agentverse/tasks/tasksolving/brainstorming/config.yaml

cnt_agents: &cnt_agents 4
max_turn: &max_turn 3
max_inner_turns: &max_inner_turns 0

task_description: |-
  generate ideas of building a compressed hydrogen storage station in Ohio
  
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 a summarizer. 
    Your task is to categorize and summarize the ideas in the chat history.
    Please add the speaker of each idea to the beginning of the content.

    The question of the discussing is to ${task_description}. Below is the chat history:
    
  solver_append_prompt: &solver_append_prompt |-
    # Output format
    1. (Speaker1): (Ideas of Speaker 1 in a single line)
    2. (Speaker2): (Ideas of Speaker 2 in a single line)
    3. (Speaker3): (Ideas of Speaker 3 in a single line)
    ...

    Please merge all ideas of one speaker into one item.

  critic_prepend_prompt: &critic_prepend_prompt |-
    You are ${role_description}. You are in a discussion group, aiming to ${task_description}.

  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.

  evaluator_prepend_prompt: &evaluator_prepend_prompt |-

  evaluator_append_prompt: &evaluator_append_prompt |-
    Your task is to evaluate the ideas in the solution.
 
    The goal is to ${task_description}.
    
    Please rate the ideas in the content in the following dimensions:
        1. Comprehensiveness:Are they comprehensive enough to cover all the 
           important aspects a engineering project may have?
        2. Detailedness: Are they detailed enough to be implemented?
        3. Feasibility: Are they reasonable and practical?
        4. Novelty: Are they creative and innovative?
    
    0 means the idea is like random generated ideas,
    10 means the idea is perfect in that aspect.
    
    and then in the fifth line of output, give your detailed advice for the solution generators.
    You can also give advice to the human resource staff on what experts they should recruit.
    Just say the drawbacks of the ideas, no need to do compliments first.
    
  
    #Output format
    You must output in the following format:
    1. Comprehensiveness: (a score between 0 and 9)
    2. Detailedness: (a score between 0 and 9)
    3. Feasibility: (a score between 0 and 9)
    4. Novelty: (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: brainstorming
      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-3.5-turbo
      model: "gpt-3.5-turbo"
      temperature: 0
      max_tokens: 512
    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-3.5-turbo
      model: "gpt-3.5-turbo"
      temperature: 0
      max_tokens: 1024
    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-3.5-turbo
      model: "gpt-3.5-turbo"
      temperature: 0
      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-3.5-turbo
      model: gpt-3.5-turbo
      temperature: 0
      max_tokens: 1024
    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-3.5-turbo
      model: gpt-3.5-turbo
      temperature: 0.3
      max_tokens: 1024
    output_parser:
      type: evaluator
      dimensions:
        - Comprehensiveness
        - Detailedness
        - Feasibility
        - Novelty