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.chat_loaders.gmail import GMailLoaderloader = GMailLoader(creds=creds, n=3)data = loader.load()# Sometimes there can be errors which we silently ignorelen(data)    2from langchain.chat_loaders.utils import (    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 hchasetraining_data = list(map_ai_messages(data, sender="Harrison Chase <hchase@langchain.com>"))PreviousFacebook MessengerNextiMessageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
This loader goes over how to load data from GMail. There are many ways you could want to load data from GMail. This loader is currently fairly opinionated in how to do so. The way it does it is it first looks for all messages that you have sent. It then looks for messages where you are responding to a previous email. It then fetches that previous email, and creates a training example of that email, followed by your email.
This loader goes over how to load data from GMail. There are many ways you could want to load data from GMail. This loader is currently fairly opinionated in how to do so. The way it does it is it first looks for all messages that you have sent. It then looks for messages where you are responding to a previous email. It then fetches that previous email, and creates a training example of that email, followed by your email. ->: 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.chat_loaders.gmail import GMailLoaderloader = GMailLoader(creds=creds, n=3)data = loader.load()# Sometimes there can be errors which we silently ignorelen(data)    2from langchain.chat_loaders.utils import (    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 hchasetraining_data = list(map_ai_messages(data, sender="Harrison Chase <hchase@langchain.com>"))PreviousFacebook MessengerNextiMessageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.