Perform fast similarity search and clustering on large vector datasets
Faiss is a library that helps you quickly find similar items in massive datasets by comparing vectors, which is essential for tasks like image retrieval or recommendation systems. It can handle data that doesn't even fit in your computer's memory, making it super efficient for big projects. Plus, it's optimized with GPU support and various algorithms, so you get speed and flexibility without sacrificing accuracy.
Quickly find nearest neighbors in large vector sets
Accelerate searches using GPU for better performance
Handle datasets larger than RAM with optimized structures
Choose methods for precision vs speed trade-offs
Easy use with Python wrappers for seamless development
Similarity search involves finding the closest vectors in a dataset to a given query vector, efficiently computing distances like Euclidean distance to identify nearest neighbors.
The recommended way is through Conda: use 'conda install -c pytorch faiss-cpu' for CPU version or 'conda install -c pytorch faiss-gpu' for GPU support.
Faiss implements various methods like inverted files, product quantization, and HNSW for efficient search, clustering, and trade-offs between speed and precision.
This tool is best for:
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Use Faiss on your favorite device - available across multiple platforms for flexibility.
Recent data shows that Faiss has 67.9K monthly visits (-0.5% decrease from the previous month), 42.0% bounce rate, and average 1.51 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 5 countries worldwide, led by United States contributing 14% of total traffic. SEO performance is shown by 5 tracked keywords, with "faiss" being the top-performing keyword with 44.9K monthly searches. See below for more info.
Monthly Visits
67.9K
Pages per Visit
1.51
Bounce Rate
42.0%
Average Time on Site
22s
| SEO Keyword | Volume | CPC |
|---|---|---|
| faiss | 44.9K | $1.85 |
| faiss vector database | 2.6K | $3.76 |
| faiss documentation | 490 | - |
| indexflatl2 | 680 | - |
| faiss kmens | 210 | - |
| Source | Traffic Share |
|---|---|
| Direct | 39% |
| Search | 47% |
| Social | 4% |
| Referrals | 9% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 14% |
 | 8% |
 | 6% |
 | 6% |
 | 5% |
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