What Is Similarity Search? The Ultimate Guide

“The harder you work, the easier it looks” is a quote from hockey great Jonathan Toews, but it could also be the motto of similarity search. Sure, it looks effortless—presenting answers and recommendations in seconds. But the complex data flows, AI systems, and compute power arrayed behind this search technique are formidable. By quickly pinpointing matches, even in large data sets, similarity search has become a pivotal player in natural language processing, recommendation systems, fraud detection, and search engines, as well as a growing number of industry use cases, including drug discovery. But how does this nimble technology maneuver through so much unstructured data so quickly? And how is it different from, or complementary to, seasoned keyword searches? Strap on your skates and let’s explore the ins and outs of similarity search.

What Is Similarity Search?

Similarity search, also known as nearest neighbor search, is a technique used in information retrieval and data analysis that finds items in a data set that are most like a query item. This is useful in applications where the goal is to identify objects, documents, images, or other data points that share common characteristics with a given query. You can see similarity search at work in applications such as an image search engine or in a streaming service’s content recommendations.

A similarity search system creates a number set, called a vector, for each item in a data set; the vector numerically represents the item’s features. This gives a computer a numerical way to understand real-world ideas and objects, whether the items in a data set are images, text, audio, video, or other types of data.

The vectors representing many different pieces of data are then stored in a vector database and a vector index is created, enabling the data to be rapidly searched. When the data set is queried, a vector embedding is created for the features and ideas represented by the query terms, using the same algorithm originally used to create the vectors stored in the database. The database then uses algorithms to locate the closest matches to the query in the data set.

In some situations, this type of search is referred to as “semantic search” because it matches items by the properties of objects and conceptual ideas represented by an item, not by keywords in a document or pixels in an image. Similarity search’s ability to provide fast and accurate results, even with very large data sets, makes it indispensable to AI-driven systems, such as natural language processing, image recognition, and content-based filtering.

It’s not uncommon to find business applications that incorporate the best of both similarity search and traditional keyword search methods—think a recommendation system that includes up-to-date business information, such as pricing and availability. These sorts of functions can be accomplished by moving data between a specialized vector database and established data stores, or with a multimodal database that natively handles both vector data and relational data.

Similarity Search vs. Traditional Search

In basic terms, traditional search is about finding what you explicitly ask for, while similarity search is about finding what’s most like what you have or asked about.

Traditional search is often used in database queries to find exact matches or highly relevant items in structured data based on specific keywords or criteria. For example, if you search for “best ramen in San Francisco,” a traditional search engine will return web pages that contain those exact keywords, predefined closely related terms, and perhaps a numerical ranking. The focus is to ensure results are accurate and directly address the query terms.

Similarity search is geared toward finding items that are conceptually or structurally near matches to your query. It’s useful where data is unstructured or semistructured, such as images, text, or complex data points. If you’re searching for images like your photo, the similarity search will look for images that share visual features or patterns, such as grass, skyscrapers, colors, or portrayed emotions, even if they’re not identical. Or a document retrieval system might return articles that discuss similar topics or use similar language, even if the exact keywords aren’t there. In our ramen example, vectors of text contained in reviews could form the basis for the similarity search.

Key Differences

We can think about the key differences between traditional search and similarity search in several ways, including the search method’s objective, the data types used, the mathematic techniques employed, and the use cases where they’re best deployed. As we mentioned above, traditional search aims to find exact matches or highly relevant items based on specific keywords or criteria, while similarity search focuses on finding items that are conceptually or structurally similar to a query.

Traditional search is based on structured table data that’s common in enterprise applications—think rows and columns used to organize inventory or personnel records—while similarity search is better at handling unstructured or semistructured data, such as images, audio, and complex data points, often in JSON format.

There’s different math behind the two types of search. For one, traditional search relies on Boolean logic, keyword matching, and ranking algorithms to determine relevance of an item in a data set. Similarity search, on the other hand, uses vector distance metrics such as cosine similarity, Euclidean distance, and Jaccard similarity, to quantify the degree of similarity between items that are indexed. We’ll discuss these metrics in more detail later in this article. As you might guess, traditional search is more commonly used when exact results from database queries of business information retrieval systems is required, while similarity search is used in recommendation systems, image recognition, and content-based filtering.

