[azure_identity_pip]: https://pypi.org/project/azure-identity/ +[default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential +[pip]: https://pypi.org/project/pip/ +[azure_sub]: https://azure.microsoft.com/free/ diff --git a/sdk/ai/azure-ai-assistants/_meta.json b/sdk/ai/azure-ai-assistants/_meta.json new file mode 100644 index 000000000000..d641ad9b66ab --- /dev/null +++ b/sdk/ai/azure-ai-assistants/_meta.json @@ -0,0 +1,6 @@ +{ + "commit": "4f2d368e0e665192c789907ef9211b058710b828", + "repository_url": "https://github.com/Azure/azure-rest-api-specs", + "typespec_src": "specification/ai/Azure.AI.Assistants", + "@azure-tools/typespec-python": "0.44.1" +} \ No newline at end of file diff --git a/sdk/ai/azure-ai-assistants/apiview-properties.json b/sdk/ai/azure-ai-assistants/apiview-properties.json new file mode 100644 index 000000000000..a7394de71405 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/apiview-properties.json @@ -0,0 +1,286 @@ +{ + "CrossLanguagePackageId": "Azure.AI.Assistants", + "CrossLanguageDefinitionId": { + "azure.ai.assistants.models.AISearchIndexResource": "Azure.AI.Assistants.AISearchIndexResource", + "azure.ai.assistants.models.Assistant": "Azure.AI.Assistants.Assistant", + "azure.ai.assistants.models.AssistantDeletionStatus": "Azure.AI.Assistants.AssistantDeletionStatus", + "azure.ai.assistants.models.AssistantsApiResponseFormat": "Azure.AI.Assistants.AssistantsApiResponseFormat", + 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"azure.ai.assistants.models.RunStepFileSearchToolCallResults": "Azure.AI.Assistants.RunStepFileSearchToolCallResults", + "azure.ai.assistants.models.RunStepFunctionToolCall": "Azure.AI.Assistants.RunStepFunctionToolCall", + "azure.ai.assistants.models.RunStepFunctionToolCallDetails": "Azure.AI.Assistants.RunStepFunctionToolCallDetails", + "azure.ai.assistants.models.RunStepMessageCreationDetails": "Azure.AI.Assistants.RunStepMessageCreationDetails", + "azure.ai.assistants.models.RunStepMessageCreationReference": "Azure.AI.Assistants.RunStepMessageCreationReference", + "azure.ai.assistants.models.RunStepMicrosoftFabricToolCall": "Azure.AI.Assistants.RunStepMicrosoftFabricToolCall", + "azure.ai.assistants.models.RunStepOpenAPIToolCall": "Azure.AI.Assistants.RunStepOpenAPIToolCall", + "azure.ai.assistants.models.RunStepSharepointToolCall": "Azure.AI.Assistants.RunStepSharepointToolCall", + "azure.ai.assistants.models.RunStepToolCallDetails": "Azure.AI.Assistants.RunStepToolCallDetails", + "azure.ai.assistants.models.SearchConfiguration": "Azure.AI.Assistants.SearchConfiguration", + "azure.ai.assistants.models.SearchConfigurationList": "Azure.AI.Assistants.SearchConfigurationList", + "azure.ai.assistants.models.SharepointToolDefinition": "Azure.AI.Assistants.SharepointToolDefinition", + "azure.ai.assistants.models.SubmitToolOutputsAction": "Azure.AI.Assistants.SubmitToolOutputsAction", + "azure.ai.assistants.models.SubmitToolOutputsDetails": "Azure.AI.Assistants.SubmitToolOutputsDetails", + "azure.ai.assistants.models.ThreadDeletionStatus": "Azure.AI.Assistants.ThreadDeletionStatus", + "azure.ai.assistants.models.ThreadMessage": "Azure.AI.Assistants.ThreadMessage", + "azure.ai.assistants.models.ThreadMessageOptions": "Azure.AI.Assistants.ThreadMessageOptions", + "azure.ai.assistants.models.ThreadRun": "Azure.AI.Assistants.ThreadRun", + "azure.ai.assistants.models.ToolConnection": "Azure.AI.Assistants.ToolConnection", + "azure.ai.assistants.models.ToolConnectionList": "Azure.AI.Assistants.ToolConnectionList", + "azure.ai.assistants.models.ToolOutput": "Azure.AI.Assistants.ToolOutput", + "azure.ai.assistants.models.ToolResources": "Azure.AI.Assistants.ToolResources", + "azure.ai.assistants.models.TruncationObject": "Azure.AI.Assistants.TruncationObject", + "azure.ai.assistants.models.UpdateCodeInterpreterToolResourceOptions": "Azure.AI.Assistants.UpdateCodeInterpreterToolResourceOptions", + "azure.ai.assistants.models.UpdateFileSearchToolResourceOptions": "Azure.AI.Assistants.UpdateFileSearchToolResourceOptions", + "azure.ai.assistants.models.UpdateToolResourcesOptions": "Azure.AI.Assistants.UpdateToolResourcesOptions", + "azure.ai.assistants.models.VectorStore": "Azure.AI.Assistants.VectorStore", + "azure.ai.assistants.models.VectorStoreChunkingStrategyRequest": "Azure.AI.Assistants.VectorStoreChunkingStrategyRequest", + "azure.ai.assistants.models.VectorStoreAutoChunkingStrategyRequest": "Azure.AI.Assistants.VectorStoreAutoChunkingStrategyRequest", + "azure.ai.assistants.models.VectorStoreChunkingStrategyResponse": "Azure.AI.Assistants.VectorStoreChunkingStrategyResponse", + "azure.ai.assistants.models.VectorStoreAutoChunkingStrategyResponse": "Azure.AI.Assistants.VectorStoreAutoChunkingStrategyResponse", + "azure.ai.assistants.models.VectorStoreConfiguration": "Azure.AI.Assistants.VectorStoreConfiguration", + "azure.ai.assistants.models.VectorStoreConfigurations": "Azure.AI.Assistants.VectorStoreConfigurations", + "azure.ai.assistants.models.VectorStoreDataSource": "Azure.AI.Assistants.VectorStoreDataSource", + "azure.ai.assistants.models.VectorStoreDeletionStatus": "Azure.AI.Assistants.VectorStoreDeletionStatus", + "azure.ai.assistants.models.VectorStoreExpirationPolicy": "Azure.AI.Assistants.VectorStoreExpirationPolicy", + "azure.ai.assistants.models.VectorStoreFile": "Azure.AI.Assistants.VectorStoreFile", + "azure.ai.assistants.models.VectorStoreFileBatch": "Azure.AI.Assistants.VectorStoreFileBatch", + "azure.ai.assistants.models.VectorStoreFileCount": "Azure.AI.Assistants.VectorStoreFileCount", + "azure.ai.assistants.models.VectorStoreFileDeletionStatus": "Azure.AI.Assistants.VectorStoreFileDeletionStatus", + "azure.ai.assistants.models.VectorStoreFileError": "Azure.AI.Assistants.VectorStoreFileError", + "azure.ai.assistants.models.VectorStoreStaticChunkingStrategyOptions": "Azure.AI.Assistants.VectorStoreStaticChunkingStrategyOptions", + "azure.ai.assistants.models.VectorStoreStaticChunkingStrategyRequest": "Azure.AI.Assistants.VectorStoreStaticChunkingStrategyRequest", + "azure.ai.assistants.models.VectorStoreStaticChunkingStrategyResponse": "Azure.AI.Assistants.VectorStoreStaticChunkingStrategyResponse", + "azure.ai.assistants.models.OpenApiAuthType": "Azure.AI.Assistants.OpenApiAuthType", + "azure.ai.assistants.models.VectorStoreDataSourceAssetType": "Azure.AI.Assistants.VectorStoreDataSourceAssetType", + "azure.ai.assistants.models.AzureAISearchQueryType": "Azure.AI.Assistants.AzureAISearchQueryType", + "azure.ai.assistants.models.AssistantsApiResponseFormatMode": "Azure.AI.Assistants.AssistantsApiResponseFormatMode", + "azure.ai.assistants.models.ResponseFormat": "Azure.AI.Assistants.ResponseFormat", + "azure.ai.assistants.models.ListSortOrder": "Azure.AI.Assistants.ListSortOrder", + "azure.ai.assistants.models.MessageRole": "Azure.AI.Assistants.MessageRole", + "azure.ai.assistants.models.MessageBlockType": "Azure.AI.Assistants.MessageBlockType", + "azure.ai.assistants.models.ImageDetailLevel": "Azure.AI.Assistants.ImageDetailLevel", + "azure.ai.assistants.models.MessageStatus": "Azure.AI.Assistants.MessageStatus", + "azure.ai.assistants.models.MessageIncompleteDetailsReason": "Azure.AI.Assistants.MessageIncompleteDetailsReason", + "azure.ai.assistants.models.RunStatus": "Azure.AI.Assistants.RunStatus", + "azure.ai.assistants.models.IncompleteDetailsReason": "Azure.AI.Assistants.IncompleteDetailsReason", + "azure.ai.assistants.models.TruncationStrategy": "Azure.AI.Assistants.TruncationStrategy", + "azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode": "Azure.AI.Assistants.AssistantsApiToolChoiceOptionMode", + "azure.ai.assistants.models.AssistantsNamedToolChoiceType": "Azure.AI.Assistants.AssistantsNamedToolChoiceType", + "azure.ai.assistants.models.RunAdditionalFieldList": "Azure.AI.Assistants.RunAdditionalFieldList", + "azure.ai.assistants.models.RunStepType": "Azure.AI.Assistants.RunStepType", + "azure.ai.assistants.models.RunStepStatus": "Azure.AI.Assistants.RunStepStatus", + "azure.ai.assistants.models.RunStepErrorCode": "Azure.AI.Assistants.RunStepErrorCode", + "azure.ai.assistants.models.FilePurpose": "Azure.AI.Assistants.FilePurpose", + "azure.ai.assistants.models.FileState": "Azure.AI.Assistants.FileState", + "azure.ai.assistants.models.VectorStoreStatus": "Azure.AI.Assistants.VectorStoreStatus", + "azure.ai.assistants.models.VectorStoreExpirationPolicyAnchor": "Azure.AI.Assistants.VectorStoreExpirationPolicyAnchor", + "azure.ai.assistants.models.VectorStoreChunkingStrategyRequestType": "Azure.AI.Assistants.VectorStoreChunkingStrategyRequestType", + "azure.ai.assistants.models.VectorStoreFileStatus": "Azure.AI.Assistants.VectorStoreFileStatus", + "azure.ai.assistants.models.VectorStoreFileErrorCode": "Azure.AI.Assistants.VectorStoreFileErrorCode", + "azure.ai.assistants.models.VectorStoreChunkingStrategyResponseType": "Azure.AI.Assistants.VectorStoreChunkingStrategyResponseType", + "azure.ai.assistants.models.VectorStoreFileStatusFilter": "Azure.AI.Assistants.VectorStoreFileStatusFilter", + "azure.ai.assistants.models.VectorStoreFileBatchStatus": "Azure.AI.Assistants.VectorStoreFileBatchStatus", + "azure.ai.assistants.models.ThreadStreamEvent": "Azure.AI.Assistants.ThreadStreamEvent", + "azure.ai.assistants.models.RunStreamEvent": "Azure.AI.Assistants.RunStreamEvent", + "azure.ai.assistants.models.RunStepStreamEvent": "Azure.AI.Assistants.RunStepStreamEvent", + "azure.ai.assistants.models.MessageStreamEvent": "Azure.AI.Assistants.MessageStreamEvent", + "azure.ai.assistants.models.ErrorEvent": "Azure.AI.Assistants.ErrorEvent", + "azure.ai.assistants.models.DoneEvent": "Azure.AI.Assistants.DoneEvent", + "azure.ai.assistants.models.AssistantStreamEvent": "Azure.AI.Assistants.AssistantStreamEvent", + "azure.ai.assistants.AssistantsClient.create_assistant": "Azure.AI.Assistants.createAssistant", + "azure.ai.assistants.aio.AssistantsClient.create_assistant": "Azure.AI.Assistants.createAssistant", + "azure.ai.assistants.AssistantsClient.list_assistants": "Azure.AI.Assistants.listAssistants", + "azure.ai.assistants.aio.AssistantsClient.list_assistants": "Azure.AI.Assistants.listAssistants", + "azure.ai.assistants.AssistantsClient.get_assistant": "Azure.AI.Assistants.getAssistant", + "azure.ai.assistants.aio.AssistantsClient.get_assistant": "Azure.AI.Assistants.getAssistant", + "azure.ai.assistants.AssistantsClient.update_assistant": "Azure.AI.Assistants.updateAssistant", + "azure.ai.assistants.aio.AssistantsClient.update_assistant": "Azure.AI.Assistants.updateAssistant", + "azure.ai.assistants.AssistantsClient.delete_assistant": "Azure.AI.Assistants.deleteAssistant", + "azure.ai.assistants.aio.AssistantsClient.delete_assistant": "Azure.AI.Assistants.deleteAssistant", + "azure.ai.assistants.AssistantsClient.create_thread": "Azure.AI.Assistants.createThread", + "azure.ai.assistants.aio.AssistantsClient.create_thread": "Azure.AI.Assistants.createThread", + "azure.ai.assistants.AssistantsClient.get_thread": "Azure.AI.Assistants.getThread", + "azure.ai.assistants.aio.AssistantsClient.get_thread": "Azure.AI.Assistants.getThread", + "azure.ai.assistants.AssistantsClient.update_thread": "Azure.AI.Assistants.updateThread", + "azure.ai.assistants.aio.AssistantsClient.update_thread": "Azure.AI.Assistants.updateThread", + "azure.ai.assistants.AssistantsClient.delete_thread": "Azure.AI.Assistants.deleteThread", + "azure.ai.assistants.aio.AssistantsClient.delete_thread": "Azure.AI.Assistants.deleteThread", + "azure.ai.assistants.AssistantsClient.list_threads": "Azure.AI.Assistants.listThreads", + "azure.ai.assistants.aio.AssistantsClient.list_threads": "Azure.AI.Assistants.listThreads", + "azure.ai.assistants.AssistantsClient.create_message": "Azure.AI.Assistants.createMessage", + "azure.ai.assistants.aio.AssistantsClient.create_message": "Azure.AI.Assistants.createMessage", + "azure.ai.assistants.AssistantsClient.list_messages": "Azure.AI.Assistants.listMessages", + "azure.ai.assistants.aio.AssistantsClient.list_messages": "Azure.AI.Assistants.listMessages", + "azure.ai.assistants.AssistantsClient.get_message": "Azure.AI.Assistants.getMessage", + "azure.ai.assistants.aio.AssistantsClient.get_message": "Azure.AI.Assistants.getMessage", + "azure.ai.assistants.AssistantsClient.update_message": "Azure.AI.Assistants.updateMessage", + "azure.ai.assistants.aio.AssistantsClient.update_message": "Azure.AI.Assistants.updateMessage", + "azure.ai.assistants.AssistantsClient.create_run": "Azure.AI.Assistants.createRun", + "azure.ai.assistants.aio.AssistantsClient.create_run": "Azure.AI.Assistants.createRun", + "azure.ai.assistants.AssistantsClient.list_runs": "Azure.AI.Assistants.listRuns", + "azure.ai.assistants.aio.AssistantsClient.list_runs": "Azure.AI.Assistants.listRuns", + "azure.ai.assistants.AssistantsClient.get_run": "Azure.AI.Assistants.getRun", + "azure.ai.assistants.aio.AssistantsClient.get_run": "Azure.AI.Assistants.getRun", + "azure.ai.assistants.AssistantsClient.update_run": "Azure.AI.Assistants.updateRun", + "azure.ai.assistants.aio.AssistantsClient.update_run": "Azure.AI.Assistants.updateRun", + "azure.ai.assistants.AssistantsClient.submit_tool_outputs_to_run": "Azure.AI.Assistants.submitToolOutputsToRun", + "azure.ai.assistants.aio.AssistantsClient.submit_tool_outputs_to_run": "Azure.AI.Assistants.submitToolOutputsToRun", + "azure.ai.assistants.AssistantsClient.cancel_run": "Azure.AI.Assistants.cancelRun", + "azure.ai.assistants.aio.AssistantsClient.cancel_run": "Azure.AI.Assistants.cancelRun", + "azure.ai.assistants.AssistantsClient.create_thread_and_run": "Azure.AI.Assistants.createThreadAndRun", + "azure.ai.assistants.aio.AssistantsClient.create_thread_and_run": "Azure.AI.Assistants.createThreadAndRun", + "azure.ai.assistants.AssistantsClient.get_run_step": "Azure.AI.Assistants.getRunStep", + "azure.ai.assistants.aio.AssistantsClient.get_run_step": "Azure.AI.Assistants.getRunStep", + "azure.ai.assistants.AssistantsClient.list_run_steps": "Azure.AI.Assistants.listRunSteps", + "azure.ai.assistants.aio.AssistantsClient.list_run_steps": "Azure.AI.Assistants.listRunSteps", + "azure.ai.assistants.AssistantsClient.list_files": "Azure.AI.Assistants.listFiles", + "azure.ai.assistants.aio.AssistantsClient.list_files": "Azure.AI.Assistants.listFiles", + "azure.ai.assistants.AssistantsClient.delete_file": "Azure.AI.Assistants.deleteFile", + "azure.ai.assistants.aio.AssistantsClient.delete_file": "Azure.AI.Assistants.deleteFile", + "azure.ai.assistants.AssistantsClient.get_file": "Azure.AI.Assistants.getFile", + "azure.ai.assistants.aio.AssistantsClient.get_file": "Azure.AI.Assistants.getFile", + "azure.ai.assistants.AssistantsClient.list_vector_stores": "Azure.AI.Assistants.listVectorStores", + "azure.ai.assistants.aio.AssistantsClient.list_vector_stores": "Azure.AI.Assistants.listVectorStores", + "azure.ai.assistants.AssistantsClient.create_vector_store": "Azure.AI.Assistants.createVectorStore", + "azure.ai.assistants.aio.AssistantsClient.create_vector_store": "Azure.AI.Assistants.createVectorStore", + "azure.ai.assistants.AssistantsClient.get_vector_store": "Azure.AI.Assistants.getVectorStore", + "azure.ai.assistants.aio.AssistantsClient.get_vector_store": "Azure.AI.Assistants.getVectorStore", + "azure.ai.assistants.AssistantsClient.modify_vector_store": "Azure.AI.Assistants.modifyVectorStore", + "azure.ai.assistants.aio.AssistantsClient.modify_vector_store": "Azure.AI.Assistants.modifyVectorStore", + "azure.ai.assistants.AssistantsClient.delete_vector_store": "Azure.AI.Assistants.deleteVectorStore", + "azure.ai.assistants.aio.AssistantsClient.delete_vector_store": "Azure.AI.Assistants.deleteVectorStore", + "azure.ai.assistants.AssistantsClient.list_vector_store_files": "Azure.AI.Assistants.listVectorStoreFiles", + "azure.ai.assistants.aio.AssistantsClient.list_vector_store_files": "Azure.AI.Assistants.listVectorStoreFiles", + "azure.ai.assistants.AssistantsClient.create_vector_store_file": "Azure.AI.Assistants.createVectorStoreFile", + "azure.ai.assistants.aio.AssistantsClient.create_vector_store_file": "Azure.AI.Assistants.createVectorStoreFile", + "azure.ai.assistants.AssistantsClient.get_vector_store_file": "Azure.AI.Assistants.getVectorStoreFile", + "azure.ai.assistants.aio.AssistantsClient.get_vector_store_file": "Azure.AI.Assistants.getVectorStoreFile", + "azure.ai.assistants.AssistantsClient.delete_vector_store_file": "Azure.AI.Assistants.deleteVectorStoreFile", + "azure.ai.assistants.aio.AssistantsClient.delete_vector_store_file": "Azure.AI.Assistants.deleteVectorStoreFile", + "azure.ai.assistants.AssistantsClient.create_vector_store_file_batch": "Azure.AI.Assistants.createVectorStoreFileBatch", + "azure.ai.assistants.aio.AssistantsClient.create_vector_store_file_batch": "Azure.AI.Assistants.createVectorStoreFileBatch", + "azure.ai.assistants.AssistantsClient.get_vector_store_file_batch": "Azure.AI.Assistants.getVectorStoreFileBatch", + "azure.ai.assistants.aio.AssistantsClient.get_vector_store_file_batch": "Azure.AI.Assistants.getVectorStoreFileBatch", + "azure.ai.assistants.AssistantsClient.cancel_vector_store_file_batch": "Azure.AI.Assistants.cancelVectorStoreFileBatch", + "azure.ai.assistants.aio.AssistantsClient.cancel_vector_store_file_batch": "Azure.AI.Assistants.cancelVectorStoreFileBatch", + "azure.ai.assistants.AssistantsClient.list_vector_store_file_batch_files": "Azure.AI.Assistants.listVectorStoreFileBatchFiles", + "azure.ai.assistants.aio.AssistantsClient.list_vector_store_file_batch_files": "Azure.AI.Assistants.listVectorStoreFileBatchFiles" + } +} \ No newline at end of file diff --git a/sdk/ai/azure-ai-assistants/azure/__init__.py b/sdk/ai/azure-ai-assistants/azure/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/ai/azure-ai-assistants/azure/ai/__init__.py b/sdk/ai/azure-ai-assistants/azure/ai/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/__init__.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/__init__.py new file mode 100644 index 000000000000..2484b50c5378 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/__init__.py @@ -0,0 +1,32 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._client import AssistantsClient # type: ignore +from ._version import VERSION + +__version__ = VERSION + +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AssistantsClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore + +_patch_sdk() diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_client.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_client.py new file mode 100644 index 000000000000..1039fa0265d8 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_client.py @@ -0,0 +1,103 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, TYPE_CHECKING, Union +from typing_extensions import Self + +from azure.core import PipelineClient +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies +from azure.core.rest import HttpRequest, HttpResponse + +from ._configuration import AssistantsClientConfiguration +from ._operations import AssistantsClientOperationsMixin +from ._utils.serialization import Deserializer, Serializer + +if TYPE_CHECKING: + from azure.core.credentials import TokenCredential + + +class AssistantsClient(AssistantsClientOperationsMixin): + """AssistantsClient. + + :param endpoint: Project endpoint in the form of: + https://.services.ai.azure.com/api/projects/. Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + _endpoint = "{endpoint}" + self._config = AssistantsClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: PipelineClient = PipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request(self, request: HttpRequest, *, stream: bool = False, **kwargs: Any) -> HttpResponse: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.HttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + def close(self) -> None: + self._client.close() + + def __enter__(self) -> Self: + self._client.__enter__() + return self + + def __exit__(self, *exc_details: Any) -> None: + self._client.__exit__(*exc_details) diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_configuration.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_configuration.py new file mode 100644 index 000000000000..b3aa33c5f408 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_configuration.py @@ -0,0 +1,73 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING, Union + +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies + +from ._version import VERSION + +if TYPE_CHECKING: + from azure.core.credentials import TokenCredential + + +class AssistantsClientConfiguration: # pylint: disable=too-many-instance-attributes + """Configuration for AssistantsClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Project endpoint in the form of: + https://.services.ai.azure.com/api/projects/. Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + api_version: str = kwargs.pop("api_version", "2025-05-15-preview") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-assistants/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Authorization", prefix="Bearer", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.BearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.RedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.RetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/__init__.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/__init__.py new file mode 100644 index 000000000000..ee3f17d82ddc --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/__init__.py @@ -0,0 +1,25 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import AssistantsClientOperationsMixin # type: ignore + +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AssistantsClientOperationsMixin", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_operations.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_operations.py new file mode 100644 index 000000000000..9fe49309378b --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_operations.py @@ -0,0 +1,6002 @@ +# pylint: disable=too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from collections.abc import MutableMapping +from io import IOBase +import json +from typing import Any, Callable, Dict, IO, Iterator, List, Optional, TYPE_CHECKING, TypeVar, Union, overload + +from azure.core import PipelineClient +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + StreamClosedError, + StreamConsumedError, + map_error, +) +from azure.core.pipeline import PipelineResponse +from azure.core.rest import HttpRequest, HttpResponse +from azure.core.tracing.decorator import distributed_trace +from azure.core.utils import case_insensitive_dict + +from .. import models as _models +from .._configuration import AssistantsClientConfiguration +from .._utils.model_base import Model as _Model, SdkJSONEncoder, _deserialize +from .._utils.serialization import Serializer +from .._utils.utils import ClientMixinABC, prepare_multipart_form_data + +if TYPE_CHECKING: + from .. import _types +JSON = MutableMapping[str, Any] +_Unset: Any = object() +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False + + +def build_assistants_create_assistant_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_assistants_request( + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_assistant_request(assistant_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants/{assistantId}" + path_format_arguments = { + "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_update_assistant_request( # pylint: disable=name-too-long + assistant_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants/{assistantId}" + path_format_arguments = { + "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_delete_assistant_request( # pylint: disable=name-too-long + assistant_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants/{assistantId}" + path_format_arguments = { + "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_create_thread_request(**kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_thread_request(thread_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_update_thread_request(thread_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_delete_thread_request(thread_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_threads_request( + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_create_message_request(thread_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_messages_request( + thread_id: str, + *, + run_id: Optional[str] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if run_id is not None: + _params["runId"] = _SERIALIZER.query("run_id", run_id, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_message_request(thread_id: str, message_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages/{messageId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "messageId": _SERIALIZER.url("message_id", message_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_update_message_request(thread_id: str, message_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages/{messageId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "messageId": _SERIALIZER.url("message_id", message_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_create_run_request( + thread_id: str, *, include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if include is not None: + _params["include[]"] = _SERIALIZER.query("include", include, "[str]", div=",") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_runs_request( + thread_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_run_request(thread_id: str, run_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_update_run_request(thread_id: str, run_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_submit_tool_outputs_to_run_request( # pylint: disable=name-too-long + thread_id: str, run_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/submit_tool_outputs" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_cancel_run_request(thread_id: str, run_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/cancel" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_create_thread_and_run_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/runs" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_run_step_request( + thread_id: str, + run_id: str, + step_id: str, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/steps/{stepId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + "stepId": _SERIALIZER.url("step_id", step_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if include is not None: + _params["include[]"] = _SERIALIZER.query("include", include, "[str]", div=",") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_run_steps_request( + thread_id: str, + run_id: str, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/steps" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if include is not None: + _params["include[]"] = _SERIALIZER.query("include", include, "[str]", div=",") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_files_request( + *, purpose: Optional[Union[str, _models.FilePurpose]] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if purpose is not None: + _params["purpose"] = _SERIALIZER.query("purpose", purpose, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_upload_file_request(**kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_delete_file_request(file_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files/{fileId}" + path_format_arguments = { + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_file_request(file_id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files/{fileId}" + path_format_arguments = { + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_file_content_request( # pylint: disable=name-too-long + file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/octet-stream") + + # Construct URL + _url = "/files/{fileId}/content" + path_format_arguments = { + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_vector_stores_request( # pylint: disable=name-too-long + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_create_vector_store_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_vector_store_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_modify_vector_store_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_delete_vector_store_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_vector_store_files_request( # pylint: disable=name-too-long + vector_store_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if filter is not None: + _params["filter"] = _SERIALIZER.query("filter", filter, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_create_vector_store_file_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_vector_store_file_request( # pylint: disable=name-too-long + vector_store_id: str, file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files/{fileId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_delete_vector_store_file_request( # pylint: disable=name-too-long + vector_store_id: str, file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files/{fileId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_create_vector_store_file_batch_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_get_vector_store_file_batch_request( # pylint: disable=name-too-long + vector_store_id: str, batch_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches/{batchId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "batchId": _SERIALIZER.url("batch_id", batch_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_cancel_vector_store_file_batch_request( # pylint: disable=name-too-long + vector_store_id: str, batch_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches/{batchId}/cancel" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "batchId": _SERIALIZER.url("batch_id", batch_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_assistants_list_vector_store_file_batch_files_request( # pylint: disable=name-too-long + vector_store_id: str, + batch_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches/{batchId}/files" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "batchId": _SERIALIZER.url("batch_id", batch_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if filter is not None: + _params["filter"] = _SERIALIZER.query("filter", filter, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +class AssistantsClientOperationsMixin( # pylint: disable=too-many-public-methods + ClientMixinABC[PipelineClient, AssistantsClientConfiguration] +): + + @overload + def create_assistant( + self, + *, + model: str, + content_type: str = "application/json", + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :keyword model: The ID of the model to use. Required. + :paramtype model: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: The name of the new assistant. Default value is None. + :paramtype name: str + :keyword description: The description of the new assistant. Default value is None. + :paramtype description: str + :keyword instructions: The system instructions for the new assistant to use. Default value is + None. + :paramtype instructions: str + :keyword tools: The collection of tools to enable for the new assistant. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, the ``code_interpreter`` + tool requires a list of file IDs, while the ``file_search`` tool requires a list of vector + store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_assistant( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_assistant( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_assistant( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + model: str = _Unset, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword model: The ID of the model to use. Required. + :paramtype model: str + :keyword name: The name of the new assistant. Default value is None. + :paramtype name: str + :keyword description: The description of the new assistant. Default value is None. + :paramtype description: str + :keyword instructions: The system instructions for the new assistant to use. Default value is + None. + :paramtype instructions: str + :keyword tools: The collection of tools to enable for the new assistant. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, the ``code_interpreter`` + tool requires a list of file IDs, while the ``file_search`` tool requires a list of vector + store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + if body is _Unset: + if model is _Unset: + raise TypeError("missing required argument: model") + body = { + "description": description, + "instructions": instructions, + "metadata": metadata, + "model": model, + "name": name, + "response_format": response_format, + "temperature": temperature, + "tool_resources": tool_resources, + "tools": tools, + "top_p": top_p, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_assistant_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_assistants( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfAssistant: + """Gets a list of assistants that were previously created. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfAssistant. The OpenAIPageableListOfAssistant is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfAssistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfAssistant] = kwargs.pop("cls", None) + + _request = build_assistants_list_assistants_request( + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfAssistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_assistant(self, assistant_id: str, **kwargs: Any) -> _models.Assistant: + """Retrieves an existing assistant. + + :param assistant_id: Identifier of the assistant. Required. + :type assistant_id: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + _request = build_assistants_get_assistant_request( + assistant_id=assistant_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_assistant( + self, + assistant_id: str, + *, + content_type: str = "application/json", + model: Optional[str] = None, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword model: The ID of the model to use. Default value is None. + :paramtype model: str + :keyword name: The modified name for the assistant to use. Default value is None. + :paramtype name: str + :keyword description: The modified description for the assistant to use. Default value is None. + :paramtype description: str + :keyword instructions: The modified system instructions for the new assistant to use. Default + value is None. + :paramtype instructions: str + :keyword tools: The modified collection of tools to enable for the assistant. Default value is + None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, + the ``code_interpreter`` tool requires a list of file IDs, while the ``file_search`` tool + requires a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_assistant( + self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_assistant( + self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_assistant( + self, + assistant_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + model: Optional[str] = None, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword model: The ID of the model to use. Default value is None. + :paramtype model: str + :keyword name: The modified name for the assistant to use. Default value is None. + :paramtype name: str + :keyword description: The modified description for the assistant to use. Default value is None. + :paramtype description: str + :keyword instructions: The modified system instructions for the new assistant to use. Default + value is None. + :paramtype instructions: str + :keyword tools: The modified collection of tools to enable for the assistant. Default value is + None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, + the ``code_interpreter`` tool requires a list of file IDs, while the ``file_search`` tool + requires a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + if body is _Unset: + body = { + "description": description, + "instructions": instructions, + "metadata": metadata, + "model": model, + "name": name, + "response_format": response_format, + "temperature": temperature, + "tool_resources": tool_resources, + "tools": tools, + "top_p": top_p, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_assistant_request( + assistant_id=assistant_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_assistant(self, assistant_id: str, **kwargs: Any) -> _models.AssistantDeletionStatus: + """Deletes an assistant. + + :param assistant_id: Identifier of the assistant. Required. + :type assistant_id: str + :return: AssistantDeletionStatus. The AssistantDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_assistant_request( + assistant_id=assistant_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_thread( + self, + *, + content_type: str = "application/json", + messages: Optional[List[_models.ThreadMessageOptions]] = None, + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword messages: The initial messages to associate with the new thread. Default value is + None. + :paramtype messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_thread( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + messages: Optional[List[_models.ThreadMessageOptions]] = None, + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword messages: The initial messages to associate with the new thread. Default value is + None. + :paramtype messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"messages": messages, "metadata": metadata, "tool_resources": tool_resources} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_thread_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_thread(self, thread_id: str, **kwargs: Any) -> _models.AssistantThread: + """Gets information about an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + _request = build_assistants_get_thread_request( + thread_id=thread_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_thread( + self, + thread_id: str, + *, + content_type: str = "application/json", + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_thread( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_thread( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_thread( + self, + thread_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata, "tool_resources": tool_resources} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_thread_request( + thread_id=thread_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_thread(self, thread_id: str, **kwargs: Any) -> _models.ThreadDeletionStatus: + """Deletes an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :return: ThreadDeletionStatus. The ThreadDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_thread_request( + thread_id=thread_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_threads( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfAssistantThread: + """Gets a list of threads that were previously created. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfAssistantThread. The OpenAIPageableListOfAssistantThread is + compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfAssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfAssistantThread] = kwargs.pop("cls", None) + + _request = build_assistants_list_threads_request( + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfAssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_message( + self, + thread_id: str, + *, + role: Union[str, _models.MessageRole], + content: "_types.MessageInputContent", + content_type: str = "application/json", + attachments: Optional[List[_models.MessageAttachment]] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword role: The role of the entity that is creating the message. Allowed values include: + ``user``, which indicates the message is sent by an actual user (and should be + used in most cases to represent user-generated messages), and ``assistant``, + which indicates the message is generated by the agent (use this value to insert + messages from the agent into the conversation). Known values are: "user" and "assistant". + Required. + :paramtype role: str or ~azure.ai.assistants.models.MessageRole + :keyword content: The content of the initial message. This may be a basic string (if you only + need text) or an array of typed content blocks (for example, text, image_file, + image_url, and so on). Is either a str type or a [MessageInputContentBlock] type. Required. + :paramtype content: str or list[~azure.ai.assistants.models.MessageInputContentBlock] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword attachments: A list of files attached to the message, and the tools they should be + added to. Default value is None. + :paramtype attachments: list[~azure.ai.assistants.models.MessageAttachment] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_message( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_message( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_message( + self, + thread_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + role: Union[str, _models.MessageRole] = _Unset, + content: "_types.MessageInputContent" = _Unset, + attachments: Optional[List[_models.MessageAttachment]] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword role: The role of the entity that is creating the message. Allowed values include: + ``user``, which indicates the message is sent by an actual user (and should be + used in most cases to represent user-generated messages), and ``assistant``, + which indicates the message is generated by the agent (use this value to insert + messages from the agent into the conversation). Known values are: "user" and "assistant". + Required. + :paramtype role: str or ~azure.ai.assistants.models.MessageRole + :keyword content: The content of the initial message. This may be a basic string (if you only + need text) or an array of typed content blocks (for example, text, image_file, + image_url, and so on). Is either a str type or a [MessageInputContentBlock] type. Required. + :paramtype content: str or list[~azure.ai.assistants.models.MessageInputContentBlock] + :keyword attachments: A list of files attached to the message, and the tools they should be + added to. Default value is None. + :paramtype attachments: list[~azure.ai.assistants.models.MessageAttachment] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + if body is _Unset: + if role is _Unset: + raise TypeError("missing required argument: role") + if content is _Unset: + raise TypeError("missing required argument: content") + body = {"attachments": attachments, "content": content, "metadata": metadata, "role": role} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_message_request( + thread_id=thread_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_messages( + self, + thread_id: str, + *, + run_id: Optional[str] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfThreadMessage: + """Gets a list of messages that exist on a thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword run_id: Filter messages by the run ID that generated them. Default value is None. + :paramtype run_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadMessage. The OpenAIPageableListOfThreadMessage is compatible + with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadMessage] = kwargs.pop("cls", None) + + _request = build_assistants_list_messages_request( + thread_id=thread_id, + run_id=run_id, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_message(self, thread_id: str, message_id: str, **kwargs: Any) -> _models.ThreadMessage: + """Gets an existing message from an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + _request = build_assistants_get_message_request( + thread_id=thread_id, + message_id=message_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_message( + self, + thread_id: str, + message_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_message( + self, thread_id: str, message_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_message( + self, thread_id: str, message_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_message( + self, + thread_id: str, + message_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_message_request( + thread_id=thread_id, + message_id=message_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_run( + self, + thread_id: str, + *, + assistant_id: str, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + content_type: str = "application/json", + model: Optional[str] = None, + instructions: Optional[str] = None, + additional_instructions: Optional[str] = None, + additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword assistant_id: The ID of the assistant that should run the thread. Required. + :paramtype assistant_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword model: The overridden model name that the assistant should use to run the thread. + Default value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions that the assistant should use to run + the thread. Default value is None. + :paramtype instructions: str + :keyword additional_instructions: Additional instructions to append at the end of the + instructions for the run. This is useful for modifying the behavior + on a per-run basis without overriding other instructions. Default value is None. + :paramtype additional_instructions: str + :keyword additional_messages: Adds additional messages to the thread before creating the run. + Default value is None. + :paramtype additional_messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tools: The overridden list of enabled tools that the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort + to use only the number of completion tokens specified, across multiple turns of the run. If + the run exceeds the number of + completion tokens specified, the run will end with status ``incomplete``. See + ``incomplete_details`` for more info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_run( + self, + thread_id: str, + body: JSON, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: JSON + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_run( + self, + thread_id: str, + body: IO[bytes], + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: IO[bytes] + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_run( + self, + thread_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + assistant_id: str = _Unset, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + model: Optional[str] = None, + instructions: Optional[str] = None, + additional_instructions: Optional[str] = None, + additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword assistant_id: The ID of the assistant that should run the thread. Required. + :paramtype assistant_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword model: The overridden model name that the assistant should use to run the thread. + Default value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions that the assistant should use to run + the thread. Default value is None. + :paramtype instructions: str + :keyword additional_instructions: Additional instructions to append at the end of the + instructions for the run. This is useful for modifying the behavior + on a per-run basis without overriding other instructions. Default value is None. + :paramtype additional_instructions: str + :keyword additional_messages: Adds additional messages to the thread before creating the run. + Default value is None. + :paramtype additional_messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tools: The overridden list of enabled tools that the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort + to use only the number of completion tokens specified, across multiple turns of the run. If + the run exceeds the number of + completion tokens specified, the run will end with status ``incomplete``. See + ``incomplete_details`` for more info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if assistant_id is _Unset: + raise TypeError("missing required argument: assistant_id") + body = { + "additional_instructions": additional_instructions, + "additional_messages": additional_messages, + "assistant_id": assistant_id, + "instructions": instructions, + "max_completion_tokens": max_completion_tokens, + "max_prompt_tokens": max_prompt_tokens, + "metadata": metadata, + "model": model, + "parallel_tool_calls": parallel_tool_calls, + "response_format": response_format, + "stream": stream_parameter, + "temperature": temperature, + "tool_choice": tool_choice, + "tools": tools, + "top_p": top_p, + "truncation_strategy": truncation_strategy, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_run_request( + thread_id=thread_id, + include=include, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_runs( + self, + thread_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfThreadRun: + """Gets a list of runs for a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadRun. The OpenAIPageableListOfThreadRun is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadRun] = kwargs.pop("cls", None) + + _request = build_assistants_list_runs_request( + thread_id=thread_id, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Gets an existing run from an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_assistants_get_run_request( + thread_id=thread_id, + run_id=run_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_run( + self, + thread_id: str, + run_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + *, + tool_outputs: List[_models.ToolOutput], + content_type: str = "application/json", + stream_parameter: Optional[bool] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.assistants.models.ToolOutput] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword stream_parameter: If true, returns a stream of events that happen during the Run as + server-sent events, terminating when the run enters a terminal state. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + tool_outputs: List[_models.ToolOutput] = _Unset, + stream_parameter: Optional[bool] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.assistants.models.ToolOutput] + :keyword stream_parameter: If true, returns a stream of events that happen during the Run as + server-sent events, terminating when the run enters a terminal state. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if tool_outputs is _Unset: + raise TypeError("missing required argument: tool_outputs") + body = {"stream": stream_parameter, "tool_outputs": tool_outputs} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_submit_tool_outputs_to_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def cancel_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Cancels a run of an in progress thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_assistants_cancel_run_request( + thread_id=thread_id, + run_id=run_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_thread_and_run( + self, + *, + assistant_id: str, + content_type: str = "application/json", + thread: Optional[_models.AssistantThreadCreationOptions] = None, + model: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :keyword assistant_id: The ID of the assistant for which the thread should be created. + Required. + :paramtype assistant_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword thread: The details used to create the new thread. If no thread is provided, an empty + one will be created. Default value is None. + :paramtype thread: ~azure.ai.assistants.models.AssistantThreadCreationOptions + :keyword model: The overridden model that the assistant should use to run the thread. Default + value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions the assistant should use to run the + thread. Default value is None. + :paramtype instructions: str + :keyword tools: The overridden list of enabled tools the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: Override the tools the assistant can use for this run. This is useful + for modifying the behavior on a per-run basis. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.UpdateToolResourcesOptions + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort to use only + the number of completion tokens specified, across multiple turns of the run. If the run + exceeds the number of completion tokens + specified, the run will end with status ``incomplete``. See ``incomplete_details`` for more + info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread_and_run( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread_and_run( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_thread_and_run( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + assistant_id: str = _Unset, + thread: Optional[_models.AssistantThreadCreationOptions] = None, + model: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword assistant_id: The ID of the assistant for which the thread should be created. + Required. + :paramtype assistant_id: str + :keyword thread: The details used to create the new thread. If no thread is provided, an empty + one will be created. Default value is None. + :paramtype thread: ~azure.ai.assistants.models.AssistantThreadCreationOptions + :keyword model: The overridden model that the assistant should use to run the thread. Default + value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions the assistant should use to run the + thread. Default value is None. + :paramtype instructions: str + :keyword tools: The overridden list of enabled tools the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: Override the tools the assistant can use for this run. This is useful + for modifying the behavior on a per-run basis. