What is a Vector Search?

Imagine searching in a distributed database and finding not just exact keyword matches, but results that truly understand what you mean. 

In modern scalable data infrastructure – think cloud-native apps running on distributed databases like YugabyteDB – developers are adopting vector search to power smarter, AI-driven search features. This technique lets you query data by meaning and similarity, which is a big leap from traditional keyword search.

This article breaks down vector search for cloud-native developers, database architects, and platform engineers, covering what it is, how it works with AI, and how it differs from other search approaches.

What is a Vector Search in Simple Terms?

Vector search is a way to find similar content by comparing data in a vector space rather than comparing raw text. In simple terms, it converts text, images, and other unstructured data into high-dimensional vectors (lists of numbers) that represent the meaning of that content. 

You can think of these vectors as numeric fingerprints of the data – if two pieces of content have similar meaning or context, their “fingerprints” (vectors) will be mathematically close to each other. Vector search is sometimes called nearest neighbor search because it works by finding the nearest vectors to your query’s vector.

Unlike traditional keyword search, which looks for exact word matches, vector search uses machine learning to understand context. For example, a classic search engine might treat the words “movie” and “film” as different, but a vector search can recognize they’re related because their vectors will be nearby in the vector space. 

This means a vector search engine can retrieve relevant results even if the exact keywords aren’t present, by focusing on semantic similarity. It’s an approach heavily used in AI applications: the search finds items that feel similar in meaning to the query, which is why it’s great for things like finding similar documents, images, or user recommendations.

A high-level view of vector search: data (documents, images, etc.) is translated into embeddings (vectors) using AI models. A user’s query is also converted into a vector. The search engine then finds the closest vectors to the query’s vector in this high-dimensional space, returning results that are most similar in meaning. This allows finding matches by concept rather than exact keywords.

So, how does it work under the hood? 

In a vector search engine, each item in your dataset (say a piece of text, an image, or an audio clip) is processed by a machine learning model to produce a vector embedding. These embeddings are simply arrays of numbers, often with hundreds of dimensions, that capture the item’s key features or topics. When you submit a query, the system also converts the query into a vector using a similar model. 

Now, searching becomes a matter of mathematics: the engine measures the distance between the query vector and all the data vectors to find which items are closest (most similar) to your query. The closer two vectors are, the more related their content is. By using algorithms known as approximate nearest neighbor (ANN) methods, a vector search can quickly find the top N closest matches even in huge datasets.

What is an Example of a Vector?

In math, a vector is simply an ordered list of numbers, like [3.5, -2.1, 0.7]. In AI and data science, a vector usually refers to a numeric representation of data that captures some meaning or features of that data. It’s also called an embedding. For example, a short piece of text (say, “cat”) might be represented by a vector of 300 numbers, each number encoding some aspect of the word’s meaning or context. 

Likewise, an image of a cat can be converted into a vector (perhaps 512 numbers long) that encodes visual features like fur texture, shape, and so on. These vectors themselves might look meaningless to us – just lists of numbers – but to a computer, they are rich with information about the content.

For a concrete example, consider a sentence like “behavior of parrots in the Amazon.” A language model could turn that sentence into a vector embedding, perhaps something like [-0.127, 0.394, -0.281, 0.735, -0.019, …, 0.158]. Each position in this vector (each number) corresponds to some latent feature the model has learned – maybe one dimension measures how much the text is about animals, another relates to geography (Amazon rainforest vs. Amazon.com), another to behavior or action. 

The exact numbers aren’t important to humans, but what matters is that similar pieces of text will end up with vectors that are close together. If we take another sentence like “behavior of macaws in the Brazilian jungle” and embed it, its vector would be very close to the “parrots” sentence, indicating the two have a similar meaning.

How does a query or data become a vector? Here’s a step-by-step, simple example of how vectorization and vector search work for, say, a text query:

1. Choose or train an embedding model: First, you need a machine learning model that knows how to represent your data as vectors. This could be a pre-trained language model for text (like BERT) or a vision model for images (like ResNet or CLIP).
   
2. Convert data to vectors: For all the items in your database or index, run them through the model to get their vector embeddings. Store these vectors, typically in a vector database or an index that supports fast vector similarity lookup.
   
3. Convert the query to a vector: When a user makes a search (either entering text or providing an example image), the system uses the same embedding model to transform the query into its own vector representation.
   
4. Find nearest neighbor vectors: The core of vector search is finding which stored vectors are “nearest” to the query’s vector. “Near” is measured by a distance metric in vector space, often cosine similarity or Euclidean distance. Essentially, the engine calculates which vectors of your data have the smallest distance to the query vector.
   
