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int8 & Binary Embeddings for Vector Databases

1 post · Apr 2024

What this trail covers

Cohere's Embeddings API now supports int8 and binary embedding formats, enabling significant reductions in vector database storage and query latency. This allows for scaling vector databases to much larger datasets while maintaining performance, making advanced AI search and retrieval more accessible and cost-effective for enterprises.

All Cohere trails
  1. Cohere int8 & binary Embeddings - Scale Your Vector Database to Large DatasetsThis post introduces the technical implementation and benefits of int8 and binary embedding formats for Cohere's Embeddings API. It details how these formats reduce embedding size by up to 8x (int8) and 32x (binary) compared to float32, leading to substantial cost savings in vector database storage and faster query times. The post highlights the trade-offs, such as potential minor accuracy degradation with binary embeddings, and provides guidance on choosing the appropriate format based on use case requirements.