Designing a Clean RAG Project Structure | Organize Your LLM Application Properly #3

Описание: This is Part 3 of the RAG from Scratch playlist. In this video, we design the complete project structure for our Retrieval-Augmented Generation (RAG) application, including a FastAPI backend for serving APIs.

A clean architecture is critical when building production-ready AI systems. We break the project into clear modules for ingestion, embeddings, vector storage, retrieval, and generation. We also integrate FastAPI as the API layer while keeping routing, services, and business logic properly separated.

In this video, you will learn:
• How to structure a scalable RAG codebase
• Where to place ingestion, retrieval, and LLM logic
• How to organize FastAPI routes and services
• Managing configuration and environment variables
• Preparing the system for Docker and production deployment

This structure will serve as the foundation for the rest of the series as we continue building the complete RAG pipeline.

📌 GitHub Repository
All code, configurations, and examples used in this playlist are available here
👉 https://github.com/khowarlabs/rag-wit...

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