With the rise of big data, our need for efficient and scalable ways to store and search for that data rises as well. Traditional databases are not always well-suited for this task, as they are not designed to handle high-dimensional data.
Vector databases are a new type of database that are specifically designed to store and search for high-dimensional data. They use a technique called vector indexing to store and search for similar vectors. This makes them appropriate for applications such as natural language processing and image recognition.
What is a Vector Database?
A vector database is a type of database that stores data as high-dimensional vectors. Vectors are mathematical objects that signal a point in space. Features, such as the text of a document, the pixels of an image, or the features of a person’s face are encoded in a vector, with the property that vectors with a small distance between each other are similar. This is done through vector embedding.
How do Vector Databases Work?
Vector databases use a technique called vector indexing to store and search for similar vectors. Vector indexing involves creating an index of all the vectors in the database. This index is then used to quickly find vectors that are similar to a given vector. As vectors are numerical, machines can efficiently calculate the distance between them. There are many different distance functions that can be used, such as Euclidean distance, Manhattan distance, and cosine similarity. These give a notion of similarity between two vectors.
Advantages of Vector Databases
Vector databases offer a number of advantages over traditional databases, including:
Efficient similarity search: Vector databases are very efficient at finding similar vectors.
Scalability: Vector databases are scalable to large datasets.
Flexibility: Vector databases are flexible and can be used for a variety of applications. Any data that suits vector embedding can be efficiently stored in a Vector Database
