Build a Powerful API with Minimal Brain Power!

Table of Contents

1. Introduction
2. Setting Up the Environment
3. Creating the Backend
   1. Installing Nest.js
   2. Running the Postgres Database
   3. Generating the Nest.js Project
   4. Testing the Hello World Endpoint
4. Installing and Configuring Prisma
   1. Installing Prisma
   2. Configuring Prisma
   3. Updating the Database URL
   4. Validating the Schema
   5. Running Prisma Commands
   6. Adding the Prisma Service
5. Creating the User Object and Schema
   1. Generating the Prisma Schema
   2. Defining the User Model
   3. Pushing Updates to the Database
   4. Generating TypeScript Types
6. Exploring the Prisma Studio
7. Generating the User Module, Controller, and Service
   1. Generating User Files Using the CLI
   2. Creating CRUD Operations
   3. Handling Errors
8. Testing the API Endpoints
9. Conclusion

Introduction

In this article, we will learn how to build a super simple API using only ChatGPT. While it may not be perfect, with a little bit of prompt engineering, it can get us most of the way there. As time goes on, it will only keep getting better. So let's start learning now!

Setting Up the Environment

Before we dive into the coding part, let's set up our environment. We will be using Nest.js as our backend node server, Prisma as our ORM, and Postgres as our database. To start, we need to download Docker Desktop to run our Postgres database in a local container. Once the database is up and running, we can proceed to the next step.

Creating the Backend

Installing Nest.js

The first thing we need to do is install Node.js if we haven't done that already. Once Node.js is installed, we can use the command-line interface (CLI) to install Nest.js globally. This will allow us to generate a new Nest.js project.

Running the Postgres Database

Now that our Nest.js project is set up, we can proceed to run the Postgres database in a Docker container. We'll use a specific command to launch the database, and it should be connected to our Nest.js project.

Generating the Nest.js Project

Once the database is up and running, we can ask ChatGPT to generate our Nest.js project. We'll follow a series of steps that involve installing dependencies, creating the project, and running it locally to ensure everything works fine.

Testing the Hello World Endpoint

With our app running locally, we can use ThunderClient, a VS Code extension similar to Postman, to exercise our API calls. We can start by hitting the hello world endpoint to validate that everything is working as expected.

Installing and Configuring Prisma

Now that our Nest.js project is functional, we can move on to setting up Prisma, our ORM, and connecting it to our local database.

Installing Prisma

To install Prisma, we'll use the npm install command. We'll also need to configure Prisma to work with our local database.

Updating the Database URL

We'll need to update the database URL in the environment file to reflect our username, password, and database table name.

Validating the Schema

Next, we need to validate the Prisma schema with the database we're using. This step will ensure that our schema matches the structure of our database.

Running Prisma Commands

We can run a couple of commands to generate TypeScript types Based on our database schema. We can also use Prisma Studio, a tool that allows us to Interact with our database like an Excel spreadsheet.

Adding the Prisma Service

To integrate Prisma into our codebase, we need to Create the Prisma service file. We can use the Nest CLI to generate the service and copy the necessary code into it. Additionally, we need to install the Prisma client to ensure our imports work properly.

Creating the User Object and Schema

With the Prisma setup complete, we can now focus on creating the user object and defining its schema.

Generating the Prisma Schema

We'll ask ChatGPT to generate the Prisma schema for our user object, which will have an ID, a name, and an email. The response will also include inferred types for the different fields and ensure email uniqueness.

Defining the User Model

Once we have the user model from the Prisma schema, we can copy it into our codebase. We'll then use Prisma DB push to push these updates to the database and Prisma generate to create TypeScript types.

Exploring the Prisma Studio

Prisma Studio provides a user-friendly interface to interact with our database. By running Prisma Studio, we can access our user table and add data as needed.

Generating the User Module, Controller, and Service

To manage users in our system, we need to create the user module, controller, and service files. We can use the CLI again to generate these files, which will include basic CRUD operations for the user entity.

Generating User Files Using the CLI

Using the Nest CLI, we can generate the necessary files for the user module, such as the controller and service.

Creating CRUD Operations

The CLI-generated files will include CRUD operations for the user controller and service, allowing us to create, retrieve, update, and remove user objects.

Handling Errors

While the generated code is functional, it lacks error handling. It's recommended to wrap each database call in a try-catch block to handle any potential exceptions.

Testing the API Endpoints

With our API code complete, we can test the endpoints to ensure everything is working as expected. We'll run our local development server and use ThunderClient to make API calls, such as creating a user, retrieving all users, updating a user, and deleting a user.

Conclusion

In this article, we learned how to build a simple REST API using ChatGPT, Nest.js, and Prisma. Despite some hiccups along the way, we were able to create an API to manage users in our system. By following the steps outlined in this article, You can leverage these technologies to level up your coding skills. Give it a try and let us know in the comments if you want to see more articles on using ChatGPT in your development process.

