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.openpublishing.redirection.json

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{
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"redirections": [
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{
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"source_path": "articles/network-watcher/network-watcher-security-group-view-powershell.md",
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"redirect_url": "/previous-versions/azure/network-watcher/network-watcher-security-group-view-powershell",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/network-watcher/network-watcher-security-group-view-cli.md",
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"redirect_url": "/previous-versions/azure/network-watcher/network-watcher-security-group-view-cli",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/storage/blobs/blob-v11-samples-dotnet.md",
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"redirect_url": "/previous-versions/azure/storage/blobs/blob-v11-samples-dotnet",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/storage/blobs/blob-v11-samples-javascript.md",
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"redirect_url": "/previous-versions/azure/storage/blobs/blob-v11-samples-javascript",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/storage/blobs/blob-v2-samples-python.md",
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"redirect_url": "/previous-versions/azure/storage/blobs/blob-v2-samples-python",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/storage/common/geo-redundant-design-legacy.md",
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"redirect_url": "/previous-versions/azure/storage/common/geo-redundant-design-legacy",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/germany/index.yml",
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"redirect_url": "/previous-versions/azure/germany/index",
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"source_path_from_root":"/articles/aks/generation-2-vm-windows.md",
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"redirect_url":"/azure/aks/generation-2-vm",
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"redirect_document_id":false
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},
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{
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"source_path_from_root":"/articles/cosmos-db/high-availability.md",
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"redirect_url":"/azure/reliability/reliability-cosmos-db-nosql.md",
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"redirect_document_id":false
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}
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]
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}

articles/active-directory-b2c/custom-policies-series-sign-up-or-sign-in-federation.md

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Just like in sign-in with a local account, you need to configure the [Microsoft Entra Technical Profiles](active-directory-technical-profile.md), which you use to connect to Microsoft Entra ID storage, to store or read a user social account.
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1. In the `ContosoCustomPolicy.XML` file, locate the `AAD-UserRead` technical profile and then add a new technical profile by using the following code:
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1. In the `ContosoCustomPolicy.XML` file, locate the `AAD-UserRead` technical profile and then add a new technical profile below it by using the following code:
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```xml
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<TechnicalProfile Id="AAD-UserWriteUsingAlternativeSecurityId">
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```xml
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<OutputClaim ClaimTypeReferenceId="authenticationSource" DefaultValue="localIdpAuthentication" AlwaysUseDefaultValue="true" />
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```
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Make sure you also add the `authenticationSource` claim in the output claims collection of the `UserSignInCollector` self-asserted technical profile.
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1. In the `UserJourneys` section, add a new user journey, `LocalAndSocialSignInAndSignUp` by using the following code:
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articles/ai-services/content-moderator/includes/tool-deprecation.md

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> [!IMPORTANT]
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> Azure Content Moderator is being deprecated in February 2024, and will be retired by February 2027. It is being replaced by [Azure AI Content Safety](/azure/ai-services/content-safety/overview), which offers advanced AI features and enhanced performance.
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> Azure Content Moderator is deprecated as of February 2024 and will be retired by February 2027. It is replaced by [Azure AI Content Safety](/azure/ai-services/content-safety/overview), which offers advanced AI features and enhanced performance.
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>
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> Azure AI Content Safety is a comprehensive solution designed to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety is suitable for many scenarios such as online marketplaces, gaming companies, social messaging platforms, enterprise media companies, and K-12 education solution providers. Here's an overview of its features and capabilities:
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>

articles/ai-services/content-moderator/index.yml

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### YamlMime:Landing
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title: Content Moderator documentation # < 60 chars
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summary: "The Azure Content Moderator API checks text, image, and video content for material that is potentially offensive, risky, or otherwise undesirable. Content Moderator is being deprecated in February 2024, and will be retired by February 2027. It is being replaced by Azure AI Content Safety, which offers advanced AI features and enhanced performance. Azure AI Content Safety is a comprehensive solution designed to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety is suitable for many scenarios such as online marketplaces, gaming companies, social messaging platforms, enterprise media companies, and K-12 education solution providers." # < 160 chars
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summary: "Azure Content Moderator is deprecated as of February 2024 and will be retired by February 2027. It is replaced by Azure AI Content Safety, which offers advanced AI features and enhanced performance. Azure AI Content Safety is a comprehensive solution designed to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety is suitable for many scenarios such as online marketplaces, gaming companies, social messaging platforms, enterprise media companies, and K-12 education solution providers." # < 160 chars
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metadata:
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title: Content Moderator Documentation - Quickstarts, Tutorials, API Reference - Azure AI services | Microsoft Docs

