调整后端
This commit is contained in:
180
_backend/main.py
180
_backend/main.py
@@ -25,101 +25,104 @@ model_client = OpenAIChatCompletionClient(
|
||||
},
|
||||
)
|
||||
|
||||
async def main(task: str = "") -> dict:
|
||||
user = UserProxyAgent("user_agent", input_func=input)
|
||||
user = UserProxyAgent("User", input_func=input)
|
||||
|
||||
scientist_team = create_scientist_team()
|
||||
engineer_team = create_engineer_team()
|
||||
# await code_executor.start()
|
||||
robot_platform = create_robot_team()
|
||||
analyst_team = create_analyst_team()
|
||||
scientist_team = create_scientist_team(model_client=model_client)
|
||||
engineer_team = create_engineer_team()
|
||||
# await code_executor.start()
|
||||
robot_platform = create_robot_team()
|
||||
analyst_team = create_analyst_team()
|
||||
|
||||
result = {}
|
||||
planning_agent = AssistantAgent(
|
||||
"PlanningAgent",
|
||||
description="An agent for planning tasks, this agent should be the first to engage when given a new task.",
|
||||
model_client=model_client,
|
||||
system_message="""
|
||||
You are a planning agent.
|
||||
Your job is to break down complex Materials science research tasks into smaller, manageable subtasks.
|
||||
Assign these subtasks to the appropriate sub-teams; not all sub-teams are required to participate in every task.
|
||||
Your sub-teams are:
|
||||
1. User: A human agent to whom you transfer information whenever you need to confirm your execution steps to a human.
|
||||
2. Scientist: A professional team of material scientists who are mainly responsible for consulting on material synthesis, structure, application and properties.
|
||||
- The scientist team has the following members:
|
||||
2.1 Synthesis Scientist: who is good at giving perfect and correct synthesis solutions.
|
||||
2.2 Structure Scientist: focusing on agents of structural topics in materials science.
|
||||
2.3 Property Scientist: focuses on physical and chemistry property topics in materials science.
|
||||
2.4 Application Scientist: Focus on practical applications of materials, such as devices, chips, etc.
|
||||
3. Engineer: A team of professional engineers who are responsible for writing code, visualizing experimental schemes, converting experimental schemes to JSON, and more.
|
||||
- The engineer team has the following members:
|
||||
3.1 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.
|
||||
3.2 Software engineer: A professional software engineers will coding with Python.
|
||||
3.3 Code reviewer: A professional code reviewer will review the code written by software engineers and execute it.
|
||||
3.4 Scheme Plotter: An agent responsible for converting a expriment scheme into a Mermaid flowchart.
|
||||
4. Executor: A robotic platform is responsible for performing automated synthesis experiments, automated characterization experiments, and collecting experimental datas.
|
||||
- The Executor team has the following members:
|
||||
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.
|
||||
4.2 RobotWorkstation_Agent: This agent is called by the mobile robot agent, do not plan it alone.
|
||||
4.3 DataCollector_Agent: This agent collects experimental data and experimental logs from the characterization device in the robot platform and stores them.
|
||||
5. Analyst: A team of data analysts who are responsible for analyzing and visualizing experimental data and logs.
|
||||
- The Data Analysis team has the following members:
|
||||
5.1 Expriment_Analyst: The agent of data analysts who are responsible for analyzing experimental data and logs.
|
||||
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.
|
||||
5.3 Data_Visulizer: The agent of data visulizers who are responsible for visualizing experimental data and logs.
|
||||
|
||||
You only plan and delegate tasks - you do not execute them yourself.
|
||||
|
||||
result = {}
|
||||
planning_agent = AssistantAgent(
|
||||
"PlanningAgent",
|
||||
description="An agent for planning tasks, this agent should be the first to engage when given a new task.",
|
||||
model_client=model_client,
|
||||
system_message="""
|
||||
You are a planning agent.
|
||||
Your job is to break down complex Materials science research tasks into smaller, manageable subtasks.
|
||||
Assign these subtasks to the appropriate sub-teams; not all sub-teams are required to participate in every task.
|
||||
Your sub-teams are:
|
||||
1. User: A human agent to whom you transfer information whenever you need to confirm your execution steps to a human.
|
||||
2. Scientist: A professional team of material scientists who are mainly responsible for consulting on material synthesis, structure, application and properties.
|
||||
- The scientist team has the following members:
|
||||
2.1 Synthesis Scientist: who is good at giving perfect and correct synthesis solutions.
|
||||
2.2 Structure Scientist: focusing on agents of structural topics in materials science.
|
||||
2.3 Property Scientist: focuses on physical and chemistry property topics in materials science.
|
||||
2.4 Application Scientist: Focus on practical applications of materials, such as devices, chips, etc.
|
||||
3. Engineer: A team of professional engineers who are responsible for writing code, visualizing experimental schemes, converting experimental schemes to JSON, and more.
|
||||
- The engineer team has the following members:
|
||||
3.1 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.
|
||||
3.2 Software engineer: A professional software engineers will coding with Python.
|
||||
3.3 Code reviewer: A professional code reviewer will review the code written by software engineers and execute it.
|
||||
3.4 Scheme Plotter: An agent responsible for converting a expriment scheme into a Mermaid flowchart.
|
||||
4. Executor: A robotic platform is responsible for performing automated synthesis experiments, automated characterization experiments, and collecting experimental datas.
|
||||
- The Executor team has the following members:
|
||||
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.
