Jan 16
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This video is your complete guide to mastering FastAPI and using it as a backbone for Generative AI applications. Whether you’re a beginner exploring REST APIs or an intermediate developer diving deeper into Python web frameworks, this structured series will walk you step by step through the fundamentals, best practices, and production-ready standards of FastAPI. By the end of this video, you’ll be able to: Understand the core concepts of REST APIs and HTTP communication. Build and scale APIs using FastAPI, Uvicorn, and Starlette. Implement request handling, response models, and validation with Pydantic. Work with asynchronous Python (async/await) for performance optimization. Deploy APIs for real-world Generative AI applications. Structure projects with industry-standard best practices for configuration, environment handling, and deployment. Full Code: https://github.com/akash-balakrishnan-22/fastapi-basic 📌 Video Roadmap 1️⃣ What is REST API? Introduction to REST architecture and its role in modern APIs. 2️⃣ How FastAPI fits into the Python ecosystem Discover where FastAPI stands in comparison to Django, Flask, and other frameworks. 3️⃣ Why FastAPI? Learn what makes FastAPI the go-to framework: speed, type hints, async-first design. 4️⃣ Core HTTP Concepts HTTP Methods JSON Communication HTTP Response Codes 5️⃣ Getting Started with FastAPI Setting up the environment Understanding Uvicorn, Starlette, and FastAPI basics 6️⃣ Decorators in Python Learn how decorators power FastAPI endpoints. 7️⃣ Pydantic Models Data validation, serialization, and type enforcement with Pydantic. 8️⃣ Request & Response Handling GET requests for retrieving data POST requests for creating/updating data Best practices for choosing request types 9️⃣ Request Body & Validation Path, Query, and Body parameters Handling form data and file uploads 🔟 Async Programming in FastAPI Async/await explained 1️⃣1️⃣ Expose a GenAI Application using an API Bridge FastAPI with Generative AI workflows. 1️⃣2️⃣ Environment Variables & Config Management Using .env files and configuration for secure, scalable projects. 1️⃣3️⃣ FastAPI Project Structure (Industry Standards) 1️⃣4️⃣ Deployment with Uvicorn & Gunicorn Using Gunicorn as a process manager Scaling with multiple Uvicorn workers 👉 This series balances theory + hands-on coding, making it perfect for: Python developers entering API development. Machine Learning/AI engineers building production-ready apps. Beginners curious about modern web frameworks. 📢 Stay tuned and follow along with the step-by-step tutorials to become confident in building and deploying APIs with FastAPI for GenAI 🚀 RestAPI Intro - 00:00 - 06:11 FastAPI History - 06:11 - 13:18 Why FastAPI - 13:18 - 19:15 HTTP Methods - 19:15 - 32:55 FastAPI Basics - 32:55 - 45:45 Decorator - 45:45 - 50:36 Pydantic - 50:36 - 56:15 GET and POST Method - 56:15 - 01:05:25 POST Body Types - 01:05:25 - 01:14:29 Async Await - 01:14:29 - 01:27:42 FastAPI in GenAI app - 01:27:42 - 01:38:28 .env Config File - 01:38:28 - 01:46:52 Industry standards - 01:46:52 - 01:52:34 Folder Structure in Code - 01:52:34 - 02:05:06 FastAPI in Production - 02:05:06 - 02:15:16 #aiwithakash #genai #codingintamil #rag #mcp #modecontextprotocol #aiintamil #fastapi #restapi #flasktutorial #fastapitutorial #restapi