A RAG app only helps people once it is running somewhere they can reach it. Deployment means wrapping your pipeline in an API, packaging it so it runs the same way everywhere, and hosting it on a cloud platform.
The three videos show three common routes. The first turns a Python RAG project into a public API hosted on AWS Lambda. The second packages a FastAPI app with Docker and deploys it to Google Cloud Run with a custom domain. The third deploys a PDF chat app built with LangChain, including its frontend and a managed Postgres database, to DigitalOcean.
The FastAPI and Docker video is not specific to RAG, but the same steps apply to any Python API. Once your app is live, come back to the previous modules on evaluation and guardrails to keep an eye on answer quality and safety. Then put everything together in the projects.
An advanced tutorial that turns a Python RAG/AI project into a public API and hosts it in the cloud on AWS Lambda.
Video 55minContainerizes a FastAPI app with a multi-stage Dockerfile and deploys it on Google Cloud Run, with a custom domain. General FastAPI, but the same steps work for a RAG API.
Video 1h 19minTakes a PDF chat app built with LangChain from local to live: the backend on DigitalOcean, the frontend on App Platform, and a managed Postgres database.
Video 36min