AI-Powered Onboarding Flow

This demo showcases an intelligent conversation flow for user onboarding, using OpenAI's GPT models to provide natural language processing and validation.

How It Works

1. Conversation Flow Architecture

graph TD
    A[User Input] --> B[Load/Create User]
    B --> C{Current Step?}
    C --> |Has Input| D[Validate Input]
    D --> |Valid| E[Store & Advance]
    D --> |Invalid| F[Ask for Clarification]
    C --> |No Input| G[Generate Step Prompt]
    E --> H{More Steps?}
    H --> |Yes| G
    H --> |No| I[Complete]

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2. Step Progression

The onboarding process collects the following in this shown order:

• First Name
• Last Name Collection
• Company Information
• Job Function
• Job Title
• Referral Source

Each step includes:

• Main prompt for the user
• System context for AI
• Validation rules
• Field mapping in database

3. AI Integration

The system uses two types of AI interactions per step:

• Input validation: Ensures provided information matches expected format
• Response generation: Creates contextual, natural-language responses

Tech Stack

• API Framework: Flask (using factory pattern)
• Database: PostgreSQL
• ORM: SQLAlchemy
• LLM: OpenAI GPT-4

Quick Start

Installation

1. Clone the repository:

git clone https://github.com/yourusername/onboarding.git
cd onboarding

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Create a .env file in the project root:

FLASK_ENV=development
SECRET_KEY=<your-secret-key>
DATABASE_URL=postgresql://<user>:<password>@localhost:5432/<database>
OPENAI_API_KEY=<your-openai-api-key>
OPENAI_MODEL_NAME=gpt-4

5. Initialize the database:

flask db init
flask db upgrade

Project Structure

onboarding/
├── app/
│   ├── __init__.py           # App factory
│   ├── routes.py             # API endpoints + utility functions
│   ├── models.py             # SQLAlchemy models
│   └── config.py             # Configuration
├── migrations/               # Database migrations
├── tests/                    # Test files
├── .env                      # Environment variables
├── requirements.txt          # Dependencies
└── app.py                  # Application entry point

Note: For demo purposes, utility functions and helper classes are defined in routes.py. In a production environment, these should be properly organized into separate modules.

Running the Application

Activate virtual environment:

Mac/Linux

source venv/bin/activate  # 

Windows:

venv\Scripts\activate

Start the Flask server:

flask run -p 8000 --debug

API Endpoints

User Management

GET /api/user/uuid:user_id

Retrieve a specific user's information

curl http://localhost:5000/api/user/123e4567-e89b-12d3-a456-426614174000

GET /api/user

List all users

curl http://localhost:5000/api/user

PUT /api/user/uuid:user_id

Update user information

curl -X PUT http://localhost:5000/api/user/123e4567-e89b-12d3-a456-426614174000 \
     -H "Content-Type: application/json" \
     -d '{"first_name": "John", "last_name": "Doe"}'

POST /api/user

Create a new user

curl -X POST http://localhost:5000/api/user \
     -H "Content-Type: application/json" \
     -d '{}'

Onboarding Flow

POST /api/onboarding

Handle onboarding conversation

curl -X POST http://localhost:5000/api/onboarding \
     -H "Content-Type: application/json" \
     -d '{"user_id": "123e4567-e89b-12d3-a456-426614174000", "input": "John"}'

Known Limitations & TODOs

• Implement proper authentication
• Add request validation
• Implement rate limiting
• Organize utility functions
• Add comprehensive error handling
• Add logging
• Add tests