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Episode 1: App's Architecture & Intro to Fast API

Introduction to the series and we will see high-level app's architecture and about Fast API and why we choose it.

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Build Your Own ChatGPT with Python & FastAPI — Part 1: Understanding FastAPI

Imagine building your own ChatGPT-like application from scratch.

An application that supports:

  • Real-time streaming responses
  • Conversation history
  • Modern chat interface
  • Database-backed conversations
  • Integration with powerful AI models like GPT-4

That's exactly what we're going to build throughout this series.

By the end, you'll have a production-style AI application that you can customize, extend, and even deploy yourself.

Prerequisites

You don't need to be an expert developer to follow along.

The following knowledge is enough:

  • Basic Python
  • Basic SQL
  • Basic React
  • Basic FastAPI
  • A little curiosity about how AI applications work

Even if you're a beginner, you should be able to follow most of the concepts because every important topic will be explained with practical examples and visuals.

Before Writing Code: Understanding the Architecture

One of the biggest mistakes beginners make is jumping straight into coding.

Before we write a single line of code, we need to understand what we're building.

Trust me—understanding the architecture first will save you hours of debugging later.

Our ChatGPT clone will consist of four major components:

1. React Frontend

The frontend is what users interact with.

It will provide:

  • Chat interface
  • Message rendering
  • Conversation history
  • User interactions

We'll cover React in detail in upcoming parts.

2. FastAPI Backend

The backend acts as the brain of our application.

It will:

  • Receive user requests
  • Communicate with the AI model
  • Store conversations
  • Return responses to the frontend

This is where FastAPI comes in.

3. PostgreSQL Database

We need a database to store:

  • Users
  • Conversations
  • Messages
  • Application data

For this project, we'll use PostgreSQL.

4. AI Model

Finally, we need an LLM (Large Language Model).

We'll use GPT-4 through OpenAI's API to generate intelligent responses.

What is FastAPI?

The official description of FastAPI is:

FastAPI is a modern, high-performance Python framework for building APIs using async programming, automatic validation, type hints, and auto-generated documentation.

At first glance, this sentence can seem complicated.

Let's break it down piece by piece.

FastAPI is a Modern, High-Performance Framework

FastAPI is a Python framework built on top of:

  • Starlette
  • Pydantic

It was designed specifically for modern API development.

One of the reasons FastAPI is so fast is its excellent support for asynchronous programming.

Understanding Async Programming

Consider this traditional synchronous function:

def get_user():
    data = db.query(...)
    return data

This code works perfectly.

However, there's a problem.

When db.query() runs, the function waits until the database returns a response.

During that waiting period, the thread is blocked.

If thousands of users send requests simultaneously, those blocked threads become a bottleneck.

Async Version

Now look at the asynchronous version:

async def get_user():
    data = await db.query(...)
    return data

Notice the async and await keywords.

When the database query is running, FastAPI doesn't waste time waiting.

Instead, the event loop can continue processing other requests.

This allows FastAPI applications to handle many concurrent requests efficiently.

Why It Matters

For applications like:

  • ChatGPT clones
  • Real-time chat systems
  • APIs
  • Streaming services

Async programming can significantly improve scalability and performance.

Automatic Validation with Pydantic

FastAPI uses Pydantic models to validate incoming data automatically.

Consider the following example:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

@app.post("/users")
async def create_user(user: User):
    return user

Here, we've defined a schema called User.

The API expects every request to follow this structure.

Valid Request

{
  "name": "Hari",
  "age": 24
}

This request is accepted because:

  • name is a string
  • age is an integer

Both fields match the schema.

Invalid Request

{
  "name": "Hari",
  "age": "twenty four"
}

This request fails validation.

Why?

Because the schema expects age to be an integer, but the request sends a string.

FastAPI automatically detects this mismatch and returns a validation error.

This is what people mean when they say FastAPI provides automatic validation.

Type Hints in FastAPI

Another key feature of FastAPI is its heavy use of Python type hints.

Consider this endpoint:

@app.get("/users/{id}")
async def get_user(id: int):
    return {"id": id}

Notice the id: int.

This tells FastAPI that the incoming value must be an integer.

FastAPI uses these type hints to:

  • Validate data
  • Convert compatible values automatically
  • Generate documentation
  • Improve editor support

If a user sends an invalid value that cannot be converted into an integer, FastAPI returns a validation error.

This is why type hints play such an important role in FastAPI applications.

Auto-Generated Documentation

One of FastAPI's most loved features is automatic API documentation.

The moment you create endpoints, FastAPI generates interactive documentation automatically.

This documentation is powered by Swagger UI.

With Swagger UI, you can:

  • View all endpoints
  • Test APIs directly from the browser
  • See request schemas
  • Explore responses

Without writing any additional code.

For beginners, this feature is incredibly useful because it makes API testing much easier.

Why We Chose FastAPI for This Project

We're building a ChatGPT-style application.

That means we need:

  • High performance
  • Async support
  • Strong validation
  • Clean API design
  • Easy testing

FastAPI provides all of these out of the box.

That's why it has become one of the most popular backend frameworks in the Python ecosystem.

What's Next?

Now that we understand what FastAPI is and why we're using it, we're ready to move one step closer to building our ChatGPT clone.

In the next part, we'll take a high-level look at PostgreSQL and understand how it will help us store conversations, messages, and application data.

See you in Part 2.

Thanks for reading!

If you have any questions, feel free to ask me in:

Always happy to help fellow developers.

Subscribe to My Youtube Channel : https://www.youtube.com/@hari.maverick