Fashion Store Email Processor - Auto Respond & Order Processing

Overview

This project implements an intelligent email processing system for a fashion store that automatically handles customer inquiries and order requests. The system uses three specialized AI agents working in concert to provide a seamless customer experience.

Core Agents

1. Email Classification Agent

• Primary Function: Analyzes and categorizes incoming emails
• Capabilities:
  • Uses GPT-4 for accurate intent classification
  • Distinguishes between "product inquiry" and "order request"
  • Handles various email formats and writing styles
  • Maintains classification accuracy metrics
• Output: Generates email-classification sheet with email IDs and categories

2. Order Processing Agent

• Primary Function: Manages order fulfillment and inventory
• Capabilities:
  • Extracts product IDs and quantities from order requests
  • Verifies product availability in stock
  • Updates inventory levels automatically
  • Generates professional order confirmation emails
  • Suggests alternatives for out-of-stock items
• Output: Creates order-status and order-response sheets

3. Product Inquiry Agent

• Primary Function: Handles product information requests
• Capabilities:
  • Uses TF-IDF and cosine similarity for product matching
  • Provides detailed product information
  • Generates personalized responses with relevant recommendations
  • Handles complex queries about product features and availability
• Output: Generates inquiry-response sheet with detailed product information

Technical Implementation

Dependencies

pandas
numpy
openai
langchain
faiss-cpu
tqdm
gspread
google-auth
gspread-dataframe
regex
rapidfuzz

Key Components

1. EmailProcessor Class

   • Unified class handling all email processing tasks
   • Implements vector-based product search
   • Manages Google Sheets integration

2. Product Matching

   • Uses FAISS for efficient vector similarity search
   • Implements fuzzy matching for product names
   • Handles partial matches and similar products

3. Response Generation

   • Context-aware response generation using GPT-4
   • Professional tone adaptation
   • Includes product details, pricing, and availability

Setup Instructions

1. Install required packages:

pip install pandas numpy openai langchain faiss-cpu tqdm gspread google-auth gspread-dataframe regex rapidfuzz

2. Configure API Keys:

   • Set up OpenAI API key
   • Configure Google Sheets credentials

3. Run the notebook:

   • Open Solve_Business_Problems_with_AI_.ipynb in Jupyter
   • Execute cells in sequence
   • Results will be saved to Google Sheets

Output Format

The system generates four main outputs:

1. Email Classification

   • email_id
   • category (product inquiry/order request)

2. Order Status

   • email_id
   • product_id
   • quantity
   • status (created/out of stock)

3. Order Responses

   • email_id
   • response (confirmation/out of stock notification)

4. Inquiry Responses

   • email_id
   • response (product information/recommendations)

Best Practices

1. Error Handling

   • Robust error handling for API calls
   • Fallback mechanisms for product matching
   • Graceful degradation when services are unavailable

2. Performance Optimization

   • Efficient vector search using FAISS
   • Batch processing for multiple emails
   • Caching of product vectors

3. Security

   • Secure API key management
   • Data validation and sanitization
   • Access control for Google Sheets

Limitations

1. API rate limits for OpenAI
2. Token limits for GPT-4 responses
3. Google Sheets API quotas
4. Processing time for large datasets

Future Improvements

1. Implement caching for frequently accessed products
2. Add support for multiple languages
3. Enhance product matching algorithms
4. Implement real-time stock updates
5. Add support for image-based product queries

Contributing

Feel free to submit issues and enhancement requests!