Containerize RAG API with Docker

This is Part 2 of the 4-part DevOps × AI Series. While you can start here, we recommend completing Project #1: Build a RAG API with FastAPI first to understand the API you'll be containerizing. Project #1 teaches you to build the RAG API from scratch, making this Docker project easier to follow. If you skip Project #1, this tutorial includes a 15-20 minute quick setup to get the API files you need (app.py, embed.py, k8s.txt) before containerizing.

No, completing Project #1 (Build a RAG API with FastAPI) is not required but highly recommended. This NextWork project guides you through a quick setup if you haven't built the RAG API yet, covering all necessary files (app.py, embed.py, k8s.txt) and dependencies. However, completing Project #1 first provides deeper context about how the API works, making the containerization process easier to understand. The setup takes approximately 15-20 minutes if starting fresh.

Docker Desktop works on macOS (10.15 or later), Windows 10/11 (64-bit with WSL 2), and modern Linux distributions. Your computer needs at least 4GB of RAM and 10GB of free disk space for Docker, images, and the project files. This NextWork project provides step-by-step installation instructions for each operating system, including WSL 2 setup for Windows users and troubleshooting guides for common installation issues like virtualization settings and permission errors.

Docker Desktop is completely free for personal use, education, and small businesses (fewer than 250 employees and under $10 million in annual revenue). Docker Hub offers unlimited free public repositories where you can share your containerized projects. This entire NextWork Docker project uses only free tiers with zero cloud costs or credit card requirements. All tools (Docker Desktop, Docker Hub, Ollama, Python) remain free even after completing the project, making this an ideal learning pathway with no financial barriers.

The Docker container connects to Ollama using the special hostname host.docker.internal (Mac/Windows) or your machine's IP address (Linux). This NextWork tutorial shows you exactly how to modify your app.py file to add the host parameter to ollama.generate(), rebuild your Docker image, and test the connection. The fix takes 5 minutes to implement. This error occurs because localhost inside a container refers to the container itself, not your actual computer where Ollama runs.

Yes, this NextWork project teaches production Docker workflows identical to those used by Netflix, Uber, Spotify, and thousands of tech companies. You'll learn core DevOps skills including Dockerfile creation, multi-stage builds, image optimization, container networking, and Docker Hub registry operations. These are the exact same containerization techniques that DevOps Engineers, Site Reliability Engineers, and Cloud Infrastructure Engineers use daily to deploy and scale applications serving millions of users.

A Docker image is a read-only template containing your application code, dependencies, and configuration - think of it as a blueprint. A Docker container is a running instance of that image - the actual execution environment. In this NextWork project, you build one Docker image of your RAG API (containing Python, FastAPI, ChromaDB, and your code), then run that image as a container. You can create multiple containers from the same image, each running independently with isolated resources.