This repository contains a simple chat application that demonstrates how to deploy a serverless application on Genezio.
To seamlessly deploy it on your Genezio account click the following button:
During this workshop you are going to be provided with an Open AI API key. Check out this shared spreadsheet to get it.
This repository contains a small challenge for you to interact on your own with the Genezio platform. The challenge consists of adding a new endpoint to the chat application that allows deleting a conversation and its messages.
Your TODO is to implement the DELETE endpoint for deleting a conversation and all its messages:
// file: express/app.ts
// DELETE /conversations/:conversationId - Delete a conversation and its messages
// TODO Implement the logic for this endpoint
app.delete('/conversations/:conversationId', async (req: Request, res:Response) => {
res.json({ message: "Not implemented yet" });
return;
});The method is called on the frontend side when the user clicks on the delete button for a conversation.
const handleDeletedConversation = async (conversationId: string) => {
const response = await deleteConversation(conversationId);
// ...
};Note: The solution can be found solution.md file or in the solution branch of this repository.
The chat application provides functionalities to:
- Fetch all conversations.
- Fetch all messages from a specific conversation.
- Chat with an LLM agent.
- Serverless architecture with Genezio for easy deployment.
- Mongo for persistent data storage (with fallback to mock data for the live demo).
- Simple REST API for interacting with chat conversations and messages.
The application consists of the following components:
- Serverless Express Backend: Provides a REST API for interacting with chat conversations and messages. Deployed in a serverless environment using Genezio.
- Mongo Database: Stores conversations and messages. If not available, mock data will be used.
- React Frontend: Provides a simple interface for interacting with the chat application.
Feel free to use this repository as a starting point for implementing your on RAG application over OpenAI API.
If you encounter any issues, please leave a [GitHub issue] and I'll try to help you.