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I don’t have the experiments to prove this, but from my experience it’s highly variable between embedding models.

Larger, more capable embedding models are better able to separate the different uses of a given word in the embedding space, smaller models are not.

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I'm using Voyage-3.5-lite at halfvec(2048), which with my limited research, seems to be one of the best embedding models. There's semi-sophisticated (breaking on paragraphs, sentences) ~300 token chunking.

When Claude is using our embed endpoint to embed arbitrary text as a search vector, it should work pretty well cross-domains. One can also use compositions of centroids (averages) of vectors in our database, as search vectors.

I was thinking about it a fair bit lately. We have all sorts of benchmarks that describe a lot of factors in detail, but all those are very abstract and yet, those do not seem to map clearly to well observed behaviors. I think we need to think of a different way to list those.

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