Jerry Liu: prediction

Transcript context

…It uses GPT to build a knowledge tree in a bottoms-up fashion by applying a summarization prompt for each node. Yep. Which I like that original vision. Your messaging roundabout then was also that you're creating optimized data structures. What's the sort of journey to that and how does that contrast with LlamaIndex today? Okay. Maybe I can tell a little bit about the beginning intuitions. I think when I first started, this really wasn't supposed to be something that was like a toolkit that people use. It was more just like a system. And the way I wanted to think about the system was more a thought exercise of how language models with their reasoning capabilities, if you just treat them as like brains, can organize information and then traverse it. So I didn't want to think about embeddings, right? To me, embeddings just felt like it was just an external thing that was like, well, it was just external to trying to actually tap into the capabilities of language models themselves, right? I really wanted to see, you know, just as like a human brain could like synthesize stuff, could we create some sort of like structure where this neural CPU, if you will, can like organize a bunch of information, you know, auto-summarize a bunch of stuff and then also traverse the structure that I created. That was the inspiration for this initial tree index, to be honest. And I think I said this in the first tweet, it actually works super well, right? Like GPT-4 obviously is much better at reasoning. I'm one of the first to say, you know, you shouldn't use anything pre-GPT-4 for anything that requires complex reasoning because it's just going to be unreliable, okay, disregarding stuff like fine tuning. But it worked okay. But I think it definitely struck a chord with kind of like the Twitter crowd, which is just like new ideas at the time, I guess, just like thinking about how you can actually bake this into some sort of application. Because I think what I also ended up discovering was the fact that there was starting to become a wave of developers building on top of GPT-3 and people were starting to realize that what makes them really useful is to apply them on top of your personal data. And so even if the solution itself was kind of like primitive at the time, like the problem statement itself was very powerful. And so I think being motivated by the problem statement, right, like this broad mission of how do I unlock elements on top of the data also contributed to the development of LOM index to the state it is today. And so I think part of the reason, you know, our toolkit has evolved beyond the just existing set of like data structures is we really tried to take a step back and think, okay, what exactly are the tools that would actually make this useful for a developer? And then, you know, somewhere around December, we made an active effort to basically like push towards that direction, make the code base more modular, right, more friendly as an open source library. And then also start adding in like embeddings, start thinking into practical considerations like latency, cost, performance, those types of things. And then really motivated by that mission, like start expanding the scope of the toolkit towards like covering the life cycle of like data ingestion and querying. , performance, those types of things. And then really motivated by that mission, like start expanding the scope of the toolkit towards like covering the life cycle of like data ingestion and querying. Where you also added Llamahub and yeah, so I think that was in like January on the data loading side. And so we started adding like some data loaders, saw an opportunity there, started adding more stuff on the retrieval querying side, right? We still have like the core data structures, but how do you actually make them more modular and kind of like decouple storing state from the types of like queries that you could run on top of this a little bit. And then starting to get into more complex interactions, like chain of thought reasoning, routing and, you know, like agent loops.…

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