|
|
|
|
|
by dmezzetti
704 days ago
|
|
Based on what you're looking to do, it sounds like Retrieval Augmented Generation (RAG) should help. This article has an example on how to do that with txtai: https://neuml.hashnode.dev/build-rag-pipelines-with-txtai RAG sounds sophisticated but it's actually quite simple. For each question, a database (vector database, keyword, relational etc) is first searched. The top n results are then inserted into a prompt and that is what is run with the LLM. Before fine-tuning, I'd try that out first. I'm planning to have another example notebook out soon building on this. |
|
An example of how I might provide references with page numbers or chapter names would be great (even if this means a more complex text-extraction pipeline). As would examples showing anything I can do to indicate differences that are obvious to me but that an LLM would be unlikely to pick up, such as the previously mentioned in-character vs out-of-character distinction. This is mostly relevant for asking questions about the setting, where in-character information might be suspect ("unreliable narrator"), while out-of-character information is generally fully accurate.
Tangentially, is this something that I could reasonably experiment with without a GPU? While I do have a 4090, it's in my Windows gaming machine, which isn't really set up for AI/LLM/etc development.