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TernaryCodec

Struct TernaryCodec 

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#[non_exhaustive]
pub struct TernaryCodec { pub dim: usize, }
Expand description

BitNet b1.58 ternary codec.

Stateless aside from dim. Per-vector scaling (absmean) is computed in VectorCodec::encode and stored in the header’s global_scale field.

Fields (Non-exhaustive)§

This struct is marked as non-exhaustive
Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.
§dim: usize

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impl TernaryCodec

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pub fn new(dim: usize) -> Self

Trait Implementations§

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impl VectorCodec for TernaryCodec

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type Quantized = TernaryQuantized

The packed quantized form. Must be convertible to a UnifiedQuantizedVector reference via AsRef.
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type Query = TernaryQuery

The prepared query form (codec-specific).
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fn encode(&self, v: &[f32]) -> TernaryQuantized

Encode a single FP32 vector into the codec’s packed form.
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fn prepare_query(&self, q: &[f32]) -> TernaryQuery

Prepare a query for distance computations against this codec. May rotate, normalize, build a LUT, etc.
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fn fast_symmetric_distance( &self, q: &Self::Quantized, v: &Self::Quantized, ) -> f32

Fast symmetric distance — bitwise / heuristic. Used during HNSW upper-layer routing. Both arguments are quantized; no scalar correction; no outlier resolution. Hot path; must be inline-friendly.
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fn exact_asymmetric_distance( &self, q: &TernaryQuery, v: &Self::Quantized, ) -> f32

Exact asymmetric distance — full ADC with scalar correction and sparse outlier resolution. Used at base-layer rerank only. Slower but high-fidelity.
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fn adc_lut(&self, _q: &Self::Query) -> Option<AdcLut>

Optional: precompute ADC lookup table for codecs that use one (PQ, IVF-PQ, TurboQuant). Returns None for codecs that don’t (RaBitQ, BBQ, ternary, binary).
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fn train(&mut self, samples: &[&[f32]]) -> Result<(), CodecError>

Optional: fit the codec’s learned parameters on a set of training vectors. Read more

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