Have you ever wondered how Netflix recommends your next favorite show or how Google seems to know exactly what you’re searching for? These everyday conveniences are powered by a clever technology called vector search.
This guide will break it down in simple terms, so you can understand how it works and why it’s important.
Unlike traditional keyword-based search that relies on exact matches, vector search understands the meaning behind your query, leading to much better search results.
Vector search is based on vector representations of data. These representations, also known as vector embeddings, are numeric representations of data points.
Think of vector search like this: instead of just storing words, documents, images, or videos as raw data, these things are converted into a string of numbers that capture their key characteristics.
These numbers represent the semantic relationships between different pieces of information. For example, the vector for "king" might be closer to the vector for "queen" than it is to the vector for "apple," reflecting their semantic relationship.
This numeric representation lets machine learning models understand and compare the data. When you make a vector search, the system converts your query into a vector embedding also.
It then uses algorithms like nearest neighbor search to find the data points in the dataset with the closest vector embeddings. The closer the vectors, the more semantically similar the items are. This means vector search can find items that are related in meaning, even if they don't share the same keywords.
Vector search is used in data retrieval and machine learning for many uses, like semantic search, recommendation systems, and image recognition. Because it focuses on meaning, vector search is a very important part of modern intelligent search experiences.
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Think back to your high school physics class. A vector is like an arrow that has two things: a size (called magnitude) and a direction. For example, gravity is a vector. It has a certain strength (the pull toward Earth) and points in a specific direction (toward the center of the Earth).
Now, when it comes to words and searching, a vector works similarly. Each word is turned into an arrow, but instead of just two traits (size and direction), a word gets thousands of traits!
These traits represent the word’s meaning and how it relates to other words. Words that mean similar things end up near each other in something called “vector space.”
Vector search is a huge improvement to information retrieval as it understands the meaning, not just your keywords. This difference enables the solution to find similar results to the concept you're looking for.
And vector search works very well with even huge amounts of data, such as millions or billions of documents.
This improvement of semantic understanding unlocks better, more relevant search experiences across many data types like text, images, and code. Ultimately, vector search empowers tools ranging from personalized recommendations to advanced AI (artificial intelligence) assistants, significantly improving how we find information.
Here’s the interesting part: computers use a process called “embedding” to turn words into vectors. This process uses a special model (think of it like a very smart program) that understands how words relate to each other. For example:
Pretty awesome, right?
By contrast, vector search focuses on meaning instead of just matching words. It represents data and queries as vector embeddings, which are numerical representations of their semantic content. These embeddings are points in a high-dimensional space, where proximity indicates semantic similarity.
Therefore, a vector search for "dog" might also retrieve results for "pet," "animal," or "puppy" because these words are close to "dog" in vector space. This allows vector search to uncover related information even if you don't use exact words.
Think of a librarian who understands the underlying meaning of your query and can suggest other relevant resources beyond just those containing your specific words. Essentially, vector search captures the semantic relationship between concepts, enabling it to find results that are conceptually similar, even if they lack lexical similarity.
This makes vector similarity search more powerful for discovering related content and understanding the intent behind a query.
Vector search has a ton of uses or applications. Here are a few examples:
Platforms like Netflix and Amazon use vector search to suggest things you might like based on what you’ve already watched or bought. This personalized approach improves user engagement and satisfaction.
This allows for a more comprehensive and contextually aware search experience, going beyond simple keyword matching.
So you can search for an image using text, search for text using an image, or even use a combination of both. This ability unlocks richer and more intuitive search experiences, going beyond traditional text-based or image-based search alone.
In Natural Language Processing (NLP), vector search powers many different applications, from chatbots to sophisticated AI assistants. By representing text as vectors, these applications can understand the semantic meaning of language.
Vector search is essential for NLP tasks like document clustering, topic modeling, and semantic search, allowing AI to process and understand language more effectively.
At the core of vector search lies the concept of nearest neighbors. Data points (whether documents or images) are turned into vector embeddings, which are numerical representations that capture semantic meaning.
These vector embeddings are then indexed for efficient retrieval. When a a search query is submitted, it too is converted into a query of vector embeddings.
The search engine then uses algorithms, like k nearest neighbor algorithms (kNN), to find the data points with the most similar vector embeddings to the query vector. These similar vectors represent the most relevant results.
While kNN provides high accuracy, it can be computationally expensive, especially when executing efficiently in high dimensional spaces.
Therefore, many vector search implementations use approximate nearest neighbor (ANN) algorithms. These algorithms sacrifice perfect accuracy to significantly reduce the computational resources required and improve search speed.
This is a good trade off, especially when dealing with massive datasets, as the benefits of faster search outweigh the slight loss in precision. ANN algorithms enable efficient vector search at scale, making it practical for real-world uses.
Approximate Nearest Neighbor (ANN) algorithms are crucial for efficient vector search in high-dimensional spaces. Traditional nearest neighbor algorithms, while accurate, can be computationally expensive. ANN algorithms significantly reduce this computational burden by sacrificing some accuracy.
This trade-off dramatically improves search efficiency, making it practical to find matches within massive datasets. ANN methods are essential for scaling vector search to handle real-world applications.
Grooper, our intelligent document processing platform, makes vector search easy to use. Here’s how vector search works with Grooper:
In addition, Grooper’s no-code platform means you don’t need to be a tech wizard to use it. Everything is set up to work smoothly without needing a large team of programmers.
Grooper improves vector search by empowering companies to process and unify all their structured and unstructured data with comprehensive access and retrieval. An especially important feature is Grooper's ability to integrate with large language models (LLMs) and embedding technologies, enabling simple and context-aware search functionalities.
Here are several benefits of vector search:
The implementation of vector search in Grooper improves retrieval accuracy and supports complex queries. The big benefit is tailored solutions that match specific business needs. As a result, Grooper's vector search can be a vital tool for companies looking to transform their data into actionable insights.
Want to learn more about how Grooper can bring the power of AI vector applications search to your fingertips? Read more about Grooper.
Contact us to get a demo of vector search with your documents!