Revolutionizing Search: Understanding the Power of Vector Search

The amount of data and information in data centers is increasing at an exponential rate. The amount of information online is so huge, that now the storage of this information isn’t the only concern for organizations. Along with the storage, organizations now also have to manage the art of accessing this Big Data in an efficient manner. To serve this purpose, Vector Search algorithms have been helping various institutions address the same. This article discusses how vector search has revolutionized the whole concept of accessing data on the web.

What is Vector Search?

To understand what is this whole talk of vector search about, we first need to have a look at what exactly is a vector. Vector is a mathematical term meaning a representation of data in a multi-dimensional space. These vectors are used to represent various types of data, such as text, images, or any other structured or unstructured information. Vector Search is an algorithm that searches for information in a database by mapping each data item to a vector representation of itself. The key innovation behind vector search lies in these vectors capturing not just the raw data but also the relationships and similarities between data items.

Vector Space Models?

A very obvious question when discussing vectors is how exactly are data items represented as vectors? “Vector Space Models” is the answer to this question. Vector Space Models are mathematical techniques that map data items to a vector representation where each dimension of the space corresponds to a unique term. The way these models work is by identifying underlying relationships between words, documents, or any other textual elements within a multi-dimensional space.

Vector Search v/s Traditional Search

We understand that you will not be willing to agree that Vector Search algorithms are better than Traditional Search algorithms without looking at facts and figures. So here’s a detailed analysis of the same just for you:

How does vector search work?

Now that we have an idea of what big data and vector search is, let us see how it exactly works.

Vector search engines — known as vector database, semantic, or cosine search — find the nearest neighbors to a given (vectorized) query.

There are basically three methods to the vector search algorithm, let us discuss each of them one by one.

Vector Embedding

Wouldn’t it be simple to store data in simply one form? Thinking about it, a database having data points in one fixed form will make it so much easier and more efficient to carry out operations and computations on the database. In vector search, vector embedding is how one can do so. Vector embeddings are the numeric representation of data and related context, stored in high dimensional (dense) vectors.

Similarity Score

Another method under vector search that simplifies comparing two datasets is the similarity score. The idea of similarity score is that if two data points are similar their vector representation will be similar as well. By indexing both queries and documents with vector embeddings, you find similar documents as the nearest neighbors of your query.

ANN Algorithm

The ANN algorithm is yet another method to account for the similarity between two datasets. The reason why the ANN algorithm is efficient is because it sacrifices perfect accuracy in exchange for executing efficiently in high dimensional embedding spaces, at scale. This proves to be effective relative to the traditional nearest neighbor algorithms like the k-nearest neighbor algorithm (kNN) which leads to excessive execution times and zaps computational resources.

Applications of Vector Search

A search algorithm as advanced as Vector Search has numerous applications for businesses and organizations. Let’s have a look at some of the fields and aspects in which Vector Search is proving to be a helping hand. 

1. Netflix: Netflix uses vector search to recommend movies and TV shows based on a user's viewing history. It considers the content of what you've watched and suggests similar titles.

1. Amazon: Amazon employs vector search to recommend products to users. If you search for a particular product, it suggests related items that others have found interesting or purchased together.

1. Google Images: Google Images allows users to search for images using keywords. It also uses vector search to find visually similar images. For example, if you search for "Eiffel Tower," it can show you pictures of the Eiffel Tower from various angles and sources.

1. Virtual Assistants: Virtual assistants like Siri and Google Assistant utilize vector search to understand and respond to spoken or typed queries, providing answers that match the user's intent.

1. Spotify: Spotify employs vector search to suggest music tracks and playlists based on your listening history and preferences. It can recommend songs with similar musical characteristics to your favorite tracks.

1. Ad Targeting: Advertisers use vector search to target ads to users based on their interests and online behavior, increasing the relevance of advertisements.

Limitations of Vector Search

Now, of course, Vector Search algorithms too, just like any other algorithm have some limitations to it.

1. High-Dimensional Space: Since the dimensional space used to map vectors is multi-dimensional, the data points become sparse which can impact the efficiency and accuracy of similarity calculations.

1. Data Quality: The quality of data wholly depends on the quality of the vector representations. If a correct Vector Space Model is not chosen to represent data points as vectors, the quality of data retrieval will have to suffer.
2. Lack of Historical Data: Recommender systems using vector search may struggle when dealing with new users or items because there is insufficient historical data to create meaningful vectors.