What does HackerNews think of gpt_index?

GPT Index is a project consisting of a set of data structures designed to make it easier to use large external knowledge bases with LLMs.

Language: Python

This is exactly what LlamaIndex is meant to solve!

A set of data structures to augment LLM's with your data: https://github.com/jerryjliu/gpt_index

This is what we've designed LlamaIndex for! https://github.com/jerryjliu/gpt_index. Designed to help you "index" over a large doc corpus in different ways for use with LLM prompts.
(creator of gpt index / llamaindex here https://github.com/jerryjliu/gpt_index)

Funny that we had just rebranded our tool from GPT Index to LlamaIndex about a week ago to avoid potential trademark issues with OpenAI, and turns out Meta has similar ideas around LLM+llama puns :). Must mean the name is good though!

Also very excited to try plugging in the LLaMa model into LlamaIndex, will report the results.

> Think of ingesting a whole novel that takes place over a few years.

I did exactly that with Asimov's Let's Get Together using https://github.com/jerryjliu/gpt_index. It's a short story that's only 8,846 words, so it's not quite a novel, much less the whole of the Harry Potter series, but it was able to answer questions that required information from different parts of the text all at the same time.

It requires multiple passes of incremental summarization so it is of course much slower than making a single call to the model, but I stand by my assertion that these things just aren't much problem in practice. They are only a problem if you're trying to paste them into ChatGPT or the GPT-3 playground window or something like that.

People are solving the problems with building these systems in the real world almost as fast as the problems arise in the first place.

The things I've seen all use hosted language models. For example https://github.com/jerryjliu/gpt_index depends on LangChain, which wraps APIs from hosted LLMs: https://langchain.readthedocs.io/en/latest/reference/modules...

AFAIK there's no GPT-3-like LLM that's easy to run at home, because the number of parameters is so so large. Your gaming PC's GPU won't have enough RAM to hold the model. For example, gpt-neox-20b needs about 40GB of RAM: https://huggingface.co/EleutherAI/gpt-neox-20b/discussions/1...

I don't think this kind of prompt leakage really matters from a strategy perspective. Pretty much every breakout internet biz in the past 20 years has been like "an HN comment poster can build that prototype". So what really matters is this thing is up live, it's being relatively widely used, it's integrated with Bing, it's pulling the right summaries of the search results into GPT's very length-constrained prompt context..

Here's yesterday's thread on this prompt context pattern: https://news.ycombinator.com/item?id=34477543

I've been experimenting with the 'gpt index' project <https://github.com/jerryjliu/gpt_index> and it doesn't seem like "oh just put summaries of stuff in the prompt" works for everything -- like I added all the Seinfeld scripts and was asking questions like "list every event related to a coat or jacket" and the insights were not great -- so you have to find the situations in which this makes sense. I found one example output that was pretty good, by asking it to list inflation related news by date given a couple thousand snippets: https://twitter.com/firasd/status/1617405987710988288

The trick I describe in this article - using embedding similarity to find relevant content, then constructing a prompt to try to answer a question - is showing up in a whole bunch of different places right now.

https://github.com/jerryjliu/gpt_index is a particularly interesting implementation under very active development at the moment.

https://github.com/jerryjliu/gpt_index

GPT Index is a project consisting of a set of data structures that are created using LLMs and can be traversed using LLMs in order to answer queries.

Also, another library to check out is GPT Index (https://github.com/jerryjliu/gpt_index) which takes a more "data structure" approach (and actually uses langchain for some stuff under the hood).
There's a lot of work going on in this area right now. Check out GPT Index:

https://github.com/jerryjliu/gpt_index https://github.com/jerryjliu/gpt_index/blob/main/examples/pa...

GPT isn't multi-modal yet (so no images), but that's coming.

> https://github.com/jerryjliu/gpt_index

Could you share anything (e.g. how many rows of data and tokens in each row) around how much it cost you to use GPT Index? It looks interesting, but it seems it'd be expensive.

Looks interesting, but need more details about set up and pricing. This is another approach that doesn't require fine tuning https://github.com/jerryjliu/gpt_index