GMail

此加载器介绍了如何从 GMail 加载数据。从 GMail 加载数据的方式有很多种。此加载器目前在如何执行此操作方面相当有主见。它的工作方式是首先查找您发送的所有消息。然后，它查找您回复以前电子邮件的消息。然后，它获取以前的电子邮件，并创建该电子邮件的训练示例，后跟您的电子邮件。

请注意，这里存在明显的局限性。例如，创建的所有示例仅查看以前的电子邮件以获取上下文。

使用方法

• 设置 Google 开发者帐户：转到 Google 开发者控制台，创建一个项目，并为该项目启用 Gmail API。这将为您提供一个 credentials.json 文件，您稍后需要该文件。

• 安装 Google 客户端库：运行以下命令以安装 Google 客户端库

%pip install --upgrade --quiet  google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client

import os.path

from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow

SCOPES = ["https://www.googleapis.com/auth/gmail.readonly"]


creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists("email_token.json"):
    creds = Credentials.from_authorized_user_file("email_token.json", SCOPES)
# If there are no (valid) credentials available, let the user log in.
if not creds or not creds.valid:
    if creds and creds.expired and creds.refresh_token:
        creds.refresh(Request())
    else:
        flow = InstalledAppFlow.from_client_secrets_file(
            # your creds file here. Please create json file as here https://cloud.google.com/docs/authentication/getting-started
            "creds.json",
            SCOPES,
        )
        creds = flow.run_local_server(port=0)
    # Save the credentials for the next run
    with open("email_token.json", "w") as token:
        token.write(creds.to_json())

from langchain_community.chat_loaders.gmail import GMailLoader

API 参考：GMailLoader

loader = GMailLoader(creds=creds, n=3)

data = loader.load()

# Sometimes there can be errors which we silently ignore
len(data)

2

from langchain_community.chat_loaders.utils import (
    map_ai_messages,
)

API 参考：map_ai_messages

# This makes messages sent by hchase@langchain.com the AI Messages
# This means you will train an LLM to predict as if it's responding as hchase
training_data = list(
    map_ai_messages(data, sender="Harrison Chase <hchase@langchain.com>")
)