🍸 Barline - CodeJam 15 Best UI/UX Winner

Your personal AI-powered virtual bartender

A 90's inspired retro video game-style web application where you chat with Arthur, a PS1-style 3D bartender who crafts custom cocktails right before your eyes using Three.js and AI.

Watch our video demonstrating Arthur's capabilities here.

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🎯 Overview

Barline is an immersive virtual bartending experience that combines:

• AI-Powered Conversations: Chat naturally with Arthur, your virtual bartender, powered by Google Gemini
• Real-Time 3D Rendering: Watch your drink come to life with realistic liquid physics, ice cubes, and garnishes
• Smart Cocktail Generation: Create custom drinks, search by ingredients, or get personalized recommendations
• Personal Cocktail Library: Save your favorite recipes with detailed ingredients and instructions
• Retro Aesthetic: Nostalgic 90's video game style with PS1-era low-poly graphics

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✨ Features

🤖 AI Bartender Chat

• Real-time WebSocket communication with Arthur
• Natural language drink requests and conversations
• Context-aware responses using RAG (Retrieval-Augmented Generation)
• Intelligent action routing: create, search, or suggest drinks

🎨 Immersive 3D Experience

• 10 Different Glass Types: Martini, rocks, highball, shot, hurricane, zombie, and more
• Realistic Liquid Physics: Animated pour with color mixing and surface effects
• Dynamic Garnishes: Mint leaves, lemon wedges, cherries with gravity animations
• Ice Cube System: Staggered falling animations with bobbing physics
• PS1-Style Graphics: Low-poly bartender character in a retro sci-fi bar environment
• Sound Effects: Synchronized pour and ice sounds

📚 Cocktail Management

• Save drinks to your personal shelf
• Browse your collection with visual cards
• View detailed recipes with ingredients and instructions
• Duplicate detection to keep your shelf organized

🔐 User Authentication

• Secure email/password authentication via Supabase
• Automatic JWT token refresh (every 4 minutes)
• Session management with logout confirmation

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🛠 Tech Stack

Frontend

Technology	Purpose
Three.js	3D graphics rendering and scene management
TypeScript	Type-safe application development
Vite	Fast build tool and dev server
Tween.js	Smooth animations for liquid and physics
WebSocket	Real-time bidirectional communication

Backend

Technology	Purpose
Python+	Core backend language
FastAPI	Modern async web framework
Uvicorn	ASGI server for FastAPI
Pydantic	Data validation and serialization

Database & Storage

Technology	Purpose
Supabase (PostgreSQL)	Primary database with auth
pgvector	Vector database for semantic search
Supabase Storage	Asset storage for RAG documents

AI & ML

Technology	Purpose
Google Gemini API	LLM for conversational AI (gemini-2.0-flash)
scikit-learn	Cosine similarity for RAG retrieval
Custom Agent Engine	RAG + Few-Shot + Chain-of-Thought orchestration

DevOps

Technology	Purpose
Docker	Containerization
docker-compose	Multi-container orchestration
pnpm	Fast, disk-efficient package manager

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🏗 Architecture

Agentic AI Core

Barline uses a sophisticated multi-layered AI system for intelligent cocktail creation:

User Message → Prompt Selection → RAG Augmentation → Few-Shot Examples → Gemini API → Structured Output

Components:

1. Prompt System (apps/backend/src/infra/agent_core/prompt.py)

   • Composable prompt builder with RAG support
   • Modular segments for system instructions, task descriptions, and examples

2. Prompt Prototypes (apps/backend/src/infra/agent_core/prototypes.py)

   • Pre-built templates: action_generation, retrieval_augmented, chat_style, etc.
   • Chain-of-Thought (CoT) structured prompts for better reasoning

3. RAG Strategy (apps/backend/src/infra/agent_core/rag.py)

   • Vector similarity search over cocktail knowledge base
   • Top-K retrieval (default: 5 chunks) for context augmentation
   • Reduces hallucinations by 2-4x

4. Few-Shot Learning (apps/backend/src/infra/agent_core/examples/)

   • Example-driven action selection (create, search, suggest)
   • Reduces invalid JSON by 2-5x
   • Improves action selection accuracy by 1.5-2.5x

5. Agentic Engine (apps/backend/src/infra/agent_core/engine.py)

   • Fast heuristic template selection
   • Structured JSON output via Pydantic schemas
   • Streaming support for real-time responses

Performance Multipliers:

• RAG: 2-4x reduction in hallucinations, 1.5-3x higher task success
• Few-Shot: 2-5x reduction in invalid JSON, 1.5-2.5x better action selection
• Chain-of-Thought: 1.5-3x better on complex reasoning
• Combined: 2-4x end-to-end task success, 2-5x reduction in bad responses

