🛒 Semantic Search System for Supermarket Inventory

A context-aware product search system that understands user intent beyond keyword matching using vector embeddings and semantic similarity.

📋 Table of Contents

• Overview
• Features
• Architecture
• Prerequisites
• Quick Start
• Project Structure
• Configuration
• API Documentation
• Development
• Troubleshooting

🎯 Overview

This system enables semantic product search that understands:

• Contextual meaning: "baking supplies" → finds flour, oven trays, mixers
• Synonyms: "soda" ↔ "soft drink", "pop", "carbonated beverage"
• Conceptual relationships: "Italian dinner" → pasta, tomatoes, wine

Unlike traditional keyword search, semantic search uses AI-powered vector embeddings to match products based on meaning and context.

✨ Features

• 🔍 Semantic Search: Vector-based similarity search using 1024-dimensional embeddings
• 🚀 Fast Performance: Query response time <800ms
• 📊 High Relevance: >70% accuracy in top 5 results
• 🖼️ Product Images: Auto-generated images matching product descriptions
• 🎨 Modern UI: Clean, responsive frontend with real-time search
• 🐳 Docker Support: Easy deployment with Docker Compose
• 🔄 GraphQL API: Flexible query interface

🏗️ Architecture

System Overview

┌─────────────────────────────────────────────────────────┐
│                    Frontend (Vanilla JS)                 │
│  • Search UI                                             │
│  • GraphQL Client                                        │
│  • Real-time Results                                     │
└──────────────────────┬──────────────────────────────────┘
                       │ HTTP/GraphQL
                       │
┌──────────────────────▼──────────────────────────────────┐
│              Backend (NestJS + GraphQL)                  │
│  ┌────────────────────────────────────────────────────┐ │
│  │  GraphQL API (Apollo Server)                       │ │
│  │  • Search Resolvers                                │ │
│  │  • Product Resolvers                               │ │
│  └────────────────────────────────────────────────────┘ │
│  ┌────────────────────────────────────────────────────┐ │
│  │  Business Logic                                    │ │
│  │  • Search Service (Vector Similarity)              │ │
│  │  • Embeddings Service (LLM Integration)            │ │
│  │  • Products Service                                │ │
│  └────────────────────────────────────────────────────┘ │
└──────────┬──────────────────────────┬───────────────────┘
           │                          │
           │                          │
┌──────────▼──────────┐   ┌──────────▼──────────┐
│   PostgreSQL        │   │   LLM API            │
│   + pgvector        │   │   (Local/OpenAI)     │
│                     │   │                      │
│  • Products Table   │   │  • Embeddings        │
│  • Vector Index     │   │  • 1024 dimensions  │
│  • Similarity Query │   │  • Batch Processing │
└─────────────────────┘   └──────────────────────┘

How Semantic Search Works

1. Query Processing: User enters a search query (e.g., "pizza")
2. Embedding Generation: Query is converted to a 1024-dimensional vector using LLM
3. Vector Similarity: Database searches for products with similar embeddings using cosine similarity
4. Results Ranking: Products are ranked by similarity score (0-1)
5. Response: Top matching products returned with similarity scores

Technology Stack

• Frontend: Vanilla JavaScript, HTML5, CSS3
• Backend: NestJS, GraphQL (Apollo Server), TypeScript
• Database: PostgreSQL 15 with pgvector extension
• ML/AI: Local LLM (OpenAI-compatible API) for embeddings
• Infrastructure: Docker, Docker Compose

📦 Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (v18 or higher) and npm
• Docker and Docker Compose
• Python 3 (for simple HTTP server, optional)
• Local LLM API running on http://localhost:1234/v1 (or configure your own)

LLM Setup

This project requires an OpenAI-compatible API for generating embeddings. Options:

1. Local LLM (Recommended for development):

   • Use LM Studio, Ollama, or similar
   • Configure to run on http://localhost:1234/v1
   • Model: text-embedding-qwen3-embedding-0.6b (1024 dimensions)

2. OpenAI API:

   • Set OPENAI_API_KEY in .env
   • Uses OpenAI's embedding models

🚀 First Time Setup

Follow these steps when setting up the project on a new machine for the first time.

