In the video titled "ChatGPT Plugins: Build Your Own in Python!", published by James Briggs, the creator demonstrates how to build and deploy a custom ChatGPT plugin using Python. James Briggs walks the viewers through the process of creating a plugin that retrieves information about the LineChain Python library and integrates it with OpenAI's ChatGPT.
The video begins by introducing the concept of plugins, which are similar to tools or agents that assist large language models in performing specific tasks. In this case, the plugin will help ChatGPT interact with a vector database containing information about LineChain. The video explains the architecture and components involved, such as the API, Pinecone vector database, and the interaction between ChatGPT and the outside world.
James Briggs demonstrates how to create a custom plugin by forking OpenAI's ChatGPT Retrieval Plugin repository on GitHub and cloning it to a local machine. The main focus is on the server-side components, such as the API endpoints for updating and querying the database. The video also explains how the API interacts with the Pinecone vector database and the OpenAI embedding model to store and retrieve information.
To deploy the API, the video shows how to use DigitalOcean, a cloud hosting platform. The deployment process involves setting up environment variables, such as the Bearer token, OpenAI API key, and Pinecone API key, which are required for authentication and access to various services.
Once the API is deployed, the video demonstrates how to use a Google Colab notebook to send data to the API, which is then stored in the Pinecone vector database. The data is processed and embedded using OpenAI's embedding model before being stored.
Next, the video shows how to query the API using example questions related to LineChain. The queries are sent to the API, which returns relevant information from the Pinecone vector database. The video then demonstrates how to integrate the custom plugin with ChatGPT, which involves updating the OpenAPI YAML file and installing the plugin within the ChatGPT interface.
In conclusion, the video provides a comprehensive walkthrough of building and deploying a custom ChatGPT plugin using Python. Although the process has some complexities, the video showcases the potential of ChatGPT plugins in enhancing the capabilities of large language models.
