🔍 Code Extractor

Demonstrates example usage of the VendorEmailExtractor class by searching for vendor emails across Office 365 mailboxes and displaying results.

Purpose

This function serves as a demonstration and testing entry point for the VendorEmailExtractor system. It loads configuration from a separate config file, initializes the extractor with Office 365 and OpenAI credentials, performs a comprehensive search for a specific vendor (Merck) across all mailboxes without limits, and displays formatted results including email counts, confidence scores, and mailbox distribution.

Source Code

def main():
    """Example usage"""
    # Load configuration
    try:
        from vendor_email_config import (
            TENANT_ID, CLIENT_ID, CLIENT_SECRET, 
            OPENAI_API_KEY, DOMAIN
        )
    except ImportError:
        print("ERROR: vendor_email_config.py not found!")
        print("Please create it from the O365_APP_SETUP_GUIDE.md instructions")
        return
    
    # Create extractor
    extractor = VendorEmailExtractor(
        tenant_id=TENANT_ID,
        client_id=CLIENT_ID,
        client_secret=CLIENT_SECRET,
        openai_api_key=OPENAI_API_KEY,
        domain=DOMAIN
    )
    
    # Example: Extract for single vendor across ALL mailboxes
    print("\n=== Single Vendor Test - All Mailboxes ===")
    print("Searching all vicebio.com mailboxes for: Merck")
    print("No limits: All mailboxes, all emails, all time\n")
    
    df = extractor.extract_for_vendor(
        vendor_name="Merck",
        max_mailboxes=None,  # Search ALL mailboxes
        max_emails_per_mailbox=None,  # No email limit - get ALL matching emails
        days_back=None  # No date limit
    )
    
    print(f"\n{'='*60}")
    print("TEST COMPLETE")
    print(f"{'='*60}")
    print(f"Found {len(df)} total records")
    
    if not df.empty:
        print(f"\nUnique vendor emails: {df['vendor_email'].nunique()}")
        print("\nTop results:")
        for email in df['vendor_email'].unique()[:10]:
            count = len(df[df['vendor_email'] == email])
            conf = df[df['vendor_email'] == email]['confidence'].mode()[0]
            mailboxes = df[df['vendor_email'] == email]['found_in_mailbox'].nunique()
            print(f"  • {email}")
            print(f"    Confidence: {conf}, Found in {count} emails across {mailboxes} mailboxes")
    else:
        print("\n⚠️  No vendor emails found for Merck")
        print("This may be normal if Merck is not in email communications")
    
    print(f"\n{'='*60}\n")

Return Value

This function does not return any value (implicitly returns None). It performs side effects by printing results to stdout and potentially creating a VendorEmailExtractor instance that may cache data.

Dependencies

• msal
• requests
• pandas
• openai
• pathlib

Required Imports

from vendor_email_config import TENANT_ID, CLIENT_ID, CLIENT_SECRET, OPENAI_API_KEY, DOMAIN

Conditional/Optional Imports

These imports are only needed under specific conditions:

from vendor_email_config import TENANT_ID, CLIENT_ID, CLIENT_SECRET, OPENAI_API_KEY, DOMAIN

Usage Example

if __name__ == '__main__':
    main()

Best Practices

• Ensure vendor_email_config.py exists before running this function, or it will exit early with an error message
• The function searches ALL mailboxes with no limits (max_mailboxes=None, max_emails_per_mailbox=None, days_back=None), which may take significant time and API quota for large organizations
• This is intended as a demonstration/testing function, not for production use - consider adding parameters for vendor name and search limits
• The function assumes VendorEmailExtractor class is defined in the same module
• Error handling is minimal - only catches ImportError for missing config file
• Results are printed to stdout rather than returned, making it unsuitable for programmatic use
• Consider wrapping the main logic in try-except blocks to handle API errors gracefully
• For production use, extract the core logic into a separate function that returns data rather than printing it

Tags

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