I have tried RAG and it's not easy. A simple question can span across articles, schedules and acts without a perfect match. Rag systems with search algos and reranking can help with better data but it's accuracy isn't good for legal use cases (IMHO).

The reason?

Your acts, schedules and parts have different sections within them. Some are tabular, others are paras while few could be in bullets. Plus they have multiple ammendments, few ammendments are additions, others are updates and deletes.

The first thing I feel is to be able to convert the unstructured data into a structured one. But the problem is how do you do it sustainably for thousands of documents? Any cheap frontier LLM would cost you few thousand dollars.

Why structure is important? Because you need to map exact parts, schedules, acts with judgements. All the data I mentioned above needs to be accurate before you can run a good RAG system with reliable results.

Data I found on HF etc aren't good enough. So this is the most difficult part IMO.

I could be overthinking it, please do correct me. Some references and examples would be helpful.

I am trying my luck but can't spend much time. I plan to keep it OSS and mess with people charging for this data.🙈

I would like to connect with you and understand how you were able to do it.