We started by setting up the project structure, ensuring that each component was well-organized and easy to navigate. The main Java directory was divided into several packages: configuration, models, services, controllers, and utilities. This organization was crucial for maintaining clarity and efficiency throughout the development process.
The configuration package was the first step, where we set up connections to various APIs. This involved creating SlackConfig, ChatGPTConfig, and GeminiConfig files, each containing the necessary credentials and URLs for their respective services. These configurations were essential for initializing API clients within the services, ensuring seamless communication with external platforms.
Next, we focused on the models, which represented the data structures used in the application. By creating classes like SlackMessage, ChatGPTResponse, and GeminiResponse, we ensured that data from Slack, ChatGPT, and Google Gemini APIs could be handled effectively.
The heart of the project lay in the service layer, where we implemented the core logic. SlackService was designed to handle sending and receiving messages from Slack, while leveraging ChatGPTService and GeminiService to process these messages through external APIs. Each service was responsible for sending requests, receiving responses, and managing any errors that occurred during these interactions.
Controllers acted as intermediaries between user inputs and the services. We developed SlackController to process incoming Slack messages and coordinate with SlackService for managing responses. Similarly, ChatGPTController and GeminiController were responsible for handling requests to their respective APIs.
Finally, we created utility classes like MessageFormatter and ResponseHandler. These utilities were crucial for formatting API responses for Slack and managing error logging, ensuring that the bot operated smoothly and efficiently.
So, this is an example of a conversation with our Slack AI bot: