DOCs.md

AI-Powered Customer Support System Documentation

Table of Contents

1. System Overview
2. Core Features
3. Technical Architecture
4. Database Schema
5. API Endpoints
6. AI Integration
7. Authentication Flow
8. Error Handling
9. Deployment Guide
10. Future Enhancements

System Overview

An intelligent ticketing system that automates customer support processes using:

• Flask backend with RESTful API
• Google Gemini for AI-powered responses
• SQLite database for data persistence
• Role-based access control

Core Features

1. Automated Ticket Processing

   • AI categorization and prioritization
   • Sentiment analysis (1-5 scale)
   • SLA deadline calculation

2. Conversation Management

   • Full conversation history
   • Context-aware responses
   • Human-in-the-loop approval

3. Performance Analytics

   • Response time tracking
   • Resolution metrics
   • Agent productivity

Technical Architecture

Client (Browser) 
  │
  ├── Flask Application (Python)
  │   ├── Routes/Controllers
  │   ├── AI Service (Gemini)
  │   └── Database ORM
  │
  └── SQLite Database
      ├── Users
      ├── Tickets
      └── Conversations

Database Schema

Users Table

cursor.execute('''
    CREATE TABLE IF NOT EXISTS users (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        name TEXT NOT NULL,
        email TEXT UNIQUE NOT NULL,
        password TEXT NOT NULL,
        role TEXT DEFAULT 'agent',
        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    )
''')

Tickets Table

cursor.execute('''
    CREATE TABLE IF NOT EXISTS tickets (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        customer_name TEXT NOT NULL,
        issue TEXT NOT NULL,
        status TEXT DEFAULT 'Open',
        category TEXT,
        priority INTEGER DEFAULT 2,
        sentiment INTEGER,
        deadline TIMESTAMP,
        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    )
''')

Conversations Table

cursor.execute('''
    CREATE TABLE IF NOT EXISTS conversations (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        ticket_id INTEGER,
        sender_type TEXT,
        message TEXT,
        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
        FOREIGN KEY (ticket_id) REFERENCES tickets(id)
    )
''')

API Endpoints

Authentication

Endpoint	Method	Description
/register	POST	User registration
/login	POST	User authentication
/logout	GET	Session termination

Ticket Management

Endpoint	Method	Description
/tickets	GET	List all tickets
/tickets	POST	Create new ticket
/ticket/<ticket_id>	GET	Get ticket details
/ticket/<ticket_id>	POST	Add response to ticket

AI Integration

Sentiment Analysis

def analyze_sentiment(text):
    prompt = f"Analyze sentiment (1-5, 5=angry) of: '{text}'. Respond ONLY with number."
    try:
        return int(model.generate_content(prompt).text)
    except:
        return 3  # Default neutral

Response Generation

def generate_response(conversation_history):
    prompt = f"""As a support agent, draft a concise reply to:
    {conversation_history}
    Guidelines:
    - Be empathetic
    - Offer solution/next steps
    - Keep under 100 words"""
    return model.generate_content(prompt).text

Authentication Flow

Registration

@app.route('/register', methods=['POST'])
def register():
    hashed_pw = generate_password_hash(request.form['password'])
    cursor.execute('INSERT INTO users (name, email, password) VALUES (?, ?, ?)',
                  (request.form['name'], request.form['email'], hashed_pw))
    conn.commit()

Login

@app.route('/login', methods=['POST'])
def login():
    user = cursor.execute('SELECT * FROM users WHERE email = ?', 
                         (request.form['email'],)).fetchone()
    if user and check_password_hash(user['password'], request.form['password']):
        session['user_id'] = user['id']

Error Handling

Database Operations

try:
    conn = sqlite3.connect('database.db')
    # Database operations
except sqlite3.Error as e:
    flash(f"Database error: {str(e)}", 'danger')
    conn.rollback()
finally:
    conn.close()

AI Service Fallbacks

try:
    ai_response = model.generate_content(prompt).text
except Exception as e:
    ai_response = "We're looking into your issue and will respond shortly."

Deployment Guide

Requirements

pip install flask google-generativeai werkzeug

Configuration

1. Set environment variables:

   export FLASK_SECRET_KEY='your_secret_key'
   export GEMINI_API_KEY='your_api_key'

2. Initialize database:

   python -c "from app import init_db; init_db()"

Running

flask run --host=0.0.0.0 --port=5000

Future Enhancements

1. Real-time Notifications
   • WebSocket integration for live updates
2. Multi-channel Support
   • Email, WhatsApp, and social media integration
3. Advanced Analytics
   • Predictive modeling for ticket volumes
4. Mobile Optimization
   • Responsive design for mobile agents

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