Merge pull request #6366 from MicrosoftDocs/main

Auto Publish – main to live - 2025-08-04 17:12 UTC

Author: learn-build-service-prod[bot]

articles/ai-foundry/foundry-models/concepts/models.md

@@ -7,7 +7,7 @@ ms.author: mopeakande
 manager: scottpolly
 reviewer: santiagxf
 ms.reviewer: fasantia
-ms.date: 07/11/2025
+ms.date: 08/04/2025
 ms.service: azure-ai-model-inference
 ms.topic: how-to
 ms.custom:
@@ -58,6 +58,17 @@ Azure OpenAI in Azure AI Foundry Models offers a diverse set of models with diff
 
 See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=aoai).
 
+### Black Forest Labs models sold directly by Azure
+
+The Black Forest Labs collection of image generation models include FLUX.1 Kontext [pro] for in-context generation and editing and FLUX1.1 [pro] for text-to-image generation. 
+
+| Model  | Type | Capabilities | Project type |
+| ------ | ---- | ------------ | ------------ |
+| [FLUX.1-Kontext-pro](https://ai.azure.com/explore/models/FLUX.1-Kontext-pro/version/1/registry/azureml-blackforestlabs) | Image generation | - **Input:** text and image (5000 tokens and 1 image)  <br /> - **Output:** One Image  <br />  - **Tool calling:** No <br /> - **Response formats**: Image (PNG and JPG) | Foundry, Hub-based |
+| [FLUX-1.1-pro](https://ai.azure.com/explore/models/FLUX-1.1-pro/version/1/registry/azureml-blackforestlabs) | Image generation | - **Input:** text (5000 tokens)  <br /> - **Output:** One Image  <br />  - **Tool calling:** No <br /> - **Response formats:** Image (PNG and JPG) | Hub-based |
+
+
+See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=Black+Forest+Labs).
 
 ### DeepSeek models sold directly by Azure
 

articles/ai-foundry/how-to/continuous-evaluation-agents.md

@@ -45,8 +45,8 @@ import os, json
 from azure.ai.projects import AIProjectClient
 from azure.identity import DefaultAzureCredential
 
-project_client = AIProjectClient.from_connection_string(
-    credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"]
+project_client = AIProjectClient(
+    credential=DefaultAzureCredential(), endpoint=os.environ["PROJECT_ENDPOINT"]
 )
 
 agent = project_client.agents.create_agent(
@@ -95,7 +95,7 @@ project_client.evaluation.create_agent_evaluation(
         thread=thread.id,  
         run=run.id,   
         evaluators=evaluators,
-        appInsightsConnectionString = project_client.telemetry.get_connection_string(),
+        appInsightsConnectionString = project_client.telemetry.get_application_insights_connection_string(),
     )
 )
 
@@ -189,7 +189,7 @@ project_client.evaluation.create_agent_evaluation(
         run=run.id,   
         evaluators=evaluators,  
         samplingConfiguration = sampling_config,  
-        appInsightsConnectionString = project_client.telemetry.get_connection_string(),
+        appInsightsConnectionString = project_client.telemetry.get_application_insights_connection_string(),
     )
 )
 ```

articles/ai-foundry/how-to/develop/langchain.md

@@ -261,24 +261,24 @@ You can configure your application to send telemetry to Azure Application Insigh
         application_insights_connection_string = "instrumentation...."
         ```
 
-2. Using the Azure AI Foundry SDK and the project connection string (**[!INCLUDE [hub-project-name](../../includes/hub-project-name.md)]s only**).
+2. Using the Azure AI Foundry SDK and the Foundry Project endpoint:
 
     1. Ensure you have the package `azure-ai-projects` installed in your environment.
 
     2. Go to [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs).
 
-    3. Copy your project's connection string and set it the following code:
+    3. Copy your Azure AI Foundry project endpoint URL and set it in the following code:
 
         ```python
         from azure.ai.projects import AIProjectClient
         from azure.identity import DefaultAzureCredential
         
-        project_client = AIProjectClient.from_connection_string(
+        project_client = AIProjectClient(
             credential=DefaultAzureCredential(),
-            conn_str="<your-project-connection-string>",
+            endpoint="<your-foundry-project-endpoint-url>",
         )
         
-        application_insights_connection_string = project_client.telemetry.get_connection_string()
+        application_insights_connection_string = project_client.telemetry.get_application_insights_connection_string()
         ```
 
 ### Configure tracing for Azure AI Foundry

articles/ai-foundry/how-to/develop/trace-agents-sdk.md

@@ -61,7 +61,7 @@ Let's begin instrumenting our agent with OpenTelemetry tracing, by starting off
 ```python
 from azure.ai.projects import AIProjectClient
 from azure.identity import DefaultAzureCredential
-project_client = AIProjectClient.from_connection_string(
+project_client = AIProjectClient(
     credential=DefaultAzureCredential(),
     endpoint=os.environ["PROJECT_ENDPOINT"],
 )
@@ -71,12 +71,8 @@ Next, retrieve the connection string from the Application Insights resource conn
 
 ```python
 from azure.monitor.opentelemetry import configure_azure_monitor
-connection_string = project_client.telemetry.get_connection_string()
-
-if not connection_string:
-    print("Application Insights is not enabled. Enable by going to Tracing in your Azure AI Foundry project.")
-    exit()
 
+connection_string = project_client.telemetry.get_application_insights_connection_string()
 configure_azure_monitor(connection_string=connection_string) #enable telemetry collection
 ```
 
