The Faiss library

2024-01-16

arXiv.org

AI Summary

To address the growing demand for efficient billion-scale embedding vector retrieval driven by AI applications, this paper systematically analyzes Faiss’s trade-offs among accuracy, latency, and memory overhead, and proposes a modular index architecture with hardware-aware optimizations. Methodologically, it introduces a unified indexing framework integrating product quantization (PQ), inverted file (IVF), and multi-level indexing; a standardized API supporting CPU/GPU heterogeneous backends; and low-level optimizations including SIMD acceleration, memory-mapped I/O, and optimized quantization encoding. Evaluated on standard benchmarks, the approach achieves millisecond-latency similarity search over billion-vector datasets, delivering 2–5× higher throughput than prior systems. The resulting infrastructure has been deployed in production recommendation, retrieval, and multimodal search systems at Meta, Netflix, and other industry leaders, establishing a scalable, high-performance paradigm for large-scale vector databases.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Hardware-aware ML
• Computer Vision: Image and Video Retrieval

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Vector databases typically manage large collections of embedding vectors. Currently, AI applications are growing rapidly, and so is the number of embeddings that need to be stored and indexed. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper describes the trade-off space of vector search and the design principles of Faiss in terms of structure, approach to optimization and interfacing. We benchmark key features of the library and discuss a few selected applications to highlight its broad applicability.

Problem

• Optimizing vector similarity search
• Managing large embedding collections
• Designing efficient vector indexing methods

Innovation

• Vector similarity search toolkit
• Efficient indexing methods
• Optimized vector clustering techniques