[thomasahle]

> part of your llm prompt (usually your most recent question?) gets fed as a query for the embedding/vector database

How is it embedded? Using a separere embedding model, like Bert or something? Or do you use the LLM itself somehow? Also, how do you create content for the vector database keys themselves? Also just some arbitrary off the shelf embedding? Or do you train it as part of training the LLM?

[brucethemoose2]

Yeah its completely seperate. The LLM just gets some extra text in the prompt, that is all. The text you want to insert is "encoded" into the database which is not particularly compute expensive. You can read about one such implementation here: https://github.com/chroma-core/chroma

[CamperBob2]

One thing I don't understand is how feeding the entire conversation back as a prefix for every prompt doesn't waste the entire 4K-token context almost immediately. I'd swear that a given ChatGPT window is stateful, somehow, just for that reason alone... but everything I've read suggests that it's not.

[thomasahle]

Have you tried something like Memory Transformers https://arxiv.org/abs/2006.11527 where you move the k/v pairs that don't fit in the context window to a vector db? Seems like a more general approach, but I have tested then against each other.