Open nelsonic opened 1 year ago
This sounds like an awesome idea. However, I have an "implementation" question.
Assuming we're using privateGPT
, we'd need a source of data for suggestions. Would we create our own embeddings? Since we can't create a simple Langchain
application that communicates with ChatGPT
to get suggestions (we would use Guidance
to get tag suggestions), I'm wondering how we're going to do this.
Perhaps I'm missing an important detail? 🤔
There are many potential (good) usecases for
ML
that can improve the lives/workflows of the people using ourApp
. However we want to avoid going down too much of an "AI" rabbit hole before we have a clearly defined feature. Butttt .... I think it's fair to say that we all want to try somethingLLM
-related ... 💭So the idea with this issue is just to open the discussion. (please share your thoughts 🙏)
What are potential use-cases where we can feed data e.g.
text
,links
orimages
into a model and get useful data out that will add value to the people using ourApp
?I've put a
priority-2
on this because noperson
using theMVP/App
has requested auto-suggestion/tagging. (yet!) We think it could be a compelling use-case to auto-categorise anitem
based on the content. and suggesttags
and even summarise longer bits ofpublic
text e.g. someone saves a link to a blog post. It might be useful to summarise that content and pull keywords out astags
.We definitely want to avoid "Tech/solutions searching for a problem" https://en.wiktionary.org/wiki/solution_in_search_of_a_problem 🙄
Requirements
people
using theMVP/App
i.e. no sending data toOpenAI
(MSFT
... 🙄)€20/month
or if we need to self-host it our own hardware we can easily do this e.g: using ourM1 Mac Mini
: https://gist.github.com/cedrickchee/e8d4cb0c4b1df6cc47ce8b18457ebde0privateGPT
looks like a good starting point: https://github.com/imartinez/privateGPT If you have found an easier/simpler way of running an LLM on our own hardware/infra (e.g. Fly.io) please share 🙏