[ot][spam][crazy][corporate[ makingeasy useful tools/behabior/agi?

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

here are part of the relevance notes from the 3rd. leaving these open
on a terminal was likely one of the things that helped me rebuild the
ability to think of it.

> i’d like to describe the baby computer relevance pattern we engaged recently.
> 1. information is converted to embeddings and stored in a vector cloud.
> 2. when a task is performed, the task is also converted to embeddings, and nearby points
>    retrieved from the vector cloud
> 3. each retrieved information is paired with the task, and a tiny summary is made of what is relevant to the task.
> 4. the tiny summaries are collected together with the task, and it is performed in context with them.

something i am adding today is that if there is a feedback loop around
3 then it seems much more reasonable to make the system consistent and
flexible and correct.

after i made the above vectordb notes i learned that langchain also
has many non-vectordb approaches that basically involve manually
processing each chunk.

one could describe a vectordb as a heuristic for what regions to
consider, analogous to a heuristic that formed tiny summaries of the
regions for purpose of considering them, and then selected from among
those summaries, with optional hierarchy.

something else that it turns out langchain has is a pluggable and
composable interface for memory, so you can add memory strategies in
and transparently treat short context models as if they are long
context, or as if they have access to external data.

it all of course takes dev work. [it seems a valuable concept for
things that effectively accelerate such too.]