How to create your own ChatGPT Plugin?

In this article, we will explain in simple terms how you can create your own plugin for ChatGPT.

Plugins are like special add-ons that help make ChatGPT even more powerful and capable. They take the existing abilities of ChatGPT to a whole new level! By following the steps we provide, you can develop your own plugin and share it with millions of people who use ChatGPT.

A Brief Introduction to Plugins

Think of ChatGPT Plugins as special tools that expand what ChatGPT can do. They help make up for any missing features and make ChatGPT even more useful.

For example, if ChatGPT doesn’t know much about the weather or how to suggest healthy recipes based on your health conditions, you can create a plugin to teach it those things.

Creating Your Own ChatGPT Plugin

To create a ChatGPT plugin, it is essential to have a solid understanding of the ChatGPT API. The API provides the interface for interacting with the model and exchanging messages.

Familiarize yourself with the API documentation to gain insights into its features and capabilities. This knowledge will be invaluable as you design and implement your plugin.

Here’s a video guide for beginners on how to create a ChatGPT plugin:

Setting Up the Development Environment

You should have a suitable development environment set up. You will need a programming language of your choice, such as Python, and the necessary tools and libraries to build and deploy your plugin. You can use whatever software language you want.

Defining Plugin Functionality

Clearly define the functionality of your plugin. Identify the problem you aim to solve or the enhancements you wish to bring to the system.

Consider the specific use cases and the target audience for your plugin. This initial planning will help you stay focused and ensure that your plugin meets the desired objectives.

Implementing the Plugin

Once you have a clear understanding of the desired functionality, it’s time to implement your ChatGPT plugin.

Utilize ChatGPT API to integrate your custom features. Depending on your chosen programming language, you can make API calls to send and receive messages from ChatGPT. Remember to follow best practices and design patterns to ensure clean and maintainable code.

Developing the Plugin

We will walk you through the process of developing a ChatGPT plugin. Our focus will be on building a web application, creating a ChatGPT plugin, and deploying it remotely using

Building a Web App

You should set up a development environment. To start, we recommend using Dev Containers. Update the devcontainer.json file by adding, Docker, and Poetry.

After setting up the environment, test that everything is functioning correctly. Create a dummy endpoint that responds with a { "Hello": "World" } object when called.

Use PyTest to set up tests that check if your instance is running and if your endpoint responds.

You can automate these tests and server setup using a makefile. Verify the connectivity by visiting localhost:8000/docs, which should display the Swagger UI for your endpoint.

It’s time to move on to implementing the plugin-specific functions.

Implementing a Function for Vector Embeddings

Since our plugin connects a vector database to ChatGPT, we need to generate vector embeddings. These embeddings are used when upserting documents to our database and performing vector search.

We chose to use the ada-002 model as specified by OpenAI, but any vectorizer can be used for querying purposes.

Next, implement functions to initialize our plugin Python client and the instance. Check if a schema exists, and if not, add one.

Initialize the Database and Adding a Dependency for the Client

Integrate the functions defined above to automatically initialize our plugin instance and client when starting the ChatGPT plugin server.

Utilize FastAPI’s lifespan feature in the main server script. After initialization, any logic for server shutdown can be included. For our plugin, no specific actions are required.

At this point, you have completed the initial setup and testing of the server. Now, it’s time to implement endpoints that will enable ChatGPT to interact with your plugin in various ways.

Development of the Plugin specific endpoints

To enhance the functionality of ChatGPT with our plugin, we have implemented three specific endpoints: /upsert, /query, and /delete.

These endpoints enable ChatGPT to interact with the plugin instance, allowing it to add objects, query and search through objects, and delete objects if necessary.

Depending on the prompt and context, ChatGPT independently determines which endpoint to use for generating the response. Let’s take a closer look at each endpoint:

/upsert endpoint

The main objective of the /upsert endpoint is to test whether the appropriate status code is received and to ensure the successful upsertion of content, ID, and vectors.

/query endpoint

The primary goal of the /query endpoint is to verify that the correct number of objects is returned and that the expected document is included in the response.

