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Python: Improve agent getting started samples #10667

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51 changes: 29 additions & 22 deletions python/samples/getting_started_with_agents/azure_ai_agent/step1_azure_ai_agent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,9 @@
from semantic_kernel.functions.kernel_function_decorator import kernel_function

###################################################################
# The following sample demonstrates how to create a simple, #
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Just as a note, I've been updating these blocks to be contained within """<text> """ which makes it much easier to manage - no worries about having to add comment symbols to the end of lines and such...

# Azure AI agent that answers questions about a sample menu #
# using a Semantic Kernel Plugin. #
# The following sample demonstrates how to create an Azure AI #
# agent that answers questions about a sample menu using a #
# Semantic Kernel Plugin. #
###################################################################


Expand All @@ -36,8 +36,18 @@ def get_item_price(
return "$9.99"


# Simulate a conversation with the agent
USER_INPUTS = [
"Hello",
"What is the special soup?",
"How much does that cost?",
"Thank you",
]


async def main() -> None:
ai_agent_settings = AzureAIAgentSettings.create()
assert ai_agent_settings.project_connection_string, "Please provide a valid Azure AI connection string." # nosec

async with (
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I think we could probably clean up this a little bit by internally creating the AzureAIAgentSettings in the get_client method. User's can still pass in values to the method that take precedence, similar to what we do elsewhere.

DefaultAzureCredential() as creds,
Expand All @@ -46,55 +56,52 @@ async def main() -> None:
conn_str=ai_agent_settings.project_connection_string.get_secret_value(),
) as client,
):
AGENT_NAME = "Host"
AGENT_INSTRUCTIONS = "Answer questions about the menu."

# Create agent definition
# 1. Create an agent on the Azure AI agent service
agent_definition = await client.agents.create_agent(
model=ai_agent_settings.model_deployment_name,
name=AGENT_NAME,
instructions=AGENT_INSTRUCTIONS,
name="Host",
instructions="Answer questions about the menu.",
)

# Create the AzureAI Agent
# 2. Create a Semantic Kernel agent for the Azure AI agent
agent = AzureAIAgent(
client=client,
definition=agent_definition,
# Optionally configure polling options
# polling_options=RunPollingOptions(run_polling_interval=timedelta(seconds=1)),
)

# Add the sample plugin to the kernel
# 3. Add a plugin to the agent via the kernel
agent.kernel.add_plugin(MenuPlugin(), plugin_name="menu")

# Create a new thread
# 4. Create a new thread on the Azure AI agent service
thread = await client.agents.create_thread()

user_inputs = [
"Hello",
"What is the special soup?",
"How much does that cost?",
"Thank you",
]

try:
for user_input in user_inputs:
# Add the user input as a chat message
for user_input in USER_INPUTS:
# 5. Add the user input as a chat message
await agent.add_chat_message(
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As part of this PR, could we update the agent's add_chat_message signature to allow one to pass in a str, and if it's a str instance, we turn it into a ChatMessageContent(AuthorRole.USER, content=message).

thread_id=thread.id, message=ChatMessageContent(role=AuthorRole.USER, content=user_input)
)
print(f"# User: '{user_input}'")
# Invoke the agent for the specified thread
# 6. Invoke the agent for the specified thread for response
async for content in agent.invoke(
thread_id=thread.id,
temperature=0.2, # override the agent-level temperature setting with a run-time value
):
if content.role != AuthorRole.TOOL:
print(f"# Agent: {content.content}")
finally:
# 7. Cleanup: Delete the thread and agent
await client.agents.delete_thread(thread.id)
await client.agents.delete_agent(agent.id)

# Sample Output:
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Similarly, I've been adding some sample output like:

"""
Sample Output:

# User: 'Hello"
...
"""

# User: 'Hello'
# Agent: Hello! How can I assist you today?
# User: 'What is the special soup?'
# ...


if __name__ == "__main__":
asyncio.run(main())
38 changes: 22 additions & 16 deletions python/samples/getting_started_with_agents/azure_ai_agent/step2_azure_ai_agent_chat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,9 +44,12 @@ async def should_agent_terminate(self, agent, history):
Consider suggestions when refining an idea.
"""

TASK = "a slogan for a new line of electric cars."


async def main():
ai_agent_settings = AzureAIAgentSettings.create()
assert ai_agent_settings.project_connection_string, "Please provide a valid Azure AI connection string." # nosec

async with (
DefaultAzureCredential() as creds,
Expand All @@ -55,55 +58,58 @@ async def main():
conn_str=ai_agent_settings.project_connection_string.get_secret_value(),
) as client,
):
# Create the reviewer agent definition
# 1. Create the reviewer agent on the Azure AI agent service
reviewer_agent_definition = await client.agents.create_agent(
model=ai_agent_settings.model_deployment_name,
name=REVIEWER_NAME,
instructions=REVIEWER_INSTRUCTIONS,
)
# Create the reviewer Azure AI Agent