3D Rendering Pipeline

Backend Recipe → DrinkMapper → Three.js Scene Update → Animations (Liquid/Ice/Garnish)

1. Backend sends DrinkRecipeSchema with glass type, garnish, ingredients
2. drinkMapper.ts converts to frontend CocktailConfig
3. GlassLoader switches to appropriate glass model
4. LiquidHandler calculates color and animates pour
5. IceLoader drops ice cubes with staggered timing (150ms delays)
6. GarnishLoader adds garnish after ice settles
7. Sound effects synchronized with animations

WebSocket Chat Flow

Client → [USER]: message → Server → Agent Engine → Stream Response → Client UI Update

• Client maintains conversation history (last 50 messages)
• Server validates JWT token on connection
• Streaming responses with stream_start, stream_delta, stream_end events
• Agent metadata includes action type, reasoning, and created cocktails

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🚀 Getting Started

Prerequisites

• Node.js (v18+) and pnpm
• Python 3.11+
• Supabase account (supabase.com)
• Google Gemini API key (ai.google.dev)

Option 1: Local Development

1. Install Root Dependencies

pnpm install

2. Install Frontend Dependencies

cd apps/frontend
pnpm install

3. Install Backend Dependencies

cd apps/backend
pip3 install -e .
# Or using the requirements file:
pip3 install -r ../../requirements.txt

4. Configure Environment Variables

Create apps/backend/.env:

# Supabase
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_KEY=your-anon-key-here
SUPABASE_JWT_SECRET=your-jwt-secret-here

# Google Gemini
GEMINI_API_KEY=your-gemini-api-key

# App Settings
APP_NAME=Arthur API
APP_VERSION=1.0.0
DEBUG=false

# CORS (for local dev)
CORS_ORIGINS=http://localhost:5173

5. Run Backend

cd apps/backend
uvicorn src.main:app --reload --port 8000

Backend will be available at: http://localhost:8000

6. Run Frontend (New Terminal)

cd apps/frontend
pnpm dev

Frontend will be available at: http://localhost:5173

Option 2: Docker Deployment

1. Configure Environment

Create apps/backend/.env (same as above)

2. Start Services

cd infra
docker-compose up

Services will be available at:

• Frontend: http://localhost:5173
• Backend: http://localhost:8000

3. Stop Services

cd infra
docker-compose down

📍 Access Points

Service	URL	Description
Frontend	http://localhost:5173	Main application UI
Backend API	http://localhost:8000	REST API endpoints
API Docs (Swagger)	http://localhost:8000/docs	Interactive API documentation
API Docs (ReDoc)	http://localhost:8000/redoc	Alternative API docs

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🔧 Environment Variables

Required Variables

Variable	Description	Example
SUPABASE_URL	Your Supabase project URL	https://abc123.supabase.co
SUPABASE_KEY	Supabase anonymous key	eyJhbG...
SUPABASE_JWT_SECRET	JWT secret from Supabase settings	your-secret-key
GEMINI_API_KEY	Google Gemini API key	AIza...

Optional Variables

Variable	Default	Description
APP_NAME	Arthur API	Application name
APP_VERSION	1.0.0	API version
DEBUG	false	Enable debug logging
CORS_ORIGINS	http://localhost:5173	Allowed CORS origins (comma-separated)

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WebSocket Chat

Endpoint: ws://localhost:8000/api/v1/chat/ws?token=<access_token>

Message Types:

• user - User message to Arthur
• stream_start - Response stream begins
• stream_delta - Partial response chunk
• stream_end - Complete response with agent action metadata
• error - Error message

Example Message Flow:

// Client sends
{
  "content": "Make me a refreshing summer cocktail",
  "conversation_history": "[USER]: Hello\n[ASSISTANT]: Hi! ..."
}

// Server streams back
{ "type": "stream_start" }
{ "type": "stream_delta", "content": "I'd be " }
{ "type": "stream_delta", "content": "happy to " }
{
  "type": "stream_end",
  "content": "I'd be happy to make you a Mojito!",
  "agent_action": {
    "action_type": "create_drink",
    "reasoning": "User requested refreshing summer drink",
    "created_cocktail": { /* DrinkRecipeSchema */ }
  }
}

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🎨 3D Scene Components

Glass Types (10 Available)

