What is Vector Search

What is Vector Search?

Vector search, also called “nearest neighbor search”, uses machine learning (ML) to capture the meaning and semantic context of unstructured data like text, images, audio, and video by converting it into numeric vector representations. Think of it as a translator, converting human language into vectors that machines can understand conceptually. 

Vector search uses vector embeddings – kind of like numeric fingerprints – that capture the essence of a piece of data. The search algorithm understands the context of query by analyzing embeddings that are nearest or most similar in the high-dimensional space of the vector (high-dimensional, in this context, means the vectors have tens, hundreds, or even thousands of dimensions.) The closer two vectors are, the more related the underlying data is. Distance between vectors is how the ML model understands context of every piece of a query (e.g., “degree of similarity”) and can retrieve relevant results.

For example, if you search for “behavior of parrots in the amazon” a vector search engine will analyze this sentence and understand you mean the Amazon rain forest (versus the ecommerce bohemoth).

Vector Search Examples

Vector search uses math and algorithms to understand search context, which is how it produces relevant results. There are many use cases for the technology, and you’ve likely come across some of them. Here are examples:

Semantic Search

Goes beyond literal keyword match to understand actual search intent (e.g., what you want versus exactly what you’re typing). For example, let’s say you want to watch a kid-friendly movie on a Friday night. You search for “best streaming movies for family night.” The vector search engine will list results for kid-appropriate movies even if they don’t contain the exact query in the retrieved content. 

Personalized Recommendations

In this case, the vector search engine matches your search with hyper-personalized content or product recommendations based on semantic similarity to your tastes. It works by representing user profiles, preferences, and interests as vectors. This is how your favorite retail website can seemingly read your mind by suggesting items that are semantically aligned with things like related interests, purchase patterns, reviews, and other signals including products you’ve viewed.

Content Discovery

Media platforms use vector search to help you explore and discover new shows, movies, songs, or articles that are semantically related to your existing interests. This is how Netflix sucks you in by making irresistible suggestions for shows you may have never thought to search for explicitly.  

Natural language interactions

Vector search is what powers AI language models and chatbots like ChatGPT or customer service bots on a business’s website. These tools engage in back-and-forth dialog by understanding the full context and meaning behind your statements.  This allows them to deviate from scripted paths and engage in conversation by giving you dynamically generated, relevant responses.

Unstructured Data Analytics

By turning unstructured data into something the machine understands, vector search can efficiently analyze this data and synthesize valuable insights using large datasets. Importantly, it uses semantic similarity versus just keywords, which means it can draw more relevant conclusions from patterns and connections in the data than it otherwise would if it was restricted to keyword analysis alone.

What are Vector Embeddings?

A vector embedding is a numerical representation that captures the meaning and semantic relationships of a piece of data like text, images, or audio. It converts that unstructured data into a high-dimensional vector or array of numbers. 

This embedding acts like a fingerprint or summary, allowing the data to be compared to other embeddings to find contextually similar matches based on their proximity in the high-dimensional vector space. Vector embeddings are the foundation of what make vector search engines work – they’re what the ML model uses to understand the intent behind a question or open-ended query.

How does a Vector Search Engine Work?

Vector search represents data points as vectors, meaning each piece of data like a text document or image is converted into a list or array of numbers that captures its semantic content in a high-dimensional vector space.  

To help you visualize how this works under the hood, here’s an example of how a vector search engine might translate a query.

  • Text: “behavior of parrots in the Amazon”
  • Vector embedding: [-0.127, 0.394, -0.281, 0.735, -0.019, …, 0.158]

As noted, a vector embedding is a long list or array of numbers, typically hundreds or thousands of dimensions. Each dimension (number in the vector) corresponds to some aspect or feature that the ML model has learned to encode the meaning and context.

For the example phrase above, the vector could have dimensions that represent concepts like:

  • Animals (higher values for phrases about animals)
  • Location (dimensions representing Amazon rainforest vs. other places)
  • Behavior (higher values for phrases describing behaviors/actions)

The exact values don’t mean much to humans, but the proximity of two vectors in this high-dimensional space is what represents their semantic similarity to the ML model. So, if we converted “behavior of macaws in the Brazilian jungle” to a vector, it would be quite close to the parrots example, indicating they have similar meaning.

When you enter a search query like this, it gets converted to a vector embedding. The search engine then finds the nearest vector neighbors in its corpus to retrieve the most semantically relevant results for that query vector.

Vector Search and AI

Vector search leverages machine learning models, including large language models (LLMs), to create vector embeddings that numerically represent data. These embeddings are generated by training models on extensive datasets. The process is analogous to teaching a child to speak and understand language—the more examples the model is exposed to, the better it becomes at producing accurate and relevant results.

Once these models are trained, they can take new queries, convert them into embeddings, and search for the nearest neighboring embeddings in the corpus (i.e., the dataset fully converted into vector form) to retrieve the most semantically relevant matches.

Vector Search vs Neural Search vs Keyword Search

Vector search often gets used interchangeably with “neural search” (a subset of vector search). Both differ from traditional keyword search and there are distinctions between vector and neural search that are helpful to understand. Each has its own strengths and limitations. Here’s a quick cheat sheet on the three approaches:

  • Vector search – Converts data to high-dimensional vector embeddings that capture semantic meaning and context, then finds the nearest neighboring vectors. That’s how it can retrieve contextually relevant matches.
  • Neural search – A type of vector search that specifically leverages neural networks and deep learning models to generate the vector embeddings representing data. This is an important distinction because neural networks, particularly those involved in deep learning, are very effective at understanding complex patterns and nuances in data (e.g., synonyms, conceptual similarities, etc.)
  • Keyword search – The traditional search approach of scanning for literal keyword matches and ranking results based on frequency of those words occurring. It can’t inherently understand semantic context or intent behind queries.

Difference Between Vector Search and Semantic Search

Vector search is the underlying technique that enables semantic search capabilities— semantic search doesn’t work without vector embeddings. By creating high-dimensional vector embeddings that capture semantic meaning and context, vector search enables semantic search to retrieve results that match the user’s intended meaning. And, as already noted, that allows the human and the search engine to break free from the shackles of literal keyword-based matching and querying.

Why is Vector Search Important?

Vector search unlocks new capabilities that make search experiences more intuitive, relevant, and valuable for users. By understanding the semantic relevance of a user’s query – the intent of the searcher – the search engine can go far beyond simple keyword matching. This is the magic that enables you to personalize recommendations based on a customer’s unique interests and behaviors. 

It’s the technology that powers media platforms and websites like Netflix, Google, and Spotify. All these companies use vector search to enable understand and retrieve content that matches user intent. It’s this same technology that underpins the latest AI language models and chatbots – tools that humans can engage with using natural, contextual dialog.  

By capturing semantic meaning and context through vector embeddings, vector search allows search engines, websites and applications to break free from restrictive keyword-based retrieval. This enhanced understanding of context and intent is how vector search engines retrieve the most relevant information, recommendations, and results.

How can Vector Search Benefit Your Business?

Vector search can significantly elevate the search and discovery experience for your business and customers. Truly understanding user intent and context enables ecommerce websites and other businesses to deliver relevant, personalized results that match what people are searching for. 

At Monetate, our personalized search solution uses vector search to transform on-site discovery into a satisfying digital experience. Advanced natural language processing deciphers nuanced search intent while market-leading ML models dynamically optimize results for each unique visitor based on real-time behavior signals. 

Personalized search is part of Monetate’s comprehensive suite of personalization capabilities that include segmentation, testing and recommendations. Our platform is designed to help you optimize your entire search journey for maximum business impact.