The crazy thing is I fell asleep a few hours ago wondering how to feed the insurance terms and conditions to ChatGPT so that I could query them, and then I woke up and saw this.
All this AI progress opens up so many possibilities that it's almost anxiety-inducing
We are using a scraper for HTML and parsing docs in Node.js.
The tough part for us is normalizing all of the data and working with various file types and encodings.
You can try it out for free here.
Edit: I tried this one with an 800kb .txt and after digesting it, it got two answers wrong (but at least related to the text) and then started spitting out "I don't know". I asked "what is this document?" because I saw with my previous test that it can get blocked and be working with a 404 page ("this document is a page that says suspicious activity request denied") but this time it just said "I don't know."
We are slowly working through the issues reported in this thread. Thanks for the kind and constructive feedback!
First question: are there other tools that do this with ChatGPT? carrolldunham says that Bing can do it. But still?
Second question: what are your plans for the future? Could we integrate your tool into a website, with a pre-made list of documents and web pages, and our members, or customers, can use the tool to get answers? And if I look further, could we imagine a similar tool to answer questions received by email?
Third question: the tool works with French documents, do you intend to translate the interface?
I am actually interested in creating such a chat dedicated to one product as a example / hobby project -- any pointers appreciated on where I can start, and what already implemented opensource solutions I can leverage for quick results. Thanks!
So I uploaded a desert recipe and noticed there is no formatting to the answer text. It is one big paragraph.
Other than that, the idea is great, but I got more "I don't know" than anything.
Is this a company? Or just a cool thing you hacked on the weekend? I ask it because there are tons of companies trying to do the same. How are you planning to monetize or standout?
There's definitely an enterprise use-case for this.
1) Allow it to take a search term, do a web search and allow the user to select from those results.
2) Allow it to look at more than one document.
4) When a user question can't be answered, prompt the user to allow your system to web search and then include those documents.
5) Create a version that can be run locally for those of us with private data. You should charge a lot for that version (~$100+k if the customer provides the hardware, and $1m+ if you have to provide the hardware (blackbox)).
6) Detect research papers and read the citations. You may have to ask the user for a SSO key to get the citations from paywalled sites.
7) Abstract responses need to be made more concrete. See if you can train the model to provide an example or describe the purpose or intuition when it responds.
Here's the full text in reverse-chronological order:
> By what measure is it popular? >> I don't know.
> Is neverthrow a popular npm library? >> Yes, neverthrow is a popular npm library. It is a fully-tested and type-checked npm package that provides type-safe error handling in TypeScript. It is designed to help users encode failability into the typesystem and provide a friendly API that lets them deal with failable values in a safe way.
> How do I install the neverthrow package? >> To install the neverthrow package, you can use the npm package manager. In your terminal, run the command `npm install neverthrow` and the package will be installed.
> What is this blog post about? >> This blog post is about type-safe error handling in TypeScript, and how to encode failability into the typesystem using the Result type. It explains how to use the neverthrow package to create a Result type, and how to use the map and mapErr methods to handle success and failure cases.
> Enter a URL to read: >> https://gdelgado.ca/type-safe-error-handling-in-typescript