A common situation in IRL problems with long time horizons is the need to perform multiple very different subtasks. For example, imagine a model trained to remember a poem and then spell it out in blocks in a game of minecraft. The data for the poem itself and the appropriate minecraft functions probably have very different embeddings, but in practice it would probably be useful to ensure the memories for how to use minecraft functions are queried when that poem is queried.

It seems like just querying a RAG DB for the vectors with the highest cosine similarity won’t be super useful for this task. A query for poems will just find poem-like data. But we don’t just want to find things with similar embeddings to poems, we want to find data that is useful. Has there been any research into this time-series / associative type of RAG?

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