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

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@@ -67,7 +67,7 @@ For example, your connection string may look something like:
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`https://myresource.services.ai.azure.com/api/projects/myproject`
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Set this connection string as an environment variable named `ENDPOINT_STRING`.
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Set this connection string as an environment variable named `PROJECT_ENDPOINT`.
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```csharp
@@ -84,7 +84,7 @@ public class Sample_Agent
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{
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static async Task Main()
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{
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var connectionString = Environment.GetEnvironmentVariable("ENDPOINT_STRING");
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var connectionString = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
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AgentsClient client = new AgentsClient(connectionString, new DefaultAzureCredential());
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‎articles/ai-services/agents/includes/quickstart-python.md

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

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