[routerl]

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.

[perfmode]

Where are LLMs used in this workflow? For creating embeddings?

[routerl]

Yes.