| Use RAG. Fine tune will bias something to return specific answers. It's great for tone and classification. It's terrible for information. If you get info out of it, it's because it's a consistent hallucination. Embeddings will turn the whole thing into a bunch of numbers. So something like Sentinel will probably match with similar feats. Embeddings are perfect for searching. You can convert images and sound to these numbers too. But these numbers can't be stored in any regular DB. Most of the time it's somewhere in memory, then thrown out. I haven't looked deep into txtai but it looks like what it does. This is okay, but it's a little slow and wasteful as you're running the embeddings each time. So that's what vector DBs are for. But unless you're running this at scale where every cent adds up, you don't really need one. As for preprocessing, many embedding models are already good enough. I'd say try it first, try different models, then tweak as needed. Generally proprietary models do better than open source, but there's likely an open source one designed for game books, which would do best on an unprocessed D&D book. However it's likely to be poor at matching pages afaik, unless you attach that info. |