So you draw some distillates out of the conversation, do a vector embedding of each, and insert the pairs into pg. Then retrieval is distill -> find top N nearby vectors. Indexing, etc. Seems like a smart way to organize long term memory.

Also I can't help but notice that this solution combines technologies whose origins literally span 6 decades: relational databases (80's), vector embeddings (00's), and LLMs (20's).

What kinds of prompts do you use to get the distillates, and what shape are they? I guess I've been assuming you extract more than one distillation out of a conversation per memory access, do you?