not rude at all, it's a common sentiment. TopK gives you all the capabilities under one roof -- text, vectors, filters, embeddings, re-ranking, all behind a single API (so single SDK, single vendor, single bill, single observability plane, etc). This simplifies your integration (less code on your part) and gives you more flexibility to define custom scoring rules (elastic-style) but over text + vectors + any other pre-computed factors. Take a look at [this](https://docs.topk.io/concepts/unified-retrieval#custom-scori...) docs page which goes into detail if you are interested.
I'd be curious to learn about your setup though. Vespa comes with lexical search so what made you choose this combo? Was Elastic already present in your stack before adopting Vespa?
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disclaimer: co-founder of topk here :)
not rude at all, it's a common sentiment. TopK gives you all the capabilities under one roof -- text, vectors, filters, embeddings, re-ranking, all behind a single API (so single SDK, single vendor, single bill, single observability plane, etc). This simplifies your integration (less code on your part) and gives you more flexibility to define custom scoring rules (elastic-style) but over text + vectors + any other pre-computed factors. Take a look at [this](https://docs.topk.io/concepts/unified-retrieval#custom-scori...) docs page which goes into detail if you are interested.
I'd be curious to learn about your setup though. Vespa comes with lexical search so what made you choose this combo? Was Elastic already present in your stack before adopting Vespa?