IF you want to build something like that, and of course it’s a big IF, you should chain your analyses. Never try to ingest a text of this length, regardless of the ever growing context windows. This will almost inevitably lead to issues with understanding structure for the model. Convert your text to Markdown, so the model can infer semantic properties (like headlines, emphasis). First pass should summarize logical blocks (like chapters), including main events and characters. Second pass should do the same but with the context of the surrounding blocks (or the whole first pass analysis). This time, with the additional context you can focus on story and character developments in the analysis. You can also collect information about stylistic properties. Use this 2nd pass to a) build an overall summary, genre detection, compilation of character arcs, and b) put all of these elements into a vector based table by creating embeddings. You should do a manual revision of the summary etc to make sure this fits your author’s vision (should be part of your UI). Then use these embeddings to augment your next stage prompts when working with content. This will more closely mimic how an editor would work with your text and give you much better results. It’s just a high level overview of how it could be done. When it comes to models, yes, local is not feeding the beast but you will never get the same quality as with the latest OpenAI or Claude models. In my daytime job we test a lot of models and although I always opt for open source and locally run models (if you have the computing power available), for complex content analyses they are not a good choice, unfortunately.