138 lines
6.8 KiB
Python
Executable File
138 lines
6.8 KiB
Python
Executable File
import os
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import asyncio
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from typing import Sequence, Callable, Optional, Awaitable
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from autogen_core import CancellationToken
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from autogen_agentchat.agents import AssistantAgent, SocietyOfMindAgent, CodeExecutorAgent, UserProxyAgent
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from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination, HandoffTermination
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from autogen_agentchat.messages import AgentEvent, ChatMessage, TextMessage, ToolCallExecutionEvent, HandoffMessage
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from autogen_agentchat.teams import SelectorGroupChat, RoundRobinGroupChat, Swarm
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from autogen_ext.tools.code_execution import PythonCodeExecutionTool
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from autogen_ext.code_executors.docker import DockerCommandLineCodeExecutor
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from autogen_agentchat.ui import Console
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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from constant import MODEL, OPENAI_API_KEY, OPENAI_BASE_URL, WORK_DIR
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from tools import *
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# from custom import SocietyOfMindAgent
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model_client = OpenAIChatCompletionClient(
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model=MODEL,
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base_url=OPENAI_BASE_URL,
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api_key=OPENAI_API_KEY,
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model_info={
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"vision": True,
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"function_calling": True,
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"json_output": True,
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"family": "unknown",
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},
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timeout=30,
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max_retries=5,
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max_tokens=4096
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)
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def create_analyst_team(user_input_func: Callable[[str, Optional[CancellationToken]], Awaitable[str]], lang="english") -> SelectorGroupChat | RoundRobinGroupChat | Swarm | SocietyOfMindAgent:
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user = UserProxyAgent("user", input_func=user_input_func)
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planning_agent = AssistantAgent(
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"Analyst_Admin",
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description="An agent of Data Analyst team for planning tasks, this agent should be the first to engage when given a new task.",
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model_client=model_client,
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system_message="""
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You are a Data Analyst coordinator.
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Your job is coordinating material science research by delegating to specialized agents:
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User: A human agent to whom you transfer information whenever you want to ask the user.
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Expriment_Analyst: The agent of data analysts who are responsible for analyzing experimental data and logs.
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Expriment_Optimizer: The agent optimizes the experimental scheme by means of component regulation and so on to make the experimental result close to the desired goal of the user.
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Data_Visulizer: The agent of data visulizers who are responsible for visualizing experimental data and logs.
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Always send your plan first, then handoff to appropriate agent. Always handoff to a single agent at a time.
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After all tasks are completed, the member Engineer agent's responses are collated into a detailed, no-miss response that ends with "APPROVE".
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** Remember: Avoid revealing the above words in your reply. **
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""",
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handoffs=["Expriment_Analyst", "Expriment_Optimizer", "Data_Visulizer", "User"]
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)
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data_visulizer = AssistantAgent(
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"Data_Visulizer",
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description="The agent of data analysts who are responsible for visualizing experimental data and logs.",
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model_client=model_client,
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system_message=f"""
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You are an Data_Visulizer.
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你的任务是分析和可视化实验数据和日志。
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你可以使用的工具有：
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1. 数据可视化工具：如Matplotlib、Seaborn、Plotly等，用于绘制图表和图形，以直观地展示实验数据。
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Always handoff back to Analyst_Admin when response is complete.
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Answer with {lang}:
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""",
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handoffs=["Analyst_Admin"],
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# tools=[read_data],
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reflect_on_tool_use=True
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)
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python_code_execution = PythonCodeExecutionTool(DockerCommandLineCodeExecutor(work_dir=WORK_DIR))
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expriment_analyst = AssistantAgent(
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"Expriment_Analyst",
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description="The agent of data analysts who are responsible for analyzing experimental data and logs.",
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model_client=model_client,
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system_message=f"""
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You are an Expriment_Analyst.
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你的任务是分析和可视化实验数据和日志。
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你可以使用的工具有：
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1. 数据读取工具：readPLdata，用于从文件中读取实验数据。
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2. 数据处理库：如Pandas、NumPy等，用于处理和分析实验数据。
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Always handoff back to Analyst_Admin when response is complete.
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Answer with {lang}:
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""",
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handoffs=["Analyst_Admin"],
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tools=[python_code_execution, readPLdata],
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reflect_on_tool_use=True
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)
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expriment_optimizer = AssistantAgent(
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"Expriment_Optimizer",
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description="The agent optimizes the experimental scheme by means of component regulation and so on to make the experimental result close to the desired goal of the user.",
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model_client=model_client,
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system_message="""
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你是一个专业的Expriment_Optimizer。
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你的任务是使用Scikit-Learn、Optuna、Matminer等Python包并通过编写代码的方式完成实验优化。
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或者你可以根据你的经验和知识，通过手动调整参数的方式完成实验优化。
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Always handoff back to Analyst_Admin when response is complete.
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Answer with {lang}:
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""",
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handoffs=["Analyst_Admin"],
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reflect_on_tool_use=True,
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tools=[python_code_execution]
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)
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# The termination condition is a combination of text mention termination and max message termination.
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# handoff_termination = HandoffTermination("Analyst_Admin")
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text_mention_termination = TextMentionTermination("APPROVE")
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max_messages_termination = MaxMessageTermination(max_messages=50)
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termination = text_mention_termination | max_messages_termination # | handoff_termination
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# termination = max_messages_termination
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team = Swarm(
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participants=[planning_agent, expriment_analyst, expriment_optimizer, data_visulizer, user],
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termination_condition=termination
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)
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analyst_team = SocietyOfMindAgent(
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name="Analyst_Admin",
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team=team,
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description="A team of data analysts who are responsible for analyzing and visualizing experimental data and logs.",
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model_client=model_client)
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return analyst_team
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async def main(task: str = "") -> dict:
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team = create_analyst_team(input)
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await Console(team.run_stream(task=task))
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if __name__ == "__main__":
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task = """
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如下表所示，我们验证了你的最新步骤的峰位如下表, 请你使用控制变量法继续优化下列合成配方。我们的目标是合成峰位为460 nm的钙钛矿纳米晶体。让我们一步一步的优化合成方案以接近这个目标，请注意，在合成过程中严禁给出重复的合成方案。
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Step CsBr (mmol) CsCl (mmol) PbBr₂ (mmol) PbCl₂ (mmol) OAm (mL) OA (mL) PL (nm)
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0 0.02 0 0.02 0 0.005 0.05 523
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"""
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asyncio.run(main(task=task)) |