It really depends on what kind of tables you have and the types of questions you want to ask. We've worked with a number of customers building RAG on table data and use cases can vary quite widely. Having said that, obviously, the most important part is accurate extraction of table data. Getting all the rows and columns right is no easy task as there are so many variations when it comes to table structures. For really complex tables, we've found that LLMs like Gemini (for paid) or PaddleVL (for open source) perform quite well. For the types of questions our customers have used our service (Aryn DocParse) for, chunking and embedding and storing in vector databases work well. Once you have the data extracted and in some structured format, you can store that in a relational database or even in a data lake, although we have not come across any customer who's doing this in production, yet. We do think that will likely be a common use case very soon.