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FastAPI with GenAI and AgenticAI project - From Basic to AI
Last updated 8/2026
Created by Hitesh Choudhary, Piyush Garg
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 74 Lectures ( 10h 36m ) | Size: 22.5 GB

Build production-grade FastAPI backends through 9 real projects: REST APIs, databases, auth, WebSockets, AI and RAG.



What you'll learn
⚡ Understand how FastAPI works under the hood; ASGI, Uvicorn, Starlette and Pydantic; to write fast, production-grade code.
⚡ Build and structure real REST APIs with correct HTTP verbs, response models, pagination and clean error handling.
⚡ Design databases and perform CRUD operations using SQLModel, plus work with MongoDB for document-based projects.
⚡ Secure APIs with header/API-key authentication, and manage application lifespan and dependency injection.
⚡ Integrate AI into backends: build LLM-powered features, image pipelines, WebSockets, SSE and a full RAG system.
⚡ Structure clean, maintainable projects using separation of concerns, Pydantic models and modular routers.

Requirements
❗ Basic Python knowledge; data types, loops, functions, and a little about decorators and async is enough.
❗ Familiarity with virtual environments (uv, venv or similar) to keep project dependencies isolated and portable.
❗ A code editor of your choice; VS Code, Cursor, Zed, Vim, anything - and Python installed on your machine.
❗ No prior FastAPI or backend framework experience needed. We build everything step by step, from scratch.

Description
This is not another short, surface-level tutorial. This is a deep, project-first course onFastAPI - built for developers who want to understand what actually happens under the hood andwrite fast, clean, production-grade backend code.

We start from the foundations: what a backend really is, why FastAPI exists, and how its core pieces;ASGI, Uvicorn, Starlette, and Pydantic - work together to make it so performant. Once you understand the request-response lifecycle and why FastAPI is fast, you stop writing sloppy code and start building applications that can serve the world.

From there, it is projects, projects, and more projects. You will build a menu API, a pincode lookup service with custom exceptions and validators, a database-backed CRUD app with SQLModel, a multi-parameter filtering and stats service, and a header-based authentication system. Then we go further into modern, AI-centric territory, Server-Sent Events, MongoDB-powered document apps,LLM-driven features, image processing pipelines, and a complete Retrieval-Augmented Generation (RAG) system using vector databases.

Along the way you will learn the practices that matter; separation of concerns, Pydantic models, dependency injection, lifespan events, pagination, and clean project structure; plus how to work effectively with AI coding tools the way developers actually do today.

The videos are intentionally longer than the usual Udemy format because real understanding needs room to breathe. This is a senior developer talking to you like a peer, sharing hard-won experience, not a polished script.
By the end, you will be a confident FastAPI backend developer, ready to build robust, high-performance applications and pair them with any frontend or AI workload. This course helps you to not only master FastAPI but also to master Generative AI, RAG in production and building Agents with modern FastAPI ecosystem of python

Who this course is for
⭐ Python developers who want to become backend developers using a modern, high-performance framework.
⭐ Data science and AI enthusiasts who need to serve models and build APIs around LLMs and RAG applications.
⭐ Students and self-taught programmers ready to build real, portfolio-worthy projects instead of toy examples.


Homepage

Code:

https://anonymz.com/?https://www.udemy.com/course/fastapi-with-genai-and-agenticai-project

Code:

https://nitroflare.com/view/EB5A134E588CCA3/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part1.rar
https://nitroflare.com/view/809F5EEF7617365/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part2.rar
https://nitroflare.com/view/851160136EBA988/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part3.rar
https://nitroflare.com/view/820BBA29D2DED0B/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part4.rar
https://nitroflare.com/view/9D96D9EA8C544E9/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part5.rar

Code:

https://rapidgator.net/file/974121bc1a00e3c0d5e94e1db14fa7b6/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part1.rar.html
https://rapidgator.net/file/1b6d0b84ec7a506a2ae098062fc296e2/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part2.rar.html
https://rapidgator.net/file/8f48367f9dc7a5e53cd34a2195aeb0ed/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part3.rar.html
https://rapidgator.net/file/dd21d23c3fe830f43b0e1295f866f172/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part4.rar.html
https://rapidgator.net/file/ae86ca86eb8efd71df217fcfe92cd09c/FastAPI_with_GenAI_and_AgenticAI_project_-_From_Basic_to_AI.part5.rar.html