wehub-resource-sync
117 lines
4.5 KiB
Python
117 lines
4.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from azure.core.credentials import TokenCredential
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from azure.identity import AzureCliCredential
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from semantic_kernel.agents import AgentGroupChat, AzureAssistantAgent, ChatCompletionAgent
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from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureOpenAISettings
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from semantic_kernel.contents import AnnotationContent
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from semantic_kernel.contents.utils.author_role import AuthorRole
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from semantic_kernel.kernel import Kernel
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"""
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The following sample demonstrates how to create an OpenAI
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assistant using either Azure OpenAI or OpenAI, a chat completion
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agent and have them participate in a group chat working with
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image content.
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Note: This sample use the `AgentGroupChat` feature of Semantic Kernel, which is
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no longer maintained. For a replacement, consider using the `GroupChatOrchestration`.
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Read more about the `GroupChatOrchestration` here:
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https://learn.microsoft.com/semantic-kernel/frameworks/agent/agent-orchestration/group-chat?pivots=programming-language-python
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Here is a migration guide from `AgentGroupChat` to `GroupChatOrchestration`:
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https://learn.microsoft.com/semantic-kernel/support/migration/group-chat-orchestration-migration-guide?pivots=programming-language-python
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"""
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def _create_kernel_with_chat_completion(service_id: str, credential: TokenCredential) -> Kernel:
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kernel = Kernel()
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kernel.add_service(AzureChatCompletion(service_id=service_id, credential=credential))
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return kernel
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async def main():
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credential = AzureCliCredential()
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# Create the client using Azure OpenAI resources and configuration
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client = AzureAssistantAgent.create_client(credential=credential)
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# Get the code interpreter tool and resources
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code_interpreter_tool, code_interpreter_resources = AzureAssistantAgent.configure_code_interpreter_tool()
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# Create the assistant definition
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definition = await client.beta.assistants.create(
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model=AzureOpenAISettings().chat_deployment_name,
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name="Analyst",
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instructions="Create charts as requested without explanation",
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tools=code_interpreter_tool,
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tool_resources=code_interpreter_resources,
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)
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# Create the AzureAssistantAgent instance using the client and the assistant definition
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analyst_agent = AzureAssistantAgent(client=client, definition=definition)
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service_id = "summary"
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summary_agent = ChatCompletionAgent(
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kernel=_create_kernel_with_chat_completion(service_id=service_id),
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instructions="Summarize the entire conversation for the user in natural language.",
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name="Summarizer",
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)
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# Create the AgentGroupChat object, which will manage the chat between the agents
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# We don't always need to specify the agents in the chat up front
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# As shown below, calling `chat.invoke(agent=<agent>)` will automatically add the
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# agent to the chat
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chat = AgentGroupChat()
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try:
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user_and_agent_inputs = (
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(
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"""
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Graph the percentage of storm events by state using a pie chart:
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State, StormCount
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TEXAS, 4701
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KANSAS, 3166
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IOWA, 2337
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ILLINOIS, 2022
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MISSOURI, 2016
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GEORGIA, 1983
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MINNESOTA, 1881
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WISCONSIN, 1850
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NEBRASKA, 1766
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NEW YORK, 1750
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""".strip(),
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analyst_agent,
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),
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(None, summary_agent),
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)
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for input, agent in user_and_agent_inputs:
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if input:
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await chat.add_chat_message(input)
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print(f"# {AuthorRole.USER}: '{input}'")
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async for content in chat.invoke(agent=agent):
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print(f"# {content.role} - {content.name or '*'}: '{content.content}'")
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if len(content.items) > 0:
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for item in content.items:
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if (
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isinstance(agent, AzureAssistantAgent)
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and isinstance(item, AnnotationContent)
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and item.file_id
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):
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print(f"\n`{item.quote}` => {item.file_id}")
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response_content = await agent.client.files.content(item.file_id)
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print(response_content.text)
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finally:
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await client.beta.assistants.delete(analyst_agent.id)
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if __name__ == "__main__":
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asyncio.run(main())
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