Enables customers to find the products they're looking for more quickly, leading to higher satisfaction rates and increased sales.
High Precision and Recall. Outperforms other embedding models by a wide margin.
Really Fast, no GPU needed! Up to 180 embeddings per second on a single CPU core.
Flexible experience.Designed for Mobile, Tablet, and Desktop result pages.
Deploy in your stack with zero lock-in. Build your store on your terms, not someone else's expensive API.
Easily plug it in to your existing site-search with Mighty connectors!
Example for 10,000 query requests, using only one CPU core.
Add as many cores as needed for linear scalability!
Latency: Returns vectors in 8 milliseconds
Throughput: 136 queries per second
Summary: Total: 73.2846 secs Slowest: 0.0119 secs Fastest: 0.0071 secs Average: 0.0073 secs Requests/sec: 136.4544 Total data: 619100001 bytes Size/request: 61910 bytes Response time histogram: 0.007 [1] | 0.008 [9358]|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.008 [637] |■■■ 0.009 [2] | 0.009 [0] | 0.010 [0] | 0.010 [0] | 0.010 [0] | 0.011 [0] | 0.011 [1] | 0.012 [1] | Latency distribution: 10% in 0.0073 secs 25% in 0.0073 secs 50% in 0.0073 secs 75% in 0.0073 secs 90% in 0.0074 secs 95% in 0.0076 secs 99% in 0.0078 secs Details (average, fastest, slowest): DNS+dialup: 0.0000 secs, 0.0071 secs, 0.0119 secs DNS-lookup: 0.0000 secs, 0.0000 secs, 0.0005 secs req write: 0.0000 secs, 0.0000 secs, 0.0011 secs resp wait: 0.0073 secs, 0.0071 secs, 0.0109 secs resp read: 0.0000 secs, 0.0000 secs, 0.0007 secs Status code distribution: [200] 10000 responses
Model | ndcg@1 | ndcg@4 |
---|---|---|
max.io/ecommerce-encoder-v01 (Ours) | 0.77 | 0.73 |
sentence-transformers/all-MiniLM-L6-v2 | 0.68 | 0.65 |
*nDCG for Amazon-Science ESCI ranking benchmark.
Bundle it with a Mighty cross-encoder for even better results!