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by dmitrykan
1717 days ago
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Such a great question! I think vector search is designed to solve a specific task, like finding K nearest neighbors for your given vector embedding. If you have a few hundred thousand, a million, it might work fine with a linear scan. Again, this depends on your use case. But if you have tens of millions, hundreds of millions or even a billion of vectors, you will need something more robust to store, index and search over this data. An example in NLP world is BERT-like NNs, that allow you to embed your text into a dense vector representation. |
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I might say transformer-based NNs instead. The problem with cross-attentional models like BERT is that they won't scale to large datasets. They are more often used in reranking results within an IR pipeline. However, even for that use they require distillation.