# 2. Create a Semantic Kernel agent for the reviewer Azure AI agent
agent_reviewer = AzureAIAgent(
client=client,
definition=reviewer_agent_definition,
)

# Create the copy writer agent definition
# 3. Create the copy writer agent on the Azure AI agent service
copy_writer_agent_definition = await client.agents.create_agent(
model=ai_agent_settings.model_deployment_name,
name=COPYWRITER_NAME,
instructions=COPYWRITER_INSTRUCTIONS,
)
# Create the copy writer Azure AI Agent

# 4. Create a Semantic Kernel agent for the copy writer Azure AI agent
agent_writer = AzureAIAgent(
client=client,
definition=copy_writer_agent_definition,
)

# 5. Place the agents in a group chat with a custom termination strategy
chat = AgentGroupChat(
agents=[agent_writer, agent_reviewer],
termination_strategy=ApprovalTerminationStrategy(agents=[agent_reviewer], maximum_iterations=10),
)

input = "a slogan for a new line of electric cars."

try:
await chat.add_chat_message(ChatMessageContent(role=AuthorRole.USER, content=input))
print(f"# {AuthorRole.USER}: '{input}'")

# 6. Add the task as a message to the group chat
await chat.add_chat_message(ChatMessageContent(role=AuthorRole.USER, content=TASK))
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This is a spot in my assistant agent PR where I've also allowed for await chat.add_chat_message(TASK) and we create the ChatMessageContent on behalf of the caller.

print(f"# {AuthorRole.USER}: '{TASK}'")
# 7. Invoke the chat
async for content in chat.invoke():
print(f"# {content.role} - {content.name or '*'}: '{content.content}'")

print(f"# IS COMPLETE: {chat.is_complete}")

print("*" * 60)
print("Chat History (In Descending Order):\n")
async for message in chat.get_chat_messages(agent=agent_reviewer):
print(f"# {message.role} - {message.name or '*'}: '{message.content}'")
finally:
# 8. Cleanup: Delete the agents
await chat.reset()
await client.agents.delete_agent(agent_reviewer.id)
await client.agents.delete_agent(agent_writer.id)

# Sample Output:
# AuthorRole.USER: 'a slogan for a new line of electric cars.'
# AuthorRole.ASSISTANT - CopyWriter: '"Charge Ahead: Drive the Future."'
# AuthorRole.ASSISTANT - ArtDirector: 'This slogan has a nice ring to it and captures the ...'
# AuthorRole.ASSISTANT - CopyWriter: '"Plug In. Drive Green."'
# ...


if __name__ == "__main__":
asyncio.run(main())
62 changes: 44 additions & 18 deletions ...mples/getting_started_with_agents/azure_ai_agent/step3_azure_ai_agent_code_interpreter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,12 @@
# a coding question. #
###################################################################

TASK = "Use code to determine the values in the Fibonacci sequence that that are less then the value of 101."


async def main() -> None:
ai_agent_settings = AzureAIAgentSettings.create()
assert ai_agent_settings.project_connection_string, "Please provide a valid Azure AI connection string." # nosec

async with (
DefaultAzureCredential() as creds,
Expand All @@ -26,43 +29,66 @@ async def main() -> None:
conn_str=ai_agent_settings.project_connection_string.get_secret_value(),
) as client,
):
# 1. Create an agent with a code interpreter on the Azure AI agent service
code_interpreter = CodeInterpreterTool()

# Create agent definition
agent_definition = await client.agents.create_agent(
model=ai_agent_settings.model_deployment_name,
tools=code_interpreter.definitions,
tool_resources=code_interpreter.resources,
)

# Create the AzureAI Agent
# 2. Create a Semantic Kernel agent for the Azure AI agent
agent = AzureAIAgent(
client=client,
definition=agent_definition,
)

# Create a new thread
# 3. Create a new thread on the Azure AI agent service
thread = await client.agents.create_thread()

user_inputs = [
"Use code to determine the values in the Fibonacci sequence that that are less then the value of 101."
]

try:
for user_input in user_inputs:
# Add the user input as a chat message
await agent.add_chat_message(
thread_id=thread.id, message=ChatMessageContent(role=AuthorRole.USER, content=user_input)
)
print(f"# User: '{user_input}'")
# Invoke the agent for the specified thread
async for content in agent.invoke(thread_id=thread.id):
if content.role != AuthorRole.TOOL:
print(f"# Agent: {content.content}")
# 4. Add the task as a chat message
await agent.add_chat_message(
thread_id=thread.id, message=ChatMessageContent(role=AuthorRole.USER, content=TASK)
)
print(f"# User: '{TASK}'")
# 5. Invoke the agent for the specified thread for response
async for content in agent.invoke(thread_id=thread.id):
if content.role != AuthorRole.TOOL:
print(f"# Agent: {content.content}")
finally:
# 6. Cleanup: Delete the thread and agent
await client.agents.delete_thread(thread.id)
await client.agents.delete_agent(agent.id)