We should note here that in many business use cases, a system equipped with retrieval-augmented generation (RAG) will use both query techniques together with an LLM to marry semantic search returns with up-to-date corporate data for the most accurate and helpful outputs for business purposes. For example, a recommendation engine will match an item based on a similarity search along with a price and availability drawn from a traditional SQL query and provide that information to an LLM to generate an easy-to-understand answer in natural language.

Key Takeaways

• Similarity search, sometimes called nearest neighbor search, is an increasingly popular search method and a cornerstone technology for RAG and other AI-driven systems.
• Similarity search works by detecting and retrieving items in a data set that are most like query terms.
• Similarity search is a vital technology in natural language processing, recommendation systems, and image search applications.

Similarity Search Explained

Similarity search is a technique in data science and machine learning that seeks to quickly find items in a data set that are most like a query item. How do these systems know that items in a data set, such as an image, piece of text, or audio file, are alike? The system runs this data through a sophisticated AI model that quantifies the real-world features of each item so they can be evaluated mathematically. The numbers that describe an item are called its vector embedding. Vector embeddings give computers numbers they can work with that represent the ideas and objects found in unstructured data. A vector database stores, indexes, and enables searching of large numbers of vectors, where each one represents an individual item in high-dimensional space. That makes it possible to mathematically determine how close, or similar, two items are to each other.

The system then identifies the closest matches based on a well-known distance metric, such as Euclidean distance, cosine similarity, or Jaccard similarity. Data scientists developing a similarity search system will choose metrics and search algorithms based on the type of data being searched and the type of work the system does, such as anomaly detection, product recommendation, or natural language processing. For example, an algorithm such as approximate nearest neighbor (ANN) is designed to speed up the similarity search process by providing a trade-off between accuracy and speed—especially in data sets that may contain billions of items. Popular ANN methods include Annoy, an open source library that provides a tree-like structure for efficient search, and Faiss, which uses advanced indexing techniques to handle billions of vectors.

How Does Similarity Search Work?

Similarity search works by identifying the features that are alike between a query and items in the data set being searched. This is done most often through techniques such as vector embeddings, indexing, and nearest neighbor search. Here’s a closer look at the steps involved:

• Vector embeddings generation: Vector embeddings are numerical representations of features found in unstructured or semistructured data. Creating the embeddings involves converting raw data, such as text, images, or audio, into strings of numbers called vectors, which capture the data’s essential features and context. There are a host of AI models that are used to generate these embeddings. For example, in text, Cohere’s Embed model creates vectors that reflect the semantic and syntactic relationships between words, allowing similar words to be close to one another in the vector space. Note that the field of vector embeddings is rapidly evolving, and many innovative open source models are available in the Open Neural Network Exchange.
• Indexing and querying: Indexing is the process of organizing and storing vector embeddings in a way that allows for efficient searching and retrieval. In a vector database, each item in the data set gets a vector embedding that describes it, and the vectors are arranged in the index to enable the system to rapidly find similar vectors. A vector is also calculated for each search query. This lets the database rapidly search its index to determine which items are most like the query.
• Performing the search: The search starts with the query being converted into a vector using the same technique applied to the data set items. The search algorithm then uses its vector to find the nearest neighbors to this query vector. These neighbors are most similar to the query. The results are often ranked based on their similarity scores, and the top matches are returned to the user or processed further to find the very best results for the query.

Similarity Search Advantages and Limitation

Similarity search is a powerful tool that’s advantageous for certain applications, especially those involving unstructured data. However, it’s crucial to be aware of its limitations and choose appropriate techniques and metrics for the specific problem at hand.

Advantages include

• Efficiency: Similarity search achieves fast and accurate retrieval of results by indexing items logically using algorithms that can quickly locate the most similar items in a large data set—without the need for exhaustive comparisons.
• Personalization: Using similarity search, applications can personalize suggestions for users. The application does this by analyzing user behavior and preferences and generating vector embeddings that capture the user’s tastes and interests. A similarity search can then quickly identify and suggest products, articles, or media.
• Versatility/scalability: The ability to efficiently search diverse and complex data types, such as text, video, and audio, lets the system be adapted to different use cases: content-based filtering, fraud detection, and many others.
• Cost-effectiveness: The efficiencies in similarity search can translate into lower operational costs and better performance. The search method’s efficient indexing techniques and targeted algorithms lower the time and computational resources needed to find similar items. This can be extremely beneficial for very large data sets.

