Merge pull request #41 from MicrosoftDocs/main

8/29/2024 AM Publish
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diff --git a/.gitignore b/.gitignore
@@ -1,3 +1,7 @@
+# Operating System files
+.DS_Store
+Thumbs.db
+
 log/
 obj/
 _site/

diff --git a/articles/ai-services/openai/how-to/deployment-types.md b/articles/ai-services/openai/how-to/deployment-types.md
@@ -30,7 +30,7 @@ Azure OpenAI offers three types of deployments. These provide a varied level of
 
 | **Offering** | **Global-Batch** | **Global-Standard** | **Standard** | **Provisioned**  |
 |---|:---|:---|:---|:---|
-| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br> For use cases that do not have data processing residency requirements.| Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. <br><br> For production applications that do not have data processing residency requirements. | For customers with data residency requirements. Optimized for low to medium volume. | Real-time scoring for large consistent volume. Includes the highest commitments and limits.|
+| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br> For use cases that do not have data processing residency requirements.| Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. | For customers with data residency requirements. Optimized for low to medium volume. | Real-time scoring for large consistent volume. Includes the highest commitments and limits.|
 | **How it works**         | Offline processing via files |Traffic may be routed anywhere in the world | | |
 | **Getting started**      | [Global-Batch](./batch.md) | [Model deployment](./create-resource.md) | [Model deployment](./create-resource.md) | [Provisioned onboarding](./provisioned-throughput-onboarding.md) |
 | **Cost**                 | [Least expensive option](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> 50% less cost compared to Global Standard prices. Access to all new models with larger quota allocations.  | [Global deployment pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |  [Regional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | May experience cost savings for consistent usage |

diff --git a/articles/ai-studio/how-to/costs-plan-manage.md b/articles/ai-studio/how-to/costs-plan-manage.md
@@ -18,7 +18,10 @@ author: Blackmist
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
-This article describes how you plan for and manage costs for Azure AI Studio. First, you use the Azure pricing calculator to help plan for Azure AI Studio costs before you add any resources for the service to estimate costs. Next, as you add Azure resources, review the estimated costs. 
+This article describes how you plan for and manage costs for Azure AI Studio. First, you use the Azure pricing calculator to help plan for Azure AI Studio costs before you add any resources for the service to estimate costs. Next, as you add Azure resources, review the estimated costs.
+
+> [!TIP]
+> Azure AI Studio does not have a specific page in the Azure pricing calculator. Azure AI Studio is composed of several other Azure services, some of which are optional. This article provides information on using the pricing calculator to estimate costs for these services.
 
 You use Azure AI services in Azure AI Studio. Costs for Azure AI services are only a portion of the monthly costs in your Azure bill. You're billed for all Azure services and resources used in your Azure subscription, including the third-party services.
 

diff --git a/articles/ai-studio/how-to/deploy-models-cohere-command.md b/articles/ai-studio/how-to/deploy-models-cohere-command.md
@@ -231,7 +231,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -244,7 +244,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 
@@ -256,13 +256,15 @@ Cohere Command chat models can create JSON outputs. Set `response_format` to `js
 
 
 ```python
+from azure.ai.inference.models import ChatCompletionsResponseFormatJSON
+
 response = client.complete(
     messages=[
         SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using."
                       " the following format: { ""answer"": ""response"" }."),
         UserMessage(content="How many languages are in the world?"),
     ],
-    response_format={ "type": ChatCompletionsResponseFormat.JSON_OBJECT }
+    response_format=ChatCompletionsResponseFormatJSON()
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-cohere-embed.md b/articles/ai-studio/how-to/deploy-models-cohere-embed.md
@@ -617,12 +617,12 @@ Cohere Embed V3 models can optimize the embeddings based on its use case.
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
-| Web requests                              | Bash              | [Command-R](https://aka.ms/samples/cohere-command-r/webrequests) - [Command-R+](https://aka.ms/samples/cohere-command-r-plus/webrequests) |
+| Web requests                              | Bash              | [cohere-embed.ipynb](https://aka.ms/samples/embed-v3/webrequests) |
 | Azure AI Inference package for JavaScript | JavaScript        | [Link](https://aka.ms/azsdk/azure-ai-inference/javascript/samples)  |
 | Azure AI Inference package for Python     | Python            | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples)      |
-| OpenAI SDK (experimental)                 | Python            | [Link](https://aka.ms/samples/cohere-command/openaisdk)             |
-| LangChain                                 | Python            | [Link](https://aka.ms/samples/cohere/langchain)                     |
-| Cohere SDK                                | Python            | [Link](https://aka.ms/samples/cohere-python-sdk)                    |
+| OpenAI SDK (experimental)                 | Python            | [Link](https://aka.ms/samples/cohere-embed/openaisdk)             |
+| LangChain                                 | Python            | [Link](https://aka.ms/samples/cohere-embed/langchain)                     |
+| Cohere SDK                                | Python            | [Link](https://aka.ms/samples/cohere-embed/cohere-python-sdk)                    |
 | LiteLLM SDK                               | Python            | [Link](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/cohere/litellm.ipynb) |
 
 #### Retrieval Augmented Generation (RAG) and tool use samples

diff --git a/articles/ai-studio/how-to/deploy-models-jais.md b/articles/ai-studio/how-to/deploy-models-jais.md
@@ -201,7 +201,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -214,7 +214,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-llama.md b/articles/ai-studio/how-to/deploy-models-llama.md
@@ -255,7 +255,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -268,7 +268,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-mistral-nemo.md b/articles/ai-studio/how-to/deploy-models-mistral-nemo.md
@@ -209,7 +209,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -222,7 +222,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 
@@ -234,13 +234,15 @@ Mistral Nemo chat model can create JSON outputs. Set `response_format` to `json_
 
 
 ```python
+from azure.ai.inference.models import ChatCompletionsResponseFormatJSON
+
 response = client.complete(
     messages=[
         SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using."
                       " the following format: { ""answer"": ""response"" }."),
         UserMessage(content="How many languages are in the world?"),
     ],
-    response_format={ "type": ChatCompletionsResponseFormat.JSON_OBJECT }
+    response_format=ChatCompletionsResponseFormatJSON()
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-mistral-open.md b/articles/ai-studio/how-to/deploy-models-mistral-open.md
@@ -257,7 +257,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -270,7 +270,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-mistral.md b/articles/ai-studio/how-to/deploy-models-mistral.md
@@ -239,7 +239,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -252,7 +252,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 
@@ -264,13 +264,15 @@ Mistral premium chat models can create JSON outputs. Set `response_format` to `j
 
 
 ```python
+from azure.ai.inference.models import ChatCompletionsResponseFormatJSON
+
 response = client.complete(
     messages=[
         SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using."
                       " the following format: { ""answer"": ""response"" }."),
         UserMessage(content="How many languages are in the world?"),
     ],
-    response_format={ "type": ChatCompletionsResponseFormat.JSON_OBJECT }
+    response_format=ChatCompletionsResponseFormatJSON()
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-phi-3-5-moe.md b/articles/ai-studio/how-to/deploy-models-phi-3-5-moe.md
@@ -219,7 +219,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -232,7 +232,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-phi-3-5-vision.md b/articles/ai-studio/how-to/deploy-models-phi-3-5-vision.md
@@ -215,7 +215,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -228,7 +228,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-phi-3-vision.md b/articles/ai-studio/how-to/deploy-models-phi-3-vision.md
@@ -215,7 +215,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -228,7 +228,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/ai-studio/how-to/deploy-models-phi-3.md b/articles/ai-studio/how-to/deploy-models-phi-3.md
@@ -256,7 +256,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -269,7 +269,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/ai-studio/index.yml b/articles/ai-studio/index.yml
@@ -8,6 +8,7 @@ metadata:
   ms.service: azure-ai-studio
   ms.custom:
     - build-2024
+    - copilot-learning-hub
   ms.topic: landing-page
   ms.reviewer: eur
   ms.author: eur

diff --git a/articles/ai-studio/reference/reference-model-inference-api.md b/articles/ai-studio/reference/reference-model-inference-api.md
@@ -318,7 +318,7 @@ The following example shows the response for a chat completion request indicatin
 
 ```python
 import json
-from azure.ai.inference.models import SystemMessage, UserMessage, ChatCompletionsResponseFormat
+from azure.ai.inference.models import SystemMessage, UserMessage, ChatCompletionsResponseFormatJSON
 from azure.core.exceptions import HttpResponseError
 
 try:
@@ -327,7 +327,7 @@ try:
             SystemMessage(content="You are a helpful assistant."),
             UserMessage(content="How many languages are in the world?"),
         ],
-        response_format={ "type": ChatCompletionsResponseFormat.JSON_OBJECT }
+        response_format=ChatCompletionsResponseFormatJSON()
     )
 except HttpResponseError as ex:
     if ex.status_code == 422:

diff --git a/articles/machine-learning/.openpublishing.redirection.machine-learning.json b/articles/machine-learning/.openpublishing.redirection.machine-learning.json
@@ -2972,8 +2972,13 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/v-fake/how-to-cicd-data-ingestion.md",
-        "redirect_url": "/azure/machine-learning/how-to-cicd-data-ingestion",
-        "redirect_document_id": true
+        "redirect_url": "/azure/machine-learning/how-to-devops-machine-learning",
+        "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/v1/how-to-cicd-data-ingestion.md",
+        "redirect_url": "/azure/machine-learning/how-to-devops-machine-learning",
+        "redirect_document_id": false
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-debug-pipelines-application-insights.md",

diff --git a/articles/machine-learning/component-reference/add-columns.md b/articles/machine-learning/component-reference/add-columns.md
@@ -18,8 +18,6 @@ This article describes a component in Azure Machine Learning designer.
 
 Use this component to concatenate two datasets. You combine all columns from the two datasets that you specify as inputs to create a single dataset. If you need to concatenate more than two datasets, use several instances of **Add Columns**.
 
-
-
 ## How to configure Add Columns
 1. Add the **Add Columns** component to your pipeline.
 
@@ -42,4 +40,4 @@ If there are two columns with the same name in the input datasets, a numeric suf
 
 ## Next steps
 
-See the [set of components available](component-reference.md) to Azure Machine Learning. 
+See the [set of components available](component-reference.md) to Azure Machine Learning.
diff --git a/articles/machine-learning/how-to-deploy-models-phi-3-5-moe.md b/articles/machine-learning/how-to-deploy-models-phi-3-5-moe.md
@@ -219,7 +219,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](reference-model-inference-api.md).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -232,7 +232,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/machine-learning/how-to-deploy-models-phi-3-5-vision.md b/articles/machine-learning/how-to-deploy-models-phi-3-5-vision.md
@@ -214,7 +214,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](reference-model-inference-api.md).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -227,7 +227,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```
 

diff --git a/articles/machine-learning/how-to-deploy-models-phi-3-vision.md b/articles/machine-learning/how-to-deploy-models-phi-3-vision.md
@@ -213,7 +213,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](reference-model-inference-api.md).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormat
+from azure.ai.inference.models import ChatCompletionsResponseFormatText
 
 response = client.complete(
     messages=[
@@ -226,7 +226,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
+    response_format=ChatCompletionsResponseFormatText(),
 )
 ```