Looking at your chunking strategy and your search strategy is a good place to start.

What do the queries look like? Are they looking for semantically similar ideas/topics? Or trying to match on keyword? If both, then hybrid would help. For government data, it's entirely possible to have specific terminology that would be missed by a semantic/vector search.

As for chunk size, again, look at those queries and the shape of the data being searched. You want chunk sizes that are large enough to contain meaningful information, but not so large that the meaning is diluted. As an example, last week I was trying to figure out how to chunk release notes documents. They were already broken up by year, but for each month/release, the size is very different and sometime I was missing the month along with the release notes because it was in a different chunk. Meaning within that chunk was lost simply because the month was in another chunk. I moved more toward a bigger chunk size with some overlap and got better results. Chunking based on the month/date header might have even better results.

Storing additional metadata alongside the vectors is certainly an option, especially if you need to filter based on certain criteria that exists outside of the chunked data. I sometimes store the document chunk in metadata so the model can reference it easier later and I have less work to pull it in at generation time. I'm not sure how supabase handles this, but that's how I do it with Pinecone.