Module quantization 

Scalar Quantization (SQ8) and Binary Quantization for memory-efficient vector storage.

This module implements quantization strategies to reduce memory usage:

Benefits

Metric	f32	SQ8	Binary
RAM/vector (768d)	3 KB	770 bytes	96 bytes
Cache efficiency	Baseline	~4x better	~32x better
Recall loss	0%	~0.5-1%	~5-10%

Structs

BinaryQuantizedVector

A binary quantized vector using 1-bit per dimension.

PQCodebook

Per-subspace centroid tables learned with k-means.

PQVector

Compressed representation of a vector: one centroid id per subspace.

ProductQuantizer

Product quantizer model and helpers for train/encode/decode.

QuantizedVector

A quantized vector using 8-bit scalar quantization.

Enums

StorageMode

Storage mode for vectors.

Functions

cosine_similarity_quantized

Computes approximate cosine similarity between a query (f32) and quantized vector.

cosine_similarity_quantized_simd

SIMD-optimized cosine similarity between f32 query and SQ8 vector.

distance_pq

Asymmetric distance computation (ADC): query is f32, candidate is PQ-coded.

dot_product_quantized

Computes the approximate dot product between a query vector (f32) and a quantized vector.

dot_product_quantized_simd

SIMD-optimized dot product between f32 query and SQ8 quantized vector.

euclidean_squared_quantized

Computes the approximate squared Euclidean distance between a query (f32) and quantized vector.

euclidean_squared_quantized_simd

SIMD-optimized squared Euclidean distance between f32 query and SQ8 vector.