144 lines
8.3 KiB
Python
Executable File
144 lines
8.3 KiB
Python
Executable File
import asyncio
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from typing import Sequence
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from autogen_agentchat.agents import AssistantAgent, SocietyOfMindAgent, 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
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from autogen_agentchat.teams import SelectorGroupChat, RoundRobinGroupChat
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from autogen_agentchat.ui import Console
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from autogen_agentchat.base import Handoff
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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
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from scientist_team import create_scientist_team
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from engineer_team import create_engineer_team
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from robot_platform import create_robot_team
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from analyst_team import create_analyst_team
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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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)
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async def main(task: str = "") -> dict:
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user = UserProxyAgent("user_agent", input_func=input)
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scientist_team = create_scientist_team()
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engineer_team = create_engineer_team()
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# await code_executor.start()
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robot_platform = create_robot_team()
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analyst_team = create_analyst_team()
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result = {}
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planning_agent = AssistantAgent(
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"PlanningAgent",
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description="An agent 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 planning agent.
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Your job is to break down complex Materials science research tasks into smaller, manageable subtasks.
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Assign these subtasks to the appropriate sub-teams; not all sub-teams are required to participate in every task.
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Your sub-teams are:
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1. User: A human agent to whom you transfer information whenever you need to confirm your execution steps to a human.
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2. Engineer: A team of professional engineers who are responsible for writing code, visualizing experimental schemes, converting experimental schemes to JSON, and more.
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- The engineer team has the following members:
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2.1 Structural engineer: A professional structural engineer who focus on converting natural language synthesis schemes to JSON or XML formated scheme, and then upload this JSON to S3 Storage.
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2.2 ML engineer: A professional machine learning engineers will use Python to implement various machine learning algorithms to model data.
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2.3 SandBox environment: A computer terminal that performs no other action than running Python scripts (provided to it quoted in ```python code blocks), or sh shell scripts (provided to it quoted in ```sh code blocks).
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2.4 Scheme Plotter: An agent responsible for converting a expriment scheme into a Mermaid flowchart.
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3. Scientist: A professional team of material scientists who are mainly responsible for consulting on material synthesis, structure, application and properties.
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- The scientist team has the following members:
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3.1 Synthesis Scientist: who is good at giving perfect and correct synthesis solutions.
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3.2 Structure Scientist: focusing on agents of structural topics in materials science.
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3.3 Property Scientist: focuses on physical and chemistry property topics in materials science.
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3.4 Application Scientist: Focus on practical applications of materials, such as devices, chips, etc.
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4. Executor: A robotic platform is responsible for performing automated synthesis experiments, automated characterization experiments, and collecting experimental datas.
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- The Executor team has the following members:
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4.1 MobileRobot_Agent: This agent controls the mobile robot by calling the funciton sendScheme2MobileRobot to place the experimental container into the robot workstation. This agent called before RobotWorkstation_Agent.
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4.2 RobotWorkstation_Agent: This agent is called by the mobile robot agent, do not plan it alone.
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4.3 DataCollector_Agent: This agent collects experimental data and experimental logs from the characterization device in the robot platform and stores them.
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5. Analyst: A team of data analysts who are responsible for analyzing and visualizing experimental data and logs.
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- The Data Analysis team has the following members:
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5.1 Expriment_Analyst: The agent of data analysts who are responsible for analyzing experimental data and logs.
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5.2 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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5.3 Data_Visulizer: The agent of data visulizers who are responsible for visualizing experimental data and logs.
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You only plan and delegate tasks - you do not execute them yourself.
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回答时你需要初始化/更新如下任务分配表和Mermaid流程图，并按顺序执行，使用如下格式并利用：
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| Team_name | Member_name | sub-task |
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| ----------- | ------------- | ------------------------------------ |
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| <team_name> | <member_name> | <status: brief sub-task description> |
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```mermaid
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graph TD
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User[User]
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subgraph <team_name>
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A1[<member_name>]
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end
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style xxx # 推荐多样的风格
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...
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User --> A1
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...
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```
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每次回答时，你需要清晰明确的指出已经完成的子任务下一步子任务，使用如下格式：
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**已完成子任务：**
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1. <team> : <subtask>
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**Next sub-task:**
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n. <team> : <subtask>
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You can end with "HUMAN" if you need to, which means you need human approval or other advice or instructions;
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After plan and delegate tasks are complete, end with "START";
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Determine if all sub-teams have completed their tasks, and if so, summarize the findings and end with "TERMINATE".
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""",
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reflect_on_tool_use=False
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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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text_mention_termination = TextMentionTermination("TERMINATE")
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max_messages_termination = MaxMessageTermination(max_messages=200)
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termination = text_mention_termination | max_messages_termination
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# The selector function is a function that takes the current message thread of the group chat
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# and returns the next speaker's name. If None is returned, the LLM-based selection method will be used.
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def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:
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if messages[-1].source != planning_agent.name:
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return planning_agent.name # Always return to the planning agent after the other agents have spoken.
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elif "HUMAN" in messages[-1].content:
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return user.name
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return None
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team = SelectorGroupChat(
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[planning_agent, user, scientist_team, engineer_team, robot_platform, analyst_team],
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model_client=model_client, # Use a smaller model for the selector.
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termination_condition=termination,
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selector_func=selector_func,
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)
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await Console(team.run_stream(task=task))
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# await code_executor.stop()
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# async for message in team.run_stream(task=task):
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# if isinstance(message, TextMessage):
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# print(f"----------------{message.source}----------------\n {message.content}")
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# result[message.source] = message.content
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# elif isinstance(message, ToolCallExecutionEvent):
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# print(f"----------------{message.source}----------------\n {message.content}")
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# result[message.source] = [content.content for content in message.content]
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return result
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# Example usage in another function
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async def main_1():
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# result = await main(input("Enter your instructions below: \n"))
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result = await main("Let the robot synthesize CsPbBr3 nanocubes at room temperature")
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# result = await main("查一下CsPbBr3的晶体结构")
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print(result)
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if __name__ == "__main__":
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asyncio.run(main_1())
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