Overview

The Greenlight Project is designed to showcase a practical application of backend development using Go. Inspired by the "Let's Go Further" book by Alex Edwards, this project focuses on creating a high-performance, reliable, and easy-to-maintain backend service for managing movie data.

Getting Started

To get started with the Greenlight Project, follow the instructions below.

Prerequisites

• Go (version 1.16 or higher)
• Git
• PostgreSQL

Installation

Clone the repository

git clone https://github.com/ovaixe/greenlight.git
cd greenlight

Set up environment variables

Create a .envrc file in the root directory and add the following variables:

export GREENLIGHT_DB_DSN=your_database_connection_string

Install dependencies

make vendor

Usage

To run the project locally:

Start the application

make run/api

API Endpoints

Movies

• GET /v1/movies: Retrieve a list of movies
  

• POST /v1/movies: Add a new movie
  

• GET /v1/movies/{id}: Retrieve a specific movie by ID
  

• PATCH /v1/movies/{id}: Update an existing movie
  

• DELETE /v1/movies/{id}: Delete an existing movie
  

• **GET /v1/movies/{id}/similar**: Get similar movies based on content
  

• **GET /v1/movies/search/semantic?q=query**: Semantic search for movies
  

Users & Authentication

• POST /v1/users: Register a new user
  

• PUT /v1/users/activated: Activate a registered user
  

• POST /v1/tokens/authentication: Create an authentication token for user
  

AI-Powered Movie Recommendations

This project includes AI-based movie recommendations using vector similarity search.

Features

• Semantic Search: Find movies using natural language queries
• Similar Movies: Get recommendations based on movie content
• Automatic Embeddings: Movie embeddings are generated automatically when creating/updating movies

Setup

1. Install pgvector Extension

CREATE EXTENSION IF NOT EXISTS vector;

2. Run Database Migrations

make db/migrations/up

3. Start the Embedding Service

cd cmd/embedding-service
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python embedding_service.py

Or use the Makefile:

make run/embedding-service

4. Populate Embeddings for Existing Movies

make populate/embeddings

5. Start the API Server

make run/api

Usage Examples

Semantic Search

curl "http://localhost:4000/v1/movies/search/semantic?q=space+adventure" \
  -H "Authorization: Bearer YOUR_TOKEN"

Get Similar Movies

curl "http://localhost:4000/v1/movies/1/similar?limit=5" \
  -H "Authorization: Bearer YOUR_TOKEN"

License

This project is licensed under the MIT License. See the LICENSE file for details.