And would you believe at this point you're 55% of the way along the journey?
Uh, it's been a while since I've thrown that stat out there, but congratulations.
That's really awesome.
Uh, so we have made progress with rag.
We're taking it slowly because it's about to get real as we get into vector databases.
But at this point, uh, not only do you have the sort of foundational understanding of why we're going
to be using vectors, but you can now use Lang chain to load in documents, to split them, to add in
metadata.
And hopefully you've played around with that and you've satisfied yourself about how it's working and
you're ready for the real deal.
We next time are going to take these chunks and convert them into vectors using OpenAI's OpenAI embeddings,
which was one of the encoding llms that we talked about.
We're then going to store the vectors in an open source vector data store called chroma, which is an
extremely popular open source vector database.
And it's terrific.
And we're going to enjoy putting our vectors in there, because we're then going to visualize them and
see them and get a sense of what does it even mean to have a vector in a database.
So lots to be done.
I will see you next time.