Presented by DataRoot Labs
As OpenAI invites developers off the waitlist, ChatGPT Plugins are slowly rolling out -- and they’re changing the game. Large language models are potentially transformative, but they’re also still limited to the information in their training data, can be out of date and can also be fairly general. Additionally, they’re limited to text output. But with OpenAI plugins, developers can connect ChatGPT to third-party applications to eliminate these restrictions and unlock a vast range of possible use cases.
Developers can create APIs that retrieve real-time information -- like stock prices, sports scores and top headlines. The Wolfram plugin gives ChatGPT access to computation, math, curated knowledge and real-time data and visualization through Wolfram|Alpha and Wolfram Language. Other plugins can dive into knowledge bases, or perform tasks like booking a flight (see the plugins created by Kayak and Expedia), making reservations (OpenTable’s entry), shopping for groceries (Instacart’s plugin) and more, without a user leaving the chat window.
ChatGPT has developed two of its own plugins: a web browser plugin that can retrieve up-to-the-moment information from the web, and a code interpreter plugin, which tests the code that ChatGPT writes in a live environment, in order to minimize errors.
As more developers are being invited to create plugins and user interest is growing, the market is exploding. Creating a plugin that enhances or customizes the functionality of GPT provides businesses a significant opportunity to reach a vast user base, increasing visibility and potential customer engagement. And if that plugin is innovative, solving a specific user need or pain point, it can generate a lot of interest and use, driving indirect traffic back to the business's core offerings.
How do you take advantage of the surge in GPT traffic, and the valuable platform a plugin offers for businesses to showcase innovation, enhance brand awareness and stimulate growth? Here’s a start-to-finish guide.
Building a ChatGPT plugin
To test a ChatGPT plugin, you first need to gain developer access from OpenAI. This process involves visiting the ChatGPT Plugins page, filling out a sign-up form with your personal details and a description of the plugin you intend to create, and optionally providing additional details about your company. Once you've completed the form, you simply submit your information.
Creating a ChatGPT plugin with Python involves several steps, from setting up the environment to defining the plugin's behavior. Here is a high-level overview of the process:
Set up the environment First, ensure you have Python (3.6 or later) installed on your system. You'll also need an OpenAI account to access the ChatGPT API.
Install dependencies You'll need to install a few Python libraries, most importantly the openai package for interacting with the OpenAI API. You can do this using pip: `pip install openai.'
Create the plugin schema A ChatGPT plugin requires a schema, which defines the plugin's input and output. This could be a JSON file. It includes the plugin's name, version, and required API inputs and outputs.
Define the plugin's behavior The behavior of the plugin is defined in a Python function. This function takes the user's message as input and generates a response. Here's where you'll incorporate the logic for your plugin.
Create the API endpoint You need to create an HTTP endpoint for your plugin. You could use a Python web framework like Flask or FastAPI to do this. The endpoint receives the user's input from the OpenAI API, passes it to your Python function, and then sends the response back to the OpenAI API.
Test and deploy the plugin Before deploying, you should thoroughly test your plugin to ensure it behaves as expected. After testing, you can deploy your plugin to a server. You could use a platform like Heroku, AWS, or Google Cloud for this.
Register the plugin with OpenAI After deploying, you need to provide the URL of your API endpoint to OpenAI. It will use this URL to send user inputs to your plugin and receive responses.
In case you don’t have access to the developer API for plugins, you can test the same process using LangChain. The plugin won’t be present in the interface, but you can prepare the whole plugin backend in advance.
Challenges and risks to watch out for
There are several risks to be aware of while designing and building your plugin, including security vulnerabilities, quality control and compatibility challenges among others. Poorly developed or maintained plugins can impact user experience and introduce bugs or crashes.
In addition, ethical considerations are essential to prevent biases, discriminatory behavior or inappropriate content in plugins.
Plugins could also increase safety challenges, with harmful or unintended actions that bad actors can take advantage of, to defraud, mislead or abuse others. And by increasing the range of possible applications, plugins may raise the risk of negative consequences from mistaken or misaligned actions taken by the model in new domains.
OpenAI is working to develop safety-by-design mitigations which restrict risky plugin behaviors and improve transparency in the user experience by exposing how and when a plugin is operating. Developers can also help mitigate risks by establishing a robust plugin development process, conducting security audits, enforcing quality assurance practices and carefully vetting and monitoring plugins before integration.
In summary, plugins turbocharge ChatGPT by augmenting its capabilities beyond providing information and summaries to actually performing tasks. They also open up new possibilities for your product, enabling users to engage with your product and service in a more efficient and impactful manner.
But never lose sight of this: the plugin needs to be developed well with usability, security, privacy and ethical concerns in mind.
Ivan Didur is a co-founder and CTO of DataRoot Labs. As the head of technology, Ivan manages the entire tech team, designs projects architecture, and makes critical technology decisions. With hands-on expertise in core AI technologies like Computer Vision, NLP, and Deep Reinforcement Learning, Ivan eagerly tests new AI methods in our research lab. If they meet his high standards, they are swiftly integrated into production. Ivan also mentors aspiring Data Scientists at DataRoot University.
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