Preconditioning Vectors: Making Elasticsearch VectorDB BBQ Work for Every Vector
Elasticsearch has introduced preconditioning techniques to enhance the performance of its vector database, particularly when using Better Binary Quantization (BBQ). This method applies a random orthogonal rotation to vectors before quantization, improving recall significantly for various types of data. The article discusses the benefits of preconditioning and provides benchmarks demonstrating its effectiveness in increasing recall rates.
- ▪Elasticsearch offers a comprehensive search toolkit for developers, including vector search and REST APIs.
- ▪Preconditioning applies a linear transformation to vectors before quantization, redistributing variance evenly across dimensions.
- ▪Benchmarks show that preconditioning can improve recall rates by nearly 75% for certain datasets.
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From vector search to powerful REST APIs, Elasticsearch offers developers the most extensive search toolkit. Dive into our sample notebooks in the Elasticsearch Labs repo to try something new. You can also start your free trial or run Elasticsearch locally today.Elasticsearch as a vector database offers comprehensive quantization techniques like Better Binary Quantization (BBQ). BBQ and other similarly modern quantization techniques compress vectors down to as little as a single bit per dimension, reducing memory use while retaining impressively accurate distance approximation. For vectors generated from deep learning models, such as Cohere models, this works really well; however, for other kinds of vectors, such as image data or histogram features, recall can be impacted heavily.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Elasticsearch Labs.
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