articles/ai-services/openai/assistants-reference-runs.md

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@@ -5,7 +5,7 @@ description: Learn how to use Azure OpenAI's Python & REST API runs with Assista
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manager: nitinme
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ms.service: azure-ai-openai
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ms.topic: conceptual
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ms.date: 02/01/2024
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ms.date: 04/16/2024
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author: mrbullwinkle
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ms.author: mbullwin
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recommendations: false
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| `failed_at`| integer or null | The Unix timestamp (in seconds) for when the run step failed.|
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| `completed_at`| integer or null | The Unix timestamp (in seconds) for when the run step completed.|
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| `metadata`| map | Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format. Keys can be a maximum of 64 characters long and values can be a maximum of 512 characters long.|
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## Stream a run result (preview)
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Stream the result of executing a Run or resuming a Run after submitting tool outputs. You can stream events after:
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* [Create Thread and Run](#create-thread-and-run)
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* [Create Run](#create-run)
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* [Submit Tool Outputs](#submit-tool-outputs-to-run)
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To stream a result, pass `"stream": true` while creating a run. The response will be a [Server-Sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events) stream.
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### Streaming example
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```python
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from typing_extensions import override
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from openai import AssistantEventHandler
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# First, we create a EventHandler class to define
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# how we want to handle the events in the response stream.
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class EventHandler(AssistantEventHandler):
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@override
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def on_text_created(self, text) -> None:
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print(f"\nassistant > ", end="", flush=True)
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@override
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def on_text_delta(self, delta, snapshot):
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print(delta.value, end="", flush=True)
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def on_tool_call_created(self, tool_call):
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print(f"\nassistant > {tool_call.type}\n", flush=True)
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def on_tool_call_delta(self, delta, snapshot):
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if delta.type == 'code_interpreter':
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if delta.code_interpreter.input:
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print(delta.code_interpreter.input, end="", flush=True)
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if delta.code_interpreter.outputs:
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print(f"\n\noutput >", flush=True)
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for output in delta.code_interpreter.outputs:
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if output.type == "logs":
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print(f"\n{output.logs}", flush=True)
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# Then, we use the `create_and_stream` SDK helper
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# with the `EventHandler` class to create the Run
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# and stream the response.
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with client.beta.threads.runs.stream(
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thread_id=thread.id,
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assistant_id=assistant.id,
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instructions="Please address the user as Jane Doe. The user has a premium account.",
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event_handler=EventHandler(),
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) as stream:
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stream.until_done()
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```
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## Message delta object
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Represents a message delta. For example any changed fields on a message during streaming.
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|Name | Type | Description |
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|--- |--- |--- |
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| `id` | string | The identifier of the message, which can be referenced in API endpoints. |
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| `object` | string | The object type, which is always `thread.message.delta`. |
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| `delta` | object | The delta containing the fields that have changed on the Message. |
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## Run step delta object
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Represents a run step delta. For example any changed fields on a run step during streaming.
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|Name | Type | Description |
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|--- |--- |--- |
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| `id` | string | The identifier of the run step, which can be referenced in API endpoints. |
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| `object` | string | The object type, which is always `thread.run.step.delta`. |
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| `delta` | object | The delta containing the fields that have changed on the run step.
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## Assistant stream events
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Represents an event emitted when streaming a Run. Each event in a server-sent events stream has an event and data property:
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```json
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event: thread.created
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data: {"id": "thread_123", "object": "thread", ...}
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```
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Events are emitted whenever a new object is created, transitions to a new state, or is being streamed in parts (deltas). For example, `thread.run.created` is emitted 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.
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|Name | Type | Description |
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|--- |--- |--- |
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| `thread.created` | `data` is a thread. | Occurs when a new thread is created. |
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| `thread.run.created` | `data` is a run. | Occurs when a new run is created. |
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| `thread.run.queued` | `data` is a run. | Occurs when a run moves to a queued status. |
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| `thread.run.in_progress` | `data` is a run. | Occurs when a run moves to an in_progress status. |
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| `thread.run.requires_action` | `data` is a run. | Occurs when a run moves to a `requires_action` status. |
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| `thread.run.completed` | `data` is a run. | Occurs when a run is completed. |
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| `thread.run.failed` | `data` is a run. | Occurs when a run fails. |
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| `thread.run.cancelling` | `data` is a run. | Occurs when a run moves to a `cancelling` status. |
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| `thread.run.cancelled` | `data` is a run. | Occurs when a run is cancelled. |
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| `thread.run.expired` | `data` is a run. | Occurs when a run expires. |
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| `thread.run.step.created` | `data` is a run step. | Occurs when a run step is created. |
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| `thread.run.step.in_progress` | `data` is a run step. | Occurs when a run step moves to an `in_progress` state. |
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| `thread.run.step.delta` | `data` is a run step delta. | Occurs when parts of a run step are being streamed. |
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| `thread.run.step.completed` | `data` is a run step. | Occurs when a run step is completed. |
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| `thread.run.step.failed` | `data` is a run step. | Occurs when a run step fails. |
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| `thread.run.step.cancelled` | `data` is a run step. | Occurs when a run step is cancelled. |
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| `thread.run.step.expired` | `data` is a run step. | Occurs when a run step expires. |
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| `thread.message.created` | `data` is a message. | Occurs when a message is created. |
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| `thread.message.in_progress` | `data` is a message. | Occurs when a message moves to an in_progress state. |
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| `thread.message.delta` | `data` is a message delta. | Occurs when parts of a Message are being streamed. |
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| `thread.message.completed` | `data` is a message. | Occurs when a message is completed. |
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| `thread.message.incomplete` | `data` is a message. | Occurs when a message ends before it is completed. |
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| `error` | `data` is an error. | Occurs when an error occurs. This can happen due to an internal server error or a timeout. |
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| `done` | `data` is `[DONE]` | Occurs when a stream ends. |

