Authenticate and Fetch Emails

First, register an app in the Azure AD portal and grant Mail.Read permissions. Use the msal library to obtain an access token and fetch the latest messages.

python

import requests
from msal import PublicClientApplication

# Configuration (Replace with your Azure App details)
CLIENT_ID = 'your_client_id'
TENANT_ID = 'your_tenant_id'
AUTHORITY = f"login.microsoftonline.com{TENANT_ID}"
app = PublicClientApplication(CLIENT_ID, authority=AUTHORITY)

# Standard flow to get access token...
# URL: graph.microsoft.com

Preprocess Email Content

Email bodies often contain messy HTML. Clean this text before feeding it to the AI to reduce token usage and improve accuracy.

Use BeautifulSoup to strip HTML tags.

Remove extra whitespace and boilerplate signatures.

Intelligent Extraction with AI

Rather than writing complex Regex, use an LLM with a specific prompt to extract structured data (e.g., JSON).

Prompt Example:

"Extract the following as a JSON object: Vendor Name, Amount, and Due Date (YYYY-MM-DD) from this email body: [Email Content]"

python

import openai
response = openai.ChatCompletion.create(model="gpt-4", messages=[{"role": "user", "content": prompt}])

# The result is ready for a pandas DataFrame or database storage.

Best Practices for ShareTech Readers

Safety First: Never hardcode your API keys. Use environment variables via python-dotenv.

Rate Limiting: If processing thousands of emails, implement back-off strategies to avoid hitting Graph API or OpenAI rate limits.

Validation: Always validate the AI's JSON output before inserting it into your database to prevent data corruption.

Conclusion

Combining Microsoft Graph with AI transforms your inbox from a wall of text into a structured database. By following this workflow, you can automate routine data entry and focus on higher-level analysis.

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Automating Email Data Extraction with Python and AI