Unity AI - Platform

AI-powered

• Reporting system that converts natural language questions into SQL queries with Metabase integration.
• Application assessment system allowing for autonomous background application review and scoring.

Quick Start

1. Navigate to applications directory:

cd applications

2. Configure environment:

cp .env.example .env
# Edit .env with your Azure OpenAI, JWT, and Metabase credentials

3. Start the application:

docker-compose up --build

4. Access the application:
   • Application: http://localhost
   • Testing with JWT: http://localhost/?token=YOUR_JWT_TOKEN

Architecture

Single Combined Container: Flask serves both the Angular frontend (static files) and backend API endpoints.

Browser (localhost:80) → Flask (container port 8080)
                          ├── /api/* → Backend API
                          └── /*     → Angular static files

Key Features

• AI Query Generation: Natural language to SQL conversion using Azure OpenAI
• Smart Authentication: JWT with hybrid local/production modes (URL tokens for localhost, PostMessage for production)
• Origin Security: ORIGIN_URL environment variable for iframe origin validation
• Chat Management: Conversation history and AI-powered SQL explanations
• Admin Dashboard: Feedback collection and administrative controls
• Multi-tenant: Configurable database mappings and tenant isolation
• Vector Search: PostgreSQL with pgvector for intelligent schema embeddings

Project Structure

unity-ai/
├── applications/
│   ├── Unity.AI.Reporting.Backend/    # Flask API + AI SQL generation
│   ├── Unity.AI.Reporting.Frontend/   # Angular chat interface
│   ├── Dockerfile                     # Combined frontend+backend build
│   ├── docker-compose.yml             # Local development setup
│   ├── .env.example                   # Configuration template
│   └── README.md                      # Deployment guide
├── documentation/                      # Project documentation
└── .github/workflows/                 # CI/CD pipelines

Documentation

• Applications README - Detailed setup and deployment
• Environment Configuration - Environment variables guide
• Manual Deployment Guide - OpenShift deployment

Tech Stack

• Frontend: Angular 21, TypeScript 5.9, Vitest 4
• Backend: Flask 3.1 (Python 3.13), Azure OpenAI, LangChain, pgvector
• Database: PostgreSQL 17 with pgvector extension
• Container: Docker (multi-stage build, OpenShift compatible)

License

Licensed under the MIT License. See LICENSE for details.

Contributing

This is a BC Government project. For questions or issues, please contact the development team.