Limitations include

• Complexity: Generating vector embeddings, choosing appropriate similarity measures, and implementing efficient indexing and querying algorithms are tasks that require a high level of computer science and data management expertise. This can be a barrier for organizations that want to build their own systems but lack the necessary technical skills.
• Resource intensiveness: Although similarity search can be a cost effective way to search large data sets, there are still costs to consider for your use case. For example, the process of generating embeddings, building indexes, and performing queries can require a lot of time and computational power. This can lead to higher costs and more demanding infrastructure requirements.
• Data preparation requirement: To offer the most relevant results, similarity search needs high-quality data and thorough preprocessing. For example, raw data often needs to be cleaned, normalized, and transformed into a suitable format before the system can generate embeddings. This preparation step can be time-consuming.
• Privacy issues: Keeping data private and complying with relevant regulations is an important part of any similarity search system, especially when it’s handling production workloads. This can add complexity and overhead to the implementation.

Core Concepts of Similarity Search

An understanding of the core concepts of similarity search is essential for effectively implementing and using the technology in your applications. The techniques and technologies below work together to deliver the desired results.

Vector Representations

Vector representation is the process where the features and characteristics of stored content are converted into numerical vectors in a multidimensional space. These vectors capture the essentials of the data item, such as the meaning of words in text, the visual elements in images, or the patterns in audio. The resultant vector that describes an item is its vector embedding. By creating vectors for data as well as queries, a vector database can use representations to efficiently measure and compare the closeness of different items and queries.

Distance Metrics

Distance metrics are essential in similarity search because they quantify the similarity or dissimilarity between vectors. The choice of distance metric depends on the nature of the data and the specific requirements of the application. Common distance metrics include Euclidean distance, which measures the straight-line distance between two points; cosine similarity, which assesses the cosine of the angle between two vectors to determine their orientation; and Jaccard similarity, which is useful for comparing sets of features represented in vectors even if they’re different sizes.

Similarity Search Techniques and Algorithms

An organization will choose a similarity search technique based on the end goal of its application. For example, is it building a system for anomaly detection, image search, or natural language processing? These techniques incorporate the distance metrics mentioned above to achieve their task. Two popular techniques are KNN and ANN, described below:

K-nearest neighbors, or KNN: In a similarity search based on the KNN technique, a query vector is compared to a set of data vectors, and the algorithm identifies the “k” data points that are closest to the query based on a chosen distance metric, such as Euclidean distance or cosine similarity. KNN predicts the category or value of a new piece of data or query by comparing it with close neighbors in the data set, assuming that similar data will be located near each other in the vector space.

KNN calculates the distances between the query and all data in the set, which makes it computationally expensive, especially with large data sets. Despite this, KNN can be effective for many applications, including recommendation systems, image recognition, and anomaly detection.

Approximate nearest neighbor, or ANN: ANN is a technique used in similarity search to efficiently find elements in a data set that are extremely close to the vector representing a query—but without needing to compute exact distances to every single point. This method is good for large-scale data sets where exact nearest neighbor search would require too much compute power to be worthwhile. ANN algorithms, such as locality-sensitive hashing (LSH) and tree-based methods, do an approximate search by reducing the dimensionality of the data or using indexing structures to quickly narrow down the potential candidates. The results may not be perfectly accurate, but they’re often sufficiently close for practical purposes. ANN is commonly used in applications like image searches and natural language processing.

Applications of Similarity Search

Similarity search is popular for several types of applications. One might encounter similarity search when presented with recommendations from a streaming service or answers from a search engine. But this search technique can also be found in the background in finance and data security. Here’s a look at other popular applications of similarity search:

