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0c5aed0489
* add test * fix test * fix --------- Co-authored-by: AnonymousRepoSub <“shaokunzhang529@outlook.com” >
114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
import os
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import sys
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import pytest
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import autogen
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from autogen import AssistantAgent, UserProxyAgent
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from autogen.agentchat.contrib.agent_optimizer import AgentOptimizer
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sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
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sys.path.append(os.path.join(os.path.dirname(__file__), "../.."))
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from conftest import reason, skip_openai
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from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST
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here = os.path.abspath(os.path.dirname(__file__))
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@pytest.mark.skipif(
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skip_openai,
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reason=reason,
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)
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def test_record_conversation():
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problem = "Simplify $\\sqrt[3]{1+8} \\cdot \\sqrt[3]{1+\\sqrt[3]{8}}"
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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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llm_config = {
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"config_list": config_list,
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"timeout": 60,
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"cache_seed": 42,
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}
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assistant = AssistantAgent("assistant", system_message="You are a helpful assistant.", llm_config=llm_config)
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user_proxy = UserProxyAgent(
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name="user_proxy",
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human_input_mode="NEVER",
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code_execution_config={
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"work_dir": f"{here}/test_agent_scripts",
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"use_docker": "python:3",
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"timeout": 60,
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},
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max_consecutive_auto_reply=3,
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)
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user_proxy.initiate_chat(assistant, message=problem)
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optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config)
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optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
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assert len(optimizer._trial_conversations_history) == 1
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assert len(optimizer._trial_conversations_performance) == 1
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assert optimizer._trial_conversations_performance[0]["Conversation 0"] == 1
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optimizer.reset_optimizer()
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assert len(optimizer._trial_conversations_history) == 0
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assert len(optimizer._trial_conversations_performance) == 0
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@pytest.mark.skipif(
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skip_openai,
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reason=reason,
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)
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def test_step():
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problem = "Simplify $\\sqrt[3]{1+8} \\cdot \\sqrt[3]{1+\\sqrt[3]{8}}"
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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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llm_config = {
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"config_list": config_list,
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"timeout": 60,
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"cache_seed": 42,
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}
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assistant = AssistantAgent(
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"assistant",
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system_message="You are a helpful assistant.",
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llm_config=llm_config,
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)
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user_proxy = UserProxyAgent(
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name="user_proxy",
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human_input_mode="NEVER",
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code_execution_config={
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"work_dir": f"{here}/test_agent_scripts",
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"use_docker": "python:3",
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"timeout": 60,
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},
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max_consecutive_auto_reply=3,
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)
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optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config)
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user_proxy.initiate_chat(assistant, message=problem)
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optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
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register_for_llm, register_for_exector = optimizer.step()
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print("-------------------------------------")
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print("register_for_llm:")
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print(register_for_llm)
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print("register_for_exector")
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print(register_for_exector)
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for item in register_for_llm:
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assistant.update_function_signature(**item)
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if len(register_for_exector.keys()) > 0:
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user_proxy.register_function(function_map=register_for_exector)
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print("-------------------------------------")
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print("Updated assistant.llm_config:")
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print(assistant.llm_config)
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print("Updated user_proxy._function_map:")
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print(user_proxy._function_map)
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