# Sample Output:
# User: 'Use code to determine the values in the Fibonacci sequence that that are less then the value of 101.'
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Wouldn't this be easier to read like:

"""
Sample Output:
User: 'Use code to determine the values in the Fibonacci sequence that that are less then the value of 101.'
Agent: # Function to generate Fibonacci sequence values less than a given limit
def fibonacci_less_than(limit):
    fib_sequence = []
    a, b = 0, 1
    while a < limit:
        fib_sequence.append(a)
        a, b = b, a + b
    a, b = 0, 1
    while a < limit:
        fib_sequence.append(a)
    a, b = 0, 1
    while a < limit:
    a, b = 0, 1
    a, b = 0, 1
    while a < limit:
        fib_sequence.append(a)
        a, b = b, a + b
    return fib_sequence

Generate Fibonacci sequence values less than 101
fibonacci_values = fibonacci_less_than(101)
fibonacci_values
Agent: The values in the Fibonacci sequence that are less than 101 are:

\[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89\]
"""

# Agent: # Function to generate Fibonacci sequence values less than a given limit
# def fibonacci_less_than(limit):
# fib_sequence = []
# a, b = 0, 1
# while a < limit:
# fib_sequence.append(a)
# a, b = b, a + b
# a, b = 0, 1
# while a < limit:
# fib_sequence.append(a)
# a, b = 0, 1
# while a < limit:
# a, b = 0, 1
# a, b = 0, 1
# while a < limit:
# fib_sequence.append(a)
# a, b = b, a + b
# return fib_sequence
#
# Generate Fibonacci sequence values less than 101
# fibonacci_values = fibonacci_less_than(101)
# fibonacci_values
# Agent: The values in the Fibonacci sequence that are less than 101 are:
#
# \[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89\]


if __name__ == "__main__":
asyncio.run(main())
41 changes: 24 additions & 17 deletions ...on/samples/getting_started_with_agents/azure_ai_agent/step4_azure_ai_agent_file_search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,13 +12,21 @@

###################################################################
# The following sample demonstrates how to create a simple, #
# Azure AI agent that uses the code interpreter tool to answer #
# a coding question. #
# Azure AI agent that uses a file search tool to answer user #
# questions. #
###################################################################

# Simulate a conversation with the agent
USER_INPUTS = [
"Who is the youngest employee?",
"Who works in sales?",
"I have a customer request, who can help me?",
]


async def main() -> None:
ai_agent_settings = AzureAIAgentSettings.create()
assert ai_agent_settings.project_connection_string, "Please provide a valid Azure AI connection string." # nosec

async with (
DefaultAzureCredential() as creds,
Expand All @@ -27,56 +35,55 @@ async def main() -> None:
conn_str=ai_agent_settings.project_connection_string.get_secret_value(),
) as client,
):
# 1. Read and upload the file to the Azure AI agent service
pdf_file_path = os.path.join(
os.path.dirname(os.path.dirname(os.path.realpath(__file__))), "resources", "employees.pdf"
)

file: OpenAIFile = await client.agents.upload_file_and_poll(file_path=pdf_file_path, purpose="assistants")

vector_store: VectorStore = await client.agents.create_vector_store_and_poll(
file_ids=[file.id], name="my_vectorstore"
)

# Create file search tool with resources followed by creating agent
# 2. Create file search tool with uploaded resources
file_search = FileSearchTool(vector_store_ids=[vector_store.id])

# Create agent definition
# 3. Create an agent on the Azure AI agent service with the file search tool
agent_definition = await client.agents.create_agent(
model=ai_agent_settings.model_deployment_name,
tools=file_search.definitions,
tool_resources=file_search.resources,
)

# Create the AzureAI Agent
# 4. Create a Semantic Kernel agent for the Azure AI agent
agent = AzureAIAgent(
client=client,
definition=agent_definition,
)

# Create a new thread
# 5. Create a new thread on the Azure AI agent service
thread = await client.agents.create_thread()

user_inputs = [
"Who is the youngest employee?",
"Who works in sales?",
"I have a customer request, who can help me?",
]

try:
for user_input in user_inputs:
# Add the user input as a chat message
for user_input in USER_INPUTS:
# 6. Add the user input as a chat message
await agent.add_chat_message(
thread_id=thread.id, message=ChatMessageContent(role=AuthorRole.USER, content=user_input)
)
print(f"# User: '{user_input}'")
# Invoke the agent for the specified thread
# 7. Invoke the agent for the specified thread for response
async for content in agent.invoke(thread_id=thread.id):
if content.role != AuthorRole.TOOL:
print(f"# Agent: {content.content}")
finally:
# 8. Cleanup: Delete the thread and agent
await client.agents.delete_thread(thread.id)
await client.agents.delete_agent(agent.id)

# Sample Output:
# User: 'Who is the youngest employee?'
# Agent: The youngest employee is Teodor Britton, who is an accountant and was born on January 9, 1997...
# User: 'Who works in sales?'


if __name__ == "__main__":
asyncio.run(main())
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