# FALCONN-LIB/FALCONN

FAst Lookups of Cosine and Other Nearest Neighbors (based on fast locality-sensitive hashing)

Repository: https://github.com/FALCONN-LIB/FALCONN
Canonical: https://ross.abutalabs.com/products/falconn
Language: C
License: MIT
License Family: permissive
Topics: nearest-neighbor-search, lsh, cosine-similarity, locality-sensitive-hashing, sketches, fast-lookups, falconn
Last push: 2024-06-01T14:32:16+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3927, "days_push": 823, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1159, forks 194 (observed 2026-08-28T04:03:48.641465+00:00)

## What it is
FALCONN is a C++ library (with a Python wrapper) implementing locality-sensitive hashing (LSH) data structures for fast nearest neighbor search in high-dimensional spaces, primarily under cosine similarity. It is header-only, depends on Eigen and FFHT, and is optimized for dense and sparse data with multi-probe LSH.

## Use cases
- find nearest neighbors by cosine similarity in high-dimensional vectors
- fast similarity search over a million embedding vectors
- maximum inner product search on a memory budget
- nearest neighbor search under Euclidean distance
- build an LSH index for sparse feature vectors
- fast approximate nearest neighbor lookup in milliseconds per query

## When to choose
- you need LSH-based approximate nearest neighbor search with a restrictive RAM budget
- you work in C++ or Python with dense or sparse high-dimensional data
- you want a lightweight header-only library with minimal dependencies

## When to avoid
- you need graph-based ANN methods like HNSW that often outperform LSH on large RAM budgets
- you need GPU acceleration or distributed indexing
- you need actively developed features beyond the current LSH families

## Facets
- artifact type: library
- maturity: maintenance
- function: search-engine, machine-learning
- domain: machine-learning, databases
- platform: cpp, python, cross-platform
- tags: locality-sensitive-hashing, nearest-neighbor-search, ann, cosine-similarity, header-only, high-dimensional-data, algorithms, search

## Member repositories
- FALCONN-LIB/FALCONN (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:48.641465+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:31:51.307109+00:00, confidence not recorded.
  - readme: https://github.com/FALCONN-LIB/FALCONN (fetched 2026-08-28T04:03:48.641465+00:00, sha 3451184860dd)
- Data as of 2026-08-30T08:39:29.467469+00:00.