Beginners Guide to GPT4 API & ChatGPT 3.5 Turbo API Tutorial

The video provides a comprehensive guide on integrating OpenAI's GPT-3.5 and GPT-4 chat APIs into web applications using Microsoft Azure's serverless technology. It covers setting up an OpenAI account, utilizing Node.js and VS Code for coding, and deploying applications on Azure. The tutorial walks through creating a REST server, configuring OpenAI APIs, and building a frontend for user interaction. It emphasizes the importance of message history and system context in chat completions. Sponsored by Microsoft, the video also guides on deploying the application to Azure, enabling global access.

• The focus is on integrating GPT-3.5 and GPT-4 into web applications or software using OpenAI's chat API.
• The tutorial is beginner-friendly and covers setting up an application from scratch using serverless technology on Microsoft Azure.

"This is a crash course to show how to integrate GPT-4 and GPT-3.5 and its latest chat API into your next website application or software."

• The aim is to teach users how to customize and deploy their applications, interfacing directly with OpenAI's models.

• Users need to create an account on OpenAI or log in using Google to access the dashboard.
• The dashboard provides documentation, API references, examples, and a playground for testing ideas.

"Head to Google search OpenAI or just go to open.com. First, you'll need an account."

• The documentation is consistent across GPT-3.5 and GPT-4, providing a unified resource for developers.

• Node.js is necessary for creating a REST server; version 18.15 LTS is recommended.
• Visual Studio Code (VS Code) is suggested for its plugins that enhance the coding experience.

"I'll download Node.js; this is a JavaScript runtime. It'll allow me to create a simple REST server."

• The project should be initialized using npm, and a new folder should be created for organization.

• Initialize the project with npm init, which creates a package.json file.
• Essential packages include Express, OpenAI, body-parser, and CORS.

"I'm going to initialize the project. To do this, I'm going to run npm init in the terminal."

• These packages are necessary for setting up the server and interfacing with OpenAI.

• Import necessary modules from OpenAI, including configuration and API classes.
• Set up the configuration with the organization ID and API key from OpenAI.

"I'm going to import some of these modules that we've created. Firstly, I'll do OpenAI."

• This configuration is crucial for initializing the OpenAI API and making requests.

• Use async/await to query the chat model, specifically GPT-3.5 turbo for cost efficiency.
• Set up the model and messages array to interact with the chat API.

"I'm going to query the chat model. To do this, I'm going to set a const value here called completion."

• The response from the model can be logged using console commands to verify functionality.

• Adjust the package.json to allow import statements by setting the type to module.
• Run the application using Node.js to test the integration with OpenAI.

"Now I can open up the terminal and test this out. I'm going to pass in node calling index.js."

• This step ensures that the setup is correct and the application can successfully query OpenAI's models.

These notes provide a comprehensive overview of the steps and considerations necessary for integrating OpenAI's GPT-3.5 and GPT-4 into applications, focusing on account setup, software requirements, project initialization, and testing.

• The speaker explains the process of setting up a web server using Node.js and Express.
• Key libraries used include Express, body-parser, and CORS.
• The server is initialized and set to listen on port 3000.
• A basic GET request is configured to handle browser access.

"I'm going to import Express from Express as well as a few other libraries. These will include body-parser as well as CORS."

• The speaker highlights the necessary libraries for setting up the server.

"I'm going to pass in const app equals Express, and I'm also going to set a port being 3,000."

• Express is initialized, and the server is set to listen on port 3000.

• The speaker describes changing the server request method from GET to POST for interactivity.
• POST requests allow for sending messages to the API and maintaining a message history.

"I'm going to change this from a GET request to a POST request, and I'm going to listen for messages that get sent as part of that POST request."

• The transition to POST requests is made to handle interactive communication.

• A basic HTML frontend is created to interact with the server.
• The frontend includes an input form for user messages and a chat log for displaying messages.

"I'm going to create a file called index.html and then I'm going to head to Google to search for a basic HTML starter."

• The speaker outlines the creation of an HTML file to serve as the user interface.

"I'll remove the script and start writing my own, but as part of that, I want to have a form that a user performs with an input and a submit button."

• The HTML frontend is customized to include an input form and a submit button.

• JavaScript is used to handle form submissions and send messages to the server.
• Event listeners are set up to manage form submission and prevent page reloads.
• Messages are reset after submission, and new message elements are created for display.

"I'm going to do an event listen on that form for any time it is submitted, and I'm going to run a function here which will pass in E and I'll do an e.preventDefault so it doesn't reload the page."

• JavaScript is used to manage form submissions and prevent default page reload behavior.

"I'm going to create a new div element and I'm going to call this the message element. It's going to create a div and that div will have a class that says message and message sent."

• New message elements are dynamically created and styled for display in the chat log.

• A fetch request is used to send POST requests to the server.
• The response is processed in JSON format, and new messages are appended to the chat log.

"I'm going to do a fetch request, but this will be a POST request to the web server on Port 3000 on Local Host."

• A fetch request is configured to send messages to the server and handle responses.

"The response will be in JSON format, so what I'll do is AR res.JSON to enable us to be able to view that."

• JSON format is used to process server responses and update the chat log.

• Message history is implemented to maintain a conversation context.
• A system message is added to define the assistant's role, and messages are stored in an array.

"Unlike traditional OpenAI models, the chat completion allows you to have history. Let's actually add that in."

• The speaker discusses the importance of maintaining message history for context.

"The history is saved as an array of messages between the user and the assistant, with a system message being there at the start to give context to how the actual chatbot should work."

• The implementation of message history is crucial for creating a coherent conversation flow.

