[vikp]

I was in a similar boat, and I built a project called Endless Academy - https://www.endless.academy/ . It helped me both brush up on some new tools, and scratch an itch to build something.

To start, just using an LLM API (like Anthropic or OpenAI), and a light wrapper like microsoft guidance will be enough for the AI piece. If you want to get more complex, you can add in semantic search with an embedding model and a vector database. But don't do that off the bat.

For your use case, you won't need ML off the bat, either. When/if you do need ML models like classifiers, I'd use scikit-learn.

For the queries, I would skip pandas, and just use SQL. You can use an LLM to turn natural language into SQL queries, then just show the query results in an interface. The hardest part will actually be mapping the queries into the interface, and vice versa.

For my stack, I used FastAPI for the backend, and SvelteKit for the frontend. I highly recommend this stack for LLM applications - the async paradigm works well for streaming LLM outputs, and you get nice reactivity on the frontend.