What Is Vector Search? The Ultimate Guide

Our daily lives are filled with vector searches going on behind the scenes. Looking for a new show to stream? The options suggested to you are most likely generated by a vector search–based engine. Making bank transactions online? That system may use vector search to detect anomalies that hint of fraud. Using a chatbot in your work? Vector search helps the LLM powering the system get answers right, even about day-to-day operational information.

Let’s learn how vector search’s fast and accurate yet nuanced data storage and search method works and where it might lead us in the future.

What Is Vector Search?

Vector search is a technique used in information retrieval and machine learning to quickly locate items in a large data set. It does this by storing and grouping items based on their vector representations. These representations, also called vector embeddings, are strings of numbers that correspond to the many attributes of an item, whether that item is a word, document, image, or audio or video file.

The vector embedding for an image of a dog in the park, for example, will describe the dog’s white color, its squat shape, and possibly its breed. It will also describe the wooded setting, the cloudless sky, and myriad other attributes of the image. All these attributes are assigned numbers in vector embeddings, which can be hundreds or even thousands of numbers long and are generally created by sophisticated machine learning algorithms running on neural networks. Now, a computer running a vector database can use the embeddings to group, compare, and quickly locate items based on nuanced, hierarchical relationships.

Vector search is sometimes called “similarity search” or “nearest neighbor search” because of the way it facilitates grouping and matching of items to speed the search process. So, if someone asks for an image of a “happy dog in a park,” the vector search system will quickly and accurately find images stored near those words in the vector database index.

In business, vector embeddings can be applied to documents, products, customer communications, and other commercial assets and blended with business data, such as up-to-date pricing or availability records. When vector search is combined with a technology called retrieval-augmented generation, or RAG, it allows powerful large language models (LLMs) to deliver answers informed by an organization’s operational data. That brings generative AI to bear on a variety of business use cases, including sales, research, product development, and many others.

Key Takeaways

• Vector search enables efficient and scalable search in high-dimensional data, allowing for fast retrieval of similar items from large data sets.
• In vector search, numerical representations are assigned to the features or attributes of an item. This is most often done using AI models with sophisticated algorithms.
• Vector search employs databases with specially designed indexes that allow them to find items based on their “proximity” in the index. This differs from traditional search methods that often require exact keyword matching.
• Vector search is a key component of popular online services, such as search engines, recommendation systems for retail products or streaming suggestions, and the anomaly detection common in finance and cybersecurity.
• More recently, vector search is combined with a technology called retrieval-augmented generation, or RAG, to help businesses combine the power of generic large language models with their own corporate data stores.

Vector Search Explained

For vector search to work, items in a data set must first be assigned vector embeddings and indexed in a way that facilitates fast retrieval of similar items. Vector embeddings are strings of numbers that correspond to the many attributes of an item—whether that item is a word, document, image, or video or audio file. Embeddings are assigned by sophisticated machine learning algorithms and allow for searches that focus on the attributes of an item rather than finding results based on keywords.

In practice, you might ask an LLM-powered chatbot to show you bikes that are good for riding on dirt and trails. That query would be assigned a vector embedding that corresponds to the attributes of those words. A vector search would then return bikes with vector embeddings that rank as similar to the vector embeddings for those words. The result would be populated with mountain bikes or gravel bikes.

Here’s how that works: When your query is made, the words of the query are transformed into a vector embedding and placed in a vector database in the same high-dimensional space as the items in the bike data set. High-dimensional data refers to the many features of an object, not the object itself. For example, a bike’s features could be represented by vectors for its color, frame material, tire type, handlebar configuration, crankset type, brake type, price, or other factors. The database indexes this collection of features in a high-dimensional space where it can use distance metrics and similarity measures to return bikes with features close to your query words in the index.

In this way, a retailer could use a vector database to match people with bikes that suit their needs. The seller can also pair that search result with business data in its relational database for up-to-date pricing and availability. In an increasingly popular scenario, the business would use RAG technology to let an LLM-powered chatbot search through a large database of bikes in inventory, plus up-to-date business data using a multimodel database that handles relational data and vector data as a native data type. This simplifies the architecture so customers can use natural language prompts to interact with the business.