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.UpdateToolResourcesOptions + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort to use only + the number of completion tokens specified, across multiple turns of the run. If the run + exceeds the number of completion tokens + specified, the run will end with status ``incomplete``. See ``incomplete_details`` for more + info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if assistant_id is _Unset: + raise TypeError("missing required argument: assistant_id") + body = { + "assistant_id": assistant_id, + "instructions": instructions, + "max_completion_tokens": max_completion_tokens, + "max_prompt_tokens": max_prompt_tokens, + "metadata": metadata, + "model": model, + "parallel_tool_calls": parallel_tool_calls, + "response_format": response_format, + "stream": stream_parameter, + "temperature": temperature, + "thread": thread, + "tool_choice": tool_choice, + "tool_resources": tool_resources, + "tools": tools, + "top_p": top_p, + "truncation_strategy": truncation_strategy, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_thread_and_run_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_run_step( + self, + thread_id: str, + run_id: str, + step_id: str, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + **kwargs: Any + ) -> _models.RunStep: + """Gets a single run step from a thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param step_id: Identifier of the run step. Required. + :type step_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :return: RunStep. The RunStep is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.RunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.RunStep] = kwargs.pop("cls", None) + + _request = build_assistants_get_run_step_request( + thread_id=thread_id, + run_id=run_id, + step_id=step_id, + include=include, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.RunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_run_steps( + self, + thread_id: str, + run_id: str, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfRunStep: + """Gets a list of run steps from a thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfRunStep. The OpenAIPageableListOfRunStep is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfRunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfRunStep] = kwargs.pop("cls", None) + + _request = build_assistants_list_run_steps_request( + thread_id=thread_id, + run_id=run_id, + include=include, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfRunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_files( + self, *, purpose: Optional[Union[str, _models.FilePurpose]] = None, **kwargs: Any + ) -> _models.FileListResponse: + """Gets a list of previously uploaded files. + + :keyword purpose: The purpose of the file. Known values are: "fine-tune", "fine-tune-results", + "assistants", "assistants_output", "batch", "batch_output", and "vision". Default value is + None. + :paramtype purpose: str or ~azure.ai.assistants.models.FilePurpose + :return: FileListResponse. The FileListResponse is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.FileListResponse + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileListResponse] = kwargs.pop("cls", None) + + _request = build_assistants_list_files_request( + purpose=purpose, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileListResponse, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def _upload_file(self, body: _models._models.UploadFileRequest, **kwargs: Any) -> _models.OpenAIFile: ... + @overload + def _upload_file(self, body: JSON, **kwargs: Any) -> _models.OpenAIFile: ... + + @distributed_trace + def _upload_file(self, body: Union[_models._models.UploadFileRequest, JSON], **kwargs: Any) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :param body: Multipart body. Is either a UploadFileRequest type or a JSON type. Required. + :type body: ~azure.ai.assistants.models._models.UploadFileRequest or JSON + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + _body = body.as_dict() if isinstance(body, _Model) else body + _file_fields: List[str] = ["file"] + _data_fields: List[str] = ["purpose", "filename"] + _files, _data = prepare_multipart_form_data(_body, _file_fields, _data_fields) + + _request = build_assistants_upload_file_request( + api_version=self._config.api_version, + files=_files, + data=_data, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_file(self, file_id: str, **kwargs: Any) -> _models.FileDeletionStatus: + """Delete a previously uploaded file. + + :param file_id: The ID of the file to delete. Required. + :type file_id: str + :return: FileDeletionStatus. The FileDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.FileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_file_request( + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_file(self, file_id: str, **kwargs: Any) -> _models.OpenAIFile: + """Returns information about a specific file. Does not retrieve file content. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + _request = build_assistants_get_file_request( + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def _get_file_content(self, file_id: str, **kwargs: Any) -> Iterator[bytes]: + """Retrieves the raw content of a specific file. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: Iterator[bytes] + :rtype: Iterator[bytes] + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[Iterator[bytes]] = kwargs.pop("cls", None) + + _request = build_assistants_get_file_content_request( + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", True) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + deserialized = response.iter_bytes() + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_vector_stores( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStore: + """Returns a list of vector stores. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStore. The OpenAIPageableListOfVectorStore is compatible + with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfVectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStore] = kwargs.pop("cls", None) + + _request = build_assistants_list_vector_stores_request( + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_vector_store( + self, + *, + content_type: str = "application/json", + file_ids: Optional[List[str]] = None, + name: Optional[str] = None, + store_configuration: Optional[_models.VectorStoreConfiguration] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword file_ids: A list of file IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Default value is None. + :paramtype file_ids: list[str] + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword store_configuration: The vector store configuration, used when vector store is created + from Azure asset URIs. Default value is None. + :paramtype store_configuration: ~azure.ai.assistants.models.VectorStoreConfiguration + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Only applicable if file_ids is non-empty. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_vector_store( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_ids: Optional[List[str]] = None, + name: Optional[str] = None, + store_configuration: Optional[_models.VectorStoreConfiguration] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_ids: A list of file IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Default value is None. + :paramtype file_ids: list[str] + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword store_configuration: The vector store configuration, used when vector store is created + from Azure asset URIs. Default value is None. + :paramtype store_configuration: ~azure.ai.assistants.models.VectorStoreConfiguration + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Only applicable if file_ids is non-empty. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + if body is _Unset: + body = { + "chunking_strategy": chunking_strategy, + "configuration": store_configuration, + "expires_after": expires_after, + "file_ids": file_ids, + "metadata": metadata, + "name": name, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_vector_store_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStore: + """Returns the vector store object matching the specified ID. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + _request = build_assistants_get_vector_store_request( + vector_store_id=vector_store_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def modify_vector_store( + self, + vector_store_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def modify_vector_store( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def modify_vector_store( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def modify_vector_store( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"expires_after": expires_after, "metadata": metadata, "name": name} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_modify_vector_store_request( + vector_store_id=vector_store_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStoreDeletionStatus: + """Deletes the vector store object matching the specified ID. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :return: VectorStoreDeletionStatus. The VectorStoreDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_vector_store_request( + vector_store_id=vector_store_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_vector_store_files( + self, + vector_store_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.assistants.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_assistants_list_vector_store_files_request( + vector_store_id=vector_store_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_vector_store_file( + self, + vector_store_id: str, + *, + content_type: str = "application/json", + file_id: Optional[str] = None, + data_source: Optional[_models.VectorStoreDataSource] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword file_id: Identifier of the file. Default value is None. + :paramtype file_id: str + :keyword data_source: Azure asset ID. Default value is None. + :paramtype data_source: ~azure.ai.assistants.models.VectorStoreDataSource + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_vector_store_file( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_id: Optional[str] = None, + data_source: Optional[_models.VectorStoreDataSource] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_id: Identifier of the file. Default value is None. + :paramtype file_id: str + :keyword data_source: Azure asset ID. Default value is None. + :paramtype data_source: ~azure.ai.assistants.models.VectorStoreDataSource + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"chunking_strategy": chunking_strategy, "data_source": data_source, "file_id": file_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_vector_store_file_request( + vector_store_id=vector_store_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_vector_store_file(self, vector_store_id: str, file_id: str, **kwargs: Any) -> _models.VectorStoreFile: + """Retrieves a vector store file. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param file_id: Identifier of the file. Required. + :type file_id: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + _request = build_assistants_get_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_vector_store_file( + self, vector_store_id: str, file_id: str, **kwargs: Any + ) -> _models.VectorStoreFileDeletionStatus: + """Delete a vector store file. This will remove the file from the vector store but the file itself + will not be deleted. + To delete the file, use the delete file endpoint. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param file_id: Identifier of the file. Required. + :type file_id: str + :return: VectorStoreFileDeletionStatus. The VectorStoreFileDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_vector_store_file_batch( + self, + vector_store_id: str, + *, + content_type: str = "application/json", + file_ids: Optional[List[str]] = None, + data_sources: Optional[List[_models.VectorStoreDataSource]] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword file_ids: List of file identifiers. Default value is None. + :paramtype file_ids: list[str] + :keyword data_sources: List of Azure assets. Default value is None. + :paramtype data_sources: list[~azure.ai.assistants.models.VectorStoreDataSource] + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file_batch( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file_batch( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_vector_store_file_batch( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_ids: Optional[List[str]] = None, + data_sources: Optional[List[_models.VectorStoreDataSource]] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_ids: List of file identifiers. Default value is None. + :paramtype file_ids: list[str] + :keyword data_sources: List of Azure assets. Default value is None. + :paramtype data_sources: list[~azure.ai.assistants.models.VectorStoreDataSource] + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"chunking_strategy": chunking_strategy, "data_sources": data_sources, "file_ids": file_ids} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_vector_store_file_batch_request( + vector_store_id=vector_store_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Retrieve a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param batch_id: Identifier of the file batch. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_assistants_get_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def cancel_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Cancel a vector store file batch. This attempts to cancel the processing of files in this batch + as soon as possible. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param batch_id: Identifier of the file batch. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_assistants_cancel_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_vector_store_file_batch_files( + self, + vector_store_id: str, + batch_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files in a batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param batch_id: Identifier of the file batch. Required. + :type batch_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.assistants.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_assistants_list_vector_store_file_batch_files_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_patch.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_operations/_patch.py @@ -0,0 +1,21 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_patch.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_patch.py @@ -0,0 +1,21 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_types.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_types.py new file mode 100644 index 000000000000..af5212be9e0f --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_types.py @@ -0,0 +1,24 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import List, TYPE_CHECKING, Union + +if TYPE_CHECKING: + from . import models as _models +AssistantsApiResponseFormatOption = Union[ + str, + str, + "_models.AssistantsApiResponseFormatMode", + "_models.AssistantsApiResponseFormat", + "_models.ResponseFormatJsonSchemaType", +] +MessageInputContent = Union[str, List["_models.MessageInputContentBlock"]] +MessageAttachmentToolDefinition = Union["_models.CodeInterpreterToolDefinition", "_models.FileSearchToolDefinition"] +AssistantsApiToolChoiceOption = Union[ + str, str, "_models.AssistantsApiToolChoiceOptionMode", "_models.AssistantsNamedToolChoice" +] diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/__init__.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/__init__.py new file mode 100644 index 000000000000..8026245c2abc --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/__init__.py @@ -0,0 +1,6 @@ +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/model_base.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/model_base.py new file mode 100644 index 000000000000..49d5c7259389 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/model_base.py @@ -0,0 +1,1232 @@ +# pylint: disable=too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=protected-access, broad-except + +import copy +import calendar +import decimal +import functools +import sys +import logging +import base64 +import re +import typing +import enum +import email.utils +from datetime import datetime, date, time, timedelta, timezone +from json import JSONEncoder +import xml.etree.ElementTree as ET +from collections.abc import MutableMapping +from typing_extensions import Self +import isodate +from azure.core.exceptions import DeserializationError +from azure.core import CaseInsensitiveEnumMeta +from azure.core.pipeline import PipelineResponse +from azure.core.serialization import _Null + +_LOGGER = logging.getLogger(__name__) + +__all__ = ["SdkJSONEncoder", "Model", "rest_field", "rest_discriminator"] + +TZ_UTC = timezone.utc +_T = typing.TypeVar("_T") + + +def _timedelta_as_isostr(td: timedelta) -> str: + """Converts a datetime.timedelta object into an ISO 8601 formatted string, e.g. 'P4DT12H30M05S' + + Function adapted from the Tin Can Python project: https://github.com/RusticiSoftware/TinCanPython + + :param timedelta td: The timedelta to convert + :rtype: str + :return: ISO8601 version of this timedelta + """ + + # Split seconds to larger units + seconds = td.total_seconds() + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + days, hours = divmod(hours, 24) + + days, hours, minutes = list(map(int, (days, hours, minutes))) + seconds = round(seconds, 6) + + # Build date + date_str = "" + if days: + date_str = "%sD" % days + + if hours or minutes or seconds: + # Build time + time_str = "T" + + # Hours + bigger_exists = date_str or hours + if bigger_exists: + time_str += "{:02}H".format(hours) + + # Minutes + bigger_exists = bigger_exists or minutes + if bigger_exists: + time_str += "{:02}M".format(minutes) + + # Seconds + try: + if seconds.is_integer(): + seconds_string = "{:02}".format(int(seconds)) + else: + # 9 chars long w/ leading 0, 6 digits after decimal + seconds_string = "%09.6f" % seconds + # Remove trailing zeros + seconds_string = seconds_string.rstrip("0") + except AttributeError: # int.is_integer() raises + seconds_string = "{:02}".format(seconds) + + time_str += "{}S".format(seconds_string) + else: + time_str = "" + + return "P" + date_str + time_str + + +def _serialize_bytes(o, format: typing.Optional[str] = None) -> str: + encoded = base64.b64encode(o).decode() + if format == "base64url": + return encoded.strip("=").replace("+", "-").replace("/", "_") + return encoded + + +def _serialize_datetime(o, format: typing.Optional[str] = None): + if hasattr(o, "year") and hasattr(o, "hour"): + if format == "rfc7231": + return email.utils.format_datetime(o, usegmt=True) + if format == "unix-timestamp": + return int(calendar.timegm(o.utctimetuple())) + + # astimezone() fails for naive times in Python 2.7, so make make sure o is aware (tzinfo is set) + if not o.tzinfo: + iso_formatted = o.replace(tzinfo=TZ_UTC).isoformat() + else: + iso_formatted = o.astimezone(TZ_UTC).isoformat() + # Replace the trailing "+00:00" UTC offset with "Z" (RFC 3339: https://www.ietf.org/rfc/rfc3339.txt) + return iso_formatted.replace("+00:00", "Z") + # Next try datetime.date or datetime.time + return o.isoformat() + + +def _is_readonly(p): + try: + return p._visibility == ["read"] + except AttributeError: + return False + + +class SdkJSONEncoder(JSONEncoder): + """A JSON encoder that's capable of serializing datetime objects and bytes.""" + + def __init__(self, *args, exclude_readonly: bool = False, format: typing.Optional[str] = None, **kwargs): + super().__init__(*args, **kwargs) + self.exclude_readonly = exclude_readonly + self.format = format + + def default(self, o): # pylint: disable=too-many-return-statements + if _is_model(o): + if self.exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + return {k: v for k, v in o.items() if k not in readonly_props} + return dict(o.items()) + try: + return super(SdkJSONEncoder, self).default(o) + except TypeError: + if isinstance(o, _Null): + return None + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, self.format) + try: + # First try datetime.datetime + return _serialize_datetime(o, self.format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return super(SdkJSONEncoder, self).default(o) + + +_VALID_DATE = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" + r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") +_VALID_RFC7231 = re.compile( + r"(Mon|Tue|Wed|Thu|Fri|Sat|Sun),\s\d{2}\s" + r"(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s\d{4}\s\d{2}:\d{2}:\d{2}\sGMT" +) + + +def _deserialize_datetime(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + attr = attr.upper() + match = _VALID_DATE.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + return date_obj + + +def _deserialize_datetime_rfc7231(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize RFC7231 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + match = _VALID_RFC7231.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + return email.utils.parsedate_to_datetime(attr) + + +def _deserialize_datetime_unix_timestamp(attr: typing.Union[float, datetime]) -> datetime: + """Deserialize unix timestamp into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + return datetime.fromtimestamp(attr, TZ_UTC) + + +def _deserialize_date(attr: typing.Union[str, date]) -> date: + """Deserialize ISO-8601 formatted string into Date object. + :param str attr: response string to be deserialized. + :rtype: date + :returns: The date object from that input + """ + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + if isinstance(attr, date): + return attr + return isodate.parse_date(attr, defaultmonth=None, defaultday=None) # type: ignore + + +def _deserialize_time(attr: typing.Union[str, time]) -> time: + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :rtype: datetime.time + :returns: The time object from that input + """ + if isinstance(attr, time): + return attr + return isodate.parse_time(attr) + + +def _deserialize_bytes(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + return bytes(base64.b64decode(attr)) + + +def _deserialize_bytes_base64(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return bytes(base64.b64decode(encoded)) + + +def _deserialize_duration(attr): + if isinstance(attr, timedelta): + return attr + return isodate.parse_duration(attr) + + +def _deserialize_decimal(attr): + if isinstance(attr, decimal.Decimal): + return attr + return decimal.Decimal(str(attr)) + + +def _deserialize_int_as_str(attr): + if isinstance(attr, int): + return attr + return int(attr) + + +_DESERIALIZE_MAPPING = { + datetime: _deserialize_datetime, + date: _deserialize_date, + time: _deserialize_time, + bytes: _deserialize_bytes, + bytearray: _deserialize_bytes, + timedelta: _deserialize_duration, + typing.Any: lambda x: x, + decimal.Decimal: _deserialize_decimal, +} + +_DESERIALIZE_MAPPING_WITHFORMAT = { + "rfc3339": _deserialize_datetime, + "rfc7231": _deserialize_datetime_rfc7231, + "unix-timestamp": _deserialize_datetime_unix_timestamp, + "base64": _deserialize_bytes, + "base64url": _deserialize_bytes_base64, +} + + +def get_deserializer(annotation: typing.Any, rf: typing.Optional["_RestField"] = None): + if annotation is int and rf and rf._format == "str": + return _deserialize_int_as_str + if rf and rf._format: + return _DESERIALIZE_MAPPING_WITHFORMAT.get(rf._format) + return _DESERIALIZE_MAPPING.get(annotation) # pyright: ignore + + +def _get_type_alias_type(module_name: str, alias_name: str): + types = { + k: v + for k, v in sys.modules[module_name].__dict__.items() + if isinstance(v, typing._GenericAlias) # type: ignore + } + if alias_name not in types: + return alias_name + return types[alias_name] + + +def _get_model(module_name: str, model_name: str): + models = {k: v for k, v in sys.modules[module_name].__dict__.items() if isinstance(v, type)} + module_end = module_name.rsplit(".", 1)[0] + models.update({k: v for k, v in sys.modules[module_end].__dict__.items() if isinstance(v, type)}) + if isinstance(model_name, str): + model_name = model_name.split(".")[-1] + if model_name not in models: + return model_name + return models[model_name] + + +_UNSET = object() + + +class _MyMutableMapping(MutableMapping[str, typing.Any]): + def __init__(self, data: typing.Dict[str, typing.Any]) -> None: + self._data = data + + def __contains__(self, key: typing.Any) -> bool: + return key in self._data + + def __getitem__(self, key: str) -> typing.Any: + return self._data.__getitem__(key) + + def __setitem__(self, key: str, value: typing.Any) -> None: + self._data.__setitem__(key, value) + + def __delitem__(self, key: str) -> None: + self._data.__delitem__(key) + + def __iter__(self) -> typing.Iterator[typing.Any]: + return self._data.__iter__() + + def __len__(self) -> int: + return self._data.__len__() + + def __ne__(self, other: typing.Any) -> bool: + return not self.__eq__(other) + + def keys(self) -> typing.KeysView[str]: + """ + :returns: a set-like object providing a view on D's keys + :rtype: ~typing.KeysView + """ + return self._data.keys() + + def values(self) -> typing.ValuesView[typing.Any]: + """ + :returns: an object providing a view on D's values + :rtype: ~typing.ValuesView + """ + return self._data.values() + + def items(self) -> typing.ItemsView[str, typing.Any]: + """ + :returns: set-like object providing a view on D's items + :rtype: ~typing.ItemsView + """ + return self._data.items() + + def get(self, key: str, default: typing.Any = None) -> typing.Any: + """ + Get the value for key if key is in the dictionary, else default. + :param str key: The key to look up. + :param any default: The value to return if key is not in the dictionary. Defaults to None + :returns: D[k] if k in D, else d. + :rtype: any + """ + try: + return self[key] + except KeyError: + return default + + @typing.overload + def pop(self, key: str) -> typing.Any: ... # pylint: disable=arguments-differ + + @typing.overload + def pop(self, key: str, default: _T) -> _T: ... # pylint: disable=signature-differs + + @typing.overload + def pop(self, key: str, default: typing.Any) -> typing.Any: ... # pylint: disable=signature-differs + + def pop(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + """ + Removes specified key and return the corresponding value. + :param str key: The key to pop. + :param any default: The value to return if key is not in the dictionary + :returns: The value corresponding to the key. + :rtype: any + :raises KeyError: If key is not found and default is not given. + """ + if default is _UNSET: + return self._data.pop(key) + return self._data.pop(key, default) + + def popitem(self) -> typing.Tuple[str, typing.Any]: + """ + Removes and returns some (key, value) pair + :returns: The (key, value) pair. + :rtype: tuple + :raises KeyError: if D is empty. + """ + return self._data.popitem() + + def clear(self) -> None: + """ + Remove all items from D. + """ + self._data.clear() + + def update(self, *args: typing.Any, **kwargs: typing.Any) -> None: # pylint: disable=arguments-differ + """ + Updates D from mapping/iterable E and F. + :param any args: Either a mapping object or an iterable of key-value pairs. + """ + self._data.update(*args, **kwargs) + + @typing.overload + def setdefault(self, key: str, default: None = None) -> None: ... + + @typing.overload + def setdefault(self, key: str, default: typing.Any) -> typing.Any: ... # pylint: disable=signature-differs + + def setdefault(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + """ + Same as calling D.get(k, d), and setting D[k]=d if k not found + :param str key: The key to look up. + :param any default: The value to set if key is not in the dictionary + :returns: D[k] if k in D, else d. + :rtype: any + """ + if default is _UNSET: + return self._data.setdefault(key) + return self._data.setdefault(key, default) + + def __eq__(self, other: typing.Any) -> bool: + try: + other_model = self.__class__(other) + except Exception: + return False + return self._data == other_model._data + + def __repr__(self) -> str: + return str(self._data) + + +def _is_model(obj: typing.Any) -> bool: + return getattr(obj, "_is_model", False) + + +def _serialize(o, format: typing.Optional[str] = None): # pylint: disable=too-many-return-statements + if isinstance(o, list): + return [_serialize(x, format) for x in o] + if isinstance(o, dict): + return {k: _serialize(v, format) for k, v in o.items()} + if isinstance(o, set): + return {_serialize(x, format) for x in o} + if isinstance(o, tuple): + return tuple(_serialize(x, format) for x in o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, format) + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, enum.Enum): + return o.value + if isinstance(o, int): + if format == "str": + return str(o) + return o + try: + # First try datetime.datetime + return _serialize_datetime(o, format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return o + + +def _get_rest_field( + attr_to_rest_field: typing.Dict[str, "_RestField"], rest_name: str +) -> typing.Optional["_RestField"]: + try: + return next(rf for rf in attr_to_rest_field.values() if rf._rest_name == rest_name) + except StopIteration: + return None + + +def _create_value(rf: typing.Optional["_RestField"], value: typing.Any) -> typing.Any: + if not rf: + return _serialize(value, None) + if rf._is_multipart_file_input: + return value + if rf._is_model: + return _deserialize(rf._type, value) + if isinstance(value, ET.Element): + value = _deserialize(rf._type, value) + return _serialize(value, rf._format) + + +class Model(_MyMutableMapping): + _is_model = True + # label whether current class's _attr_to_rest_field has been calculated + # could not see _attr_to_rest_field directly because subclass inherits it from parent class + _calculated: typing.Set[str] = set() + + def __init__(self, *args: typing.Any, **kwargs: typing.Any) -> None: + class_name = self.__class__.__name__ + if len(args) > 1: + raise TypeError(f"{class_name}.__init__() takes 2 positional arguments but {len(args) + 1} were given") + dict_to_pass = { + rest_field._rest_name: rest_field._default + for rest_field in self._attr_to_rest_field.values() + if rest_field._default is not _UNSET + } + if args: # pylint: disable=too-many-nested-blocks + if isinstance(args[0], ET.Element): + existed_attr_keys = [] + model_meta = getattr(self, "_xml", {}) + + for rf in self._attr_to_rest_field.values(): + prop_meta = getattr(rf, "_xml", {}) + xml_name = prop_meta.get("name", rf._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + # attribute + if prop_meta.get("attribute", False) and args[0].get(xml_name) is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].get(xml_name)) + continue + + # unwrapped element is array + if prop_meta.get("unwrapped", False): + # unwrapped array could either use prop items meta/prop meta + if prop_meta.get("itemsName"): + xml_name = prop_meta.get("itemsName") + xml_ns = prop_meta.get("itemNs") + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + items = args[0].findall(xml_name) # pyright: ignore + if len(items) > 0: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, items) + continue + + # text element is primitive type + if prop_meta.get("text", False): + if args[0].text is not None: + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].text) + continue + + # wrapped element could be normal property or array, it should only have one element + item = args[0].find(xml_name) + if item is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, item) + + # rest thing is additional properties + for e in args[0]: + if e.tag not in existed_attr_keys: + dict_to_pass[e.tag] = _convert_element(e) + else: + dict_to_pass.update( + {k: _create_value(_get_rest_field(self._attr_to_rest_field, k), v) for k, v in args[0].items()} + ) + else: + non_attr_kwargs = [k for k in kwargs if k not in self._attr_to_rest_field] + if non_attr_kwargs: + # actual type errors only throw the first wrong keyword arg they see, so following that. + raise TypeError(f"{class_name}.__init__() got an unexpected keyword argument '{non_attr_kwargs[0]}'") + dict_to_pass.update( + { + self._attr_to_rest_field[k]._rest_name: _create_value(self._attr_to_rest_field[k], v) + for k, v in kwargs.items() + if v is not None + } + ) + super().__init__(dict_to_pass) + + def copy(self) -> "Model": + return Model(self.__dict__) + + def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: + if f"{cls.__module__}.{cls.__qualname__}" not in cls._calculated: + # we know the last nine classes in mro are going to be 'Model', '_MyMutableMapping', 'MutableMapping', + # 'Mapping', 'Collection', 'Sized', 'Iterable', 'Container' and 'object' + mros = cls.__mro__[:-9][::-1] # ignore parents, and reverse the mro order + attr_to_rest_field: typing.Dict[str, _RestField] = { # map attribute name to rest_field property + k: v for mro_class in mros for k, v in mro_class.__dict__.items() if k[0] != "_" and hasattr(v, "_type") + } + annotations = { + k: v + for mro_class in mros + if hasattr(mro_class, "__annotations__") + for k, v in mro_class.__annotations__.items() + } + for attr, rf in attr_to_rest_field.items(): + rf._module = cls.__module__ + if not rf._type: + rf._type = rf._get_deserialize_callable_from_annotation(annotations.get(attr, None)) + if not rf._rest_name_input: + rf._rest_name_input = attr + cls._attr_to_rest_field: typing.Dict[str, _RestField] = dict(attr_to_rest_field.items()) + cls._calculated.add(f"{cls.__module__}.{cls.__qualname__}") + + return super().__new__(cls) + + def __init_subclass__(cls, discriminator: typing.Optional[str] = None) -> None: + for base in cls.__bases__: + if hasattr(base, "__mapping__"): + base.__mapping__[discriminator or cls.__name__] = cls # type: ignore + + @classmethod + def _get_discriminator(cls, exist_discriminators) -> typing.Optional["_RestField"]: + for v in cls.__dict__.values(): + if isinstance(v, _RestField) and v._is_discriminator and v._rest_name not in exist_discriminators: + return v + return None + + @classmethod + def _deserialize(cls, data, exist_discriminators): + if not hasattr(cls, "__mapping__"): + return cls(data) + discriminator = cls._get_discriminator(exist_discriminators) + if discriminator is None: + return cls(data) + exist_discriminators.append(discriminator._rest_name) + if isinstance(data, ET.Element): + model_meta = getattr(cls, "_xml", {}) + prop_meta = getattr(discriminator, "_xml", {}) + xml_name = prop_meta.get("name", discriminator._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + if data.get(xml_name) is not None: + discriminator_value = data.get(xml_name) + else: + discriminator_value = data.find(xml_name).text # pyright: ignore + else: + discriminator_value = data.get(discriminator._rest_name) + mapped_cls = cls.__mapping__.get(discriminator_value, cls) # pyright: ignore # pylint: disable=no-member + return mapped_cls._deserialize(data, exist_discriminators) + + def as_dict(self, *, exclude_readonly: bool = False) -> typing.Dict[str, typing.Any]: + """Return a dict that can be turned into json using json.dump. + + :keyword bool exclude_readonly: Whether to remove the readonly properties. + :returns: A dict JSON compatible object + :rtype: dict + """ + + result = {} + readonly_props = [] + if exclude_readonly: + readonly_props = [p._rest_name for p in self._attr_to_rest_field.values() if _is_readonly(p)] + for k, v in self.items(): + if exclude_readonly and k in readonly_props: # pyright: ignore + continue + is_multipart_file_input = False + try: + is_multipart_file_input = next( + rf for rf in self._attr_to_rest_field.values() if rf._rest_name == k + )._is_multipart_file_input + except StopIteration: + pass + result[k] = v if is_multipart_file_input else Model._as_dict_value(v, exclude_readonly=exclude_readonly) + return result + + @staticmethod + def _as_dict_value(v: typing.Any, exclude_readonly: bool = False) -> typing.Any: + if v is None or isinstance(v, _Null): + return None + if isinstance(v, (list, tuple, set)): + return type(v)(Model._as_dict_value(x, exclude_readonly=exclude_readonly) for x in v) + if isinstance(v, dict): + return {dk: Model._as_dict_value(dv, exclude_readonly=exclude_readonly) for dk, dv in v.items()} + return v.as_dict(exclude_readonly=exclude_readonly) if hasattr(v, "as_dict") else v + + +def _deserialize_model(model_deserializer: typing.Optional[typing.Callable], obj): + if _is_model(obj): + return obj + return _deserialize(model_deserializer, obj) + + +def _deserialize_with_optional(if_obj_deserializer: typing.Optional[typing.Callable], obj): + if obj is None: + return obj + return _deserialize_with_callable(if_obj_deserializer, obj) + + +def _deserialize_with_union(deserializers, obj): + for deserializer in deserializers: + try: + return _deserialize(deserializer, obj) + except DeserializationError: + pass + raise DeserializationError() + + +def _deserialize_dict( + value_deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj: typing.Dict[typing.Any, typing.Any], +): + if obj is None: + return obj + if isinstance(obj, ET.Element): + obj = {child.tag: child for child in obj} + return {k: _deserialize(value_deserializer, v, module) for k, v in obj.items()} + + +def _deserialize_multiple_sequence( + entry_deserializers: typing.List[typing.Optional[typing.Callable]], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + return type(obj)(_deserialize(deserializer, entry, module) for entry, deserializer in zip(obj, entry_deserializers)) + + +def _deserialize_sequence( + deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + if isinstance(obj, ET.Element): + obj = list(obj) + return type(obj)(_deserialize(deserializer, entry, module) for entry in obj) + + +def _sorted_annotations(types: typing.List[typing.Any]) -> typing.List[typing.Any]: + return sorted( + types, + key=lambda x: hasattr(x, "__name__") and x.__name__.lower() in ("str", "float", "int", "bool"), + ) + + +def _get_deserialize_callable_from_annotation( # pylint: disable=too-many-return-statements, too-many-branches + annotation: typing.Any, + module: typing.Optional[str], + rf: typing.Optional["_RestField"] = None, +) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + if not annotation: + return None + + # is it a type alias? + if isinstance(annotation, str): + if module is not None: + annotation = _get_type_alias_type(module, annotation) + + # is it a forward ref / in quotes? + if isinstance(annotation, (str, typing.ForwardRef)): + try: + model_name = annotation.__forward_arg__ # type: ignore + except AttributeError: + model_name = annotation + if module is not None: + annotation = _get_model(module, model_name) # type: ignore + + try: + if module and _is_model(annotation): + if rf: + rf._is_model = True + + return functools.partial(_deserialize_model, annotation) # pyright: ignore + except Exception: + pass + + # is it a literal? + try: + if annotation.__origin__ is typing.Literal: # pyright: ignore + return None + except AttributeError: + pass + + # is it optional? + try: + if any(a for a in annotation.__args__ if a == type(None)): # pyright: ignore + if len(annotation.__args__) <= 2: # pyright: ignore + if_obj_deserializer = _get_deserialize_callable_from_annotation( + next(a for a in annotation.__args__ if a != type(None)), module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_with_optional, if_obj_deserializer) + # the type is Optional[Union[...]], we need to remove the None type from the Union + annotation_copy = copy.copy(annotation) + annotation_copy.__args__ = [a for a in annotation_copy.__args__ if a != type(None)] # pyright: ignore + return _get_deserialize_callable_from_annotation(annotation_copy, module, rf) + except AttributeError: + pass + + # is it union? + if getattr(annotation, "__origin__", None) is typing.Union: + # initial ordering is we make `string` the last deserialization option, because it is often them most generic + deserializers = [ + _get_deserialize_callable_from_annotation(arg, module, rf) + for arg in _sorted_annotations(annotation.__args__) # pyright: ignore + ] + + return functools.partial(_deserialize_with_union, deserializers) + + try: + if annotation._name == "Dict": # pyright: ignore + value_deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[1], module, rf # pyright: ignore + ) + + return functools.partial( + _deserialize_dict, + value_deserializer, + module, + ) + except (AttributeError, IndexError): + pass + try: + if annotation._name in ["List", "Set", "Tuple", "Sequence"]: # pyright: ignore + if len(annotation.__args__) > 1: # pyright: ignore + entry_deserializers = [ + _get_deserialize_callable_from_annotation(dt, module, rf) + for dt in annotation.__args__ # pyright: ignore + ] + return functools.partial(_deserialize_multiple_sequence, entry_deserializers, module) + deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[0], module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_sequence, deserializer, module) + except (TypeError, IndexError, AttributeError, SyntaxError): + pass + + def _deserialize_default( + deserializer, + obj, + ): + if obj is None: + return obj + try: + return _deserialize_with_callable(deserializer, obj) + except Exception: + pass + return obj + + if get_deserializer(annotation, rf): + return functools.partial(_deserialize_default, get_deserializer(annotation, rf)) + + return functools.partial(_deserialize_default, annotation) + + +def _deserialize_with_callable( + deserializer: typing.Optional[typing.Callable[[typing.Any], typing.Any]], + value: typing.Any, +): # pylint: disable=too-many-return-statements + try: + if value is None or isinstance(value, _Null): + return None + if isinstance(value, ET.Element): + if deserializer is str: + return value.text or "" + if deserializer is int: + return int(value.text) if value.text else None + if deserializer is float: + return float(value.text) if value.text else None + if deserializer is bool: + return value.text == "true" if value.text else None + if deserializer is None: + return value + if deserializer in [int, float, bool]: + return deserializer(value) + if isinstance(deserializer, CaseInsensitiveEnumMeta): + try: + return deserializer(value) + except ValueError: + # for unknown value, return raw value + return value + if isinstance(deserializer, type) and issubclass(deserializer, Model): + return deserializer._deserialize(value, []) + return typing.cast(typing.Callable[[typing.Any], typing.Any], deserializer)(value) + except Exception as e: + raise DeserializationError() from e + + +def _deserialize( + deserializer: typing.Any, + value: typing.Any, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + if isinstance(value, PipelineResponse): + value = value.http_response.json() + if rf is None and format: + rf = _RestField(format=format) + if not isinstance(deserializer, functools.partial): + deserializer = _get_deserialize_callable_from_annotation(deserializer, module, rf) + return _deserialize_with_callable(deserializer, value) + + +def _failsafe_deserialize( + deserializer: typing.Any, + value: typing.Any, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + try: + return _deserialize(deserializer, value, module, rf, format) + except DeserializationError: + _LOGGER.warning( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + +def _failsafe_deserialize_xml( + deserializer: typing.Any, + value: typing.Any, +) -> typing.Any: + try: + return _deserialize_xml(deserializer, value) + except DeserializationError: + _LOGGER.warning( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + +class _RestField: + def __init__( + self, + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + is_discriminator: bool = False, + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, + ): + self._type = type + self._rest_name_input = name + self._module: typing.Optional[str] = None + self._is_discriminator = is_discriminator + self._visibility = visibility + self._is_model = False + self._default = default + self._format = format + self._is_multipart_file_input = is_multipart_file_input + self._xml = xml if xml is not None else {} + + @property + def _class_type(self) -> typing.Any: + return getattr(self._type, "args", [None])[0] + + @property + def _rest_name(self) -> str: + if self._rest_name_input is None: + raise ValueError("Rest name was never set") + return self._rest_name_input + + def __get__(self, obj: Model, type=None): # pylint: disable=redefined-builtin + # by this point, type and rest_name will have a value bc we default + # them in __new__ of the Model class + item = obj.get(self._rest_name) + if item is None: + return item + if self._is_model: + return item + return _deserialize(self._type, _serialize(item, self._format), rf=self) + + def __set__(self, obj: Model, value) -> None: + if value is None: + # we want to wipe out entries if users set attr to None + try: + obj.__delitem__(self._rest_name) + except KeyError: + pass + return + if self._is_model: + if not _is_model(value): + value = _deserialize(self._type, value) + obj.__setitem__(self._rest_name, value) + return + obj.__setitem__(self._rest_name, _serialize(value, self._format)) + + def _get_deserialize_callable_from_annotation( + self, annotation: typing.Any + ) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + return _get_deserialize_callable_from_annotation(annotation, self._module, self) + + +def rest_field( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, +) -> typing.Any: + return _RestField( + name=name, + type=type, + visibility=visibility, + default=default, + format=format, + is_multipart_file_input=is_multipart_file_input, + xml=xml, + ) + + +def rest_discriminator( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + visibility: typing.Optional[typing.List[str]] = None, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, +) -> typing.Any: + return _RestField(name=name, type=type, is_discriminator=True, visibility=visibility, xml=xml) + + +def serialize_xml(model: Model, exclude_readonly: bool = False) -> str: + """Serialize a model to XML. + + :param Model model: The model to serialize. + :param bool exclude_readonly: Whether to exclude readonly properties. + :returns: The XML representation of the model. + :rtype: str + """ + return ET.tostring(_get_element(model, exclude_readonly), encoding="unicode") # type: ignore + + +def _get_element( + o: typing.Any, + exclude_readonly: bool = False, + parent_meta: typing.Optional[typing.Dict[str, typing.Any]] = None, + wrapped_element: typing.Optional[ET.Element] = None, +) -> typing.Union[ET.Element, typing.List[ET.Element]]: + if _is_model(o): + model_meta = getattr(o, "_xml", {}) + + # if prop is a model, then use the prop element directly, else generate a wrapper of model + if wrapped_element is None: + wrapped_element = _create_xml_element( + model_meta.get("name", o.__class__.__name__), + model_meta.get("prefix"), + model_meta.get("ns"), + ) + + readonly_props = [] + if exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + + for k, v in o.items(): + # do not serialize readonly properties + if exclude_readonly and k in readonly_props: + continue + + prop_rest_field = _get_rest_field(o._attr_to_rest_field, k) + if prop_rest_field: + prop_meta = getattr(prop_rest_field, "_xml").copy() + # use the wire name as xml name if no specific name is set + if prop_meta.get("name") is None: + prop_meta["name"] = k + else: + # additional properties will not have rest field, use the wire name as xml name + prop_meta = {"name": k} + + # if no ns for prop, use model's + if prop_meta.get("ns") is None and model_meta.get("ns"): + prop_meta["ns"] = model_meta.get("ns") + prop_meta["prefix"] = model_meta.get("prefix") + + if prop_meta.get("unwrapped", False): + # unwrapped could only set on array + wrapped_element.extend(_get_element(v, exclude_readonly, prop_meta)) + elif prop_meta.get("text", False): + # text could only set on primitive type + wrapped_element.text = _get_primitive_type_value(v) + elif prop_meta.get("attribute", False): + xml_name = prop_meta.get("name", k) + if prop_meta.get("ns"): + ET.register_namespace(prop_meta.get("prefix"), prop_meta.get("ns")) # pyright: ignore + xml_name = "{" + prop_meta.get("ns") + "}" + xml_name # pyright: ignore + # attribute should be primitive type + wrapped_element.set(xml_name, _get_primitive_type_value(v)) + else: + # other wrapped prop element + wrapped_element.append(_get_wrapped_element(v, exclude_readonly, prop_meta)) + return wrapped_element + if isinstance(o, list): + return [_get_element(x, exclude_readonly, parent_meta) for x in o] # type: ignore + if isinstance(o, dict): + result = [] + for k, v in o.items(): + result.append( + _get_wrapped_element( + v, + exclude_readonly, + { + "name": k, + "ns": parent_meta.get("ns") if parent_meta else None, + "prefix": parent_meta.get("prefix") if parent_meta else None, + }, + ) + ) + return result + + # primitive case need to create element based on parent_meta + if parent_meta: + return _get_wrapped_element( + o, + exclude_readonly, + { + "name": parent_meta.get("itemsName", parent_meta.get("name")), + "prefix": parent_meta.get("itemsPrefix", parent_meta.get("prefix")), + "ns": parent_meta.get("itemsNs", parent_meta.get("ns")), + }, + ) + + raise ValueError("Could not serialize value into xml: " + o) + + +def _get_wrapped_element( + v: typing.Any, + exclude_readonly: bool, + meta: typing.Optional[typing.Dict[str, typing.Any]], +) -> ET.Element: + wrapped_element = _create_xml_element( + meta.get("name") if meta else None, meta.get("prefix") if meta else None, meta.get("ns") if meta else None + ) + if isinstance(v, (dict, list)): + wrapped_element.extend(_get_element(v, exclude_readonly, meta)) + elif _is_model(v): + _get_element(v, exclude_readonly, meta, wrapped_element) + else: + wrapped_element.text = _get_primitive_type_value(v) + return wrapped_element + + +def _get_primitive_type_value(v) -> str: + if v is True: + return "true" + if v is False: + return "false" + if isinstance(v, _Null): + return "" + return str(v) + + +def _create_xml_element(tag, prefix=None, ns=None): + if prefix and ns: + ET.register_namespace(prefix, ns) + if ns: + return ET.Element("{" + ns + "}" + tag) + return ET.Element(tag) + + +def _deserialize_xml( + deserializer: typing.Any, + value: str, +) -> typing.Any: + element = ET.fromstring(value) # nosec + return _deserialize(deserializer, element) + + +def _convert_element(e: ET.Element): + # dict case + if len(e.attrib) > 0 or len({child.tag for child in e}) > 1: + dict_result: typing.Dict[str, typing.Any] = {} + for child in e: + if dict_result.get(child.tag) is not None: + if isinstance(dict_result[child.tag], list): + dict_result[child.tag].append(_convert_element(child)) + else: + dict_result[child.tag] = [dict_result[child.tag], _convert_element(child)] + else: + dict_result[child.tag] = _convert_element(child) + dict_result.update(e.attrib) + return dict_result + # array case + if len(e) > 0: + array_result: typing.List[typing.Any] = [] + for child in e: + array_result.append(_convert_element(child)) + return array_result + # primitive case + return e.text diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/serialization.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/serialization.py new file mode 100644 index 000000000000..eb86ea23c965 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/serialization.py @@ -0,0 +1,2032 @@ +# pylint: disable=line-too-long,useless-suppression,too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +# pyright: reportUnnecessaryTypeIgnoreComment=false + +from base64 import b64decode, b64encode +import calendar +import datetime +import decimal +import email +from enum import Enum +import json +import logging +import re +import sys +import codecs +from typing import ( + Dict, + Any, + cast, + Optional, + Union, + AnyStr, + IO, + Mapping, + Callable, + MutableMapping, + List, +) + +try: + from urllib import quote # type: ignore +except ImportError: + from urllib.parse import quote +import xml.etree.ElementTree as ET + +import isodate # type: ignore +from typing_extensions import Self + +from azure.core.exceptions import DeserializationError, SerializationError +from azure.core.serialization import NULL as CoreNull + +_BOM = codecs.BOM_UTF8.decode(encoding="utf-8") + +JSON = MutableMapping[str, Any] + + +class RawDeserializer: + + # Accept "text" because we're open minded people... + JSON_REGEXP = re.compile(r"^(application|text)/([a-z+.]+\+)?json$") + + # Name used in context + CONTEXT_NAME = "deserialized_data" + + @classmethod + def deserialize_from_text(cls, data: Optional[Union[AnyStr, IO]], content_type: Optional[str] = None) -> Any: + """Decode data according to content-type. + + Accept a stream of data as well, but will be load at once in memory for now. + + If no content-type, will return the string version (not bytes, not stream) + + :param data: Input, could be bytes or stream (will be decoded with UTF8) or text + :type data: str or bytes or IO + :param str content_type: The content type. + :return: The deserialized data. + :rtype: object + """ + if hasattr(data, "read"): + # Assume a stream + data = cast(IO, data).read() + + if isinstance(data, bytes): + data_as_str = data.decode(encoding="utf-8-sig") + else: + # Explain to mypy the correct type. + data_as_str = cast(str, data) + + # Remove Byte Order Mark if present in string + data_as_str = data_as_str.lstrip(_BOM) + + if content_type is None: + return data + + if cls.JSON_REGEXP.match(content_type): + try: + return json.loads(data_as_str) + except ValueError as err: + raise DeserializationError("JSON is invalid: {}".format(err), err) from err + elif "xml" in (content_type or []): + try: + + try: + if isinstance(data, unicode): # type: ignore + # If I'm Python 2.7 and unicode XML will scream if I try a "fromstring" on unicode string + data_as_str = data_as_str.encode(encoding="utf-8") # type: ignore + except NameError: + pass + + return ET.fromstring(data_as_str) # nosec + except ET.ParseError as err: + # It might be because the server has an issue, and returned JSON with + # content-type XML.... + # So let's try a JSON load, and if it's still broken + # let's flow the initial exception + def _json_attemp(data): + try: + return True, json.loads(data) + except ValueError: + return False, None # Don't care about this one + + success, json_result = _json_attemp(data) + if success: + return json_result + # If i'm here, it's not JSON, it's not XML, let's scream + # and raise the last context in this block (the XML exception) + # The function hack is because Py2.7 messes up with exception + # context otherwise. + _LOGGER.critical("Wasn't XML not JSON, failing") + raise DeserializationError("XML is invalid") from err + elif content_type.startswith("text/"): + return data_as_str + raise DeserializationError("Cannot deserialize content-type: {}".format(content_type)) + + @classmethod + def deserialize_from_http_generics(cls, body_bytes: Optional[Union[AnyStr, IO]], headers: Mapping) -> Any: + """Deserialize from HTTP response. + + Use bytes and headers to NOT use any requests/aiohttp or whatever + specific implementation. + Headers will tested for "content-type" + + :param bytes body_bytes: The body of the response. + :param dict headers: The headers of the response. + :returns: The deserialized data. + :rtype: object + """ + # Try to use content-type from headers if available + content_type = None + if "content-type" in headers: + content_type = headers["content-type"].split(";")[0].strip().lower() + # Ouch, this server did not declare what it sent... + # Let's guess it's JSON... + # Also, since Autorest was considering that an empty body was a valid JSON, + # need that test as well.... + else: + content_type = "application/json" + + if body_bytes: + return cls.deserialize_from_text(body_bytes, content_type) + return None + + +_LOGGER = logging.getLogger(__name__) + +try: + _long_type = long # type: ignore +except NameError: + _long_type = int + +TZ_UTC = datetime.timezone.utc + +_FLATTEN = re.compile(r"(? None: + self.additional_properties: Optional[Dict[str, Any]] = {} + for k in kwargs: # pylint: disable=consider-using-dict-items + if k not in self._attribute_map: + _LOGGER.warning("%s is not a known attribute of class %s and will be ignored", k, self.