A better way to do this might be to use the embedding API. That allows you to upload a text corpus and to then get vectors. You can then calculate the cosign similarity for a search string on those to get relevant results of clustered text from the uploaded corpus.
Would you mind explaining this and maybe dumbing it down? Sounds useful
Show HN: ChatGPT and Document Parser = Ghost
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Re: Show HN: ChatGPT and Document Parser = Ghost
#32A better way to do this might be to use the embedding API. That allows you to upload a text corpus and to then get vectors. You can then calculate the cosign similarity for a search string on those to get relevant results of clustered text from the uploaded corpus.
It's like, imagine there's a complex machine with large panels full of buttons and levers - and then, someone covered the panels with tapestry. Beautiful tapestry, showing artistic interpretations of things mundane and holy, trivialities of everyday life next to impossible dreams. And then, people were told the machine is to be operated by touching that tapestry, and that the artworks are the guide to understanding it and using it effectively. And then a whole religion formed around studying patterns in the tapestry. To me, prompt engineering is that religion.
There's an actual interface to the machine hidden under all the clever wordplay. A precise, formalized one. An interface that eats tokens and spits out probabilities. I just don't get why most talk - even seemingly specialist talk - about LLMs is ignoring it entirely, and focuses on the tapestry that's just obscuring the nature of the model, effectively making everything more difficult.
Re: Show HN: ChatGPT and Document Parser = Ghost
#33A better way to do this might be to use the embedding API. That allows you to upload a text corpus and to then get vectors. You can then calculate the cosign similarity for a search string on those to get relevant results of clustered text from the uploaded corpus.
I don't get why people bother with chat interface and textual prompts. The whole concept of "prompt engineering" sounds to me like a practical joke that got out of hand. It's like, imagine there's a complex machine with large panels full of buttons and levers - and then, someone covered the panels with tapestry. Beautiful tapestry, showing artistic interpretations of things mundane and holy, trivialities of everyday…
I was on a call this morning and heard someone refer to two of their team members as "Prompt Engineers" as if that were an actual role.
Re: Show HN: ChatGPT and Document Parser = Ghost
#34Projects like these (using embeddings) are great, but what I'm looking for is something that can ingest an entire book (let's say a fiction book) then answer questions about the entire content (and not just by effectively doing a text search over your input, but actually "understanding" the entire contents of the book); I presume such a thing is not possible with ChatGPT (without fine-tuning), correct?
Right now that’s not a use case supported out of the box by ChatGPT. It also seems to be one of the most important limitations of ChatGPT, and a lot of people/teams are looking for solutions.
Re: Show HN: ChatGPT and Document Parser = Ghost
#35Re: Show HN: ChatGPT and Document Parser = Ghost
#36Re: Show HN: ChatGPT and Document Parser = Ghost
#37A better way to do this might be to use the embedding API. That allows you to upload a text corpus and to then get vectors. You can then calculate the cosign similarity for a search string on those to get relevant results of clustered text from the uploaded corpus.
Would you mind explaining this and maybe dumbing it down? Sounds useful
Re: Show HN: ChatGPT and Document Parser = Ghost
#38It's byok. Keys are not persisted. You can choose chat-gpt-turbo or text-davinci.
Limit is 2.4M tokens per call, working to get higher too.
Re: Show HN: ChatGPT and Document Parser = Ghost
#39Earlier quoted context omitted.
Right now that’s not a use case supported out of the box by ChatGPT. It also seems to be one of the most important limitations of ChatGPT, and a lot of people/teams are looking for solutions.
I work in consulting and this is literally the use case that every single client wants right now - the ability to ingest a corpus of documents into ChatGPT or similar and then have it generate responses based on natural language questions. Right now most people are faking it by running the search using some other tool like Solr/ES and then taking the snippets that are returned and assembling them into a prompt that g…
Which option seems to you to be the best alternative? And where do you see the future of this?