Why Is Vector Search Important?

Vector search is important because it plays a starring role in a wide variety of applications that benefit from quickly finding similar items and analyzing high-dimensional data. This constitutes a growing number of use cases, including the emerging imperative to help businesses take advantage of generative AI. When combined with RAG, vector search can securely guide a generic LLM through a trove of business documents and up-to-date operational data to provide outputs that are relevant to stakeholders. The result is a conversational front end on business data that delivers significant value. Benefits can include improvements in customer service, sales prospecting, human resources, content marketing, and many other use cases.

Vector search techniques are designed to handle large-scale data sets efficiently, facilitating the lightning-fast comparison of an item’s vector representation against the rest of the data set. This leads to two other important use cases: recommendations and anomaly detection.

Vector search is a key component of the personalized recommendation systems now common in online retail and entertainment streaming. It allows these applications to quickly find and recommend items based on user preferences, demographics, or habits, and thus, if all goes as planned, enhance the user experience and increase customer engagement. Vector search has also become a vital technology in domains where unusual or dissimilar items must be immediately spotted and alerted on, such as fraud detection, network security, or quality control.

How Does Vector Search Work?

In vector search, items in the database are assigned vector embeddings that describe their attributes and their meaning. Then the search query is also assigned a vector embedding. The database matches those up and returns items that most closely resemble the vector embedding of the search query.

This graphic helps to visualize the process:

How Do Vector Databases Work?

Vectors are arrays of numbers that represent the many attributes of an item, whether that item is a word, a document, an image, a song, or a movie. A vector database puts these vectors in a special index that groups similar items—enabling lightning-fast “similarity searches.” Let’s see how it works to recommend just the right movie.

Viewer vectors

The database stores vectors that describe customers based on their demographics, preferences, and past choices.

Movie vectors

The database stores vectors that describe each movie, such as its location, language, genre, and actors and then indexes similar movies “close” together.

Database

Our viewer’s vector brings it “close” to vectors of many sci-fi movies in the database index.

Video recommendations

The database returns the closest matches, and the recommendation system displays them for the viewer to peruse.

Traditional vs. Vector Search

Traditional search and vector search differ in their approaches to finding and retrieving information from databases. Traditional search, commonly used in relational databases, is very efficient when sifting through data structured into rows and columns full of, for example, product or customer data. Vector search, in contrast, efficiently searches and finds similar items in unstructured data, such as text, images, and video and audio files.

Key Differences

Here are a few of the ways traditional search and vector search differ. Knowing the key characteristics is important when choosing the right database technology for your organization.

Dimensionality. Traditional search methods are designed for low-dimensional data, where the number of attributes or features is small. Think of a city’s average high and low temperature on a given date. Vector search, on the other hand, is specifically designed to handle high-dimensional data, where the number of dimensions or attributes for a given item can be very large.

Similarity vs. exact match. Traditional search focuses on finding exact or approximate matches based on keywords or textual similarity—that is, the semantics or overall message of the text. Vector search, on the other hand, aims to find similar items by calculating feature similarity between the query item and the items in the data set. It does this using distance metrics or similarity measures that evaluate how closely data points represented as vectors resemble one another. Vector search allows for more nuanced and flexible retrieval of similar items rather than relying solely on exact matches.

Scalability. Traditional search methods can struggle with scalability when dealing with large, high-dimensional data sets. Vector search techniques, on the other hand, use specialized data structures and algorithms that enable fast retrieval of similar items, even in massive data sets.

Benefits of Vector Search

Vector search is a powerful technique found in a variety of applications, such as recommendation systems and anomaly detection engines. The overarching benefit is its ability to provide much more accurate and relevant search results compared with traditional keyword-based search methods. This can result in improved user experience, increased engagement, and ultimately higher conversion rates for businesses. Here are some common examples of benefits.

