Creating a Conversational API Using ChatGPT with Node.js
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Chapter 1: Introduction to the ChatGPT API
The ChatGPT API is a powerful tool for developing interactive applications, particularly when used with Node.js and Express. However, while the documentation is thorough, understanding how to effectively engage with the API can be challenging. In this chapter, I'll share my experiences and insights gained from developing an application that heavily utilizes ChatGPT, along with a quick start guide.
Tips & Best Practices
The ChatGPT API operates using three primary message roles that must be included with each communication:
- "System" Messages: These provide detailed control over the model's outputs. If you wish to create a model with a distinct personality, you can specify that using the "system" role.
- "Assistant" Messages: These should include prior messages from the model, maintaining the context of the conversation. Keeping these in the correct order is essential.
- "User" Messages: This encompasses the inquiries or statements made by the user.
Maintaining the conversation's context is vital for a cohesive user experience. Simply sending random messages to ChatGPT won't optimize the interaction for your specific needs.
Section 1.1: Development Process
When developing your application, consider these key points:
- Ensure the conversation history is preserved and sent back with each new user message. Consistency in ordering is crucial.
- Set up your environment to allow for quick iterations with hot reloading, as you'll frequently tweak prompts and data handling.
- Be explicit about your initial system context during the seed phase. Numerous resources discuss prompt engineering, so ensure your first message is well-crafted.
Be Mindful of These Factors:
- Using system prompts does not train the model; it merely guides it toward your intended use case.
- "Hallucination" refers to instances when the model fabricates or inaccurately conveys information. This can be problematic in scenarios requiring specialized knowledge.
Now, let's dive into the initial implementation!
Section 1.2: Setting Up Your Environment
Assuming you have NodeJS installed, along with a code editor like Visual Studio Code and Docker, we can get started. If you're unfamiliar with setting up these tools, you might find the process a bit confusing. At the end of this article, I’ll provide a link to a GitHub repository containing the full code, which can run without Docker for those who prefer that route.
To begin, scaffold your project in the Terminal at the root of your code repository:
npx create-react-app web --template typescript
mkdir api api/src
touch docker-compose.yaml api/Dockerfile web/Dockerfile
cd api
npm init -y
touch src/index.ts
We'll populate these files with basic Docker configurations to facilitate rapid development.
After confirming that the Docker configurations work with docker-compose up, we can begin developing the API. Move to the API project and run the following command:
npm i typescript nodemon node-ts express @types/express cors @types/cors openai
These dependencies provide the foundational libraries necessary for our API to function effectively. Let’s scaffold a simple Express API to demonstrate its capabilities:
After this setup, you can verify that your API responds with a "Hello World" message by executing:
Now we have the basic structure for an API that can relay user conversations to the ChatGPT API! Before diving further, it’s essential to conceptualize what this application aims to achieve beyond just a re-skinned ChatGPT interface.
Understanding User Personas
To create a more engaging experience, we can implement distinct personas that users can interact with. This approach will help users who may not fully understand how to guide the AI. For our application, we will establish four personas: Craig the Builder, Tom the Gardener, Kate the Bartender, and Jen the Beautician.
Now that we have our personas defined, we can create a straightforward API endpoint to retrieve them. A quick curl request will confirm that the persona list is accessible.
Chapter 2: Building the API Endpoints
Interactions with ChatGPT follow a structured loop: initializing conversations, user inputs, and model responses. A user might engage in multiple conversations at once, some brief and others more extended. The sequence of messages exchanged can significantly affect the conversation's quality.
The first video, "Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)," provides insights into effectively utilizing ChatGPT with your data.
The second video, "Using OpenAI's ChatGPT API to Build a Conversational AI Chatbot | Python | Code," illustrates how to create a conversational AI chatbot using Python.
API Endpoint Overview
For our application, users will initiate conversations by sending a POST request with the chosen persona and a unique conversation ID generated on the front end. This structure allows us to effortlessly resume conversations later.
- POST: New conversations initialized with a persona and message.
- PUT: Continues the conversation by adding new user input.
- DELETE: Removes conversations as requested by the user.
This architecture ensures that conversations are stored and retrieved effectively, allowing users to manage their interactions seamlessly.
Conclusion
Building an application with the ChatGPT API necessitates a comprehensive understanding of its roles, a robust backend structure, and an intuitive frontend. The API's three primary roles—"system," "assistant," and "user"—are fundamental to maintaining meaningful interactions.
By laying a strong project foundation and introducing personas, you can enhance user engagement. This article outlined the essential steps to kickstart your project, define personas, and create the needed API endpoints. For a deeper dive, refer to the linked GitHub repository for the complete codebase, which is easy to set up and modify to suit your unique needs.