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.
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.
Why it matters
Three concrete levers for cutting retrieval cost without re-architecting: asymmetric query and document models, vector quantizationShrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.Full definition →, and Matryoshka dimension truncation, each with its tradeoff named.
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