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Merge pull request #10 from azure-ai-foundry/python-tools-estraight
Python tools estraight
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‎doc-samples/agents/python/project-client/azure_functions.py‎

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# pylint: disable=line-too-long,useless-suppression
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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to use azure function agent operations from
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the Azure Agents service using a synchronous client.
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USAGE:
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python sample_agents_azure_functions.py
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Before running the sample:
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pip install azure-ai-projects azure-identity
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Set these environment variables with your own values:
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1) PROJECT_CONNECTION_STRING - The project connection string, as found in the overview page of your
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Azure AI Foundry project.
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2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
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the "Models + endpoints" tab in your Azure AI Foundry project.
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3) STORAGE_SERVICE_ENDPONT - the storage service queue endpoint, triggering Azure function.
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Please see Getting Started with Azure Functions page for more information on Azure Functions:
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https://learn.microsoft.com/azure/azure-functions/functions-get-started
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"""
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# <imports>
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import os
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from azure.ai.projects import AIProjectClient
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from azure.ai.agents.models import AzureFunctionStorageQueue, AzureFunctionTool, MessageRole
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from azure.identity import DefaultAzureCredential
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# </imports>
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# <client_initialization>
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endpoint = os.environ["PROJECT_ENDPOINT"]
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model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"]
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with AIProjectClient(
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endpoint=endpoint,
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credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),
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) as project_client:
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# </client_initialization>
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storage_service_endpoint = os.environ["STORAGE_SERVICE_ENDPOINT"]
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# [START create_agent_with_azure_function_tool]
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# <azure_function_tool_setup>
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azure_function_tool = AzureFunctionTool(
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name="foo",
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description="Get answers from the foo bot.",
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parameters={
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "The question to ask."},
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"outputqueueuri": {"type": "string", "description": "The full output queue uri."},
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},
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},
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input_queue=AzureFunctionStorageQueue(
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queue_name="azure-function-foo-input",
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storage_service_endpoint=storage_service_endpoint,
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),
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output_queue=AzureFunctionStorageQueue(
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queue_name="azure-function-tool-output",
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storage_service_endpoint=storage_service_endpoint,
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),
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)
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# </azure_function_tool_setup>
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# <agent_creation>
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agent = project_client.agents.create_agent(
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model=os.environ["MODEL_DEPLOYMENT_NAME"],
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name="azure-function-agent-foo",
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instructions=f"You are a helpful support agent. Use the provided function any time the prompt contains the string 'What would foo say?'. When you invoke the function, ALWAYS specify the output queue uri parameter as '{storage_service_endpoint}/azure-function-tool-output'. Always responds with \"Foo says\" and then the response from the tool.",
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tools=azure_function_tool.definitions,
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)
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print(f"Created agent, agent ID: {agent.id}")
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# [END create_agent_with_azure_function_tool]
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# </agent_creation>
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# <thread_management>
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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 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="What is the most prevalent element in the universe? What would foo say?",
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)
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print(f"Created message, message ID: {message.id}")
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# </thread_management>
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# <message_processing>
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run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
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if run.status == "failed":
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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 agent
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last_msg = messages.get_last_text_message_by_role(MessageRole.AGENT)
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if last_msg:
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print(f"Last Message: {last_msg.text.value}")
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# </message_processing>
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# <cleanup>
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# Delete the agent once done
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result = project_client.agents.delete_agent(agent.id)
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if result.deleted:
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print(f"Deleted agent {result.id}")
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else:
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print(f"Failed to delete agent {result.id}")
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# </cleanup>

‎doc-samples/agents/python/project-client/code_interpreter/code_interpreter.py‎

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# pylint: disable=line-too-long,useless-suppression
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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to use agent operations with code interpreter from
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the Azure Agents service using a synchronous client.
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USAGE:
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python sample_agents_code_interpreter.py
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Before running the sample:
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pip install azure-ai-projects azure-identity
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Set these environment variables with your own values:
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1) PROJECT_ENDPOINT - The project endpoint, as found in the overview page of your
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Azure AI Foundry project.
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2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
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the "Models + endpoints" tab in your Azure AI Foundry project.
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"""
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# <imports>
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import os
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from azure.ai.projects import AIProjectClient
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from azure.ai.agents.models import CodeInterpreterTool
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from azure.ai.agents.models import FilePurpose, MessageRole
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from azure.identity import DefaultAzureCredential
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from pathlib import Path
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# </imports>
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# <client_initialization>
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endpoint = os.environ["PROJECT_ENDPOINT"]
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model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"]
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with AIProjectClient(
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endpoint=endpoint,
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credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),
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) as project_client:
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# </client_initialization>
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# Upload a file and wait for it to be processed
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# [START upload_file_and_create_agent_with_code_interpreter]
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# <file_upload>
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file = project_client.agents.upload_file_and_poll(
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file_path=str(Path(__file__).parent / "nifty_500_quarterly_results.csv"), purpose=FilePurpose.AGENTS
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)
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print(f"Uploaded file, file ID: {file.id}")
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# </file_upload>
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# <code_interpreter_setup>
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code_interpreter = CodeInterpreterTool(file_ids=[file.id])
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# </code_interpreter_setup>
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# <agent_creation>
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# Create agent with code interpreter tool and tools_resources
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agent = project_client.agents.create_agent(
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model=os.environ["MODEL_DEPLOYMENT_NAME"],
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name="my-assistant",
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instructions="You are helpful assistant",
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tools=code_interpreter.definitions,
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tool_resources=code_interpreter.resources,
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)
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# [END upload_file_and_create_agent_with_code_interpreter]
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print(f"Created agent, agent ID: {agent.id}")
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# </agent_creation>
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# <thread_management>
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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 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 bar chart in TRANSPORTATION sector for the operating profit from the uploaded csv file and provide file to me?",
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)
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print(f"Created message, message ID: {message.id}")
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# </thread_management>
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# <message_processing>
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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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# </message_processing>
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# <file_handling>
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project_client.agents.delete_file(file.id)
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print("Deleted file")
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# [START get_messages_and_save_files]
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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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for image_content in messages.image_contents:
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file_id = image_content.image_file.file_id
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print(f"Image File ID: {file_id}")
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file_name = f"{file_id}_image_file.png"
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project_client.agents.save_file(file_id=file_id, file_name=file_name)
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print(f"Saved image file to: {Path.cwd() / file_name}")
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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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# [END get_messages_and_save_files]
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# </file_handling>
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last_msg = messages.get_last_text_message_by_role(MessageRole.AGENT)
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if last_msg:
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print(f"Last Message: {last_msg.text.value}")
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# <cleanup>
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project_client.agents.delete_agent(agent.id)
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print("Deleted agent")
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# </cleanup>

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