Builder for SearcherOptions
.
Builder()
|
abstract SearcherOptions |
build()
|
abstract SearcherOptions.Builder | |
abstract SearcherOptions.Builder |
setL2Normalize(boolean l2Normalize)
Sets whether to normalize the embedding feature vector with L2 norm.
|
abstract SearcherOptions.Builder |
setMaxResults(int maxResults)
Sets the maximum number of nearest neighbor results to return.
|
abstract SearcherOptions.Builder |
setQuantize(boolean quantize)
Sets whether the embedding should be quantized to bytes via scalar quantization.
|
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 |
---|
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 |
---|
Sets the maximum number of nearest neighbor results to return. Defaults to 5
maxResults |
---|
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 |
---|