naveedjanmo

Ayo this looks sick

This is an interesting idea. I'll have a think about a way to start measuring it. In Unriddle, any responses given that aren't drawn from the document are prefaced with a message to that effect. The bot usually says something like "I appreciate your curiosity about [query], but as an AI assistant, my primary focus is to provide advice on [document description]."

Yeah I think you're right - the 32k context window for GPT-4 (not available for everyone yet) is already enough for research papers. I'm using a library called Langchain, there's also LlamaIndex.

I was playing around with Dune inside of Unriddle yesterday and it seemed to work pretty well for describing the overarching narrative. https://app.unriddle.ai/bot/55fee905-1174-4b33-8e67-5dfe8301...

But I expect this kind of querying will be much better as the context windows for LLMs increase

*API calls to GPT-4

It's just the GPT-4 API - the chunks are sent as part of a prompt. In that case it won't use data from all chunks but it will try to find any chunks that provide descriptions of the document. I've found with research papers, for example, it fetches parts of the introduction and abstract.

Right now it's just one pdf per bot but yeah you could hack it by merging the pdfs and then generating a new bot. Interesting suggestion - did you notice a particular hallucination? What kind of docs would be high vs. low temperature?

I'm just exploring whether or not it's useful. If people continue using it and/or it evolves into something more useful, I'll likely charge a monthly fee for access to certain features (e.g. longer pdfs, merging multiple pdfs into one bot, having multiple bots). The plan is to keep it partially free though

There's an OpenAI API for vectorizing which I'm accessing through the Langchain library :)

Vectorisation is done via OpenAI's embedding API. And the chunking/querying is happens through the Langchain library. But there are a few different ways of doing it - another good library is LLamaIndex.

Hey! I'm the developer of Unriddle - it works using text embeddings. The document is split into small chunks and each chunk is assigned a numerical representation, or "vector", of its semantic meaning and relation to the other chunks. When a user prompts this too is assigned a vector and then compared to the rest of the chunks. The similar chunks are then fed into GPT-4 along with the query, ensuring the total number of words doesn't exceed the context window limit.

Thanks for the feedback. I get your point but with Unriddle users curate information for the index that sits on top of GPT-4. It's just one doc for now but in the future it will be multiple - this curation of info sources is where the "own" part comes in.

Thanks! It's free to use for now and will always be at least partially free :)

Yeah it's only pdfs for now. What other file types did you want to try? Max file size is 600 pages/50 mbs

Oh interesting, I hadn't come across this one. Thanks for sharing

The document is first broken up into chunks. When a user prompts, Unriddle finds the most relevant chunks and then feeds them into GPT-3.

Hey man! Thanks for the comments. You're right - adding a feedback button is probably the way to go. Would you be up for having a quick 15/20 min call to share your thoughts? I'm @naveedjanmo on Twitter if you prefer to talk there :)

Which browser did you have in mind? It only works in Chrome and Brave for now, but I'm planning to get a web app running soon :)

Thanks man. I'm using OpenAI's text davinci 003

Turn any article into a few sentences with one click. Squish uses a machine-learning model to generate a shorter, more concise version of a body of text while still maintaining its key points and main ideas.