Quick Start Guide

Get up and running with entropyDB in 5 minutes

Prerequisites

Docker Desktop or Docker Engine (v20.10+)
8GB RAM minimum (16GB recommended)
A terminal/command line interface

Step 1: Install entropyDB

Pull the latest entropyDB Docker image:

docker pull entropydb/entropydb:latest

This downloads the latest stable version of entropyDB (~2GB)

Step 2: Start the Database

Launch an entropyDB instance with default configuration:

docker run -d \
  --name entropydb \
  -p 5432:5432 \
  -p 8080:8080 \
  -e ENTROPY_PASSWORD=your_secure_password \
  entropydb/entropydb:latest

Ports: 5432 for SQL connections, 8080 for HTTP API

Step 3: Connect to entropyDB

Connect using the built-in CLI:

docker exec -it entropydb entropy-cli

Or use any PostgreSQL-compatible client:

psql -h localhost -p 5432 -U entropy -d entropydb

Step 4: Run Your First Query

Create a table and insert data:

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name TEXT NOT NULL,
  email TEXT UNIQUE,
  created_at TIMESTAMP DEFAULT NOW()
);

INSERT INTO users (name, email) 
VALUES 
  ('Alice Johnson', 'alice@example.com'),
  ('Bob Smith', 'bob@example.com');

SELECT * FROM users;

Step 5: Try Multi-Model Features

Add a JSON document column and vector embeddings:

-- Add JSON metadata
ALTER TABLE users ADD COLUMN metadata JSONB;

UPDATE users 
SET metadata = '{"preferences": {"theme": "dark"}, "tags": ["premium"]}'
WHERE name = 'Alice Johnson';

-- Add vector embeddings for semantic search
ALTER TABLE users ADD COLUMN embedding VECTOR(768);

-- Query JSON data
SELECT name, metadata->'preferences'->>'theme' as theme
FROM users
WHERE metadata @> '{"tags": ["premium"]}';

Next Steps

Installation Guide

Learn about production deployment options

Core Concepts

Understand entropyDB architecture

SQL Reference

Complete SQL documentation

Vector Search

Build AI-powered applications

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