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Understanding How Vector Search Works

Explore the world of Vector Search and discover how it works in an understandable manner! Dive into the fundamentals of this technology and learn how it can help you identify items quickly and precisely. Whether you’re a computer enthusiast or just inquisitive, this blog will explain Vector Search in simple terms, making it accessible to everyone. Join us on a trip to uncover the wonders of this amazing search tool!

Retailers looking to improve their online user experience, raise conversion rates, and boost online sales will find that Vector Search changes everything.

We will examine this disruptive technology’s inner workings in more detail in this piece.

Information represented mathematically

The way data is represented and compared is where vector and keyword search diverge most.

Conventional search techniques focus on the presence or absence of certain phrases, treating data as a collection of words. In contrast, vector embeddings are used by vector search to represent data points.

In essence, vectors are mathematical representations of any kind of object, including images, texts, and products.

An e-commerce catalog’s products are all converted into vectors in high-dimensional spaces. Different product qualities can be represented by these dimensions. A movie could, for instance, be represented as a vector with each dimension denoting a feature (actors, director, genre, etc.).

The search algorithm can capture complex correlations and similarities between products since it operates in high-dimensional regions. In this multi-dimensional space, for instance, products with comparable qualities will have closer-fitting vectors.

Vectorization methods

A variety of methods, including Word2Vec and Doc2Vec as well as deep learning models like recurrent neural networks (RNNs) and convolutional neural networks (CNNs), can be used to construct vector representations. The data and the issue you are attempting to address will determine the machine learning technique you should use.

The best search accuracy may be obtained in an e-commerce setup using techniques like word embeddings for textual product descriptions, product embeddings based on their attributes, session data embeddings (which add up to a user profile), and hybrid models that combine multiple vectorization methods.


After all of your objects have vector representations, you must index them to enable effective retrieval.

Data structures like search trees or more sophisticated methods like approximate nearest neighbor (ANN) indexes or locality-sensitive hashing (LSH) are frequently used for this. Fast nearest-neighbor searches are made possible by these data structures.

Search Query Vector

Similar to how things are vectorized, queries submitted by users or systems are also vectorized. In order to locate the pertinent products that are in stock, the query vector—which reflects the traits or characteristics of the query—will be utilized as the foundation for similarity.

Similarity Metrics

A technique to quantify the similarity between the search query vector and the vectors of the items in the dataset is necessary in order to locate related items in vector databases. Among the common metrics used to measure similarity are cosine similarity, Euclidean distance, L2 distance, and Jaccard similarity.

Greater vector similarity results from proximity in space, but greater distance results in less shared traits. Companies that use vector search engines can find the distance metrics to the nearest query-related vectors in a space by running nearest-neighbor searches.

A hybrid search engine that blends keyword- and vector-based techniques provides a more robust and versatile solution, delivering highly relevant search results in a variety of settings.

But how are keyword and vector search results combined?

There are other approaches, but this is how we handle it at Prefixbox:

Vector-based search results showcase traditional keyword-based search scores, including the matching score, which signifies the relevance of a search result to the query, and the popularity score, reflecting its popularity. Alongside these scores, similarity scores are incorporated, calculated from the mathematical representation of the keyword.

Combining the result lists is done through blending. This can be accomplished by assigning weights to these scores and scaling and normalizing them in order to obtain the optimal ranking.


Using their vector representations, comparable items can be found in a huge dataset using a technique called vector search. Applications include image retrieval, product suggestions for online retailers, and content recommendations.

It’s the crucial component that enhances search engine optimization and recommendation systems’ ability to provide more intelligent recommendations.

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