Github 工具包
这GithubToolkit 包含使 LLM 代理能够与 GitHub 存储库交互的工具。
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，请转到 API 参考。
设置
概括地说，我们将：
- 安装 pygithub 库
- 创建 Github 应用
- 设置环境变量
- 将工具传递给您的代理
toolkit.get_tools()
要启用单个工具的自动跟踪，请设置您的 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.我们还需要pygithub屬地：
%pip install --upgrade --quiet pygithub langchain-community
2. 创建 Github 应用程序
按照此处的说明创建并注册 Github 应用程序。确保您的应用程序具有以下存储库权限：
- 提交状态 （只读）
- 内容（读取和写入）
- 问题（读取和写入）
- 元数据（只读）
- 拉取请求（读取和写入）
注册应用程序后，您必须授予应用程序访问您授权它执行作的每个存储库的权限。在此处使用 github.com 上的应用程序设置。
3. 设置环境变量
在初始化 Agent 之前，需要设置以下环境变量：
- GITHUB_APP_ID - 在应用的常规设置中找到的六位数字
- GITHUB_APP_PRIVATE_KEY- 应用的私钥 .pem 文件的位置，或该文件的字符串全文。
- GITHUB_REPOSITORY - 您希望机器人对其执行作的 Github 存储库的名称。必须遵循 {username}/{repo-name} 格式。首先确保应用程序已添加到此存储库！
- 可选：GITHUB_BRANCH - 机器人将在其中进行提交的分支。默认为
repo.default_branch. - 可选：GITHUB_BASE_BRANCH - PR 所基于的存储库的基本分支。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
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)
API 参考：GitHubToolkit | GitHubAPIWrapper
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：
下面将详细解释这些步骤中的每一个。
-
Get Issues（获取问题）- 从存储库中提取问题。
-
Get Issue - 获取有关特定问题的详细信息。
-
Comment on Issue - 发布有关特定问题的评论。
-
Create Pull Request（创建拉取请求）- 创建从机器人的工作分支到基本分支的拉取请求。
-
Create File（创建文件）- 在存储库中创建新文件。
-
Read File （读取文件） - 从存储库中读取文件。
-
Update File（更新文件）- 更新存储库中的文件。
-
Delete File （删除文件） - 从存储库中删除文件。
包括发布工具
默认情况下，该工具包不包括与发布相关的工具。您可以通过设置include_release_tools=True初始化 Toolkit 时：
toolkit = GitHubToolkit.from_github_api_wrapper(github, include_release_tools=True)
设置include_release_tools=True将包括以下工具：
-
Get Latest Release （获取最新版本） - 从存储库中提取最新版本。
-
Get Releases- 从存储库中提取最新的 5 个版本。
-
Get Release- 按标签名称从存储库中获取特定发行版，例如
v1.0.0.
在代理内使用
我们需要一个 LLM 或聊天模型：
选择聊天模式：
pip install -qU "langchain[openai]"
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
from langchain.chat_models import init_chat_model
llm = init_chat_model("gpt-4o-mini", model_provider="openai")
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
并向其发出一个查询：
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()
================================[1m Human Message [0m=================================
What is the title of issue 24888?
==================================[1m Ai Message [0m==================================
Tool Calls:
get_issue (call_iSYJVaM7uchfNHOMJoVPQsOi)
Call ID: call_iSYJVaM7uchfNHOMJoVPQsOi
Args:
issue_number: 24888
=================================[1m Tool Message [0m=================================
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.com/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.com/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"}
==================================[1m Ai Message [0m==================================
The title of issue 24888 is "Standardize KV-Store Docs".
API 参考
有关所有GithubToolkit功能和配置可参考 API 参考。