Using GPT4All as a research tool

[Pachuqismo]

On my PC I've just installed GPT4All and selected as model the 'Llama 3 8B Instruct'. It is presently indexing my Calibre library to what it calls LocalDocs.

For anyone interested, I suggest to check it out at GPT4All's homepage (it's free and promises to fully respect privacy). It's not that much of an install and that way you can quiery the AI to your heart's content to get an idea of its use.

I expect it to be a significant tool to access specific information in my largely non-fiction library, but by no means do I consider it a replacement for Calibre for the simple reason that it is not - and doesn't pretend to be - a library manager.

Among the benefits listed by the LLM, I particularly paid attention to:

- Contextual search: allowing for additional questions and provision of additional context to refine results
- Natural Language Processing
- Integration with Chat interface

For anyone linking to their Calibre library, and who have many books, I suggest to create a sub-library in its own folder and run GPT4All on that. Do it with books that you know fairly well, so you have some possibility for evaluting produced results.

Checking GitHub and elsewhere, I understand that a GPT4All summarizer is in the pipeline, which would really be nice.

However, I would still carry on what I've done so far, which is to export my marked text to a text file (which my ePub reader allows to create and export). Then I use - say DuckDuckGo's Chat AI - to consolidate it into a coherent summary. From now on I expect to use GPT4All to do that.

I bet, at least for a long time, this sort of wetware curated summary (i.e. using my brain) will surpase what any AI have to offer, since who-better-to-know than I what is important for me.

Unless you're fanatically interested in certain topics and just prefer to enjoy fictions, all of this may surely not be important. If you're an (aspiring) writer then it may be different.

What I expect from GPT4All is to better understand the three existential threats presently facing humanity, which I find to be: Climate Change, Depletion of Biodiversity and Inequality at Local, Country and Global levels.

I got lots of relevant literature in my library but finding it is a challenge that I hope will be reduced significantly with the use of AI.

Cheers!

[JSWolf]

I hope the answer is never to AI being added to calibre.

[Quoth]

Originally Posted by Pachuqismo

On my PC I've just installed GPT4All and selected as model the 'Llama 3 8B Instruct'. It is presently indexing my Calibre library to what it calls LocalDocs.

For anyone interested, I suggest to check it out at GPT4All's homepage (it's free and promises to fully respect privacy).

But the makers don't respect copyright. The whole subject is full of lies and anything useful is simply scraped. In reality it's a search engine that doesn't quote sources and plagiarises. It's also very inefficient and thus damaging to the environment.

[BetterRed]

Moderator Notice

I moved this from the Calibre forum to the Related Tools subforum.

I have deleted the almost identical post in Plugins.

BR

[Quoth]

Calibre built-in Full Text Search (FTS) is very powerful, with phrase and near features.
You can run the search, when index is up to date, on a sub-range of titles by any normal Calibre search.

It's fantastic for research.

[Pachuqismo]

@Quoth

I'll be sure to check out the Calibre FTS results.

With respect to your previous response I think you're referring to the means used and present stranglehold Big Tech has on LLMs where they try to force people to use their cloud-based systems. I surely agree that this is a very negative development and that's why I'm keeping an eye on open source alternatives.

Also, AI has already had significant impact on various scientific domains enhancing research and practical applications. Just ask an AI

1. Weather Prediction (1950s): Early AI algorithms improved meteorological forecasting. Reference: Charney & Shukla, 1981.
2. Medical Imaging (1970s): AI began analyzing X-rays and CT scans to assist in disease diagnosis. Reference: Doi, 2007.
3. Protein Folding (1990s): AI predicted protein structures, enhancing understanding of biological processes. Reference: Baker, 2000.
4. Genomics (2000s): AI analyzed genomic data, advancing personalized medicine and genetic disease understanding. Reference: Shendure & Ji, 2008.
5. Drug Discovery (2010s): AI models accelerated drug candidate identification and molecular interaction predictions. Reference: Vamathevan et al., 2019.
6. Climate Modeling (2010s): AI improved climate change predictions and weather pattern analysis. Reference: Rasp et al., 2018.
7. Radiology (2016): Deep learning models achieved radiologist-level accuracy in analyzing medical images. Reference: Esteva et al., 2017.
8. COVID-19 Research (2020): AI analyzed COVID-19 data for outbreak predictions and vaccine development. Reference: Wang et al., 2020.
9. AI in Astronomy (2020s): AI analyzed astronomical data, leading to new discoveries in celestial phenomena. Reference: McKinney et al., 2020.
10. Environmental Science (2021): AI monitored environmental changes, aiding conservation efforts. Reference: Levin et al., 2021.