chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from azure.identity import AzureCliCredential
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from semantic_kernel import Kernel
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from semantic_kernel.agents import AgentGroupChat, ChatCompletionAgent
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from semantic_kernel.agents.strategies import (
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KernelFunctionSelectionStrategy,
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KernelFunctionTerminationStrategy,
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)
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from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
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from semantic_kernel.contents import ChatHistoryTruncationReducer
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from semantic_kernel.functions import KernelFunctionFromPrompt
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"""
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The following sample demonstrates how to create a simple,
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agent group chat that utilizes a Reviewer Chat Completion
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Agent along with a Writer Chat Completion Agent to
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complete a user's task.
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This is the full code sample for the Semantic Kernel Learn Site: How-To: Coordinate Agent Collaboration
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using Agent Group Chat
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https://learn.microsoft.com/semantic-kernel/frameworks/agent/examples/example-agent-collaboration?pivots=programming-language-python
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"""
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# Define agent names
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REVIEWER_NAME = "Reviewer"
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WRITER_NAME = "Writer"
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def create_kernel() -> Kernel:
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"""Creates a Kernel instance with an Azure OpenAI ChatCompletion service."""
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kernel = Kernel()
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kernel.add_service(service=AzureChatCompletion(credential=AzureCliCredential()))
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return kernel
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async def main():
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# Create a single kernel instance for all agents.
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kernel = create_kernel()
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# Create ChatCompletionAgents using the same kernel.
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agent_reviewer = ChatCompletionAgent(
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kernel=kernel,
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name=REVIEWER_NAME,
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instructions="""
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Your responsibility is to review and identify how to improve user provided content.
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If the user has provided input or direction for content already provided, specify how to address this input.
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Never directly perform the correction or provide an example.
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Once the content has been updated in a subsequent response, review it again until it is satisfactory.
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RULES:
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- Only identify suggestions that are specific and actionable.
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- Verify previous suggestions have been addressed.
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- Never repeat previous suggestions.
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""",
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)
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agent_writer = ChatCompletionAgent(
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kernel=kernel,
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name=WRITER_NAME,
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instructions="""
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Your sole responsibility is to rewrite content according to review suggestions.
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- Always apply all review directions.
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- Always revise the content in its entirety without explanation.
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- Never address the user.
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""",
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)
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# Define a selection function to determine which agent should take the next turn.
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selection_function = KernelFunctionFromPrompt(
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function_name="selection",
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prompt=f"""
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Examine the provided RESPONSE and choose the next participant.
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State only the name of the chosen participant without explanation.
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Never choose the participant named in the RESPONSE.
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Choose only from these participants:
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- {REVIEWER_NAME}
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- {WRITER_NAME}
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Rules:
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- If RESPONSE is user input, it is {REVIEWER_NAME}'s turn.
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- If RESPONSE is by {REVIEWER_NAME}, it is {WRITER_NAME}'s turn.
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- If RESPONSE is by {WRITER_NAME}, it is {REVIEWER_NAME}'s turn.
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RESPONSE:
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{{{{$lastmessage}}}}
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""",
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)
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# Define a termination function where the reviewer signals completion with "yes".
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termination_keyword = "yes"
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termination_function = KernelFunctionFromPrompt(
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function_name="termination",
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prompt=f"""
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Examine the RESPONSE and determine whether the content has been deemed satisfactory.
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If the content is satisfactory, respond with a single word without explanation: {termination_keyword}.
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If specific suggestions are being provided, it is not satisfactory.
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If no correction is suggested, it is satisfactory.
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RESPONSE:
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{{{{$lastmessage}}}}
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""",
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)
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history_reducer = ChatHistoryTruncationReducer(target_count=1)
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# Create the AgentGroupChat with selection and termination strategies.
