Merge pull request #4726 from aahill/release-build-agents-2

Release build agents 2

Author: v-dirichards

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

@@ -26,12 +26,12 @@ Tracing solves this by allowing you to clearly see the inputs and outputs of eac
 The Agents playground in the Azure AI Foundry portal lets you trace threads and runs that your agents produce. To open a trace, select **Thread info** in an active thread. You can also optionally select **Metrics** to enable automatic evaluations of the model's performance across several dimensions of **AI quality** and **Risk and safety**. 
 
 > [!NOTE]
-> * Evaluations are available for 24 hours after they're generated.
+> Evaluation results are available for 24 hours before expiring. To get evaluation results, select your desired metrics and chat with your agent.
 > * Evaluations are not available in the following regions.
->     * australiaeast
->     * japaneast
->     * southindia
->     * uksouth
+>     * `australiaeast`
+>     * `japaneast`
+>     * `southindia`
+>     * `uksouth`
 
 :::image type="content" source="../media/ai-foundry-tracing.png" alt-text="A screenshot of the agent playground in the Azure AI Foundry portal." lightbox="../media/ai-foundry-tracing.png":::
 

articles/ai-services/agents/how-to/virtual-networks.md

@@ -186,7 +186,7 @@ Use the following code to create and run an agent. To run this code, you need to
 
 `<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>`
 
-[!INCLUDE [connection-string-portal](../includes/connection-string-portal.md)]
+[!INCLUDE [connection-string-portal](../includes/endpoint-string-portal.md)]
 
 `HostName` can be found by navigating to your `discovery_url` and removing the leading `https://` and trailing `/discovery`. To find your `discovery_url`, run this CLI command:
 

articles/ai-services/agents/includes/connection-string-portal.md

@@ -0,0 +1,12 @@
+---
+manager: nitinme
+author: aahill
+ms.author: aahi
+ms.service: azure-ai-agent-service
+ms.topic: include
+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":::

articles/ai-services/agents/includes/endpoint-string-portal.md

@@ -57,23 +57,17 @@ 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 get the endpoint for your project. This string is in the format:
 
-`<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>`
+`https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>`
 
-[!INCLUDE [connection-string-portal](connection-string-portal.md)]
-
-`HostName` can be found by navigating to your `discovery_url` and removing the leading `https://` and trailing `/discovery`. To find your `discovery_url`, run this CLI command:
-
-```azurecli
-az ml workspace show -n {project_name} --resource-group {resource_group_name} --query discovery_url
-```
+[!INCLUDE [endpoint-string-portal](endpoint-string-portal.md)]
 
 For example, your connection string may look something like:
 
-`eastus.api.azureml.ms;12345678-abcd-1234-9fc6-62780b3d3e05;my-resource-group;my-project-name`
+`https://myresource.services.ai.azure.com/api/projects/myproject`
 
-Set this connection string as an environment variable named `PROJECT_CONNECTION_STRING`.
+Set this connection string as an environment variable named `PROJECT_ENDPOINT`.
 
 
 ```csharp
@@ -90,7 +84,7 @@ public class Sample_Agent
 {
     static async Task Main()
     {
-        var connectionString = Environment.GetEnvironmentVariable("PROJECT_CONNECTION_STRING");
+        var connectionString = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
 
         AgentsClient client = new AgentsClient(connectionString, new DefaultAzureCredential());
 

articles/ai-services/agents/includes/quickstart-csharp.md

@@ -67,7 +67,7 @@ Use the following code to create and run an agent. To run this code, you will ne
 
 `<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>`
 
-[!INCLUDE [connection-string-portal](connection-string-portal.md)]
+[!INCLUDE [connection-string-portal](endpoint-string-portal.md)]
 
 `HostName` can be found by navigating to your `discovery_url` and removing the leading `https://` and trailing `/discovery`. To find your `discovery_url`, run this CLI command:
 

articles/ai-services/agents/includes/quickstart-javascript.md

@@ -53,7 +53,7 @@ Use the following code to create and run an agent. To run this code, you will ne
 
 `<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>`
 
-[!INCLUDE [connection-string-portal](connection-string-portal.md)]
+[!INCLUDE [connection-string-portal](endpoint-string-portal.md)]
 
 `HostName` can be found by navigating to your `discovery_url` and removing the leading `https://` and trailing `/discovery`. To find your `discovery_url`, run this CLI command:
 

articles/ai-services/agents/includes/quickstart-python-openai.md

@@ -48,104 +48,74 @@ 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 get the endpoint for your project. This string is in the format:
 
-`<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>`
+`https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>`
 
-[!INCLUDE [connection-string-portal](connection-string-portal.md)]
-
-`HostName` can be found by navigating to your `discovery_url` and removing the leading `https://` and trailing `/discovery`. To find your `discovery_url`, run this CLI command:
-
-```azurecli
-az ml workspace show -n {project_name} --resource-group {resource_group_name} --query discovery_url
-```
+[!INCLUDE [endpoint-string-portal](endpoint-string-portal.md)]
 
 For example, your connection string may look something like:
 
-`eastus.api.azureml.ms;12345678-abcd-1234-9fc6-62780b3d3e05;my-resource-group;my-project-name`
+`https://myresource.services.ai.azure.com/api/projects/myproject`
+
+Set this connection string as an environment variable named `PROJECT_ENDPOINT`.
 
-Set this connection string as an environment variable named `PROJECT_CONNECTION_STRING`.
 
