Jerry Liu: belief

Transcript context

…Um, our SEC Insights is a full stack LLM chatbot application, um, that does. Analysis of your sec 10 K and 10 Q filings. And so the goal for building this project is really twofold. The reason we started building this was one, it was a great way to dog food, the production readiness for our library. We actually ended up like adding a bunch of stuff and fixing a ton of bugs because of this. And I think it was great because like, you know, thinking about how we handle like callbacks streaming, actually generating like reliable sub responses and bubbling up sources, citations. These are all things that like, you know, if you're just building the library in isolation, you don't really think about it. But if you're trying to tie this into a downstream application, like it really starts mattering for your error messages. When you talk about bubbling up stuff for like sources, like if you go into SEC Insights and you type something, you can actually see the highlights in the right side. That was something that like took a little bit of like, um, understanding to figure out how to build wall. And so it was great for dog fooding improvement of the library itself. And then as we're building the app, um, the second thing was we're starting to talk to users and just like trying to showcase like kind of, uh, bigger companies, like the potential of LLM index as a framework, because these days obviously building a chatbot, right. With Streamlight or something, it'll take you like 30 minutes or an hour. Like there's plenty of templates out there on LLM index, like train, like you can just build a chatbot, but how do you build something that kind of like satisfies some of these, uh, this like criteria of surfacing, like citations, being transparent, seeing like, uh, having a good UX, um, and then also being able to handle different types of questions, right? Like more complex questions that compare different documents. That's something that I think people are still trying to explore. And so what we did was like, we showed, well, first like organizations, the possibilities of like what you can do when you actually build something like this. And then after like, you know, we kind of like stealth launched this for fun, just as a separate project, uh, just to see if we could get feedback from users who are using this world to see like, you know, how we can improve stuff. And then we were thought, we thought like, ah, you know, we built this, right? Obviously we're not going to sell like a financial app. Like that's not really our, in our wheelhouse, but we're just going to open source the entire thing. And so that now is basically just like a really nice, like full stack app template you can use and customize on your own, right. To build your own chatbot, whether it is a really financial documents or like other types of documents. Um, and it provides like a nice template for basically anybody to kind of like go in and get started. ild your own chatbot, whether it is a really financial documents or like other types of documents. Um, and it provides like a nice template for basically anybody to kind of like go in and get started. There's certain components though, that like aren't released yet that we're going to going to, and then next few weeks, like one is just like kind of more detailed guides on like different modular components within it. So if you're like a full stack developer, you can go in and actually take the pieces that you want and actually kind of build your own custom flows. The second piece is like, take, there's like certain components in there that might not be directly related to the LLM app that would be nice to just like have people use, uh, an example is the PDF viewer, like the PDF viewer with like citations. I think we're just going to give that right. So, you know, you could be using any library you want, but then you can just, you know, just drop in a PDF viewer. Right.…

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