• Scalability. Vector search takes advantage of optimized data structures and algorithms to enable fast retrieval of likely matches—even in massive data sets. This scalability is vital for applications dealing with large amounts of unstructured data.
• Efficient retrieval. By representing items as vectors and using specialized data structures and algorithms, vector search techniques enable efficient retrieval of similar items from large data sets. This is essential for content and retail recommendation systems, as well as searches for unstructured data, such as images or text.
• Flexibility. Unlike traditional search methods that rely on exact or approximate matches, vector search quickly calculates the similarity between the meaning of a search and the items in a data set. This allows for more nuanced and flexible retrieval of relevant items.
• Semantic understanding. A vector representation can capture complex relationships and similarities between items based on their features or attributes. Semantic understanding allows for more accurate and meaningful retrieval of similar items, even when the items may not have explicit similarities.
• Personalization. Vector search helps retailers or streaming services find and present similar items based on user preferences or item attributes. This personalization has been proven to increase engagement and sales and lead to a better customer experience.
• Reduced dimensionality. Over time, vector search techniques can eliminate irrelevant or redundant dimensions in vector embeddings, allowing future searches to focus on the most informative vectors for an application’s needs. This reduction in dimensionality can improve search performance by paring down the number of dimensions being considered in the search.
• Support for complex queries. Instead of relying on the keyword matching or exact matches used in traditional search techniques, vector search enables similarity searches to be combined with traditional search techniques, such as SQL WHERE clauses, table joins, analytics, property graphs, and spatial and JSON queries.

Challenges of Vector Search

While vector search offers significant benefits, it’s important to be aware of possible shortfalls in the technology and employ appropriate strategies to overcome them. In general, vector search can be challenging due to the complexity of working with high-dimensional data, as we’ll discuss. Ensuring the accuracy and relevance of search results can also be a challenge as noise and outliers can impact the effectiveness of the technology.

Overall, effectively using vector search requires an understanding of the underlying data and algorithms and an ability to tackle problems inherent in the technology, including the following:

• High dimensionality. Vector search works with high-dimensional data. That poses a challenge because as the number of dimensions increases, the cost of calculating the vector distance also rises. That can decrease search performance and has been called the “curse of dimensionality.” There are techniques to help mitigate this challenge, such as dimensionality reduction via vector quantization, which is a lossy data compression technique used in machine learning. It works by mapping vectors from a multidimensional space to a finite set of values in a lower-dimensional subspace, using clustering algorithms to identify similar vectors.
• Semantic gap. Vector search relies on vector representations of items to calculate similarity. There can, however, be a gap between the vector representation and the actual attributes of an item. For example, two bikes might be semantically similar but have different vector representations due to variations in the vectorization process. Bridging this semantic gap can mean going back to the vectorization process and capturing more accurate semantic features of items in their vector representations.
• Garbage collection. Vector search relies on vector indexes to organize and search large data sets of high-dimensional data. To work efficiently, however, obsolete information must be removed from the index when objects or values change. This problem has long been solved in relational databases, but it’s an ongoing challenge for vector indexes.
• Vector quality. Vectors will be only as good as the data they’re derived from. Vectors are created by ML models specifically for that purpose, and different models will produce higher quality vectors for certain types of data. Knowing the type of data being stored, such as text, images, or audio files, will help identify the right embedding model to use.
• Scalability. While vector search techniques are designed to handle large-scale data sets, extremely large or rapidly growing data sets can still be a problem. They can stretch memory requirements and lengthen search times. A mix of approaches are used to address this challenge, including more efficient indexing structures, distributed computing, and parallel processing techniques.
• Cold start problem. When a new item is introduced to a data set, it may not have a well-defined vector representation, or it may have limited data available for similarity calculations. This can make it difficult to accurately identify similar items, a situation referred to as “the cold start problem.” In vector search-powered recommendation engines, workarounds can include content-based recommendations, or when available, combining user-based content and item-based content to mitigate the cold start problem.
• Interpretability. Because vector representations are strings of numbers that are not easily interpreted by humans, it can sometimes be difficult to understand why certain items are considered similar by the vector search process. In areas where the explainability of AI choices is important, such as in the legal or medical fields, this lack of interpretability can be a limitation. Techniques that help people visualize the data analysis behind vector search results can help with interpretability.