articles/ai-services/openai/concepts/models.md

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# Azure OpenAI Service models
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Azure OpenAI Service is powered by a diverse set of models with different capabilities and price points. Model availability varies by region. For GPT-3 and other models retiring in July 2024, see [Azure OpenAI Service legacy models](./legacy-models.md).
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Azure OpenAI Service is powered by a diverse set of models with different capabilities and price points. Model availability varies by region. For GPT-3 and other models retiring in July 2024, see [Azure OpenAI Service legacy models](./legacy-models.md).
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| Models | Description |
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|--|--|
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| [GPT-4 Turbo 🆕](#gpt-4-turbo) | The latest most capable Azure OpenAI models with multimodal versions which can accept both text and images as input. |
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| [GPT-4 Turbo **NEW**](#gpt-4-turbo) | The latest most capable Azure OpenAI models with multimodal versions, which can accept both text and images as input. |
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| [GPT-4](#gpt-4) | A set of models that improve on GPT-3.5 and can understand and generate natural language and code. |
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| [GPT-3.5](#gpt-35) | A set of models that improve on GPT-3 and can understand and generate natural language and code. |
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| [Embeddings](#embeddings-models) | A set of models that can convert text into numerical vector form to facilitate text similarity. |
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See [model versions](../concepts/model-versions.md) to learn about how Azure OpenAI Service handles model version upgrades, and [working with models](../how-to/working-with-models.md) to learn how to view and configure the model version settings of your GPT-4 deployments.
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| Model ID | Max Request (tokens) | Training Data (up to) |
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| --- | :--- | :---: |
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| `gpt-4` (0314) | 8,192 | Sep 2021 |
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| `gpt-4-32k`(0314) | 32,768 | Sep 2021 |
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| `gpt-4` (0613) | 8,192 | Sep 2021 |
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| `gpt-4-32k` (0613) | 32,768 | Sep 2021 |
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| `gpt-4` (1106-Preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000 <br> Output: 4,096 | Apr 2023 |
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| `gpt-4` (0125-Preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000 <br> Output: 4,096 | Dec 2023 |
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| `gpt-4` (vision-preview)**<sup>2</sup>**<br>**GPT-4 Turbo with Vision Preview** | Input: 128,000 <br> Output: 4,096 | Apr 2023 |
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| `gpt-4` (turbo-2024-04-09) 🆕 <br>**GPT-4 Turbo with Vision GA** | Input: 128,000 <br> Output: 4,096 | Dec 2023 |
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**<sup>1</sup>** GPT-4 Turbo Preview = `gpt-4` (0125-Preview) or `gpt-4` (1106-Preview). To deploy this model, under **Deployments** select model **gpt-4**. Under version select (0125-Preview) or (1106-Preview).
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**<sup>2</sup>** GPT-4 Turbo with Vision Preview = `gpt-4` (vision-preview). To deploy this model, under **Deployments** select model **gpt-4**. For **Model version** select **vision-preview**.
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| Model ID | Description | Max Request (tokens) | Training Data (up to) |
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| --- | :--- |:--- |:---: |
