What is Vector Search?: A Comprehensive Guide

What is vector search?

Vector search is a method in artificial intelligence and data retrieval that uses mathematical vectors to represent and efficiently search through complex, unstructured data.

The tech industry is buzzing right now with all of the opportunities for change that predictive AI and generative AI bring to how we interact with information. At the center of this AI revolution is the concept of vector search, also known as nearest neighbor search, which enables AI models to find specific sets of information in a collection that are the most closely related to a prescribed query.

Unlike traditional searching models like keyword search that look to find exact matches of information, vector search represents data points as vectors, which have direction and magnitude, in a high-dimensional space. With vector search, the individual dimensions define a specific attribute or feature. The search compares the similarity of the query vector to the possible vector paths that can and do traverse all of the dimensions. Implementing a vector search engine marks a significant advancement, enabling more sophisticated and accurate searches through large and complex datasets.

Vector search works by associating similar vector representations and converting queries into the same vector representation. With both query and data represented as vectors, finding related data becomes a function of searching for the closest data representations to your query representation, known as nearest neighbors. Unlike traditional search algorithms that use keywords, word frequency, or word similarity, vector search uses the distance representation embedded into the vectorization of the dataset to find similarity and semantic relationships.

Why is vector search important?

Vector search is the latest evolution of how information is categorized and accessed. Like many transformative changes, vector search brings a whole new approach to unlocking power from the data we gather.

Vector search taps into the intrinsic value of categorizing data into high-dimensional vector spaces. It captures the semantic value of that data, allowing for generative AI solutions the ability to extract the contextual relevance and create new relevant content based on that context. Vector search's contextual relevance can be applied to a variety of use cases:

• Similarity Retrieval - Provides applications with the ability to have a thesaurus – not just for words – for the entirety of the users' data set. This enables adaptation to contextual input more directly, allowing users to find variations that suit their requirements quickly.
• Content Filtering and Recommendation - Vector search provides a more fine-tuned approach to filtering content. Moving beyond the limited scope of keyword association to an approach that considers hundreds or thousands of contextual data points and helps identify additional content with similar attributes.
• Interactive User Experience - With vector search, users can more directly interact with large data sets to hone in on relevant information more quickly. Instead of searching product documentation using a specific keyword, users can now interact with the documentation using natural language processing, not only getting more relevant results for their queries but also getting additional information around those queries that they may not know to ask.
• Retrieval-Augmented Generation - One of the most significant benefits generative AI brings is that we can now bridge the gap between predicting outcomes and responding to outcomes. Vector search is the foundation for retrieval-augmented generation (RAG) architectures because it provides the ability to glean semantic value from the datasets we have and, more importantly, continually add additional context to those datasets augmenting the outputs to be more and more relevant.

How does vector search work?

Nearest neighbor search is at the core of vector search, and there are several different algorithms for finding nearest neighbors depending on how much compute you want to allocate and/or how accurate you want your result to be.

K-nearest neighbor algorithms (kNN) provide the most accurate results but also require the highest amount of computational resources and execution time. For most use cases, Approximate Nearest Neighbor (ANN) is preferred as it provides significantly better execution efficiency in high dimensional spaces at the cost of perfect accuracy of the results. ANN allows vector search operations for large language models or models that require extremely large datasets to operate at scale. With larger datasets, the sacrifice of result accuracy becomes less of an issue because more data yields better results, especially if you introduce algorithms like hierarchical navigable small worlds (HNSW).

What are vector embeddings?

The way vector search calculates and uses nearest neighbor algorithms is by transforming all data into vector embeddings. A vector embedding, in its most basic form, is a mathematical representation of an object as a list of numbers. Once in this numerical representation, the semantic similarity of objects now becomes a function of proximity in a vector space. This numerical translation is known as 'vector representation,' which is crucial in defining how objects are positioned and compared within the multidimensional vector space. It's this vector representation that enables the precise calculation of similarities and differences between various data points.

Once real-world objects are represented as a list of numbers, they can be plotted on a multidimensional graph, and depending on how close one object is to another determines how similar a given object is to another.

Once objects are stored as vectors, they can be stored in a vector database, which is purpose-built to provide efficient storage and retrieval of large datasets of vector embeddings so that vector search operations can be used at scale.

The difference between traditional vs vector search

Unlike traditional search that compares keywords or indexes, vector search provides the ability to compare the entirety of the query against the entirety of the data being searched and represent how closely each result is to the given query with context. This distinction highlights the superiority of how a vector search engine works in managing complex search queries, transcending the limitations of keyword-based traditional search systems. But what does this mean and what is its impact?

Traditional keyword search has thrived when exact results are requested. If I know the specific details of what I am looking for then it is easy for me to find it. Take, for example, all the information for a given account, name, phone number, address, preferences, etc. Traditional search can yield a good result if you have one of these pieces of information readily available.

But what if I knew of an upcoming service outage in a specific area and I wanted to do a more broadly scoped query like all accounts that are on a given street? Well, that type of search is more difficult with a traditional search because I don’t have an exact match. This is where the concept of similarity search becomes crucial, as vector search excels in identifying and retrieving information that is not just identical but semantically similar to the query.

