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Github 工具包

Github 工具包包含使 LLM 代理能够与 Github 存储库进行交互的工具。该工具是 PyGitHub 库的包装器。

有关所有 GithubToolkit 功能和配置的详细文档，请访问 API 参考。

设置​

从高层次来看，我们将

  1. 安装 pygithub 库
  2. 创建 Github 应用
  3. 设置环境变量
  4. 使用 toolkit.get_tools() 将工具传递给您的代理

如果您想从单个工具运行中获取自动跟踪，也可以通过取消以下注释来设置您的 LangSmith API 密钥

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装​

1. 安装依赖项​

此集成在 langchain-community 中实现。我们还需要 pygithub 依赖项

%pip install --upgrade --quiet  pygithub langchain-community

2. 创建 Github 应用​

按照此处的说明创建和注册 Github 应用。确保您的应用具有以下 存储库权限：

  • 提交状态（只读）
  • 内容（读写）
  • 问题（读写）
  • 元数据（只读）
  • 拉取请求（读写）

应用注册后，您必须授予应用权限以访问您希望它操作的每个存储库。使用 github.com 上的应用设置。

3. 设置环境变量​

在初始化代理之前，需要设置以下环境变量

  • GITHUB_APP_ID- 在您的应用的常规设置中找到的六位数字
  • GITHUB_APP_PRIVATE_KEY- 您的应用的私钥 .pem 文件的位置，或该文件的完整文本作为字符串。
  • GITHUB_REPOSITORY- 您希望机器人操作的 Github 存储库的名称。必须遵循格式 {用户名}/{仓库名称}。确保已将应用添加到此存储库！
  • 可选：GITHUB_BRANCH- 机器人将进行提交的分支。默认为 repo.default_branch。
  • 可选：GITHUB_BASE_BRANCH- 您的仓库的基础分支，拉取请求将基于此分支。默认为 repo.default_branch。
import getpass
import os

for env_var in [
    "GITHUB_APP_ID",
    "GITHUB_APP_PRIVATE_KEY",
    "GITHUB_REPOSITORY",
]:
    if not os.getenv(env_var):
        os.environ[env_var] = getpass.getpass()

实例化​

现在我们可以实例化我们的工具包

from langchain_community.agent_toolkits.github.toolkit import GitHubToolkit
from langchain_community.utilities.github import GitHubAPIWrapper

github = GitHubAPIWrapper()
toolkit = GitHubToolkit.from_github_api_wrapper(github)

工具​

查看可用工具

tools = toolkit.get_tools()

for tool in tools:
    print(tool.name)
Get Issues
Get Issue
Comment on Issue
List open pull requests (PRs)
Get Pull Request
Overview of files included in PR
Create Pull Request
List Pull Requests' Files
Create File
Read File
Update File
Delete File
Overview of existing files in Main branch
Overview of files in current working branch
List branches in this repository
Set active branch
Create a new branch
Get files from a directory
Search issues and pull requests
Search code
Create review request

这些工具的用途如下

下面将详细解释每个步骤。

  1. 获取问题- 从存储库中获取问题。

  2. 获取问题- 获取有关特定问题的详细信息。

  3. 在问题上评论- 在特定问题上发布评论。

  4. 创建拉取请求- 从机器人的工作分支创建到基础分支的拉取请求。

  5. 创建文件- 在存储库中创建新文件。

  6. 读取文件- 从存储库中读取文件。

  7. 更新文件- 更新存储库中的文件。

  8. 删除文件- 从存储库中删除文件。

在代理中使用​

我们将需要一个 LLM 或聊天模型

pip install -qU langchain-openai
import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

使用工具子集初始化代理

from langgraph.prebuilt import create_react_agent

tools = [tool for tool in toolkit.get_tools() if tool.name == "Get Issue"]
assert len(tools) == 1
tools[0].name = "get_issue"

agent_executor = create_react_agent(llm, tools)
API 参考：create_react_agent

并向其发出查询

example_query = "What is the title of issue 24888?"

events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()
================================ Human Message =================================

What is the title of issue 24888?
================================== Ai Message ==================================
Tool Calls:
  get_issue (call_iSYJVaM7uchfNHOMJoVPQsOi)
 Call ID: call_iSYJVaM7uchfNHOMJoVPQsOi
  Args:
    issue_number: 24888
================================= Tool Message =================================
Name: get_issue

