MEMORY SYSTEM OVERHAUL. Alright for those following along, I previously had a system where I would store memory inside a JSON file. However, I have been researching how real agents store memory and they seem to do it very differently. In short, they store memory by embedding text into a vector. This process is done by turning a string into a vector with 800+ dimensions, like: <v1, v2, v3……. v800>. This way the model can find similar meaning setences, ideas, etc. using what it does best, numbers. It matches the vector to the next closest vectors to it when searching through memory. This makes finding things in memory wayyyyyyyyyyyy easier and faster because the model can both find stuff in memory that may not have any matching keywords but mean the same thing(like “I am broke” and “I have no money” would be plotted next to each other even though the main keywords aren’t shared between each other, this is a rough example though) and it can also do it in a way more efficient manner. This is called semantic searching(searching by meaning, not keywords). I also have an SQLite database, mostly used for project data. The model stores stuff like project id and summaries about projects in neat, clean rows. This helps the model find stuff better because it can now search by id, but it also makes it easier for me to see stuff :).