__class__) + elif k in self._validation and self._validation[k].get("readonly", False): + _LOGGER.warning("Readonly attribute %s will be ignored in class %s", k, self.__class__) + else: + setattr(self, k, kwargs[k]) + + def __eq__(self, other: Any) -> bool: + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are equal + :rtype: bool + """ + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + return False + + def __ne__(self, other: Any) -> bool: + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are not equal + :rtype: bool + """ + return not self.__eq__(other) + + def __str__(self) -> str: + return str(self.__dict__) + + @classmethod + def enable_additional_properties_sending(cls) -> None: + cls._attribute_map["additional_properties"] = {"key": "", "type": "{object}"} + + @classmethod + def is_xml_model(cls) -> bool: + try: + cls._xml_map # type: ignore + except AttributeError: + return False + return True + + @classmethod + def _create_xml_node(cls): + """Create XML node. + + :returns: The XML node + :rtype: xml.etree.ElementTree.Element + """ + try: + xml_map = cls._xml_map # type: ignore + except AttributeError: + xml_map = {} + + return _create_xml_node(xml_map.get("name", cls.__name__), xml_map.get("prefix", None), xml_map.get("ns", None)) + + def serialize(self, keep_readonly: bool = False, **kwargs: Any) -> JSON: + """Return the JSON that would be sent to server from this model. + + This is an alias to `as_dict(full_restapi_key_transformer, keep_readonly=False)`. + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param bool keep_readonly: If you want to serialize the readonly attributes + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, keep_readonly=keep_readonly, **kwargs + ) + + def as_dict( + self, + keep_readonly: bool = True, + key_transformer: Callable[[str, Dict[str, Any], Any], Any] = attribute_transformer, + **kwargs: Any + ) -> JSON: + """Return a dict that can be serialized using json.dump. + + Advanced usage might optionally use a callback as parameter: + + .. code::python + + def my_key_transformer(key, attr_desc, value): + return key + + Key is the attribute name used in Python. Attr_desc + is a dict of metadata. Currently contains 'type' with the + msrest type and 'key' with the RestAPI encoded key. + Value is the current value in this object. + + The string returned will be used to serialize the key. + If the return type is a list, this is considered hierarchical + result dict. + + See the three examples in this file: + + - attribute_transformer + - full_restapi_key_transformer + - last_restapi_key_transformer + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param bool keep_readonly: If you want to serialize the readonly attributes + :param function key_transformer: A key transformer function. + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, key_transformer=key_transformer, keep_readonly=keep_readonly, **kwargs + ) + + @classmethod + def _infer_class_models(cls): + try: + str_models = cls.__module__.rsplit(".", 1)[0] + models = sys.modules[str_models] + client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} + if cls.__name__ not in client_models: + raise ValueError("Not Autorest generated code") + except Exception: # pylint: disable=broad-exception-caught + # Assume it's not Autorest generated (tests?). Add ourselves as dependencies. + client_models = {cls.__name__: cls} + return client_models + + @classmethod + def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Self: + """Parse a str using the RestAPI syntax and return a model. + + :param str data: A str using RestAPI structure. JSON by default. + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises DeserializationError: if something went wrong + :rtype: Self + """ + deserializer = Deserializer(cls._infer_class_models()) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def from_dict( + cls, + data: Any, + key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None, + content_type: Optional[str] = None, + ) -> Self: + """Parse a dict using given key extractor return a model. + + By default consider key + extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor + and last_rest_key_case_insensitive_extractor) + + :param dict data: A dict using RestAPI structure + :param function key_extractors: A key extractor function. + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises DeserializationError: if something went wrong + :rtype: Self + """ + deserializer = Deserializer(cls._infer_class_models()) + deserializer.key_extractors = ( # type: ignore + [ # type: ignore + attribute_key_case_insensitive_extractor, + rest_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + if key_extractors is None + else key_extractors + ) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def _flatten_subtype(cls, key, objects): + if "_subtype_map" not in cls.__dict__: + return {} + result = dict(cls._subtype_map[key]) + for valuetype in cls._subtype_map[key].values(): + result.update(objects[valuetype]._flatten_subtype(key, objects)) # pylint: disable=protected-access + return result + + @classmethod + def _classify(cls, response, objects): + """Check the class _subtype_map for any child classes. + We want to ignore any inherited _subtype_maps. + + :param dict response: The initial data + :param dict objects: The class objects + :returns: The class to be used + :rtype: class + """ + for subtype_key in cls.__dict__.get("_subtype_map", {}).keys(): + subtype_value = None + + if not isinstance(response, ET.Element): + rest_api_response_key = cls._get_rest_key_parts(subtype_key)[-1] + subtype_value = response.get(rest_api_response_key, None) or response.get(subtype_key, None) + else: + subtype_value = xml_key_extractor(subtype_key, cls._attribute_map[subtype_key], response) + if subtype_value: + # Try to match base class. Can be class name only + # (bug to fix in Autorest to support x-ms-discriminator-name) + if cls.__name__ == subtype_value: + return cls + flatten_mapping_type = cls._flatten_subtype(subtype_key, objects) + try: + return objects[flatten_mapping_type[subtype_value]] # type: ignore + except KeyError: + _LOGGER.warning( + "Subtype value %s has no mapping, use base class %s.", + subtype_value, + cls.__name__, + ) + break + else: + _LOGGER.warning("Discriminator %s is absent or null, use base class %s.", subtype_key, cls.__name__) + break + return cls + + @classmethod + def _get_rest_key_parts(cls, attr_key): + """Get the RestAPI key of this attr, split it and decode part + :param str attr_key: Attribute key must be in attribute_map. + :returns: A list of RestAPI part + :rtype: list + """ + rest_split_key = _FLATTEN.split(cls._attribute_map[attr_key]["key"]) + return [_decode_attribute_map_key(key_part) for key_part in rest_split_key] + + +def _decode_attribute_map_key(key): + """This decode a key in an _attribute_map to the actual key we want to look at + inside the received data. + + :param str key: A key string from the generated code + :returns: The decoded key + :rtype: str + """ + return key.replace("\\.", ".") + + +class Serializer: # pylint: disable=too-many-public-methods + """Request object model serializer.""" + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + _xml_basic_types_serializers = {"bool": lambda x: str(x).lower()} + days = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"} + months = { + 1: "Jan", + 2: "Feb", + 3: "Mar", + 4: "Apr", + 5: "May", + 6: "Jun", + 7: "Jul", + 8: "Aug", + 9: "Sep", + 10: "Oct", + 11: "Nov", + 12: "Dec", + } + validation = { + "min_length": lambda x, y: len(x) < y, + "max_length": lambda x, y: len(x) > y, + "minimum": lambda x, y: x < y, + "maximum": lambda x, y: x > y, + "minimum_ex": lambda x, y: x <= y, + "maximum_ex": lambda x, y: x >= y, + "min_items": lambda x, y: len(x) < y, + "max_items": lambda x, y: len(x) > y, + "pattern": lambda x, y: not re.match(y, x, re.UNICODE), + "unique": lambda x, y: len(x) != len(set(x)), + "multiple": lambda x, y: x % y != 0, + } + + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: + self.serialize_type = { + "iso-8601": Serializer.serialize_iso, + "rfc-1123": Serializer.serialize_rfc, + "unix-time": Serializer.serialize_unix, + "duration": Serializer.serialize_duration, + "date": Serializer.serialize_date, + "time": Serializer.serialize_time, + "decimal": Serializer.serialize_decimal, + "long": Serializer.serialize_long, + "bytearray": Serializer.serialize_bytearray, + "base64": Serializer.serialize_base64, + "object": self.serialize_object, + "[]": self.serialize_iter, + "{}": self.serialize_dict, + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_transformer = full_restapi_key_transformer + self.client_side_validation = True + + def _serialize( # pylint: disable=too-many-nested-blocks, too-many-branches, too-many-statements, too-many-locals + self, target_obj, data_type=None, **kwargs + ): + """Serialize data into a string according to type. + + :param object target_obj: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str, dict + :raises SerializationError: if serialization fails. + :returns: The serialized data. + """ + key_transformer = kwargs.get("key_transformer", self.key_transformer) + keep_readonly = kwargs.get("keep_readonly", False) + if target_obj is None: + return None + + attr_name = None + class_name = target_obj.__class__.__name__ + + if data_type: + return self.serialize_data(target_obj, data_type, **kwargs) + + if not hasattr(target_obj, "_attribute_map"): + data_type = type(target_obj).__name__ + if data_type in self.basic_types.values(): + return self.serialize_data(target_obj, data_type, **kwargs) + + # Force "is_xml" kwargs if we detect a XML model + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + is_xml_model_serialization = kwargs.setdefault("is_xml", target_obj.is_xml_model()) + + serialized = {} + if is_xml_model_serialization: + serialized = target_obj._create_xml_node() # pylint: disable=protected-access + try: + attributes = target_obj._attribute_map # pylint: disable=protected-access + for attr, attr_desc in attributes.items(): + attr_name = attr + if not keep_readonly and target_obj._validation.get( # pylint: disable=protected-access + attr_name, {} + ).get("readonly", False): + continue + + if attr_name == "additional_properties" and attr_desc["key"] == "": + if target_obj.additional_properties is not None: + serialized.update(target_obj.additional_properties) + continue + try: + + orig_attr = getattr(target_obj, attr) + if is_xml_model_serialization: + pass # Don't provide "transformer" for XML for now. Keep "orig_attr" + else: # JSON + keys, orig_attr = key_transformer(attr, attr_desc.copy(), orig_attr) + keys = keys if isinstance(keys, list) else [keys] + + kwargs["serialization_ctxt"] = attr_desc + new_attr = self.serialize_data(orig_attr, attr_desc["type"], **kwargs) + + if is_xml_model_serialization: + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + xml_prefix = xml_desc.get("prefix", None) + xml_ns = xml_desc.get("ns", None) + if xml_desc.get("attr", False): + if xml_ns: + ET.register_namespace(xml_prefix, xml_ns) + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + serialized.set(xml_name, new_attr) # type: ignore + continue + if xml_desc.get("text", False): + serialized.text = new_attr # type: ignore + continue + if isinstance(new_attr, list): + serialized.extend(new_attr) # type: ignore + elif isinstance(new_attr, ET.Element): + # If the down XML has no XML/Name, + # we MUST replace the tag with the local tag. But keeping the namespaces. + if "name" not in getattr(orig_attr, "_xml_map", {}): + splitted_tag = new_attr.tag.split("}") + if len(splitted_tag) == 2: # Namespace + new_attr.tag = "}".join([splitted_tag[0], xml_name]) + else: + new_attr.tag = xml_name + serialized.append(new_attr) # type: ignore + else: # That's a basic type + # Integrate namespace if necessary + local_node = _create_xml_node(xml_name, xml_prefix, xml_ns) + local_node.text = str(new_attr) + serialized.append(local_node) # type: ignore + else: # JSON + for k in reversed(keys): # type: ignore + new_attr = {k: new_attr} + + _new_attr = new_attr + _serialized = serialized + for k in keys: # type: ignore + if k not in _serialized: + _serialized.update(_new_attr) # type: ignore + _new_attr = _new_attr[k] # type: ignore + _serialized = _serialized[k] + except ValueError as err: + if isinstance(err, SerializationError): + raise + + except (AttributeError, KeyError, TypeError) as err: + msg = "Attribute {} in object {} cannot be serialized.\n{}".format(attr_name, class_name, str(target_obj)) + raise SerializationError(msg) from err + return serialized + + def body(self, data, data_type, **kwargs): + """Serialize data intended for a request body. + + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: dict + :raises SerializationError: if serialization fails. + :raises ValueError: if data is None + :returns: The serialized request body + """ + + # Just in case this is a dict + internal_data_type_str = data_type.strip("[]{}") + internal_data_type = self.dependencies.get(internal_data_type_str, None) + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + if internal_data_type and issubclass(internal_data_type, Model): + is_xml_model_serialization = kwargs.setdefault("is_xml", internal_data_type.is_xml_model()) + else: + is_xml_model_serialization = False + if internal_data_type and not isinstance(internal_data_type, Enum): + try: + deserializer = Deserializer(self.dependencies) + # Since it's on serialization, it's almost sure that format is not JSON REST + # We're not able to deal with additional properties for now. + deserializer.additional_properties_detection = False + if is_xml_model_serialization: + deserializer.key_extractors = [ # type: ignore + attribute_key_case_insensitive_extractor, + ] + else: + deserializer.key_extractors = [ + rest_key_case_insensitive_extractor, + attribute_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + data = deserializer._deserialize(data_type, data) # pylint: disable=protected-access + except DeserializationError as err: + raise SerializationError("Unable to build a model: " + str(err)) from err + + return self._serialize(data, data_type, **kwargs) + + def url(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL path. + + :param str name: The name of the URL path parameter. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :returns: The serialized URL path + :raises TypeError: if serialization fails. + :raises ValueError: if data is None + """ + try: + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + + if kwargs.get("skip_quote") is True: + output = str(output) + output = output.replace("{", quote("{")).replace("}", quote("}")) + else: + output = quote(str(output), safe="") + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return output + + def query(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL query. + + :param str name: The name of the query parameter. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str, list + :raises TypeError: if serialization fails. + :raises ValueError: if data is None + :returns: The serialized query parameter + """ + try: + # Treat the list aside, since we don't want to encode the div separator + if data_type.startswith("["): + internal_data_type = data_type[1:-1] + do_quote = not kwargs.get("skip_quote", False) + return self.serialize_iter(data, internal_data_type, do_quote=do_quote, **kwargs) + + # Not a list, regular serialization + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + if kwargs.get("skip_quote") is True: + output = str(output) + else: + output = quote(str(output), safe="") + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) + + def header(self, name, data, data_type, **kwargs): + """Serialize data intended for a request header. + + :param str name: The name of the header. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :raises TypeError: if serialization fails. + :raises ValueError: if data is None + :returns: The serialized header + """ + try: + if data_type in ["[str]"]: + data = ["" if d is None else d for d in data] + + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) + + def serialize_data(self, data, data_type, **kwargs): + """Serialize generic data according to supplied data type. + + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :raises AttributeError: if required data is None. + :raises ValueError: if data is None + :raises SerializationError: if serialization fails. + :returns: The serialized data. + :rtype: str, int, float, bool, dict, list + """ + if data is None: + raise ValueError("No value for given attribute") + + try: + if data is CoreNull: + return None + if data_type in self.basic_types.values(): + return self.serialize_basic(data, data_type, **kwargs) + + if data_type in self.serialize_type: + return self.serialize_type[data_type](data, **kwargs) + + # If dependencies is empty, try with current data class + # It has to be a subclass of Enum anyway + enum_type = self.dependencies.get(data_type, data.__class__) + if issubclass(enum_type, Enum): + return Serializer.serialize_enum(data, enum_obj=enum_type) + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.serialize_type: + return self.serialize_type[iter_type](data, data_type[1:-1], **kwargs) + + except (ValueError, TypeError) as err: + msg = "Unable to serialize value: {!r} as type: {!r}." + raise SerializationError(msg.format(data, data_type)) from err + return self._serialize(data, **kwargs) + + @classmethod + def _get_custom_serializers(cls, data_type, **kwargs): # pylint: disable=inconsistent-return-statements + custom_serializer = kwargs.get("basic_types_serializers", {}).get(data_type) + if custom_serializer: + return custom_serializer + if kwargs.get("is_xml", False): + return cls._xml_basic_types_serializers.get(data_type) + + @classmethod + def serialize_basic(cls, data, data_type, **kwargs): + """Serialize basic builting data type. + Serializes objects to str, int, float or bool. + + Possible kwargs: + - basic_types_serializers dict[str, callable] : If set, use the callable as serializer + - is_xml bool : If set, use xml_basic_types_serializers + + :param obj data: Object to be serialized. + :param str data_type: Type of object in the iterable. + :rtype: str, int, float, bool + :return: serialized object + """ + custom_serializer = cls._get_custom_serializers(data_type, **kwargs) + if custom_serializer: + return custom_serializer(data) + if data_type == "str": + return cls.serialize_unicode(data) + return eval(data_type)(data) # nosec # pylint: disable=eval-used + + @classmethod + def serialize_unicode(cls, data): + """Special handling for serializing unicode strings in Py2. + Encode to UTF-8 if unicode, otherwise handle as a str. + + :param str data: Object to be serialized. + :rtype: str + :return: serialized object + """ + try: # If I received an enum, return its value + return data.value + except AttributeError: + pass + + try: + if isinstance(data, unicode): # type: ignore + # Don't change it, JSON and XML ElementTree are totally able + # to serialize correctly u'' strings + return data + except NameError: + return str(data) + return str(data) + + def serialize_iter(self, data, iter_type, div=None, **kwargs): + """Serialize iterable. + + Supported kwargs: + - serialization_ctxt dict : The current entry of _attribute_map, or same format. + serialization_ctxt['type'] should be same as data_type. + - is_xml bool : If set, serialize as XML + + :param list data: Object to be serialized. + :param str iter_type: Type of object in the iterable. + :param str div: If set, this str will be used to combine the elements + in the iterable into a combined string. Default is 'None'. + Defaults to False. + :rtype: list, str + :return: serialized iterable + """ + if isinstance(data, str): + raise SerializationError("Refuse str type as a valid iter type.") + + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + is_xml = kwargs.get("is_xml", False) + + serialized = [] + for d in data: + try: + serialized.append(self.serialize_data(d, iter_type, **kwargs)) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized.append(None) + + if kwargs.get("do_quote", False): + serialized = ["" if s is None else quote(str(s), safe="") for s in serialized] + + if div: + serialized = ["" if s is None else str(s) for s in serialized] + serialized = div.join(serialized) + + if "xml" in serialization_ctxt or is_xml: + # XML serialization is more complicated + xml_desc = serialization_ctxt.get("xml", {}) + xml_name = xml_desc.get("name") + if not xml_name: + xml_name = serialization_ctxt["key"] + + # Create a wrap node if necessary (use the fact that Element and list have "append") + is_wrapped = xml_desc.get("wrapped", False) + node_name = xml_desc.get("itemsName", xml_name) + if is_wrapped: + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + else: + final_result = [] + # All list elements to "local_node" + for el in serialized: + if isinstance(el, ET.Element): + el_node = el + else: + el_node = _create_xml_node(node_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + if el is not None: # Otherwise it writes "None" :-p + el_node.text = str(el) + final_result.append(el_node) + return final_result + return serialized + + def serialize_dict(self, attr, dict_type, **kwargs): + """Serialize a dictionary of objects. + + :param dict attr: Object to be serialized. + :param str dict_type: Type of object in the dictionary. + :rtype: dict + :return: serialized dictionary + """ + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_data(value, dict_type, **kwargs) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized[self.serialize_unicode(key)] = None + + if "xml" in serialization_ctxt: + # XML serialization is more complicated + xml_desc = serialization_ctxt["xml"] + xml_name = xml_desc["name"] + + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + for key, value in serialized.items(): + ET.SubElement(final_result, key).text = value + return final_result + + return serialized + + def serialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements + """Serialize a generic object. + This will be handled as a dictionary. If object passed in is not + a basic type (str, int, float, dict, list) it will simply be + cast to str. + + :param dict attr: Object to be serialized. + :rtype: dict or str + :return: serialized object + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + return attr + obj_type = type(attr) + if obj_type in self.basic_types: + return self.serialize_basic(attr, self.basic_types[obj_type], **kwargs) + if obj_type is _long_type: + return self.serialize_long(attr) + if obj_type is str: + return self.serialize_unicode(attr) + if obj_type is datetime.datetime: + return self.serialize_iso(attr) + if obj_type is datetime.date: + return self.serialize_date(attr) + if obj_type is datetime.time: + return self.serialize_time(attr) + if obj_type is datetime.timedelta: + return self.serialize_duration(attr) + if obj_type is decimal.Decimal: + return self.serialize_decimal(attr) + + # If it's a model or I know this dependency, serialize as a Model + if obj_type in self.dependencies.values() or isinstance(attr, Model): + return self._serialize(attr) + + if obj_type == dict: + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_object(value, **kwargs) + except ValueError: + serialized[self.serialize_unicode(key)] = None + return serialized + + if obj_type == list: + serialized = [] + for obj in attr: + try: + serialized.append(self.serialize_object(obj, **kwargs)) + except ValueError: + pass + return serialized + return str(attr) + + @staticmethod + def serialize_enum(attr, enum_obj=None): + try: + result = attr.value + except AttributeError: + result = attr + try: + enum_obj(result) # type: ignore + return result + except ValueError as exc: + for enum_value in enum_obj: # type: ignore + if enum_value.value.lower() == str(attr).lower(): + return enum_value.value + error = "{!r} is not valid value for enum {!r}" + raise SerializationError(error.format(attr, enum_obj)) from exc + + @staticmethod + def serialize_bytearray(attr, **kwargs): # pylint: disable=unused-argument + """Serialize bytearray into base-64 string. + + :param str attr: Object to be serialized. + :rtype: str + :return: serialized base64 + """ + return b64encode(attr).decode() + + @staticmethod + def serialize_base64(attr, **kwargs): # pylint: disable=unused-argument + """Serialize str into base-64 string. + + :param str attr: Object to be serialized. + :rtype: str + :return: serialized base64 + """ + encoded = b64encode(attr).decode("ascii") + return encoded.strip("=").replace("+", "-").replace("/", "_") + + @staticmethod + def serialize_decimal(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Decimal object to float. + + :param decimal attr: Object to be serialized. + :rtype: float + :return: serialized decimal + """ + return float(attr) + + @staticmethod + def serialize_long(attr, **kwargs): # pylint: disable=unused-argument + """Serialize long (Py2) or int (Py3). + + :param int attr: Object to be serialized. + :rtype: int/long + :return: serialized long + """ + return _long_type(attr) + + @staticmethod + def serialize_date(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Date object into ISO-8601 formatted string. + + :param Date attr: Object to be serialized. + :rtype: str + :return: serialized date + """ + if isinstance(attr, str): + attr = isodate.parse_date(attr) + t = "{:04}-{:02}-{:02}".format(attr.year, attr.month, attr.day) + return t + + @staticmethod + def serialize_time(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Time object into ISO-8601 formatted string. + + :param datetime.time attr: Object to be serialized. + :rtype: str + :return: serialized time + """ + if isinstance(attr, str): + attr = isodate.parse_time(attr) + t = "{:02}:{:02}:{:02}".format(attr.hour, attr.minute, attr.second) + if attr.microsecond: + t += ".{:02}".format(attr.microsecond) + return t + + @staticmethod + def serialize_duration(attr, **kwargs): # pylint: disable=unused-argument + """Serialize TimeDelta object into ISO-8601 formatted string. + + :param TimeDelta attr: Object to be serialized. + :rtype: str + :return: serialized duration + """ + if isinstance(attr, str): + attr = isodate.parse_duration(attr) + return isodate.duration_isoformat(attr) + + @staticmethod + def serialize_rfc(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into RFC-1123 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises TypeError: if format invalid. + :return: serialized rfc + """ + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + except AttributeError as exc: + raise TypeError("RFC1123 object must be valid Datetime object.") from exc + + return "{}, {:02} {} {:04} {:02}:{:02}:{:02} GMT".format( + Serializer.days[utc.tm_wday], + utc.tm_mday, + Serializer.months[utc.tm_mon], + utc.tm_year, + utc.tm_hour, + utc.tm_min, + utc.tm_sec, + ) + + @staticmethod + def serialize_iso(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into ISO-8601 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises SerializationError: if format invalid. + :return: serialized iso + """ + if isinstance(attr, str): + attr = isodate.parse_datetime(attr) + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + if utc.tm_year > 9999 or utc.tm_year < 1: + raise OverflowError("Hit max or min date") + + microseconds = str(attr.microsecond).rjust(6, "0").rstrip("0").ljust(3, "0") + if microseconds: + microseconds = "." + microseconds + date = "{:04}-{:02}-{:02}T{:02}:{:02}:{:02}".format( + utc.tm_year, utc.tm_mon, utc.tm_mday, utc.tm_hour, utc.tm_min, utc.tm_sec + ) + return date + microseconds + "Z" + except (ValueError, OverflowError) as err: + msg = "Unable to serialize datetime object." + raise SerializationError(msg) from err + except AttributeError as err: + msg = "ISO-8601 object must be valid Datetime object." + raise TypeError(msg) from err + + @staticmethod + def serialize_unix(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param Datetime attr: Object to be serialized. + :rtype: int + :raises SerializationError: if format invalid + :return: serialied unix + """ + if isinstance(attr, int): + return attr + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + return int(calendar.timegm(attr.utctimetuple())) + except AttributeError as exc: + raise TypeError("Unix time object must be valid Datetime object.") from exc + + +def rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + key = attr_desc["key"] + working_data = data + + while "." in key: + # Need the cast, as for some reasons "split" is typed as list[str | Any] + dict_keys = cast(List[str], _FLATTEN.split(key)) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = working_data.get(working_key, data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + return working_data.get(key) + + +def rest_key_case_insensitive_extractor( # pylint: disable=unused-argument, inconsistent-return-statements + attr, attr_desc, data +): + key = attr_desc["key"] + working_data = data + + while "." in key: + dict_keys = _FLATTEN.split(key) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = attribute_key_case_insensitive_extractor(working_key, None, working_data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + if working_data: + return attribute_key_case_insensitive_extractor(key, None, working_data) + + +def last_rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + """Extract the attribute in "data" based on the last part of the JSON path key. + + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute + """ + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_extractor(dict_keys[-1], None, data) + + +def last_rest_key_case_insensitive_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + """Extract the attribute in "data" based on the last part of the JSON path key. + + This is the case insensitive version of "last_rest_key_extractor" + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute + """ + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_case_insensitive_extractor(dict_keys[-1], None, data) + + +def attribute_key_extractor(attr, _, data): + return data.get(attr) + + +def attribute_key_case_insensitive_extractor(attr, _, data): + found_key = None + lower_attr = attr.lower() + for key in data: + if lower_attr == key.lower(): + found_key = key + break + + return data.get(found_key) + + +def _extract_name_from_internal_type(internal_type): + """Given an internal type XML description, extract correct XML name with namespace. + + :param dict internal_type: An model type + :rtype: tuple + :returns: A tuple XML name + namespace dict + """ + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + xml_name = internal_type_xml_map.get("name", internal_type.__name__) + xml_ns = internal_type_xml_map.get("ns", None) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + return xml_name + + +def xml_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument,too-many-return-statements + if isinstance(data, dict): + return None + + # Test if this model is XML ready first + if not isinstance(data, ET.Element): + return None + + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + + # Look for a children + is_iter_type = attr_desc["type"].startswith("[") + is_wrapped = xml_desc.get("wrapped", False) + internal_type = attr_desc.get("internalType", None) + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + + # Integrate namespace if necessary + xml_ns = xml_desc.get("ns", internal_type_xml_map.get("ns", None)) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + + # If it's an attribute, that's simple + if xml_desc.get("attr", False): + return data.get(xml_name) + + # If it's x-ms-text, that's simple too + if xml_desc.get("text", False): + return data.text + + # Scenario where I take the local name: + # - Wrapped node + # - Internal type is an enum (considered basic types) + # - Internal type has no XML/Name node + if is_wrapped or (internal_type and (issubclass(internal_type, Enum) or "name" not in internal_type_xml_map)): + children = data.findall(xml_name) + # If internal type has a local name and it's not a list, I use that name + elif not is_iter_type and internal_type and "name" in internal_type_xml_map: + xml_name = _extract_name_from_internal_type(internal_type) + children = data.findall(xml_name) + # That's an array + else: + if internal_type: # Complex type, ignore itemsName and use the complex type name + items_name = _extract_name_from_internal_type(internal_type) + else: + items_name = xml_desc.get("itemsName", xml_name) + children = data.findall(items_name) + + if len(children) == 0: + if is_iter_type: + if is_wrapped: + return None # is_wrapped no node, we want None + return [] # not wrapped, assume empty list + return None # Assume it's not there, maybe an optional node. + + # If is_iter_type and not wrapped, return all found children + if is_iter_type: + if not is_wrapped: + return children + # Iter and wrapped, should have found one node only (the wrap one) + if len(children) != 1: + raise DeserializationError( + "Tried to deserialize an array not wrapped, and found several nodes '{}'. Maybe you should declare this array as wrapped?".format( + xml_name + ) + ) + return list(children[0]) # Might be empty list and that's ok. + + # Here it's not a itertype, we should have found one element only or empty + if len(children) > 1: + raise DeserializationError("Find several XML '{}' where it was not expected".format(xml_name)) + return children[0] + + +class Deserializer: + """Response object model deserializer. + + :param dict classes: Class type dictionary for deserializing complex types. + :ivar list key_extractors: Ordered list of extractors to be used by this deserializer. + """ + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") + + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: + self.deserialize_type = { + "iso-8601": Deserializer.deserialize_iso, + "rfc-1123": Deserializer.deserialize_rfc, + "unix-time": Deserializer.deserialize_unix, + "duration": Deserializer.deserialize_duration, + "date": Deserializer.deserialize_date, + "time": Deserializer.deserialize_time, + "decimal": Deserializer.deserialize_decimal, + "long": Deserializer.deserialize_long, + "bytearray": Deserializer.deserialize_bytearray, + "base64": Deserializer.deserialize_base64, + "object": self.deserialize_object, + "[]": self.deserialize_iter, + "{}": self.deserialize_dict, + } + self.deserialize_expected_types = { + "duration": (isodate.Duration, datetime.timedelta), + "iso-8601": (datetime.datetime), + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_extractors = [rest_key_extractor, xml_key_extractor] + # Additional properties only works if the "rest_key_extractor" is used to + # extract the keys. Making it to work whatever the key extractor is too much + # complicated, with no real scenario for now. + # So adding a flag to disable additional properties detection. This flag should be + # used if your expect the deserialization to NOT come from a JSON REST syntax. + # Otherwise, result are unexpected + self.additional_properties_detection = True + + def __call__(self, target_obj, response_data, content_type=None): + """Call the deserializer to process a REST response. + + :param str target_obj: Target data type to deserialize to. + :param requests.Response response_data: REST response object. + :param str content_type: Swagger "produces" if available. + :raises DeserializationError: if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + data = self._unpack_content(response_data, content_type) + return self._deserialize(target_obj, data) + + def _deserialize(self, target_obj, data): # pylint: disable=inconsistent-return-statements + """Call the deserializer on a model. + + Data needs to be already deserialized as JSON or XML ElementTree + + :param str target_obj: Target data type to deserialize to. + :param object data: Object to deserialize. + :raises DeserializationError: if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + # This is already a model, go recursive just in case + if hasattr(data, "_attribute_map"): + constants = [name for name, config in getattr(data, "_validation", {}).items() if config.get("constant")] + try: + for attr, mapconfig in data._attribute_map.items(): # pylint: disable=protected-access + if attr in constants: + continue + value = getattr(data, attr) + if value is None: + continue + local_type = mapconfig["type"] + internal_data_type = local_type.strip("[]{}") + if internal_data_type not in self.dependencies or isinstance(internal_data_type, Enum): + continue + setattr(data, attr, self._deserialize(local_type, value)) + return data + except AttributeError: + return + + response, class_name = self._classify_target(target_obj, data) + + if isinstance(response, str): + return self.deserialize_data(data, response) + if isinstance(response, type) and issubclass(response, Enum): + return self.deserialize_enum(data, response) + + if data is None or data is CoreNull: + return data + try: + attributes = response._attribute_map # type: ignore # pylint: disable=protected-access + d_attrs = {} + for attr, attr_desc in attributes.items(): + # Check empty string. If it's not empty, someone has a real "additionalProperties"... + if attr == "additional_properties" and attr_desc["key"] == "": + continue + raw_value = None + # Enhance attr_desc with some dynamic data + attr_desc = attr_desc.copy() # Do a copy, do not change the real one + internal_data_type = attr_desc["type"].strip("[]{}") + if internal_data_type in self.dependencies: + attr_desc["internalType"] = self.dependencies[internal_data_type] + + for key_extractor in self.key_extractors: + found_value = key_extractor(attr, attr_desc, data) + if found_value is not None: + if raw_value is not None and raw_value != found_value: + msg = ( + "Ignoring extracted value '%s' from %s for key '%s'" + " (duplicate extraction, follow extractors order)" + ) + _LOGGER.warning(msg, found_value, key_extractor, attr) + continue + raw_value = found_value + + value = self.deserialize_data(raw_value, attr_desc["type"]) + d_attrs[attr] = value + except (AttributeError, TypeError, KeyError) as err: + msg = "Unable to deserialize to object: " + class_name # type: ignore + raise DeserializationError(msg) from err + additional_properties = self._build_additional_properties(attributes, data) + return self._instantiate_model(response, d_attrs, additional_properties) + + def _build_additional_properties(self, attribute_map, data): + if not self.additional_properties_detection: + return None + if "additional_properties" in attribute_map and attribute_map.get("additional_properties", {}).get("key") != "": + # Check empty string. If it's not empty, someone has a real "additionalProperties" + return None + if isinstance(data, ET.Element): + data = {el.tag: el.text for el in data} + + known_keys = { + _decode_attribute_map_key(_FLATTEN.split(desc["key"])[0]) + for desc in attribute_map.values() + if desc["key"] != "" + } + present_keys = set(data.keys()) + missing_keys = present_keys - known_keys + return {key: data[key] for key in missing_keys} + + def _classify_target(self, target, data): + """Check to see whether the deserialization target object can + be classified into a subclass. + Once classification has been determined, initialize object. + + :param str target: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + :return: The classified target object and its class name. + :rtype: tuple + """ + if target is None: + return None, None + + if isinstance(target, str): + try: + target = self.dependencies[target] + except KeyError: + return target, target + + try: + target = target._classify(data, self.dependencies) # type: ignore # pylint: disable=protected-access + except AttributeError: + pass # Target is not a Model, no classify + return target, target.__class__.__name__ # type: ignore + + def failsafe_deserialize(self, target_obj, data, content_type=None): + """Ignores any errors encountered in deserialization, + and falls back to not deserializing the object. Recommended + for use in error deserialization, as we want to return the + HttpResponseError to users, and not have them deal with + a deserialization error. + + :param str target_obj: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + :param str content_type: Swagger "produces" if available. + :return: Deserialized object. + :rtype: object + """ + try: + return self(target_obj, data, content_type=content_type) + except: # pylint: disable=bare-except + _LOGGER.debug( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + @staticmethod + def _unpack_content(raw_data, content_type=None): + """Extract the correct structure for deserialization. + + If raw_data is a PipelineResponse, try to extract the result of RawDeserializer. + if we can't, raise. Your Pipeline should have a RawDeserializer. + + If not a pipeline response and raw_data is bytes or string, use content-type + to decode it. If no content-type, try JSON. + + If raw_data is something else, bypass all logic and return it directly. + + :param obj raw_data: Data to be processed. + :param str content_type: How to parse if raw_data is a string/bytes. + :raises JSONDecodeError: If JSON is requested and parsing is impossible. + :raises UnicodeDecodeError: If bytes is not UTF8 + :rtype: object + :return: Unpacked content. + """ + # Assume this is enough to detect a Pipeline Response without importing it + context = getattr(raw_data, "context", {}) + if context: + if RawDeserializer.CONTEXT_NAME in context: + return context[RawDeserializer.CONTEXT_NAME] + raise ValueError("This pipeline didn't have the RawDeserializer policy; can't deserialize") + + # Assume this is enough to recognize universal_http.ClientResponse without importing it + if hasattr(raw_data, "body"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text(), raw_data.headers) + + # Assume this enough to recognize requests.Response without importing it. + if hasattr(raw_data, "_content_consumed"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text, raw_data.headers) + + if isinstance(raw_data, (str, bytes)) or hasattr(raw_data, "read"): + return RawDeserializer.deserialize_from_text(raw_data, content_type) # type: ignore + return raw_data + + def _instantiate_model(self, response, attrs, additional_properties=None): + """Instantiate a response model passing in deserialized args. + + :param Response response: The response model class. + :param dict attrs: The deserialized response attributes. + :param dict additional_properties: Additional properties to be set. + :rtype: Response + :return: The instantiated response model. + """ + if callable(response): + subtype = getattr(response, "_subtype_map", {}) + try: + readonly = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("readonly") + ] + const = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("constant") + ] + kwargs = {k: v for k, v in attrs.items() if k not in subtype and k not in readonly + const} + response_obj = response(**kwargs) + for attr in readonly: + setattr(response_obj, attr, attrs.get(attr)) + if additional_properties: + response_obj.additional_properties = additional_properties # type: ignore + return response_obj + except TypeError as err: + msg = "Unable to deserialize {} into model {}. ".format(kwargs, response) # type: ignore + raise DeserializationError(msg + str(err)) from err + else: + try: + for attr, value in attrs.items(): + setattr(response, attr, value) + return response + except Exception as exp: + msg = "Unable to populate response model. " + msg += "Type: {}, Error: {}".format(type(response), exp) + raise DeserializationError(msg) from exp + + def deserialize_data(self, data, data_type): # pylint: disable=too-many-return-statements + """Process data for deserialization according to data type. + + :param str data: The response string to be deserialized. + :param str data_type: The type to deserialize to. + :raises DeserializationError: if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + if data is None: + return data + + try: + if not data_type: + return data + if data_type in self.basic_types.values(): + return self.deserialize_basic(data, data_type) + if data_type in self.deserialize_type: + if isinstance(data, self.deserialize_expected_types.get(data_type, tuple())): + return data + + is_a_text_parsing_type = lambda x: x not in [ # pylint: disable=unnecessary-lambda-assignment + "object", + "[]", + r"{}", + ] + if isinstance(data, ET.Element) and is_a_text_parsing_type(data_type) and not data.text: + return None + data_val = self.deserialize_type[data_type](data) + return data_val + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.deserialize_type: + return self.deserialize_type[iter_type](data, data_type[1:-1]) + + obj_type = self.dependencies[data_type] + if issubclass(obj_type, Enum): + if isinstance(data, ET.Element): + data = data.text + return self.deserialize_enum(data, obj_type) + + except (ValueError, TypeError, AttributeError) as err: + msg = "Unable to deserialize response data." + msg += " Data: {}, {}".format(data, data_type) + raise DeserializationError(msg) from err + return self._deserialize(obj_type, data) + + def deserialize_iter(self, attr, iter_type): + """Deserialize an iterable. + + :param list attr: Iterable to be deserialized. + :param str iter_type: The type of object in the iterable. + :return: Deserialized iterable. + :rtype: list + """ + if attr is None: + return None + if isinstance(attr, ET.Element): # If I receive an element here, get the children + attr = list(attr) + if not isinstance(attr, (list, set)): + raise DeserializationError("Cannot deserialize as [{}] an object of type {}".format(iter_type, type(attr))) + return [self.deserialize_data(a, iter_type) for a in attr] + + def deserialize_dict(self, attr, dict_type): + """Deserialize a dictionary. + + :param dict/list attr: Dictionary to be deserialized. Also accepts + a list of key, value pairs. + :param str dict_type: The object type of the items in the dictionary. + :return: Deserialized dictionary. + :rtype: dict + """ + if isinstance(attr, list): + return {x["key"]: self.deserialize_data(x["value"], dict_type) for x in attr} + + if isinstance(attr, ET.Element): + # Transform value into {"Key": "value"} + attr = {el.tag: el.text for el in attr} + return {k: self.deserialize_data(v, dict_type) for k, v in attr.items()} + + def deserialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements + """Deserialize a generic object. + This will be handled as a dictionary. + + :param dict attr: Dictionary to be deserialized. + :return: Deserialized object. + :rtype: dict + :raises TypeError: if non-builtin datatype encountered. + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + # Do no recurse on XML, just return the tree as-is + return attr + if isinstance(attr, str): + return self.deserialize_basic(attr, "str") + obj_type = type(attr) + if obj_type in self.basic_types: + return self.deserialize_basic(attr, self.basic_types[obj_type]) + if obj_type is _long_type: + return self.deserialize_long(attr) + + if obj_type == dict: + deserialized = {} + for key, value in attr.items(): + try: + deserialized[key] = self.deserialize_object(value, **kwargs) + except ValueError: + deserialized[key] = None + return deserialized + + if obj_type == list: + deserialized = [] + for obj in attr: + try: + deserialized.append(self.deserialize_object(obj, **kwargs)) + except ValueError: + pass + return deserialized + + error = "Cannot deserialize generic object with type: " + raise TypeError(error + str(obj_type)) + + def deserialize_basic(self, attr, data_type): # pylint: disable=too-many-return-statements + """Deserialize basic builtin data type from string. + Will attempt to convert to str, int, float and bool. + This function will also accept '1', '0', 'true' and 'false' as + valid bool values. + + :param str attr: response string to be deserialized. + :param str data_type: deserialization data type. + :return: Deserialized basic type. + :rtype: str, int, float or bool + :raises TypeError: if string format is not valid. + """ + # If we're here, data is supposed to be a basic type. + # If it's still an XML node, take the text + if isinstance(attr, ET.Element): + attr = attr.text + if not attr: + if data_type == "str": + # None or '', node is empty string. + return "" + # None or '', node with a strong type is None. + # Don't try to model "empty bool" or "empty int" + return None + + if data_type == "bool": + if attr in [True, False, 1, 0]: + return bool(attr) + if isinstance(attr, str): + if attr.lower() in ["true", "1"]: + return True + if attr.lower() in ["false", "0"]: + return False + raise TypeError("Invalid boolean value: {}".format(attr)) + + if data_type == "str": + return self.deserialize_unicode(attr) + return eval(data_type)(attr) # nosec # pylint: disable=eval-used + + @staticmethod + def deserialize_unicode(data): + """Preserve unicode objects in Python 2, otherwise return data + as a string. + + :param str data: response string to be deserialized. + :return: Deserialized string. + :rtype: str or unicode + """ + # We might be here because we have an enum modeled as string, + # and we try to deserialize a partial dict with enum inside + if isinstance(data, Enum): + return data + + # Consider this is real string + try: + if isinstance(data, unicode): # type: ignore + return data + except NameError: + return str(data) + return str(data) + + @staticmethod + def deserialize_enum(data, enum_obj): + """Deserialize string into enum object. + + If the string is not a valid enum value it will be returned as-is + and a warning will be logged. + + :param str data: Response string to be deserialized. If this value is + None or invalid it will be returned as-is. + :param Enum enum_obj: Enum object to deserialize to. + :return: Deserialized enum object. + :rtype: Enum + """ + if isinstance(data, enum_obj) or data is None: + return data + if isinstance(data, Enum): + data = data.value + if isinstance(data, int): + # Workaround. We might consider remove it in the future. + try: + return list(enum_obj.__members__.values())[data] + except IndexError as exc: + error = "{!r} is not a valid index for enum {!r}" + raise DeserializationError(error.format(data, enum_obj)) from exc + try: + return enum_obj(str(data)) + except ValueError: + for enum_value in enum_obj: + if enum_value.value.lower() == str(data).lower(): + return enum_value + # We don't fail anymore for unknown value, we deserialize as a string + _LOGGER.warning("Deserializer is not able to find %s as valid enum in %s", data, enum_obj) + return Deserializer.deserialize_unicode(data) + + @staticmethod + def deserialize_bytearray(attr): + """Deserialize string into bytearray. + + :param str attr: response string to be deserialized. + :return: Deserialized bytearray + :rtype: bytearray + :raises TypeError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return bytearray(b64decode(attr)) # type: ignore + + @staticmethod + def deserialize_base64(attr): + """Deserialize base64 encoded string into string. + + :param str attr: response string to be deserialized. + :return: Deserialized base64 string + :rtype: bytearray + :raises TypeError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return b64decode(encoded) + + @staticmethod + def deserialize_decimal(attr): + """Deserialize string into Decimal object. + + :param str attr: response string to be deserialized. + :return: Deserialized decimal + :raises DeserializationError: if string format invalid. + :rtype: decimal + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + return decimal.Decimal(str(attr)) # type: ignore + except decimal.DecimalException as err: + msg = "Invalid decimal {}".format(attr) + raise DeserializationError(msg) from err + + @staticmethod + def deserialize_long(attr): + """Deserialize string into long (Py2) or int (Py3). + + :param str attr: response string to be deserialized. + :return: Deserialized int + :rtype: long or int + :raises ValueError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return _long_type(attr) # type: ignore + + @staticmethod + def deserialize_duration(attr): + """Deserialize ISO-8601 formatted string into TimeDelta object. + + :param str attr: response string to be deserialized. + :return: Deserialized duration + :rtype: TimeDelta + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + duration = isodate.parse_duration(attr) + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize duration object." + raise DeserializationError(msg) from err + return duration + + @staticmethod + def deserialize_date(attr): + """Deserialize ISO-8601 formatted string into Date object. + + :param str attr: response string to be deserialized. + :return: Deserialized date + :rtype: Date + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + return isodate.parse_date(attr, defaultmonth=0, defaultday=0) + + @staticmethod + def deserialize_time(attr): + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :return: Deserialized time + :rtype: datetime.time + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + return isodate.parse_time(attr) + + @staticmethod + def deserialize_rfc(attr): + """Deserialize RFC-1123 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :return: Deserialized RFC datetime + :rtype: Datetime + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + parsed_date = email.utils.parsedate_tz(attr) # type: ignore + date_obj = datetime.datetime( + *parsed_date[:6], tzinfo=datetime.timezone(datetime.timedelta(minutes=(parsed_date[9] or 0) / 60)) + ) + if not date_obj.tzinfo: + date_obj = date_obj.astimezone(tz=TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to rfc datetime object." + raise DeserializationError(msg) from err + return date_obj + + @staticmethod + def deserialize_iso(attr): + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :return: Deserialized ISO datetime + :rtype: Datetime + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + attr = attr.upper() # type: ignore + match = Deserializer.valid_date.