Merge pull request #2108 from MicrosoftDocs/main

12/23/2024 AM Publish

Author: Taojunshen

articles/ai-services/agents/concepts/agents.md

@@ -24,9 +24,9 @@ Azure AI Agent Service is now available in public preview. The service makes it
 
 Previously, building custom AI agents needed heavy lifting even for experienced developers. While many APIs are lightweight and powerful like Azure OpenAI's chat completions API, it's inherently stateless which means that developers had to manage conversation state and chat threads, tool integrations, retrieval documents and indexes, and execute code manually.
 
-Azure AI Agents Service, as the evolution of the chat completion API and Assistants, provides a solution for these challenges. Agents support persistent automatically managed threads. This means that as a developer you no longer need to develop conversation state management systems and work around a model’s context window constraints. Agents will automatically handle the optimizations to keep the thread below the max context window of your chosen model. Once you create a thread, you can append new messages to it as users respond. Agents can also access multiple [tools](../how-to/tools/overview.md) in parallel.
+Azure AI Agent Service, as the evolution of the chat completion API and Assistants, provides a solution for these challenges. Agents support persistent automatically managed threads. This means that as a developer you no longer need to develop conversation state management systems and work around a model’s context window constraints. Agents will automatically handle the optimizations to keep the thread below the max context window of your chosen model. Once you create a thread, you can append new messages to it as users respond. Agents can also access multiple [tools](../how-to/tools/overview.md) in parallel.
 
-Azure AI Agents Service is built on the same capabilities that power Azure OpenAI's assistants. Some possible use cases range from AI-powered product recommender, sales analyst app, coding assistant, employee Q&A chatbot, and more.
+Azure AI Agent Service is built on the same capabilities that power Azure OpenAI's assistants. Some possible use cases range from AI-powered product recommender, sales analyst app, coding assistant, employee Q&A chatbot, and more.
 
 > [!IMPORTANT]
 > Retrieving untrusted data using Function Calling, Code Interpreter or File Search with file input, and agent threads functionalities could compromise the security of your agent, or the application that uses the agent.
@@ -75,7 +75,7 @@ When using the File Search tool, we recommend setting the `max_prompt_tokens` to
 
 ## Truncation strategy
 
-You can also specify a truncation strategy to control how your thread should be rendered into the model's context window. Using a truncation strategy of type `auto` will use OpenAI's default truncation strategy. Using a truncation strategy of type `last_messages` will allow you to specify the number of the most recent messages to include in the context window.
+You can also specify a truncation strategy to control how your thread should be rendered into the model's context window. Using a truncation strategy of type `auto` will use Azure OpenAI's default truncation strategy. Using a truncation strategy of type `last_messages` will allow you to specify the number of the most recent messages to include in the context window.
 
 ## See also
 * Learn more about [agents](../overview.md). <!--and [File Search](../how-to/tools/file-search.md)-->

articles/ai-services/agents/concepts/model-region-support.md

@@ -13,11 +13,11 @@ ms.custom: azure-ai-agents
 
 # Models supported by Azure AI Agent Service
 
-Agents are powered by a diverse set of models with different capabilities and price points. Model availability varies by region and cloud. Certain tools and capabilities require the latest models. The following models are available in the available SDKs. The following table is for pay-as-you-go. For information on Provisioned Throughput Unit (PTU) availability, see [provisioned throughput](../../openai/concepts/provisioned-throughput.md) in the OpenAI documentation. You can use [global standard models](../../openai/concepts/models.md#global-standard-model-availability) if they're supported in the regions listed here. 
+Agents are powered by a diverse set of models with different capabilities and price points. Model availability varies by region and cloud. Certain tools and capabilities require the latest models. The following models are available in the available SDKs. The following table is for pay-as-you-go. For information on Provisioned Throughput Unit (PTU) availability, see [provisioned throughput](../../openai/concepts/provisioned-throughput.md) in the Azure OpenAI documentation. You can use [global standard models](../../openai/concepts/models.md#global-standard-model-availability) if they're supported in the regions listed here. 
 
 ## Azure OpenAI models
 
-Azure AI Agent service supports the same models as the chat completions API in Azure OpenAI, in the following regions.
+Azure AI Agent Service supports the same models as the chat completions API in Azure OpenAI, in the following regions.
 
 | **Region**    | **gpt-4o**, **2024-05-13** | **gpt-4o**, **2024-08-06** | **gpt-4o-mini**, **2024-07-18** | **gpt-4**, **0613** | **gpt-4**, **1106-Preview** | **gpt-4**, **0125-Preview** | **gpt-4**, **vision-preview** | **gpt-4**, **turbo-2024-04-09** | **gpt-4-32k**, **0613** | **gpt-35-turbo**, **0613** | **gpt-35-turbo**, **1106** | **gpt-35-turbo**, **0125** | **gpt-35-turbo-16k**, **0613** |
 |:--------------|:--------------------------:|:--------------------------:|:-------------------------------:|:-------------------:|:---------------------------:|:---------------------------:|:-----------------------------:|:-------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:------------------------------:|
@@ -28,29 +28,29 @@ Azure AI Agent service supports the same models as the chat completions API in A
 | westus        | ✅                         | ✅                          | ✅                            | -                   | ✅                          | -                           | ✅                            | ✅                               | - | -                          | ✅                         | ✅                          | -                              |
 
 
-## Additional models
+## More models
 
-In addition to the supported Azure OpenAI models, you can also use the following 3rd party models with Azure AI Agent Service. 
+The Azure AI Agent Service also supports the following models from the Azure AI Foundry model catalog.
 
 * Llama 3.1-70B-instruct
 * Mistral-large-2407
 * Cohere command R+
 
 To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference it in your agent. 
 
-1. Go to the [Azure AI Foundry portal](https://ai.azure.com/) and select **Model catalog** in the left navigation menu, and scroll down to **Meta-Llama-3-70B-Instruct**. You can also find and use one of the models listed above.  
+1. Go to the [Azure AI Foundry portal](https://ai.azure.com/) and select **Model catalog** in the left navigation menu, and scroll down to **Meta-Llama-3-70B-Instruct**. You can also find and use one of the models listed previously.  
 
 1. Select **Deploy**. 
 
 1. In the Deployment options screen that appears, select **Serverless API** with Azure AI Content Safety. 
 
     :::image type="content" source="../media/llama/llama-deployment.png" alt-text="An image of the llama model project selection screen.":::
 
-1. Select your project and click **Subscribe and deploy**. 
+1. Select your project and then select **Subscribe and deploy**. 
 
     :::image type="content" source="../media/llama/llama-deployment-2.png" alt-text="An image of the llama model deployment screen.":::
 
-1. Add the serverless connection to your hub/project. The deployment name you choose will be the one you reference in your code.  
+1. Add the serverless connection to your hub/project. The deployment name you choose is the one that you reference in your code.  
 
 1. When calling agent creation API, set the `models` parameter to your deployment name. For example:
 

articles/ai-services/agents/faq.yml

@@ -19,7 +19,7 @@ sections:
       - question: |
           Do you store any data used in the AI Agent Service API? 
         answer: |
-          Yes. Unlike Chat Completions API, Azure OpenAI AI Agent Service is a stateful API, meaning it retains data. There are two types of data stored in the AI Agent Service API: 
+          Yes. Unlike Chat Completions API, Azure AI Agent Service is a stateful API, meaning it retains data. There are two types of data stored in the AI Agent Service API: 
           * Stateful entities: Threads, messages, and runs created during AI Agent Service use. 
           * Files: Uploaded during AI Agent Service setup or as part of a message.  
       - question: |
@@ -41,7 +41,7 @@ sections:
       - question: |
           Where is data stored geographically?
         answer: |
-          Azure OpenAI AI Agent Service endpoints are regional, and data is stored in the same region as the endpoint. For more information, see the [Azure data residency documentation](https://azure.microsoft.com/explore/global-infrastructure/data-residency/#overview). 
+          Azure AI Agent Service endpoints are regional, and data is stored in the same region as the endpoint. For more information, see the [Azure data residency documentation](https://azure.microsoft.com/explore/global-infrastructure/data-residency/#overview). 
       - question: |
           How am I charged for AI Agent Service?
         answer: |

articles/ai-services/agents/how-to/tools/azure-functions.md

@@ -17,7 +17,7 @@ zone_pivot_groups: selection-function-calling
 
 ::: zone pivot="overview"
 
-The Azure AI Agents service integrates with Azure Functions, enabling you to create intelligent, event-driven applications with minimal overhead. This combination allows AI-driven workflows to leverage the scalability and flexibility of serverless computing, making it easier to build and deploy solutions that respond to real-time events or complex workflows. 
+The Azure AI Agent Service integrates with Azure Functions, enabling you to create intelligent, event-driven applications with minimal overhead. This combination allows AI-driven workflows to leverage the scalability and flexibility of serverless computing, making it easier to build and deploy solutions that respond to real-time events or complex workflows. 
 
 Azure Functions provide support for triggers and bindings, which simplify how your AI Agents interact with external systems and services. Triggers determine when a function executes—such as an HTTP request, message from a queue, or a file upload to Azure Blob Storage and allows agents to act dynamically based on incoming events. 
 
@@ -31,7 +31,7 @@ Meanwhile, bindings facilitate streamlined connections to input or output data s
 
 ## Prepare your local environment
 
-The following examples highlight how to use the Azure AI Agent service function calling where function calls are placed on a storage queue by the Agent service to be processed by an Azure Function listening to that queue.
+The following examples highlight how to use the Azure AI Agent Service function calling where function calls are placed on a storage queue by the Agent Service to be processed by an Azure Function listening to that queue.
 
 You can find the template and code used here on [GitHub](https://github.com/Azure-Samples/azure-functions-ai-services-agent-python).
 
@@ -136,7 +136,7 @@ Start by defining an Azure Function queue trigger function that will process fun
 
 ```python
 # Function to get the weather from an Azure Storage queue where the AI Agent will send function call information
-# It returns the mock weather to an output queue with the correlation id for the AI Agent service to pick up the result of the function call
+# It returns the mock weather to an output queue with the correlation id for the AI Agent Service to pick up the result of the function call
 @app.function_name(name="GetWeather")
 @app.queue_trigger(arg_name="msg", queue_name="input", connection="STORAGE_CONNECTION")  
 def process_queue_message(msg: func.QueueMessage) -> None:
@@ -245,7 +245,7 @@ print(f"Created message, message ID: {message.id}")
 
 # Run the agent
 run = project_client.agents.create_run(thread_id=thread.id, assistant_id=agent.id)
-# Monitor and process the run status. The function call should be placed on the input queue by the Agent service for the Azure Function to pick up when requires_action is returned
+# Monitor and process the run status. The function call should be placed on the input queue by the Agent Service for the Azure Function to pick up when requires_action is returned
 while run.status in ["queued", "in_progress", "requires_action"]:
     time.sleep(1)
     run = project_client.agents.get_run(thread_id=thread.id, run_id=run.id)

articles/ai-services/agents/how-to/tools/bing-grounding.md

@@ -1,5 +1,5 @@
 ---
-title: 'How to use Grounding with Bing Search in Azure AI Agent service'
+title: 'How to use Grounding with Bing Search in Azure AI Agent Service'
 titleSuffix: Azure OpenAI
 description: Learn how to ground Azure AI Agents using Bing Search results.
 services: cognitive-services
@@ -109,7 +109,7 @@ from azure.identity import DefaultAzureCredential
 from azure.ai.projects.models import BingGroundingTool
 
 
-# Create an Azure AI Client from a connection string, copied from your AI Studio project.
+# Create an Azure AI Client from a connection string, copied from your Azure AI Foundry project.
 # At the moment, it should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<HubName>"
 # Customer needs to login to Azure subscription via Azure CLI and set the environment variables
 

articles/ai-services/agents/how-to/tools/code-interpreter.md

@@ -1,7 +1,7 @@
 ---
-title: 'How to use Azure AI Agent service Code Interpreter'
+title: 'How to use Azure AI Agent Service Code Interpreter'
 titleSuffix: Azure OpenAI
-description: Learn how to use Azure AI Agent service Code Interpreter
+description: Learn how to use Azure AI Agent Service Code Interpreter
 services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-agent-service
@@ -13,7 +13,7 @@ ms.custom: azure-ai-agents
 zone_pivot_groups: selection-code-interpreter
 ---
 
-# Azure AI Agent service Code Interpreter
+# Azure AI Agent Service Code Interpreter
 
 
 

articles/ai-services/agents/how-to/tools/file-search.md

@@ -45,7 +45,7 @@ The file search tool uses the Azure AI Search and Azure Blob Storage resources y
 - Vector stores get created using your connected Azure AI Search resource 
 <br> </br>
 
-For both agent setups, OpenAI handles the entire ingestion process, which includes:
+For both agent setups, Azure OpenAI handles the entire ingestion process, which includes:
 - Automatically parsing and chunking documents
 - Generating and storing embeddings
 - Utilizing both vector and keyword searches to retrieve relevant content for user queries. 

articles/ai-services/agents/how-to/tools/function-calling.md

@@ -1,5 +1,5 @@
 ---
-title: 'How to use Azure AI Agent service with function calling'
+title: 'How to use Azure AI Agent Service with function calling'
 titleSuffix: Azure OpenAI
 description: Learn how to use Azure AI Agents with function calling.
 services: cognitive-services
@@ -142,7 +142,7 @@ from azure.identity import DefaultAzureCredential
 from azure.ai.projects.models import FunctionTool, ToolSet
 from user_functions import user_functions # user functions which can be found in a user_functions.py file.
 
-# Create an Azure AI Client from a connection string, copied from your AI Studio project.
+# Create an Azure AI Client from a connection string, copied from your Azure AI Foundry project.
 # It should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<HubName>"
 # Customers need to login to Azure subscription via Azure CLI and set the environment variables
 

articles/ai-services/agents/how-to/tools/openapi-spec.md

@@ -1,5 +1,5 @@
 ---
-title: 'How to use Azure AI Agent service with OpenAPI Specified Tools'
+title: 'How to use Azure AI Agent Service with OpenAPI Specified Tools'
 titleSuffix: Azure OpenAI
 description: Learn how to use Azure AI Agents with OpenAPI Specified Tools.
 services: cognitive-services
@@ -12,7 +12,7 @@ ms.author: aahi
 zone_pivot_groups: selection-function-calling
 ms.custom: azure-ai-agents
 ---
-# How to use Azure AI Agent service with OpenAPI Specified Tools
+# How to use Azure AI Agent Service with OpenAPI Specified Tools
 
 ::: zone pivot="overview"
 
@@ -94,7 +94,7 @@ from azure.identity import DefaultAzureCredential
 from azure.ai.projects.models import OpenApiTool, OpenApiAnonymousAuthDetails
 
 
-# Create an Azure AI Client from a connection string, copied from your AI Studio project.
+# Create an Azure AI Client from a connection string, copied from your Azure AI Foundry project.
 # At the moment, it should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<HubName>"
 # Customer needs to login to Azure subscription via Azure CLI and set the environment variables
 

articles/ai-services/agents/how-to/tools/overview.md

@@ -1,5 +1,5 @@
 ---
-title: 'Tools available in the Azure AI Agent service'
+title: 'Tools available in the Azure AI Agent Service'
 titleSuffix: Azure OpenAI
 description: Learn about the various tools available for use by the agents you create. 
 services: cognitive-services