AHPQ.jl
AHPQ.jl is a Julia library providing training and inference for anisotropic hierarchical product quantization, compatible with ScaNN-style vector quantization and useful for building high-performance vector search pipelines.
About this tool
AHPQ.jl
Description
AHPQ.jl is a Julia library implementing the Anisotropic Hierarchical Vector Quantizer based on the Scalable Nearest Neighbours Algorithm (ScaNN). It follows the Google Research implementation (per the referenced paper and talk) and provides a configurable Max Inner Product Search (MIPS) system, written entirely in Julia with no non-Julia dependencies.
- Category: SDKs & Libraries
- Technology: Julia
- Use cases: Vector search, product quantization, MIPS-oriented indexing
- Source: https://github.com/AxelvL/AHPQ.jl
Features
-
Anisotropic Hierarchical Vector Quantizer
- Implements anisotropic hierarchical product/vector quantization.
- Trains a vector quantization tree (VQ-tree) with an anisotropic loss function.
- Optimized for Max Inner Product Search (MIPS) rather than standard L2 distance.
-
ScaNN-based Algorithm
- Based on Google Research’s Scalable Nearest Neighbours Algorithm (ScaNN).
- Follows the approach described in the associated research paper and SlidesLive presentation.
-
IVFADC-style Architecture
- Can be viewed as an IVFADC setup (inverted file with asymmetric distance computation).
- Uses vector quantization for coarse partitioning and refined distance evaluation.
-
Julia-native Implementation
- Written 100% in Julia.
- No non-Julian dependencies, easing installation and integration into Julia projects.
-
Configurable MIPS System
- Provides a highly configurable Max Inner Product Search pipeline.
- Designed for building high-performance vector search systems using product quantization.
(The repository content snippet does not expose further API-level or configuration details.)
Pricing
- Not applicable (open-source library; no pricing information provided in the available content).
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