Merge pull request #5231 from MicrosoftDocs/main

PhilKang0704 · web-flow · commit 51eec00bd086 · 2025-05-28T13:30:46.000+08:00
5/28/2025 11:00 AM IST Publish
diff --git a/articles/ai-services/agents/how-to/tools/azure-ai-search-samples.md b/articles/ai-services/agents/how-to/tools/azure-ai-search-samples.md
@@ -320,10 +320,10 @@ agentClient.Administration.DeleteAgent(agent.Id);
 :::zone pivot="javascript"
 
 ## Create an Azure AI Client
-First, create an Azure AI Client using the connection string of your project.
+First, create an Azure AI Client using the endpoint your project.
 
 ```javascript
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project connection string>";
+const projectEndpoint = process.env["PROJECT_ENDPOINT"];
 
 if (!projectString) {
   throw new Error("AZURE_AI_PROJECTS_CONNECTION_STRING must be set in the environment variables");
diff --git a/articles/ai-services/agents/how-to/tools/bing-code-samples.md b/articles/ai-services/agents/how-to/tools/bing-code-samples.md
@@ -49,7 +49,7 @@ Use this article to find step-by-step instructions and code samples for Groundin
 
 ## Create a project client
 
-Create a client object, which will contain the connection string for connecting to your AI project and other resources.
+Create a client object, which will contain the endpoint for connecting to your AI project and other resources.
 
 ```python
 import os
@@ -315,7 +315,7 @@ agentClient.Administration.DeleteAgent(agentId: agent.Id);
 
 ## Create a project client
 
-Create a client object, which will contain the connection string for connecting to your AI project and other resources.
+Create a client object, which will contain the endpoint for connecting to your AI project and other resources.
 
 ```javascript
 const { AgentsClient, ToolUtility, isOutputOfType } = require("@azure/ai-agents");
@@ -324,7 +324,7 @@ const { DefaultAzureCredential } = require("@azure/identity");
 
 require("dotenv/config");
 
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project connection string>";
+const projectEndpoint = process.env["PROJECT_ENDPOINT"];
 
 // Create an Azure AI Client
 const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
diff --git a/articles/ai-services/agents/how-to/tools/code-interpreter-samples.md b/articles/ai-services/agents/how-to/tools/code-interpreter-samples.md
@@ -259,7 +259,7 @@ Finally, delete the thread and the agent to clean up the resources created in th
 
 ## Create a project client 
 
-To use code interpreter, first you need to create a project client, which will contain a connection string to your AI project, and will be used to authenticate API calls.
+To use code interpreter, first you need to create a project client, which will contain an endpoint to your AI project, and will be used to authenticate API calls.
 
 ```javascript
 const { AgentsClient, isOutputOfType, ToolUtility } = require("@azure/ai-agents");
@@ -269,7 +269,7 @@ const fs = require("fs");
 const path = require("node:path");
 require("dotenv/config");
 
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project connection string>";
+const projectEndpoint = process.env["PROJECT_ENDPOINT"];
 
 // Create an Azure AI Client
 const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
diff --git a/articles/ai-services/agents/how-to/tools/file-search-upload-files.md b/articles/ai-services/agents/how-to/tools/file-search-upload-files.md
@@ -45,7 +45,7 @@ Use this article to find step-by-step instructions and code samples for uploadin
 
 ## Create a project client
 
-Create a client object that contains the connection string for connecting to your AI project and other resources.
+Create a client object that contains the endpoint for connecting to your AI project and other resources.
 
 ```python
 import os
@@ -154,7 +154,7 @@ for message in messages.data:
 
 ## Create a project client
 
-Create a client object that contains the connection string for connecting to your AI project and other resources.
+Create a client object that contains the endpoint for connecting to your AI project and other resources.
 
 ```csharp
 using Azure;
@@ -346,7 +346,7 @@ agentClient.Administration.DeleteAgent(agent.Id);
 
 ## Create a project client
 
-Create a client object that contains the connection string for connecting to your AI project and other resources.
+Create a client object that contains the endpoint for connecting to your AI project and other resources.
 
 ```javascript
 const { AgentsClient, isOutputOfType, ToolUtility } = require("@azure/ai-agents");
@@ -356,7 +356,7 @@ const { DefaultAzureCredential } = require("@azure/identity");
 const fs = require("fs");
 require("dotenv/config");
 
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project connection string>";
+const projectEndpoint = process.env["PROJECT_ENDPOINT"];
 
 // Create an Azure AI Client
 const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
diff --git a/articles/ai-services/agents/how-to/tools/function-calling.md b/articles/ai-services/agents/how-to/tools/function-calling.md
@@ -490,7 +490,7 @@ const { DefaultAzureCredential } = require("@azure/identity");
 
 require("dotenv/config");
 
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project connection string>";
+const projectEndpoint = process.env["PROJECT_ENDPOINT"];
 
 const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
 const functionToolExecutor = new FunctionToolExecutor();
diff --git a/articles/ai-services/agents/how-to/tools/openapi-spec-samples.md b/articles/ai-services/agents/how-to/tools/openapi-spec-samples.md
@@ -183,7 +183,7 @@ After the interaction is complete, the script performs cleanup by deleting the c
 
 ## Create a project client
 
-Create a client object that contains the connection string for connecting to your AI project and other resources.
+Create a client object that contains the endpoint for connecting to your AI project and other resources.
 
