Over 80% of world’s data is unstructured i.e, Audio, Video, Documents. One cannot store this data in traditional database. Traditional databases are great at storing and retrieving data based on exact matches. However, the world of AI necessitates a more nuanced approach to evaluate data based on similar characteristics. Vector embeddings are essential for this data task. So, Forget rows and columns! Vector databases store data as "vectors" in a high-dimensional space, enabling rapid searches based on similarity. Unlike traditional databases, they excel at handling unstructured data like images, video, text and audio.
The similar vectors are clustered together. Need to find visually similar images? A vector database analyzes the image itself (pixels), not just keywords, for accurate results.
Vectors are arrays of numbers that can represent complex data like text, images, videos and audios, generated by a machine learning model. These vectors are represented in a continuous, multi-dimensional space known as an embedding, which are generated by embedding models. The embedding models are specialized to convert the vector data into an embedding. Vector databases store and index the output of an embedding model. Vector embeddings are a numerical representation of data, grouping sets of data based on semantic meaning or similar features across virtually any data type.
For example, consider the words “doctor” and “physician”. They refer to same profession even though they’re spelled different. In AI applications for semantic search, vector representations of “doctor” and “physician” need to capture their semantic equivalence. In machine learning, embeddings are high-dimensional vectors that encode this semantic information. These vector embeddings are crucial for powering recommendation engines, voice assistants, and AI applications like ChatGPT, Gemini.
image credits: KDNuggets
Imagine a database that understands the essence of your data, not just the literal meaning. That's the power of vector databases! They go beyond storing raw information like text or images and instead capture their core characteristics using vector embeddings. These embeddings are like unique fingerprints, allowing the database to find similar data points quickly and efficiently.
Here's the magic behind the scenes:
For example, say you have a database of product images. A traditional database might struggle to find similar items based on a blurry picture. But a vector database can analyze the color, shape, and overall composition, allowing you to find visually similar products with ease.
The best part? Vector databases aren't one-trick ponies. They offer the full range of CRUD (Create, Read, Update, Delete) operations you'd expect from any database. So you can manage your data effectively while unlocking the power of similarity search.
Vector databases excel at similarity searches. They can rapidly find embeddings similar to a query embedding, which is essential for applications like: