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]."
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
Got it. Thank you
Currently is there any limit on the pdf sizes or number of pages to upload. For example, if I upload my entire book pdf (500pages), it will cost you a lot.
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.
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.
Oh so there is pre-processing to find the useful portions? What are you using for the pre-processing?
I feel that it's inevitable that OpenAI et al. will be able to handle large PDF documents eventually. But until then I'm sure there's a lot of value of in this kind of pre-processing/chunking.
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.
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.
Thanks a lot! Do you _have_ to do vectorization and querying with the same LLM? Can someone do vectorization with 1 and do querying with reevant chunks with another?
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.
The title is misleading; giving the wrong idea or impression. People who work in the field may understand what is actually going on, but I get the feeling that your target market is much broader than that, and I doubt that the majority of that market will interpret "Create your own GPT4 ..." to instead actually mean "Unriddle users curate information for the index that sits on top of a GPT-4 API that OpenAI owns and we use on your behalf" (or whatever actually goes on)
Can it ingest multiple PDFs in the same 'context', or would I have to assemble it all into one (under the 50mb limit)?
What is it using to
Can we (provoke you to) set the model temperature in the conversation to either minimize the hallucinating or increase the 'conjecture/BS/marketing-claims' factor?
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?
Yes. See this comment [0] Another HNer with API access tried just ingesting the paper without context and some instructions and model-temp=0 and got better results.
I've also found in my area that it'll happily hallucinate stuff -- after all, it has zero actual understanding, it just predicts the most likely filler in the given context.
Tamping that down and just getting it to cut out the BS/overconfidence response patterns and reply with "I know X and I don't know Y" would be incredibly useful.
When we get back an "IDK", we can probe in a different way, but falsely thinking that we know something when we are actually still ignorant is worse than just knowing we've not yet got an answer.
