🍽️ Restaurant Discovery AI Agent

Redis-powered restaurant discovery with intelligent dining assistance.

An AI-powered restaurant discovery platform that combines Redis's speed with LangGraph's intelligent workflow orchestration. Get personalized restaurant recommendations, smart dining suggestions, and lightning-fast responses through semantic caching.

App screenshots

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Tech Stack

• Node.js (v18+) + Express - Backend runtime and API framework
• Redis - Restaurant store, agentic AI memory, conversational history, and semantic caching
• Redis LangCache API - Semantic caching for LLM responses
• LangGraph - AI workflow orchestration
• OpenAI API - GPT-4 for intelligent responses and embeddings for vector search
• HTML + CSS + Vanilla JS - Frontend UI

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Product Features

• Smart Restaurant Discovery: AI-powered assistant helps you find restaurants, discover cuisines, and manage your reservations. Both text and vector-based search across restaurants
• Dining Intelligence: Get restaurant recommendations with detailed information for any cuisine or occasion using RAG (Retrieval Augmented Generation)
• Demo Reservation System: Reservation management - add, view, and manage restaurant reservations (Simplified demo implementation, not a production-ready reservation system)

Technical features

• Redis as memory layer: For fast data retrieval
• Vector Search: Find restaurants using AI-powered similarity search
• Semantic Cache: Similar queries return instantly using Redis LangCache
• LangGraph Workflows: AI agent routing, tool selection
• Multi-tool Agent: Restaurant tools, search tools, reservation tools, and knowledge tools

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Getting started

1. Configure environment variables

cp .env.example .env
# Edit .env with your Redis URL, OpenAI key, and LangCache credentials

2. Installation

Option 1: Docker (Recommended)

Quick start:

# Build and start
docker compose build
docker compose up -d

# Load data (first time only)
docker compose exec relish-app npm run load-restaurants
docker compose exec relish-app npm run seed-dummy-users

# View logs
docker compose logs -f relish-app

Subsequent runs:

docker compose up

To stop the service and remove containers:

# Stop and remove containers
docker compose down

Option 2: Manual setup

1. Install dependencies:

   npm install

2. Load restaurant data

   npm run load-restaurants

3. Load sample users

   npm run seed-dummy-users

4. Start the server

   npm start

3. Access the app

Visit http://localhost:3000/?name=ashwin or any of the seeded users in your browser. Default port number can be customized via environment variables defined in the .env configuration file.

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Architecture

Technical architecture

Project architecture

│
├── package.json
├── index.js
├── config.js
├── modules/
│   ├── restaurants/          # Restaurant Component
│   │   ├── api/                 # REST API endpoints
│   │   ├── domain/              # Business logic and services
│   │   └── data/                # Data access layer
│   ├── reservations/         # Reservation Business Component
│   │   ├── api/
│   │   ├── domain/
│   │   └── data/
│   ├── chat/                 # Chat/Cache Component
│   │   ├── api/
│   │   ├── domain/
│   │   └── data/
│   ├── users/                # User Business Component
│   │   ├── domain/
│   │   └── data/
│   ├── ai/                   # Agentic AI Layer
│   
├── client/                   # Frontend assets
├── views/                    # HTML and Handlebars templates
├── scripts/                  # Data loading scripts
└── README.md                 # This file

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API Endpoints

• POST /api/chat - Main chat interface for AI restaurant assistant
• GET /api/restaurants/search - Search restaurants with text/vector similarity
• POST /api/reservations/add - Add restaurant reservations (demo implementation)
• GET /api/reservations - View reservation history (demo implementation)
• DELETE /api/reservations - Cancel reservations (demo implementation)

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Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes following Conventional Commits (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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Maintainers

• Ashwin Hariharan - @booleanhunter

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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🐞 Reporting Issues

If you find a bug or have a feature request, please open an issue in the repository.

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Motivations

This project is a learning-focused demonstration designed to help developers understand:

• AI/ML Concepts: Semantic search, vector embeddings, semantic caching, and agentic AI workflows
• Full-Stack AI Architecture: How to organize AI applications using clean architecture principles and modular design patterns
• Integration Patterns: Wiring together Redis, LangGraph, OpenAI, and LangCache in a real-world application

What this project focuses on:

• ✅ Semantic search and vector similarity
• ✅ LLM-powered agentic workflows with tool calling
• ✅ Semantic caching for performance optimization
• ✅ Clean, modular architecture for full-stack AI applications

What this project does NOT focus on:

• ❌ Complete business logic (e.g., real reservation systems, payment processing)
• ❌ Production-ready authentication/authorization

The features in this app such as reservation and user management are intentionally simplified to focus on demonstrating AI workflows and architecture patterns.

📖 Read More: Learn about the architectural principles in this blog post.

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