[8.x] Add support for bitwise inner-product in painless (#116082) #116285
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This adds bitwise inner product to painless. The idea here is: - For two bit arrays, which we determine to be a byte array whose dimensions match `dense_vector.dim/8`, we simply return bitwise `&` - For a stored bit array (remember, with `dense_vector.dim/8` bytes), sum up the provided byte or float array using the bit array as a mask. This is effectively supporting asynchronous quantization. A prime example of how this works is: https://github.com/cohere-ai/BinaryVectorDB Basically, you do your initial search against the binary space and then rerank with a differently quantized vector allowing for more information without additional storage space. closes: elastic#111232
benwtrent
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:Search Relevance/Vectors
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@elasticmachine update branch |
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Backports the following commits to 8.x: