Great writeup but ... pretty sure ChromaDB is open source and not "Google's database"?

Maybe a bit off-topic: For my PhD, I wanted to leverage LLMs and AI to speed up the literature review process*. Due to time constraints, this never really lifted off for me. At the time I checked (about 6 months ago), several tools were already available (NotebookLM, Anara, Connected Papers, ZotAI, Litmaps, Consensus, Research Rabbit) supporting Literature Review. They have all pros and cons (and different scopes), but my biggest requirement would be to do this on my Zotero bibliographic collection (available offline as PDF/ePub).

ZotAI can use LMStudio (for embeddings and LLM models), but at that time, ZotAI was super slow and buggy.

Instead of going through the valley of sorrows (as threatofrain shared in the blog post - thanks for that), is there a more or less out-of-the-box solution (paid or free) for the demand (RAG for local literature review support)?

*If I am honest, it was rather a procrastination exercise, but this is for sure relatable for readers of HN :-D

51 visitors in real-time.

I love those site features!

In a submission of a few days ago there was something similar.

I love it when a website gives a hint to the old web :)

I made something similar in my project. My more difficult task has been choice the right approach to chunking long documents. I used both structural and semantic chunking approach. The semantic one helped to better store vectors in vectorial DB. I used QDrant and openAi embedding model.

And some have been saying that RAGs are obsolete—that the context window of a modern LLM is adequate (preferable?). The example I recently read was that the contexts are large enough for the entire "The Lord of the Rings" books.

That may be, but then there's an entire law library, the entirety of Wikipedia (and the example in this article of 451 GB). Surely those are at least an order of magnitude larger than Tolkien's prose and might still benefit from a RAG.