• Image search: When you ask an AI application to find images based on your query or example image, it will most likely use a similarity search to locate those images. The system converts images into feature vectors so algorithms can compare these vectors to ones stored for each element in its data set and identify images with similar characteristics. The system then efficiently retrieves the most similar images from a large database. This is useful in applications such as reverse image search, where users can upload an image to find similar or identical ones, and in content-based image retrieval systems that retrieve images based on written descriptions. In another example, manufacturing quality control compares images of newly created parts with known good and bad samples to identify pieces for additional scrutiny.
• Recommendation systems: When you arrive at the app of a retailer or streaming service and find recommendations, you know their systems have done a similarity search based on your preferences and past behaviors. These systems convert user preferences and item attributes into vectors and index them in a high-dimensional space where product vectors are also indexed. Then they calculate the similarity between these vectors using metrics like cosine similarity or Euclidean distance, resulting in a short list of items most likely to be of interest to you. For example, a movie recommendation system might capture your past choices and preferences as vectors, allowing the system to recommend movies that are like those you’ve already enjoyed. Similarity search’s enablement of fast, accurate personalization has made it a cornerstone of ecommerce, streaming services, and social media platforms.
• Fraud detection: When your retailer or financial institution scans for fraudulent transactions, they often employ similarity search. It helps them identify unusual patterns or anomalies in data that may indicate fraudulent activity. By representing transactions or user behaviors as vectors, these systems can compare new data points to historical data to find the closest matches. If a new transaction or behavior is different enough from its nearest neighbors, it can be flagged as suspicious. By helping to detect outliers and anomalies, similarity search has become crucial in financial services and other businesses looking to stem losses or mitigate security threats.
• Business data exploration: Similarity search can, for example, help a businessperson explore corporate data with natural language prompts instead of writing SQL statements. With similarity search and RAG, data exploration and visualization can take the form of a conversation between a businessperson and a tabular data set or a semistructured document store.
• Healthcare and drug discovery: The healthcare and biotech industries are putting similarity search to work in several ways. By vectorizing large volumes of industry data, a similarity search can uncover contextually relevant studies, compounds, or mechanisms that may have been overlooked by traditional keyword-based search methods, helping people in these industries connect the dots in new ways. In chemical databases and compound libraries, similarity search has the potential to identify matches based on pharmacological properties to accelerate drug discovery while lowering costs. These same pattern-matching abilities can help discover new relationships in gene expression data, protein sequencing, and other large biological or chemical data sets.

Tools and Libraries

There are several tools and libraries designed to help organizations implement similarity search efficiently, but they differ in their approaches and features. Here are some examples:

• Annoy, short for Approximate Nearest Neighbors Oh Yeah, is a lightweight, efficient library for approximate nearest neighbor search that was developed by Spotify. It’s particularly useful for applications where speed and memory efficiency are key considerations. Annoy builds a tree-like structure to index vectors, allowing for fast retrieval of approximate nearest neighbors. Annoy can be integrated into a variety of programming environments, including Python and C++.
• Faiss, short for Facebook AI Similarity Search, is an open source library developed by Facebook AI Research that’s now widely used in applications including recommendation systems, image recognition, and natural language processing. Faiss is optimized for high performance similarity search and can handle billions of vectors on a single machine. It supports multiple distance metrics and indexing methods, including flat, inverted file (IVF), and Hierarchical Navigable Small World (HNSW) graphs.
• Milvus is an open source, cloud native vector database designed for similarity search in a variety of items, including images, videos, and text. Milvus supports multiple indexing algorithms and distance metrics, and it can be deployed in the cloud or in a lite version on a device. It’s known for its flexibility and ease of integration with other data processing and machine learning frameworks, making it a popular choice for a wide variety of similarity search applications.
• Pinecone is a cloud-based vector database designed for similarity search in large-scale applications. It offers a solution that simplifies the process of storing, indexing, and querying high-dimensional vectors, making it popular for tasks including recommendation systems, image search, and natural language processing. It supports a variety of distance metrics and provides APIs for quick integrations with existing systems.
• Oracle AI Database is a multimodal database that offers native AI vector search on the strategic data stores of large enterprises. It lets developers easily bring AI-powered similarity search to business data without managing and integrating multiple databases or compromising functionality, security, and consistency. Organizations including large enterprises and fast-moving startups use it to enable ultrasophisticated AI search applications.

Enhance Similarity Search with Oracle AI Vector Search

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Similarity Search FAQs

How can similarity search benefit my enterprise?

An AI vector search system in your enterprise can make it much easier for people to explore data stores and documents using native language prompts. It can also help your organization build personalization into the services you provide for customers, such as a recommendation engine for online retail.

What types of data can be used in similarity search?

Similarity search can be used with any data that has a vector embedding, but it’s most often used with unstructured or semistructured data, such as text, images, video, and audio files.

How does similarity search improve customer experiences?

Similarity search can improve the customer experience by personalizing and suggesting content for customers based on their preferences and past choices.

How scalable is similarity search for large data sets?

Similarity search is a very flexible and scalable search method. It handles large data sets by indexing vector data in a way that makes it easy to locate and return similar items to a query.