/delete endpoint:

The /delete endpoint aims to validate the response and ensure that the removal of an object results in a decrease in the total number of objects in the instance.

These three endpoints provide the foundation for extending the functionality of your plugin. Depending on the specific features you want to implement, you can create additional endpoints and test them accordingly.

Step 4: Prepare plugin manifest files

To enable ChatGPT, specifically GPT4.0, to utilize the endpoints exposed by your plugin, you need to create two important files: ai-plugin.json and openapi.yaml.


This file contains essential information such as the app name, logo assets, and most importantly, the name_for_model field, which determines how the model refers to the plugin, and the description_for_model field, which provides a description that the model can understand.


The openapi.yaml file plays a crucial role in describing the endpoints to ChatGPT. It specifies the details of each endpoint, allowing ChatGPT to comprehend and utilize them effectively.

Note that generating the .yaml file can be simplified by first generating the spec in JSON format using SwaggerUI. Then, you can use an online converter to convert the JSON file to the YAML format.

Both ai-plugin.json and openapi.yaml are vital components for ChatGPT to understand and utilize your plugin’s exposed endpoints accurately.

It’s important to provide comprehensive and clear descriptions in these files. ChatGPT relies on these descriptions not only to determine when to use the endpoints but also how to use them correctly.

If ChatGPT is not utilizing your endpoints correctly, consider improving the descriptions for both the plugin and the endpoints. OpenAI provides best practices for creating these descriptions, so it’s beneficial to follow their guidelines.

Keep in mind that the descriptions, doc strings, and other related information should not exceed the context length, as they count against the model’s context limit.

Local deployment of your plugin and testing using the ChatGPT user interface

To test your plugin locally and ensure its proper functionality, you need to allow cross requests from http://localhost:8000 and to your plugin’s server. This can be achieved using FastAPI’s CORSMiddleware middleware.

After setting up the CORS middleware, you can run your plugin locally and test all the endpoints through the ChatGPT user interface. This allows you to verify that the endpoints are functioning correctly.

Prepare to deploy the plugin remotely to

Once you have thoroughly tested the plugin locally, you can proceed with remote deployment to and install it into ChatGPT. Here’s an outline of the steps involved:

  1. Create a remote Instance using your API’s Cloud Services.
  2. Add a Dockerfile, which is a modified version of the template provided by OpenAI. This Dockerfile is used to set up your environment remotely and start the server.
  3. Update the plugin manifest configuration files ai-plugin.json and openapi.yaml to incorporate authentication using a bearer token and your newly created cloud instance instead of localhost.
  4. Update the app to ensure that all communication is authenticated.

Deploy to and Install in ChatGPT

The final step involves deploying your plugin to and installing it in ChatGPT.

Once deployed, you can open ChatGPT in your browser and install your plugin by specifying the hosted URL and providing the bearer token for authentication.

By completing these steps, you will have successfully deployed your plugin and made it available for use in ChatGPT.

To create a successful ChatGPT plugin, prioritize the user experience. Design intuitive and user-friendly interfaces that make interacting with your plugin effortless.

Security and Privacy Considerations

When developing a ChatGPT plugin, it is crucial to prioritize security and privacy. Follow best practices for data handling and storage to protect user information.

Be transparent about the data your plugin collects, if any, and ensure compliance with relevant privacy regulations. By addressing security and privacy concerns, you build trust with your users and foster long-term adoption of your plugin.

Documentation and Support

Provide comprehensive documentation for your ChatGPT plugin, including installation instructions, API references, and code examples.

Clear and concise documentation empowers users to understand and leverage the full potential of your plugin. Additionally, establish channels for user support, such as a dedicated website, forums, or a support email address, to address any questions or issues that users may encounter.


Creating your own ChatGPT plugin opens up a world of possibilities for customization and innovation. By following the outlined steps in this article, you can create your own plugin.

Remember to keep the user experience at the forefront, prioritize security and privacy, and provide thorough documentation and support. With your plugin, you can enhance the capabilities of ChatGPT and create unique AI-powered experiences.