Building a Redis AI Vector Search Boilerplate with Spec-Driven Development

Introduction

In the era of AI-powered applications, traditional keyword search feels increasingly outdated. Users expect search experiences that understand context and meaning, not just exact word matches. That's why I built this Redis AI Vector Search Boilerplate - a modern full-stack application that demonstrates how to implement intelligent semantic search using Redis vector similarity and Google GenAI embeddings.

What is Redis AI Vector Search Boilerplate?

This project is a comprehensive boilerplate that enables developers to quickly implement AI-powered semantic search in their applications. Instead of searching for exact keywords, users can ask natural language questions like "How to implement real-time chat?" and get relevance-ranked results that understand the intent behind their query.

The application combines the power of Redis for high-performance vector storage, Google GenAI for generating text embeddings, and modern web technologies to create a seamless search experience. It's designed as both a functional application and an educational resource for developers wanting to understand vector similarity search.

Tech Stack & Architecture

The project leverages a modern serverless architecture with carefully chosen technologies:

Frontend Technologies

• React 19 - Latest React features for modern UI development
• TypeScript - Type safety throughout the application
• Tailwind CSS - Utility-first styling with dark/light theme support
• Vite - Fast build tool and development server
• Radix UI - Accessible component primitives

Backend Technologies

• Deno - Modern JavaScript/TypeScript runtime for serverless functions
• Redis - Vector database for high-performance similarity search
• Google GenAI - Text embedding generation for semantic understanding

Development Tools

• Kiro AI - Spec-driven development and automation
• ESLint - Code quality and consistency
• pnpm - Fast package management

Development Methodology: Kiro Specifications

This project follows spec-driven development using Kiro AI specifications, which proved instrumental in maintaining code quality and project coherence. The methodology involves three core components:

• Requirements: User stories and acceptance criteria for each feature
• Design: Technical architecture and implementation approach
• Tasks: Discrete, trackable implementation steps

Specifications Created:

1. Redis AI Boilerplate - Core application functionality with semantic search interface
2. Cloudflare-Deno Integration - Seamless deployment pipeline for full-stack serverless architecture
3. Item Deletion - CRUD operations with proper error handling and user feedback
4. Deno Server Polish - Performance optimization and production readiness

Each specification follows the EARS (Easy Approach to Requirements Syntax) format, ensuring clear, testable requirements that guide implementation decisions.

Key Features

1. Semantic Search: Natural language queries powered by 768-dimensional vector embeddings
2. Real-time CRUD Operations: Add, search, retrieve, and delete items with instant feedback
3. Vector Similarity Scoring: Cosine similarity calculations for relevance ranking
4. Multi-platform Deployment: Compatible with Deno Deploy and Cloudflare Pages
5. Responsive Design: Modern UI with dark/light theme support
6. Development Automation: 23 Kiro hooks for workflow automation

Demo

uratmangun / redis-boilerplate

Redis AI Vector Search Boilerplate

A modern full-stack application demonstrating AI-powered semantic search using Redis vector similarity, built with React, TypeScript, and Deno. This project showcases how to implement intelligent search functionality with text embeddings and vector databases.

🚀 Features

• AI Semantic Search: Natural language queries powered by Google GenAI embeddings
• Vector Similarity: Redis-based vector search with cosine similarity
• Real-time UI: Modern React interface with dark/light theme support
• Serverless Ready: Deno Deploy and Cloudflare Pages compatible
• Full CRUD Operations: Add, search, retrieve, and delete items
• Responsive Design: Mobile-friendly interface with Tailwind CSS
• Type Safety: Full TypeScript implementation across frontend and backend

🛠 Tech Stack

Frontend

• React 19 - Modern UI library with latest features
• TypeScript - Type-safe development
• Tailwind CSS - Utility-first styling
• Vite - Fast build tool and dev server
• React Router - Client-side routing
• Radix UI - Accessible component primitives

Backend

…

View on GitHub

Getting Started

Setting up the project is straightforward with the provided configuration:

# Clone the repository
git clone https://github.com/uratmangun/redis-boilerplate.git
cd redis-boilerplate

# Install dependencies
pnpm install

# Configure environment
cp .env.example .env
# Edit .env with your Redis URL and Google AI API key

# Start development servers
pnpm dev

The application supports various Redis providers including local development, Upstash for serverless deployments, Redis Cloud, and Railway.

Conclusion

Building this Redis AI Vector Search Boilerplate was an enlightening journey into the intersection of AI and modern web development. The combination of vector similarity search, serverless architecture, and spec-driven development created a robust foundation for intelligent applications.

The project demonstrates that implementing AI-powered features doesn't have to be complex - with the right architecture and tools, developers can create sophisticated search experiences that truly understand user intent.

Technical Deep Dive

Project Structure

The codebase is organized for clarity and maintainability:

• src/components/ - React UI components with TypeScript
• functions/ - Deno serverless functions for API endpoints
• utils/ - Shared utilities for Redis operations and text embeddings
• .kiro/specs/ - Comprehensive development specifications
• .kiro/hooks/ - Automated development workflows

Key Dependencies

• @google/generative-ai - Google GenAI for text embeddings
• redis - Redis client for Deno
• react - UI framework with latest features
• tailwindcss - Utility-first CSS framework
• @radix-ui/react-slot - Accessible component primitives

Development Workflow

The spec-driven approach with Kiro AI enabled systematic feature development:

1. Requirements gathering with user stories and acceptance criteria
2. Technical design with architecture decisions and trade-offs
3. Implementation through discrete, trackable tasks
4. Automated documentation and workflow management through hooks

This methodology proved invaluable for maintaining project coherence and ensuring all features met their intended requirements while providing clear progress tracking throughout development.