Near-optimal vector quantization from Google's ICLR 2026 paper — 95% recall, 5x compression, zero preprocessing, pure Python FAISS replacement
-
Updated
Mar 28, 2026 - Python
Near-optimal vector quantization from Google's ICLR 2026 paper — 95% recall, 5x compression, zero preprocessing, pure Python FAISS replacement
High-performance database management system
Hybrid B+ Tree and HNSW index for efficient k-NN search with scalar filtering using probabilistic optimization
Optimizing multi-attribute filtering for Approximate Nearest Neighbor (ANN) search using HNSW. This project integrates bitsets and Roaring Bitmaps into HNSWLib to accelerate query performance by reducing evaluation costs for large attribute datasets.
PostgreSQL TurboQuant Index for PGVector
Protein homology search using transformer-based embeddings and Approximate Nearest Neighbor methods for efficient biological similarity detection
Approximate Nearest Neighbor search implementation in C, including LSH, Hypercube, IVF, and Product Quantization algorithms
Learning-based Approximate Nearest Neighbor search using neural classifiers for partition prediction and efficient retrieval
Edit repository details Description Website Topics (separate with spaces) Include in the home page Releases Packages Deployments
Vector Databases: Use Cases, Algorithms and Key Features
Lightweight HNSW for experimenting with approximate nearest neighbors.
Transform-domain representation enabling 3–4× storage reduction with direct ANN search and novel multi-resolution signals. UK patent application under accelerated examination (Green Channel).
Add a description, image, and links to the approximate-nearest-neighbor topic page so that developers can more easily learn about it.
To associate your repository with the approximate-nearest-neighbor topic, visit your repo's landing page and select "manage topics."