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize datetime object." + raise DeserializationError(msg) from err + return date_obj + + @staticmethod + def deserialize_unix(attr): + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param int attr: Object to be serialized. + :return: Deserialized datetime + :rtype: Datetime + :raises DeserializationError: if format invalid + """ + if isinstance(attr, ET.Element): + attr = int(attr.text) # type: ignore + try: + attr = int(attr) + date_obj = datetime.datetime.fromtimestamp(attr, TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to unix datetime object." + raise DeserializationError(msg) from err + return date_obj diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/utils.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/utils.py new file mode 100644 index 000000000000..89abe90d9245 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_utils/utils.py @@ -0,0 +1,67 @@ +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from abc import ABC +import json +from typing import Any, Dict, Generic, IO, List, Mapping, Optional, TYPE_CHECKING, Tuple, TypeVar, Union + +from .._utils.model_base import Model, SdkJSONEncoder + +if TYPE_CHECKING: + from .serialization import Deserializer, Serializer + + +TClient = TypeVar("TClient") +TConfig = TypeVar("TConfig") + + +class ClientMixinABC(ABC, Generic[TClient, TConfig]): + """DO NOT use this class. It is for internal typing use only.""" + + _client: TClient + _config: TConfig + _serialize: "Serializer" + _deserialize: "Deserializer" + + +# file-like tuple could be `(filename, IO (or bytes))` or `(filename, IO (or bytes), content_type)` +FileContent = Union[str, bytes, IO[str], IO[bytes]] + +FileType = Union[ + # file (or bytes) + FileContent, + # (filename, file (or bytes)) + Tuple[Optional[str], FileContent], + # (filename, file (or bytes), content_type) + Tuple[Optional[str], FileContent, Optional[str]], +] + + +def serialize_multipart_data_entry(data_entry: Any) -> Any: + if isinstance(data_entry, (list, tuple, dict, Model)): + return json.dumps(data_entry, cls=SdkJSONEncoder, exclude_readonly=True) + return data_entry + + +def prepare_multipart_form_data( + body: Mapping[str, Any], multipart_fields: List[str], data_fields: List[str] +) -> Tuple[List[FileType], Dict[str, Any]]: + files: List[FileType] = [] + data: Dict[str, Any] = {} + for multipart_field in multipart_fields: + multipart_entry = body.get(multipart_field) + if isinstance(multipart_entry, list): + files.extend([(multipart_field, e) for e in multipart_entry]) + elif multipart_entry: + files.append((multipart_field, multipart_entry)) + + for data_field in data_fields: + data_entry = body.get(data_field) + if data_entry: + data[data_field] = serialize_multipart_data_entry(data_entry) + + return files, data diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/_version.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_version.py new file mode 100644 index 000000000000..be71c81bd282 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/_version.py @@ -0,0 +1,9 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +VERSION = "1.0.0b1" diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/__init__.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/__init__.py new file mode 100644 index 000000000000..4fea30ca6925 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/__init__.py @@ -0,0 +1,29 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._client import AssistantsClient # type: ignore + +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AssistantsClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore + +_patch_sdk() diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_client.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_client.py new file mode 100644 index 000000000000..248b5175bfa2 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_client.py @@ -0,0 +1,107 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, Awaitable, TYPE_CHECKING, Union +from typing_extensions import Self + +from azure.core import AsyncPipelineClient +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies +from azure.core.rest import AsyncHttpResponse, HttpRequest + +from .._utils.serialization import Deserializer, Serializer +from ._configuration import AssistantsClientConfiguration +from ._operations import AssistantsClientOperationsMixin + +if TYPE_CHECKING: + from azure.core.credentials_async import AsyncTokenCredential + + +class AssistantsClient(AssistantsClientOperationsMixin): + """AssistantsClient. + + :param endpoint: Project endpoint in the form of: + https://.services.ai.azure.com/api/projects/. Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + _endpoint = "{endpoint}" + self._config = AssistantsClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request( + self, request: HttpRequest, *, stream: bool = False, **kwargs: Any + ) -> Awaitable[AsyncHttpResponse]: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = await client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.AsyncHttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + async def close(self) -> None: + await self._client.close() + + async def __aenter__(self) -> Self: + await self._client.__aenter__() + return self + + async def __aexit__(self, *exc_details: Any) -> None: + await self._client.__aexit__(*exc_details) diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_configuration.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_configuration.py new file mode 100644 index 000000000000..637f56b4a09d --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_configuration.py @@ -0,0 +1,75 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING, Union + +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies + +from .._version import VERSION + +if TYPE_CHECKING: + from azure.core.credentials_async import AsyncTokenCredential + + +class AssistantsClientConfiguration: # pylint: disable=too-many-instance-attributes + """Configuration for AssistantsClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Project endpoint in the form of: + https://.services.ai.azure.com/api/projects/. Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + api_version: str = kwargs.pop("api_version", "2025-05-15-preview") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-assistants/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Authorization", prefix="Bearer", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.AsyncBearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.AsyncRedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.AsyncRetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/__init__.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/__init__.py new file mode 100644 index 000000000000..ee3f17d82ddc --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/__init__.py @@ -0,0 +1,25 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import AssistantsClientOperationsMixin # type: ignore + +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AssistantsClientOperationsMixin", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_operations.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_operations.py new file mode 100644 index 000000000000..2ea5340442b0 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_operations.py @@ -0,0 +1,4866 @@ +# pylint: disable=line-too-long,useless-suppression,too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from collections.abc import MutableMapping +from io import IOBase +import json +from typing import Any, AsyncIterator, Callable, Dict, IO, List, Optional, TYPE_CHECKING, TypeVar, Union, overload + +from azure.core import AsyncPipelineClient +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + StreamClosedError, + StreamConsumedError, + map_error, +) +from azure.core.pipeline import PipelineResponse +from azure.core.rest import AsyncHttpResponse, HttpRequest +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.core.utils import case_insensitive_dict + +from ... import models as _models +from ..._operations._operations import ( + build_assistants_cancel_run_request, + build_assistants_cancel_vector_store_file_batch_request, + build_assistants_create_assistant_request, + build_assistants_create_message_request, + build_assistants_create_run_request, + build_assistants_create_thread_and_run_request, + build_assistants_create_thread_request, + build_assistants_create_vector_store_file_batch_request, + build_assistants_create_vector_store_file_request, + build_assistants_create_vector_store_request, + build_assistants_delete_assistant_request, + build_assistants_delete_file_request, + build_assistants_delete_thread_request, + build_assistants_delete_vector_store_file_request, + build_assistants_delete_vector_store_request, + build_assistants_get_assistant_request, + build_assistants_get_file_content_request, + build_assistants_get_file_request, + build_assistants_get_message_request, + build_assistants_get_run_request, + build_assistants_get_run_step_request, + build_assistants_get_thread_request, + build_assistants_get_vector_store_file_batch_request, + build_assistants_get_vector_store_file_request, + build_assistants_get_vector_store_request, + build_assistants_list_assistants_request, + build_assistants_list_files_request, + build_assistants_list_messages_request, + build_assistants_list_run_steps_request, + build_assistants_list_runs_request, + build_assistants_list_threads_request, + build_assistants_list_vector_store_file_batch_files_request, + build_assistants_list_vector_store_files_request, + build_assistants_list_vector_stores_request, + build_assistants_modify_vector_store_request, + build_assistants_submit_tool_outputs_to_run_request, + build_assistants_update_assistant_request, + build_assistants_update_message_request, + build_assistants_update_run_request, + build_assistants_update_thread_request, + build_assistants_upload_file_request, +) +from ..._utils.model_base import Model as _Model, SdkJSONEncoder, _deserialize +from ..._utils.utils import ClientMixinABC, prepare_multipart_form_data +from .._configuration import AssistantsClientConfiguration + +if TYPE_CHECKING: + from ... import _types +JSON = MutableMapping[str, Any] +_Unset: Any = object() +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + + +class AssistantsClientOperationsMixin( # pylint: disable=too-many-public-methods + ClientMixinABC[AsyncPipelineClient, AssistantsClientConfiguration] +): + + @overload + async def create_assistant( + self, + *, + model: str, + content_type: str = "application/json", + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :keyword model: The ID of the model to use. Required. + :paramtype model: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: The name of the new assistant. Default value is None. + :paramtype name: str + :keyword description: The description of the new assistant. Default value is None. + :paramtype description: str + :keyword instructions: The system instructions for the new assistant to use. Default value is + None. + :paramtype instructions: str + :keyword tools: The collection of tools to enable for the new assistant. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, the ``code_interpreter`` + tool requires a list of file IDs, while the ``file_search`` tool requires a list of vector + store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_assistant( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_assistant( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_assistant( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + model: str = _Unset, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword model: The ID of the model to use. Required. + :paramtype model: str + :keyword name: The name of the new assistant. Default value is None. + :paramtype name: str + :keyword description: The description of the new assistant. Default value is None. + :paramtype description: str + :keyword instructions: The system instructions for the new assistant to use. Default value is + None. + :paramtype instructions: str + :keyword tools: The collection of tools to enable for the new assistant. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, the ``code_interpreter`` + tool requires a list of file IDs, while the ``file_search`` tool requires a list of vector + store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + if body is _Unset: + if model is _Unset: + raise TypeError("missing required argument: model") + body = { + "description": description, + "instructions": instructions, + "metadata": metadata, + "model": model, + "name": name, + "response_format": response_format, + "temperature": temperature, + "tool_resources": tool_resources, + "tools": tools, + "top_p": top_p, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_assistant_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_assistants( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfAssistant: + """Gets a list of assistants that were previously created. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfAssistant. The OpenAIPageableListOfAssistant is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfAssistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfAssistant] = kwargs.pop("cls", None) + + _request = build_assistants_list_assistants_request( + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfAssistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_assistant(self, assistant_id: str, **kwargs: Any) -> _models.Assistant: + """Retrieves an existing assistant. + + :param assistant_id: Identifier of the assistant. Required. + :type assistant_id: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + _request = build_assistants_get_assistant_request( + assistant_id=assistant_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_assistant( + self, + assistant_id: str, + *, + content_type: str = "application/json", + model: Optional[str] = None, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword model: The ID of the model to use. Default value is None. + :paramtype model: str + :keyword name: The modified name for the assistant to use. Default value is None. + :paramtype name: str + :keyword description: The modified description for the assistant to use. Default value is None. + :paramtype description: str + :keyword instructions: The modified system instructions for the new assistant to use. Default + value is None. + :paramtype instructions: str + :keyword tools: The modified collection of tools to enable for the assistant. Default value is + None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, + the ``code_interpreter`` tool requires a list of file IDs, while the ``file_search`` tool + requires a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_assistant( + self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_assistant( + self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_assistant( + self, + assistant_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + model: Optional[str] = None, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.ToolResources] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword model: The ID of the model to use. Default value is None. + :paramtype model: str + :keyword name: The modified name for the assistant to use. Default value is None. + :paramtype name: str + :keyword description: The modified description for the assistant to use. Default value is None. + :paramtype description: str + :keyword instructions: The modified system instructions for the new assistant to use. Default + value is None. + :paramtype instructions: str + :keyword tools: The modified collection of tools to enable for the assistant. Default value is + None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: A set of resources that are used by the assistant's tools. The + resources are specific to the type of tool. For example, + the ``code_interpreter`` tool requires a list of file IDs, while the ``file_search`` tool + requires a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Default value is + None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword response_format: The response format of the tool calls used by this assistant. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + if body is _Unset: + body = { + "description": description, + "instructions": instructions, + "metadata": metadata, + "model": model, + "name": name, + "response_format": response_format, + "temperature": temperature, + "tool_resources": tool_resources, + "tools": tools, + "top_p": top_p, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_assistant_request( + assistant_id=assistant_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_assistant(self, assistant_id: str, **kwargs: Any) -> _models.AssistantDeletionStatus: + """Deletes an assistant. + + :param assistant_id: Identifier of the assistant. Required. + :type assistant_id: str + :return: AssistantDeletionStatus. The AssistantDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_assistant_request( + assistant_id=assistant_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_thread( + self, + *, + content_type: str = "application/json", + messages: Optional[List[_models.ThreadMessageOptions]] = None, + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword messages: The initial messages to associate with the new thread. Default value is + None. + :paramtype messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_thread( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + messages: Optional[List[_models.ThreadMessageOptions]] = None, + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword messages: The initial messages to associate with the new thread. Default value is + None. + :paramtype messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"messages": messages, "metadata": metadata, "tool_resources": tool_resources} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_thread_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_thread(self, thread_id: str, **kwargs: Any) -> _models.AssistantThread: + """Gets information about an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + _request = build_assistants_get_thread_request( + thread_id=thread_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_thread( + self, + thread_id: str, + *, + content_type: str = "application/json", + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_thread( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_thread( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_thread( + self, + thread_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + tool_resources: Optional[_models.ToolResources] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while + the ``file_search`` tool requires + a list of vector store IDs. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.ToolResources + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata, "tool_resources": tool_resources} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_thread_request( + thread_id=thread_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_thread(self, thread_id: str, **kwargs: Any) -> _models.ThreadDeletionStatus: + """Deletes an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :return: ThreadDeletionStatus. The ThreadDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_thread_request( + thread_id=thread_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_threads( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfAssistantThread: + """Gets a list of threads that were previously created. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfAssistantThread. The OpenAIPageableListOfAssistantThread is + compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfAssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfAssistantThread] = kwargs.pop("cls", None) + + _request = build_assistants_list_threads_request( + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfAssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_message( + self, + thread_id: str, + *, + role: Union[str, _models.MessageRole], + content: "_types.MessageInputContent", + content_type: str = "application/json", + attachments: Optional[List[_models.MessageAttachment]] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword role: The role of the entity that is creating the message. Allowed values include: + ``user``, which indicates the message is sent by an actual user (and should be + used in most cases to represent user-generated messages), and ``assistant``, + which indicates the message is generated by the agent (use this value to insert + messages from the agent into the conversation). Known values are: "user" and "assistant". + Required. + :paramtype role: str or ~azure.ai.assistants.models.MessageRole + :keyword content: The content of the initial message. This may be a basic string (if you only + need text) or an array of typed content blocks (for example, text, image_file, + image_url, and so on). Is either a str type or a [MessageInputContentBlock] type. Required. + :paramtype content: str or list[~azure.ai.assistants.models.MessageInputContentBlock] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword attachments: A list of files attached to the message, and the tools they should be + added to. Default value is None. + :paramtype attachments: list[~azure.ai.assistants.models.MessageAttachment] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_message( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_message( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_message( + self, + thread_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + role: Union[str, _models.MessageRole] = _Unset, + content: "_types.MessageInputContent" = _Unset, + attachments: Optional[List[_models.MessageAttachment]] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword role: The role of the entity that is creating the message. Allowed values include: + ``user``, which indicates the message is sent by an actual user (and should be + used in most cases to represent user-generated messages), and ``assistant``, + which indicates the message is generated by the agent (use this value to insert + messages from the agent into the conversation). Known values are: "user" and "assistant". + Required. + :paramtype role: str or ~azure.ai.assistants.models.MessageRole + :keyword content: The content of the initial message. This may be a basic string (if you only + need text) or an array of typed content blocks (for example, text, image_file, + image_url, and so on). Is either a str type or a [MessageInputContentBlock] type. Required. + :paramtype content: str or list[~azure.ai.assistants.models.MessageInputContentBlock] + :keyword attachments: A list of files attached to the message, and the tools they should be + added to. Default value is None. + :paramtype attachments: list[~azure.ai.assistants.models.MessageAttachment] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + if body is _Unset: + if role is _Unset: + raise TypeError("missing required argument: role") + if content is _Unset: + raise TypeError("missing required argument: content") + body = {"attachments": attachments, "content": content, "metadata": metadata, "role": role} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_message_request( + thread_id=thread_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_messages( + self, + thread_id: str, + *, + run_id: Optional[str] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfThreadMessage: + """Gets a list of messages that exist on a thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword run_id: Filter messages by the run ID that generated them. Default value is None. + :paramtype run_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadMessage. The OpenAIPageableListOfThreadMessage is compatible + with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadMessage] = kwargs.pop("cls", None) + + _request = build_assistants_list_messages_request( + thread_id=thread_id, + run_id=run_id, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_message(self, thread_id: str, message_id: str, **kwargs: Any) -> _models.ThreadMessage: + """Gets an existing message from an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + _request = build_assistants_get_message_request( + thread_id=thread_id, + message_id=message_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_message( + self, + thread_id: str, + message_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_message( + self, thread_id: str, message_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_message( + self, thread_id: str, message_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_message( + self, + thread_id: str, + message_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param message_id: Identifier of the message. Required. + :type message_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_message_request( + thread_id=thread_id, + message_id=message_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_run( + self, + thread_id: str, + *, + assistant_id: str, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + content_type: str = "application/json", + model: Optional[str] = None, + instructions: Optional[str] = None, + additional_instructions: Optional[str] = None, + additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword assistant_id: The ID of the assistant that should run the thread. Required. + :paramtype assistant_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword model: The overridden model name that the assistant should use to run the thread. + Default value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions that the assistant should use to run + the thread. Default value is None. + :paramtype instructions: str + :keyword additional_instructions: Additional instructions to append at the end of the + instructions for the run. This is useful for modifying the behavior + on a per-run basis without overriding other instructions. Default value is None. + :paramtype additional_instructions: str + :keyword additional_messages: Adds additional messages to the thread before creating the run. + Default value is None. + :paramtype additional_messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tools: The overridden list of enabled tools that the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort + to use only the number of completion tokens specified, across multiple turns of the run. If + the run exceeds the number of + completion tokens specified, the run will end with status ``incomplete``. See + ``incomplete_details`` for more info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_run( + self, + thread_id: str, + body: JSON, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: JSON + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_run( + self, + thread_id: str, + body: IO[bytes], + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Required. + :type body: IO[bytes] + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_run( + self, + thread_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + assistant_id: str = _Unset, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + model: Optional[str] = None, + instructions: Optional[str] = None, + additional_instructions: Optional[str] = None, + additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword assistant_id: The ID of the assistant that should run the thread. Required. + :paramtype assistant_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword model: The overridden model name that the assistant should use to run the thread. + Default value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions that the assistant should use to run + the thread. Default value is None. + :paramtype instructions: str + :keyword additional_instructions: Additional instructions to append at the end of the + instructions for the run. This is useful for modifying the behavior + on a per-run basis without overriding other instructions. Default value is None. + :paramtype additional_instructions: str + :keyword additional_messages: Adds additional messages to the thread before creating the run. + Default value is None. + :paramtype additional_messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :keyword tools: The overridden list of enabled tools that the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort + to use only the number of completion tokens specified, across multiple turns of the run. If + the run exceeds the number of + completion tokens specified, the run will end with status ``incomplete``. See + ``incomplete_details`` for more info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if assistant_id is _Unset: + raise TypeError("missing required argument: assistant_id") + body = { + "additional_instructions": additional_instructions, + "additional_messages": additional_messages, + "assistant_id": assistant_id, + "instructions": instructions, + "max_completion_tokens": max_completion_tokens, + "max_prompt_tokens": max_prompt_tokens, + "metadata": metadata, + "model": model, + "parallel_tool_calls": parallel_tool_calls, + "response_format": response_format, + "stream": stream_parameter, + "temperature": temperature, + "tool_choice": tool_choice, + "tools": tools, + "top_p": top_p, + "truncation_strategy": truncation_strategy, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_run_request( + thread_id=thread_id, + include=include, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_runs( + self, + thread_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfThreadRun: + """Gets a list of runs for a specified thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadRun. The OpenAIPageableListOfThreadRun is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadRun] = kwargs.pop("cls", None) + + _request = build_assistants_list_runs_request( + thread_id=thread_id, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Gets an existing run from an existing thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_assistants_get_run_request( + thread_id=thread_id, + run_id=run_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_run( + self, + thread_id: str, + run_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_update_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + *, + tool_outputs: List[_models.ToolOutput], + content_type: str = "application/json", + stream_parameter: Optional[bool] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.assistants.models.ToolOutput] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword stream_parameter: If true, returns a stream of events that happen during the Run as + server-sent events, terminating when the run enters a terminal state. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + tool_outputs: List[_models.ToolOutput] = _Unset, + stream_parameter: Optional[bool] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.assistants.models.ToolOutput] + :keyword stream_parameter: If true, returns a stream of events that happen during the Run as + server-sent events, terminating when the run enters a terminal state. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if tool_outputs is _Unset: + raise TypeError("missing required argument: tool_outputs") + body = {"stream": stream_parameter, "tool_outputs": tool_outputs} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_submit_tool_outputs_to_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def cancel_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Cancels a run of an in progress thread. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_assistants_cancel_run_request( + thread_id=thread_id, + run_id=run_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_thread_and_run( + self, + *, + assistant_id: str, + content_type: str = "application/json", + thread: Optional[_models.AssistantThreadCreationOptions] = None, + model: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :keyword assistant_id: The ID of the assistant for which the thread should be created. + Required. + :paramtype assistant_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword thread: The details used to create the new thread. If no thread is provided, an empty + one will be created. Default value is None. + :paramtype thread: ~azure.ai.assistants.models.AssistantThreadCreationOptions + :keyword model: The overridden model that the assistant should use to run the thread. Default + value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions the assistant should use to run the + thread. Default value is None. + :paramtype instructions: str + :keyword tools: The overridden list of enabled tools the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: Override the tools the assistant can use for this run. This is useful + for modifying the behavior on a per-run basis. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.UpdateToolResourcesOptions + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort to use only + the number of completion tokens specified, across multiple turns of the run. If the run + exceeds the number of completion tokens + specified, the run will end with status ``incomplete``. See ``incomplete_details`` for more + info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread_and_run( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread_and_run( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_thread_and_run( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + assistant_id: str = _Unset, + thread: Optional[_models.AssistantThreadCreationOptions] = None, + model: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List[_models.ToolDefinition]] = None, + tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, + stream_parameter: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional[_models.TruncationObject] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + parallel_tool_calls: Optional[bool] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword assistant_id: The ID of the assistant for which the thread should be created. + Required. + :paramtype assistant_id: str + :keyword thread: The details used to create the new thread. If no thread is provided, an empty + one will be created. Default value is None. + :paramtype thread: ~azure.ai.assistants.models.AssistantThreadCreationOptions + :keyword model: The overridden model that the assistant should use to run the thread. Default + value is None. + :paramtype model: str + :keyword instructions: The overridden system instructions the assistant should use to run the + thread. Default value is None. + :paramtype instructions: str + :keyword tools: The overridden list of enabled tools the assistant should use to run the + thread. Default value is None. + :paramtype tools: list[~azure.ai.assistants.models.ToolDefinition] + :keyword tool_resources: Override the tools the assistant can use for this run. This is useful + for modifying the behavior on a per-run basis. Default value is None. + :paramtype tool_resources: ~azure.ai.assistants.models.UpdateToolResourcesOptions + :keyword stream_parameter: If ``true``, returns a stream of events that happen during the Run + as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. Default + value is None. + :paramtype stream_parameter: bool + :keyword temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Default + value is None. + :paramtype temperature: float + :keyword top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Default value is None. + :paramtype top_p: float + :keyword max_prompt_tokens: The maximum number of prompt tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. Default + value is None. + :paramtype max_prompt_tokens: int + :keyword max_completion_tokens: The maximum number of completion tokens that may be used over + the course of the run. The run will make a best effort to use only + the number of completion tokens specified, across multiple turns of the run. If the run + exceeds the number of completion tokens + specified, the run will end with status ``incomplete``. See ``incomplete_details`` for more + info. Default value is None. + :paramtype max_completion_tokens: int + :keyword truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Default value is None. + :paramtype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of + the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice Default value is None. + :paramtype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :keyword response_format: Specifies the format that the model must output. Is one of the + following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. + :paramtype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. + Default value is None. + :paramtype parallel_tool_calls: bool + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if assistant_id is _Unset: + raise TypeError("missing required argument: assistant_id") + body = { + "assistant_id": assistant_id, + "instructions": instructions, + "max_completion_tokens": max_completion_tokens, + "max_prompt_tokens": max_prompt_tokens, + "metadata": metadata, + "model": model, + "parallel_tool_calls": parallel_tool_calls, + "response_format": response_format, + "stream": stream_parameter, + "temperature": temperature, + "thread": thread, + "tool_choice": tool_choice, + "tool_resources": tool_resources, + "tools": tools, + "top_p": top_p, + "truncation_strategy": truncation_strategy, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_thread_and_run_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_run_step( + self, + thread_id: str, + run_id: str, + step_id: str, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + **kwargs: Any + ) -> _models.RunStep: + """Gets a single run step from a thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :param step_id: Identifier of the run step. Required. + :type step_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :return: RunStep. The RunStep is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.RunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.RunStep] = kwargs.pop("cls", None) + + _request = build_assistants_get_run_step_request( + thread_id=thread_id, + run_id=run_id, + step_id=step_id, + include=include, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.RunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_run_steps( + self, + thread_id: str, + run_id: str, + *, + include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfRunStep: + """Gets a list of run steps from a thread run. + + :param thread_id: Identifier of the thread. Required. + :type thread_id: str + :param run_id: Identifier of the run. Required. + :type run_id: str + :keyword include: A list of additional fields to include in the response. + Currently the only supported value is + ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result + content. Default value is None. + :paramtype include: list[str or ~azure.ai.assistants.models.RunAdditionalFieldList] + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfRunStep. The OpenAIPageableListOfRunStep is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfRunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfRunStep] = kwargs.pop("cls", None) + + _request = build_assistants_list_run_steps_request( + thread_id=thread_id, + run_id=run_id, + include=include, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfRunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_files( + self, *, purpose: Optional[Union[str, _models.FilePurpose]] = None, **kwargs: Any + ) -> _models.FileListResponse: + """Gets a list of previously uploaded files. + + :keyword purpose: The purpose of the file. Known values are: "fine-tune", "fine-tune-results", + "assistants", "assistants_output", "batch", "batch_output", and "vision". Default value is + None. + :paramtype purpose: str or ~azure.ai.assistants.models.FilePurpose + :return: FileListResponse. The FileListResponse is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.FileListResponse + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileListResponse] = kwargs.pop("cls", None) + + _request = build_assistants_list_files_request( + purpose=purpose, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileListResponse, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def _upload_file(self, body: _models._models.UploadFileRequest, **kwargs: Any) -> _models.OpenAIFile: ... + @overload + async def _upload_file(self, body: JSON, **kwargs: Any) -> _models.OpenAIFile: ... + + @distributed_trace_async + async def _upload_file( + self, body: Union[_models._models.UploadFileRequest, JSON], **kwargs: Any + ) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :param body: Multipart body. Is either a UploadFileRequest type or a JSON type. Required. + :type body: ~azure.ai.assistants.models._models.UploadFileRequest or JSON + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + _body = body.as_dict() if isinstance(body, _Model) else body + _file_fields: List[str] = ["file"] + _data_fields: List[str] = ["purpose", "filename"] + _files, _data = prepare_multipart_form_data(_body, _file_fields, _data_fields) + + _request = build_assistants_upload_file_request( + api_version=self._config.api_version, + files=_files, + data=_data, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_file(self, file_id: str, **kwargs: Any) -> _models.FileDeletionStatus: + """Delete a previously uploaded file. + + :param file_id: The ID of the file to delete. Required. + :type file_id: str + :return: FileDeletionStatus. The FileDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.FileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_file_request( + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_file(self, file_id: str, **kwargs: Any) -> _models.OpenAIFile: + """Returns information about a specific file. Does not retrieve file content. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + _request = build_assistants_get_file_request( + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def _get_file_content(self, file_id: str, **kwargs: Any) -> AsyncIterator[bytes]: + """Retrieves the raw content of a specific file. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: AsyncIterator[bytes] + :rtype: AsyncIterator[bytes] + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None) + + _request = build_assistants_get_file_content_request( + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", True) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + deserialized = response.iter_bytes() + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_vector_stores( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStore: + """Returns a list of vector stores. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStore. The OpenAIPageableListOfVectorStore is compatible + with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfVectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStore] = kwargs.pop("cls", None) + + _request = build_assistants_list_vector_stores_request( + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_vector_store( + self, + *, + content_type: str = "application/json", + file_ids: Optional[List[str]] = None, + name: Optional[str] = None, + store_configuration: Optional[_models.VectorStoreConfiguration] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword file_ids: A list of file IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Default value is None. + :paramtype file_ids: list[str] + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword store_configuration: The vector store configuration, used when vector store is created + from Azure asset URIs. Default value is None. + :paramtype store_configuration: ~azure.ai.assistants.models.VectorStoreConfiguration + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Only applicable if file_ids is non-empty. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_vector_store( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_ids: Optional[List[str]] = None, + name: Optional[str] = None, + store_configuration: Optional[_models.VectorStoreConfiguration] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_ids: A list of file IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Default value is None. + :paramtype file_ids: list[str] + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword store_configuration: The vector store configuration, used when vector store is created + from Azure asset URIs. Default value is None. + :paramtype store_configuration: ~azure.ai.assistants.models.VectorStoreConfiguration + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Only applicable if file_ids is non-empty. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + if body is _Unset: + body = { + "chunking_strategy": chunking_strategy, + "configuration": store_configuration, + "expires_after": expires_after, + "file_ids": file_ids, + "metadata": metadata, + "name": name, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_vector_store_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStore: + """Returns the vector store object matching the specified ID. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + _request = build_assistants_get_vector_store_request( + vector_store_id=vector_store_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def modify_vector_store( + self, + vector_store_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def modify_vector_store( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def modify_vector_store( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def modify_vector_store( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: The name of the vector store. Default value is None. + :paramtype name: str + :keyword expires_after: Details on when this vector store expires. Default value is None. + :paramtype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"expires_after": expires_after, "metadata": metadata, "name": name} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_modify_vector_store_request( + vector_store_id=vector_store_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStoreDeletionStatus: + """Deletes the vector store object matching the specified ID. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :return: VectorStoreDeletionStatus. The VectorStoreDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_vector_store_request( + vector_store_id=vector_store_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_vector_store_files( + self, + vector_store_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.assistants.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_assistants_list_vector_store_files_request( + vector_store_id=vector_store_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_vector_store_file( + self, + vector_store_id: str, + *, + content_type: str = "application/json", + file_id: Optional[str] = None, + data_source: Optional[_models.VectorStoreDataSource] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword file_id: Identifier of the file. Default value is None. + :paramtype file_id: str + :keyword data_source: Azure asset ID. Default value is None. + :paramtype data_source: ~azure.ai.assistants.models.VectorStoreDataSource + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_vector_store_file( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_id: Optional[str] = None, + data_source: Optional[_models.VectorStoreDataSource] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_id: Identifier of the file. Default value is None. + :paramtype file_id: str + :keyword data_source: Azure asset ID. Default value is None. + :paramtype data_source: ~azure.ai.assistants.models.VectorStoreDataSource + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"chunking_strategy": chunking_strategy, "data_source": data_source, "file_id": file_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_vector_store_file_request( + vector_store_id=vector_store_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_vector_store_file(self, vector_store_id: str, file_id: str, **kwargs: Any) -> _models.VectorStoreFile: + """Retrieves a vector store file. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param file_id: Identifier of the file. Required. + :type file_id: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + _request = build_assistants_get_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_vector_store_file( + self, vector_store_id: str, file_id: str, **kwargs: Any + ) -> _models.VectorStoreFileDeletionStatus: + """Delete a vector store file. This will remove the file from the vector store but the file itself + will not be deleted. + To delete the file, use the delete file endpoint. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param file_id: Identifier of the file. Required. + :type file_id: str + :return: VectorStoreFileDeletionStatus. The VectorStoreFileDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_assistants_delete_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_vector_store_file_batch( + self, + vector_store_id: str, + *, + content_type: str = "application/json", + file_ids: Optional[List[str]] = None, + data_sources: Optional[List[_models.VectorStoreDataSource]] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword file_ids: List of file identifiers. Default value is None. + :paramtype file_ids: list[str] + :keyword data_sources: List of Azure assets. Default value is None. + :paramtype data_sources: list[~azure.ai.assistants.models.VectorStoreDataSource] + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file_batch( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file_batch( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_vector_store_file_batch( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_ids: Optional[List[str]] = None, + data_sources: Optional[List[_models.VectorStoreDataSource]] = None, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_ids: List of file identifiers. Default value is None. + :paramtype file_ids: list[str] + :keyword data_sources: List of Azure assets. Default value is None. + :paramtype data_sources: list[~azure.ai.assistants.models.VectorStoreDataSource] + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"chunking_strategy": chunking_strategy, "data_sources": data_sources, "file_ids": file_ids} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_assistants_create_vector_store_file_batch_request( + vector_store_id=vector_store_id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Retrieve a vector store file batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param batch_id: Identifier of the file batch. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_assistants_get_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def cancel_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Cancel a vector store file batch. This attempts to cancel the processing of files in this batch + as soon as possible. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param batch_id: Identifier of the file batch. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_assistants_cancel_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_vector_store_file_batch_files( + self, + vector_store_id: str, + batch_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files in a batch. + + :param vector_store_id: Identifier of the vector store. Required. + :type vector_store_id: str + :param batch_id: Identifier of the file batch. Required. + :type batch_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.assistants.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.assistants.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.assistants.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_assistants_list_vector_store_file_batch_files_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_patch.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_operations/_patch.py @@ -0,0 +1,21 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_patch.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/aio/_patch.py @@ -0,0 +1,21 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/__init__.