Faiss
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Overview
HEALTH SCORE
Healthy
82out of 100
Solid maintainer coverage and security posture. Development cadence keeps the score from reaching Excellent.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Healthy(82/100)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
Median response time is 3 days.
Bus factor
19 active maintainers currently have merge rights.
Organization Diversity
Contributors span 739 organizations.
Project Achievements
Leaderboards
About
Faiss is a library for efficient similarity search and clustering of dense vectors, developed by Facebook Research. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also includes support for different similarity metrics and various optimization methods for fast and accurate vector search.
First commit
Software value
$6.8M
License(s)
MIT
Data sources
Git
GitHub