How to Create Your Own ChatGPT Plugin | Weaviate - vector database
Plugins are essential in expanding ChatGPT's functionality, allowing it to leverage third-party resources for better conversations and interactions. With the use of plugins, users can improve ChatGPT's capabilities without the need to retrain the underlying GPT model. This article aims to guide you on how to create your own ChatGPT plugin and connect it to Weaviate's vector database.
Step 1: Setup the Development Environment
To set up your development environment, you can use Dev Containers. Update the devcontainer.json file by adding Fly.io, Docker, and Poetry. You can also find other dev container templates that may suit your preferences.
Step 2: Test the Setup
Setting up tests using PyTest allows you to check if your Weaviate instance is up and running. You can also test if your Fast API endpoint is responding. These tests are defined to ensure that your development environment is working correctly. Additionally, create a makefile to automate running tests and firing up the endpoint. Make sure to specify a run command to spin up the server locally and check if the network connectivity settings are all set up correctly.
Step 3: Implement a Function to Get Vector Embeddings
Since the purpose of this plugin is to connect ChatGPT to a vector database, you will need to define a way to generate vector embeddings. This function is used to generate and store vector embeddings for your documents and vectorize queries when querying and performing vector search over the vector database.
Step 4: Implement Function to Initialize the Weaviate Client and Vector Database
Implementing a couple of functions to initialize the Weaviate python client and initialize the Weaviate instance by adding a schema if it doesn't exist is crucial. This step will enable your plugin to connect to Weaviate's vector database.
Step 5: Initialize the Database and Add a Dependency for the Weaviate Client
Integrate the usage of the initialization functions so that the Weaviate instance and connection of your client are automatically initialized every time the ChatGPT plugin server starts. Use FastAPI's lifespan feature in the main server python script to accomplish this task.
After completing the initial server setup and testing, you can move on to implement endpoints that will allow ChatGPT to interact with your plugin. The available endpoints include querying, upserting documents, and deleting them.
Conclusion
Creating a ChatGPT plugin that connects to Weaviate's vector database is an excellent way to improve ChatGPT's capabilities. Follow the steps outlined in this article to build your own plugin and contribute to the development of generative AI.
