Hi everyone!

I'm excited to share my submission for Databricks AppQuest Quest 5.

For this quest, I extended the Lakebase starter application from Quest 4 into an intelligent Knowledge Base and note-taking application.

🔗 Deployed Application:

https://my-data-bricks-app-7474657569273252.aws.databricksapps.com/

✨ Features

• Knowledge-base note creation and management
• Markdown editing and rendering
• Note organization with tags and priorities
• Persistent data using Databricks Lakebase PostgreSQL
• RAG-based AI assistant that can use my stored notes
• Deployed application running on Databricks Apps

🧠 Prompting Approach

I first created and maintained a PLAN.md file to define the application requirements and architecture.

I then used prompts such as:

"Read PLAN.md and understand the existing Lakebase starter application and its architecture."

"Based on PLAN.md and the RAG template, create a plan to transform the existing todo application into a knowledge-base note-taking application with a RAG assistant."

For debugging and deployment, I also asked the agent to inspect errors, identify the root cause and apply the required fixes before redeploying the application.

🛠️ How I built it

I started with the Lakebase starter application from Quest 4 and used the PLAN.md workflow to define the new requirements. I then integrated the RAG functionality, updated the frontend and backend, tested the application locally, and deployed the final application using the Databricks CLI.

🎟️ Training Voucher Customization

Yes (Customized with 5 significant features: RAG AI Assistant, Markdown Editor/Preview, Tagging System, Interconnected Note Backlinks, Time Tracking).

Thanks to the Databricks and AngelHack teams for creating this hands-on AppQuest experience! 🚀

#Databricks #DatabricksAppQuest #DatabricksApps #Lakebase #RAG #AI