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chat = AgentGroupChat(
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agents=[agent_reviewer, agent_writer],
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selection_strategy=KernelFunctionSelectionStrategy(
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initial_agent=agent_reviewer,
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function=selection_function,
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kernel=kernel,
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result_parser=lambda result: str(result.value[0]).strip() if result.value[0] is not None else WRITER_NAME,
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history_variable_name="lastmessage",
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history_reducer=history_reducer,
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),
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termination_strategy=KernelFunctionTerminationStrategy(
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agents=[agent_reviewer],
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function=termination_function,
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kernel=kernel,
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result_parser=lambda result: termination_keyword in str(result.value[0]).lower(),
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history_variable_name="lastmessage",
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maximum_iterations=10,
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history_reducer=history_reducer,
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),
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)
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print(
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"Ready! Type your input, or 'exit' to quit, 'reset' to restart the conversation. "
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"You may pass in a file path using @<path_to_file>."
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)
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is_complete = False
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while not is_complete:
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print()
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user_input = input("User > ").strip()
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if not user_input:
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continue
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if user_input.lower() == "exit":
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is_complete = True
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break
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if user_input.lower() == "reset":
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await chat.reset()
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print("[Conversation has been reset]")
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continue
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# Try to grab files from the script's current directory
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if user_input.startswith("@") and len(user_input) > 1:
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file_name = user_input[1:]
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script_dir = os.path.dirname(os.path.abspath(__file__))
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file_path = os.path.join(script_dir, file_name)
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try:
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if not os.path.exists(file_path):
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print(f"Unable to access file: {file_path}")
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continue
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with open(file_path, encoding="utf-8") as file:
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user_input = file.read()
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except Exception:
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print(f"Unable to access file: {file_path}")
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continue
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# Add the current user_input to the chat
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await chat.add_chat_message(message=user_input)
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try:
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async for response in chat.invoke():
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if response is None or not response.name:
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continue
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print()
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print(f"# {response.name.upper()}:\n{response.content}")
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except Exception as e:
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print(f"Error during chat invocation: {e}")
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# Reset the chat's complete flag for the new conversation round.
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chat.is_complete = False
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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import os
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from azure.identity import AzureCliCredential
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from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
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from semantic_kernel.contents import StreamingFileReferenceContent
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logging.basicConfig(level=logging.ERROR)
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"""
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The following sample demonstrates how to create a simple,
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OpenAI assistant agent that utilizes the code interpreter
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to analyze uploaded files.
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This is the full code sample for the Semantic Kernel Learn Site: How-To: Open AI Assistant Agent Code Interpreter
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https://learn.microsoft.com/semantic-kernel/frameworks/agent/examples/example-assistant-code?pivots=programming-language-python
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""" # noqa: E501
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# Let's form the file paths that we will later pass to the assistant
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csv_file_path_1 = os.path.join(
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os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
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"resources",
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"PopulationByAdmin1.csv",
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)
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csv_file_path_2 = os.path.join(
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os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
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"resources",
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"PopulationByCountry.csv",
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)
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async def download_file_content(agent: AzureAssistantAgent, file_id: str):
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try:
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# Fetch the content of the file using the provided method
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response_content = await agent.client.files.content(file_id)
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# Get the current working directory of the file
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current_directory = os.path.dirname(os.path.abspath(__file__))
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# Define the path to save the image in the current directory
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file_path = os.path.join(
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current_directory, # Use the current directory of the file
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f"{file_id}.png", # You can modify this to use the actual filename with proper extension
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)
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# Save content to a file asynchronously
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with open(file_path, "wb") as file:
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file.write(response_content.content)
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print(f"File saved to: {file_path}")
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except Exception as e:
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print(f"An error occurred while downloading file {file_id}: {str(e)}")
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async def download_response_image(agent: AzureAssistantAgent, file_ids: list[str]):
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if file_ids:
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# Iterate over file_ids and download each one
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for file_id in file_ids:
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await download_file_content(agent, file_id)
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async def main():
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# Create the client using Azure OpenAI resources and configuration
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client = AzureAssistantAgent.create_client(credential=AzureCliCredential())
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# Upload the files to the client
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file_ids: list[str] = []
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for path in [csv_file_path_1, csv_file_path_2]:
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with open(path, "rb") as file:
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file = await client.files.create(file=file, purpose="assistants")
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file_ids.append(file.id)
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# Get the code interpreter tool and resources
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code_interpreter_tools, code_interpreter_tool_resources = AzureAssistantAgent.configure_code_interpreter_tool(
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file_ids=file_ids
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)
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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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instructions="""
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Analyze the available data to provide an answer to the user's question.
