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c75655a340
* add message field * send * message func doc str * test dict message * retiring soon * generate_init_message docstr * remove todo * update notebook * CompressibleAgent * update notebook * add test * retrieve agent * update test * summary_method args * summary * carryover * dict message * update nested doc * generate_init_message * fix typo * update docs for mathchat * Fix missing message * Add docstrings * model * notebook * default naming --------- Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: kevin666aa <yrwu000627@gmail.com> Co-authored-by: Li Jiang <bnujli@gmail.com> Co-authored-by: Li Jiang <lijiang1@microsoft.com>
119 lines
3.9 KiB
Python
Executable File
119 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python3 -m pytest
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from autogen import AssistantAgent, UserProxyAgent
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from autogen import GroupChat, GroupChatManager
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import asyncio
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from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST
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import pytest
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from conftest import skip_openai
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import autogen
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from typing import Literal
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from typing_extensions import Annotated
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from autogen import initiate_chats
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@pytest.mark.skipif(skip_openai, reason="requested to skip openai tests")
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@pytest.mark.asyncio
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async def test_async_chats():
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config_list = autogen.config_list_from_json(
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OAI_CONFIG_LIST,
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file_location=KEY_LOC,
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)
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financial_tasks = [
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"""What are the full names of NVDA and TESLA.""",
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"""Get their stock price.""",
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"""Analyze pros and cons. Keep it short.""",
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]
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writing_tasks = ["""Develop a short but engaging blog post using any information provided."""]
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financial_assistant_1 = AssistantAgent(
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name="Financial_assistant_1",
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llm_config={"config_list": config_list},
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)
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financial_assistant_2 = AssistantAgent(
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name="Financial_assistant_2",
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llm_config={"config_list": config_list},
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)
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writer = AssistantAgent(
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name="Writer",
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llm_config={"config_list": config_list},
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is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
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system_message="""
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You are a professional writer, known for
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your insightful and engaging articles.
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You transform complex concepts into compelling narratives.
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Reply "TERMINATE" in the end when everything is done.
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""",
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)
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user = UserProxyAgent(
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name="User",
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human_input_mode="NEVER",
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is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
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code_execution_config={
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"last_n_messages": 1,
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"work_dir": "tasks",
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"use_docker": False,
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}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
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)
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def my_summary_method(recipient, sender, summary_args):
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return recipient.chat_messages[sender][0].get("content", "")
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chat_res = await user.a_initiate_chats(
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[
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{
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"chat_id": 1,
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"recipient": financial_assistant_1,
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"message": financial_tasks[0],
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"silent": False,
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"summary_method": my_summary_method,
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},
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{
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"chat_id": 2,
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"prerequisites": [1],
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"recipient": financial_assistant_2,
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"message": financial_tasks[1],
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"silent": True,
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"summary_method": "reflection_with_llm",
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},
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{
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"chat_id": 3,
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"prerequisites": [1, 2],
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"recipient": financial_assistant_1,
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"message": financial_tasks[2],
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"summary_method": "last_msg",
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"clear_history": False,
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},
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{
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"chat_id": 4,
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"prerequisites": [1, 2, 3],
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"recipient": writer,
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"message": writing_tasks[0],
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"carryover": "I want to include a figure or a table of data in the blogpost.",
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"summary_method": "last_msg",
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},
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]
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)
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last_chat_id = 4
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chat_w_writer = chat_res[last_chat_id]
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print(chat_w_writer.chat_history, chat_w_writer.summary, chat_w_writer.cost)
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all_res = user.get_chat_results()
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writer_res = user.get_chat_results(last_chat_id)
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# print(blogpost.summary, insights_and_blogpost)
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print(writer_res.summary, writer_res.cost)
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print(all_res[1].human_input)
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print(all_res[1].summary)
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print(all_res[1].chat_history)
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print(all_res[2].summary)
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if __name__ == "__main__":
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test_async_chats()
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