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| `gpt-4` (turbo-2024-04-09) <br>**GPT-4 Turbo with Vision** | **Latest GA model** <br> - Replacement for all GPT-4 preview models (`vision-preview`, `1106-Preview`, `0125-Preview`). <br> - [**Feature availability**](#gpt-4-turbo) is currently different depending on method of input, and deployment type. <br> - Does **not support** enhancements. | Input: 128,000 <br> Output: 4,096 | Dec 2023 |
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| `gpt-4` (0125-Preview)*<br>**GPT-4 Turbo Preview** | **Preview Model** <br> -Replaces 1106-Preview <br>- Better code generation performance <br> - Reduces cases where the model doesn't complete a task <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000 <br> Output: 4,096 | Dec 2023 |
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| `gpt-4` (vision-preview)<br>**GPT-4 Turbo with Vision Preview** | **Preview model** <br> - Accepts text and image input. <br> - Supports enhancements <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000 <br> Output: 4,096 | Apr 2023 |
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| `gpt-4` (1106-Preview)<br>**GPT-4 Turbo Preview** | **Preview Model** <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000 <br> Output: 4,096 | Apr 2023 |
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| `gpt-4-32k` (0613) | **Older GA model** <br> - Basic function calling with tools | 32,768 | Sep 2021 |
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| `gpt-4` (0613) | **Older GA model** <br> - Basic function calling with tools | 8,192 | Sep 2021 |
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| `gpt-4-32k`(0314) | **Older GA model** <br> - Deprecated function calling | 32,768 | Sep 2021 |
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| `gpt-4` (0314) | **Older GA model** <br> - Deprecated function calling | 8,192 | Sep 2021 |
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> [!CAUTION]
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> We don't recommend using preview models in production. We will upgrade all deployments of preview models to future preview versions and a stable version. Models designated preview do not follow the standard Azure OpenAI model lifecycle.
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> We don't recommend using preview models in production. We will upgrade all deployments of preview models to either future preview versions or to the latest stable/GA version. Models designated preview do not follow the standard Azure OpenAI model lifecycle.
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> [!NOTE]
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> Version `0314` of `gpt-4` and `gpt-4-32k` will be retired no earlier than July 5, 2024. Version `0613` of `gpt-4` and `gpt-4-32k` will be retired no earlier than September 30, 2024. See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.
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- GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview previously released as version 1106-preview.
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- GPT-4 version 0125-preview completes tasks such as code generation more completely compared to gpt-4-1106-preview. Because of this, depending on the task, customers may find that GPT-4-0125-preview generates more output compared to the gpt-4-1106-preview. We recommend customers compare the outputs of the new model. GPT-4-0125-preview also addresses bugs in gpt-4-1106-preview with UTF-8 handling for non-English languages. GPT-4 version `turbo-2024-04-09` is the latest GA release and replaces `0125-Preview`, `1106-preview`, and `vision-preview`.
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- GPT-4 version 0125-preview completes tasks such as code generation more completely compared to gpt-4-1106-preview. Because of this, depending on the task, customers may find that GPT-4-0125-preview generates more output compared to the gpt-4-1106-preview. We recommend customers compare the outputs of the new model. GPT-4-0125-preview also addresses bugs in gpt-4-1106-preview with UTF-8 handling for non-English languages. GPT-4 version `turbo-2024-04-09` is the latest GA release and replaces `0125-Preview`, `1106-preview`, and `vision-preview`.
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> [!IMPORTANT]
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>

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