 ```javascript
 const { AgentsClient, isOutputOfType, ToolUtility } = require("@azure/ai-agents");
@@ -192,7 +192,7 @@ const { DefaultAzureCredential } = require("@azure/identity");
 const fs = require("fs");
 require("dotenv/config");
 
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project connection string>";
+const projectEndpoint = process.env["PROJECT_ENDPOINT"];
 
 // Create an Azure AI Client
 const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
diff --git a/articles/ai-services/agents/includes/endpoint-string-portal.md b/articles/ai-services/agents/includes/endpoint-string-portal.md
@@ -8,5 +8,5 @@ ms.date: 12/11/2024
 ---
 
 > [!TIP]
-> You can also find your connection string in the **overview** for your project in the [Azure AI Foundry portal](https://ai.azure.com/), under **Libraries** > **Azure AI Foundry**.
-> :::image type="content" source="../media/quickstart/portal-endpoint-string.png" alt-text="A screenshot showing the connection string in the Azure AI Foundry portal." lightbox="../media/quickstart/portal-endpoint-string.png":::
+> You can also find your endpoint in the **overview** for your project in the [Azure AI Foundry portal](https://ai.azure.com/), under **Libraries** > **Azure AI Foundry**.
+> :::image type="content" source="../media/quickstart/portal-endpoint-string.png" alt-text="A screenshot showing the endpoint in the Azure AI Foundry portal." lightbox="../media/quickstart/portal-endpoint-string.png":::
diff --git a/articles/ai-services/agents/includes/quickstart-csharp.md b/articles/ai-services/agents/includes/quickstart-csharp.md
@@ -31,11 +31,8 @@ dotnet new console
 
 Install the .NET package to your project. For example if you're using the .NET CLI, run the following command.
 
->[!Note]
-> Azure.AI.Agents.Persistent is only available as a prelease version. Please use the "-prerelease" flag to add the package until a release version becomes available.
-
 ```console
-dotnet add package Azure.AI.Agents.Persistent --prerelease
+dotnet add package Azure.AI.Agents.Persistent
 dotnet add package Azure.Identity
 ```
 
@@ -51,11 +48,11 @@ Use the following code to create and run an agent. To run this code, you will ne
 
 [!INCLUDE [endpoint-string-portal](endpoint-string-portal.md)]
 
-For example, your connection string may look something like:
+For example, your endpoint may look something like:
 
 `https://myresource.services.ai.azure.com/api/projects/myproject`
 
-Set this connection string in an appsetting variable named `ProjectEndpoint`.
+Set this endpoint in an appsetting variable named `ProjectEndpoint`.
 
 
 ```csharp
diff --git a/articles/ai-services/agents/includes/quickstart-javascript.md b/articles/ai-services/agents/includes/quickstart-javascript.md
@@ -59,11 +59,11 @@ Use the following code to create and run an agent. To run this code, you will ne
 
 [!INCLUDE [endpoint-string-portal](endpoint-string-portal.md)]
 
-For example, your connection string may look something like:
+For example, your endpoint may look something like:
 
 `https://myresource.services.ai.azure.com/api/projects/myproject`
 
-Set this connection string as an environment variable named `PROJECT_ENDPOINT` in a `.env` file.
+Set this endpoint as an environment variable named `PROJECT_ENDPOINT` in a `.env` file.
 
 > [!IMPORTANT] 
 > * This quickstart code uses environment variables for sensitive configuration. Never commit your `.env` file to version control by making sure `.env` is listed in your `.gitignore` file.
@@ -92,7 +92,7 @@ const fs = require("fs");
 const path = require("node:path");
 require("dotenv/config");
 
-const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project connection string>";
+const projectEndpoint = process.env["PROJECT_ENDPOINT"];
 const modelDeploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "gpt-4o";
 
 async function main() {
diff --git a/articles/ai-services/agents/includes/quickstart-python-openai.md b/articles/ai-services/agents/includes/quickstart-python-openai.md
diff --git a/articles/ai-services/agents/includes/quickstart-python.md b/articles/ai-services/agents/includes/quickstart-python.md
@@ -45,11 +45,11 @@ Use the following code to create and run an agent. To run this code, you will ne
 
 [!INCLUDE [endpoint-string-portal](endpoint-string-portal.md)]
 
-For example, your connection string may look something like:
+For example, your endpoint may look something like:
 
 `https://myresource.services.ai.azure.com/api/projects/myproject`
 
-Set this connection string as an environment variable named `PROJECT_ENDPOINT`.
+Set this endpoint as an environment variable named `PROJECT_ENDPOINT`.
 