• The process involves deconstructing an array to provide a history of messages from the front end.
• A new value called messages is created using a schema from OpenAI documentation.
• The schema includes a role and content, which are essential for message structuring.
• A New Message constant is created with the schema, dynamically inserting content from a text input.
• The messages array is updated by pushing the new message into it.
• The server is updated to handle the messages array instead of a single message.

"I'm going to create a new const value called New Message and this const value will have that schema."

• The creation of a new constant for messages ensures that the schema is adhered to, maintaining consistency.

"Finally, I want this added to the messages array so I'll do a simple messages.push with the new message being in there."

• The new message is appended to the messages array, enabling a history of interactions.

• The server response is incorporated into the message history by creating a new assistant message.
• The assistant's role is included in the message schema, with content from the server's response.
• The message history is updated to reflect conversations accurately.

"I'm going to do new assistant message I'm going to paste in that schema for the role being assistant this time and I'm going to pass in data completions content."

• This ensures that responses from the server are correctly formatted and integrated into the message history.

• Initially, the application runs locally, requiring deployment to make it accessible online.
• The deployment is planned to be on Microsoft Azure Cloud, allowing global access.
• The process includes creating a serverless function using Azure Functions.

"All of this however is a local host meaning you have to run it on your own computer so next I want to put this up online so that anyone can access."

• The transition from a local setup to a cloud-based system aims to provide broader accessibility.

"What I want to do is create a function that's going to run without any servers while we can do this through the user interface I'm going to do this inside of vs code."

• This approach leverages serverless architecture to streamline deployment and management.

• The deployment involves using Azure Functions, specifically a serverless app.
• A unique name, runtime, and resource pool are selected for the function.
• The function is developed and deployed using Visual Studio Code with Azure extensions.

"I'll need to select a runtime so I'll put it on node.js on version 18 I'll need a resource pool I'll just select the default for us East."

• The selection of runtime and resource pool is crucial for the proper functioning of the deployed application.

"Now to create the work space for this project I'm going to go to workspaces and I'm going to select the little lightning Arrow which basically creates a new project."

• The creation of a workspace is a foundational step for organizing and developing the project within Azure.

• The function app is named and configured, with a workspace created for project management.
• The project setup includes selecting a language and model version, along with a trigger type.
• The setup is facilitated by Visual Studio Code, providing a structured environment for development.

"The functions app will now show Adrian Azure GPT Now to create the workspace for this project I'm going to go to workspaces and I'm going to select the little lightning Arrow which basically creates a new project."

• The naming and configuration of the function app are essential for identifying and managing the project.

"Finally I'm going to give it a name I'll give the name GPT function what's cool is that the code along with the files are created for you."

• The automatic creation of code and files enhances efficiency, allowing developers to focus on customization and functionality.

• Begin by setting up a local development environment using Azure and Visual Studio Code (VS Code).
• Connect to Azure Storage automatically created for the project.
• Start debugging the local instance using a specific local address.

"I'm going to expand out the workplace and I'm going to select start debugging. I'll connect this to my Azure storage which was automatically created here called Adrian Azure with some random number at the end and this will start up the local instance of this project in the terminal."

• Explanation: This outlines the initial steps for setting up a local environment in Azure, connecting to storage, and starting the debugging process.

• Deploy the workspace to the cloud using Azure's deployment options.
• Overwrite existing deployments if necessary during the deployment process.
• Access the Azure dashboard to verify the deployment.

"I have the option here next to the Thunderbolt as a cloud with an up Arrow to deploy the workspace to the cloud. I'm going to deploy it to the Adrian Azure GPT functions app that I've created."

• Explanation: Describes the process for deploying a local project to the Azure cloud, including overwriting existing deployments.

• Install OpenAI using npm and ensure it is listed in package.json.
• Transfer OpenAI configuration from an Express server to the Azure function.
• Use require statements instead of import statements for module inclusion.

"I'm going to install it first running npm install open Ai and next I'm going to make sure that it's in the package.json. It's the only Library we need in this case."

• Explanation: Details the steps to install and configure OpenAI within an Azure function, highlighting the use of npm and require statements.

• Remove unnecessary syntax and context logging from the function.
• Change the response format to JSON and update the function to handle POST requests.
• Adapt the function to use Azure-specific logging and request handling.

"I'm going to get rid of all the other syntax inside of this function we don't need the context. log which is kind of like console.log and we don't need the name which is queried from the URL."

• Explanation: Explains the modifications made to streamline the Azure function, focusing on response format and logging.

• Restart and test the modified Azure function locally to ensure it works with the new configurations.
• Update front-end requests to match the new local address.

"I'm going to restart it I'm going to just start up a new instance and this will start it up on the same IP address I had before on Local Host."

• Explanation: Describes the testing process for the Azure function, including restarting and updating local addresses for front-end requests.

• Deploy the updated Azure function to the cloud, including the OpenAI module and POST request configurations.
• Replace local URLs with online URLs for cloud testing.

"I'm going to head over to Azure and this is the easiest part I select the up Arrow to deploy this function to the cloud overriding the previous one I had."

• Explanation: Outlines the steps to deploy the updated function to the cloud, emphasizing URL changes for cloud-based testing.

• Enable Cross-Origin Resource Sharing (CORS) to allow access from different origins.
• Test the function again to ensure it responds correctly over the internet.

"I'll jump back to Azure head to the functions app select the function app application here and search up cores here I'm going to select API cores on the left hand side and select star to enable me to access it from anywhere."

• Explanation: Details the process of enabling CORS to allow the Azure function to be accessed from various origins, ensuring proper internet functionality.