 ```python
 import os
-from azure.ai.projects import AIProjectClient
-from azure.ai.projects.models import CodeInterpreterTool
+import os, time
 from azure.identity import DefaultAzureCredential
-from typing import Any
+from azure.ai.projects import AIProjectClient
 from pathlib import Path
 
-# 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>;<ProjectName>"
-# HostName can be found by navigating to your discovery_url and removing the leading "https://" and trailing "/discovery"
-# To find your discovery_url, run the CLI command: az ml workspace show -n {project_name} --resource-group {resource_group_name} --query discovery_url
-# Project Connection example: eastus.api.azureml.ms;12345678-abcd-1234-9fc6-62780b3d3e05;my-resource-group;my-project-name
-# Customer needs to login to Azure subscription via Azure CLI and set the environment variables
+# Retrieve the project endpoint from environment variables
+project_endpoint = os.environ["PROJECT_ENDPOINT"]
 
-project_client = AIProjectClient.from_connection_string(
-    credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"]
+# Initialize the AIProjectClient
+project_client = AIProjectClient(
+    endpoint=project_endpoint,
+    credential=DefaultAzureCredential(),
+    api_version="latest",
 )
 
-with project_client:
-    # Create an instance of the CodeInterpreterTool
-    code_interpreter = CodeInterpreterTool()
+# Initialize the FunctionTool with user-defined functions
+functions = FunctionTool(functions=user_functions)
 
-    # The CodeInterpreterTool needs to be included in creation of the agent
+with project_client:
+    # Create an agent with custom functions
     agent = project_client.agents.create_agent(
-        model="gpt-4o-mini",
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
         name="my-agent",
-        instructions="You are helpful agent",
-        tools=code_interpreter.definitions,
-        tool_resources=code_interpreter.resources,
+        instructions="You are a helpful agent"
     )
-    print(f"Created agent, agent ID: {agent.id}")
+    print(f"Created agent, ID: {agent.id}")
 
-    # Create a thread
-    thread = project_client.agents.create_thread()
-    print(f"Created thread, thread ID: {thread.id}")
+# Create a thread for communication
+thread = project_client.agents.threads.create()
+print(f"Created thread, ID: {thread.id}")
 
-    # Create a message
-    message = project_client.agents.create_message(
-        thread_id=thread.id,
-        role="user",
-        content="Could you please create a bar chart for the operating profit using the following data and provide the file to me? Company A: $1.2 million, Company B: $2.5 million, Company C: $3.0 million, Company D: $1.8 million",
-    )
-    print(f"Created message, message ID: {message.id}")
-
-    # Run the agent
-    run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
-    print(f"Run finished with status: {run.status}")
-
-    if run.status == "failed":
-        # Check if you got "Rate limit is exceeded.", then you want to get more quota
-        print(f"Run failed: {run.last_error}")
-
-    # Get messages from the thread
-    messages = project_client.agents.list_messages(thread_id=thread.id)
-    print(f"Messages: {messages}")
-
-    # Get the last message from the sender
-    last_msg = messages.get_last_text_message_by_role("assistant")
-    if last_msg:
-        print(f"Last Message: {last_msg.text.value}")
-
-    # Generate an image file for the bar chart
-    for image_content in messages.image_contents:
-        print(f"Image File ID: {image_content.image_file.file_id}")
-        file_name = f"{image_content.image_file.file_id}_image_file.png"
-        project_client.agents.save_file(file_id=image_content.image_file.file_id, file_name=file_name)
-        print(f"Saved image file to: {Path.cwd() / file_name}")
-
-    # Print the file path(s) from the messages
-    for file_path_annotation in messages.file_path_annotations:
-        print(f"File Paths:")
-        print(f"Type: {file_path_annotation.type}")
-        print(f"Text: {file_path_annotation.text}")
-        print(f"File ID: {file_path_annotation.file_path.file_id}")
-        print(f"Start Index: {file_path_annotation.start_index}")
-        print(f"End Index: {file_path_annotation.end_index}")
-        project_client.agents.save_file(file_id=file_path_annotation.file_path.file_id, file_name=Path(file_path_annotation.text).name)
-
-    # Delete the agent once done
-    project_client.agents.delete_agent(agent.id)
-    print("Deleted agent")
+# Create a message in the thread
+message = project_client.agents.messages.create(
+    thread_id=thread.id,
+    role="user",  # Role of the message sender
+    content="When was Microsoft founded?",  # Message content
+)
+print(f"Created message, ID: {message['id']}")
+
+# Create and process an agent run in the thread
+run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
+print(f"Run finished with status: {run.status}")
+
+# Check if the run failed
+if run.status == "failed":
+    print(f"Run failed: {run.last_error}")
+
+# Fetch and log all messages from the thread
+messages = project_client.agents.messages.list(thread_id=thread.id)
+for message in messages.data:
+    print(f"Role: {message.role}, Content: {message.content}")
+
+# Delete the agent after use
+project_client.agents.delete_agent(agent.id)
+print("Deleted agent")
 ```

articles/ai-services/agents/includes/quickstart-python.md

@@ -58,7 +58,7 @@ Use the following code to create and run an agent. To run this code, you will ne
 
 `<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>`
 
-[!INCLUDE [connection-string-portal](connection-string-portal.md)]
+[!INCLUDE [connection-string-portal](endpoint-string-portal.md)]
 
 `HostName` can be found by navigating to your `discovery_url` and removing the leading `https://` and trailing `/discovery`. To find your `discovery_url`, run this CLI command: