AI Overviews are coming to your Gmail at work

Introduction

In this tutorial, you'll learn how to work with AI-powered email summarization technology similar to what's coming to Gmail. We'll build a simple email summarization tool that demonstrates the core concepts behind AI Overviews. This hands-on project will teach you how to process email data, extract key information, and generate human-readable summaries using basic AI techniques.

Prerequisites

Before starting this tutorial, you'll need:

• A computer with internet access
• Basic understanding of Python programming
• Python 3.6 or higher installed
• Some familiarity with email formats and data structures

Step-by-step instructions

Step 1: Set up your development environment

Install required Python packages

First, we need to install the necessary Python libraries. Open your terminal or command prompt and run:

pip install python-dotenv openai

This installs the required packages for handling environment variables and connecting to OpenAI's API.

Step 2: Create a sample email dataset

Prepare sample email data

Let's create a simple dataset of sample emails that we'll use to demonstrate summarization:

import json

# Sample email data
sample_emails = [
    {
        "id": "1",
        "subject": "Project Update - Q4 Budget Review",
        "sender": "[email protected]",
        "date": "2023-10-15",
        "body": "Hi team, I wanted to share the Q4 budget review results. The marketing department has requested an additional $50,000 for social media campaigns. The development team has completed the mobile app redesign phase and is moving to the testing phase. We've also secured a new partnership with TechCorp that will bring in $100,000 in revenue. Please review the attached spreadsheet for detailed breakdowns."
    },
    {
        "id": "2",
        "subject": "Meeting Rescheduled - Product Launch",
        "sender": "[email protected]",
        "date": "2023-10-16",
        "body": "Hello everyone, I need to reschedule our product launch meeting from Thursday to Friday at 2 PM. The marketing team has finalized the campaign strategy and we're ready to proceed. We'll be presenting the new features and the launch timeline. Please confirm your availability for Friday."
    },
    {
        "id": "3",
        "subject": "New Client Onboarding Process",
        "sender": "[email protected]",
        "date": "2023-10-17",
        "body": "Dear Clients, we're excited to welcome you to our platform. The onboarding process includes account setup, training sessions, and integration with your existing systems. Our support team will be available 24/7 for any questions. Please complete the initial setup by Friday to ensure smooth transition."
    }
]

# Save sample data to file
with open('sample_emails.json', 'w') as f:
    json.dump(sample_emails, f, indent=2)

print("Sample email data created successfully!")

This creates a realistic dataset of emails that we can use to test our summarization system. Each email has a unique ID, subject, sender, date, and body content.

Step 3: Set up OpenAI API access

Create API key and environment file

Before we can use AI summarization, we need to get an API key from OpenAI. Visit platform.openai.com to create an account and generate your API key. Then create a file called .env in your project directory:

OPENAI_API_KEY=your_actual_api_key_here

Replace your_actual_api_key_here with your real API key. This keeps your key secure and out of your code.

Step 4: Create the email summarizer

Build the core summarization function

Now we'll create the main summarization function that will process our emails:

import openai
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize OpenAI client
openai.api_key = os.getenv('OPENAI_API_KEY')


def summarize_email(email_content, email_subject):
    """
    Generate a summary for a single email
    """
    prompt = f"""
    Please provide a concise summary of the following email:
    
    Subject: {email_subject}
    
    Body: {email_content}
    
    Summary:"""
    
    try:
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are an email summarization assistant. Provide concise, clear summaries of emails."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=150,
            temperature=0.3
        )
        
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error generating summary: {str(e)}"


def process_emails(emails):
    """
    Process multiple emails and generate summaries
    """
    summaries = []
    
    for email in emails:
        summary = summarize_email(email['body'], email['subject'])
        summaries.append({
            'id': email['id'],
            'subject': email['subject'],
            'summary': summary,
            'sender': email['sender']
        })
        
    return summaries

This function uses OpenAI's GPT model to analyze email content and generate human-readable summaries. The temperature setting of 0.3 makes the responses more focused and consistent.

Step 5: Run the summarization process

Execute the email processing

Now let's create a main execution script that loads our sample emails and generates summaries:

import json

# Load sample emails
with open('sample_emails.json', 'r') as f:
    emails = json.load(f)

# Process emails and generate summaries
summaries = process_emails(emails)

# Display results
print("\n=== EMAIL SUMMARIES ===\n")
for summary in summaries:
    print(f"ID: {summary['id']}")
    print(f"Subject: {summary['subject']}")
    print(f"Summary: {summary['summary']}")
    print(f"From: {summary['sender']}")
    print("-" * 50)

This script loads our sample emails, processes them through our summarization function, and displays the results in a readable format.

Step 6: Test with real email data

Extend functionality for real-world usage

Let's enhance our tool to handle more complex scenarios:

def create_overview(email_summaries):
    """
    Create an overview from multiple email summaries
    """
    # Combine all summaries into one document
    combined_text = "\n\n".join([f"{s['subject']}: {s['summary']}" for s in email_summaries])
    
    prompt = f"""
    Please create a comprehensive overview of the following emails:
    
    {combined_text}
    
    Overview:"""
    
    try:
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are an email overview assistant. Create a comprehensive summary that captures the key points from multiple emails."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=200,
            temperature=0.5
        )
        
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error creating overview: {str(e)}"

# Test the overview creation
overview = create_overview(summaries)
print("\n=== OVERVIEW SUMMARY ===\n")
print(overview)

This creates a higher-level summary that pulls together key points from multiple emails, similar to how Gmail's AI Overviews would work.

Summary

In this tutorial, you've learned how to build a basic email summarization system that demonstrates the core concepts behind AI Overviews in Gmail. You've created a working system that can process email data, generate individual summaries, and create comprehensive overviews from multiple emails. This hands-on approach gives you practical experience with AI-powered text processing while showing how the technology works at a fundamental level.

The key concepts covered include: setting up AI API access, processing structured email data, creating prompt engineering for AI models, and building multi-step summarization workflows. This foundation can be expanded to work with real email accounts, more sophisticated AI models, and additional features like sentiment analysis or action item extraction.