Artificial intelligence (AI) relies heavily on the utilization of vectorized data, transforming real-world information into a format that can be analyzed, searched, and manipulated effectively.
At the core of this process are vector databases, which store the data generated through AI modeling and provide access during AI inference. This article delves into the concept of vector databases, exploring how vector data is applied in AI and machine learning, discussing high-dimensional data, vector embedding, the storage challenges associated with vector data, and the companies offering vector database solutions.
Understanding High-Dimensional Data
Vector data is a specialized form of high-dimensional data, characterized by having significantly more features or values associated with a single data point compared to the number of samples collected. Historically, low-dimensional data—where data points have few values—was more prevalent. However, the advancement in data collection has allowed for high-dimensional data to thrive. A prime example of this is contemporary AI that processes complex information, such as speech or images, which can have numerous attributes and contexts.
What are Vectors?
Vectors are one of several types of data representations where quantities are expressed using single or more complex numerical arrangements. In mathematical terms, a scalar is a single value, such as 5 or 0.5, while a vector is a one-dimensional array, for example, [0.5, 5]. A matrix extends this concept into two dimensions, like:
[[0.5, 5],
[5, 0.5],
[0.5, 5]]Tensors take this even further into three or more dimensions. For instance, a 3D tensor could depict color values in an image (for red, green, and blue), while a 4D tensor may include time by sequencing 3D tensors in a video context. Tensors are multi-dimensional arrays of numbers capable of representing intricate data, making them invaluable in frameworks like TensorFlow and PyTorch used for AI and deep learning.
What is Vector Embedding?
In AI, tensors facilitate the storage and manipulation of data. Tensor-based frameworks provide essential tools for creating tensors and executing computations on them. For instance, when a natural language request is made to ChatGPT, it is parsed for meaning and context, subsequently represented as a multi-dimensional tensor. This transformation turns real-world subjects into a format suitable for mathematical operations, a process known as vector embedding.
To respond to a query, the processed numerical representation can be compared to existing vector-embedded data, allowing for the retrieval of relevant answers. This concept—ingesting and representing data, then comparing and responding—can be applied to various AI scenarios, including image analysis or consumer behavior studies.
What are Vector Databases?
Vector databases are specifically designed to store high-dimensional vector data, organizing data points into clusters based on their similarities. They offer the speed and performance essential for generative AI applications. According to Gartner, by 2026, over 30% of enterprises are expected to adopt vector databases to build foundational models using pertinent business data.
In contrast to traditional relational databases, which are structured in rows and columns, data points in vector databases exist as vectors with multiple dimensions. While traditional databases handle structured data with predefined variables, vector databases accommodate values that exist across multiple scales, allowing for representation of characteristics found in unstructured data—such as nuances in colors or the arrangement of pixels in an image.
Transforming unstructured data into a format suitable for traditional relational databases to prepare it for AI implementation is feasible, though it poses challenges. The methods of searching also diverge between the two; traditional SQL databases require precise values for searches, while vector databases allow for less exact representations, retrieving results based on related data and patterns that might be invisible to conventional databases.
Storage Challenges of Vector Databases
AI modeling requires writing vector embeddings into databases with massive volumes of often non-numeric data like words, sounds, or images. During inference, AI compares these vector-embedded data with new queries through high-performance processors, predominantly graphical processing units (GPUs) that handle extensive processing loads.
Vector databases face significant I/O demands, especially during modeling, making scalability and potential data portability vital for efficient processing. They can be indexed to speed up searches and evaluate the distance between vectors, enabling results based on similarity—crucial for applications like recommendation systems, semantic searches, image recognition, and natural language processing.
Key Players in Vector Database Supply
Numerous proprietary and open-source database products exist, including offerings from DataStax, Elastic, Milvus, Pinecone, Singlestore, and Weaviate. There are also extensions for existing databases, such as PostgreSQL’s pgvector, vector search capabilities in Apache Cassandra, and Redis’s vector database features.
Platforms like IBM watsonx.data also integrate vector database functionalities. Additionally, major cloud service providers—AWS, Google Cloud, and Microsoft Azure—offer vector database capabilities both in their own services and through third-party options available in their marketplaces.