AI邮件自动化2026：GPT-4 + Gmail API实现智能收件箱管理

利用AI自动分类、回复和管理您的邮件收件箱。

可自动化的功能

自动分类邮件（支持、销售、垃圾、紧急）

为常见请求类型生成回复草稿

从邮件线程中提取行动项

将邮件路由至正确的团队成员

总结长邮件线程

设置：Gmail API

bash
pip install google-auth google-auth-oauthlib google-api-python-client openai

python
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.oauth2.credentials import Credentials
import base64
import os
SCOPES = ['https://www.googleapis.com/auth/gmail.modify']
def get_gmail_service():
    creds = None
    if os.path.exists('token.json'):
        creds = Credentials.from_authorized_user_file('token.json', SCOPES)
    if not creds or not creds.valid:
        flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES)
        creds = flow.run_local_server(port=0)
        with open('token.json', 'w') as f:
            f.write(creds.to_json())
    return build('gmail', 'v1', credentials=creds)

读取邮件

python
from openai import OpenAI
openai = OpenAI()
service = get_gmail_service()
def get_unread_emails(max_results: int = 10):
    results = service.users().messages().list(
        userId='me',
        labelIds=['INBOX', 'UNREAD'],
        maxResults=max_results
    ).execute()
    
    emails = []
    for msg in results.get('messages', []):
        email = service.users().messages().get(userId='me', id=msg['id'], format='full').execute()
        
        headers = {h['name']: h['value'] for h in email['payload']['headers']}
        body = ''
        if 'parts' in email['payload']:
            for part in email['payload']['parts']:
                if part['mimeType'] == 'text/plain' and 'data' in part['body']:
                    body = base64.urlsafe_b64decode(part['body']['data']).decode()
                    break
        
        emails.append({
            'id': msg['id'],
            'from': headers.get('From', ''),
            'subject': headers.get('Subject', ''),
            'body': body[:2000]  # 截断以符合token限制
        })
    
    return emails

AI邮件分类

python
import json
def classify_email(email: dict) -> dict:
    r = openai.chat.completions.create(
        model='gpt-4o-mini',
        messages=[{
            'role': 'user',
            'content': f'Classify this email. Return JSON with fields: '
                       f'category (support/sales/spam/internal/urgent/newsletter), '
                       f'priority (high/medium/low), '
                       f'sentiment (positive/neutral/negative), '
                       f'summary (one sentence).\n\n'
                       f'From: {email["from"]}\n'
                       f'Subject: {email["subject"]}\n'
                       f'Body: {email["body"]}'
        }],
        response_format={'type': 'json_object'}
    )
    return json.loads(r.choices[0].message.content)
处理所有未读邮件
emails = get_unread_emails(20)
for email in emails:
    classification = classify_email(email)
    print(f'{email["subject"]}: {classification["category"]} ({classification["priority"]})')
    print(f'  摘要: {classification["summary"]}')

自动生成回复草稿

python
def generate_reply(email: dict, context: str = '') -> str:
    r = openai.chat.completions.create(
        model='gpt-4o',
        messages=[{
            'role': 'system',
            'content': 'You are an professional email assistant. '
                       'Write clear, concise, professional email replies. '
                       'Match the tone of the original email.'
        }, {
            'role': 'user',
            'content': f'Write a reply to this email.\n\n'
                       f'Original email:\n'
                       f'From: {email["from"]}\n'
                       f'Subject: {email["subject"]}\n'
                       f'Body: {email["body"]}\n\n'
                       f'Context/instructions: {context or "Be helpful and professional."}'
        }]
    )
    return r.choices[0].message.content
def save_draft(service, email_id: str, reply_text: str):
    original = service.users().messages().get(userId='me', id=email_id, format='full').execute()
    headers = {h['name']: h['value'] for h in original['payload']['headers']}
    
    import email.mime.text
    msg = email.mime.text.MIMEText(reply_text)
    msg['To'] = headers.get('From', '')
    msg['Subject'] = 'Re: ' + headers.get('Subject', '')
    msg['In-Reply-To'] = headers.get('Message-Id', '')
    
    raw = base64.urlsafe_b64encode(msg.as_bytes()).decode()
    service.users().drafts().create(
        userId='me',
        body={'message': {'raw': raw, 'threadId': original['threadId']}}
    ).execute()

结论

AI邮件自动化为知识工作者每周节省数小时。从分类开始，然后为高量请求类型添加自动草稿生成。在生产环境中发送前，务必审查AI生成的草稿。