> Facts are the meaning pulled out of each episode, stored as subject-predicate-object records with a plain summary and timestamps for when the fact was introduced and when it was invalidated (subject=person, predicate=works_at, object=company). Facts form a graph with typed edges between them: X is in tension with Y, A is derived from B, J supersedes K.

I've always thought that knowledge graphs/expert systems, and even the broader concept of entity-attribute-value storage, got an unfairly bad reputation because of the 1970s/1980s "AI Winter."

And I think that perhaps this reputation is why so much of the oxygen in the RAG space has been consumed by the notion that "RAG = retrieval of fragments by vector similarity."

The difference now from decades ago, of course, is that now LLMs can do both the job of maintaining that graph at scale, and being able to agentically run successive queries to explore for best practices in any situation! And these have reached the scalability where any small business can build and use their own expert system.

I really want to see this approach win, because I think there's such an opportunity to explore even more data structures and approaches from the past and how their impact can be reimagined. If LLMs do indeed approach AGI, it will be in large part due to the ability to use tools (there's some evolutionary irony there, too) - and we should be trying every kind of underlying storage for those tools that we can, standing on the shoulders of giants.

(And curious what database you use for the knowledge graph - those are also a place where we stand on the shoulders of giants!)