Three levers for cutting retrieval cost in production
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Building Gen AI prototypes is easy. Scaling them to production is hard. Our guide shows you how to optimize retrieval for accuracy, speed, AND cost. Covers asymmetric retrieval using Voyage 4, vector quantization, and Matryoshka Learning. Link below 👇
⚖️ Asymmetric retrieval: Use different embedding models for queries and documents. Voyage 4's shared embedding space lets you embed queries with voyage-4-lite (fast & cheap) while searching documents embedded with voyage-4-large (high quality). Perfect for high-volume applications where query costs add up quickly.
🗜️ Vector quantization: Compress embeddings from high-precision (float32) to low-precision (int8, binary) formats. Massively reduces memory footprint and accelerates search with minimal accuracy loss.
🪆 Matryoshka Learning: Train embeddings such that early dimensions capture the most important info information. This allows you to truncate embeddings to reduce storage costs and speed up search while preserving semantic meaning. Voyage 4 supports 2048, 1024, 512, and 256 dimensions from a single model.
Context
Search can take less storage by containing fewer numbers or by storing each number with less precision. Voyage AI's retrieval guide thread describes both approaches for Voyage 4.
Matryoshka training puts useful information into the early dimensions of an embedding, allowing a shorter representation to remain useful for search. instead changes the numerical format, such as moving from 32-bit floating-point values to 8-bit integers. These address different parts of the storage requirement: the length of each vector and the space occupied by each entry. Neither requires keeping the largest, highest-precision representation as the only deployment option.
- embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
- quantization — Shrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.
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