Fullstack RAG Application with PydanticAI, FastAPI, LanceDB and Streamlit part 2 - Dockerizing

Description

Join us in this detailed tutorial where we take our full stack PydanticAI RAG application, and dockerize it for seamless integration and later deployment. With FastAPI backend and Streamlit frontend, we'll create two separate Docker containers for each component and ensure communication between them. By the end, you'll know how to set up containers for both frontend and backend services, use UV for package management and workspaces, and prepare for deployment on Azure. Check out the previous video on how to build the RAG application and stay tuned for the next one where we'll deploy this setup to Azure. Previous video https://www.youtube.com/watch?v=KdeoPglfpb8 Github repo https://github.com/AIgineerAB/AI_engineering_course/tree/main/20_docker_containerization #lancedb #docker #pydanticai #gemini 00:00 Introduction to Ragbit project 00:56 Setting Up the Project 03:31 Organizing the Project Structure 05:28 Configuring Workspaces 08:20 Preparing for Dockerization 14:33 Creating Docker Files 21:21 Docker Compose Setup 26:06 Testing and Verifying 29:48 Conclusion and Next Steps