For example you can have a table of "knowledge" as I described earlier, but you can just add easily have a table of the conversation history, or have both.

In fact it's quite popular afaik to store the conversation this way because then if you query on a topic you've queried before, even if the conversation history has gone behind the size of the context, it can still retrieve that history. So yes, what you describe is a good idea/would work/is being done.

It really all comes down to the non model logic/regular programming of how your vector db is queried and how you mix those query results in with the user's query to the LLM.

For example you could embed their query as I described, then search the conversation history + general information storage in the vector db and mix the results. You can even feed it back into itself in a multi step process a la "agents" where your "thought process" takes the user query and breaks it down further by querying the LLM with a different prompt; instead of "you are a helpful assistant" it can be "you have x categories of information in the database, given query {query} specify what data to be extracted for further processing" obv that's a fake general idea prompt but I hope you understand.

Well there's technically no model training involved here but I guess you consider the corpus of conversation data a kind of training, and yeah that would be RLHF based which LLMs learn pretty heavily on afaik (I've not fine tuned my own yet).

You can fine tune models to be better at certain things or respond in certain ways, this is usually done via a kind of reinforcement learning (with human feedback...idk why it's called this, any human feedback is surely just supervised learning right?) this is useful for example, to take a model trained on all kinds of text from everywhere, then fine tune it on text from scifi novels, to make it particularly good at writing scifi fiction.

A fine tune I would say is more the "personality" of the underlying LLM. Saying this, you can ask an LLM to play a character, but the underlying "personality" of the LLM is still manufacturing said character.

Vector databases are more for knowledge store, as if your LLM personality had a table full off open books in front of them; world atlases, a notebook of the conversation you've been having, etc.

Eg, personality: LLM fine tune on all David Attenborough narration = personality like a biologist/natural historian

Knowledge base = chunks of text from scientific papers on chemistry + chunks of the current conversation

Which with some clever vector db queries/feeding back into model = bot that talks like Attenboroughish but knows about chemistry.

Tbf the feedback model it's better to use something strict, ie instruct based model, bc your internal thought steps are heavily goal orientated, all of the personality can be added with the final step using your fine tune.