An Introduction To Vector Databases: Why Its Data Retrieval Method Is Different

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To say we live in a data-driven world would be an understatement. Data is the future of innovation, and corporations invest huge amounts of time and resources into securing data in order to improve their services.

The vector database is one relatively new database that is making waves in the data management industry. This is because vector databases store and retrieve data in a different way than traditional databases. Instead of storing data in tables or a defined data model, vector databases store data in a multi-dimensional space. The ability of a vector database to efficiently handle and extract meaning from large and intricate datasets makes it an essential asset across industries. Many businesses are seeing the vector database’s value, which is evident in the fact that the global vector database market size is expected to reach $6.4 billion by 2030. In this blog, we will explore what a vector database is and how its data retrieval methods are different.

How Does a Vector Database Store Data?

What makes a vector database unique among databases is how it stores data on a vector. A vector is a mathematical representation of an object’s features stored in a vector database as a list of numbers. This data can be anything, from an image or video to a whole text document or even a single word. The object is inputted into an embedding model, which uses an algorithm to convert it into a vector embedding and store it in a vector index. The data is then sorted through vector indexing and grouped based on their similarity and contextual relationships.

For example, if creating a vector database to store a large collection of videos, each video will be an individual vector embedding that will contain multiple data points such as the title, length, and content. Within the database, similar videos will automatically be clustered together. However, further indexing will also group together data points from each similar vector, e.g., All videos of the same length will be clustered together. It is this clustering that enables the vector databases’ unique data retrieval method.

How Does a Vector Database Retrieve Data?

A vector database can retrieve data through a similarity search (also known as a vector search). Unlike traditional databases that rely on exact matches, vector databases retrieve results based on similarity. This semantic understanding means that they can be matched even if two pieces of data aren’t identical but are contextually or semantically similar. A similarity search retrieves vectors comparable to a given query by analyzing the similarity between the query vector and vectors stored in the database.

For example, if a vector search is performed in a collection of car images and you query for Ferrari results, the similarity search would also return results on Aston Martin or Lamborghini. Users can set thresholds in the similarity search to ensure the search isn’t too wide. This is done by setting a similarity score limit. When a vector database performs a similarity search, it will calculate how close each vector is based on a similarity score. It is possible to set similarity scores so that only the vectors above the specified threshold are returned as search results, allowing users to control the level of relevance and precision in the retrieved results.

How is a Vector Database’s Data Retrieval Method Used?

This unique retrieval method has many benefits in the real world that make vector databases extremely useful. One of the most common applications of a vector database is for recommendation systems. An ecommerce store can use a vector search to recommend products based on previous purchases or even their browsing history. When a customer searches for a specific product, they will also get similar results, which will enhance customer satisfaction and potentially increase sales. Another important area where vector databases are becoming increasingly vital is generative AI.

Vector databases that are used by chatbots can distinguish the semantic essence of phrases or sentences, which enables them to identify matches that might not be identical in terms of wording but are contextually similar. This allows chatbots to search for the best answers based on the user’s query and respond accurately. Because vector databases can be continuously updated, they can be used to train AI models beyond their initial dataset. This allows companies to easily tailor AI models to their specific requirements and ensure they are always up-to-date with the latest changes.

Data is a source of knowledge, which is crucial to business efficiency, and vector databases are fast becoming one of the most effective tools a business can employ to store and use the data they collect. As all industries become more data-driven, more businesses are expected to adopt vector databases.