mini-RAG | 24 | Celery Basics | Step 1/2

Welcome to mini-RAG Ep. 24 in the series "minirag: From Notebook to Production"! In this video, we kick off Step 1 of 2 on integrating Celery into your Python backend – ideal for ML workflows, RAG pipelines, or any production-grade system that needs asynchronous task processing. Whether you're building a Retrieval-Augmented Generation (RAG) app, an API service, or automating ML pipelines – Celery is the go-to tool for distributed task queues in Python. == What You’ll Learn: 00:00 – Why this course 04:34 – Celery Life Cycle: Producers, Brokers, Workers 14:01 – Key Celery Terms: Tasks, Queues, Results 16:47 – Redis vs RabbitMQ: Which Broker to Choose? 20:46 – Docker Setup: Redis & RabbitMQ for Celery 34:45 – Basic Celery App Configuration 01:02:05 – Write Your First Celery Task 01:31:49 – Refactor a FastAPI Endpoint to Use Celery Codes: https://github.com/bakrianoo/mini-rag/tree/tut-016 == Technologies Covered: Celery (Python Task Queue) FastAPI Docker & Docker Compose Redis & RabbitMQ Async Background Task Architecture