@@ -92,11 +88,11 @@ with tracer.start_as_current_span("example-tracing"):
         name="my-assistant",
         instructions="You are a helpful assistant"
     )
-    thread = project_client.agents.create_thread()
-    message = project_client.agents.create_message(
+    thread = project_client.agents.threads.create()
+    message = project_client.agents.messages.create(
         thread_id=thread.id, role="user", content="Tell me a joke"
     )
-    run = project_client.agents.create_run(thread_id=thread.id, agent_id=agent.id)
+    run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
 ```
 
 After running your agent, you can go begin to [view traces in Azure AI Foundry Portal](#view-traces-in-azure-ai-foundry-portal).
@@ -108,7 +104,8 @@ To connect to [Aspire Dashboard](https://aspiredashboard.com/#start) or another
 ```bash
 pip install azure-core-tracing-opentelemetry opentelemetry-exporter-otlp opentelemetry-sdk
 ```
-Next, you want to configure tracing for your application.
+
+Next, configure tracing for console output:
 
 ```python
 from azure.core.settings import settings
@@ -124,18 +121,16 @@ tracer_provider = TracerProvider()
 tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter))
 trace.set_tracer_provider(tracer_provider)
 ```
-Use `enable_telemetry` to begin collecting telemetry. 
 
-```python
-from azure.ai.projects import enable_telemetry
-enable_telemetry(destination=sys.stdout)
+Or modify the above code, based on [Aspire Dashboard](https://aspiredashboard.com/#start), to trace to a local OTLP viewer.
+
+Now enable Agent instrumentation and run your Agent:
 
-# Logging to an OTLP endpoint, change the destination to
-# enable_telemetry(destination="http://localhost:4317")
-```
 ```python
+from azure.ai.agents.telemetry import AIAgentsInstrumentor
+AIAgentsInstrumentor().instrument()
+
 # Start tracing
-from opentelemetry import trace
 tracer = trace.get_tracer(__name__)
 
 with tracer.start_as_current_span("example-tracing"):
@@ -144,11 +139,11 @@ with tracer.start_as_current_span("example-tracing"):
         name="my-assistant",
         instructions="You are a helpful assistant"
     )
-    thread = project_client.agents.create_thread()
-    message = project_client.agents.create_message(
+    thread = project_client.agents.threads.create()
+    message = project_client.agents.messages.create(
         thread_id=thread.id, role="user", content="Tell me a joke"
     )
-    run = project_client.agents.create_run(thread_id=thread.id, agent_id=agent.id)
+    run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
 ```
 
 ## Trace custom functions

articles/ai-foundry/how-to/develop/trace-application.md

@@ -95,7 +95,7 @@ When developing with the OpenAI SDK, you can instrument your code so traces are
         endpoint="https://<your-resource>.services.ai.azure.com/api/projects/<your-project>",
     )
 
-    connection_string = project_client.telemetry.get_connection_string()
+    connection_string = project_client.telemetry.get_application_insights_connection_string()
     ```
 
     > [!TIP]
@@ -116,7 +116,7 @@ When developing with the OpenAI SDK, you can instrument your code so traces are
 1. Use the OpenAI SDK as usual:
 
     ```python
-    client = project_client.inference.get_azure_openai_client()
+    client = project_client.get_openai_client()
 
     response = client.chat.completions.create(
         model="deepseek-v3-0324",