With vector search, this type of query becomes a function of searching based on the street and finding all the data representations nearest to that query's vector embedding, yielding highly accurate results quickly and efficiently. It is this ability to elastically compare and search these large data types that vector search is specifically designed for.

Vector search use cases and applications

The integration of vector search within machine learning models opens up various applications across industries, enhancing the precision and efficiency of these models in processing large datasets. While vector search is ideally suited for obvious use cases like semantic search, it also enables many applications with the potential to transform industries fundamentally.

The most talked about use case today is the ability to leverage vector databases and vector search for natural language processing (NLP) using large language models (LLMs). The ability to convert large volumes of documents to vector embeddings that can be searched using natural language enables customer interactions that are faster, more efficient, and more satisfying for end users because answers to questions can be serviced based on the closest match (neighbors) to the question asked. No longer does a developer have to search length documentation to understand how a function works, no longer does an end user have to search a FAQ to find how to use a specific feature of your product. Users can interact directly by asking questions and receiving detailed answers with immediate feedback.

Things like chatbots, recommendation engines, searching large volumes of unstructured data like audio, video, or IoT sensor data, and telemetry have all become a function of vector search, vector databases, and vector embeddings.

E-commerce

Take, for example, e-commerce, where product recommendations can be tailored to specific requests over time. Context and history are important, and with vector search, user preferences, interests, hobbies, and information can be built into a representation of their profile. Then, you can make recommendations based on how similar the result from their query matches their profile.

Suppose somebody is searching for tennis balls. On the surface, they are probably a tennis player, but what if their profile says they don’t like playing sports? Maybe tennis racquets aren't precisely what they want, but their profile states they have three dogs. So instead of recommending tennis racquets, maybe recommending dog fetch toys is the more accurate result. By having objects represented as vectors, this similarity-based matching becomes easy, and customers get the results they want, leading to higher satisfaction and user engagement.

Content discovery

What about finding information that is broader in scope than a specific query? Streaming services like Netflix and YouTube have popularized this with the ability to discover new shows or content based on similar things you have watched in the past.

For example, imagine you are shopping for a new car to replace your SUV, and you want to see all the options, like a specific brand and model. With traditional search, you would get articles comparing that brand and model to other brands and models based on keywords, but you have to do the leg work to see how they compare. With vector search, your query can be vectorized quickly compared against all the brand and model features and a recommendation for what brands and models have that feature can be returned quickly due to how near they are to your request. You get a fast response with accurate results without knowing the specific brands and models you might be interested in. The application of semantic search in content discovery platforms, powered by vector search, allows for a more personalized and intuitive user experience, as the system can better comprehend user interests and preferences.

Natural language processing (NLP)

The most prominent use case today, however, is in natural language processing. Solutions like ChatGPT have revolutionized how we interact with information and data and how that data is presented for use.

Before the ability to leverage vector search chatbots, virtual assistants and language translators all had to leverage keyword relations to provide information. However, with vector search, interactions can be conversational because models can generate responses to questions with information that is semantically similar to what the query is asking.

For example, with traditional search, somebody might search for a template for a memo about a new building dedication, but with vector search, AI can write the memo for you under the constraints you provide, using natural language processing to complete the next word in the memo based on nearest neighbor vectors from similar documents process in an AI large language model.

Similarity search in NLP applications allows for a more nuanced understanding and response to user queries, going beyond mere keyword matching to comprehend the intent and context of the query. It is this ability to relate information in semantically similar searches that powers AI to complete and provide natural language processing.

What are the benefits of leveraging vector search?

Leveraging vector search provides many benefits, such as:

• Ability to efficiently query and browse unstructured data.
• Provide the ability to add contextual meaning to data with embeddings and use that as part of the search criteria.
• Provide a multidimensional graphical representation of search results that can be re-ranked and filtered based on context.
• Enable more relevant results based on nearest neighbor search patterns.
• Add semantic understanding to queries, providing more accurate results on a per-user basis.
  

What are the current limitations and challenges of vector search?

While vector search has several benefits, there are also some limitations and challenges you should be aware of:

• For vector search to be effective, you need a foundation of knowledge and vector embeddings to build off of, which, depending on the dataset, take a lot of time to create.
• Traditional information storage approaches like relational databases cannot scale to the demands of vector search, so specialized vector databases, like Datastax Astra DB, are needed to access and store vector embeddings efficiently.
• Computational overhead to vectorize data can be intensive, which inline vectorization of data on demand, data pipelines, and long-term storage of those vectors, vector databases, can alleviate.

How can DataStax help with vector search?

With the rapid growth and acceleration of generative AI across all industries, we need a purpose-built way to store the massive amount of data used to drive contextual decision-making. Vector databases have been purpose-built for this task and provide a specialized solution for managing vector embeddings for AI usage. This is where the true power of a vector database coupled with vector search derives: the ability to enable contextual data both at rest and in motion to provide the core memory recall for AI processing.

While this may sound complex, vector search on Astra DB takes care of all of this for you with a fully integrated solution that provides all of the pieces you need for contextual data, from the nervous system built on data pipelines to embeddings to core memory storage and retrieval, access, and processing in an easy-to-use cloud platform. Try for free today.