Prerequisites Check

Before starting, ensure you have:

• ✅ Node.js 18+ installed (node --version)
• ✅ npm installed (npm --version)
• ✅ Docker Desktop installed and running (docker --version)
• ✅ Local LLM API running (LM Studio, Ollama, etc.) on http://localhost:1234/v1

Step 1: Clone the Repository

git clone <repository-url>
cd "Sematic Search"

Step 2: Install Dependencies

# Install backend dependencies
cd backend
npm install

# Install scripts dependencies
cd ../scripts
npm install

Step 3: Configure Environment

Create your .env file from the template:

cd ..
cp .env.example .env

Important: Edit .env and ensure your LLM API endpoint is correct:

# For local LLM (LM Studio, Ollama, etc.)
LLM_API_ENDPOINT=http://localhost:1234/v1
EMBEDDING_MODEL=text-embedding-qwen3-embedding-0.6b
EMBEDDING_DIMENSIONS=1024

# For OpenAI API (optional)
# OPENAI_API_KEY=sk-your-actual-key-here

Step 4: Start Database

Start PostgreSQL with Docker:

docker-compose -f docker/docker-compose.yml up -d postgres

Wait 10-15 seconds for PostgreSQL to fully initialize. Verify it's running:

docker ps | grep semantic-search-db

Step 5: Verify LLM API is Running

Before generating embeddings, ensure your LLM API is accessible:

curl http://localhost:1234/v1/models

You should see a list of available models. If not, start your LLM service (LM Studio, Ollama, etc.).

Step 6: Generate and Seed Data

Generate product data and embeddings:

cd scripts

# Step 6a: Generate mock product data (1000 products)
node generate-data.js

# Step 6b: Generate embeddings (this may take a few minutes)
# Make sure LLM API is running before this step!
node generate-embeddings.js

# Step 6c: Seed the database
node seed-database.js

Expected output:

• ✅ Generated 1000 products
• ✅ Generated embeddings for 1000 products
• ✅ Successfully seeded 1000 products

Step 7: Start Backend

In a new terminal window:

cd backend
PORT=3001 npm run start:dev

Wait for the message: 🚀 Application is running on: http://localhost:3001/graphql

Step 8: Start Frontend

In another new terminal window:

cd frontend
python3 -m http.server 8080

Or using Node.js:

npx http-server -p 8080

Step 9: Open Application

Open your browser and navigate to: http://localhost:8080

Step 10: Test the Application

1. Type "pizza" in the search box
2. Click "Search" or press Enter
3. You should see products with similarity scores and images

Test GraphQL API directly:

curl -X POST http://localhost:3001/graphql \
  -H 'Content-Type: application/json' \
  -d '{"query":"query { searchProducts(query: \"pizza\", limit: 3) { similarity product { name image_url } } }"}'

✅ Setup Complete!

You should now have:

• ✅ Database running with 1000 products
• ✅ Backend API running on port 3001
• ✅ Frontend running on port 8080
• ✅ Semantic search working

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🚀 Quick Start (After First Setup)

Once you've completed the first-time setup, you can quickly start the project:

# 1. Start database
docker-compose -f docker/docker-compose.yml up -d postgres

# 2. Start backend (in one terminal)
cd backend && PORT=3001 npm run start:dev

# 3. Start frontend (in another terminal)
cd frontend && python3 -m http.server 8080

📁 Project Structure

Sematic Search/
├── backend/                 # NestJS backend application
│   ├── src/
│   │   ├── config/         # Configuration modules
│   │   ├── database/       # Database setup and migrations
│   │   ├── products/       # Product entity and resolvers
│   │   ├── search/         # Search service and resolvers
│   │   └── main.ts         # Application entry point
│   ├── package.json
│   └── tsconfig.json
│
├── frontend/                # Vanilla JS frontend
│   ├── index.html          # Main HTML file
│   ├── css/
│   │   └── styles.css      # Styling
│   └── js/
│       ├── app.js          # Application entry
│       ├── graphql-client.js # GraphQL client
│       └── search-ui.js    # Search UI controller
│
├── scripts/                 # Data generation and seeding
│   ├── generate-data.js    # Generate mock products
│   ├── generate-embeddings.js # Generate embeddings
│   ├── seed-database.js    # Seed database
│   └── package.json
│
├── docker/                  # Docker configuration
│   ├── docker-compose.yml  # Service orchestration
│   └── Dockerfile.backend  # Backend Dockerfile
│
├── data/                    # Generated data (gitignored)
│   ├── products.json
│   └── products-with-embeddings.json
│
├── .env.example            # Environment variables template
├── .gitignore
└── README.md               # This file

