Feb 24
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In this video, we build a complete Retrieval-Augmented Generation (RAG) application from scratch using the latest LangChain version (2026), FastAPI, and Docker. We start from a blank project structure and gradually build a simple yet production-ready RAG system that can answer user questions based on document content. You’ll learn how RAG works internally, how to structure your project cleanly, and how to expose your RAG pipeline as a FastAPI API. Once the RAG application is ready, we containerize it using Docker and fully test it locally using Swagger UI. This video focuses on building strong fundamentals — validating the application layer and the Docker layer — before moving to orchestration and scaling. ⚠️ Note: Kubernetes deployment will be covered in the next video. By the end of this video, you will: ✔ Understand how RAG works in real-world systems ✔ Build a RAG pipeline using LangChain ✔ Expose RAG using FastAPI ✔ Create and test a Docker container for an AI application ✔ Be fully prepared to deploy this system on Kubernetes This video is ideal for: • AI / ML Engineers • Backend Developers • MLOps learners • Anyone building production LLM applications If you enjoyed my “Updated LangChain Version Complete Course (2026)”, this video is the perfect next step. 👉 Subscribe for the upcoming Kubernetes + scaling video.