{"number": 24888, "title": "Standardize KV-Store Docs", "body": "To make our KV-store integrations as easy to use as possible we need to make sure the docs for them are thorough and standardized. There are two parts to this: updating the KV-store docstrings and updating the actual integration docs.\r\n\r\nThis needs to be done for each KV-store integration, ideally with one PR per KV-store.\r\n\r\nRelated to broader issues #21983 and #22005.\r\n\r\n## Docstrings\r\nEach KV-store class docstring should have the sections shown in the [Appendix](#appendix) below. The sections should have input and output code blocks when relevant.\r\n\r\nTo build a preview of the API docs for the package you're working on run (from root of repo):\r\n\r\n\`\`\`shell\r\nmake api_docs_clean; make api_docs_quick_preview API_PKG=openai\r\n\`\`\`\r\n\r\nwhere `API_PKG=` should be the parent directory that houses the edited package (e.g. community, openai, anthropic, huggingface, together, mistralai, groq, fireworks, etc.). This should be quite fast for all the partner packages.\r\n\r\n## Doc pages\r\nEach KV-store [docs page](https://python.langchain.ac.cn/docs/integrations/stores/) should follow [this template](https://github.com/langchain-ai/langchain/blob/master/libs/cli/langchain_cli/integration_template/docs/kv_store.ipynb).\r\n\r\nHere is an example: https://python.langchain.ac.cn/docs/integrations/stores/in_memory/\r\n\r\nYou can use the `langchain-cli` to quickly get started with a new chat model integration docs page (run from root of repo):\r\n\r\n\`\`\`shell\r\npoetry run pip install -e libs/cli\r\npoetry run langchain-cli integration create-doc --name \"foo-bar\" --name-class FooBar --component-type kv_store --destination-dir ./docs/docs/integrations/stores/\r\n\`\`\`\r\n\r\nwhere `--name` is the integration package name without the \"langchain-\" prefix and `--name-class` is the class name without the \"ByteStore\" suffix. This will create a template doc with some autopopulated fields at docs/docs/integrations/stores/foo_bar.ipynb.\r\n\r\nTo build a preview of the docs you can run (from root):\r\n\r\n\`\`\`shell\r\nmake docs_clean\r\nmake docs_build\r\ncd docs/build/output-new\r\nyarn\r\nyarn start\r\n\`\`\`\r\n\r\n## Appendix\r\nExpected sections for the KV-store class docstring.\r\n\r\n\`\`\`python\r\n    \"\"\"__ModuleName__ completion KV-store integration.\r\n\r\n    # TODO: Replace with relevant packages, env vars.\r\n    Setup:\r\n        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.\r\n\r\n        .. code-block:: bash\r\n\r\n            pip install -U __package_name__\r\n            export __MODULE_NAME___API_KEY=\"your-api-key\"\r\n\r\n    # TODO: Populate with relevant params.\r\n    Key init args \u2014 client params:\r\n        api_key: Optional[str]\r\n            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.\r\n\r\n    See full list of supported init args and their descriptions in the params section.\r\n\r\n    # TODO: Replace with relevant init params.\r\n    Instantiate:\r\n        .. code-block:: python\r\n\r\n            from __module_name__ import __ModuleName__ByteStore\r\n\r\n            kv_store = __ModuleName__ByteStore(\r\n                # api_key=\"...\",\r\n                # other params...\r\n            )\r\n\r\n    Set keys:\r\n        .. code-block:: python\r\n\r\n            kv_pairs = [\r\n                [\"key1\", \"value1\"],\r\n                [\"key2\", \"value2\"],\r\n            ]\r\n\r\n            kv_store.mset(kv_pairs)\r\n\r\n        .. code-block:: python\r\n\r\n    Get keys:\r\n        .. code-block:: python\r\n\r\n            kv_store.mget([\"key1\", \"key2\"])\r\n\r\n        .. code-block:: python\r\n\r\n            # TODO: Example output.\r\n\r\n    Delete keys:\r\n        ..code-block:: python\r\n\r\n            kv_store.mdelete([\"key1\", \"key2\"])\r\n\r\n        ..code-block:: python\r\n    \"\"\"  # noqa: E501\r\n\`\`\`", "comments": "[]", "opened_by": "jacoblee93"}
================================== Ai Message ==================================

The title of issue 24888 is "Standardize KV-Store Docs".

API 参考​

有关所有 GithubToolkit 功能和配置的详细文档，请访问 API 参考。

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