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Always format response using markdown.
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Always include a numerical index that starts at 1 for any lists or tables.
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Always sort lists in ascending order.
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""",
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name="SampleAssistantAgent",
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tools=code_interpreter_tools,
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tool_resources=code_interpreter_tool_resources,
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)
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# Create the agent using the client and the assistant definition
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agent = AzureAssistantAgent(
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client=client,
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definition=definition,
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)
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thread: AssistantAgentThread = None
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try:
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is_complete: bool = False
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file_ids: list[str] = []
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while not is_complete:
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user_input = input("User:> ")
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if not user_input:
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continue
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if user_input.lower() == "exit":
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is_complete = True
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break
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is_code = False
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last_role = None
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async for response in agent.invoke_stream(messages=user_input, thread=thread):
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current_is_code = response.metadata.get("code", False)
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if current_is_code:
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if not is_code:
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print("\n\n```python")
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is_code = True
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print(response.content, end="", flush=True)
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else:
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if is_code:
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print("\n```")
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is_code = False
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last_role = None
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if hasattr(response, "role") and response.role is not None and last_role != response.role:
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print(f"\n# {response.role}: ", end="", flush=True)
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last_role = response.role
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print(response.content, end="", flush=True)
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file_ids.extend([
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item.file_id for item in response.items if isinstance(item, StreamingFileReferenceContent)
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])
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thread = response.thread
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if is_code:
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print("```\n")
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print()
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await download_response_image(agent, file_ids)
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file_ids.clear()
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finally:
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print("\nCleaning up resources...")
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[await client.files.delete(file_id) for file_id in file_ids]
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await thread.delete() if thread else None
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await client.beta.assistants.delete(agent.id)
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,118 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from azure.identity import AzureCliCredential
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from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
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from semantic_kernel.contents import StreamingAnnotationContent
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"""
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The following sample demonstrates how to create a simple,
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OpenAI assistant agent that utilizes the vector store
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to answer questions based on the uploaded documents.
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This is the full code sample for the Semantic Kernel Learn Site: How-To: Open AI Assistant Agent File Search
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https://learn.microsoft.com/semantic-kernel/frameworks/agent/examples/example-assistant-search?pivots=programming-language-python
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"""
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def get_filepath_for_filename(filename: str) -> str:
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base_directory = os.path.join(
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os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
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"resources",
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)
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return os.path.join(base_directory, filename)
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filenames = [
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"Grimms-The-King-of-the-Golden-Mountain.txt",
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"Grimms-The-Water-of-Life.txt",
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"Grimms-The-White-Snake.txt",
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]
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async def main():
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# Create the client using Azure OpenAI resources and configuration
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client = AzureAssistantAgent.create_client(credential=AzureCliCredential())
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# Upload the files to the client
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file_ids: list[str] = []
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for path in [get_filepath_for_filename(filename) for filename in filenames]:
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with open(path, "rb") as file:
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file = await client.files.create(file=file, purpose="assistants")
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file_ids.append(file.id)
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vector_store = await client.vector_stores.create(
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name="assistant_search",
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file_ids=file_ids,
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)
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# Get the file search tool and resources
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file_search_tools, file_search_tool_resources = AzureAssistantAgent.configure_file_search_tool(
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vector_store_ids=vector_store.id
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)
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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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instructions="""
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The document store contains the text of fictional stories.
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Always analyze the document store to provide an answer to the user's question.
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Never rely on your knowledge of stories not included in the document store.
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Always format response using markdown.
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""",
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name="SampleAssistantAgent",
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tools=file_search_tools,
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tool_resources=file_search_tool_resources,
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)
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# Create the agent using the client and the assistant definition
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agent = AzureAssistantAgent(
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client=client,
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definition=definition,
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)
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thread: AssistantAgentThread = None
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try:
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is_complete: bool = False
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while not is_complete:
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user_input = input("User:> ")
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if not user_input:
|
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continue
|
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if user_input.lower() == "exit":
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is_complete = True
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break
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footnotes: list[StreamingAnnotationContent] = []
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async for response in agent.invoke_stream(messages=user_input, thread=thread):
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footnotes.extend([item for item in response.items if isinstance(item, StreamingAnnotationContent)])
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print(f"{response.content}", end="", flush=True)
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if not thread:
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thread = response.thread
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print()
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if len(footnotes) > 0:
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for footnote in footnotes:
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print(
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f"\n`{footnote.quote}` => {footnote.file_id} "
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f"(Index: {footnote.start_index} - {footnote.end_index})"
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)
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finally:
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print("\nCleaning up resources...")
