Friday, February 7, 2025

Improve Similarity Search With Intel Scalable Vector Search

Overview

At the heart of the AI revolution is Vector search. It gives apps access to unstructured material that is semantically important. A performance package for billion-scale similarity search, Scalable Vector Search (SVS) provides dimensionality reduction, vector compression, and high-speed computing optimizations.

By reducing the quanta of fetched data, the new vector compression and dimensionality reduction algorithms can further speed up computations by relieving the strain on memory. Continue reading to find out how SVS sets the bar for memory footprint and search performance. This series’ upcoming blogs will go into greater detail about RAG systems, dimensionality reduction, and vector compression.

High-dimensional vectors have emerged as the standard data representation for unstructured data in recent years, including computer code, photos, music, video, text, and genomics. These vectors often referred to as embeddings are created in such a way that, based on a similarity function, semantically related vectors are near to one another.

Similarity search
Image Credit To Intel

Vector search, often called similarity search, is a common problem that involves finding the vectors that are most similar to a query out of possibly billions of vectors. It is applicable to an expanding number of applications, including ad matching, recommender systems, question answering, image production, and natural language processing.

Vector Search in a Nutshell

The prevalence of large datasets with thousands of dimensions is rising. Modern large language embedding models, for instance, produce vectors with dimensions ranging from 768 to 1536 (and more recent models even reach 4096). Finding the nearest neighbors by a linear scan of a billion high-dimensional vectors also known as an accurate nearest neighbor search becomes too sluggish to satisfy application needs.

Neighbor search
Image Credit To Intel

If it can tolerate a slight inaccuracy in the nearest neighbors that are provided (i.e., some vectors may be obtained wrongly), it can avoid doing a full scan. This approach, known as approximate nearest neighbor search, provides two solutions to this dilemma:

  • Data structures called vector indices are used to arrange the vectors such that a search only retrieves a subset of the complete set, minimizing memory visits.
  • Data encoding formats known as vector representations compress each vector to increase retrieval speed, reduce memory usage, and maintain computational kernel efficiency.

Vector Representations similarity search

Vector search, often called similarity search, is a common problem that involves finding the vectors that are most similar to a query out of possibly billions of vectors. It is applicable to an expanding number of applications, including ad matching, recommender systems, question answering, image production, and natural language processing.

Similarity search
Image Credit To Intel
  • Speed of similarity computation: It don’t want to unduly slow down the search, thus similarity calculations must stay quick and easy.
  • The amount of memory these are preserving is indicated by the compression rate. In the most basic case, it obtain a compression rate of 2 by compressing a vector’s elements from single-precision (FP32) to half-precision (FP16) floating-point values.
  • Search accuracy: In the majority of contemporary use cases, a high degree of precision is necessary. While maintaining the necessary target accuracy, it wish to conserve memory and memory bandwidth. One unresolved issue in search is achieving high compression rates in this high-accuracy environment.
  • Training complexity: The model should be relatively quick to train if the compression technique is data-driven (e.g., machine learning). It could take too long to train a deep neural network to compress vectors.
  • Mutability: The compression technique must be resistant to changes in the vector collection’s distribution. If a shift necessitates (partially) retraining a model, this must happen fast to allow index mutability.
  • Encoding speed: The preferred compression technique must be used to encode any newly added vectors to the database. Index mutability (see the preceding section) will be jeopardized if such encoding is too computationally demanding. For instance, despite the fact that other researchers have looked into using deep neural networks for vector compression, deployments are still constrained by the models’ lengthy inference runtime.
  • Distribution of queries: It’s important to maintain search accuracy, especially when query vectors differ from database vectors in certain ways.

Scalar quantization, vector quantization, and dimensionality reduction are the three types of modification that can be used to convert the majority of vectors into representations. These categories are increasingly combined in certain solutions to produce compounded benefits.

Intel Scalable Vector Search (SVS)

As discussed in the preceding sections, many billion-scale similarity search methods lack the high-performance computing block-and-tackling required to extract adequate memory savings and vectorized distance computation efficiency. To overcome these constraints, it have created Intel Scalable Vector Search (SVS), which makes use of innovative vector compression methods and highly optimized vector index implementations. Vector similarity search is offered by SVS:

  • On billions of high-dimensional vectors
  • Superior accuracy
  • Cutting-edge speed
  • While consuming less memory than its substitutes

This makes it possible for developers of frameworks and applications to use similarity search to maximize performance on Intel Xeon CPUs (2nd generation and newer). SVS provides a straightforward, feature-rich Python API that works with the majority of common libraries. To make it easier to integrate SVS into applications that require high performance, it is also built in C++. See how SVS works by watching the demo below.

Drakshi
Drakshi
Since June 2023, Drakshi has been writing articles of Artificial Intelligence for govindhtech. She was a postgraduate in business administration. She was an enthusiast of Artificial Intelligence.
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