[jusgu]

Great work! One of the things that would be incredibly useful/interesting would be generating a reusable script with an LLM, instead of just grabbing the data. In theory, this should result in a massive cost reduction (no need to call the LLM every time) as long as the source code doesn’t change which would make it sustainable for constant and frequent monitoring.

[diptanu]

This approach was studied in a paper called Evaporate+ - https://www.vldb.org/pvldb/vol17/p92-arora.pdf They used active learning to pick the best function among candidate functions generated by the LLM on a sampled set of data.

[andrew_zhong]

I’ve worked on this exact problem when extracting feeds from news websites. Yes calling LLM each time is costly so I use LLM for the first time to extract robust css selectors and the following times just relying on those instead of incurring further LLM cost.

[ushakov]

Thank you! I’m working on supporting local llms via llama.cpp currently, so cost won’t be an issue anymore

[nbbaier]

Given that the ollama API is openai compatible, that should be a drop in, no?

[fermisea]

I'm working on this problem now. It's possible in some sources - whenever the HTML structure is enough that you map it to the feature of interest - but it could also happen that the information is hidden within the text, which makes it virtually impossible

[nbbaier]

This is a really nice idea. Wonder what the prompt would look like for that.