- Terminology (A to D)
- AI Capability Control
- Asset Performance
- Bayes Theorem
- Big Data
- Chatbot: A Beginner’s Guide
- Computational Thinking
- Computer Vision
- Confusion Matrix
- Convolutional Neural Networks
- Data Fabric
- Data Storytelling
- Data Science
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- Deep Reinforcement Learning
- Diffusion Models
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- Terminology (E to K)
- Edge AI
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- Generative AI
- Generative Adversarial Network
- Generative vs. Discriminative
- Gradient Boosting
- Gradient Descent
- Few-Shot Learning
- Image Classification
- IT Operations (ITOps)
- Incident Automation
- Influence Engineering
- K-Means Clustering
- K-Nearest Neighbors
- Terminology (L to Q)
- Terminology (R to Z)
Table Of Contents
Modern data search is a complex domain. Vector similarity search, or VSS, represents data with contextual depth and returns more relevant information to the consumers in response to a search query. Let’s take a simple example.
Search queries like “data science” and “science fiction” refer to different types of content despite both having a common word (“science”). A traditional search technique would match common phrases to return relevant results, which would be inaccurate in this case. Vector similarity search would consider the actual search intent and meaning of these search queries to return a more accurate response.
This article will discuss various aspects of vector similarity search, such as its components, challenges, benefits, and use cases. Let’s begin.
What is Vector Similarity Search (VSS)?
Vector similarity search finds and retrieves contextually similar information from large collections of structured or unstructured data by transforming it into numerical representations known as vectors or embeddings.
VSS can manage a variety of data formats, including numerical, categorical, textual, image, and video. It converts each object in a data corpus to a high-dimensional vector representation corresponding to its relevant format (discussed in the next section).
Most commonly, VSS locates comparable objects, such as similar phrases or paragraphs, or finds related images in vast image retrieval systems. Big consumer companies like Amazon, eBay, and Spotify use this technology to improve search results for millions of users, i.e., serve relevant content that users would most likely want to buy, watch, or listen to.
Three Main Components of Vector Similarity Search
Before we understand how vector similarity search works, let’s look at its major components. Primarily, there are three essential components for implementing an effective VSS methodology:
- Vector embeddings: Embeddings represent different data types in a mathematical format, i.e., an ordered array or set of numbers. They identify patterns in the data using mathematical calculations.
- Distance or similarity metrics: These are mathematical functions that calculate how similar or closely related two vectors are.
- Search algorithms: Algorithms help find similar vectors to a given search query. For instance, K-Nearest Neighbors or KNN algorithm is frequently used in VSS-enabled search systems to determine K vectors in a dataset that are most similar to a given input query.
Now, let’s discuss how these components work in a search system.
How Vector Similarity Search Works?
The first step in implementing vector similarity search is representing or describing objects in the data corpus as vector embeddings. It uses different vector embedding methods, such as GloVe, Word2vec, and BERT, to map objects to the vector space.
For each data format, like text, audio, and video, VSS builds different embedding models, but the end result of this process is a numerical array representation.
The next step is to create an index that can arrange similar objects together using these numerical representations. An algorithm like KNN serves as the foundation for implementing search similarity. However, to index similar terms, search systems use modern approaches, such as Locality Sensitive Hashing (LSH) and Approximate Nearest Neighbor (ANNOY).
Also, VSS algorithms calculate a similarity or distance measure, such as Euclidean distance, cosine similarity, or Jaccard similarity, to compare all vector representations in the data collection and return similar content in response to a user query.
Major Challenges & Benefits of Vector Similarity Search
Overall, the aim is to find common characteristics among data objects. However, this process presents several potential challenges.
Main Challenges of Implementing VSS
- Different vector embedding techniques and similarity measures present different outcomes. Choosing the appropriate configurations for similarity search systems is the main challenge.
- For large datasets, VSS is computationally costly and needs high-performance GPUs to create large-scale indexes.
- Vectors with too many dimensions may not accurately represent the data's authentic structure and connections. Hence, the vector embedding process must be lossless, which is a challenge.
Currently, the VSS technology is under continuous development and improvement. However, it can still provide many benefits for a company or product’s search experience.
Benefits of VSS
- VSS allows search systems to locate similar objects incredibly fast on varied data types.
- VSS ensures efficient memory management since it converts all data objects into numerical embeddings that machines can easily process.
- VSS can classify objects on new search queries that the system may not have encountered from the consumers.
- VSS is an excellent method for dealing with poor and incomplete data because it can find contextually similar objects even if they aren't a perfect match.
- Most importantly, it can detect and cluster related objects at scale (variable data volumes).
Major Business Use Cases of Vector Similarity Search
In commercial business, VSS technology can revolutionize a wide range of industries and applications. Some of these use cases include:
- Questions answering: The vector similarity search can locate related questions in Q&A forums that are nearly identical, allowing for more precise and pertinent responses for end users.
- Semantic web search: Vector similarity search can locate related documents or web pages depending on the “closeness” of their vector representations. It aims to increase the relevancy of web search results.
- Product recommendations: Vector similarity search can make personalized product recommendations based on the consumer's browsing or search history.
- Better healthcare delivery: Healthcare researchers and practitioners utilize vector similarity search to optimize clinical trials by analyzing vector representations of relevant medical research.
Today, it is no longer viable to manage, analyze, and search data using conventional SQL-based techniques. Internet consumers ask complex queries on the web – seemingly simple for humans but incredibly complex for machines (search engines) to interpret. It is a long-standing challenge for machines to decipher different forms of data in machine-understandable format.
Vector similarity search makes it possible for search systems to better understand the context of commercial information.
Want to read more insightful AI-related content? Visit unite.ai.
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