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/__init__.py new file mode 100644 index 000000000000..851d85e3303e --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/__init__.py @@ -0,0 +1,434 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + + +from ._models import ( # type: ignore + AISearchIndexResource, + Assistant, + AssistantDeletionStatus, + AssistantThread, + AssistantThreadCreationOptions, + AssistantsApiResponseFormat, + AssistantsNamedToolChoice, + AzureAISearchResource, + AzureAISearchToolDefinition, + AzureFunctionBinding, + AzureFunctionDefinition, + AzureFunctionStorageQueue, + AzureFunctionToolDefinition, + BingCustomSearchToolDefinition, + BingGroundingToolDefinition, + CodeInterpreterToolDefinition, + CodeInterpreterToolResource, + ConnectedAgentDetails, + ConnectedAgentToolDefinition, + FileDeletionStatus, + FileListResponse, + FileSearchRankingOptions, + FileSearchToolCallContent, + FileSearchToolDefinition, + FileSearchToolDefinitionDetails, + FileSearchToolResource, + FunctionDefinition, + FunctionName, + FunctionToolDefinition, + IncompleteRunDetails, + MessageAttachment, + MessageContent, + MessageDelta, + MessageDeltaChunk, + MessageDeltaContent, + MessageDeltaImageFileContent, + MessageDeltaImageFileContentObject, + MessageDeltaTextAnnotation, + MessageDeltaTextContent, + MessageDeltaTextContentObject, + MessageDeltaTextFileCitationAnnotation, + MessageDeltaTextFileCitationAnnotationObject, + MessageDeltaTextFilePathAnnotation, + MessageDeltaTextFilePathAnnotationObject, + MessageDeltaTextUrlCitationAnnotation, + MessageDeltaTextUrlCitationDetails, + MessageImageFileContent, + MessageImageFileDetails, + MessageImageFileParam, + MessageImageUrlParam, + MessageIncompleteDetails, + MessageInputContentBlock, + MessageInputImageFileBlock, + MessageInputImageUrlBlock, + MessageInputTextBlock, + MessageTextAnnotation, + MessageTextContent, + MessageTextDetails, + MessageTextFileCitationAnnotation, + MessageTextFileCitationDetails, + MessageTextFilePathAnnotation, + MessageTextFilePathDetails, + MessageTextUrlCitationAnnotation, + MessageTextUrlCitationDetails, + MicrosoftFabricToolDefinition, + OpenAIFile, + OpenAIPageableListOfAssistant, + OpenAIPageableListOfAssistantThread, + OpenAIPageableListOfRunStep, + OpenAIPageableListOfThreadMessage, + OpenAIPageableListOfThreadRun, + OpenAIPageableListOfVectorStore, + OpenAIPageableListOfVectorStoreFile, + OpenApiAnonymousAuthDetails, + OpenApiAuthDetails, + OpenApiConnectionAuthDetails, + OpenApiConnectionSecurityScheme, + OpenApiFunctionDefinition, + OpenApiManagedAuthDetails, + OpenApiManagedSecurityScheme, + OpenApiToolDefinition, + RequiredAction, + RequiredFunctionToolCall, + RequiredFunctionToolCallDetails, + RequiredToolCall, + ResponseFormatJsonSchema, + ResponseFormatJsonSchemaType, + RunCompletionUsage, + RunError, + RunStep, + RunStepAzureAISearchToolCall, + RunStepBingGroundingToolCall, + RunStepCodeInterpreterImageOutput, + RunStepCodeInterpreterImageReference, + RunStepCodeInterpreterLogOutput, + RunStepCodeInterpreterToolCall, + RunStepCodeInterpreterToolCallDetails, + RunStepCodeInterpreterToolCallOutput, + RunStepCompletionUsage, + RunStepCustomSearchToolCall, + RunStepDelta, + RunStepDeltaChunk, + RunStepDeltaCodeInterpreterDetailItemObject, + RunStepDeltaCodeInterpreterImageOutput, + RunStepDeltaCodeInterpreterImageOutputObject, + RunStepDeltaCodeInterpreterLogOutput, + RunStepDeltaCodeInterpreterOutput, + RunStepDeltaCodeInterpreterToolCall, + RunStepDeltaDetail, + RunStepDeltaFileSearchToolCall, + RunStepDeltaFunction, + RunStepDeltaFunctionToolCall, + RunStepDeltaMessageCreation, + RunStepDeltaMessageCreationObject, + RunStepDeltaToolCall, + RunStepDeltaToolCallObject, + RunStepDetails, + RunStepError, + RunStepFileSearchToolCall, + RunStepFileSearchToolCallResult, + RunStepFileSearchToolCallResults, + RunStepFunctionToolCall, + RunStepFunctionToolCallDetails, + RunStepMessageCreationDetails, + RunStepMessageCreationReference, + RunStepMicrosoftFabricToolCall, + RunStepOpenAPIToolCall, + RunStepSharepointToolCall, + RunStepToolCall, + RunStepToolCallDetails, + SearchConfiguration, + SearchConfigurationList, + SharepointToolDefinition, + SubmitToolOutputsAction, + SubmitToolOutputsDetails, + ThreadDeletionStatus, + ThreadMessage, + ThreadMessageOptions, + ThreadRun, + ToolConnection, + ToolConnectionList, + ToolDefinition, + ToolOutput, + ToolResources, + TruncationObject, + UpdateCodeInterpreterToolResourceOptions, + UpdateFileSearchToolResourceOptions, + UpdateToolResourcesOptions, + VectorStore, + VectorStoreAutoChunkingStrategyRequest, + VectorStoreAutoChunkingStrategyResponse, + VectorStoreChunkingStrategyRequest, + VectorStoreChunkingStrategyResponse, + VectorStoreConfiguration, + VectorStoreConfigurations, + VectorStoreDataSource, + VectorStoreDeletionStatus, + VectorStoreExpirationPolicy, + VectorStoreFile, + VectorStoreFileBatch, + VectorStoreFileCount, + VectorStoreFileDeletionStatus, + VectorStoreFileError, + VectorStoreStaticChunkingStrategyOptions, + VectorStoreStaticChunkingStrategyRequest, + VectorStoreStaticChunkingStrategyResponse, +) + +from ._enums import ( # type: ignore + AssistantStreamEvent, + AssistantsApiResponseFormatMode, + AssistantsApiToolChoiceOptionMode, + AssistantsNamedToolChoiceType, + AzureAISearchQueryType, + DoneEvent, + ErrorEvent, + FilePurpose, + FileState, + ImageDetailLevel, + IncompleteDetailsReason, + ListSortOrder, + MessageBlockType, + MessageIncompleteDetailsReason, + MessageRole, + MessageStatus, + MessageStreamEvent, + OpenApiAuthType, + ResponseFormat, + RunAdditionalFieldList, + RunStatus, + RunStepErrorCode, + RunStepStatus, + RunStepStreamEvent, + RunStepType, + RunStreamEvent, + ThreadStreamEvent, + TruncationStrategy, + VectorStoreChunkingStrategyRequestType, + VectorStoreChunkingStrategyResponseType, + VectorStoreDataSourceAssetType, + VectorStoreExpirationPolicyAnchor, + VectorStoreFileBatchStatus, + VectorStoreFileErrorCode, + VectorStoreFileStatus, + VectorStoreFileStatusFilter, + VectorStoreStatus, +) +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AISearchIndexResource", + "Assistant", + "AssistantDeletionStatus", + "AssistantThread", + "AssistantThreadCreationOptions", + "AssistantsApiResponseFormat", + "AssistantsNamedToolChoice", + "AzureAISearchResource", + "AzureAISearchToolDefinition", + "AzureFunctionBinding", + "AzureFunctionDefinition", + "AzureFunctionStorageQueue", + "AzureFunctionToolDefinition", + "BingCustomSearchToolDefinition", + "BingGroundingToolDefinition", + "CodeInterpreterToolDefinition", + "CodeInterpreterToolResource", + "ConnectedAgentDetails", + "ConnectedAgentToolDefinition", + "FileDeletionStatus", + "FileListResponse", + "FileSearchRankingOptions", + "FileSearchToolCallContent", + "FileSearchToolDefinition", + "FileSearchToolDefinitionDetails", + "FileSearchToolResource", + "FunctionDefinition", + "FunctionName", + "FunctionToolDefinition", + "IncompleteRunDetails", + "MessageAttachment", + "MessageContent", + "MessageDelta", + "MessageDeltaChunk", + "MessageDeltaContent", + "MessageDeltaImageFileContent", + "MessageDeltaImageFileContentObject", + "MessageDeltaTextAnnotation", + "MessageDeltaTextContent", + "MessageDeltaTextContentObject", + "MessageDeltaTextFileCitationAnnotation", + "MessageDeltaTextFileCitationAnnotationObject", + "MessageDeltaTextFilePathAnnotation", + "MessageDeltaTextFilePathAnnotationObject", + "MessageDeltaTextUrlCitationAnnotation", + "MessageDeltaTextUrlCitationDetails", + "MessageImageFileContent", + "MessageImageFileDetails", + "MessageImageFileParam", + "MessageImageUrlParam", + "MessageIncompleteDetails", + "MessageInputContentBlock", + "MessageInputImageFileBlock", + "MessageInputImageUrlBlock", + "MessageInputTextBlock", + "MessageTextAnnotation", + "MessageTextContent", + "MessageTextDetails", + "MessageTextFileCitationAnnotation", + "MessageTextFileCitationDetails", + "MessageTextFilePathAnnotation", + "MessageTextFilePathDetails", + "MessageTextUrlCitationAnnotation", + "MessageTextUrlCitationDetails", + "MicrosoftFabricToolDefinition", + "OpenAIFile", + "OpenAIPageableListOfAssistant", + "OpenAIPageableListOfAssistantThread", + "OpenAIPageableListOfRunStep", + "OpenAIPageableListOfThreadMessage", + "OpenAIPageableListOfThreadRun", + "OpenAIPageableListOfVectorStore", + "OpenAIPageableListOfVectorStoreFile", + "OpenApiAnonymousAuthDetails", + "OpenApiAuthDetails", + "OpenApiConnectionAuthDetails", + "OpenApiConnectionSecurityScheme", + "OpenApiFunctionDefinition", + "OpenApiManagedAuthDetails", + "OpenApiManagedSecurityScheme", + "OpenApiToolDefinition", + "RequiredAction", + "RequiredFunctionToolCall", + "RequiredFunctionToolCallDetails", + "RequiredToolCall", + "ResponseFormatJsonSchema", + "ResponseFormatJsonSchemaType", + "RunCompletionUsage", + "RunError", + "RunStep", + "RunStepAzureAISearchToolCall", + "RunStepBingGroundingToolCall", + "RunStepCodeInterpreterImageOutput", + "RunStepCodeInterpreterImageReference", + "RunStepCodeInterpreterLogOutput", + "RunStepCodeInterpreterToolCall", + "RunStepCodeInterpreterToolCallDetails", + "RunStepCodeInterpreterToolCallOutput", + "RunStepCompletionUsage", + "RunStepCustomSearchToolCall", + "RunStepDelta", + "RunStepDeltaChunk", + "RunStepDeltaCodeInterpreterDetailItemObject", + "RunStepDeltaCodeInterpreterImageOutput", + "RunStepDeltaCodeInterpreterImageOutputObject", + "RunStepDeltaCodeInterpreterLogOutput", + "RunStepDeltaCodeInterpreterOutput", + "RunStepDeltaCodeInterpreterToolCall", + "RunStepDeltaDetail", + "RunStepDeltaFileSearchToolCall", + "RunStepDeltaFunction", + "RunStepDeltaFunctionToolCall", + "RunStepDeltaMessageCreation", + "RunStepDeltaMessageCreationObject", + "RunStepDeltaToolCall", + "RunStepDeltaToolCallObject", + "RunStepDetails", + "RunStepError", + "RunStepFileSearchToolCall", + "RunStepFileSearchToolCallResult", + "RunStepFileSearchToolCallResults", + "RunStepFunctionToolCall", + "RunStepFunctionToolCallDetails", + "RunStepMessageCreationDetails", + "RunStepMessageCreationReference", + "RunStepMicrosoftFabricToolCall", + "RunStepOpenAPIToolCall", + "RunStepSharepointToolCall", + "RunStepToolCall", + "RunStepToolCallDetails", + "SearchConfiguration", + "SearchConfigurationList", + "SharepointToolDefinition", + "SubmitToolOutputsAction", + "SubmitToolOutputsDetails", + "ThreadDeletionStatus", + "ThreadMessage", + "ThreadMessageOptions", + "ThreadRun", + "ToolConnection", + "ToolConnectionList", + "ToolDefinition", + "ToolOutput", + "ToolResources", + "TruncationObject", + "UpdateCodeInterpreterToolResourceOptions", + "UpdateFileSearchToolResourceOptions", + "UpdateToolResourcesOptions", + "VectorStore", + "VectorStoreAutoChunkingStrategyRequest", + "VectorStoreAutoChunkingStrategyResponse", + "VectorStoreChunkingStrategyRequest", + "VectorStoreChunkingStrategyResponse", + "VectorStoreConfiguration", + "VectorStoreConfigurations", + "VectorStoreDataSource", + "VectorStoreDeletionStatus", + "VectorStoreExpirationPolicy", + "VectorStoreFile", + "VectorStoreFileBatch", + "VectorStoreFileCount", + "VectorStoreFileDeletionStatus", + "VectorStoreFileError", + "VectorStoreStaticChunkingStrategyOptions", + "VectorStoreStaticChunkingStrategyRequest", + "VectorStoreStaticChunkingStrategyResponse", + "AssistantStreamEvent", + "AssistantsApiResponseFormatMode", + "AssistantsApiToolChoiceOptionMode", + "AssistantsNamedToolChoiceType", + "AzureAISearchQueryType", + "DoneEvent", + "ErrorEvent", + "FilePurpose", + "FileState", + "ImageDetailLevel", + "IncompleteDetailsReason", + "ListSortOrder", + "MessageBlockType", + "MessageIncompleteDetailsReason", + "MessageRole", + "MessageStatus", + "MessageStreamEvent", + "OpenApiAuthType", + "ResponseFormat", + "RunAdditionalFieldList", + "RunStatus", + "RunStepErrorCode", + "RunStepStatus", + "RunStepStreamEvent", + "RunStepType", + "RunStreamEvent", + "ThreadStreamEvent", + "TruncationStrategy", + "VectorStoreChunkingStrategyRequestType", + "VectorStoreChunkingStrategyResponseType", + "VectorStoreDataSourceAssetType", + "VectorStoreExpirationPolicyAnchor", + "VectorStoreFileBatchStatus", + "VectorStoreFileErrorCode", + "VectorStoreFileStatus", + "VectorStoreFileStatusFilter", + "VectorStoreStatus", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_enums.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_enums.py new file mode 100644 index 000000000000..07dc0d9dc410 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_enums.py @@ -0,0 +1,546 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from enum import Enum +from azure.core import CaseInsensitiveEnumMeta + + +class AssistantsApiResponseFormatMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Represents the mode in which the model will handle the return format of a tool call.""" + + AUTO = "auto" + """Default value. Let the model handle the return format.""" + NONE = "none" + """Setting the value to ``none``, will result in a 400 Bad request.""" + + +class AssistantsApiToolChoiceOptionMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Specifies how the tool choice will be used.""" + + NONE = "none" + """The model will not call a function and instead generates a message.""" + AUTO = "auto" + """The model can pick between generating a message or calling a function.""" + + +class AssistantsNamedToolChoiceType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available tool types for assistants named tools.""" + + FUNCTION = "function" + """Tool type ``function``""" + CODE_INTERPRETER = "code_interpreter" + """Tool type ``code_interpreter``""" + FILE_SEARCH = "file_search" + """Tool type ``file_search``""" + BING_GROUNDING = "bing_grounding" + """Tool type ``bing_grounding``""" + MICROSOFT_FABRIC = "fabric_dataagent" + """Tool type ``fabric_dataagent``""" + SHAREPOINT = "sharepoint_grounding" + """Tool type ``sharepoint_grounding``""" + AZURE_AI_SEARCH = "azure_ai_search" + """Tool type ``azure_ai_search``""" + BING_CUSTOM_SEARCH = "bing_custom_search" + """Tool type ``bing_custom_search``""" + CONNECTED_AGENT = "connected_agent" + """Tool type ``connected_agent``""" + + +class AssistantStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Each event in a server-sent events stream has an ``event`` and ``data`` property: + + + + .. code-block:: + + event: thread.created + data: {"id": "thread_123", "object": "thread", ...} + + We emit events whenever a new object is created, transitions to a new state, or is being + streamed in parts (deltas). For example, we emit ``thread.run.created`` when a new run + is created, ``thread.run.completed`` when a run completes, and so on. When an Assistant chooses + to create a message during a run, we emit a ``thread.message.created event``, a + ``thread.message.in_progress`` event, many ``thread.message.delta`` events, and finally a + ``thread.message.completed`` event. + + We may add additional events over time, so we recommend handling unknown events gracefully + in your code. + """ + + THREAD_CREATED = "thread.created" + """Event sent when a new thread is created. The data of this event is of type AssistantThread""" + THREAD_RUN_CREATED = "thread.run.created" + """Event sent when a new run is created. The data of this event is of type ThreadRun""" + THREAD_RUN_QUEUED = "thread.run.queued" + """Event sent when a run moves to ``queued`` status. The data of this event is of type ThreadRun""" + THREAD_RUN_IN_PROGRESS = "thread.run.in_progress" + """Event sent when a run moves to ``in_progress`` status. The data of this event is of type + ThreadRun""" + THREAD_RUN_REQUIRES_ACTION = "thread.run.requires_action" + """Event sent when a run moves to ``requires_action`` status. The data of this event is of type + ThreadRun""" + THREAD_RUN_COMPLETED = "thread.run.completed" + """Event sent when a run is completed. The data of this event is of type ThreadRun""" + THREAD_RUN_INCOMPLETE = "thread.run.incomplete" + """Event sent when a run ends incomplete. The data of this event is of type ThreadRun""" + THREAD_RUN_FAILED = "thread.run.failed" + """Event sent when a run fails. The data of this event is of type ThreadRun""" + THREAD_RUN_CANCELLING = "thread.run.cancelling" + """Event sent when a run moves to ``cancelling`` status. The data of this event is of type + ThreadRun""" + THREAD_RUN_CANCELLED = "thread.run.cancelled" + """Event sent when a run is cancelled. The data of this event is of type ThreadRun""" + THREAD_RUN_EXPIRED = "thread.run.expired" + """Event sent when a run is expired. The data of this event is of type ThreadRun""" + THREAD_RUN_STEP_CREATED = "thread.run.step.created" + """Event sent when a new thread run step is created. The data of this event is of type RunStep""" + THREAD_RUN_STEP_IN_PROGRESS = "thread.run.step.in_progress" + """Event sent when a run step moves to ``in_progress`` status. The data of this event is of type + RunStep""" + THREAD_RUN_STEP_DELTA = "thread.run.step.delta" + """Event sent when a run step is being streamed. The data of this event is of type + RunStepDeltaChunk""" + THREAD_RUN_STEP_COMPLETED = "thread.run.step.completed" + """Event sent when a run step is completed. The data of this event is of type RunStep""" + THREAD_RUN_STEP_FAILED = "thread.run.step.failed" + """Event sent when a run step fails. The data of this event is of type RunStep""" + THREAD_RUN_STEP_CANCELLED = "thread.run.step.cancelled" + """Event sent when a run step is cancelled. The data of this event is of type RunStep""" + THREAD_RUN_STEP_EXPIRED = "thread.run.step.expired" + """Event sent when a run step is expired. The data of this event is of type RunStep""" + THREAD_MESSAGE_CREATED = "thread.message.created" + """Event sent when a new message is created. The data of this event is of type ThreadMessage""" + THREAD_MESSAGE_IN_PROGRESS = "thread.message.in_progress" + """Event sent when a message moves to ``in_progress`` status. The data of this event is of type + ThreadMessage""" + THREAD_MESSAGE_DELTA = "thread.message.delta" + """Event sent when a message is being streamed. The data of this event is of type + MessageDeltaChunk""" + THREAD_MESSAGE_COMPLETED = "thread.message.completed" + """Event sent when a message is completed. The data of this event is of type ThreadMessage""" + THREAD_MESSAGE_INCOMPLETE = "thread.message.incomplete" + """Event sent before a message is completed. The data of this event is of type ThreadMessage""" + ERROR = "error" + """Event sent when an error occurs, such as an internal server error or a timeout.""" + DONE = "done" + """Event sent when the stream is done.""" + + +class AzureAISearchQueryType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available query types for Azure AI Search tool.""" + + SIMPLE = "simple" + """Query type ``simple``""" + SEMANTIC = "semantic" + """Query type ``semantic``""" + VECTOR = "vector" + """Query type ``vector``""" + VECTOR_SIMPLE_HYBRID = "vector_simple_hybrid" + """Query type ``vector_simple_hybrid``""" + VECTOR_SEMANTIC_HYBRID = "vector_semantic_hybrid" + """Query type ``vector_semantic_hybrid``""" + + +class DoneEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Terminal event indicating the successful end of a stream.""" + + DONE = "done" + """Event sent when the stream is done.""" + + +class ErrorEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Terminal event indicating a server side error while streaming.""" + + ERROR = "error" + """Event sent when an error occurs, such as an internal server error or a timeout.""" + + +class FilePurpose(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible values denoting the intended usage of a file.""" + + FINE_TUNE = "fine-tune" + """Indicates a file is used for fine tuning input.""" + FINE_TUNE_RESULTS = "fine-tune-results" + """Indicates a file is used for fine tuning results.""" + ASSISTANTS = "assistants" + """Indicates a file is used as input to assistants.""" + ASSISTANTS_OUTPUT = "assistants_output" + """Indicates a file is used as output by assistants.""" + BATCH = "batch" + """Indicates a file is used as input to .""" + BATCH_OUTPUT = "batch_output" + """Indicates a file is used as output by a vector store batch operation.""" + VISION = "vision" + """Indicates a file is used as input to a vision operation.""" + + +class FileState(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The state of the file.""" + + UPLOADED = "uploaded" + """The file has been uploaded but it's not yet processed. This state is not returned by Azure + OpenAI and exposed only for + compatibility. It can be categorized as an inactive state.""" + PENDING = "pending" + """The operation was created and is not queued to be processed in the future. It can be + categorized as an inactive state.""" + RUNNING = "running" + """The operation has started to be processed. It can be categorized as an active state.""" + PROCESSED = "processed" + """The operation has successfully processed and is ready for consumption. It can be categorized as + a terminal state.""" + ERROR = "error" + """The operation has completed processing with a failure and cannot be further consumed. It can be + categorized as a terminal state.""" + DELETING = "deleting" + """The entity is in the process to be deleted. This state is not returned by Azure OpenAI and + exposed only for compatibility. + It can be categorized as an active state.""" + DELETED = "deleted" + """The entity has been deleted but may still be referenced by other entities predating the + deletion. It can be categorized as a + terminal state.""" + + +class ImageDetailLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Specifies an image's detail level. Can be 'auto', 'low', 'high', or an unknown future value.""" + + AUTO = "auto" + """Automatically select an appropriate detail level.""" + LOW = "low" + """Use a lower detail level to reduce bandwidth or cost.""" + HIGH = "high" + """Use a higher detail level—potentially more resource-intensive.""" + + +class IncompleteDetailsReason(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The reason why the run is incomplete. This will point to which specific token limit was reached + over the course of the run. + """ + + MAX_COMPLETION_TOKENS = "max_completion_tokens" + """Maximum completion tokens exceeded""" + MAX_PROMPT_TOKENS = "max_prompt_tokens" + """Maximum prompt tokens exceeded""" + + +class ListSortOrder(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The available sorting options when requesting a list of response objects.""" + + ASCENDING = "asc" + """Specifies an ascending sort order.""" + DESCENDING = "desc" + """Specifies a descending sort order.""" + + +class MessageBlockType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Specifies the kind of content block within a message. Could be text, an image file, an external + image URL, or an unknown future type. + """ + + TEXT = "text" + """Indicates a block containing text content.""" + IMAGE_FILE = "image_file" + """Indicates a block referencing an internally uploaded image file.""" + IMAGE_URL = "image_url" + """Indicates a block referencing an external image URL.""" + + +class MessageIncompleteDetailsReason(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """A set of reasons describing why a message is marked as incomplete.""" + + CONTENT_FILTER = "content_filter" + """The run generating the message was terminated due to content filter flagging.""" + MAX_TOKENS = "max_tokens" + """The run generating the message exhausted available tokens before completion.""" + RUN_CANCELLED = "run_cancelled" + """The run generating the message was cancelled before completion.""" + RUN_FAILED = "run_failed" + """The run generating the message failed.""" + RUN_EXPIRED = "run_expired" + """The run generating the message expired.""" + + +class MessageRole(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible values for roles attributed to messages in a thread.""" + + USER = "user" + """The role representing the end-user.""" + ASSISTANT = "assistant" + """The role representing the assistant.""" + + +class MessageStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible execution status values for a thread message.""" + + IN_PROGRESS = "in_progress" + """A run is currently creating this message.""" + INCOMPLETE = "incomplete" + """This message is incomplete. See incomplete_details for more information.""" + COMPLETED = "completed" + """This message was successfully completed by a run.""" + + +class MessageStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Message operation related streaming events.""" + + THREAD_MESSAGE_CREATED = "thread.message.created" + """Event sent when a new message is created. The data of this event is of type ThreadMessage""" + THREAD_MESSAGE_IN_PROGRESS = "thread.message.in_progress" + """Event sent when a message moves to ``in_progress`` status. The data of this event is of type + ThreadMessage""" + THREAD_MESSAGE_DELTA = "thread.message.delta" + """Event sent when a message is being streamed. The data of this event is of type + MessageDeltaChunk""" + THREAD_MESSAGE_COMPLETED = "thread.message.completed" + """Event sent when a message is completed. The data of this event is of type ThreadMessage""" + THREAD_MESSAGE_INCOMPLETE = "thread.message.incomplete" + """Event sent before a message is completed. The data of this event is of type ThreadMessage""" + + +class OpenApiAuthType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Authentication type for OpenApi endpoint. Allowed types are: + + * Anonymous (no authentication required) + * Connection (requires connection_id to endpoint, as setup in AI Foundry) + * Managed_Identity (requires audience for identity based auth). + """ + + ANONYMOUS = "anonymous" + CONNECTION = "connection" + MANAGED_IDENTITY = "managed_identity" + + +class ResponseFormat(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible API response formats.""" + + TEXT = "text" + """``text`` format should be used for requests involving any sort of ToolCall.""" + JSON_OBJECT = "json_object" + """Using ``json_object`` format will limit the usage of ToolCall to only functions.""" + + +class RunAdditionalFieldList(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """A list of additional fields to include in the response.""" + + FILE_SEARCH_CONTENTS = "step_details.tool_calls[*].file_search.results[*].content" + """File search result content.""" + + +class RunStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible values for the status of an assistant thread run.""" + + QUEUED = "queued" + """Represents a run that is queued to start.""" + IN_PROGRESS = "in_progress" + """Represents a run that is in progress.""" + REQUIRES_ACTION = "requires_action" + """Represents a run that needs another operation, such as tool output submission, to continue.""" + CANCELLING = "cancelling" + """Represents a run that is in the process of cancellation.""" + CANCELLED = "cancelled" + """Represents a run that has been cancelled.""" + FAILED = "failed" + """Represents a run that failed.""" + COMPLETED = "completed" + """Represents a run that successfully completed.""" + EXPIRED = "expired" + """Represents a run that expired before it could otherwise finish.""" + + +class RunStepErrorCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible error code values attributable to a failed run step.""" + + SERVER_ERROR = "server_error" + """Represents a server error.""" + RATE_LIMIT_EXCEEDED = "rate_limit_exceeded" + """Represents an error indicating configured rate limits were exceeded.""" + + +class RunStepStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible values for the status of a run step.""" + + IN_PROGRESS = "in_progress" + """Represents a run step still in progress.""" + CANCELLED = "cancelled" + """Represents a run step that was cancelled.""" + FAILED = "failed" + """Represents a run step that failed.""" + COMPLETED = "completed" + """Represents a run step that successfully completed.""" + EXPIRED = "expired" + """Represents a run step that expired before otherwise finishing.""" + + +class RunStepStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Run step operation related streaming events.""" + + THREAD_RUN_STEP_CREATED = "thread.run.step.created" + """Event sent when a new thread run step is created. The data of this event is of type RunStep""" + THREAD_RUN_STEP_IN_PROGRESS = "thread.run.step.in_progress" + """Event sent when a run step moves to ``in_progress`` status. The data of this event is of type + RunStep""" + THREAD_RUN_STEP_DELTA = "thread.run.step.delta" + """Event sent when a run step is being streamed. The data of this event is of type + RunStepDeltaChunk""" + THREAD_RUN_STEP_COMPLETED = "thread.run.step.completed" + """Event sent when a run step is completed. The data of this event is of type RunStep""" + THREAD_RUN_STEP_FAILED = "thread.run.step.failed" + """Event sent when a run step fails. The data of this event is of type RunStep""" + THREAD_RUN_STEP_CANCELLED = "thread.run.step.cancelled" + """Event sent when a run step is cancelled. The data of this event is of type RunStep""" + THREAD_RUN_STEP_EXPIRED = "thread.run.step.expired" + """Event sent when a run step is expired. The data of this event is of type RunStep""" + + +class RunStepType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible types of run steps.""" + + MESSAGE_CREATION = "message_creation" + """Represents a run step to create a message.""" + TOOL_CALLS = "tool_calls" + """Represents a run step that calls tools.""" + + +class RunStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Run operation related streaming events.""" + + THREAD_RUN_CREATED = "thread.run.created" + """Event sent when a new run is created. The data of this event is of type ThreadRun""" + THREAD_RUN_QUEUED = "thread.run.queued" + """Event sent when a run moves to ``queued`` status. The data of this event is of type ThreadRun""" + THREAD_RUN_IN_PROGRESS = "thread.run.in_progress" + """Event sent when a run moves to ``in_progress`` status. The data of this event is of type + ThreadRun""" + THREAD_RUN_REQUIRES_ACTION = "thread.run.requires_action" + """Event sent when a run moves to ``requires_action`` status. The data of this event is of type + ThreadRun""" + THREAD_RUN_COMPLETED = "thread.run.completed" + """Event sent when a run is completed. The data of this event is of type ThreadRun""" + THREAD_RUN_INCOMPLETE = "thread.run.incomplete" + """Event sent when a run ends incomplete. The data of this event is of type ThreadRun""" + THREAD_RUN_FAILED = "thread.run.failed" + """Event sent when a run fails. The data of this event is of type ThreadRun""" + THREAD_RUN_CANCELLING = "thread.run.cancelling" + """Event sent when a run moves to ``cancelling`` status. The data of this event is of type + ThreadRun""" + THREAD_RUN_CANCELLED = "thread.run.cancelled" + """Event sent when a run is cancelled. The data of this event is of type ThreadRun""" + THREAD_RUN_EXPIRED = "thread.run.expired" + """Event sent when a run is expired. The data of this event is of type ThreadRun""" + + +class ThreadStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Thread operation related streaming events.""" + + THREAD_CREATED = "thread.created" + """Event sent when a new thread is created. The data of this event is of type AssistantThread""" + + +class TruncationStrategy(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible truncation strategies for the thread.""" + + AUTO = "auto" + """Default value. Messages in the middle of the thread will be dropped to fit the context length + of the model.""" + LAST_MESSAGES = "last_messages" + """The thread will truncate to the ``lastMessages`` count of recent messages.""" + + +class VectorStoreChunkingStrategyRequestType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of chunking strategy.""" + + AUTO = "auto" + STATIC = "static" + + +class VectorStoreChunkingStrategyResponseType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of chunking strategy.""" + + OTHER = "other" + STATIC = "static" + + +class VectorStoreDataSourceAssetType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of vector storage asset. Asset type may be a uri_asset, in this case it should contain + asset URI ID, + in the case of id_asset it should contain the data ID. + """ + + URI_ASSET = "uri_asset" + """Azure URI""" + ID_ASSET = "id_asset" + """The data ID""" + + +class VectorStoreExpirationPolicyAnchor(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Describes the relationship between the days and the expiration of this vector store.""" + + LAST_ACTIVE_AT = "last_active_at" + """The expiration policy is based on the last time the vector store was active.""" + + +class VectorStoreFileBatchStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The status of the vector store file batch.""" + + IN_PROGRESS = "in_progress" + """The vector store is still processing this file batch.""" + COMPLETED = "completed" + """the vector store file batch is ready for use.""" + CANCELLED = "cancelled" + """The vector store file batch was cancelled.""" + FAILED = "failed" + """The vector store file batch failed to process.""" + + +class VectorStoreFileErrorCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Error code variants for vector store file processing.""" + + SERVER_ERROR = "server_error" + """An server error occurred.""" + INVALID_FILE = "invalid_file" + """The file is not valid.""" + UNSUPPORTED_FILE = "unsupported_file" + """The file is of unsupported type.""" + + +class VectorStoreFileStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Vector store file status.""" + + IN_PROGRESS = "in_progress" + """The file is currently being processed.""" + COMPLETED = "completed" + """The file has been successfully processed.""" + FAILED = "failed" + """The file has failed to process.""" + CANCELLED = "cancelled" + """The file was cancelled.""" + + +class VectorStoreFileStatusFilter(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Query parameter filter for vector store file retrieval endpoint.""" + + IN_PROGRESS = "in_progress" + """Retrieve only files that are currently being processed""" + COMPLETED = "completed" + """Retrieve only files that have been successfully processed""" + FAILED = "failed" + """Retrieve only files that have failed to process""" + CANCELLED = "cancelled" + """Retrieve only files that were cancelled""" + + +class VectorStoreStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Vector store possible status.""" + + EXPIRED = "expired" + """expired status indicates that this vector store has expired and is no longer available for use.""" + IN_PROGRESS = "in_progress" + """in_progress status indicates that this vector store is still processing files.""" + COMPLETED = "completed" + """completed status indicates that this vector store is ready for use.""" diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_models.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_models.py new file mode 100644 index 000000000000..cf0a0bad98ee --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_models.py @@ -0,0 +1,7002 @@ +# pylint: disable=line-too-long,useless-suppression,too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=useless-super-delegation + +import datetime +from typing import Any, Dict, List, Literal, Mapping, Optional, TYPE_CHECKING, Union, overload + +from .._utils.model_base import Model as _Model, rest_discriminator, rest_field +from .._utils.utils import FileType +from ._enums import ( + MessageBlockType, + OpenApiAuthType, + RunStepType, + VectorStoreChunkingStrategyRequestType, + VectorStoreChunkingStrategyResponseType, +) + +if TYPE_CHECKING: + from .. import _types, models as _models + + +class AISearchIndexResource(_Model): + """A AI Search Index resource. + + :ivar index_connection_id: An index connection id in an IndexResource attached to this + assistant. Required. + :vartype index_connection_id: str + :ivar index_name: The name of an index in an IndexResource attached to this assistant. + Required. + :vartype index_name: str + :ivar query_type: Type of query in an AIIndexResource attached to this assistant. Known values + are: "simple", "semantic", "vector", "vector_simple_hybrid", and "vector_semantic_hybrid". + :vartype query_type: str or ~azure.ai.assistants.models.AzureAISearchQueryType + :ivar top_k: Number of documents to retrieve from search and present to the model. + :vartype top_k: int + :ivar filter: Odata filter string for search resource. + :vartype filter: str + """ + + index_connection_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An index connection id in an IndexResource attached to this assistant. Required.""" + index_name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of an index in an IndexResource attached to this assistant. Required.""" + query_type: Optional[Union[str, "_models.AzureAISearchQueryType"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Type of query in an AIIndexResource attached to this assistant. Known values are: \"simple\", + \"semantic\", \"vector\", \"vector_simple_hybrid\", and \"vector_semantic_hybrid\".""" + top_k: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Number of documents to retrieve from search and present to the model.""" + filter: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Odata filter string for search resource.""" + + @overload + def __init__( + self, + *, + index_connection_id: str, + index_name: str, + query_type: Optional[Union[str, "_models.AzureAISearchQueryType"]] = None, + top_k: Optional[int] = None, + filter: Optional[str] = None, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Assistant(_Model): + """Represents an assistant that can call the model and use tools. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always assistant. Required. Default value is + "assistant". + :vartype object: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar name: The name of the assistant. Required. + :vartype name: str + :ivar description: The description of the assistant. Required. + :vartype description: str + :ivar model: The ID of the model to use. Required. + :vartype model: str + :ivar instructions: The system instructions for the assistant to use. Required. + :vartype instructions: str + :ivar tools: The collection of tools enabled for the assistant. Required. + :vartype tools: list[~azure.ai.assistants.models.ToolDefinition] + :ivar tool_resources: A set of resources that are used by the assistant's tools. The resources + are specific to the type of tool. For example, the ``code_interpreter`` + tool requires a list of file IDs, while the ``file_search`` tool requires a list of vector + store IDs. Required. + :vartype tool_resources: ~azure.ai.assistants.models.ToolResources + :ivar temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, + while lower values like 0.2 will make it more focused and deterministic. Required. + :vartype temperature: float + :ivar top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Required. + :vartype top_p: float + :ivar response_format: The response format of the tool calls used by this assistant. Is one of + the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType + :vartype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["assistant"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always assistant. Required. Default value is \"assistant\".""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the assistant. Required.""" + description: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The description of the assistant. Required.""" + model: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the model to use. Required.""" + instructions: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The system instructions for the assistant to use. Required.""" + tools: List["_models.ToolDefinition"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The collection of tools enabled for the assistant. Required.""" + tool_resources: "_models.ToolResources" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of resources that are used by the assistant's tools. The resources are specific to the + type of tool. For example, the ``code_interpreter`` + tool requires a list of file IDs, while the ``file_search`` tool requires a list of vector + store IDs. Required.""" + temperature: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output + more random, + while lower values like 0.2 will make it more focused and deterministic. Required.""" + top_p: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An alternative to sampling with temperature, called nucleus sampling, where the model considers + the results of the tokens with top_p probability mass. + So 0.1 means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. Required.""" + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The response format of the tool calls used by this assistant. Is one of the following types: + str, Union[str, \"_models.AssistantsApiResponseFormatMode\"], AssistantsApiResponseFormat, + ResponseFormatJsonSchemaType""" + metadata: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + name: str, + description: str, + model: str, + instructions: str, + tools: List["_models.ToolDefinition"], + tool_resources: "_models.ToolResources", + temperature: float, + top_p: float, + metadata: Dict[str, str], + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["assistant"] = "assistant" + + +class AssistantDeletionStatus(_Model): + """The status of an assistant deletion operation. + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'assistant.deleted'. Required. Default value is + "assistant.deleted". + :vartype object: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether deletion was successful. Required.""" + object: Literal["assistant.deleted"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'assistant.deleted'. Required. Default value is + \"assistant.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["assistant.deleted"] = "assistant.deleted" + + +class AssistantsApiResponseFormat(_Model): + """An object describing the expected output of the model. If ``json_object`` only ``function`` + type ``tools`` are allowed to be passed to the Run. + If ``text`` the model can return text or any value needed. + + :ivar type: Must be one of ``text`` or ``json_object``. Known values are: "text" and + "json_object". + :vartype type: str or ~azure.ai.assistants.models.ResponseFormat + """ + + type: Optional[Union[str, "_models.ResponseFormat"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Must be one of ``text`` or ``json_object``. Known values are: \"text\" and \"json_object\".""" + + @overload + def __init__( + self, + *, + type: Optional[Union[str, "_models.ResponseFormat"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssistantsNamedToolChoice(_Model): + """Specifies a tool the model should use. Use to force the model to call a specific tool. + + :ivar type: the type of tool. If type is ``function``, the function name must be set. Required. + Known values are: "function", "code_interpreter", "file_search", "bing_grounding", + "fabric_dataagent", "sharepoint_grounding", "azure_ai_search", "bing_custom_search", and + "connected_agent". + :vartype type: str or ~azure.ai.assistants.models.AssistantsNamedToolChoiceType + :ivar function: The name of the function to call. + :vartype function: ~azure.ai.assistants.models.FunctionName + """ + + type: Union[str, "_models.AssistantsNamedToolChoiceType"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """the type of tool. If type is ``function``, the function name must be set. Required. Known + values are: \"function\", \"code_interpreter\", \"file_search\", \"bing_grounding\", + \"fabric_dataagent\", \"sharepoint_grounding\", \"azure_ai_search\", \"bing_custom_search\", + and \"connected_agent\".""" + function: Optional["_models.FunctionName"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the function to call.""" + + @overload + def __init__( + self, + *, + type: Union[str, "_models.AssistantsNamedToolChoiceType"], + function: Optional["_models.FunctionName"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssistantThread(_Model): + """Information about a single thread associated with an assistant. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread'. Required. Default value is "thread". + :vartype object: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the type + of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires a list + of vector store IDs. Required. + :vartype tool_resources: ~azure.ai.assistants.models.ToolResources + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'thread'. Required. Default value is \"thread\".""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + tool_resources: "_models.ToolResources" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of resources that are made available to the assistant's tools in this thread. The + resources are specific to the type + of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires a list + of vector store IDs. Required.""" + metadata: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + tool_resources: "_models.ToolResources", + metadata: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread"] = "thread" + + +class AssistantThreadCreationOptions(_Model): + """The details used to create a new assistant thread. + + :ivar messages: The initial messages to associate with the new thread. + :vartype messages: list[~azure.ai.assistants.models.ThreadMessageOptions] + :ivar tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires + a list of vector store IDs. + :vartype tool_resources: ~azure.ai.assistants.models.ToolResources + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + messages: Optional[List["_models.ThreadMessageOptions"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The initial messages to associate with the new thread.""" + tool_resources: Optional["_models.ToolResources"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A set of resources that are made available to the assistant's tools in this thread. The + resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires + a list of vector store IDs.""" + metadata: Optional[Dict[str, str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + messages: Optional[List["_models.ThreadMessageOptions"]] = None, + tool_resources: Optional["_models.ToolResources"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AzureAISearchResource(_Model): + """A set of index resources used by the ``azure_ai_search`` tool. + + :ivar index_list: The indices attached to this assistant. There can be a maximum of 1 index + resource attached to the assistant. + :vartype index_list: list[~azure.ai.assistants.models.AISearchIndexResource] + """ + + index_list: Optional[List["_models.AISearchIndexResource"]] = rest_field( + name="indexes", visibility=["read", "create", "update", "delete", "query"] + ) + """The indices attached to this assistant. There can be a maximum of 1 index + resource attached to the assistant.""" + + @overload + def __init__( + self, + *, + index_list: Optional[List["_models.AISearchIndexResource"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ToolDefinition(_Model): + """An abstract representation of an input tool definition that an assistant can use. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AzureAISearchToolDefinition, AzureFunctionToolDefinition, BingCustomSearchToolDefinition, + BingGroundingToolDefinition, CodeInterpreterToolDefinition, ConnectedAgentToolDefinition, + MicrosoftFabricToolDefinition, FileSearchToolDefinition, FunctionToolDefinition, + OpenApiToolDefinition, SharepointToolDefinition + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AzureAISearchToolDefinition(ToolDefinition, discriminator="azure_ai_search"): + """The input definition information for an Azure AI search tool as used to configure an assistant. + + :ivar type: The object type, which is always 'azure_ai_search'. Required. Default value is + "azure_ai_search". + :vartype type: str + """ + + type: Literal["azure_ai_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'azure_ai_search'. Required. Default value is + \"azure_ai_search\".""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="azure_ai_search", **kwargs) + + +class AzureFunctionBinding(_Model): + """The structure for keeping storage queue name and URI. + + :ivar type: The type of binding, which is always 'storage_queue'. Required. Default value is + "storage_queue". + :vartype type: str + :ivar storage_queue: Storage queue. Required. + :vartype storage_queue: ~azure.ai.assistants.models.AzureFunctionStorageQueue + """ + + type: Literal["storage_queue"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The type of binding, which is always 'storage_queue'. Required. Default value is + \"storage_queue\".""" + storage_queue: "_models.AzureFunctionStorageQueue" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Storage queue. Required.""" + + @overload + def __init__( + self, + *, + storage_queue: "_models.AzureFunctionStorageQueue", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.type: Literal["storage_queue"] = "storage_queue" + + +class AzureFunctionDefinition(_Model): + """The definition of Azure function. + + :ivar function: The definition of azure function and its parameters. Required. + :vartype function: ~azure.ai.assistants.models.FunctionDefinition + :ivar input_binding: Input storage queue. The queue storage trigger runs a function as messages + are added to it. Required. + :vartype input_binding: ~azure.ai.assistants.models.AzureFunctionBinding + :ivar output_binding: Output storage queue. The function writes output to this queue when the + input items are processed. Required. + :vartype output_binding: ~azure.ai.assistants.models.AzureFunctionBinding + """ + + function: "_models.FunctionDefinition" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The definition of azure function and its parameters. Required.""" + input_binding: "_models.AzureFunctionBinding" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Input storage queue. The queue storage trigger runs a function as messages are added to it. + Required.""" + output_binding: "_models.AzureFunctionBinding" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Output storage queue. The function writes output to this queue when the input items are + processed. Required.""" + + @overload + def __init__( + self, + *, + function: "_models.FunctionDefinition", + input_binding: "_models.AzureFunctionBinding", + output_binding: "_models.AzureFunctionBinding", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AzureFunctionStorageQueue(_Model): + """The structure for keeping storage queue name and URI. + + :ivar storage_service_endpoint: URI to the Azure Storage Queue service allowing you to + manipulate a queue. Required. + :vartype storage_service_endpoint: str + :ivar queue_name: The name of an Azure function storage queue. Required. + :vartype queue_name: str + """ + + storage_service_endpoint: str = rest_field( + name="queue_service_endpoint", visibility=["read", "create", "update", "delete", "query"] + ) + """URI to the Azure Storage Queue service allowing you to manipulate a queue. Required.""" + queue_name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of an Azure function storage queue. Required.""" + + @overload + def __init__( + self, + *, + storage_service_endpoint: str, + queue_name: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AzureFunctionToolDefinition(ToolDefinition, discriminator="azure_function"): + """The input definition information for a azure function tool as used to configure an assistant. + + :ivar type: The object type, which is always 'azure_function'. Required. Default value is + "azure_function". + :vartype type: str + :ivar azure_function: The definition of the concrete function that the function tool should + call. Required. + :vartype azure_function: ~azure.ai.assistants.models.AzureFunctionDefinition + """ + + type: Literal["azure_function"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'azure_function'. Required. Default value is + \"azure_function\".""" + azure_function: "_models.AzureFunctionDefinition" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The definition of the concrete function that the function tool should call. Required.""" + + @overload + def __init__( + self, + *, + azure_function: "_models.AzureFunctionDefinition", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="azure_function", **kwargs) + + +class BingCustomSearchToolDefinition(ToolDefinition, discriminator="bing_custom_search"): + """The input definition information for a Bing custom search tool as used to configure an + assistant. + + :ivar type: The object type, which is always 'bing_custom_search'. Required. Default value is + "bing_custom_search". + :vartype type: str + :ivar bing_custom_search: The list of search configurations used by the bing custom search + tool. Required. + :vartype bing_custom_search: ~azure.ai.assistants.models.SearchConfigurationList + """ + + type: Literal["bing_custom_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'bing_custom_search'. Required. Default value is + \"bing_custom_search\".""" + bing_custom_search: "_models.SearchConfigurationList" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The list of search configurations used by the bing custom search tool. Required.""" + + @overload + def __init__( + self, + *, + bing_custom_search: "_models.SearchConfigurationList", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="bing_custom_search", **kwargs) + + +class BingGroundingToolDefinition(ToolDefinition, discriminator="bing_grounding"): + """The input definition information for a bing grounding search tool as used to configure an + assistant. + + :ivar type: The object type, which is always 'bing_grounding'. Required. Default value is + "bing_grounding". + :vartype type: str + :ivar bing_grounding: The list of connections used by the bing grounding tool. Required. + :vartype bing_grounding: ~azure.ai.assistants.models.ToolConnectionList + """ + + type: Literal["bing_grounding"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'bing_grounding'. Required. Default value is + \"bing_grounding\".""" + bing_grounding: "_models.ToolConnectionList" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The list of connections used by the bing grounding tool. Required.""" + + @overload + def __init__( + self, + *, + bing_grounding: "_models.ToolConnectionList", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="bing_grounding", **kwargs) + + +class CodeInterpreterToolDefinition(ToolDefinition, discriminator="code_interpreter"): + """The input definition information for a code interpreter tool as used to configure an assistant. + + :ivar type: The object type, which is always 'code_interpreter'. Required. Default value is + "code_interpreter". + :vartype type: str + """ + + type: Literal["code_interpreter"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'code_interpreter'. Required. Default value is + \"code_interpreter\".""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="code_interpreter", **kwargs) + + +class CodeInterpreterToolResource(_Model): + """A set of resources that are used by the ``code_interpreter`` tool. + + :ivar file_ids: A list of file IDs made available to the ``code_interpreter`` tool. There can + be a maximum of 20 files + associated with the tool. + :vartype file_ids: list[str] + :ivar data_sources: The data sources to be used. This option is mutually exclusive with the + ``fileIds`` property. + :vartype data_sources: list[~azure.ai.assistants.models.VectorStoreDataSource] + """ + + file_ids: Optional[List[str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A list of file IDs made available to the ``code_interpreter`` tool. There can be a maximum of + 20 files + associated with the tool.""" + data_sources: Optional[List["_models.VectorStoreDataSource"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The data sources to be used. This option is mutually exclusive with the ``fileIds`` property.""" + + @overload + def __init__( + self, + *, + file_ids: Optional[List[str]] = None, + data_sources: Optional[List["_models.VectorStoreDataSource"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ConnectedAgentDetails(_Model): + """Information for connecting one agent to another as a tool. + + :ivar id: The identifier of the child agent. Required. + :vartype id: str + :ivar name: The name of the agent to be called. Required. + :vartype name: str + :ivar description: A description of what the agent does, used by the model to choose when and + how to call the agent. Required. + :vartype description: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier of the child agent. Required.""" + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the agent to be called. Required.""" + description: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A description of what the agent does, used by the model to choose when and how to call the + agent. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + name: str, + description: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ConnectedAgentToolDefinition(ToolDefinition, discriminator="connected_agent"): + """The input definition information for a connected agent tool which defines a domain specific + sub-agent. + + :ivar type: The object type, which is always 'connected_agent'. Required. Default value is + "connected_agent". + :vartype type: str + :ivar connected_agent: The sub-agent to connect. Required. + :vartype connected_agent: ~azure.ai.assistants.models.ConnectedAgentDetails + """ + + type: Literal["connected_agent"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'connected_agent'. Required. Default value is + \"connected_agent\".""" + connected_agent: "_models.ConnectedAgentDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The sub-agent to connect. Required.""" + + @overload + def __init__( + self, + *, + connected_agent: "_models.ConnectedAgentDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="connected_agent", **kwargs) + + +class FileDeletionStatus(_Model): + """A status response from a file deletion operation. + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'file'. Required. Default value is "file". + :vartype object: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether deletion was successful. Required.""" + object: Literal["file"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'file'. Required. Default value is \"file\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["file"] = "file" + + +class FileListResponse(_Model): + """The response data from a file list operation. + + :ivar object: The object type, which is always 'list'. Required. Default value is "list". + :vartype object: str + :ivar data: The files returned for the request. Required. + :vartype data: list[~azure.ai.assistants.models.OpenAIFile] + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'list'. Required. Default value is \"list\".""" + data: List["_models.OpenAIFile"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The files returned for the request. Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.OpenAIFile"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class FileSearchRankingOptions(_Model): + """Ranking options for file search. + + :ivar ranker: File search ranker. Required. + :vartype ranker: str + :ivar score_threshold: Ranker search threshold. Required. + :vartype score_threshold: float + """ + + ranker: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """File search ranker. Required.""" + score_threshold: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Ranker search threshold. Required.""" + + @overload + def __init__( + self, + *, + ranker: str, + score_threshold: float, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FileSearchToolCallContent(_Model): + """The file search result content object. + + :ivar type: The type of the content. Required. Default value is "text". + :vartype type: str + :ivar text: The text content of the file. Required. + :vartype text: str + """ + + type: Literal["text"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The type of the content. Required. Default value is \"text\".""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The text content of the file. Required.""" + + @overload + def __init__( + self, + *, + text: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.type: Literal["text"] = "text" + + +class FileSearchToolDefinition(ToolDefinition, discriminator="file_search"): + """The input definition information for a file search tool as used to configure an assistant. + + :ivar type: The object type, which is always 'file_search'. Required. Default value is + "file_search". + :vartype type: str + :ivar file_search: Options overrides for the file search tool. + :vartype file_search: ~azure.ai.assistants.models.FileSearchToolDefinitionDetails + """ + + type: Literal["file_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'file_search'. Required. Default value is \"file_search\".""" + file_search: Optional["_models.FileSearchToolDefinitionDetails"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Options overrides for the file search tool.""" + + @overload + def __init__( + self, + *, + file_search: Optional["_models.FileSearchToolDefinitionDetails"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_search", **kwargs) + + +class FileSearchToolDefinitionDetails(_Model): + """Options overrides for the file search tool. + + :ivar max_num_results: The maximum number of results the file search tool should output. The + default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 + inclusive. + + Note that the file search tool may output fewer than ``max_num_results`` results. See the file + search tool documentation for more information. + :vartype max_num_results: int + :ivar ranking_options: Ranking options for file search. + :vartype ranking_options: ~azure.ai.assistants.models.FileSearchRankingOptions + """ + + max_num_results: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The maximum number of results the file search tool should output. The default is 20 for gpt-4* + models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive. + + Note that the file search tool may output fewer than ``max_num_results`` results. See the file + search tool documentation for more information.""" + ranking_options: Optional["_models.FileSearchRankingOptions"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Ranking options for file search.""" + + @overload + def __init__( + self, + *, + max_num_results: Optional[int] = None, + ranking_options: Optional["_models.FileSearchRankingOptions"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FileSearchToolResource(_Model): + """A set of resources that are used by the ``file_search`` tool. + + :ivar vector_store_ids: The ID of the vector store attached to this assistant. There can be a + maximum of 1 vector + store attached to the assistant. + :vartype vector_store_ids: list[str] + :ivar vector_stores: The list of vector store configuration objects from Azure. + This list is limited to one element. + The only element of this list contains the list of azure asset IDs used by the search tool. + :vartype vector_stores: list[~azure.ai.assistants.models.VectorStoreConfigurations] + """ + + vector_store_ids: Optional[List[str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the vector store attached to this assistant. There can be a maximum of 1 vector + store attached to the assistant.""" + vector_stores: Optional[List["_models.VectorStoreConfigurations"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The list of vector store configuration objects from Azure. + This list is limited to one element. + The only element of this list contains the list of azure asset IDs used by the search tool.""" + + @overload + def __init__( + self, + *, + vector_store_ids: Optional[List[str]] = None, + vector_stores: Optional[List["_models.VectorStoreConfigurations"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionDefinition(_Model): + """The input definition information for a function. + + :ivar name: The name of the function to be called. Required. + :vartype name: str + :ivar description: A description of what the function does, used by the model to choose when + and how to call the function. + :vartype description: str + :ivar parameters: The parameters the functions accepts, described as a JSON Schema object. + Required. + :vartype parameters: any + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the function to be called. Required.""" + description: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A description of what the function does, used by the model to choose when and how to call the + function.""" + parameters: Any = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The parameters the functions accepts, described as a JSON Schema object. Required.""" + + @overload + def __init__( + self, + *, + name: str, + parameters: Any, + description: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionName(_Model): + """The function name that will be used, if using the ``function`` tool. + + :ivar name: The name of the function to call. Required. + :vartype name: str + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the function to call. Required.""" + + @overload + def __init__( + self, + *, + name: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionToolDefinition(ToolDefinition, discriminator="function"): + """The input definition information for a function tool as used to configure an assistant. + + :ivar type: The object type, which is always 'function'. Required. Default value is "function". + :vartype type: str + :ivar function: The definition of the concrete function that the function tool should call. + Required. + :vartype function: ~azure.ai.assistants.models.FunctionDefinition + """ + + type: Literal["function"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'function'. Required. Default value is \"function\".""" + function: "_models.FunctionDefinition" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The definition of the concrete function that the function tool should call. Required.""" + + @overload + def __init__( + self, + *, + function: "_models.FunctionDefinition", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="function", **kwargs) + + +class IncompleteRunDetails(_Model): + """Details on why the run is incomplete. Will be ``null`` if the run is not incomplete. + + :ivar reason: The reason why the run is incomplete. This indicates which specific token limit + was reached during the run. Required. Known values are: "max_completion_tokens" and + "max_prompt_tokens". + :vartype reason: str or ~azure.ai.assistants.models.IncompleteDetailsReason + """ + + reason: Union[str, "_models.IncompleteDetailsReason"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The reason why the run is incomplete. This indicates which specific token limit was reached + during the run. Required. Known values are: \"max_completion_tokens\" and + \"max_prompt_tokens\".""" + + @overload + def __init__( + self, + *, + reason: Union[str, "_models.IncompleteDetailsReason"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageAttachment(_Model): + """This describes to which tools a file has been attached. + + :ivar file_id: The ID of the file to attach to the message. + :vartype file_id: str + :ivar data_source: Azure asset ID. + :vartype data_source: ~azure.ai.assistants.models.VectorStoreDataSource + :ivar tools: The tools to add to this file. Required. + :vartype tools: list[~azure.ai.assistants.models.CodeInterpreterToolDefinition or + ~azure.ai.assistants.models.FileSearchToolDefinition] + """ + + file_id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the file to attach to the message.""" + data_source: Optional["_models.VectorStoreDataSource"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Azure asset ID.""" + tools: List["_types.MessageAttachmentToolDefinition"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The tools to add to this file. Required.""" + + @overload + def __init__( + self, + *, + tools: List["_types.MessageAttachmentToolDefinition"], + file_id: Optional[str] = None, + data_source: Optional["_models.VectorStoreDataSource"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageContent(_Model): + """An abstract representation of a single item of thread message content. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + MessageImageFileContent, MessageTextContent + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDelta(_Model): + """Represents the typed 'delta' payload within a streaming message delta chunk. + + :ivar role: The entity that produced the message. Required. Known values are: "user" and + "assistant". + :vartype role: str or ~azure.ai.assistants.models.MessageRole + :ivar content: The content of the message as an array of text and/or images. Required. + :vartype content: list[~azure.ai.assistants.models.MessageDeltaContent] + """ + + role: Union[str, "_models.MessageRole"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The entity that produced the message. Required. Known values are: \"user\" and \"assistant\".""" + content: List["_models.MessageDeltaContent"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The content of the message as an array of text and/or images. Required.""" + + @overload + def __init__( + self, + *, + role: Union[str, "_models.MessageRole"], + content: List["_models.MessageDeltaContent"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDeltaChunk(_Model): + """Represents a message delta i.e. any changed fields on a message during streaming. + + :ivar id: The identifier of the message, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``thread.message.delta``. Required. Default + value is "thread.message.delta". + :vartype object: str + :ivar delta: The delta containing the fields that have changed on the Message. Required. + :vartype delta: ~azure.ai.assistants.models.MessageDelta + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier of the message, which can be referenced in API endpoints. Required.""" + object: Literal["thread.message.delta"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always ``thread.message.delta``. Required. Default value is + \"thread.message.delta\".""" + delta: "_models.MessageDelta" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The delta containing the fields that have changed on the Message. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + delta: "_models.MessageDelta", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.message.delta"] = "thread.message.delta" + + +class MessageDeltaContent(_Model): + """The abstract base representation of a partial streamed message content payload. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + MessageDeltaImageFileContent, MessageDeltaTextContent + + :ivar index: The index of the content part of the message. Required. + :vartype index: int + :ivar type: The type of content for this content part. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + index: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The index of the content part of the message. Required.""" + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The type of content for this content part. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + index: int, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDeltaImageFileContent(MessageDeltaContent, discriminator="image_file"): + """Represents a streamed image file content part within a streaming message delta chunk. + + :ivar index: The index of the content part of the message. Required. + :vartype index: int + :ivar type: The type of content for this content part, which is always "image_file.". Required. + Default value is "image_file". + :vartype type: str + :ivar image_file: The image_file data. + :vartype image_file: ~azure.ai.assistants.models.MessageDeltaImageFileContentObject + """ + + type: Literal["image_file"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The type of content for this content part, which is always \"image_file.\". Required. Default + value is \"image_file\".""" + image_file: Optional["_models.MessageDeltaImageFileContentObject"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The image_file data.""" + + @overload + def __init__( + self, + *, + index: int, + image_file: Optional["_models.MessageDeltaImageFileContentObject"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="image_file", **kwargs) + + +class MessageDeltaImageFileContentObject(_Model): + """Represents the 'image_file' payload within streaming image file content. + + :ivar file_id: The file ID of the image in the message content. + :vartype file_id: str + """ + + file_id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The file ID of the image in the message content.""" + + @overload + def __init__( + self, + *, + file_id: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDeltaTextAnnotation(_Model): + """The abstract base representation of a streamed text content part's text annotation. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + MessageDeltaTextFileCitationAnnotation, MessageDeltaTextFilePathAnnotation, + MessageDeltaTextUrlCitationAnnotation + + :ivar index: The index of the annotation within a text content part. Required. + :vartype index: int + :ivar type: The type of the text content annotation. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + index: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The index of the annotation within a text content part. Required.""" + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The type of the text content annotation. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + index: int, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDeltaTextContent(MessageDeltaContent, discriminator="text"): + """Represents a streamed text content part within a streaming message delta chunk. + + :ivar index: The index of the content part of the message. Required. + :vartype index: int + :ivar type: The type of content for this content part, which is always "text.". Required. + Default value is "text". + :vartype type: str + :ivar text: The text content details. + :vartype text: ~azure.ai.assistants.models.MessageDeltaTextContentObject + """ + + type: Literal["text"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The type of content for this content part, which is always \"text.\". Required. Default value + is \"text\".""" + text: Optional["_models.MessageDeltaTextContentObject"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The text content details.""" + + @overload + def __init__( + self, + *, + index: int, + text: Optional["_models.MessageDeltaTextContentObject"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="text", **kwargs) + + +class MessageDeltaTextContentObject(_Model): + """Represents the data of a streamed text content part within a streaming message delta chunk. + + :ivar value: The data that makes up the text. + :vartype value: str + :ivar annotations: Annotations for the text. + :vartype annotations: list[~azure.ai.assistants.models.MessageDeltaTextAnnotation] + """ + + value: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The data that makes up the text.""" + annotations: Optional[List["_models.MessageDeltaTextAnnotation"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Annotations for the text.""" + + @overload + def __init__( + self, + *, + value: Optional[str] = None, + annotations: Optional[List["_models.MessageDeltaTextAnnotation"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDeltaTextFileCitationAnnotation(MessageDeltaTextAnnotation, discriminator="file_citation"): + """Represents a streamed file citation applied to a streaming text content part. + + :ivar index: The index of the annotation within a text content part. Required. + :vartype index: int + :ivar type: The type of the text content annotation, which is always "file_citation.". + Required. Default value is "file_citation". + :vartype type: str + :ivar file_citation: The file citation information. + :vartype file_citation: + ~azure.ai.assistants.models.MessageDeltaTextFileCitationAnnotationObject + :ivar text: The text in the message content that needs to be replaced. + :vartype text: str + :ivar start_index: The start index of this annotation in the content text. + :vartype start_index: int + :ivar end_index: The end index of this annotation in the content text. + :vartype end_index: int + """ + + type: Literal["file_citation"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The type of the text content annotation, which is always \"file_citation.\". Required. Default + value is \"file_citation\".""" + file_citation: Optional["_models.MessageDeltaTextFileCitationAnnotationObject"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The file citation information.""" + text: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The text in the message content that needs to be replaced.""" + start_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The start index of this annotation in the content text.""" + end_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The end index of this annotation in the content text.""" + + @overload + def __init__( + self, + *, + index: int, + file_citation: Optional["_models.MessageDeltaTextFileCitationAnnotationObject"] = None, + text: Optional[str] = None, + start_index: Optional[int] = None, + end_index: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_citation", **kwargs) + + +class MessageDeltaTextFileCitationAnnotationObject(_Model): # pylint: disable=name-too-long + """Represents the data of a streamed file citation as applied to a streaming text content part. + + :ivar file_id: The ID of the specific file the citation is from. + :vartype file_id: str + :ivar quote: The specific quote in the cited file. + :vartype quote: str + """ + + file_id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the specific file the citation is from.""" + quote: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The specific quote in the cited file.""" + + @overload + def __init__( + self, + *, + file_id: Optional[str] = None, + quote: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDeltaTextFilePathAnnotation(MessageDeltaTextAnnotation, discriminator="file_path"): + """Represents a streamed file path annotation applied to a streaming text content part. + + :ivar index: The index of the annotation within a text content part. Required. + :vartype index: int + :ivar type: The type of the text content annotation, which is always "file_path.". Required. + Default value is "file_path". + :vartype type: str + :ivar file_path: The file path information. + :vartype file_path: ~azure.ai.assistants.models.MessageDeltaTextFilePathAnnotationObject + :ivar start_index: The start index of this annotation in the content text. + :vartype start_index: int + :ivar end_index: The end index of this annotation in the content text. + :vartype end_index: int + :ivar text: The text in the message content that needs to be replaced. + :vartype text: str + """ + + type: Literal["file_path"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The type of the text content annotation, which is always \"file_path.\". Required. Default + value is \"file_path\".""" + file_path: Optional["_models.MessageDeltaTextFilePathAnnotationObject"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The file path information.""" + start_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The start index of this annotation in the content text.""" + end_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The end index of this annotation in the content text.""" + text: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The text in the message content that needs to be replaced.""" + + @overload + def __init__( + self, + *, + index: int, + file_path: Optional["_models.MessageDeltaTextFilePathAnnotationObject"] = None, + start_index: Optional[int] = None, + end_index: Optional[int] = None, + text: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_path", **kwargs) + + +class MessageDeltaTextFilePathAnnotationObject(_Model): + """Represents the data of a streamed file path annotation as applied to a streaming text content + part. + + :ivar file_id: The file ID for the annotation. + :vartype file_id: str + """ + + file_id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The file ID for the annotation.""" + + @overload + def __init__( + self, + *, + file_id: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageDeltaTextUrlCitationAnnotation(MessageDeltaTextAnnotation, discriminator="url_citation"): + """A citation within the message that points to a specific URL associated with the message. + Generated when the assistant uses tools such as 'bing_grounding' to search the Internet. + + :ivar index: The index of the annotation within a text content part. Required. + :vartype index: int + :ivar type: The object type, which is always 'url_citation'. Required. Default value is + "url_citation". + :vartype type: str + :ivar url_citation: The details of the URL citation. Required. + :vartype url_citation: ~azure.ai.assistants.models.MessageDeltaTextUrlCitationDetails + :ivar start_index: The first text index associated with this text annotation. + :vartype start_index: int + :ivar end_index: The last text index associated with this text annotation. + :vartype end_index: int + """ + + type: Literal["url_citation"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'url_citation'. Required. Default value is \"url_citation\".""" + url_citation: "_models.MessageDeltaTextUrlCitationDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The details of the URL citation. Required.""" + start_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first text index associated with this text annotation.""" + end_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last text index associated with this text annotation.""" + + @overload + def __init__( + self, + *, + index: int, + url_citation: "_models.MessageDeltaTextUrlCitationDetails", + start_index: Optional[int] = None, + end_index: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="url_citation", **kwargs) + + +class MessageDeltaTextUrlCitationDetails(_Model): + """A representation of a URL citation, as used in text thread message content. + + :ivar url: The URL associated with this citation. Required. + :vartype url: str + :ivar title: The title of the URL. + :vartype title: str + """ + + url: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The URL associated with this citation. Required.""" + title: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The title of the URL.""" + + @overload + def __init__( + self, + *, + url: str, + title: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageImageFileContent(MessageContent, discriminator="image_file"): + """A representation of image file content in a thread message. + + :ivar type: The object type, which is always 'image_file'. Required. Default value is + "image_file". + :vartype type: str + :ivar image_file: The image file for this thread message content item. Required. + :vartype image_file: ~azure.ai.assistants.models.MessageImageFileDetails + """ + + type: Literal["image_file"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'image_file'. Required. Default value is \"image_file\".""" + image_file: "_models.MessageImageFileDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The image file for this thread message content item. Required.""" + + @overload + def __init__( + self, + *, + image_file: "_models.MessageImageFileDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="image_file", **kwargs) + + +class MessageImageFileDetails(_Model): + """An image reference, as represented in thread message content. + + :ivar file_id: The ID for the file associated with this image. Required. + :vartype file_id: str + """ + + file_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID for the file associated with this image. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageImageFileParam(_Model): + """Defines how an internally uploaded image file is referenced when creating an image-file block. + + :ivar file_id: The ID of the previously uploaded image file. Required. + :vartype file_id: str + :ivar detail: Optional detail level for the image (auto, low, or high). Known values are: + "auto", "low", and "high". + :vartype detail: str or ~azure.ai.assistants.models.ImageDetailLevel + """ + + file_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the previously uploaded image file. Required.""" + detail: Optional[Union[str, "_models.ImageDetailLevel"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Optional detail level for the image (auto, low, or high). Known values are: \"auto\", \"low\", + and \"high\".""" + + @overload + def __init__( + self, + *, + file_id: str, + detail: Optional[Union[str, "_models.ImageDetailLevel"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageImageUrlParam(_Model): + """Defines how an external image URL is referenced when creating an image-URL block. + + :ivar url: The publicly accessible URL of the external image. Required. + :vartype url: str + :ivar detail: Optional detail level for the image (auto, low, or high). Defaults to 'auto' if + not specified. Known values are: "auto", "low", and "high". + :vartype detail: str or ~azure.ai.assistants.models.ImageDetailLevel + """ + + url: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The publicly accessible URL of the external image. Required.""" + detail: Optional[Union[str, "_models.ImageDetailLevel"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Optional detail level for the image (auto, low, or high). Defaults to 'auto' if not specified. + Known values are: \"auto\", \"low\", and \"high\".""" + + @overload + def __init__( + self, + *, + url: str, + detail: Optional[Union[str, "_models.ImageDetailLevel"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageIncompleteDetails(_Model): + """Information providing additional detail about a message entering an incomplete status. + + :ivar reason: The provided reason describing why the message was marked as incomplete. + Required. Known values are: "content_filter", "max_tokens", "run_cancelled", "run_failed", and + "run_expired". + :vartype reason: str or ~azure.ai.assistants.models.MessageIncompleteDetailsReason + """ + + reason: Union[str, "_models.MessageIncompleteDetailsReason"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The provided reason describing why the message was marked as incomplete. Required. Known values + are: \"content_filter\", \"max_tokens\", \"run_cancelled\", \"run_failed\", and + \"run_expired\".""" + + @overload + def __init__( + self, + *, + reason: Union[str, "_models.MessageIncompleteDetailsReason"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageInputContentBlock(_Model): + """Defines a single content block when creating a message. The 'type' field determines whether it + is text, an image file, or an external image URL, etc. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + MessageInputImageFileBlock, MessageInputImageUrlBlock, MessageInputTextBlock + + :ivar type: Specifies which kind of content block this is (text, image_file, image_url, etc.). + Required. Known values are: "text", "image_file", and "image_url". + :vartype type: str or ~azure.ai.assistants.models.MessageBlockType + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """Specifies which kind of content block this is (text, image_file, image_url, etc.). Required. + Known values are: \"text\", \"image_file\", and \"image_url\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageInputImageFileBlock(MessageInputContentBlock, discriminator="image_file"): + """An image-file block in a new message, referencing an internally uploaded image by file ID. + + :ivar type: Must be 'image_file' for an internally uploaded image block. Required. Indicates a + block referencing an internally uploaded image file. + :vartype type: str or ~azure.ai.assistants.models.IMAGE_FILE + :ivar image_file: Information about the referenced image file, including file ID and optional + detail level. Required. + :vartype image_file: ~azure.ai.assistants.models.MessageImageFileParam + """ + + type: Literal[MessageBlockType.IMAGE_FILE] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Must be 'image_file' for an internally uploaded image block. Required. Indicates a block + referencing an internally uploaded image file.""" + image_file: "_models.MessageImageFileParam" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Information about the referenced image file, including file ID and optional detail level. + Required.""" + + @overload + def __init__( + self, + *, + image_file: "_models.MessageImageFileParam", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=MessageBlockType.IMAGE_FILE, **kwargs) + + +class MessageInputImageUrlBlock(MessageInputContentBlock, discriminator="image_url"): + """An image-URL block in a new message, referencing an external image by URL. + + :ivar type: Must be 'image_url' for an externally hosted image block. Required. Indicates a + block referencing an external image URL. + :vartype type: str or ~azure.ai.assistants.models.IMAGE_URL + :ivar image_url: Information about the external image URL, including the URL and optional + detail level. Required. + :vartype image_url: ~azure.ai.assistants.models.MessageImageUrlParam + """ + + type: Literal[MessageBlockType.IMAGE_URL] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Must be 'image_url' for an externally hosted image block. Required. Indicates a block + referencing an external image URL.""" + image_url: "_models.MessageImageUrlParam" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Information about the external image URL, including the URL and optional detail level. + Required.""" + + @overload + def __init__( + self, + *, + image_url: "_models.MessageImageUrlParam", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=MessageBlockType.IMAGE_URL, **kwargs) + + +class MessageInputTextBlock(MessageInputContentBlock, discriminator="text"): + """A text block in a new message, containing plain text content. + + :ivar type: Must be 'text' for a text block. Required. Indicates a block containing text + content. + :vartype type: str or ~azure.ai.assistants.models.TEXT + :ivar text: The plain text content for this block. Required. + :vartype text: str + """ + + type: Literal[MessageBlockType.TEXT] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Must be 'text' for a text block. Required. Indicates a block containing text content.""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The plain text content for this block. Required.""" + + @overload + def __init__( + self, + *, + text: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=MessageBlockType.TEXT, **kwargs) + + +class MessageTextAnnotation(_Model): + """An abstract representation of an annotation to text thread message content. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + MessageTextFileCitationAnnotation, MessageTextFilePathAnnotation, + MessageTextUrlCitationAnnotation + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + :ivar text: The textual content associated with this text annotation item. Required. + :vartype text: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Default value is None.""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The textual content associated with this text annotation item. Required.""" + + @overload + def __init__( + self, + *, + type: str, + text: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextContent(MessageContent, discriminator="text"): + """A representation of a textual item of thread message content. + + :ivar type: The object type, which is always 'text'. Required. Default value is "text". + :vartype type: str + :ivar text: The text and associated annotations for this thread message content item. Required. + :vartype text: ~azure.ai.assistants.models.MessageTextDetails + """ + + type: Literal["text"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'text'. Required. Default value is \"text\".""" + text: "_models.MessageTextDetails" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The text and associated annotations for this thread message content item. Required.""" + + @overload + def __init__( + self, + *, + text: "_models.MessageTextDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="text", **kwargs) + + +class MessageTextDetails(_Model): + """The text and associated annotations for a single item of assistant thread message content. + + :ivar value: The text data. Required. + :vartype value: str + :ivar annotations: A list of annotations associated with this text. Required. + :vartype annotations: list[~azure.ai.assistants.models.MessageTextAnnotation] + """ + + value: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The text data. Required.""" + annotations: List["_models.MessageTextAnnotation"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A list of annotations associated with this text. Required.""" + + @overload + def __init__( + self, + *, + value: str, + annotations: List["_models.MessageTextAnnotation"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextFileCitationAnnotation(MessageTextAnnotation, discriminator="file_citation"): + """A citation within the message that points to a specific quote from a specific File associated + with the assistant or the message. Generated when the assistant uses the 'file_search' tool to + search files. + + :ivar text: The textual content associated with this text annotation item. Required. + :vartype text: str + :ivar type: The object type, which is always 'file_citation'. Required. Default value is + "file_citation". + :vartype type: str + :ivar file_citation: A citation within the message that points to a specific quote from a + specific file. + Generated when the assistant uses the "file_search" tool to search files. Required. + :vartype file_citation: ~azure.ai.assistants.models.MessageTextFileCitationDetails + :ivar start_index: The first text index associated with this text annotation. + :vartype start_index: int + :ivar end_index: The last text index associated with this text annotation. + :vartype end_index: int + """ + + type: Literal["file_citation"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'file_citation'. Required. Default value is \"file_citation\".""" + file_citation: "_models.MessageTextFileCitationDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A citation within the message that points to a specific quote from a specific file. + Generated when the assistant uses the \"file_search\" tool to search files. Required.""" + start_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first text index associated with this text annotation.""" + end_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last text index associated with this text annotation.""" + + @overload + def __init__( + self, + *, + text: str, + file_citation: "_models.MessageTextFileCitationDetails", + start_index: Optional[int] = None, + end_index: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_citation", **kwargs) + + +class MessageTextFileCitationDetails(_Model): + """A representation of a file-based text citation, as used in a file-based annotation of text + thread message content. + + :ivar file_id: The ID of the file associated with this citation. Required. + :vartype file_id: str + :ivar quote: The specific quote cited in the associated file. Required. + :vartype quote: str + """ + + file_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the file associated with this citation. Required.""" + quote: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The specific quote cited in the associated file. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + quote: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextFilePathAnnotation(MessageTextAnnotation, discriminator="file_path"): + """A citation within the message that points to a file located at a specific path. + + :ivar text: The textual content associated with this text annotation item. Required. + :vartype text: str + :ivar type: The object type, which is always 'file_path'. Required. Default value is + "file_path". + :vartype type: str + :ivar file_path: A URL for the file that's generated when the assistant used the + code_interpreter tool to generate a file. Required. + :vartype file_path: ~azure.ai.assistants.models.MessageTextFilePathDetails + :ivar start_index: The first text index associated with this text annotation. + :vartype start_index: int + :ivar end_index: The last text index associated with this text annotation. + :vartype end_index: int + """ + + type: Literal["file_path"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'file_path'. Required. Default value is \"file_path\".""" + file_path: "_models.MessageTextFilePathDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A URL for the file that's generated when the assistant used the code_interpreter tool to + generate a file. Required.""" + start_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first text index associated with this text annotation.""" + end_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last text index associated with this text annotation.""" + + @overload + def __init__( + self, + *, + text: str, + file_path: "_models.MessageTextFilePathDetails", + start_index: Optional[int] = None, + end_index: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_path", **kwargs) + + +class MessageTextFilePathDetails(_Model): + """An encapsulation of an image file ID, as used by message image content. + + :ivar file_id: The ID of the specific file that the citation is from. Required. + :vartype file_id: str + """ + + file_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the specific file that the citation is from. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextUrlCitationAnnotation(MessageTextAnnotation, discriminator="url_citation"): + """A citation within the message that points to a specific URL associated with the message. + Generated when the assistant uses tools such as 'bing_grounding' to search the Internet. + + :ivar text: The textual content associated with this text annotation item. Required. + :vartype text: str + :ivar type: The object type, which is always 'url_citation'. Required. Default value is + "url_citation". + :vartype type: str + :ivar url_citation: The details of the URL citation. Required. + :vartype url_citation: ~azure.ai.assistants.models.MessageTextUrlCitationDetails + :ivar start_index: The first text index associated with this text annotation. + :vartype start_index: int + :ivar end_index: The last text index associated with this text annotation. + :vartype end_index: int + """ + + type: Literal["url_citation"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'url_citation'. Required. Default value is \"url_citation\".""" + url_citation: "_models.MessageTextUrlCitationDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The details of the URL citation. Required.""" + start_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first text index associated with this text annotation.""" + end_index: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last text index associated with this text annotation.""" + + @overload + def __init__( + self, + *, + text: str, + url_citation: "_models.MessageTextUrlCitationDetails", + start_index: Optional[int] = None, + end_index: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="url_citation", **kwargs) + + +class MessageTextUrlCitationDetails(_Model): + """A representation of a URL citation, as used in text thread message content. + + :ivar url: The URL associated with this citation. Required. + :vartype url: str + :ivar title: The title of the URL. + :vartype title: str + """ + + url: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The URL associated with this citation. Required.""" + title: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The title of the URL.""" + + @overload + def __init__( + self, + *, + url: str, + title: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MicrosoftFabricToolDefinition(ToolDefinition, discriminator="fabric_dataagent"): + """The input definition information for a Microsoft Fabric tool as used to configure an assistant. + + :ivar type: The object type, which is always 'fabric_dataagent'. Required. Default value is + "fabric_dataagent". + :vartype type: str + :ivar fabric_dataagent: The list of connections used by the Microsoft Fabric tool. Required. + :vartype fabric_dataagent: ~azure.ai.assistants.models.ToolConnectionList + """ + + type: Literal["fabric_dataagent"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'fabric_dataagent'. Required. Default value is + \"fabric_dataagent\".""" + fabric_dataagent: "_models.ToolConnectionList" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The list of connections used by the Microsoft Fabric tool. Required.""" + + @overload + def __init__( + self, + *, + fabric_dataagent: "_models.ToolConnectionList", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="fabric_dataagent", **kwargs) + + +class OpenAIFile(_Model): + """Represents an assistant that can call the model and use tools. + + :ivar object: The object type, which is always 'file'. Required. Default value is "file". + :vartype object: str + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar bytes: The size of the file, in bytes. Required. + :vartype bytes: int + :ivar filename: The name of the file. Required. + :vartype filename: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar purpose: The intended purpose of a file. Required. Known values are: "fine-tune", + "fine-tune-results", "assistants", "assistants_output", "batch", "batch_output", and "vision". + :vartype purpose: str or ~azure.ai.assistants.models.FilePurpose + :ivar status: The state of the file. This field is available in Azure OpenAI only. Known values + are: "uploaded", "pending", "running", "processed", "error", "deleting", and "deleted". + :vartype status: str or ~azure.ai.assistants.models.FileState + :ivar status_details: The error message with details in case processing of this file failed. + This field is available in Azure OpenAI only. + :vartype status_details: str + """ + + object: Literal["file"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'file'. Required. Default value is \"file\".""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + bytes: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The size of the file, in bytes. Required.""" + filename: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the file. Required.""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + purpose: Union[str, "_models.FilePurpose"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The intended purpose of a file. Required. Known values are: \"fine-tune\", + \"fine-tune-results\", \"assistants\", \"assistants_output\", \"batch\", \"batch_output\", and + \"vision\".""" + status: Optional[Union[str, "_models.FileState"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The state of the file. This field is available in Azure OpenAI only. Known values are: + \"uploaded\", \"pending\", \"running\", \"processed\", \"error\", \"deleting\", and + \"deleted\".""" + status_details: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The error message with details in case processing of this file failed. This field is available + in Azure OpenAI only.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + bytes: int, + filename: str, + created_at: datetime.datetime, + purpose: Union[str, "_models.FilePurpose"], + status: Optional[Union[str, "_models.FileState"]] = None, + status_details: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["file"] = "file" + + +class OpenAIPageableListOfAssistant(_Model): + """The response data for a requested list of items. + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.assistants.models.Assistant] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.Assistant"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The requested list of items. Required.""" + first_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first ID represented in this list. Required.""" + last_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last ID represented in this list. Required.""" + has_more: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.Assistant"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfAssistantThread(_Model): + """The response data for a requested list of items. + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.assistants.models.AssistantThread] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.AssistantThread"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The requested list of items. Required.""" + first_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first ID represented in this list. Required.""" + last_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last ID represented in this list. Required.""" + has_more: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.AssistantThread"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfRunStep(_Model): + """The response data for a requested list of items. + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.assistants.models.RunStep] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.RunStep"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The requested list of items. Required.""" + first_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first ID represented in this list. Required.""" + last_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last ID represented in this list. Required.""" + has_more: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.RunStep"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfThreadMessage(_Model): + """The response data for a requested list of items. + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.assistants.models.ThreadMessage] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.ThreadMessage"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The requested list of items. Required.""" + first_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first ID represented in this list. Required.""" + last_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last ID represented in this list. Required.""" + has_more: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.ThreadMessage"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfThreadRun(_Model): + """The response data for a requested list of items. + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.assistants.models.ThreadRun] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.ThreadRun"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The requested list of items. Required.""" + first_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first ID represented in this list. Required.""" + last_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last ID represented in this list. Required.""" + has_more: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.ThreadRun"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfVectorStore(_Model): + """The response data for a requested list of items. + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.assistants.models.VectorStore] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.VectorStore"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The requested list of items. Required.""" + first_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first ID represented in this list. Required.""" + last_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last ID represented in this list. Required.""" + has_more: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.VectorStore"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfVectorStoreFile(_Model): + """The response data for a requested list of items. + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.assistants.models.VectorStoreFile] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.VectorStoreFile"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The requested list of items. Required.""" + first_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The first ID represented in this list. Required.""" + last_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last ID represented in this list. Required.""" + has_more: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.VectorStoreFile"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenApiAuthDetails(_Model): + """authentication details for OpenApiFunctionDefinition. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + OpenApiAnonymousAuthDetails, OpenApiConnectionAuthDetails, OpenApiManagedAuthDetails + + :ivar type: The type of authentication, must be anonymous/connection/managed_identity. + Required. Known values are: "anonymous", "connection", and "managed_identity". + :vartype type: str or ~azure.ai.assistants.models.OpenApiAuthType + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The type of authentication, must be anonymous/connection/managed_identity. Required. Known + values are: \"anonymous\", \"connection\", and \"managed_identity\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OpenApiAnonymousAuthDetails(OpenApiAuthDetails, discriminator="anonymous"): + """Security details for OpenApi anonymous authentication. + + :ivar type: The object type, which is always 'anonymous'. Required. + :vartype type: str or ~azure.ai.assistants.models.ANONYMOUS + """ + + type: Literal[OpenApiAuthType.ANONYMOUS] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'anonymous'. Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=OpenApiAuthType.ANONYMOUS, **kwargs) + + +class OpenApiConnectionAuthDetails(OpenApiAuthDetails, discriminator="connection"): + """Security details for OpenApi connection authentication. + + :ivar type: The object type, which is always 'connection'. Required. + :vartype type: str or ~azure.ai.assistants.models.CONNECTION + :ivar security_scheme: Connection auth security details. Required. + :vartype security_scheme: ~azure.ai.assistants.models.OpenApiConnectionSecurityScheme + """ + + type: Literal[OpenApiAuthType.CONNECTION] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'connection'. Required.""" + security_scheme: "_models.OpenApiConnectionSecurityScheme" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Connection auth security details. Required.""" + + @overload + def __init__( + self, + *, + security_scheme: "_models.OpenApiConnectionSecurityScheme", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=OpenApiAuthType.CONNECTION, **kwargs) + + +class OpenApiConnectionSecurityScheme(_Model): + """Security scheme for OpenApi managed_identity authentication. + + :ivar connection_id: Connection id for Connection auth type. Required. + :vartype connection_id: str + """ + + connection_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Connection id for Connection auth type. Required.""" + + @overload + def __init__( + self, + *, + connection_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OpenApiFunctionDefinition(_Model): + """The input definition information for an openapi function. + + :ivar name: The name of the function to be called. Required. + :vartype name: str + :ivar description: A description of what the function does, used by the model to choose when + and how to call the function. + :vartype description: str + :ivar spec: The openapi function shape, described as a JSON Schema object. Required. + :vartype spec: any + :ivar auth: Open API authentication details. Required. + :vartype auth: ~azure.ai.assistants.models.OpenApiAuthDetails + :ivar default_params: List of OpenAPI spec parameters that will use user-provided defaults. + :vartype default_params: list[str] + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the function to be called. Required.""" + description: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A description of what the function does, used by the model to choose when and how to call the + function.""" + spec: Any = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The openapi function shape, described as a JSON Schema object. Required.""" + auth: "_models.OpenApiAuthDetails" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Open API authentication details. Required.""" + default_params: Optional[List[str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of OpenAPI spec parameters that will use user-provided defaults.""" + + @overload + def __init__( + self, + *, + name: str, + spec: Any, + auth: "_models.OpenApiAuthDetails", + description: Optional[str] = None, + default_params: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OpenApiManagedAuthDetails(OpenApiAuthDetails, discriminator="managed_identity"): + """Security details for OpenApi managed_identity authentication. + + :ivar type: The object type, which is always 'managed_identity'. Required. + :vartype type: str or ~azure.ai.assistants.models.MANAGED_IDENTITY + :ivar security_scheme: Connection auth security details. Required. + :vartype security_scheme: ~azure.ai.assistants.models.OpenApiManagedSecurityScheme + """ + + type: Literal[OpenApiAuthType.MANAGED_IDENTITY] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'managed_identity'. Required.""" + security_scheme: "_models.OpenApiManagedSecurityScheme" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Connection auth security details. Required.""" + + @overload + def __init__( + self, + *, + security_scheme: "_models.OpenApiManagedSecurityScheme", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=OpenApiAuthType.MANAGED_IDENTITY, **kwargs) + + +class OpenApiManagedSecurityScheme(_Model): + """Security scheme for OpenApi managed_identity authentication. + + :ivar audience: Authentication scope for managed_identity auth type. Required. + :vartype audience: str + """ + + audience: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Authentication scope for managed_identity auth type. Required.""" + + @overload + def __init__( + self, + *, + audience: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OpenApiToolDefinition(ToolDefinition, discriminator="openapi"): + """The input definition information for an OpenAPI tool as used to configure an assistant. + + :ivar type: The object type, which is always 'openapi'. Required. Default value is "openapi". + :vartype type: str + :ivar openapi: The openapi function definition. Required. + :vartype openapi: ~azure.ai.assistants.models.OpenApiFunctionDefinition + """ + + type: Literal["openapi"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'openapi'. Required. Default value is \"openapi\".""" + openapi: "_models.OpenApiFunctionDefinition" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The openapi function definition. Required.""" + + @overload + def __init__( + self, + *, + openapi: "_models.OpenApiFunctionDefinition", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="openapi", **kwargs) + + +class RequiredAction(_Model): + """An abstract representation of a required action for an assistant thread run to continue. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + SubmitToolOutputsAction + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RequiredToolCall(_Model): + """An abstract representation of a tool invocation needed by the model to continue a run. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RequiredFunctionToolCall + + :ivar type: The object type for the required tool call. Required. Default value is None. + :vartype type: str + :ivar id: The ID of the tool call. This ID must be referenced when submitting tool outputs. + Required. + :vartype id: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type for the required tool call. Required. Default value is None.""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the tool call. This ID must be referenced when submitting tool outputs. Required.""" + + @overload + def __init__( + self, + *, + type: str, + id: str, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RequiredFunctionToolCall(RequiredToolCall, discriminator="function"): + """A representation of a requested call to a function tool, needed by the model to continue + evaluation of a run. + + :ivar id: The ID of the tool call. This ID must be referenced when submitting tool outputs. + Required. + :vartype id: str + :ivar type: The object type of the required tool call. Always 'function' for function tools. + Required. Default value is "function". + :vartype type: str + :ivar function: Detailed information about the function to be executed by the tool that + includes name and arguments. Required. + :vartype function: ~azure.ai.assistants.models.RequiredFunctionToolCallDetails + """ + + type: Literal["function"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type of the required tool call. Always 'function' for function tools. Required. + Default value is \"function\".""" + function: "_models.RequiredFunctionToolCallDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Detailed information about the function to be executed by the tool that includes name and + arguments. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + function: "_models.RequiredFunctionToolCallDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="function", **kwargs) + + +class RequiredFunctionToolCallDetails(_Model): + """The detailed information for a function invocation, as provided by a required action invoking a + function tool, that includes the name of and arguments to the function. + + :ivar name: The name of the function. Required. + :vartype name: str + :ivar arguments: The arguments to use when invoking the named function, as provided by the + model. Arguments are presented as a JSON document that should be validated and parsed for + evaluation. Required. + :vartype arguments: str + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the function. Required.""" + arguments: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The arguments to use when invoking the named function, as provided by the model. Arguments are + presented as a JSON document that should be validated and parsed for evaluation. Required.""" + + @overload + def __init__( + self, + *, + name: str, + arguments: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ResponseFormatJsonSchema(_Model): + """A description of what the response format is for, used by the model to determine how to respond + in the format. + + :ivar description: A description of what the response format is for, used by the model to + determine how to respond in the format. + :vartype description: str + :ivar name: The name of a schema. Required. + :vartype name: str + :ivar schema: The JSON schema object, describing the response format. Required. + :vartype schema: any + """ + + description: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A description of what the response format is for, used by the model to determine how to respond + in the format.""" + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of a schema. Required.""" + schema: Any = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The JSON schema object, describing the response format. Required.""" + + @overload + def __init__( + self, + *, + name: str, + schema: Any, + description: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ResponseFormatJsonSchemaType(_Model): + """The type of response format being defined: ``json_schema``. + + :ivar type: Type. Required. Default value is "json_schema". + :vartype type: str + :ivar json_schema: The JSON schema, describing response format. Required. + :vartype json_schema: ~azure.ai.assistants.models.ResponseFormatJsonSchema + """ + + type: Literal["json_schema"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Type. Required. Default value is \"json_schema\".""" + json_schema: "_models.ResponseFormatJsonSchema" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The JSON schema, describing response format. Required.""" + + @overload + def __init__( + self, + *, + json_schema: "_models.ResponseFormatJsonSchema", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.type: Literal["json_schema"] = "json_schema" + + +class RunCompletionUsage(_Model): + """Usage statistics related to the run. This value will be ``null`` if the run is not in a + terminal state (i.e. ``in_progress``, ``queued``, etc.). + + :ivar completion_tokens: Number of completion tokens used over the course of the run. Required. + :vartype completion_tokens: int + :ivar prompt_tokens: Number of prompt tokens used over the course of the run. Required. + :vartype prompt_tokens: int + :ivar total_tokens: Total number of tokens used (prompt + completion). Required. + :vartype total_tokens: int + """ + + completion_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Number of completion tokens used over the course of the run. Required.""" + prompt_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Number of prompt tokens used over the course of the run. Required.""" + total_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Total number of tokens used (prompt + completion). Required.""" + + @overload + def __init__( + self, + *, + completion_tokens: int, + prompt_tokens: int, + total_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunError(_Model): + """The details of an error as encountered by an assistant thread run. + + :ivar code: The status for the error. Required. + :vartype code: str + :ivar message: The human-readable text associated with the error. Required. + :vartype message: str + """ + + code: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The status for the error. Required.""" + message: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The human-readable text associated with the error. Required.""" + + @overload + def __init__( + self, + *, + code: str, + message: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStep(_Model): + """Detailed information about a single step of an assistant thread run. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread.run.step'. Required. Default value is + "thread.run.step". + :vartype object: str + :ivar type: The type of run step, which can be either message_creation or tool_calls. Required. + Known values are: "message_creation" and "tool_calls". + :vartype type: str or ~azure.ai.assistants.models.RunStepType + :ivar assistant_id: The ID of the assistant associated with the run step. Required. + :vartype assistant_id: str + :ivar thread_id: The ID of the thread that was run. Required. + :vartype thread_id: str + :ivar run_id: The ID of the run that this run step is a part of. Required. + :vartype run_id: str + :ivar status: The status of this run step. Required. Known values are: "in_progress", + "cancelled", "failed", "completed", and "expired". + :vartype status: str or ~azure.ai.assistants.models.RunStepStatus + :ivar step_details: The details for this run step. Required. + :vartype step_details: ~azure.ai.assistants.models.RunStepDetails + :ivar last_error: If applicable, information about the last error encountered by this run step. + Required. + :vartype last_error: ~azure.ai.assistants.models.RunStepError + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar expired_at: The Unix timestamp, in seconds, representing when this item expired. + Required. + :vartype expired_at: ~datetime.datetime + :ivar completed_at: The Unix timestamp, in seconds, representing when this completed. Required. + :vartype completed_at: ~datetime.datetime + :ivar cancelled_at: The Unix timestamp, in seconds, representing when this was cancelled. + Required. + :vartype cancelled_at: ~datetime.datetime + :ivar failed_at: The Unix timestamp, in seconds, representing when this failed. Required. + :vartype failed_at: ~datetime.datetime + :ivar usage: Usage statistics related to the run step. This value will be ``null`` while the + run step's status is ``in_progress``. + :vartype usage: ~azure.ai.assistants.models.RunStepCompletionUsage + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread.run.step"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'thread.run.step'. Required. Default value is + \"thread.run.step\".""" + type: Union[str, "_models.RunStepType"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The type of run step, which can be either message_creation or tool_calls. Required. Known + values are: \"message_creation\" and \"tool_calls\".""" + assistant_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the assistant associated with the run step. Required.""" + thread_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the thread that was run. Required.""" + run_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the run that this run step is a part of. Required.""" + status: Union[str, "_models.RunStepStatus"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The status of this run step. Required. Known values are: \"in_progress\", \"cancelled\", + \"failed\", \"completed\", and \"expired\".""" + step_details: "_models.RunStepDetails" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The details for this run step. Required.""" + last_error: "_models.RunStepError" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """If applicable, information about the last error encountered by this run step. Required.""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + expired_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this item expired. Required.""" + completed_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this completed. Required.""" + cancelled_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this was cancelled. Required.""" + failed_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this failed. Required.""" + usage: Optional["_models.RunStepCompletionUsage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Usage statistics related to the run step. This value will be ``null`` while the run step's + status is ``in_progress``.""" + metadata: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + type: Union[str, "_models.RunStepType"], + assistant_id: str, + thread_id: str, + run_id: str, + status: Union[str, "_models.RunStepStatus"], + step_details: "_models.RunStepDetails", + last_error: "_models.RunStepError", + created_at: datetime.datetime, + expired_at: datetime.datetime, + completed_at: datetime.datetime, + cancelled_at: datetime.datetime, + failed_at: datetime.datetime, + metadata: Dict[str, str], + usage: Optional["_models.RunStepCompletionUsage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.run.step"] = "thread.run.step" + + +class RunStepToolCall(_Model): + """An abstract representation of a detailed tool call as recorded within a run step for an + existing run. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepAzureAISearchToolCall, RunStepCustomSearchToolCall, RunStepBingGroundingToolCall, + RunStepCodeInterpreterToolCall, RunStepMicrosoftFabricToolCall, RunStepFileSearchToolCall, + RunStepFunctionToolCall, RunStepOpenAPIToolCall, RunStepSharepointToolCall + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Default value is None.""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the tool call. This ID must be referenced when you submit tool outputs. Required.""" + + @overload + def __init__( + self, + *, + type: str, + id: str, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepAzureAISearchToolCall(RunStepToolCall, discriminator="azure_ai_search"): + """A record of a call to an Azure AI Search tool, issued by the model in evaluation of a defined + tool, that represents + executed Azure AI search. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'azure_ai_search'. Required. Default value is + "azure_ai_search". + :vartype type: str + :ivar azure_ai_search: Reserved for future use. Required. + :vartype azure_ai_search: dict[str, str] + """ + + type: Literal["azure_ai_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'azure_ai_search'. Required. Default value is + \"azure_ai_search\".""" + azure_ai_search: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Reserved for future use. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + azure_ai_search: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="azure_ai_search", **kwargs) + + +class RunStepBingGroundingToolCall(RunStepToolCall, discriminator="bing_grounding"): + """A record of a call to a bing grounding tool, issued by the model in evaluation of a defined + tool, that represents + executed search with bing grounding. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'bing_grounding'. Required. Default value is + "bing_grounding". + :vartype type: str + :ivar bing_grounding: Reserved for future use. Required. + :vartype bing_grounding: dict[str, str] + """ + + type: Literal["bing_grounding"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'bing_grounding'. Required. Default value is + \"bing_grounding\".""" + bing_grounding: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Reserved for future use. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + bing_grounding: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="bing_grounding", **kwargs) + + +class RunStepCodeInterpreterToolCallOutput(_Model): + """An abstract representation of an emitted output from a code interpreter tool. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepCodeInterpreterImageOutput, RunStepCodeInterpreterLogOutput + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCodeInterpreterImageOutput(RunStepCodeInterpreterToolCallOutput, discriminator="image"): + """A representation of an image output emitted by a code interpreter tool in response to a tool + call by the model. + + :ivar type: The object type, which is always 'image'. Required. Default value is "image". + :vartype type: str + :ivar image: Referential information for the image associated with this output. Required. + :vartype image: ~azure.ai.assistants.models.RunStepCodeInterpreterImageReference + """ + + type: Literal["image"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'image'. Required. Default value is \"image\".""" + image: "_models.RunStepCodeInterpreterImageReference" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Referential information for the image associated with this output. Required.""" + + @overload + def __init__( + self, + *, + image: "_models.RunStepCodeInterpreterImageReference", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="image", **kwargs) + + +class RunStepCodeInterpreterImageReference(_Model): + """An image reference emitted by a code interpreter tool in response to a tool call by the model. + + :ivar file_id: The ID of the file associated with this image. Required. + :vartype file_id: str + """ + + file_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the file associated with this image. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCodeInterpreterLogOutput(RunStepCodeInterpreterToolCallOutput, discriminator="logs"): + """A representation of a log output emitted by a code interpreter tool in response to a tool call + by the model. + + :ivar type: The object type, which is always 'logs'. Required. Default value is "logs". + :vartype type: str + :ivar logs: The serialized log output emitted by the code interpreter. Required. + :vartype logs: str + """ + + type: Literal["logs"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'logs'. Required. Default value is \"logs\".""" + logs: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The serialized log output emitted by the code interpreter. Required.""" + + @overload + def __init__( + self, + *, + logs: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="logs", **kwargs) + + +class RunStepCodeInterpreterToolCall(RunStepToolCall, discriminator="code_interpreter"): + """A record of a call to a code interpreter tool, issued by the model in evaluation of a defined + tool, that + represents inputs and outputs consumed and emitted by the code interpreter. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'code_interpreter'. Required. Default value is + "code_interpreter". + :vartype type: str + :ivar code_interpreter: The details of the tool call to the code interpreter tool. Required. + :vartype code_interpreter: ~azure.ai.assistants.models.RunStepCodeInterpreterToolCallDetails + """ + + type: Literal["code_interpreter"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'code_interpreter'. Required. Default value is + \"code_interpreter\".""" + code_interpreter: "_models.RunStepCodeInterpreterToolCallDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The details of the tool call to the code interpreter tool. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + code_interpreter: "_models.RunStepCodeInterpreterToolCallDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="code_interpreter", **kwargs) + + +class RunStepCodeInterpreterToolCallDetails(_Model): + """The detailed information about a code interpreter invocation by the model. + + :ivar input: The input provided by the model to the code interpreter tool. Required. + :vartype input: str + :ivar outputs: The outputs produced by the code interpreter tool back to the model in response + to the tool call. Required. + :vartype outputs: list[~azure.ai.assistants.models.RunStepCodeInterpreterToolCallOutput] + """ + + input: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The input provided by the model to the code interpreter tool. Required.""" + outputs: List["_models.RunStepCodeInterpreterToolCallOutput"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The outputs produced by the code interpreter tool back to the model in response to the tool + call. Required.""" + + @overload + def __init__( + self, + *, + input: str, + outputs: List["_models.RunStepCodeInterpreterToolCallOutput"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCompletionUsage(_Model): + """Usage statistics related to the run step. + + :ivar completion_tokens: Number of completion tokens used over the course of the run step. + Required. + :vartype completion_tokens: int + :ivar prompt_tokens: Number of prompt tokens used over the course of the run step. Required. + :vartype prompt_tokens: int + :ivar total_tokens: Total number of tokens used (prompt + completion). Required. + :vartype total_tokens: int + """ + + completion_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Number of completion tokens used over the course of the run step. Required.""" + prompt_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Number of prompt tokens used over the course of the run step. Required.""" + total_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Total number of tokens used (prompt + completion). Required.""" + + @overload + def __init__( + self, + *, + completion_tokens: int, + prompt_tokens: int, + total_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCustomSearchToolCall(RunStepToolCall, discriminator="bing_custom_search"): + """A record of a call to a bing custom search tool, issued by the model in evaluation of a defined + tool, that represents + executed search with bing custom search. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'bing_custom_search'. Required. Default value is + "bing_custom_search". + :vartype type: str + :ivar bing_custom_search: Reserved for future use. Required. + :vartype bing_custom_search: dict[str, str] + """ + + type: Literal["bing_custom_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'bing_custom_search'. Required. Default value is + \"bing_custom_search\".""" + bing_custom_search: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Reserved for future use. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + bing_custom_search: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="bing_custom_search", **kwargs) + + +class RunStepDelta(_Model): + """Represents the delta payload in a streaming run step delta chunk. + + :ivar step_details: The details of the run step. + :vartype step_details: ~azure.ai.assistants.models.RunStepDeltaDetail + """ + + step_details: Optional["_models.RunStepDeltaDetail"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The details of the run step.""" + + @overload + def __init__( + self, + *, + step_details: Optional["_models.RunStepDeltaDetail"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaChunk(_Model): + """Represents a run step delta i.e. any changed fields on a run step during streaming. + + :ivar id: The identifier of the run step, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``thread.run.step.delta``. Required. Default + value is "thread.run.step.delta". + :vartype object: str + :ivar delta: The delta containing the fields that have changed on the run step. Required. + :vartype delta: ~azure.ai.assistants.models.RunStepDelta + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier of the run step, which can be referenced in API endpoints. Required.""" + object: Literal["thread.run.step.delta"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always ``thread.run.step.delta``. Required. Default value is + \"thread.run.step.delta\".""" + delta: "_models.RunStepDelta" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The delta containing the fields that have changed on the run step. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + delta: "_models.RunStepDelta", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.run.step.delta"] = "thread.run.step.delta" + + +class RunStepDeltaCodeInterpreterDetailItemObject(_Model): # pylint: disable=name-too-long + """Represents the Code Interpreter tool call data in a streaming run step's tool calls. + + :ivar input: The input into the Code Interpreter tool call. + :vartype input: str + :ivar outputs: The outputs from the Code Interpreter tool call. Code Interpreter can output one + or more + items, including text (``logs``) or images (``image``). Each of these are represented by a + different object type. + :vartype outputs: list[~azure.ai.assistants.models.RunStepDeltaCodeInterpreterOutput] + """ + + input: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The input into the Code Interpreter tool call.""" + outputs: Optional[List["_models.RunStepDeltaCodeInterpreterOutput"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The outputs from the Code Interpreter tool call. Code Interpreter can output one or more + items, including text (``logs``) or images (``image``). Each of these are represented by a + different object type.""" + + @overload + def __init__( + self, + *, + input: Optional[str] = None, + outputs: Optional[List["_models.RunStepDeltaCodeInterpreterOutput"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaCodeInterpreterOutput(_Model): + """The abstract base representation of a streaming run step tool call's Code Interpreter tool + output. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepDeltaCodeInterpreterImageOutput, RunStepDeltaCodeInterpreterLogOutput + + :ivar index: The index of the output in the streaming run step tool call's Code Interpreter + outputs array. Required. + :vartype index: int + :ivar type: The type of the streaming run step tool call's Code Interpreter output. Required. + Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + index: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The index of the output in the streaming run step tool call's Code Interpreter outputs array. + Required.""" + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The type of the streaming run step tool call's Code Interpreter output. Required. Default value + is None.""" + + @overload + def __init__( + self, + *, + index: int, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaCodeInterpreterImageOutput(RunStepDeltaCodeInterpreterOutput, discriminator="image"): + """Represents an image output as produced the Code interpreter tool and as represented in a + streaming run step's delta tool calls collection. + + :ivar index: The index of the output in the streaming run step tool call's Code Interpreter + outputs array. Required. + :vartype index: int + :ivar type: The object type, which is always "image.". Required. Default value is "image". + :vartype type: str + :ivar image: The image data for the Code Interpreter tool call output. + :vartype image: ~azure.ai.assistants.models.RunStepDeltaCodeInterpreterImageOutputObject + """ + + type: Literal["image"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always \"image.\". Required. Default value is \"image\".""" + image: Optional["_models.RunStepDeltaCodeInterpreterImageOutputObject"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The image data for the Code Interpreter tool call output.""" + + @overload + def __init__( + self, + *, + index: int, + image: Optional["_models.RunStepDeltaCodeInterpreterImageOutputObject"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="image", **kwargs) + + +class RunStepDeltaCodeInterpreterImageOutputObject(_Model): # pylint: disable=name-too-long + """Represents the data for a streaming run step's Code Interpreter tool call image output. + + :ivar file_id: The file ID for the image. + :vartype file_id: str + """ + + file_id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The file ID for the image.""" + + @overload + def __init__( + self, + *, + file_id: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaCodeInterpreterLogOutput(RunStepDeltaCodeInterpreterOutput, discriminator="logs"): + """Represents a log output as produced by the Code Interpreter tool and as represented in a + streaming run step's delta tool calls collection. + + :ivar index: The index of the output in the streaming run step tool call's Code Interpreter + outputs array. Required. + :vartype index: int + :ivar type: The type of the object, which is always "logs.". Required. Default value is "logs". + :vartype type: str + :ivar logs: The text output from the Code Interpreter tool call. + :vartype logs: str + """ + + type: Literal["logs"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The type of the object, which is always \"logs.\". Required. Default value is \"logs\".""" + logs: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The text output from the Code Interpreter tool call.""" + + @overload + def __init__( + self, + *, + index: int, + logs: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="logs", **kwargs) + + +class RunStepDeltaToolCall(_Model): + """The abstract base representation of a single tool call within a streaming run step's delta tool + call details. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepDeltaCodeInterpreterToolCall, RunStepDeltaFileSearchToolCall, + RunStepDeltaFunctionToolCall + + :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. + :vartype index: int + :ivar id: The ID of the tool call, used when submitting outputs to the run. Required. + :vartype id: str + :ivar type: The type of the tool call detail item in a streaming run step's details. Required. + Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + index: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The index of the tool call detail in the run step's tool_calls array. Required.""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the tool call, used when submitting outputs to the run. Required.""" + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The type of the tool call detail item in a streaming run step's details. Required. Default + value is None.""" + + @overload + def __init__( + self, + *, + index: int, + id: str, # pylint: disable=redefined-builtin + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaCodeInterpreterToolCall(RunStepDeltaToolCall, discriminator="code_interpreter"): + """Represents a Code Interpreter tool call within a streaming run step's tool call details. + + :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. + :vartype index: int + :ivar id: The ID of the tool call, used when submitting outputs to the run. Required. + :vartype id: str + :ivar type: The object type, which is always "code_interpreter.". Required. Default value is + "code_interpreter". + :vartype type: str + :ivar code_interpreter: The Code Interpreter data for the tool call. + :vartype code_interpreter: + ~azure.ai.assistants.models.RunStepDeltaCodeInterpreterDetailItemObject + """ + + type: Literal["code_interpreter"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always \"code_interpreter.\". Required. Default value is + \"code_interpreter\".""" + code_interpreter: Optional["_models.RunStepDeltaCodeInterpreterDetailItemObject"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The Code Interpreter data for the tool call.""" + + @overload + def __init__( + self, + *, + index: int, + id: str, # pylint: disable=redefined-builtin + code_interpreter: Optional["_models.RunStepDeltaCodeInterpreterDetailItemObject"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="code_interpreter", **kwargs) + + +class RunStepDeltaDetail(_Model): + """Represents a single run step detail item in a streaming run step's delta payload. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepDeltaMessageCreation, RunStepDeltaToolCallObject + + :ivar type: The object type for the run step detail object. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type for the run step detail object. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaFileSearchToolCall(RunStepDeltaToolCall, discriminator="file_search"): + """Represents a file search tool call within a streaming run step's tool call details. + + :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. + :vartype index: int + :ivar id: The ID of the tool call, used when submitting outputs to the run. Required. + :vartype id: str + :ivar type: The object type, which is always "file_search.". Required. Default value is + "file_search". + :vartype type: str + :ivar file_search: Reserved for future use. + :vartype file_search: ~azure.ai.assistants.models.RunStepFileSearchToolCallResults + """ + + type: Literal["file_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always \"file_search.\". Required. Default value is \"file_search\".""" + file_search: Optional["_models.RunStepFileSearchToolCallResults"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Reserved for future use.""" + + @overload + def __init__( + self, + *, + index: int, + id: str, # pylint: disable=redefined-builtin + file_search: Optional["_models.RunStepFileSearchToolCallResults"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_search", **kwargs) + + +class RunStepDeltaFunction(_Model): + """Represents the function data in a streaming run step delta's function tool call. + + :ivar name: The name of the function. + :vartype name: str + :ivar arguments: The arguments passed to the function as input. + :vartype arguments: str + :ivar output: The output of the function, null if outputs have not yet been submitted. + :vartype output: str + """ + + name: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the function.""" + arguments: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The arguments passed to the function as input.""" + output: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The output of the function, null if outputs have not yet been submitted.""" + + @overload + def __init__( + self, + *, + name: Optional[str] = None, + arguments: Optional[str] = None, + output: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaFunctionToolCall(RunStepDeltaToolCall, discriminator="function"): + """Represents a function tool call within a streaming run step's tool call details. + + :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. + :vartype index: int + :ivar id: The ID of the tool call, used when submitting outputs to the run. Required. + :vartype id: str + :ivar type: The object type, which is always "function.". Required. Default value is + "function". + :vartype type: str + :ivar function: The function data for the tool call. + :vartype function: ~azure.ai.assistants.models.RunStepDeltaFunction + """ + + type: Literal["function"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always \"function.\". Required. Default value is \"function\".""" + function: Optional["_models.RunStepDeltaFunction"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The function data for the tool call.""" + + @overload + def __init__( + self, + *, + index: int, + id: str, # pylint: disable=redefined-builtin + function: Optional["_models.RunStepDeltaFunction"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="function", **kwargs) + + +class RunStepDeltaMessageCreation(RunStepDeltaDetail, discriminator="message_creation"): + """Represents a message creation within a streaming run step delta. + + :ivar type: The object type, which is always "message_creation.". Required. Default value is + "message_creation". + :vartype type: str + :ivar message_creation: The message creation data. + :vartype message_creation: ~azure.ai.assistants.models.RunStepDeltaMessageCreationObject + """ + + type: Literal["message_creation"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always \"message_creation.\". Required. Default value is + \"message_creation\".""" + message_creation: Optional["_models.RunStepDeltaMessageCreationObject"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The message creation data.""" + + @overload + def __init__( + self, + *, + message_creation: Optional["_models.RunStepDeltaMessageCreationObject"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="message_creation", **kwargs) + + +class RunStepDeltaMessageCreationObject(_Model): + """Represents the data within a streaming run step message creation response object. + + :ivar message_id: The ID of the newly-created message. + :vartype message_id: str + """ + + message_id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the newly-created message.""" + + @overload + def __init__( + self, + *, + message_id: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDeltaToolCallObject(RunStepDeltaDetail, discriminator="tool_calls"): + """Represents an invocation of tool calls as part of a streaming run step. + + :ivar type: The object type, which is always "tool_calls.". Required. Default value is + "tool_calls". + :vartype type: str + :ivar tool_calls: The collection of tool calls for the tool call detail item. + :vartype tool_calls: list[~azure.ai.assistants.models.RunStepDeltaToolCall] + """ + + type: Literal["tool_calls"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always \"tool_calls.\". Required. Default value is \"tool_calls\".""" + tool_calls: Optional[List["_models.RunStepDeltaToolCall"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The collection of tool calls for the tool call detail item.""" + + @overload + def __init__( + self, + *, + tool_calls: Optional[List["_models.RunStepDeltaToolCall"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="tool_calls", **kwargs) + + +class RunStepDetails(_Model): + """An abstract representation of the details for a run step. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepMessageCreationDetails, RunStepToolCallDetails + + :ivar type: The object type. Required. Known values are: "message_creation" and "tool_calls". + :vartype type: str or ~azure.ai.assistants.models.RunStepType + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Known values are: \"message_creation\" and \"tool_calls\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepError(_Model): + """The error information associated with a failed run step. + + :ivar code: The error code for this error. Required. Known values are: "server_error" and + "rate_limit_exceeded". + :vartype code: str or ~azure.ai.assistants.models.RunStepErrorCode + :ivar message: The human-readable text associated with this error. Required. + :vartype message: str + """ + + code: Union[str, "_models.RunStepErrorCode"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The error code for this error. Required. Known values are: \"server_error\" and + \"rate_limit_exceeded\".""" + message: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The human-readable text associated with this error. Required.""" + + @overload + def __init__( + self, + *, + code: Union[str, "_models.RunStepErrorCode"], + message: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepFileSearchToolCall(RunStepToolCall, discriminator="file_search"): + """A record of a call to a file search tool, issued by the model in evaluation of a defined tool, + that represents + executed file search. + + :ivar type: The object type, which is always 'file_search'. Required. Default value is + "file_search". + :vartype type: str + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar file_search: For now, this is always going to be an empty object. Required. + :vartype file_search: ~azure.ai.assistants.models.RunStepFileSearchToolCallResults + """ + + type: Literal["file_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'file_search'. Required. Default value is \"file_search\".""" + file_search: "_models.RunStepFileSearchToolCallResults" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """For now, this is always going to be an empty object. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + file_search: "_models.RunStepFileSearchToolCallResults", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_search", **kwargs) + + +class RunStepFileSearchToolCallResult(_Model): + """File search tool call result. + + :ivar file_id: The ID of the file that result was found in. Required. + :vartype file_id: str + :ivar file_name: The name of the file that result was found in. Required. + :vartype file_name: str + :ivar score: The score of the result. All values must be a floating point number between 0 and + 1. Required. + :vartype score: float + :ivar content: The content of the result that was found. The content is only included if + requested via the include query parameter. + :vartype content: list[~azure.ai.assistants.models.FileSearchToolCallContent] + """ + + file_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the file that result was found in. Required.""" + file_name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the file that result was found in. Required.""" + score: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The score of the result. All values must be a floating point number between 0 and 1. Required.""" + content: Optional[List["_models.FileSearchToolCallContent"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The content of the result that was found. The content is only included if requested via the + include query parameter.""" + + @overload + def __init__( + self, + *, + file_id: str, + file_name: str, + score: float, + content: Optional[List["_models.FileSearchToolCallContent"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepFileSearchToolCallResults(_Model): + """The results of the file search. + + :ivar ranking_options: Ranking options for file search. + :vartype ranking_options: ~azure.ai.assistants.models.FileSearchRankingOptions + :ivar results: The array of a file search results. Required. + :vartype results: list[~azure.ai.assistants.models.RunStepFileSearchToolCallResult] + """ + + ranking_options: Optional["_models.FileSearchRankingOptions"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Ranking options for file search.""" + results: List["_models.RunStepFileSearchToolCallResult"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The array of a file search results. Required.""" + + @overload + def __init__( + self, + *, + results: List["_models.RunStepFileSearchToolCallResult"], + ranking_options: Optional["_models.FileSearchRankingOptions"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepFunctionToolCall(RunStepToolCall, discriminator="function"): + """A record of a call to a function tool, issued by the model in evaluation of a defined tool, + that represents the inputs + and output consumed and emitted by the specified function. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'function'. Required. Default value is "function". + :vartype type: str + :ivar function: The detailed information about the function called by the model. Required. + :vartype function: ~azure.ai.assistants.models.RunStepFunctionToolCallDetails + """ + + type: Literal["function"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'function'. Required. Default value is \"function\".""" + function: "_models.RunStepFunctionToolCallDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The detailed information about the function called by the model. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + function: "_models.RunStepFunctionToolCallDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="function", **kwargs) + + +class RunStepFunctionToolCallDetails(_Model): + """The detailed information about the function called by the model. + + :ivar name: The name of the function. Required. + :vartype name: str + :ivar arguments: The arguments that the model requires are provided to the named function. + Required. + :vartype arguments: str + :ivar output: The output of the function, only populated for function calls that have already + have had their outputs submitted. Required. + :vartype output: str + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the function. Required.""" + arguments: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The arguments that the model requires are provided to the named function. Required.""" + output: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The output of the function, only populated for function calls that have already have had their + outputs submitted. Required.""" + + @overload + def __init__( + self, + *, + name: str, + arguments: str, + output: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepMessageCreationDetails(RunStepDetails, discriminator="message_creation"): + """The detailed information associated with a message creation run step. + + :ivar type: The object type, which is always 'message_creation'. Required. Represents a run + step to create a message. + :vartype type: str or ~azure.ai.assistants.models.MESSAGE_CREATION + :ivar message_creation: Information about the message creation associated with this run step. + Required. + :vartype message_creation: ~azure.ai.assistants.models.RunStepMessageCreationReference + """ + + type: Literal[RunStepType.MESSAGE_CREATION] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'message_creation'. Required. Represents a run step to create + a message.""" + message_creation: "_models.RunStepMessageCreationReference" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Information about the message creation associated with this run step. Required.""" + + @overload + def __init__( + self, + *, + message_creation: "_models.RunStepMessageCreationReference", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=RunStepType.MESSAGE_CREATION, **kwargs) + + +class RunStepMessageCreationReference(_Model): + """The details of a message created as a part of a run step. + + :ivar message_id: The ID of the message created by this run step. Required. + :vartype message_id: str + """ + + message_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the message created by this run step. Required.""" + + @overload + def __init__( + self, + *, + message_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepMicrosoftFabricToolCall(RunStepToolCall, discriminator="fabric_dataagent"): + """A record of a call to a Microsoft Fabric tool, issued by the model in evaluation of a defined + tool, that represents + executed Microsoft Fabric operations. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'fabric_dataagent'. Required. Default value is + "fabric_dataagent". + :vartype type: str + :ivar microsoft_fabric: Reserved for future use. Required. + :vartype microsoft_fabric: dict[str, str] + """ + + type: Literal["fabric_dataagent"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'fabric_dataagent'. Required. Default value is + \"fabric_dataagent\".""" + microsoft_fabric: Dict[str, str] = rest_field( + name="fabric_dataagent", visibility=["read", "create", "update", "delete", "query"] + ) + """Reserved for future use. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + microsoft_fabric: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="fabric_dataagent", **kwargs) + + +class RunStepOpenAPIToolCall(RunStepToolCall, discriminator="openapi"): + """A record of a call to an OpenAPI tool, issued by the model in evaluation of a defined tool, + that represents + executed OpenAPI operations. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'openapi'. Required. Default value is "openapi". + :vartype type: str + :ivar open_api: Reserved for future use. Required. + :vartype open_api: dict[str, str] + """ + + type: Literal["openapi"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'openapi'. Required. Default value is \"openapi\".""" + open_api: Dict[str, str] = rest_field(name="openapi", visibility=["read", "create", "update", "delete", "query"]) + """Reserved for future use. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + open_api: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="openapi", **kwargs) + + +class RunStepSharepointToolCall(RunStepToolCall, discriminator="sharepoint_grounding"): + """A record of a call to a SharePoint tool, issued by the model in evaluation of a defined tool, + that represents + executed SharePoint actions. + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'sharepoint_grounding'. Required. Default value is + "sharepoint_grounding". + :vartype type: str + :ivar share_point: Reserved for future use. Required. + :vartype share_point: dict[str, str] + """ + + type: Literal["sharepoint_grounding"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'sharepoint_grounding'. Required. Default value is + \"sharepoint_grounding\".""" + share_point: Dict[str, str] = rest_field( + name="sharepoint_grounding", visibility=["read", "create", "update", "delete", "query"] + ) + """Reserved for future use. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + share_point: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="sharepoint_grounding", **kwargs) + + +class RunStepToolCallDetails(RunStepDetails, discriminator="tool_calls"): + """The detailed information associated with a run step calling tools. + + :ivar type: The object type, which is always 'tool_calls'. Required. Represents a run step that + calls tools. + :vartype type: str or ~azure.ai.assistants.models.TOOL_CALLS + :ivar tool_calls: A list of tool call details for this run step. Required. + :vartype tool_calls: list[~azure.ai.assistants.models.RunStepToolCall] + """ + + type: Literal[RunStepType.TOOL_CALLS] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'tool_calls'. Required. Represents a run step that calls + tools.""" + tool_calls: List["_models.RunStepToolCall"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A list of tool call details for this run step. Required.""" + + @overload + def __init__( + self, + *, + tool_calls: List["_models.RunStepToolCall"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=RunStepType.TOOL_CALLS, **kwargs) + + +class SearchConfiguration(_Model): + """A custom search configuration. + + :ivar connection_id: A connection in a ToolConnectionList attached to this tool. Required. + :vartype connection_id: str + :ivar instance_name: Name of the custom configuration instance given to config. Required. + :vartype instance_name: str + """ + + connection_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A connection in a ToolConnectionList attached to this tool. Required.""" + instance_name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Name of the custom configuration instance given to config. Required.""" + + @overload + def __init__( + self, + *, + connection_id: str, + instance_name: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SearchConfigurationList(_Model): + """A list of search configurations currently used by the ``bing_custom_search`` tool. + + :ivar search_configurations: The connections attached to this tool. There can be a maximum of 1 + connection + resource attached to the tool. Required. + :vartype search_configurations: list[~azure.ai.assistants.models.SearchConfiguration] + """ + + search_configurations: List["_models.SearchConfiguration"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The connections attached to this tool. There can be a maximum of 1 connection + resource attached to the tool. Required.""" + + @overload + def __init__( + self, + *, + search_configurations: List["_models.SearchConfiguration"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SharepointToolDefinition(ToolDefinition, discriminator="sharepoint_grounding"): + """The input definition information for a sharepoint tool as used to configure an assistant. + + :ivar type: The object type, which is always 'sharepoint_grounding'. Required. Default value is + "sharepoint_grounding". + :vartype type: str + :ivar sharepoint_grounding: The list of connections used by the SharePoint tool. Required. + :vartype sharepoint_grounding: ~azure.ai.assistants.models.ToolConnectionList + """ + + type: Literal["sharepoint_grounding"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'sharepoint_grounding'. Required. Default value is + \"sharepoint_grounding\".""" + sharepoint_grounding: "_models.ToolConnectionList" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The list of connections used by the SharePoint tool. Required.""" + + @overload + def __init__( + self, + *, + sharepoint_grounding: "_models.ToolConnectionList", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="sharepoint_grounding", **kwargs) + + +class SubmitToolOutputsAction(RequiredAction, discriminator="submit_tool_outputs"): + """The details for required tool calls that must be submitted for an assistant thread run to + continue. + + :ivar type: The object type, which is always 'submit_tool_outputs'. Required. Default value is + "submit_tool_outputs". + :vartype type: str + :ivar submit_tool_outputs: The details describing tools that should be called to submit tool + outputs. Required. + :vartype submit_tool_outputs: ~azure.ai.assistants.models.SubmitToolOutputsDetails + """ + + type: Literal["submit_tool_outputs"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'submit_tool_outputs'. Required. Default value is + \"submit_tool_outputs\".""" + submit_tool_outputs: "_models.SubmitToolOutputsDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The details describing tools that should be called to submit tool outputs. Required.""" + + @overload + def __init__( + self, + *, + submit_tool_outputs: "_models.SubmitToolOutputsDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="submit_tool_outputs", **kwargs) + + +class SubmitToolOutputsDetails(_Model): + """The details describing tools that should be called to submit tool outputs. + + :ivar tool_calls: The list of tool calls that must be resolved for the assistant thread run to + continue. Required. + :vartype tool_calls: list[~azure.ai.assistants.models.RequiredToolCall] + """ + + tool_calls: List["_models.RequiredToolCall"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The list of tool calls that must be resolved for the assistant thread run to continue. + Required.""" + + @overload + def __init__( + self, + *, + tool_calls: List["_models.RequiredToolCall"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ThreadDeletionStatus(_Model): + """The status of a thread deletion operation. + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'thread.deleted'. Required. Default value is + "thread.deleted". + :vartype object: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether deletion was successful. Required.""" + object: Literal["thread.deleted"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'thread.deleted'. Required. Default value is + \"thread.