Faiss is a powerful library for efficient similarity search and nearest neighbor retrieval in large-scale datasets. It is widely used in image and text processing, recommendation systems, and natural language processing.
Product | Market Share (%) |
---|---|
Faiss | 3.7% |
PostgreSQL | 16.8% |
Firebird SQL | 15.8% |
Other | 63.7% |
Users appreciate its speed, scalability, and ability to handle high-dimensional data effectively. Faiss also offers easy integration and extensive support for different programming languages.
Its valuable features include efficient search capabilities, support for large-scale datasets, various similarity measures, easy integration, and comprehensive documentation and community support.
1. Facebook 2. Airbnb 3. Pinterest 4. Twitter 5. Microsoft 6. Uber 7. LinkedIn 8. Netflix 9. Spotify 10. Adobe 11. eBay 12. Dropbox 13. Yelp 14. Salesforce 15. IBM 16. Intel 17. Nvidia 18. Qualcomm 19. Samsung 20. Sony 21. Tencent 22. Alibaba 23. Baidu 24. JD.com 25. Rakuten 26. Zillow 27. Booking.com 28. Expedia 29. TripAdvisor 30. Rakuten 31. Rakuten Viber 32. Rakuten Ichiba
Author info | Rating | Review Summary |
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Senior Software Engineer | 4.0 | I've used Faiss for a year in a retrieval-augmented generation project with Cohere and OpenAI; it’s fast, integrates well, and accurate, though limited by in-memory storage and lacks persistent storage or visualization of embeddings. |
Student at Otto-von-Guericke-Universitaet | 4.5 | I created vectors and embeddings using Hugging Face and used Faiss for query searches. Although I found Faiss valuable as a basic database, I wish I could customize algorithms to compare query mechanisms for better results. |
CEO & President at Ideyatech, Inc | 3.5 | We use Faiss for document tasks in augmented generation. It requires additional tools for smoother production integration and improvement in handling larger datasets. We previously evaluated Milvus and are currently exploring Chroma for a potential alternative. |