Vector Search Use Cases

The versatility of vector search makes it valuable across various domains. By enabling efficient retrieval, recommendation, analysis, and decision-making based on similarity calculations, vector search is giving rise to entirely new use cases as well.

1. Information retrieval. By representing documents or web pages as vectors, vector search enables rapid semantic search of unstructured data. This is great for businesses who want to pair vector search with RAG to let generic LLMs serve up answers informed by the business’s own knowledgebase.
2. Recommendation systems. Vector search is widely used in recommendation systems to provide personalized recommendations to users. Applications in areas such as customer experience marketing use vectors to represent user preferences and item attributes, allowing vector search to identify similar items and make recommendations.
3. Content-based filtering. Content-based filtering applies vectors to items and their content attributes. This allows vector search to identify items with similar content. This is often used by vector-enabled databases as a technique in recommendation systems.
4. Image and video search. Vector search is commonly used in image and video search. By representing images or videos as vectors, a vector search process, such as Google Images, can identify similar visual patterns and retrieve relevant content.
5. Natural language processing (NLP). Vector search is used in NLP for tasks such as document clustering, topic modeling, and semantic search. By representing text documents as vectors, a vector search application, such as a chatbot or digital assistant, clusters documents that are similar by considering topic area or other criteria. That allows AI models to quickly retrieve semantically similar documents.
6. Anomaly detection. By representing normal patterns as vectors, vector search can identify outliers that indicate anomalies or abnormalities, making it useful for anomaly detection in domains including construction, manufacturing quality control, network security, and fraud detection.
7. Genomics and bioinformatics. By representing genetic sequences or biological structures as vectors, vector search offers an efficient way to compare and analyze biological data, making it useful in tasks such as DNA sequence alignment, protein structure comparison, or gene expression analysis.
8. Ecommerce and retail. Vector search is used in many common ecommerce and retail applications for product recommendation and personalized shopping experiences. It does this by representing products and search criteria as vectors, then uses similarity search to identify products and recommend relevant options.
9. Music and audio analysis. Vector search is used in music and audio analysis for tasks such as music recommendation, audio similarity search, or genre classification. Popular services, such as Spotify, do this by representing music or audio signals as vectors and then employing similarity search to identify similar songs, recommend related music, or classify audio based on similarity calculations.
10. Healthcare and medical imaging. Vector search has become an important tool in healthcare and medical imaging. For example, vector search applications represent medical images or patient data as vectors and then identify similar images, assist in diagnosis, or find patients with similar characteristics for personalized treatment.

Vector Search Examples

Once you start noticing, you’ll see vector search throughout your online experiences and beyond. It’s in the search results you peruse, the movies that are recommended to you, the product suggestions you see, and much more.

Here are a few more well-known examples.

