Builder for SearcherOptions.
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Builder()
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| abstract SearcherOptions |
build()
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| abstract SearcherOptions.Builder | |
| abstract SearcherOptions.Builder |
setL2Normalize(boolean l2Normalize)
Sets whether to normalize the embedding feature vector with L2 norm.
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| abstract SearcherOptions.Builder |
setMaxResults(int maxResults)
Sets the maximum number of nearest neighbor results to return.
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| abstract SearcherOptions.Builder |
setQuantize(boolean quantize)
Sets whether the embedding should be quantized to bytes via scalar quantization.
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Sets the index file to search into.
Required if the model does not come with an index file inside. Otherwise, it can be ignore
by setting to null.
| indexFile |
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Sets whether to normalize the embedding feature vector with L2 norm. Defaults to false.
Use this option only if the model does not already contain a native L2_NORMALIZATION TFLite Op. In most cases, this is already the case and L2 norm is thus achieved through TFLite inference.
| l2Normalize |
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Sets the maximum number of nearest neighbor results to return. Defaults to 5
| maxResults |
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Sets whether the embedding should be quantized to bytes via scalar quantization. Defaults to false.
Embeddings are implicitly assumed to be unit-norm and therefore any dimension is
guaranteed to have a value in [-1.0, 1.0]. Use the l2_normalize option if this is not
the case.
| quantize |
|---|