Vector Search Is in Vogue, but Could It Really Herald the Future of Data Querying?

The buzz around predictive and generative artificial intelligence (AI) has recently brought information retrieval techniques and algorithms under the spotlight again. To condense the chatter, the consensus is that AI and machine learning (ML) will profoundly alter how we look for and engage with information. This is attributed to AI and ML's ability to access copious amounts of data and make decisions as well as correlations off of that.

IDC forecasts that by 2026, the Asia Pacific will see approximately 30% of AI models integrate multiple data modalities. This is expected to enhance learning efficiency and mitigate the existing limitations in single-modality AI solutions on everyday knowledge.

Bringing Nuance to Queries

On the back of this AI revolution, the term Vector Search has become all the rage. But what even is a vector? Simply, a vector is an array of numbers. However, they can also represent more complex objects. Think of words, sentences, images, and even audio files, and group them in a continuous high-dimensional space called embeddings. This is why Vector Search is also known as nearest neighbor search, and its game-changing qualities are exemplified in the following ways:

A deeper learning approach: Users today expect search engines to do more than just process the characters or words in a query. Instead, they must also understand the semantics of a query. This helps match meaning with intent by understanding what is implicit in a query. In e-commerce, for instance, this can offer more relevant products by understanding the reason behind the need.

Reduce time and effort querying: Due to its ability to search across different data formats—i.e., text, images, audio, video, and so on—Vector Search can understand the contextual relationships between these different formats. This empowers the models with nuance and, thus, delivers highly relevant answers quickly. From a user perspective, this allows people to unshackle themselves from relying purely on text to express intent.

Tailor results to context and domain particularities: Vector Search allows users to factor in a given context when performing queries. This enables relevance and context to go hand in hand. For instance, e-commerce will differ from pharmaceuticals. Though they may use certain terminology that overlaps, these domains cannot rely on generic results that lack relevance.

Put users first: Vector Search enables users to find information that is broader in scope than a specific query. This gives them a precise, nuanced answer that does not force them to parse through a list of results. Streaming services like Netflix and YouTube have popularized this concept, allowing users to discover new shows or content based on similarities to what they have previously watched.

With vector search, queries can be quickly compared against all relevant features, and recommendations in line with those requirements can be returned promptly. For instance, in e-commerce, this allows the customer to search for what they want and find impactful results without necessarily knowing the specific brands and models they might be interested in.

Overcoming Challenges and Anticipating the Future

While the benefits of Vector Search allow organizations to push the envelope in data querying, putting it to work is not quite as straightforward. The main challenge to overcome is to assemble the required sum of parts to implement a context-based system. These include a large language model (LLM), a vector database, and frameworks like Lang chain and LlamaIndex. It also involves integrating a regular database, which contains inventory data and generates vector embeddings. Organizations can explore the options at their disposal to transform a standard database into a vector database. This involves writing code and ensuring all components work effectively in a commercial real-time environment. The goal is to avoid performance issues or breakdowns that could harm the solution's effectiveness.

Just as vector databases provide the functional brain to AI, vector search offers the neural network for cognitive recall. Organizations then equip themselves with the neural pathways to navigate, remember, and associate that information efficiently. This is especially relevant amid the rapid growth of predictive and generative AI, which underscores the need for purpose-built data storage that drives contextual decision-making. Vector search does just that by allowing businesses to look to the future and build natural semantic search into applications or a language processing engine to provide more natural user interaction. Ultimately, this empowers users with the information they need at the right place and time.

Deb Dutta, general manager for Asia Pacific & Japan at DataStax, wrote this article.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/everything bagel