May as well ask here: what is the best way to use something like an LLM as a personal knowledge base?
I have a few thousand book, papers and articles collected over the last decade. And while I have meticulously categorised them for fast lookup, it's getting harder and harder to search for the desired info, especially in categories which I might not have explored recently.
I do have a 4070 (12 GB VRAM), so I thought that LLMs might be a solutions. But trying to figure out the whats and hows hase proven to be extremely complicated, what with deluge of techniques (fine-tuning, RAG, quantisation) that might not might not be obsolete, too many grifters hawking their own startups with thin wrappers, and a general sense that the "new shiny object" is prioritised more than actual stable solutions to real problems.
Imho opinion, and I'm no expert, but this has been working well for me:
Segment the texts into chunks that make sense (i.e. into the lengths of text you'll want to find, whether this means chapters, sub-chapters, paragraphs, etc), create embeddings of each chunk, and store the resultant vectors in a vector database. Your search workflow will then be to create an embedding of your query, and perform a distance comparison (e.g. cosine similarity) which returns ranked results. This way you can now semantically search your texts.
Everything I've mentioned above is fairly easily doable with existing LLM libraries like langchain or llamaindex. For reference, this is an RAG workflow.
I have a few thousand book, papers and articles collected over the last decade. And while I have meticulously categorised them for fast lookup, it's getting harder and harder to search for the desired info, especially in categories which I might not have explored recently.
I do have a 4070 (12 GB VRAM), so I thought that LLMs might be a solutions. But trying to figure out the whats and hows hase proven to be extremely complicated, what with deluge of techniques (fine-tuning, RAG, quantisation) that might not might not be obsolete, too many grifters hawking their own startups with thin wrappers, and a general sense that the "new shiny object" is prioritised more than actual stable solutions to real problems.