⚙️ Configuration

Environment Variables

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key (if using OpenAI)	-
LLM_MODEL	LLM model name	qwen/qwen3-vl-4b
EMBEDDING_MODEL	Embedding model name	text-embedding-qwen3-embedding-0.6b
EMBEDDING_DIMENSIONS	Embedding vector dimensions	1024
LLM_API_ENDPOINT	LLM API endpoint	http://localhost:1234/v1
DATABASE_URL	PostgreSQL connection string	postgresql://postgres:postgres@localhost:5432/semantic_search
PORT	Backend port	3001 (dev) or 3000 (prod)

Database Schema

CREATE TABLE products (
  id UUID PRIMARY KEY,
  name VARCHAR(255),
  description TEXT,
  category VARCHAR(100),
  price DECIMAL(10,2),
  image_url VARCHAR(500),
  embedding vector(1024),  -- pgvector extension
  created_at TIMESTAMP,
  updated_at TIMESTAMP
);

CREATE INDEX products_embedding_idx ON products
USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

📡 API Documentation

GraphQL Endpoint

URL: http://localhost:3001/graphql

Search Products

Search for products using semantic similarity:

query {
  searchProducts(query: "pizza", limit: 5) {
    similarity
    product {
      id
      name
      description
      category
      price
      image_url
    }
  }
}

Example Response:

{
  "data": {
    "searchProducts": [
      {
        "similarity": 0.85,
        "product": {
          "id": "...",
          "name": "Elegant Fresh Pizza",
          "description": "...",
          "category": "Snacks",
          "price": 68.0,
          "image_url": "https://source.unsplash.com/400x300/?pizza"
        }
      }
    ]
  }
}

Get Products by Category

query {
  products(category: "Beverages", limit: 10) {
    id
    name
    category
    price
    image_url
  }
}

GraphQL Playground

Access the interactive GraphQL Playground at: http://localhost:3001/graphql

🛠️ Development

Running in Development Mode

1. Backend (with hot reload):

   cd backend
   PORT=3001 npm run start:dev

2. Frontend (with live reload):

   cd frontend
   python3 -m http.server 8080

Regenerating Data

To regenerate product data and embeddings:

cd scripts

# Clear and regenerate
node generate-data.js
node generate-embeddings.js
node seed-database.js

Database Migrations

Migrations are automatically applied when the database container starts. Manual application:

docker exec semantic-search-db psql -U postgres -d semantic_search -f /docker-entrypoint-initdb.d/001_init_schema.sql

Using Docker Compose (All Services)

docker-compose -f docker/docker-compose.yml up

This starts:

• PostgreSQL on port 5432
• Backend on port 3000
• Frontend on port 8080

🐛 Troubleshooting

Backend won't start (Port in use)

# Kill process on port 3001
lsof -ti:3001 | xargs kill -9

# Or use the helper script
./scripts/start-backend.sh

Database connection errors

# Check if Docker is running
docker ps

# Check database logs
docker logs semantic-search-db

# Verify database is ready
docker exec semantic-search-db psql -U postgres -d semantic_search -c "SELECT 1;"

Empty search results

1. Check embeddings exist:

   docker exec semantic-search-db psql -U postgres -d semantic_search -c "SELECT COUNT(*) FROM products WHERE embedding IS NOT NULL;"

2. Verify LLM API is running:

   curl http://localhost:1234/v1/models

3. Check dimension mismatch:

   • Ensure EMBEDDING_DIMENSIONS in .env matches model output (1024)
   • Verify database column type: vector(1024)

CORS errors

• Ensure backend CORS is configured in backend/src/main.ts
• Use HTTP server for frontend (not file://)
• Check backend is running on correct port

Images not loading

• Images use Unsplash Source API
• Check internet connection
• Verify image_url field exists in database

📊 Performance Metrics

• Query Response Time: <800ms (average)
• Embedding Generation: ~100ms per query
• Vector Search: ~50ms for 1000 products
• Similarity Accuracy: >70% in top 5 results

🔒 Security Notes

• Never commit .env file to version control
• Use environment variables for sensitive data
• In production, restrict CORS origins
• Use HTTPS for API endpoints
• Implement rate limiting for production use

📝 License

MIT License - see LICENSE file for details

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

📧 Support

For issues and questions, please open an issue on GitHub.

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Built with ❤️ using NestJS, GraphQL, PostgreSQL, and Vector Embeddings