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[await client.files.delete(file_id) for file_id in file_ids]
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await client.vector_stores.delete(vector_store.id)
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await thread.delete() if thread else None
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await client.beta.assistants.delete(agent.id)
|
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|
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,88 @@
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# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import os
|
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import sys
|
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from datetime import datetime
|
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|
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from azure.identity import AzureCliCredential
|
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|
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from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
|
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from semantic_kernel.connectors.ai import FunctionChoiceBehavior
|
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from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
|
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from semantic_kernel.functions import KernelArguments
|
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from semantic_kernel.kernel import Kernel
|
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# Adjust the sys.path so we can use the GitHubPlugin and GitHubSettings classes
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# This is so we can run the code from the samples/learn_resources/agent_docs directory
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# If you are running code from your own project, you may not need need to do this.
|
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from plugins.GithubPlugin.github import GitHubPlugin, GitHubSettings # noqa: E402
|
||||
|
||||
"""
|
||||
The following sample demonstrates how to create a simple,
|
||||
ChatCompletionAgent to use a GitHub plugin to interact
|
||||
with the GitHub API.
|
||||
|
||||
This is the full code sample for the Semantic Kernel Learn Site: How-To: Chat Completion Agent
|
||||
|
||||
https://learn.microsoft.com/semantic-kernel/frameworks/agent/examples/example-chat-agent?pivots=programming-language-python
|
||||
"""
|
||||
|
||||
|
||||
async def main():
|
||||
kernel = Kernel()
|
||||
|
||||
# Add the AzureChatCompletion AI Service to the Kernel
|
||||
service_id = "agent"
|
||||
kernel.add_service(AzureChatCompletion(service_id=service_id, credential=AzureCliCredential()))
|
||||
|
||||
settings = kernel.get_prompt_execution_settings_from_service_id(service_id=service_id)
|
||||
# Configure the function choice behavior to auto invoke kernel functions
|
||||
settings.function_choice_behavior = FunctionChoiceBehavior.Auto()
|
||||
|
||||
# Set your GitHub Personal Access Token (PAT) value here
|
||||
gh_settings = GitHubSettings(token="") # nosec
|
||||
kernel.add_plugin(plugin=GitHubPlugin(gh_settings), plugin_name="GithubPlugin")
|
||||
|
||||
current_time = datetime.now().isoformat()
|
||||
|
||||
# Create the agent
|
||||
agent = ChatCompletionAgent(
|
||||
kernel=kernel,
|
||||
name="SampleAssistantAgent",
|
||||
instructions=f"""
|
||||
You are an agent designed to query and retrieve information from a single GitHub repository in a read-only
|
||||
manner.
|
||||
You are also able to access the profile of the active user.
|
||||
|
||||
Use the current date and time to provide up-to-date details or time-sensitive responses.
|
||||
|
||||
The repository you are querying is a public repository with the following name: microsoft/semantic-kernel
|
||||
|
||||
The current date and time is: {current_time}.
|
||||
""",
|
||||
arguments=KernelArguments(settings=settings),
|
||||
)
|
||||
|
||||
thread: ChatHistoryAgentThread = None
|
||||
is_complete: bool = False
|
||||
while not is_complete:
|
||||
user_input = input("User:> ")
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
if user_input.lower() == "exit":
|
||||
is_complete = True
|
||||
break
|
||||
|
||||
arguments = KernelArguments(now=datetime.now().strftime("%Y-%m-%d %H:%M"))
|
||||
|
||||
async for response in agent.invoke(messages=user_input, thread=thread, arguments=arguments):
|
||||
print(f"{response.content}")
|
||||
thread = response.thread
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user