Gmail 工具包 (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、草拟和发送邮件等。有关所有 GmailToolkit 功能和配置的详细文档，请转到API 参考。(This will help you getting started with the GMail toolkit. This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the API reference.)

设置

要使用此工具包，您需要设置您的凭据，这在Gmail API 文档中进行了说明。下载完credentials.json文件后，您就可以开始使用 Gmail API 了。(To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. Once you've downloaded the credentials.json file, you can start using the Gmail API.)

安装

此工具包位于langchain-google-community包中。我们需要gmail额外内容 (This toolkit lives in the langchain-google-community package. We'll need the gmail extra)

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

如果您希望从单个工具的运行中获得自动跟踪，您还可以通过取消以下注释来设置您的LangSmith API 密钥。(If you want to get automated tracing from runs of individual tools, you can also set your LangSmith API key by uncommenting below)

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

实例化

默认情况下，工具包读取本地credentials.json文件。您也可以手动提供一个Credentials对象。(By default the toolkit reads the local credentials.json file. You can also manually provide a Credentials object.)

from langchain_google_community import GmailToolkit

toolkit = GmailToolkit()

自定义身份验证

在幕后，使用以下方法创建了一个googleapi资源。您可以手动构建一个googleapi资源以获得更多身份验证控制。(Behind the scenes, a googleapi resource is created using the following methods. you can manually build a googleapi resource for more auth control.)

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)

工具

查看可用工具 (View available tools)

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集成到代理中。(Below we show how to incorporate the toolkit into an agent.)

我们将需要一个 LLM 或聊天模型 (We will need a LLM or chat model)

OpenAI

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")

Anthropic

pip install -qU langchain-anthropic

import getpass
import os

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

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

Azure

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

Google

pip install -qU langchain-google-vertexai

import getpass
import os

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

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-1.5-flash")

Cohere

pip install -qU langchain-cohere

import getpass
import os

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

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r-plus")

NVIDIA

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

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

from langchain import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

FireworksAI

pip install -qU langchain-fireworks

import getpass
import os

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

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")

Groq

pip install -qU langchain-groq

import getpass
import os

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

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

MistralAI

pip install -qU langchain-mistralai

import getpass
import os

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

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

TogetherAI

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(llm, tools)

example_query = "Draft an email to [email protected] 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 [email protected] 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: ['[email protected]']
    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 [email protected] 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，请转到API 参考。(For detailed documentation of all GmailToolkit features and configurations head to the API reference.)

相关

• 工具概念指南 (Tool conceptual guide)
• 工具操作指南 (Tool how-to guides)