🍽️ 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 + Express (Backend API)
• Redis (Restaurant store, agentic AI memory, conversational history, and semantic caching with Redis LangCache)
• OpenAI API (GPT-4 for intelligent responses and embeddings for vector search)
• LangGraph (AI workflow orchestration)
• HTML + CSS + Vanilla JS (Frontend)

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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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Setup

Follow the setup instructions below to get started.

Prerequisites

• Redis: Use Redis Cloud (no installation required)
• Redis LangCache API: Get LangCache credentials
• OpenAI API Key: Create an API key
• Node.js (v18 or higher): Download & Install Node.js

Installation

1. Clone and setup

   git clone <repository-url>
   cd relish
   npm install

2. Configure environment

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

3. Load restaurant data

   npm run load-restaurants

4. Start the server

   npm start

5. Open your browser

   http://localhost:3000
   

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Architecture

Technical architecture

Project architecture

│
├── package.json
├── index.js
├── config.js
├── services/
│   ├── restaurants/          # Restaurant Business Component
│   │   ├── api/                 # REST API endpoints
│   │   ├── domain/              # Business logic
│   │   └── data/                # Data access layer
│   ├── reservations/         # Reservation Business Component
│   │   ├── api/
│   │   ├── domain/
│   │   └── data/
│   ├── chat/                 # Chat/Cache Business Component
│   │   ├── api/
│   │   ├── domain/
│   │   └── data/
│   ├── users/                # User Business Component
│   │   ├── domain/
│   │   └── data/
│   ├── ai/                   # Agentic AI Layer
│   └── routes/
├── 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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