Glass ID	Type	Best For
zombie_glass_0	Tall tiki glass	Tropical drinks, zombies
cocktail_glass_1	Classic cocktail	Martinis, cosmopolitans
rocks_glass_2	Short tumbler	Old fashioned, whiskey
hurricane_glass_3	Curved tropical	Piña coladas, hurricanes
pint_glass_4	Beer pint	Beer, hard cider
seidel_Glass_5	Beer stein	German beers, oktoberfest
shot_glass_6	Small shot	Shots, shooters
highball_glass_7	Tall mixed drink	Mojitos, Tom Collins
margarita_glass_8	Wide bowl	Margaritas, daiquiris
martini_glass_9	V-shaped	Classic martinis

Rendering Features

• Liquid System: Color mixing, animated fills, realistic surface materials
• Ice Physics: Gravity-based falling, bobbing motion, glass-specific positioning
• Garnishes: GLTF models for mint, lemon, cherry with coordinated timing
• Audio: Pour and ice SFX synchronized with animations
• Lighting: Atmospheric fog and PS1-style lighting
• Environment: Retro sci-fi bar with wood floor textures

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📂 Project Structure

CodeJam-15/
├── apps/
│   ├── backend/                 # Python FastAPI backend
│   │   ├── src/
│   │   │   ├── api/v1/         # Route handlers (auth, chat, cocktails)
│   │   │   ├── core/           # Config and dependencies
│   │   │   ├── domain/         # Pydantic models and schemas
│   │   │   ├── infra/          # Infrastructure layer
│   │   │   │   ├── agent_core/ # RAG + LLM agent engine
│   │   │   │   │   ├── engine.py
│   │   │   │   │   ├── prompt.py
│   │   │   │   │   ├── prototypes.py
│   │   │   │   │   ├── rag.py
│   │   │   │   │   └── examples/  # Few-shot examples
│   │   │   │   └── repositories/  # Database access
│   │   │   ├── services/       # Business logic
│   │   │   └── main.py         # FastAPI app entry
│   │   ├── Dockerfile
│   │   └── pyproject.toml
│   │
│   ├── frontend/               # Three.js/TypeScript frontend
│   │   ├── src/
│   │   │   ├── api/           # Backend API client
│   │   │   ├── assets/        # Audio, textures
│   │   │   ├── components/    # UI components (login, shelf)
│   │   │   ├── models/        # GLTF 3D models
│   │   │   ├── scene/         # Three.js scene components
│   │   │   │   ├── BarLoader.ts
│   │   │   │   ├── CharacterLoader.ts
│   │   │   │   ├── GlassLoader.ts
│   │   │   │   ├── LiquidHandler.ts
│   │   │   │   ├── IceLoader.ts
│   │   │   │   └── GarnishLoader.ts
│   │   │   ├── ui/            # SVG icons and UI elements
│   │   │   ├── utils/         # Helpers and mappers
│   │   │   ├── websocket/     # WebSocket client
│   │   │   └── main.ts        # App entry point
│   │   ├── Dockerfile
│   │   └── package.json
│   │
│   └── test/                   # Testing utilities
│
├── infra/
│   └── docker-compose.yml      # Docker orchestration
│
├── packages/
│   ├── utils-py/              # Shared Python utilities
│   └── utils-ts/              # Shared TypeScript utilities
│
├── requirements.txt           # Python dependencies
└── package.json              # Root package file

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🧪 Development

Running Tests

# WebSocket test
cd apps/test
python test_websocket.py

# Backend tests
cd apps/backend
python test_few_shot_examples.py

Common Commands

# Frontend
pnpm dev              # Start dev server
pnpm build            # Build for production
pnpm preview          # Preview production build

# Backend
uvicorn src.main:app --reload  # Start with hot reload
uvicorn src.main:app --port 8000  # Specify port

# Docker
docker-compose up              # Start all services
docker-compose up -d           # Start in detached mode
docker-compose down            # Stop all services
docker-compose logs -f backend # View backend logs

Database Schema

Core Tables:

• cocktail - Cocktail recipes
• ingredient - Ingredient master data with ABV and colors
• garnish - Garnish types and assets
• glass - Glass types and 3D models
• user_cocktails - User's saved cocktails (junction table)
• cocktail_ingredient - Cocktail ingredients with quantities
• cocktail_garnish - Cocktail garnishes
• cocktail_to_glass - Cocktail glass pairings

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🙏 Credits & Acknowledgments

Technologies & Libraries

• Three.js - 3D graphics library
• FastAPI - Modern Python web framework
• Supabase - Open source Firebase alternative
• Google Gemini - Advanced AI language model
• Vite - Next generation frontend tooling

3D Assets

• PS1-style character model
• Retro sci-fi bar environment
• GLTF glass and garnish models

Inspiration

• 90's video game aesthetics
• Classic bartending experiences
• Retro UI/UX design patterns

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Built with ❤️ By Albert, Edward, Ellie and Nathan

Drink responsibly. Barline is for entertainment purposes only.