Benjamin Trent d33a03ce6b Add support for bitwise inner-product in painless (#116082) ... 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: https://github.com/elastic/elasticsearch/issues/111232		2024-11-06 09:22:04 +11:00
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vector-functions.asciidoc	Add support for bitwise inner-product in painless (#116082)	2024-11-06 09:22:04 +11:00