MetaGPT人类交互
概述
解释器支持灵活的自然语言交互。使用者可在多个位点变更需求、提供执行建议、审阅结果等。通过人类的引导，解释器可在各场景下达到更高的成功率和更好的效果。
示例：机器学习
任务
我们使用机器学习场景作为示例。在此，我们展示如何根据我们的自身需求，确认和修改解释器的计划，以及引导它重做指定任务
代码
examples/di/machine_learning.py
python examples/di/machine_learning.py --auto_run Falsepython examples/di/machine_learning.py --auto_run False运行结果
在交互过程中，我们通过自然语言多次提出需求，例如：
- 制定计划时，我们要求它整合和变更任务：“merge 4 and 5, also, change task 2, save the plot”
- 执行过程中，我们要求它更新后续任务：“confirm, but change task 3 only, use 30% as validation set”
- 执行过程中，我们要求它重做当前任务：“redo, use decision tree as the model instead”
解释器均动态地调整并执行，完成了我们的需求
机制解释
使用人类交互模式，需在解释器初始化时设置 auto_run=False 。解释器将在每次提出计划和完成计划中的每个任务时，请求人类进行审阅。结合关键词，使用者可使用自然语言告知解释器，解释器提出的计划、写出的代码应如何修改。解释器支持计划的状态管理，在新增、修改、删除任务时，会保留未受影响任务的进度。请参考如下的使用示例
解释器生成的Plan示例
json
[
{
"task_id": "1",
"dependent_task_ids": [],
"instruction": "Load the sklearn Wine recognition dataset."
},
{
"task_id": "2",
"dependent_task_ids": ["1"],
"instruction": "Perform exploratory data analysis and include a plot of the dataset features."
},
{
"task_id": "3",
"dependent_task_ids": ["1"],
"instruction": "Split the dataset into training and validation sets with a 20% validation split."
},
{
"task_id": "4",
"dependent_task_ids": ["3"],
"instruction": "Train a model on the training set to predict wine class."
},
{
"task_id": "5",
"dependent_task_ids": ["4"],
"instruction": "Evaluate the model on the validation set and show the validation accuracy."
}
][
{
"task_id": "1",
"dependent_task_ids": [],
"instruction": "Load the sklearn Wine recognition dataset."
},
{
"task_id": "2",
"dependent_task_ids": ["1"],
"instruction": "Perform exploratory data analysis and include a plot of the dataset features."
},
{
"task_id": "3",
"dependent_task_ids": ["1"],
"instruction": "Split the dataset into training and validation sets with a 20% validation split."
},
{
"task_id": "4",
"dependent_task_ids": ["3"],
"instruction": "Train a model on the training set to predict wine class."
},
{
"task_id": "5",
"dependent_task_ids": ["4"],
"instruction": "Evaluate the model on the validation set and show the validation accuracy."
}
]人类可交互内容
| 交互类型 | 所需关键词 | 示例 | 说明 |
|---|---|---|---|
| 确认 | ["confirm", "continue", "c", "yes", "y"]之一 | confirm | 确认当前计划或任务结果，并继续往后执行 |
| 确认并修改 | confirm + 任意其他内容 | confirm, and change task 3, use 30% as validation set | 确认当前任务结果，同时修改后续计划 |
| 重做 | redo | redo, fix the error by using matplotlib | 根据人类意见，重做当前任务 |
| redo, use decision tree as the model instead | |||
| 修改 | - | add a task, save the plot | 根据人类意见，修改计划 |
| delete task 5 | |||
| change task 4, use decision tree as the model specifically | |||
| merge task 4 and 5 |
解释器将根据人类意见文本，确定交互类型，优先级：确认并修改 > 确认 > 重做 > 修改