5. Retrieve and rank results: The items corresponding to those nearest vectors are retrieved as the top results. Typically, the results are already ranked by similarity (nearest first). These are the content pieces most semantically similar to the query. 

After these steps, the application can display the results to the user.

The user experiences it as if the search understood their intent, because the results are relevant in meaning, not just by literal word match. The key was that both the query and the data were compared in this vector form. Two vectors that are close mean the original contents were similar, which is why vector search finds similar items based on the distances between vectors.

What is the Difference Between Elasticsearch and Vector Search?

Elasticsearch is a well-known search engine that traditionally relies on keyword-based search, whereas vector search is a newer approach focused on similarity search using embeddings. 

Elasticsearch (traditional mode) finds documents by matching terms and using algorithms like BM25 to rank results, while vector search finds items by comparing numeric vectors representing semantic meaning. 

Let’s break down the differences and also see how they can complement each other:

Traditional Elasticsearch (Keyword Search)

Elasticsearch indexes documents using an inverted index of terms. When you search for “database scaling techniques,” Elasticsearch will look for documents containing those terms (or their stems/synonyms if configured) and score them based on frequency, field weights, etc. This method is excellent for precision with structured text queries and is incredibly fast for exact matches. 

However, it has a limitation: if a document uses different wording (e.g. “methods for scaling storage”) that doesn’t exactly match the query terms, Elasticsearch might miss it. It doesn’t inherently understand that “scaling techniques” and “methods for scaling” are related ideas – it just sees different words. In short, keyword search is literal; it can’t truly grasp the query’s intent beyond the words themselves.

Vector Search (Semantic Search)

Vector search doesn’t use an inverted index of words. Instead, it requires that content and queries be pre-processed into vectors via ML models. 

The trade-off is that vector search requires more computational overhead and specialized indexing to be fast, whereas keyword search has decades of optimization behind it. Early on, vector searches could be slower and harder to scale than keyword searches, but with technologies like ANN indexes and hardware acceleration, they can be made very efficient even at scale.

Elasticsearch vs. vector search isn’t an either/or choice – in fact, modern Elasticsearch supports vector search capabilities, combining the two methods. Recent versions of Elasticsearch introduced a dense vector field type and kNN search, so you can store embeddings and perform similarity searches directly in Elastic. For example, Elastic 8.x allows you to index vectors and run a k-NN query to get the closest vectors to a given query vector. This means you can use Elasticsearch as a rudimentary vector database alongside its classic full-text search. 

In practical terms, you might enrich your documents with embedding vectors and then use a query vector to find similar documents. A real-world example is image search: Elastic has demonstrated using vector search for image similarity, where a user uploads a photo and Elastic finds visually similar images in an index. This is something pure keyword search could never do, since images don’t have keywords until you manually tag them.

What is the Difference between Neural Search and Vector Search?

The terms neural search, vector search, and semantic search are closely related and sometimes used interchangeably, but there are subtle differences in emphasis:

• Vector search: Refers to the core technique of searching by comparing vectors (as we’ve discussed). Data is converted into high-dimensional vectors that encode meaning, and queries find results by looking for nearest neighbor vectors.
  
• Neural search: This usually means a type of vector search that specifically leverages neural networks (deep learning models) to generate those vectors. In other words, neural search is vector search powered by neural network-generated embeddings. Because neural networks are excellent at capturing complex patterns (like understanding synonyms, context, and user intent), neural search can be very powerful. The advantage of neural search is that those models can be updated or fine-tuned, and they often learn from data, giving the search system the ability to improve over time or adapt to specific domains.

To put it another way: vector search is the mechanism, neural search is the mechanism powered by a particular technology (neural nets), and semantic search is the user-facing functionality that these mechanisms deliver. 

Neural search and vector search are very closely connected – in fact, many people will just say “vector search” when they mean a search system that uses neural embeddings. 

Some also use “neural search” to imply an even more advanced scenario where the search engine not only uses vectors but might incorporate on-the-fly neural network processing (like re-ranking results with a transformer model, etc.). But at a high level, both neural and vector search deal with embeddings and similarity.

All three are tied together. For example, when you use a modern question-answering system, you’re experiencing semantic search (it finds the answer to your question’s meaning). Underneath, that system likely used neural search: perhaps a language model turned your question into a vector, matched it against a vector database of FAQs, then maybe even used a neural network to formulate the answer. And fundamentally, it was vector search that allowed matching your question to the right answer by similarity.

As AI models continue to evolve, we can expect neural search to become synonymous with semantic search, all built on the backbone of vector similarity. This is a big part of why today’s distributed databases and search platforms are evolving: to support these vector-based, AI-driven queries seamlessly alongside traditional queries, giving developers the best of both worlds when building intelligent applications.