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 typing import TYPE_CHECKING
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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 AuthorRole
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from semantic_kernel.kernel import Kernel
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if TYPE_CHECKING:
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pass
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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 to work towards
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the user's requirement. It also demonstrates how the underlying
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agent reset method is used to clear the current state of the chat
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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no longer maintained.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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# First create the ChatCompletionAgent
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chat_agent = ChatCompletionAgent(
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kernel=_create_kernel_with_chat_completion("chat", credential),
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name="chat_agent",
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instructions="""
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The user may either provide information or query on information previously provided.
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If the query does not correspond with information provided, inform the user that their query
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cannot be answered.
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""",
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)
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# Next, we will create the AzureAssistantAgent
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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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# 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="copywriter",
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instructions="""
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The user may either provide information or query on information previously provided.
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If the query does not correspond with information provided, inform the user that their query
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cannot be answered.
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""",
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)
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# Create the AzureAssistantAgent instance using the client and the assistant definition
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assistant_agent = AzureAssistantAgent(
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client=client,
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definition=definition,
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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_inputs = [
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"What is my favorite color?",
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"I like green.",
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"What is my favorite color?",
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"[RESET]",
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"What is my favorite color?",
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]
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for user_input in user_inputs:
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# Check for reset indicator
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if user_input == "[RESET]":
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print("\nResetting chat...")
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await chat.reset()
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continue
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# First agent (assistant_agent) receives the user input
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await chat.add_chat_message(user_input)
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print(f"\n{AuthorRole.USER}: '{user_input}'")
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async for message in chat.invoke(agent=assistant_agent):
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if message.content is not None:
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print(f"\n# {message.role} - {message.name or '*'}: '{message.content}'")
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# Second agent (chat_agent) just responds without new user input
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async for message in chat.invoke(agent=chat_agent):
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if message.content is not None:
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print(f"\n# {message.role} - {message.name or '*'}: '{message.content}'")
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finally:
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await chat.reset()
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await assistant_agent.client.beta.assistants.delete(assistant_agent.id)
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if __name__ == "__main__":
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asyncio.run(main())
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