support ask_review by llm

[orange-crow]

add ask_review by llm, and adjust the generated code to fit the jupyter notebook.

Features

• add 3 review_type : ["human", "llm", "confirm_all"];

Result

If the generated code is executed successfully, but the code does not match the task requirements, it can be detected through llm's ask review, and it will make modification suggestions. The example is as follows,

@pytest.mark.asyncio
async def test_ask_review_llm():
    context = [
        Message("Train a model to predict wine class using the training set."),
        Message(
               """
               from sklearn.datasets import load_wine
               wine_data = load_wine()
               plt.hist(wine_data.target, bins=len(wine_data.target_names))
               plt.xlabel('Class')
               plt.ylabel('Number of Samples')
               plt.title('Distribution of Wine Classes')
               plt.xticks(range(len(wine_data.target_names)), wine_data.target_names)
               plt.show()
               """
        ),
    ]
    rsp, confirmed = await AskReview().run(context, review_type="llm")
    assert rsp.startswith(("redo", "change"))   # -> True
    assert not confirmed                        # -> True
    pirnt(rsp)
    # ```
    # redo the task, the provided code only includes data loading and visualization, but does not include any steps related 
    # to training a model.
    # ```

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 92.30769% with 4 lines in your changes are missing coverage. Please review.

Project coverage is 82.73%. Comparing base (0271cd7) to head (5ff3cd1).
Report is 17 commits behind head on code_interpreter.

Files	Patch %	Lines
metagpt/actions/di/ask_review.py	91.30%	2 Missing ⚠️
metagpt/roles/di/data_interpreter.py	75.00%	1 Missing ⚠️
metagpt/strategy/planner.py	93.33%	1 Missing ⚠️

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Additional details and impacted files

@@                 Coverage Diff                  @@
##           code_interpreter     #959      +/-   ##
====================================================
+ Coverage             82.70%   82.73%   +0.03%     
====================================================
  Files                   223      223              
  Lines                 13129    13164      +35     
====================================================
+ Hits                  10858    10891      +33     
- Misses                 2271     2273       +2     

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[garylin2099]

建议全部写进一个prompt，没必要在代码上做条件分叉。从上下文，llm应该能知道前面的代码是成功了还是失败了

[garylin2099]

confirm_all可以改为disabled。另外，这里多了一个入参，上层planner和interpreter却没看到有引入。可以去掉auto_run，使用review_type属性，并向下做透传

[orange-crow]

planner和interpreter中的auto_run也删掉吗？

[garylin2099]

对的

[garylin2099]

add ask_review by llm, and adjust the generated code to fit the jupyter notebook.

Features

• add 3 review_type : ["human", "llm", "confirm_all"];
• update DEFAULT_SYSTEM_MSG in BaseWriteAnalysisCode, Make it clear that the executor is jupyter notebook.

Result

If the generated code is executed successfully, but the code does not match the task requirements, it can be detected through llm's ask review, and it will make modification suggestions. The example is as follows,

@pytest.mark.asyncio
async def test_ask_review_llm():
    context = [
        Message("Train a model to predict wine class using the training set."),
        Message(
               """
               from sklearn.datasets import load_wine
               wine_data = load_wine()
               plt.hist(wine_data.target, bins=len(wine_data.target_names))
               plt.xlabel('Class')
               plt.ylabel('Number of Samples')
               plt.title('Distribution of Wine Classes')
               plt.xticks(range(len(wine_data.target_names)), wine_data.target_names)
               plt.show()
               """
        ),
    ]
    rsp, confirmed = await AskReview().run(context, review_type="llm")
    assert rsp.startswith(("redo", "change"))   # -> True
    assert not confirmed                        # -> True
    pirnt(rsp)
    # ```
    # change task current task, split the task into two separate tasks: 
    # 1. Train a model to predict wine class using the training set.
    # 2. Visualize the distribution of wine classes with a histogram.
    # ```

Prefer a more practical example. Review is useful when errors occur. Perhaps give an example showing how it handles errors, such as suggesting "redo" with feedback, or "change" with updated current task instruction?