Gmail Toolkit

这将帮助您开始使用 GMail 工具包。此工具包与 GMail API 交互以读取邮件、起草和发送邮件等。 有关所有 GmailToolkit 功能和配置的详细文档，请访问 API 参考。

设置

要使用此工具包，您需要按照 Gmail API 文档中说明的步骤设置您的凭据。 下载 credentials.json 文件后，您就可以开始使用 Gmail API。

安装

此工具包位于 langchain-google-community 包中。 我们需要 gmail 额外功能

%pip install -qU langchain-google-community\[gmail\]

如果您想从各个工具的运行中获得自动跟踪，您还可以通过取消注释下方内容来设置您的 LangSmith API 密钥

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

实例化

默认情况下，该工具包读取本地 credentials.json 文件。 您也可以手动提供 Credentials 对象。

from langchain_google_community import GmailToolkit

toolkit = GmailToolkit()

API 参考:GmailToolkit

自定义身份验证

在幕后，googleapi 资源是使用以下方法创建的。 您可以手动构建 googleapi 资源以获得更多身份验证控制。

from langchain_google_community.gmail.utils import (
    build_resource_service,
    get_gmail_credentials,
)

# Can review scopes here https://developers.google.com/gmail/api/auth/scopes
# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'
credentials = get_gmail_credentials(
    token_file="token.json",
    scopes=["https://mail.google.com/"],
    client_secrets_file="credentials.json",
)
api_resource = build_resource_service(credentials=credentials)
toolkit = GmailToolkit(api_resource=api_resource)

API 参考:build_resource_service | get_gmail_credentials

工具

查看可用工具

tools = toolkit.get_tools()
tools

[GmailCreateDraft(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
 GmailSendMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
 GmailSearch(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
 GmailGetMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
 GmailGetThread(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>)]

• GmailCreateDraft
• GmailSendMessage
• GmailSearch
• GmailGetMessage
• GmailGetThread

在代理中使用

下面我们展示如何将工具包整合到代理中。

我们将需要 LLM 或聊天模型

选择聊天模型

OpenAI

Anthropic

Azure

Google Vertex

AWS

Groq

Cohere

NVIDIA

Fireworks AI

Mistral AI

Together AI

IBM watsonx

Databricks

xAI

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")

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(llm, tools)

API 参考:create_react_agent

example_query = "Draft an email to fake@fake.com thanking them for coffee."

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

================================ Human Message =================================

Draft an email to fake@fake.com thanking them for coffee.
================================== Ai Message ==================================
Tool Calls:
  create_gmail_draft (call_slGkYKZKA6h3Mf1CraUBzs6M)
 Call ID: call_slGkYKZKA6h3Mf1CraUBzs6M
  Args:
    message: Dear Fake,

I wanted to take a moment to thank you for the coffee yesterday. It was a pleasure catching up with you. Let's do it again soon!

Best regards,
[Your Name]
    to: ['fake@fake.com']
    subject: Thank You for the Coffee
================================= Tool Message =================================
Name: create_gmail_draft

Draft created. Draft Id: r-7233782721440261513
================================== Ai Message ==================================

I have drafted an email to fake@fake.com thanking them for the coffee. You can review and send it from your email draft with the subject "Thank You for the Coffee".

API 参考

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

相关

• 工具概念指南
• 工具使用指南