 ```python
 import os
diff --git a/articles/ai-services/agents/includes/quickstart-typescript.md b/articles/ai-services/agents/includes/quickstart-typescript.md
@@ -54,7 +54,7 @@ Next, to authenticate your API requests and run the program, use the [az login](
 az login
 ```
 
-Use the following code to create and run an agent. To run this code, you will need to create a connection string using information from your project. This string is in the format:
+Use the following code to create and run an agent. To run this code, you will need to create an endpoint using information from your project. This string is in the format:
 
 `<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>`
 
diff --git a/articles/ai-services/agents/overview.md b/articles/ai-services/agents/overview.md
@@ -105,7 +105,7 @@ Azure AI Foundry Agent Service provides a production-ready foundation for deploy
 | **4. Trust and safety** | Integrated [content filters](../openai/how-to/content-filters.md) help prevent misuse and mitigate prompt injection risks (XPIA). all outputs are policy-governed. |
 | **5. Enterprise integration** | Bring your own [storage](./how-to/use-your-own-resources.md#use-an-existing-azure-cosmos-db-for-nosql-account-for-thread-storage), [Azure AI Search index](./how-to/use-your-own-resources.md#use-an-existing-azure-ai-search-resource), and [virtual network](./how-to/virtual-networks.md) to meet compliance needs. |
 | **6. Observability and debugging** | Threads, tool invocations, and message traces are [fully traceable](./concepts/tracing.md); [Application Insights integration](./how-to/metrics.md) for telemetry |
-| **7. Identity and policy control** | Built on Microsoft Entra with full support for RBAC, audit logs, and enterprise conditional acces.s |
+| **7. Identity and policy control** | Built on Microsoft Entra with full support for RBAC, audit logs, and enterprise conditional access. |
 
 ## Get started with Foundry Agent Service
 
diff --git a/articles/ai-services/agents/quickstart.md b/articles/ai-services/agents/quickstart.md
@@ -34,12 +34,6 @@ Azure AI Foundry Agent Service allows you to create AI agents tailored to your n
 
 ::: zone-end
 
-::: zone pivot="programming-language-python-openai"
-
-[!INCLUDE [quickstart-python-openai](includes/quickstart-python-openai.md)]
-
-::: zone-end
-
 ::: zone pivot="programming-language-javascript"
 
 [!INCLUDE [quickstart-javascript](includes/quickstart-javascript.md)]
diff --git a/articles/search/multimodal-search-overview.md b/articles/search/multimodal-search-overview.md
@@ -4,7 +4,7 @@ titleSuffix: Azure AI Search
 description: Learn what multimodal search is, how Azure AI Search supports it for text + image content, and where to find detailed concepts, tutorials, and samples.
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 05/19/2025
+ms.date: 05/27/2025
 author: gmndrg
 ms.author: gimondra
 ---
@@ -21,7 +21,7 @@ Azure AI Search simplifies the construction of a multimodal pipeline through a g
 
 + [Azure portal multimodal functionality](search-get-started-portal-image-search.md): The step-by-step multimodal functionality in the **Import and vectorize data** wizard helps configure your data source, extraction and enrichment settings, and generate a multimodal index containing text, embedded image references, and vector embeddings.
 
-+ [Reference GitHub multimodal RAG application sample](https://aka.ms/azs-multimodal-sample-app-repo): A companion GitHub repository with sample code. The sample demonstrates how a [Retrieval Augmented Generation (RAG)](retrieval-augmented-generation-overview.md) application consumes a multimodal index and renders both textual citations and associated image snippets in the response. The repository also showcases the full process of data ingestion and indexing through code, providing developers with a programmatic alternative to the Azure portal wizard.
++ [Reference GitHub multimodal RAG application sample](https://aka.ms/azs-multimodal-sample-app-repo): A companion GitHub repository with sample code. The sample demonstrates how a [Retrieval Augmented Generation (RAG)](retrieval-augmented-generation-overview.md) application consumes a multimodal index (such as the one created by the [Azure portal multimodal functionality](search-get-started-portal-image-search.md)) and renders both textual citations and associated image snippets in the response. The repository also showcases the full process of data ingestion and indexing through code, providing developers with a programmatic alternative to the Azure portal wizard.
 
 ## Functionality enabling multimodality
 
diff --git a/zone-pivots/zone-pivot-groups.yml b/zone-pivots/zone-pivot-groups.yml
@@ -1044,8 +1044,6 @@ groups:
     title: JavaScript
   - id: programming-language-python-azure
     title: Python (Azure SDK)
-  - id: programming-language-python-openai
-    title: Python (OpenAI SDK)
   - id: programming-language-typescript
     title: TypeScript
   - id: rest-api
