agentverse/tasks/tasksolving/pythoncalculator/config.yaml

cnt_agents: &cnt_agents 4
max_turn: &max_turn 3
max_inner_turns: &max_inner_turns 2

task_description: |-
  write a simple calculator GUI using Python3.

prompts:
  role_assigner_prepend_prompt: &role_assigner_prepend_prompt |-

  role_assigner_append_prompt: &role_assigner_append_prompt |-
    You are the leader of a group of experts, now you are faced with a task:
    
    ${task_description}
    
    You can recruit ${cnt_critic_agents} expert team members in different regions.
    What experts will you recruit to better generate good ideas?
    
    Output format example:
    1. an electrical engineer specified in the filed of xxx
    2. an economist who is good at xxx
    3. a lawyer with a good knowledge of xxx
    ...
    
    ${advice}
    You don't have to give the reason.

  solver_prepend_prompt: &solver_prepend_prompt |-
    You are faced with the task:
    ${task_description}

    Below is the chat history among you and other teammates.
    
  solver_append_prompt: &solver_append_prompt |-
    Now you are going to give a new solution, based upon your former solution and the critics' opinions. Write the code step by step.

  critic_prepend_prompt: &critic_prepend_prompt |-
    Now you are ${role_description}
    
    You are in a discussion group, aiming to ${task_description}.
    
    Below is the chat history among you and other teammates.

  critic_append_prompt: &critic_append_prompt |-
    Now the group is asking your opinion about it. Based on your knowledge
    in your field, do you agree that this solution can perfectly 
    solve the problem?
    Or do you have any ideas to improve it?
    
    - If you thinks it is perfect, use the following output format:
    Action: Agree
    Action Input: Agree.
    (Do not output your reason for agreeing!)

    - If you want to give complemented opinions to improve it or to contradict with it, use the following output format:
    Action: Disagree
    Action Input: (what you want to say in one line)
    
    P.S. Always remember you are ${role_description}!
    
    If no former solution or critic opinions are given, you can just disagree and output your idea freely, based on the expertise of your role.
    Remember, the ideas should be specific and detailed enough, not just general opinions.
    
    Please control output code in 2048 tokens! (Write concise code)

  evaluator_prepend_prompt: &evaluator_prepend_prompt |-

  evaluator_append_prompt: &evaluator_append_prompt |-
    You are an professional and strict code reviewer. Your task is to evaluate the solution. The code is to ${task_description}. Your task is to evaluate the codes written by the code engineers.
    
    Please not only give a general rating points (from 0 to 9) but also give detailed comments about where and how the code can be improved. Please consider the following aspects when you are evaluating the code:
      1. The code should be able to run without any errors.
      2. The code should be able to achieve the goal specified in the task description.
      3. The code should be easy to read and understand, efficient, concise and elegant.
      4. The code should be robust.
    
    Please rate the code in the following dimensions:
        1. Completeness: Is the code snippet complete enough without unimplemented functions of methods? Is it able to run without any errors?
        2. Functionality: Is the code able to achieve the goal specified in the task description?
        3. Readability: Is the code easy to read and understand, efficient, concise and elegant?
        4. Robustness: Is the code snippet able to handle different unexpected input or other exceptions?
    
    In the fifth line of output, give your detailed advice for the engineers to better generate good codes. Only give high grade when it is really great.
  
    # Output format
    1. Completeness: (a score between 0 and 9)
    2. Functionality: (a score between 0 and 9)
    3. Readability: (a score between 0 and 9)
    4. Robustness: (a score between 0 and 9)
    5. Advice: (your advice in one line)
    
    Here is the content you have to evaluate:
    ${solution}
    

name: pipeline


environment:
  env_type: task-basic
  max_turn: *max_turn
  rule:
    role_assigner:
      type: role_description
      cnt_agents: *cnt_agents
    decision_maker:
      type: vertical-solver-first
      max_inner_turns: *max_inner_turns
    executor:
      type: none
    evaluator:
      type: basic

agents:
  - #role_assigner_agent:
    agent_type: role_assigner
    name: role assigner
    max_retry: 1000
    prepend_prompt_template: *role_assigner_prepend_prompt
    append_prompt_template: *role_assigner_append_prompt
    memory:
      memory_type: chat_history
    llm:
      llm_type: gpt-4
      model: "gpt-4"
      temperature: 0.7
      max_tokens: 256
    output_parser:
      type: role_assigner

  - #solver_agent:
    agent_type: solver
    name: Summarizer
    max_retry: 1000
    max_history: 5
    prepend_prompt_template: *solver_prepend_prompt
    append_prompt_template: *solver_append_prompt
    memory:
      memory_type: chat_history
    llm:
      llm_type: gpt-4
      model: "gpt-4"
      temperature: 0.7
      max_tokens: 2048
    output_parser:
      type: dummy

  - #critic_agents:
    agent_type: critic
    name: Reviewer
    max_retry: 1000
    max_history: 5
    role_description: |-
      Waiting to be assigned.
    prepend_prompt_template: *critic_prepend_prompt
    append_prompt_template: *critic_append_prompt
    memory:
      memory_type: chat_history
    llm:
      llm_type: gpt-4
      model: "gpt-4"
      temperature: 0.7
      max_tokens: 1024
    output_parser:
      type: critic

  - #executor_agent:
    agent_type: executor
    name: Dummy Executor
    max_retry: 1000
    memory:
      memory_type: chat_history
    llm:
      llm_type: gpt-4
      model: "gpt-4"
      temperature: 0.7
      max_tokens: 512
    output_parser:
      type: dummy

  - #evaluator_agent:
    agent_type: evaluator
    name: Evaluator
    max_retry: 1000
    role_description: |-
      Evaluator
    prepend_prompt_template: *evaluator_prepend_prompt
    append_prompt_template: *evaluator_append_prompt
    memory:
      memory_type: chat_history
    llm:
      llm_type: gpt-4
      model: "gpt-4"
      temperature: 0.7
      max_tokens: 1024
    output_parser:
      type: evaluator
      dimensions:
        - Completeness
        - Functionality
        - Readability
        - Robustness