Ever wondered how Netflix suggests movies you might like, or how Spotify creates personalized playlists? These AI-powered features often use vector similarity search under the hood. In this series, we'll build our own AI search engine using PostgreSQL with pgvector!
Let's get started...🐢
Contents
- Project Overview
- What is Vector Search?
- Step-by-Step Setup
- Troubleshooting Tips
- Quick Preview
- What's Next?
Project Overview ✨
We'll build a search engine to find similar content based on meaning, not just matching keywords. This is the same type of technology behind:
- GitHub Copilot's code suggestions
- Spotify's song recommendations
- Netflix's movie recommendations
While various tools and services support similar functionality, we'll use pgvector
to implement vector similarity search within postgreSQL.
In Part 1, we'll set up the database infrastructure. In Part 2, we'll implement the search functionality using OpenAI's embeddings.
What is Vector Search? 🔎
When AI processes content (text, code, or images), it creates a special list of numbers called embedding. Think of it as a smart summary that captures the content's meaning. Similar content will have similar numbers, making it easy to find related items.
If you're not familiar with Machine Learning, don't worry! You can easily obtain these embeddings from AI APIs like OpenAI, even without deep AI knowledge.
pgvector helps us efficiently store and search these embeddings as vectors in PostgreSQL.
Step-by-Step Setup 👣
Make sure you have Docker Desktop installed on your computer.
Project Structure
vector-search/
├── compose.yml
└── postgres/
└── schema.sql
1. Create compose.yml
services:
db:
image: pgvector/pgvector:pg17 # PostgreSQL with pgvector support
container_name: pgvector-db
environment:
POSTGRES_USER: postgres
POSTGRES_PASSWORD: password
POSTGRES_DB: example_db
ports:
- "5432:5432"
volumes:
- pgdata:/var/lib/postgresql/data
- ./postgres/schema.sql:/docker-entrypoint-initdb.d/schema.sql
volumes:
pgdata: # Stores data outside the container to ensure persistence
2. Define Database Schema
Create postgres/schema.sql
:
-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;
-- Create sample table
CREATE TABLE items (
id SERIAL PRIMARY KEY,
name VARCHAR(255) NOT NULL,
item_data JSONB,
embedding vector(1536) -- vector data
);
3. Start the Database
Run Docker Compose to build and start the PostgreSQL container with pgvector.
docker compose up --build
4. Verify the Setup
Connect to PostgreSQL:
docker exec -it pgvector-db psql -U postgres -d example_db
Check if everything is set up correctly:
-- Check installed extensions
\dx
-- Check table creation
\dt
-- Check table structure
\d items
Troubleshooting Tips 🛠️
Error: Port 5432 already in use
Change the port in compose.yml
to 5433 or another free port.
ports:
- "5433:5432"
Database not initializing properly
Remove the volume and restart.
docker-compose down -v # Remove existing volume
docker-compose up --build # Start fresh
Still not sure what's wrong?
Check the container logs.
docker compose logs db
Quick Preview 👀
Here's a quick preview of how we'll query similar items in Part 2:
-- Find items similar to a specific vector
SELECT id, name, item_data
FROM items
ORDER BY embedding <-> '[0.1, 0.2, ...]'::vector
LIMIT 5;
Replace [0.1, 0.2, ...] with an actual vector from AI models.
What's Next? 💭
We'll dive into the following topics:
- Understand what embeddings are and how they work
- Generate embeddings using OpenAI
- See how vector search works in practice
Stay tuned! 🚀
Spot any mistakes or have a better way? Please leave a comment below! 💬
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