Article

Introduction

Have you ever wanted to build a simple API but didn't want to deal with the complexities of setting up the backend? Look no further! In this article, we will learn how to build a super simple API using only ChatGPT, a powerful AI Tool. With a little bit of prompt engineering, we can create an API that gets us most of the way there. And the best part? It will only keep getting better as time goes on. So why wait? Let's dive in and start learning now!

Setting Up the Environment

Before we get started with the coding part, let's set up our environment. We will be using Nest.js as our backend node server, Prisma as our ORM, and Postgres as our database. To begin, we need to download Docker Desktop so that we can run our Postgres database in a local container. Once the database is up and running, we can move on to the next step.

Creating the Backend

Our first step in creating the backend is installing Nest.js. If you haven't already, make sure to install Node.js, as Nest.js requires it. Once Node.js is installed, we can use the command-line interface (CLI) to install Nest.js globally. This will allow us to generate a new Nest.js project with ease.

With Nest.js installed, it's time to run the Postgres database. By running a specific command, we can launch the database in a Docker container, connected to our Nest.js project. This will provide us with a solid foundation for our API.

Now that the database is up and running, let's generate our Nest.js project using ChatGPT. The process involves installing dependencies, creating the project, and running it locally to ensure everything works as expected. Following these steps will set us up with a functional backend for our API.

To make sure our application is working correctly, we'll test the hello world endpoint using ThunderClient, a VS Code extension similar to Postman. This will validate that our API is set up and running smoothly.

Installing and Configuring Prisma

Now that our Nest.js project is up and running, we can move on to installing and configuring Prisma, our ORM. To install Prisma, we'll use the npm install command. Once Prisma is installed, we need to configure it to work with our local database. This involves updating the database URL in the environment file to match our credentials.

With the database URL updated, we can now validate the Prisma schema against our database. This step ensures that our schema matches the structure of our database, preventing any conflicts or inconsistencies.

Once the schema is validated, we can run a couple of Prisma commands to generate TypeScript types based on our database schema. Additionally, we can use Prisma Studio, a powerful tool that allows us to interact with our database visually. With Prisma properly set up, we can move on to the next step.

Creating the User Object and Schema

Now comes the exciting part: creating the user object and defining its schema. We can ask ChatGPT to generate the Prisma schema for our user object, which will include fields such as ID, name, and email. The response from ChatGPT will also provide us with inferred types for the different fields, ensuring type safety in our code.

With the user model generated, we can simply copy it into our codebase. This will allow us to define the structure of our user object. Once that's done, we can push the updates to the database using Prisma DB push and generate TypeScript types using Prisma generate.

Exploring the Prisma Studio

Before we move on to the next step, let's take a moment to explore Prisma Studio. This tool provides a user-friendly interface that allows us to interact with our database as if it were an Excel spreadsheet. By running Prisma Studio, we can view our user table and easily add data to it.

Generating the User Module, Controller, and Service

To manage users in our system, we need to create the user module, controller, and service. Fortunately, we don't have to start from scratch, as the Nest CLI can generate these files for us. By running a simple command, we can generate the necessary files with all the basic CRUD operations in place.

With the user module, controller, and service files generated, our API is ready to handle user-related operations. We can create, retrieve, update, and delete user objects using the provided endpoints. It's important to note that the generated code might lack error handling, so it's recommended to add try-catch blocks when interacting with the database to handle potential exceptions gracefully.

Testing the API Endpoints

Now that our API code is complete, it's time to put it to the test. We'll run our development server locally and use ThunderClient to make API calls. This will allow us to verify that our endpoints work as expected. We can create a user, retrieve all users, update a user's information, and even delete a user.

Conclusion

In conclusion, we have successfully built a simple REST API using ChatGPT, Nest.js, and Prisma. Although there were a few challenges along the way, with some prompt engineering, we were able to overcome them and create a functional API to manage users in our system. By following the steps outlined in this article, you can also harness the power of these technologies to level up your coding skills. Give it a try and let us know in the comments if you want to see more articles on using ChatGPT in your development Journey.

Highlights

• Simple REST API using ChatGPT, Nest.js, and Prisma
• Setting up the environment with Docker, Nest.js, and Postgres
• Installing and configuring Prisma as the ORM
• Generating the user object and schema with ChatGPT
• Exploring Prisma Studio for easy database management
• Generating user module, controller, and service files using the Nest CLI
• Testing API endpoints for CRUD operations

FAQ

Q: What technologies are used in building the API? A: The API is built using ChatGPT, Nest.js, Prisma, and Postgres.

Q: Is it necessary to use Docker for the database? A: Yes, Docker is used to run the Postgres database in a local container.

Q: Can I customize the schema of the user object? A: Yes, you can define the schema based on your requirements.

Q: How can I handle errors in the generated code? A: It's recommended to add try-catch blocks when interacting with the database to handle potential exceptions.

Q: Can I test the API endpoints locally? A: Yes, you can run the local development server and use ThunderClient to test the endpoints.