Fix issues with AI-generated pull requests, powered by ChatGPT https://github.com/irgolic/AutoPR

天问 b1398b5b9e Update 'README.md' 1 year ago
.github 7ec72d450e Add '.github/workflows/autopr.yml' 1 year ago
README.md b1398b5b9e Update 'README.md' 1 year ago

README.md

AutoPR

Fix issues with AI-generated pull requests, powered by ChatGPT

Usage

1、把 AutoPR 设置到项目中

创建 .github/workflows/autopr.yml, 模板:https://github.com/irgolic/AutoPR-template/blob/main/.github/workflows/autopr.yml

里面设置一下openai key

2、创建一个label,名称 AutoPR

3、new 一个 issue,包含清晰的问题和步骤描述。

4、增加 AutoPR 标签到这个 issue

自动触发 action,创建 autopr/issue-# 分支,并发起 pull request

5、人工 Review 代码,合并代码

源码分析

采用 github action 方式自动化pull request。 触发条件:

on:
  issues:
    types: [labeled]
   

标签为 AutoPR,则继续执行,否则exit:

    curl -s -H "Authorization: token $GITHUB_TOKEN" -H "Accept: application/vnd.github+json" \
          "https://api.github.com/repos/${{ github.repository }}/collaborators/${{ github.event.sender.login }}" | jq -r '.message'

继续执行,采用的是 docker 容器方式:

docker://ghcr.io/irgolic/autopr:latest

执行 :

python -m autopr.gh_actions_entrypoint

执行 main方法,先切换到 autopr/issue-# 分支, commit,然后 push,发起pull request

    main(
        repo_path=repo_path,
        event=event,
        commit_service=commit_service,
        publish_service=publish_service,
        settings=settings,
    )

获取提取词:

    prompt = rail.get_prompt_message()


    def get_string_params(self) -> dict[str, str]:
        return {
            'issue': str(self.issue),
            'filepaths_with_token_lengths': '\n'.join([
                file_descriptor.filepaths_with_token_lengths_to_str()
                for file_descriptor in self.file_descriptors
            ]),
            'token_limit': str(self.token_limit),
        }

可以看到提取词是 issue ,其中调用 openai 接口参数如下:

        messages = [
            {"role": "system", "content": system_prompt},
        ]
        for example in examples:
            messages.append({"role": "user", "content": example[0]})
            messages.append({"role": "assistant", "content": example[1]})
        messages.append({"role": "user", "content": prompt})

        openai_response = openai.ChatCompletion.create(
            model=self.model,
            messages=messages,
            temperature=temperature,
            max_tokens=max_tokens,
        )

先循环遍历 examples ,然后再提问, 下面是 system_prompt 系统 example 提取词:

        raw_system_prompt: str = 'You are a software developer and git nerd, a helpful planning and coding assistant.',
        rail_system_prompt: str = "You are a helpful assistant, "
                                  "able to express yourself purely through JSON, "
                                  "strictly and precisely adhering to the provided XML schemas.",
    ):

💎 Examples

Well-written issues often lead to better results.

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