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.deleted"] = "thread.deleted" + + +class ThreadMessage(_Model): + """A single, existing message within an assistant thread. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread.message'. Required. Default value is + "thread.message". + :vartype object: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar thread_id: The ID of the thread that this message belongs to. Required. + :vartype thread_id: str + :ivar status: The status of the message. Required. Known values are: "in_progress", + "incomplete", and "completed". + :vartype status: str or ~azure.ai.assistants.models.MessageStatus + :ivar incomplete_details: On an incomplete message, details about why the message is + incomplete. Required. + :vartype incomplete_details: ~azure.ai.assistants.models.MessageIncompleteDetails + :ivar completed_at: The Unix timestamp (in seconds) for when the message was completed. + Required. + :vartype completed_at: ~datetime.datetime + :ivar incomplete_at: The Unix timestamp (in seconds) for when the message was marked as + incomplete. Required. + :vartype incomplete_at: ~datetime.datetime + :ivar role: The role associated with the assistant thread message. Required. Known values are: + "user" and "assistant". + :vartype role: str or ~azure.ai.assistants.models.MessageRole + :ivar content: The list of content items associated with the assistant thread message. + Required. + :vartype content: list[~azure.ai.assistants.models.MessageContent] + :ivar assistant_id: If applicable, the ID of the assistant that authored this message. + Required. + :vartype assistant_id: str + :ivar run_id: If applicable, the ID of the run associated with the authoring of this message. + Required. + :vartype run_id: str + :ivar attachments: A list of files attached to the message, and the tools they were added to. + Required. + :vartype attachments: list[~azure.ai.assistants.models.MessageAttachment] + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread.message"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'thread.message'. Required. Default value is + \"thread.message\".""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + thread_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the thread that this message belongs to. Required.""" + status: Union[str, "_models.MessageStatus"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The status of the message. Required. Known values are: \"in_progress\", \"incomplete\", and + \"completed\".""" + incomplete_details: "_models.MessageIncompleteDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """On an incomplete message, details about why the message is incomplete. Required.""" + completed_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp (in seconds) for when the message was completed. Required.""" + incomplete_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp (in seconds) for when the message was marked as incomplete. Required.""" + role: Union[str, "_models.MessageRole"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The role associated with the assistant thread message. Required. Known values are: \"user\" and + \"assistant\".""" + content: List["_models.MessageContent"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The list of content items associated with the assistant thread message. Required.""" + assistant_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """If applicable, the ID of the assistant that authored this message. Required.""" + run_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """If applicable, the ID of the run associated with the authoring of this message. Required.""" + attachments: List["_models.MessageAttachment"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A list of files attached to the message, and the tools they were added to. Required.""" + metadata: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + thread_id: str, + status: Union[str, "_models.MessageStatus"], + incomplete_details: "_models.MessageIncompleteDetails", + completed_at: datetime.datetime, + incomplete_at: datetime.datetime, + role: Union[str, "_models.MessageRole"], + content: List["_models.MessageContent"], + assistant_id: str, + run_id: str, + attachments: List["_models.MessageAttachment"], + metadata: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.message"] = "thread.message" + + +class ThreadMessageOptions(_Model): + """A single message within an agent thread, + as provided during that thread's creation for its initial state. + + :ivar role: The role of the entity that is creating the message. Allowed values include: + ``user``, which indicates the message is sent by an actual user (and should be + used in most cases to represent user-generated messages), and ``assistant``, + which indicates the message is generated by the agent (use this value to insert + messages from the agent into the conversation). Required. Known values are: "user" and + "assistant". + :vartype role: str or ~azure.ai.assistants.models.MessageRole + :ivar content: The content of the initial message. This may be a basic string (if you only + need text) or an array of typed content blocks (for example, text, image_file, + image_url, and so on). Required. Is either a str type or a [MessageInputContentBlock] type. + :vartype content: str or list[~azure.ai.assistants.models.MessageInputContentBlock] + :ivar attachments: A list of files attached to the message, and the tools they should be added + to. + :vartype attachments: list[~azure.ai.assistants.models.MessageAttachment] + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + role: Union[str, "_models.MessageRole"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The role of the entity that is creating the message. Allowed values include: + ``user``, which indicates the message is sent by an actual user (and should be + used in most cases to represent user-generated messages), and ``assistant``, + which indicates the message is generated by the agent (use this value to insert + messages from the agent into the conversation). Required. Known values are: \"user\" and + \"assistant\".""" + content: "_types.MessageInputContent" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The content of the initial message. This may be a basic string (if you only + need text) or an array of typed content blocks (for example, text, image_file, + image_url, and so on). Required. Is either a str type or a [MessageInputContentBlock] type.""" + attachments: Optional[List["_models.MessageAttachment"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A list of files attached to the message, and the tools they should be added to.""" + metadata: Optional[Dict[str, str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + role: Union[str, "_models.MessageRole"], + content: "_types.MessageInputContent", + attachments: Optional[List["_models.MessageAttachment"]] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ThreadRun(_Model): + """Data representing a single evaluation run of an assistant thread. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread.run'. Required. Default value is + "thread.run". + :vartype object: str + :ivar thread_id: The ID of the thread associated with this run. Required. + :vartype thread_id: str + :ivar assistant_id: The ID of the assistant associated with the thread this run was performed + against. Required. + :vartype assistant_id: str + :ivar status: The status of the assistant thread run. Required. Known values are: "queued", + "in_progress", "requires_action", "cancelling", "cancelled", "failed", "completed", and + "expired". + :vartype status: str or ~azure.ai.assistants.models.RunStatus + :ivar required_action: The details of the action required for the assistant thread run to + continue. + :vartype required_action: ~azure.ai.assistants.models.RequiredAction + :ivar last_error: The last error, if any, encountered by this assistant thread run. Required. + :vartype last_error: ~azure.ai.assistants.models.RunError + :ivar model: The ID of the model to use. Required. + :vartype model: str + :ivar instructions: The overridden system instructions used for this assistant thread run. + Required. + :vartype instructions: str + :ivar tools: The overridden enabled tools used for this assistant thread run. Required. + :vartype tools: list[~azure.ai.assistants.models.ToolDefinition] + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar expires_at: The Unix timestamp, in seconds, representing when this item expires. + Required. + :vartype expires_at: ~datetime.datetime + :ivar started_at: The Unix timestamp, in seconds, representing when this item was started. + Required. + :vartype started_at: ~datetime.datetime + :ivar completed_at: The Unix timestamp, in seconds, representing when this completed. Required. + :vartype completed_at: ~datetime.datetime + :ivar cancelled_at: The Unix timestamp, in seconds, representing when this was cancelled. + Required. + :vartype cancelled_at: ~datetime.datetime + :ivar failed_at: The Unix timestamp, in seconds, representing when this failed. Required. + :vartype failed_at: ~datetime.datetime + :ivar incomplete_details: Details on why the run is incomplete. Will be ``null`` if the run is + not incomplete. Required. + :vartype incomplete_details: ~azure.ai.assistants.models.IncompleteRunDetails + :ivar usage: Usage statistics related to the run. This value will be ``null`` if the run is not + in a terminal state (i.e. ``in_progress``, ``queued``, etc.). Required. + :vartype usage: ~azure.ai.assistants.models.RunCompletionUsage + :ivar temperature: The sampling temperature used for this run. If not set, defaults to 1. + :vartype temperature: float + :ivar top_p: The nucleus sampling value used for this run. If not set, defaults to 1. + :vartype top_p: float + :ivar max_prompt_tokens: The maximum number of prompt tokens specified to have been used over + the course of the run. Required. + :vartype max_prompt_tokens: int + :ivar max_completion_tokens: The maximum number of completion tokens specified to have been + used over the course of the run. Required. + :vartype max_completion_tokens: int + :ivar truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Required. + :vartype truncation_strategy: ~azure.ai.assistants.models.TruncationObject + :ivar tool_choice: Controls whether or not and which tool is called by the model. Required. Is + one of the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice + :vartype tool_choice: str or str or + ~azure.ai.assistants.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.assistants.models.AssistantsNamedToolChoice + :ivar response_format: The response format of the tool calls used in this run. Required. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType + :vartype response_format: str or str or + ~azure.ai.assistants.models.AssistantsApiResponseFormatMode or + ~azure.ai.assistants.models.AssistantsApiResponseFormat or + ~azure.ai.assistants.models.ResponseFormatJsonSchemaType + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + :ivar tool_resources: Override the tools the assistant can use for this run. This is useful for + modifying the behavior on a per-run basis. + :vartype tool_resources: ~azure.ai.assistants.models.UpdateToolResourcesOptions + :ivar parallel_tool_calls: Determines if tools can be executed in parallel within the run. + Required. + :vartype parallel_tool_calls: bool + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread.run"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'thread.run'. Required. Default value is \"thread.run\".""" + thread_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the thread associated with this run. Required.""" + assistant_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the assistant associated with the thread this run was performed against. Required.""" + status: Union[str, "_models.RunStatus"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The status of the assistant thread run. Required. Known values are: \"queued\", + \"in_progress\", \"requires_action\", \"cancelling\", \"cancelled\", \"failed\", \"completed\", + and \"expired\".""" + required_action: Optional["_models.RequiredAction"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The details of the action required for the assistant thread run to continue.""" + last_error: "_models.RunError" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last error, if any, encountered by this assistant thread run. Required.""" + model: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the model to use. Required.""" + instructions: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The overridden system instructions used for this assistant thread run. Required.""" + tools: List["_models.ToolDefinition"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The overridden enabled tools used for this assistant thread run. Required.""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + expires_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this item expires. Required.""" + started_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this item was started. Required.""" + completed_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this completed. Required.""" + cancelled_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this was cancelled. Required.""" + failed_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp, in seconds, representing when this failed. Required.""" + incomplete_details: "_models.IncompleteRunDetails" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Details on why the run is incomplete. Will be ``null`` if the run is not incomplete. Required.""" + usage: "_models.RunCompletionUsage" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Usage statistics related to the run. This value will be ``null`` if the run is not in a + terminal state (i.e. ``in_progress``, ``queued``, etc.). Required.""" + temperature: Optional[float] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The sampling temperature used for this run. If not set, defaults to 1.""" + top_p: Optional[float] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The nucleus sampling value used for this run. If not set, defaults to 1.""" + max_prompt_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The maximum number of prompt tokens specified to have been used over the course of the run. + Required.""" + max_completion_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The maximum number of completion tokens specified to have been used over the course of the run. + Required.""" + truncation_strategy: "_models.TruncationObject" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The strategy to use for dropping messages as the context windows moves forward. Required.""" + tool_choice: "_types.AssistantsApiToolChoiceOption" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Controls whether or not and which tool is called by the model. Required. Is one of the + following types: str, Union[str, \"_models.AssistantsApiToolChoiceOptionMode\"], + AssistantsNamedToolChoice""" + response_format: "_types.AssistantsApiResponseFormatOption" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The response format of the tool calls used in this run. Required. Is one of the following + types: str, Union[str, \"_models.AssistantsApiResponseFormatMode\"], + AssistantsApiResponseFormat, ResponseFormatJsonSchemaType""" + metadata: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Override the tools the assistant can use for this run. This is useful for modifying the + behavior on a per-run basis.""" + parallel_tool_calls: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Determines if tools can be executed in parallel within the run. Required.""" + + @overload + def __init__( # pylint: disable=too-many-locals + self, + *, + id: str, # pylint: disable=redefined-builtin + thread_id: str, + assistant_id: str, + status: Union[str, "_models.RunStatus"], + last_error: "_models.RunError", + model: str, + instructions: str, + tools: List["_models.ToolDefinition"], + created_at: datetime.datetime, + expires_at: datetime.datetime, + started_at: datetime.datetime, + completed_at: datetime.datetime, + cancelled_at: datetime.datetime, + failed_at: datetime.datetime, + incomplete_details: "_models.IncompleteRunDetails", + usage: "_models.RunCompletionUsage", + max_prompt_tokens: int, + max_completion_tokens: int, + truncation_strategy: "_models.TruncationObject", + tool_choice: "_types.AssistantsApiToolChoiceOption", + response_format: "_types.AssistantsApiResponseFormatOption", + metadata: Dict[str, str], + parallel_tool_calls: bool, + required_action: Optional["_models.RequiredAction"] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.run"] = "thread.run" + + +class ToolConnection(_Model): + """A connection resource. + + :ivar connection_id: A connection in a ToolConnectionList attached to this tool. Required. + :vartype connection_id: str + """ + + connection_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A connection in a ToolConnectionList attached to this tool. Required.""" + + @overload + def __init__( + self, + *, + connection_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ToolConnectionList(_Model): + """A set of connection resources currently used by either the ``bing_grounding``, + ``fabric_dataagent``, or ``sharepoint_grounding`` tools. + + :ivar connection_list: The connections attached to this tool. There can be a maximum of 1 + connection + resource attached to the tool. + :vartype connection_list: list[~azure.ai.assistants.models.ToolConnection] + """ + + connection_list: Optional[List["_models.ToolConnection"]] = rest_field( + name="connections", visibility=["read", "create", "update", "delete", "query"] + ) + """The connections attached to this tool. There can be a maximum of 1 connection + resource attached to the tool.""" + + @overload + def __init__( + self, + *, + connection_list: Optional[List["_models.ToolConnection"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ToolOutput(_Model): + """The data provided during a tool outputs submission to resolve pending tool calls and allow the + model to continue. + + :ivar tool_call_id: The ID of the tool call being resolved, as provided in the tool calls of a + required action from a run. + :vartype tool_call_id: str + :ivar output: The output from the tool to be submitted. + :vartype output: str + """ + + tool_call_id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the tool call being resolved, as provided in the tool calls of a required action from + a run.""" + output: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The output from the tool to be submitted.""" + + @overload + def __init__( + self, + *, + tool_call_id: Optional[str] = None, + output: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ToolResources(_Model): + """A set of resources that are used by the assistant's tools. The resources are specific to the + type of + tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` + tool requires a list of vector store IDs. + + :ivar code_interpreter: Resources to be used by the ``code_interpreter`` tool consisting of + file IDs. + :vartype code_interpreter: ~azure.ai.assistants.models.CodeInterpreterToolResource + :ivar file_search: Resources to be used by the ``file_search`` tool consisting of vector store + IDs. + :vartype file_search: ~azure.ai.assistants.models.FileSearchToolResource + :ivar azure_ai_search: Resources to be used by the ``azure_ai_search`` tool consisting of index + IDs and names. + :vartype azure_ai_search: ~azure.ai.assistants.models.AzureAISearchResource + """ + + code_interpreter: Optional["_models.CodeInterpreterToolResource"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Resources to be used by the ``code_interpreter`` tool consisting of file IDs.""" + file_search: Optional["_models.FileSearchToolResource"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Resources to be used by the ``file_search`` tool consisting of vector store IDs.""" + azure_ai_search: Optional["_models.AzureAISearchResource"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Resources to be used by the ``azure_ai_search`` tool consisting of index IDs and names.""" + + @overload + def __init__( + self, + *, + code_interpreter: Optional["_models.CodeInterpreterToolResource"] = None, + file_search: Optional["_models.FileSearchToolResource"] = None, + azure_ai_search: Optional["_models.AzureAISearchResource"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class TruncationObject(_Model): + """Controls for how a thread will be truncated prior to the run. Use this to control the initial + context window of the run. + + :ivar type: The truncation strategy to use for the thread. The default is ``auto``. If set to + ``last_messages``, the thread will + be truncated to the ``lastMessages`` count most recent messages in the thread. When set to + ``auto``, messages in the middle of the thread + will be dropped to fit the context length of the model, ``max_prompt_tokens``. Required. Known + values are: "auto" and "last_messages". + :vartype type: str or ~azure.ai.assistants.models.TruncationStrategy + :ivar last_messages: The number of most recent messages from the thread when constructing the + context for the run. + :vartype last_messages: int + """ + + type: Union[str, "_models.TruncationStrategy"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The truncation strategy to use for the thread. The default is ``auto``. If set to + ``last_messages``, the thread will + be truncated to the ``lastMessages`` count most recent messages in the thread. When set to + ``auto``, messages in the middle of the thread + will be dropped to fit the context length of the model, ``max_prompt_tokens``. Required. Known + values are: \"auto\" and \"last_messages\".""" + last_messages: Optional[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The number of most recent messages from the thread when constructing the context for the run.""" + + @overload + def __init__( + self, + *, + type: Union[str, "_models.TruncationStrategy"], + last_messages: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateCodeInterpreterToolResourceOptions(_Model): + """Request object to update ``code_interpreted`` tool resources. + + :ivar file_ids: A list of file IDs to override the current list of the assistant. + :vartype file_ids: list[str] + """ + + file_ids: Optional[List[str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A list of file IDs to override the current list of the assistant.""" + + @overload + def __init__( + self, + *, + file_ids: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateFileSearchToolResourceOptions(_Model): + """Request object to update ``file_search`` tool resources. + + :ivar vector_store_ids: A list of vector store IDs to override the current list of the + assistant. + :vartype vector_store_ids: list[str] + """ + + vector_store_ids: Optional[List[str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A list of vector store IDs to override the current list of the assistant.""" + + @overload + def __init__( + self, + *, + vector_store_ids: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateToolResourcesOptions(_Model): + """Request object. A set of resources that are used by the assistant's tools. The resources are + specific to the type of tool. + For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires a list of + vector store IDs. + + :ivar code_interpreter: Overrides the list of file IDs made available to the + ``code_interpreter`` tool. There can be a maximum of 20 files + associated with the tool. + :vartype code_interpreter: ~azure.ai.assistants.models.UpdateCodeInterpreterToolResourceOptions + :ivar file_search: Overrides the vector store attached to this assistant. There can be a + maximum of 1 vector store attached to the assistant. + :vartype file_search: ~azure.ai.assistants.models.UpdateFileSearchToolResourceOptions + :ivar azure_ai_search: Overrides the resources to be used by the ``azure_ai_search`` tool + consisting of index IDs and names. + :vartype azure_ai_search: ~azure.ai.assistants.models.AzureAISearchResource + """ + + code_interpreter: Optional["_models.UpdateCodeInterpreterToolResourceOptions"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Overrides the list of file IDs made available to the ``code_interpreter`` tool. There can be a + maximum of 20 files + associated with the tool.""" + file_search: Optional["_models.UpdateFileSearchToolResourceOptions"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Overrides the vector store attached to this assistant. There can be a maximum of 1 vector store + attached to the assistant.""" + azure_ai_search: Optional["_models.AzureAISearchResource"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Overrides the resources to be used by the ``azure_ai_search`` tool consisting of index IDs and + names.""" + + @overload + def __init__( + self, + *, + code_interpreter: Optional["_models.UpdateCodeInterpreterToolResourceOptions"] = None, + file_search: Optional["_models.UpdateFileSearchToolResourceOptions"] = None, + azure_ai_search: Optional["_models.AzureAISearchResource"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UploadFileRequest(_Model): + """UploadFileRequest. + + :ivar file: The file data, in bytes. Required. + :vartype file: ~azure.ai.assistants._utils.utils.FileType + :ivar purpose: The intended purpose of the uploaded file. Use ``assistants`` for Assistants and + Message files, ``vision`` for Assistants image file inputs, ``batch`` for Batch API, and + ``fine-tune`` for Fine-tuning. Required. Known values are: "fine-tune", "fine-tune-results", + "assistants", "assistants_output", "batch", "batch_output", and "vision". + :vartype purpose: str or ~azure.ai.assistants.models.FilePurpose + :ivar filename: The name of the file. + :vartype filename: str + """ + + file: FileType = rest_field( + visibility=["read", "create", "update", "delete", "query"], is_multipart_file_input=True + ) + """The file data, in bytes. Required.""" + purpose: Union[str, "_models.FilePurpose"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The intended purpose of the uploaded file. Use ``assistants`` for Assistants and Message files, + ``vision`` for Assistants image file inputs, ``batch`` for Batch API, and ``fine-tune`` for + Fine-tuning. Required. Known values are: \"fine-tune\", \"fine-tune-results\", \"assistants\", + \"assistants_output\", \"batch\", \"batch_output\", and \"vision\".""" + filename: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the file.""" + + @overload + def __init__( + self, + *, + file: FileType, + purpose: Union[str, "_models.FilePurpose"], + filename: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStore(_Model): + """A vector store is a collection of processed files can be used by the ``file_search`` tool. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``vector_store``. Required. Default value is + "vector_store". + :vartype object: str + :ivar created_at: The Unix timestamp (in seconds) for when the vector store was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar name: The name of the vector store. Required. + :vartype name: str + :ivar usage_bytes: The total number of bytes used by the files in the vector store. Required. + :vartype usage_bytes: int + :ivar file_counts: Files count grouped by status processed or being processed by this vector + store. Required. + :vartype file_counts: ~azure.ai.assistants.models.VectorStoreFileCount + :ivar status: The status of the vector store, which can be either ``expired``, ``in_progress``, + or ``completed``. A status of ``completed`` indicates that the vector store is ready for use. + Required. Known values are: "expired", "in_progress", and "completed". + :vartype status: str or ~azure.ai.assistants.models.VectorStoreStatus + :ivar expires_after: Details on when this vector store expires. + :vartype expires_after: ~azure.ai.assistants.models.VectorStoreExpirationPolicy + :ivar expires_at: The Unix timestamp (in seconds) for when the vector store will expire. + :vartype expires_at: ~datetime.datetime + :ivar last_active_at: The Unix timestamp (in seconds) for when the vector store was last + active. Required. + :vartype last_active_at: ~datetime.datetime + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["vector_store"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always ``vector_store``. Required. Default value is \"vector_store\".""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp (in seconds) for when the vector store was created. Required.""" + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the vector store. Required.""" + usage_bytes: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The total number of bytes used by the files in the vector store. Required.""" + file_counts: "_models.VectorStoreFileCount" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Files count grouped by status processed or being processed by this vector store. Required.""" + status: Union[str, "_models.VectorStoreStatus"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The status of the vector store, which can be either ``expired``, ``in_progress``, or + ``completed``. A status of ``completed`` indicates that the vector store is ready for use. + Required. Known values are: \"expired\", \"in_progress\", and \"completed\".""" + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Details on when this vector store expires.""" + expires_at: Optional[datetime.datetime] = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp (in seconds) for when the vector store will expire.""" + last_active_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp (in seconds) for when the vector store was last active. Required.""" + metadata: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + name: str, + usage_bytes: int, + file_counts: "_models.VectorStoreFileCount", + status: Union[str, "_models.VectorStoreStatus"], + last_active_at: datetime.datetime, + metadata: Dict[str, str], + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = None, + expires_at: Optional[datetime.datetime] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store"] = "vector_store" + + +class VectorStoreChunkingStrategyRequest(_Model): + """An abstract representation of a vector store chunking strategy configuration. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + VectorStoreAutoChunkingStrategyRequest, VectorStoreStaticChunkingStrategyRequest + + :ivar type: The object type. Required. Known values are: "auto" and "static". + :vartype type: str or ~azure.ai.assistants.models.VectorStoreChunkingStrategyRequestType + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Known values are: \"auto\" and \"static\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreAutoChunkingStrategyRequest(VectorStoreChunkingStrategyRequest, discriminator="auto"): + """The default strategy. This strategy currently uses a max_chunk_size_tokens of 800 and + chunk_overlap_tokens of 400. + + :ivar type: The object type, which is always 'auto'. Required. + :vartype type: str or ~azure.ai.assistants.models.AUTO + """ + + type: Literal[VectorStoreChunkingStrategyRequestType.AUTO] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'auto'. Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyRequestType.AUTO, **kwargs) + + +class VectorStoreChunkingStrategyResponse(_Model): + """An abstract representation of a vector store chunking strategy configuration. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + VectorStoreAutoChunkingStrategyResponse, VectorStoreStaticChunkingStrategyResponse + + :ivar type: The object type. Required. Known values are: "other" and "static". + :vartype type: str or ~azure.ai.assistants.models.VectorStoreChunkingStrategyResponseType + """ + + __mapping__: Dict[str, _Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """The object type. Required. Known values are: \"other\" and \"static\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreAutoChunkingStrategyResponse(VectorStoreChunkingStrategyResponse, discriminator="other"): + """This is returned when the chunking strategy is unknown. Typically, this is because the file was + indexed before the chunking_strategy concept was introduced in the API. + + :ivar type: The object type, which is always 'other'. Required. + :vartype type: str or ~azure.ai.assistants.models.OTHER + """ + + type: Literal[VectorStoreChunkingStrategyResponseType.OTHER] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'other'. Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyResponseType.OTHER, **kwargs) + + +class VectorStoreConfiguration(_Model): + """Vector storage configuration is the list of data sources, used when multiple + files can be used for the enterprise file search. + + :ivar data_sources: Data sources. Required. + :vartype data_sources: list[~azure.ai.assistants.models.VectorStoreDataSource] + """ + + data_sources: List["_models.VectorStoreDataSource"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Data sources. Required.""" + + @overload + def __init__( + self, + *, + data_sources: List["_models.VectorStoreDataSource"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreConfigurations(_Model): + """The structure, containing the list of vector storage configurations i.e. the list of azure + asset IDs. + + :ivar store_name: Name. Required. + :vartype store_name: str + :ivar store_configuration: Configurations. Required. + :vartype store_configuration: ~azure.ai.assistants.models.VectorStoreConfiguration + """ + + store_name: str = rest_field(name="name", visibility=["read", "create", "update", "delete", "query"]) + """Name. Required.""" + store_configuration: "_models.VectorStoreConfiguration" = rest_field( + name="configuration", visibility=["read", "create", "update", "delete", "query"] + ) + """Configurations. Required.""" + + @overload + def __init__( + self, + *, + store_name: str, + store_configuration: "_models.VectorStoreConfiguration", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreDataSource(_Model): + """The structure, containing Azure asset URI path and the asset type of the file used as a data + source + for the enterprise file search. + + :ivar asset_identifier: Asset URI. Required. + :vartype asset_identifier: str + :ivar asset_type: The asset type. Required. Known values are: "uri_asset" and "id_asset". + :vartype asset_type: str or ~azure.ai.assistants.models.VectorStoreDataSourceAssetType + """ + + asset_identifier: str = rest_field(name="uri", visibility=["read", "create", "update", "delete", "query"]) + """Asset URI. Required.""" + asset_type: Union[str, "_models.VectorStoreDataSourceAssetType"] = rest_field( + name="type", visibility=["read", "create", "update", "delete", "query"] + ) + """The asset type. Required. Known values are: \"uri_asset\" and \"id_asset\".""" + + @overload + def __init__( + self, + *, + asset_identifier: str, + asset_type: Union[str, "_models.VectorStoreDataSourceAssetType"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreDeletionStatus(_Model): + """Response object for deleting a vector store. + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'vector_store.deleted'. Required. Default value + is "vector_store.deleted". + :vartype object: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether deletion was successful. Required.""" + object: Literal["vector_store.deleted"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always 'vector_store.deleted'. Required. Default value is + \"vector_store.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.deleted"] = "vector_store.deleted" + + +class VectorStoreExpirationPolicy(_Model): + """The expiration policy for a vector store. + + :ivar anchor: Anchor timestamp after which the expiration policy applies. Supported anchors: + ``last_active_at``. Required. "last_active_at" + :vartype anchor: str or ~azure.ai.assistants.models.VectorStoreExpirationPolicyAnchor + :ivar days: The anchor timestamp after which the expiration policy applies. Required. + :vartype days: int + """ + + anchor: Union[str, "_models.VectorStoreExpirationPolicyAnchor"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Anchor timestamp after which the expiration policy applies. Supported anchors: + ``last_active_at``. Required. \"last_active_at\"""" + days: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The anchor timestamp after which the expiration policy applies. Required.""" + + @overload + def __init__( + self, + *, + anchor: Union[str, "_models.VectorStoreExpirationPolicyAnchor"], + days: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreFile(_Model): + """Description of a file attached to a vector store. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``vector_store.file``. Required. Default value + is "vector_store.file". + :vartype object: str + :ivar usage_bytes: The total vector store usage in bytes. Note that this may be different from + the original file + size. Required. + :vartype usage_bytes: int + :ivar created_at: The Unix timestamp (in seconds) for when the vector store file was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar vector_store_id: The ID of the vector store that the file is attached to. Required. + :vartype vector_store_id: str + :ivar status: The status of the vector store file, which can be either ``in_progress``, + ``completed``, ``cancelled``, or ``failed``. The status ``completed`` indicates that the vector + store file is ready for use. Required. Known values are: "in_progress", "completed", "failed", + and "cancelled". + :vartype status: str or ~azure.ai.assistants.models.VectorStoreFileStatus + :ivar last_error: The last error associated with this vector store file. Will be ``null`` if + there are no errors. Required. + :vartype last_error: ~azure.ai.assistants.models.VectorStoreFileError + :ivar chunking_strategy: The strategy used to chunk the file. Required. + :vartype chunking_strategy: ~azure.ai.assistants.models.VectorStoreChunkingStrategyResponse + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["vector_store.file"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always ``vector_store.file``. Required. Default value is + \"vector_store.file\".""" + usage_bytes: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The total vector store usage in bytes. Note that this may be different from the original file + size. Required.""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp (in seconds) for when the vector store file was created. Required.""" + vector_store_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the vector store that the file is attached to. Required.""" + status: Union[str, "_models.VectorStoreFileStatus"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The status of the vector store file, which can be either ``in_progress``, ``completed``, + ``cancelled``, or ``failed``. The status ``completed`` indicates that the vector store file is + ready for use. Required. Known values are: \"in_progress\", \"completed\", \"failed\", and + \"cancelled\".""" + last_error: "_models.VectorStoreFileError" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The last error associated with this vector store file. Will be ``null`` if there are no errors. + Required.""" + chunking_strategy: "_models.VectorStoreChunkingStrategyResponse" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The strategy used to chunk the file. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + usage_bytes: int, + created_at: datetime.datetime, + vector_store_id: str, + status: Union[str, "_models.VectorStoreFileStatus"], + last_error: "_models.VectorStoreFileError", + chunking_strategy: "_models.VectorStoreChunkingStrategyResponse", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.file"] = "vector_store.file" + + +class VectorStoreFileBatch(_Model): + """A batch of files attached to a vector store. + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``vector_store.file_batch``. Required. Default + value is "vector_store.files_batch". + :vartype object: str + :ivar created_at: The Unix timestamp (in seconds) for when the vector store files batch was + created. Required. + :vartype created_at: ~datetime.datetime + :ivar vector_store_id: The ID of the vector store that the file is attached to. Required. + :vartype vector_store_id: str + :ivar status: The status of the vector store files batch, which can be either ``in_progress``, + ``completed``, ``cancelled`` or ``failed``. Required. Known values are: "in_progress", + "completed", "cancelled", and "failed". + :vartype status: str or ~azure.ai.assistants.models.VectorStoreFileBatchStatus + :ivar file_counts: Files count grouped by status processed or being processed by this vector + store. Required. + :vartype file_counts: ~azure.ai.assistants.models.VectorStoreFileCount + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["vector_store.files_batch"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The object type, which is always ``vector_store.file_batch``. Required. Default value is + \"vector_store.files_batch\".""" + created_at: datetime.datetime = rest_field( + visibility=["read", "create", "update", "delete", "query"], format="unix-timestamp" + ) + """The Unix timestamp (in seconds) for when the vector store files batch was created. Required.""" + vector_store_id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the vector store that the file is attached to. Required.""" + status: Union[str, "_models.VectorStoreFileBatchStatus"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The status of the vector store files batch, which can be either ``in_progress``, ``completed``, + ``cancelled`` or ``failed``. Required. Known values are: \"in_progress\", \"completed\", + \"cancelled\", and \"failed\".""" + file_counts: "_models.VectorStoreFileCount" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Files count grouped by status processed or being processed by this vector store. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + vector_store_id: str, + status: Union[str, "_models.VectorStoreFileBatchStatus"], + file_counts: "_models.VectorStoreFileCount", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.files_batch"] = "vector_store.files_batch" + + +class VectorStoreFileCount(_Model): + """Counts of files processed or being processed by this vector store grouped by status. + + :ivar in_progress: The number of files that are currently being processed. Required. + :vartype in_progress: int + :ivar completed: The number of files that have been successfully processed. Required. + :vartype completed: int + :ivar failed: The number of files that have failed to process. Required. + :vartype failed: int + :ivar cancelled: The number of files that were cancelled. Required. + :vartype cancelled: int + :ivar total: The total number of files. Required. + :vartype total: int + """ + + in_progress: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The number of files that are currently being processed. Required.""" + completed: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The number of files that have been successfully processed. Required.""" + failed: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The number of files that have failed to process. Required.""" + cancelled: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The number of files that were cancelled. Required.""" + total: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The total number of files. Required.""" + + @overload + def __init__( + self, + *, + in_progress: int, + completed: int, + failed: int, + cancelled: int, + total: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreFileDeletionStatus(_Model): + """Response object for deleting a vector store file relationship. + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'vector_store.deleted'. Required. Default value + is "vector_store.file.deleted". + :vartype object: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A value indicating whether deletion was successful. Required.""" + object: Literal["vector_store.file.deleted"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The object type, which is always 'vector_store.deleted'. Required. Default value is + \"vector_store.file.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.file.deleted"] = "vector_store.file.deleted" + + +class VectorStoreFileError(_Model): + """Details on the error that may have occurred while processing a file for this vector store. + + :ivar code: One of ``server_error`` or ``rate_limit_exceeded``. Required. Known values are: + "server_error", "invalid_file", and "unsupported_file". + :vartype code: str or ~azure.ai.assistants.models.VectorStoreFileErrorCode + :ivar message: A human-readable description of the error. Required. + :vartype message: str + """ + + code: Union[str, "_models.VectorStoreFileErrorCode"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """One of ``server_error`` or ``rate_limit_exceeded``. Required. Known values are: + \"server_error\", \"invalid_file\", and \"unsupported_file\".""" + message: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A human-readable description of the error. Required.""" + + @overload + def __init__( + self, + *, + code: Union[str, "_models.VectorStoreFileErrorCode"], + message: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreStaticChunkingStrategyOptions(_Model): + """Options to configure a vector store static chunking strategy. + + :ivar max_chunk_size_tokens: The maximum number of tokens in each chunk. The default value is + 800. The minimum value is 100 and the maximum value is 4096. Required. + :vartype max_chunk_size_tokens: int + :ivar chunk_overlap_tokens: The number of tokens that overlap between chunks. The default value + is 400. + Note that the overlap must not exceed half of max_chunk_size_tokens. Required. + :vartype chunk_overlap_tokens: int + """ + + max_chunk_size_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The maximum number of tokens in each chunk. The default value is 800. The minimum value is 100 + and the maximum value is 4096. Required.""" + chunk_overlap_tokens: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The number of tokens that overlap between chunks. The default value is 400. + Note that the overlap must not exceed half of max_chunk_size_tokens. Required.""" + + @overload + def __init__( + self, + *, + max_chunk_size_tokens: int, + chunk_overlap_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreStaticChunkingStrategyRequest(VectorStoreChunkingStrategyRequest, discriminator="static"): + """A statically configured chunking strategy. + + :ivar type: The object type, which is always 'static'. Required. + :vartype type: str or ~azure.ai.assistants.models.STATIC + :ivar static: The options for the static chunking strategy. Required. + :vartype static: ~azure.ai.assistants.models.VectorStoreStaticChunkingStrategyOptions + """ + + type: Literal[VectorStoreChunkingStrategyRequestType.STATIC] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'static'. Required.""" + static: "_models.VectorStoreStaticChunkingStrategyOptions" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The options for the static chunking strategy. Required.""" + + @overload + def __init__( + self, + *, + static: "_models.VectorStoreStaticChunkingStrategyOptions", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyRequestType.STATIC, **kwargs) + + +class VectorStoreStaticChunkingStrategyResponse( + VectorStoreChunkingStrategyResponse, discriminator="static" +): # pylint: disable=name-too-long + """A statically configured chunking strategy. + + :ivar type: The object type, which is always 'static'. Required. + :vartype type: str or ~azure.ai.assistants.models.STATIC + :ivar static: The options for the static chunking strategy. Required. + :vartype static: ~azure.ai.assistants.models.VectorStoreStaticChunkingStrategyOptions + """ + + type: Literal[VectorStoreChunkingStrategyResponseType.STATIC] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The object type, which is always 'static'. Required.""" + static: "_models.VectorStoreStaticChunkingStrategyOptions" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The options for the static chunking strategy. Required.""" + + @overload + def __init__( + self, + *, + static: "_models.VectorStoreStaticChunkingStrategyOptions", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyResponseType.STATIC, **kwargs) diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_patch.py b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/models/_patch.py @@ -0,0 +1,21 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-assistants/azure/ai/assistants/py.typed b/sdk/ai/azure-ai-assistants/azure/ai/assistants/py.typed new file mode 100644 index 000000000000..e5aff4f83af8 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/azure/ai/assistants/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561. \ No newline at end of file diff --git a/sdk/ai/azure-ai-assistants/dev_requirements.txt b/sdk/ai/azure-ai-assistants/dev_requirements.txt new file mode 100644 index 000000000000..105486471444 --- /dev/null +++ b/sdk/ai/azure-ai-assistants/dev_requirements.txt @@ -0,0 +1,3 @@ +-e ../../../tools/azure-sdk-tools +../../core/azure-core +aiohttp \ No newline at end of file diff --git a/sdk/ai/azure-ai-assistants/sdk_packaging.toml b/sdk/ai/azure-ai-assistants/sdk_packaging.toml new file mode 100644 index 000000000000..e7687fdae93b --- /dev/null +++ b/sdk/ai/azure-ai-assistants/sdk_packaging.toml @@ -0,0 +1,2 @@ +[packaging] +auto_update = false \ No newline at end of file diff --git a/sdk/ai/azure-ai-assistants/setup.py b/sdk/ai/azure-ai-assistants/setup.py new file mode 100644 index 000000000000..45e0a1d1187c --- /dev/null +++ b/sdk/ai/azure-ai-assistants/setup.py @@ -0,0 +1,70 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + + +import os +import re +from setuptools import setup, find_packages + + +PACKAGE_NAME = "azure-ai-assistants" +PACKAGE_PPRINT_NAME = "Azure Ai Assistants" + +# a-b-c => a/b/c +package_folder_path = PACKAGE_NAME.replace("-", "/") + +# Version extraction inspired from 'requests' +with open(os.path.join(package_folder_path, "_version.py"), "r") as fd: + version = re.search(r'^VERSION\s*=\s*[\'"]([^\'"]*)[\'"]', fd.read(), re.MULTILINE).group(1) + +if not version: + raise RuntimeError("Cannot find version information") + + +setup( + name=PACKAGE_NAME, + version=version, + description="Microsoft Corporation {} Client Library for Python".format(PACKAGE_PPRINT_NAME), + long_description=open("README.md", "r").read(), + long_description_content_type="text/markdown", + license="MIT License", + author="Microsoft Corporation", + author_email="azpysdkhelp@microsoft.com", + url="https://github.com/Azure/azure-sdk-for-python/tree/main/sdk", + keywords="azure, azure sdk", + classifiers=[ + "Development Status :: 4 - Beta", + "Programming Language :: Python", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "License :: OSI Approved :: MIT License", + ], + zip_safe=False, + packages=find_packages( + exclude=[ + "tests", + # Exclude packages that will be covered by PEP420 or nspkg + "azure", + "azure.ai", + ] + ), + include_package_data=True, + package_data={ + "azure.ai.assistants": ["py.typed"], + }, + install_requires=[ + "isodate>=0.6.1", + "azure-core>=1.30.0", + "typing-extensions>=4.6.0", + ], + python_requires=">=3.9", +) diff --git a/sdk/ai/azure-ai-assistants/tsp-location.yaml b/sdk/ai/azure-ai-assistants/tsp-location.yaml new file mode 100644 index 000000000000..e61907167f6b --- /dev/null +++ b/sdk/ai/azure-ai-assistants/tsp-location.yaml @@ -0,0 +1,4 @@ +directory: specification/ai/Azure.AI.Assistants +commit: 4f2d368e0e665192c789907ef9211b058710b828 +repo: Azure/azure-rest-api-specs +additionalDirectories: diff --git a/sdk/ai/ci.yml b/sdk/ai/ci.yml index 117b9c6c785d..7e605f378276 100644 --- a/sdk/ai/ci.yml +++ b/sdk/ai/ci.yml @@ -59,3 +59,5 @@ extends: # safeName: azureaigenerative - name: azure-ai-resources safeName: azureairesources + - name: azure-ai-assistants + safeName: azureaiassistants