• Google Search. As you might imagine, Google makes extensive use of vector search technology for semantic searches and to power recommendation engines. You can find it in services such as Google Image Search, YouTube, and Google Play. In addition, Google has integrated a technology it calls BERT (Bidirectional Encoder Representations from Transformers) into its search algorithm. BERT is, Google notes, a form of vector search where queries and content are transformed into vectors that semantically represent their meanings. This allows them to better match queries with more relevant results.
• Netflix Recommendation System. Netflix owes a degree of its success to an efficient and accurate recommendation system. The system continuously collects and analyzes information based on the behaviors and preferences of one viewer and predicts what they’ll want next based on their similarity with other viewers. This is done by assigning vectors to these criteria and using a vector database to note the similarity between users and return most similar selections.
• Amazon Product Search. Accurate product search and recommendations on Amazon’s sites are the result of continual experimentation and refinement. Originally derived from a content-based filtering system based on user likes or favorite selections, the recommendation engine now relies on vector representations. These give Amazon the ability to capture complex relationships and similarities between products or customers, allowing the company to unearth hidden connections and provide recommendations that closely align with an up-to-date view of the customer’s preferences.
• Spotify Discover Weekly. How does Spotify understand your musical tastes so keenly? It’s the result of several techniques compounded to understand you and people like you, so you can broaden one another’s musical horizons. There are different AI models used throughout the system, including LLMs to analyze text; audio models that analyze and categorize audio files; and collaborative filtering, which groups listeners based on their behavior. Spotify AI experts apply vector representations to describe listeners and audio tracks using strings of numbers that a computer can quickly read and use to compare them. This is done in a vector database, which places the vectors in a special index where it becomes quickly obvious which are “close together” and, quite likely, finding your next favorite song.
• Pinterest Visual Search. Visual search is a core competency of vector search technology, and Pinterest puts it to good use. According to Pinterest, its current visual search technologies use vector embeddings to power the match systems that let users browse 200 billion plus ideas from any image.
• Shazam Music Recognition. Shazam seems like magic. But the system is just really good at applying a technique called audio fingerprinting, which is similar to a vector-based similarity search. That’s how a 20-second audio clip—intro, verse, chorus, doesn’t matter—tells you exactly what song you’re listening to. Still feels like magic.
• Facebook Similarity Search. It’s in the name. Facebook uses vector search, aka similarity search, for tasks throughout its consumer and commercial services. These include image retrieval, recommendation systems, and natural language processing.

Future of Vector Search

The future of vector search is wide open. Its central role as an enabler of generative AI for business means that research and development will only accelerate. As a result, search techniques that contribute to natural language processing tasks, such as semantic search and document clustering, should evolve and improve.

Vector search will also play a bigger role in personalized healthcare and precision medicine, as it helps practitioners delve more deeply into medical images and genomic information and explore vast troves of anonymized patient data. And, of course, when you think of the future—even the near future—you think about autonomous systems, such as self-driving cars, robotics, and intelligent agents.

These are all on the cusp of wider adoption, and vector search will enable the accurate, millisecond similarity calculations required to make them a reality.

Vector Search Made Easy with Oracle AI Vector Search

There is no better partner for your vector search system than Oracle—and no better vector database than Oracle Database 23ai. With Oracle, you can easily bring the power of similarity search to your business data without having to manage and integrate multiple databases. This simplifies data management and enhances data security by maintaining a single database for storage, retrieval, and analysis.

The system also makes it easy to bring generative AI to your vector and business data using RAG. Now, powerful LLMs can deliver answers to natural language questions informed by your organization’s information and operational data. This can drive more personalized customer service, higher sales efficiency, smoother employee onboarding, and many other benefits.

Try Oracle AI Vector Search using Oracle Database and see how easy it can be to combine your customer data, product data, and AI search for new applications or as an extension of your existing applications. Start learning now with Oracle’s AI Solutions Hub.

Vector Search FAQs

Does Google use vector search?

Google uses vector search throughout its offerings, including Image Search and YouTube recommendations. Increasingly, it’s bringing vector-based search into its core search functionality through a technology it calls BERT, or Bidirectional Encoder Representations from Transformers, which enables more accurate search returns based on semantic searches.

What is the difference between vector search and elasticsearch?

Elasticsearch is used to search and analyze text by leveraging a technology called inverted indexes, which make it easy to take words or phrases and know which documents contain them, and where. Vector search, on the other hand, uses vector indexes to enable lightning-fast similarity searches among unstructured data sets containing documents, images, video and audio files, and more.

What is vector search vs. keyword search?

Vector search and keyword search work on different principles. Keyword search is more common in structured data that’s stored in rows and columns. The system scans columns looking for words that exactly or closely match those in the search query. Vector search takes a much more nuanced approach, storing vectors that describe unstructured data and using special indexes to find related items based on a “nearest neighbor” result. Some databases are proficient at both.