FastAPI Full Crash Course with GenAI & LangChain | Build & Deploy an AI Agent as a REST API (2026)
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In this complete FastAPI + GenAI crash course, you’ll learn everything from the absolute basics of APIs to building and deploying a real AI Agent as a REST API!
๐ฅ What you’ll learn:
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What is an API & how Frontend-Backend-Database communication works
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HTTP Protocol, Routes, Path Params & Query Params explained
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HTTP Methods (GET, POST, PUT, DELETE) & CRUD Operations
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Building a complete Product CRUD API using FastAPI
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Data validation using Pydantic (DTOs & Schemas)
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HTTP Status Codes & Custom Exception Handling
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Sending Emails from FastAPI using FastAPI-Mail
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Running long tasks efficiently using Background Tasks
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Introduction to Generative AI & Large Language Models (LLMs)
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Connecting LLMs using LangChain
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Using Groq API for ultra-fast, free LLM inference
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Building an AI Agent with Tools (Google Search integration)
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Deploying your AI Agent as a REST API using LangServe
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Connecting your Agent to LangGraph Studio & Agent Chat UI
By the end of this video, you’ll have built a fully functional GenAI-powered REST API โ combining traditional backend development with modern AI Agent architecture.
โฑ๏ธ Timestamps:
00:00 Introduction
02:10 Frontend vs Backend Explained
08:24 HTTP, Routes, Ports & CRUD Basics
15:23 FastAPI Installation & Setup
29:14 Building the Product CRUD API
44:24 Path Params & Query Params
52:25 HTTP Status Codes & Error Handling
01:01:45 Pydantic DTOs and Complete CRUD Apis
01:32:57 Sending Emails via FastAPI-Mail
01:52:19 Background Tasks in FastAPI
02:03:33 What is Generative AI & LLMs?
02:10:32 Connecting LLMs with LangChain & Groq
02:26:00 What is an AI Agent?
02:32:15 Building an Agent with Tools
02:37:52 Deploying Agent as REST API (LangServe)
02:47:50 Connecting to Agent Chat UI
02:50:04 Final Recap
๐ Tech Stack Used: Python, FastAPI, Pydantic, FastAPI-Mail, LangChain, Groq API, LangServe, LangGraph
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