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People might say or enter such detailed search queries as they’re formulating the data points of what, exactly, they need to find out or track down. Most people want to search as they think naturally, not have to painstakingly construct a “promising” search phrase and then perhaps revisit and edit it to ensure that they’ve conveyed it well enough for a seemingly bone-headed search engine to figure out.

In the past, with the limited data-management capabilities available, this sophisticated level of searching was strictly out. To successfully find what you needed, you might have had to search, for instance, by entering a more conventional-sounding string like Comedy movie for children ages 14 to 18 rated G.

That was then. What’s happening now, in the age of AI?

For one thing, there’s an even bigger proliferation of unstructured data (notably, text, images, and sound), and it’s the bane of companies that want to preserve and make use of their burgeoning reams of vital information.

ChatGPT, the recent OpenAI initiative, is proving to be a large-scale player at corralling this unstructured information into a structured format.

However, vector databases are a second viable avenue for tackling unstructured data. “In an environment where businesses struggle to process unstructured data, vector databases are an exciting concept to maximize value from the data you collect,” notes technology writer Twain Taylor.

So in the field of machine learning and in data science overall, having emerged as one of the best ways to store vector embeddings, vector databases are a hot commodity. Various promising open-source vector database (and closed, as well) offerings include Faiss, Milvus, Weaviate, Pinecone, and Qdrant.

Vector databases are also being utilized as part and parcel of search providers’ tools. For example, search pioneer Algolia excels thanks to a vector database. It uses AI to convert content into numerical values; relevancy can then be determined based on proximity to the next nearest number.

What are vector embeddings?

Regardless of the provider and application, success in this industry typically comes down to the quality of the vector embeddings (also known as vector representations and word embeddings).

In traditional relational databases, information is processed and stored using predefined formatting in tables. A vector database is different: to store and retrieve unstructured data, it utilizes vector embeddings, which are typically generated using machine-learning techniques such as neural networks, which map text input to vectors.

So vector embeddings are simply numerical representations of words, phrases, and documents generated by AI models (such as large language models, LLM) in a vector space. “Vectorization” is the process of converting words into vectors. The relationships between the words are effectively captured as well. In the vector space, words with similar meanings or contexts as vectors appear to be physically close together.

Once meaning is encoded in a vector embedding, the information can be processed mathematically, allowing machines to understand the content more effectively.

That’s embeddings. There’s one issue, though: complexity.

Vector embeddings possess many attributes, representing various dimensions of data. Trying to get the right insights and manage them using a traditional database that relies on SQL commands isn’t going to work very well. So having a teammate in the form of a vector database — one configured specifically to deal with vectors and maximize performance — is key.

What then, is a vector database?

A vector database is as it sounds: expressly for storing, efficiently processing, and analyzing data sequences. By representing information in a way that machines can more easily understand, it’s ideal for tasks that involve natural language processing (NLP) and recognizing the content of images, for instance, while working on something that incorporates computer vision.

Another hallmark of a vector database is its ability to accommodate especially large datasets, including time-series data. Its ability to store data isn’t limited; in fact, its tables can have an infinite number of columns.

Advantages of vector databases

When it comes to organizing, indexing, and storing information, are vector databases superior to traditional ones? The short answer, given the state of AI technology and evolving requirements for efficient data processing, is clearly a resounding yes. Vector databases are well suited for managing vector embeddings; they excel compared with traditional databases.

Vector databases offer several advantages over traditional databases for use cases that involve similarity search, machine learning, and AI applications. In addition to better (e.g., faster) workflow performance, they’re flexible in that they can process multiple types of data. They’re horizontally scalable, which is key for real-time search through vast data stores. Plus, vector indexing in a database can be customized as needed for a company’s business model or a particular type of data.

Best uses for vector databases

With their knack for extending AI language capabilities, vector databases are a coveted commodity in a variety of business settings. They’re useful with deciphering and getting down to the true intent of search queries, applying vectors to accurately understand context and respond to what someone wants, even if the person is searching by pasting in an image. A vector database can facilitate similarity searches by utilizing the vector representation of the data. It can work with high-dimensional vectors, whereas a traditional database can’t scale effectively to achieve that. 

In the same way, recommendations on a website can be strengthened by vector database technology. Another well-suited application for vector processing is fraud identification and prevention, as the AI can do anomaly detection. In addition, the fields of finance, ecommerce, healthcare, and cybersecurity all stand to benefit from the continuing use and refinement of vector database technology.

Vectors and search

In the search industry, vector search powered by artificial intelligence is enabling more-accurate search, recommendation systems, and prediction of desired content, even with the presence of extremely large datasets.

In search, vectors are used for automating synonyms, clustering documents‍, detecting specific meaning and intent in queries, and ranking results.

A vector database relies on algorithms that use the approximate nearest neighbor (ANN) algorithm to optimize search. Vector search is only as good as the vector data in the vector database it can access, however. Best-quality data processing is critical for applications that rely on the functionality of large language models, semantic search, and generative AI. 

With vectors, computers can make sense of words such as king, queen, ruler, monarchy, and royalty by clustering them in n-dimensional space. Machine-learning models can figure out that words appearing close together in vector space — like king and queen — are related, and words that are even closer, like queen and ruler — may be synonymous.

Vectors can also be added, subtracted, and multiplied to determine meaning. One often-cited example:

king – man + woman = queen

A machine can conceivably use this reasoning to determine gender or understand how gender is related. A search engine can use this functionality to determine fairly complex things, like the deepest lake in a particular geographical area or the “best” brand of jeans for a certain demographic.

Vector-ize your search

Want to tap the power of vectors for fine-tuning your website search? Algolia NeuralSearch is powered by a breakthrough algorithm that compresses vectors. Using our API, you can quickly upgrade your search functionality to duly impress your users who need information while prospectively giving your site metrics a healthy boost.

Let’s get your site on the road to success! Contact us today.

About the authorVincent Caruana

Vincent Caruana

Senior Digital Marketing Manager, SEO

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