|
||||
4.2 RobotWorkstation_Agent: This agent is called by the mobile robot agent, do not plan it alone.
|
||||
4.3 DataCollector_Agent: This agent collects experimental data and experimental logs from the characterization device in the robot platform and stores them.
|
||||
5. Analyst: A team of data analysts who are responsible for analyzing and visualizing experimental data and logs.
|
||||
- The Data Analysis team has the following members:
|
||||
5.1 Expriment_Analyst: The agent of data analysts who are responsible for analyzing experimental data and logs.
|
||||
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.
|
||||
5.3 Data_Visulizer: The agent of data visulizers who are responsible for visualizing experimental data and logs.
|
||||
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：
|
||||
| Team_name | Member_name | sub-task |
|
||||
| ----------- | ------------- | ------------------------------------ |
|
||||
| <team_name> | <member_name> | <status: brief sub-task description> |
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
User[User]
|
||||
subgraph <team_name>
|
||||
A1[<member_name>]
|
||||
end
|
||||
style xxx # 推荐多样的风格
|
||||
...
|
||||
User --> A1
|
||||
...
|
||||
```
|
||||
|
||||
You only plan and delegate tasks - you do not execute them yourself.
|
||||
|
||||
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：
|
||||
| Team_name | Member_name | sub-task |
|
||||
| ----------- | ------------- | ------------------------------------ |
|
||||
| <team_name> | <member_name> | <status: brief sub-task description> |
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
User[User]
|
||||
subgraph <team_name>
|
||||
A1[<member_name>]
|
||||
end
|
||||
style xxx # 推荐多样的风格
|
||||
...
|
||||
User --> A1
|
||||
...
|
||||
```
|
||||
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：
|
||||
**已完成子任务：**
|
||||
1. <team> : <subtask>
|
||||
**Next sub-task:**
|
||||
n. <team> : <subtask>
|
||||
|
||||
You can end with "HUMAN" if you need to, which means you need human approval or other advice or instructions;
|
||||
After plan and delegate tasks are complete, end with "START";
|
||||
Determine if all sub-teams have completed their tasks, and if so, summarize the findings and end with "TERMINATE".
|
||||
""",
|
||||
reflect_on_tool_use=False
|
||||
)
|
||||
|
||||
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：
|
||||
**已完成子任务：**
|
||||
1. <team> : <subtask>
|
||||
**Next sub-task:**
|
||||
n. <team> : <subtask>
|
||||
|
||||
You can end with "HUMAN" if you need to, which means you need human approval or other advice or instructions;
|
||||
After plan and delegate tasks are complete, end with "START";
|
||||
Determine if all sub-teams have completed their tasks, and if so, summarize the findings and end with "TERMINATE".
|
||||
""",
|
||||
reflect_on_tool_use=False
|
||||
)
|
||||
# The termination condition is a combination of text mention termination and max message termination.
|
||||
text_mention_termination = TextMentionTermination("TERMINATE")
|
||||
max_messages_termination = MaxMessageTermination(max_messages=200)
|
||||
termination = text_mention_termination | max_messages_termination
|
||||
|
||||
# The termination condition is a combination of text mention termination and max message termination.
|
||||
text_mention_termination = TextMentionTermination("TERMINATE")
|
||||
max_messages_termination = MaxMessageTermination(max_messages=200)
|
||||
termination = text_mention_termination | max_messages_termination
|
||||
# The selector function is a function that takes the current message thread of the group chat
|
||||
# and returns the next speaker's name. If None is returned, the LLM-based selection method will be used.
|
||||
def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:
|
||||
if messages[-1].source != planning_agent.name:
|
||||
return planning_agent.name # Always return to the planning agent after the other agents have spoken.
|
||||
elif "HUMAN" in messages[-1].content:
|
||||
return user.name
|
||||
return None
|
||||
|
||||
# The selector function is a function that takes the current message thread of the group chat
|
||||
# and returns the next speaker's name. If None is returned, the LLM-based selection method will be used.
|
||||
def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:
|
||||
if messages[-1].source != planning_agent.name:
|
||||
return planning_agent.name # Always return to the planning agent after the other agents have spoken.
|
||||
elif "HUMAN" in messages[-1].content:
|
||||
return user.name
|
||||
return None
|
||||
team = SelectorGroupChat(
|
||||
[planning_agent, user, scientist_team, engineer_team, robot_platform, analyst_team],
|
||||
model_client=model_client, # Use a smaller model for the selector.
|
||||
termination_condition=termination,
|
||||
selector_func=selector_func,
|
||||
)
|
||||
|
||||
team = SelectorGroupChat(
|
||||
[planning_agent, user, scientist_team, engineer_team, robot_platform, analyst_team],
|
||||
model_client=model_client, # Use a smaller model for the selector.
|
||||
termination_condition=termination,
|
||||
selector_func=selector_func,
|
||||
)
|
||||
|
||||
|
||||
async def main(task: str = "") -> dict:
|
||||
await Console(team.run_stream(task=task))
|
||||
# await code_executor.stop()
|
||||
# async for message in team.run_stream(task=task):
|
||||
@@ -131,6 +134,7 @@ async def main(task: str = "") -> dict:
|
||||
# result[message.source] = [content.content for content in message.content]
|
||||
return result
|
||||
|
||||
|
||||
# Example usage in another function
|
||||
async def main_1():
|
||||
# result = await main(input("Enter your instructions below: \n"))
|
||||
|
||||
Reference in New Issue
Block a user