Data is king, and the storage and organization of every data type have led to a demand for databases that can keep up with new data management innovations.
In the past few years, vector databases have emerged as among the most advanced and important databases being implemented across industries. This is due to their important role in the evolution of generative AI, valued at $67.18 billion this year, and their capacity to store and sort large quantities of unstructured data. Yet, despite the increasingly widespread demand for vector databases, we are still in the infancy of this database’s potential. In this post, we will examine what a vector database is and why the future of vector databases looks very bright.
How Does a Vector Database Differ to a Traditional Database?
A traditional database organizes data into tables, rows, and columns and uses Structured Query Language (SQL) to manage and manipulate the stored data. This is one of the simplest and most effective ways to store data that fits into data tables, such as numbers, short text, and dates, and is why SQL databases continue to be one of the most widely used across industries. Vector databases store data in a mathematical construct that is called a vector.
The advantage of a vector database compared to a traditional database is that it can store unstructured data such as documents, audio and video files, and images. A vector database takes data points from the unstructured data and turns them into a vector embedding, a list of numbers that are then indexed in the database. Unlike a traditional database, which is used for finding exact matches, a vector database can search for semantically similar data points.
Why Are Vector Searches Becoming Critical?
As noted above, traditional keyword searches effectively pinpoint specific terms within documents or tables, but they fall short with unstructured data, such as videos, books, social media posts, PDFs, and audio files. Today, a vast amount of data comes from this unstructured data. Studies show that unstructured data represents 80% to 90% of all new enterprise data and is growing three times faster than structured data. In order to store and sort unstructured data efficiently, they must be converted into multiple data points.
As explained by MongoDB, “vector databases are adept at handling data points that span hundreds or even thousands of dimensions. Algorithms optimized for vector search of high dimensional vectors, such as approximate nearest neighbor (ANN) search, can swiftly identify the most similar vectors in this vast space without the need to scan every vector. This efficiency translates to faster and more resource-effective searches”. This makes vector databases crucial for generative AI applications like recommendation systems, image recognition software, and large language models (LMMs), which can provide results based on similarity rather than exact matches.
Vector Databases Are the Future
The rise of vector databases and their importance is becoming a hot topic in the tech world. A paper by CMU professor Andy Pavlo, known for his acclaimed database lectures, detailed how, in Q3 of 2023, vector databases were being used as external memory for large language models, which evolved to become known as Retrieval-Augmented Generation (RAG). RAG is a response to an increasing demand for LLMs to respond to queries from private or proprietary datasets. Using multiple data points that have been converted into vectors, RAG can perform a similarity search within the vector database to retrieve the top results. These results are then combined into prompts and sent to the LLM as input. This allows the LLM to find the best results when answering questions as a chatbot, creating new content on ChatGPT, translating languages, or analyzing documents. As AI continues to become more widely implemented, the need for vector databases to help train these models will increase.
Vector databases are highly adaptable and can be very efficient at scaling to meet changing needs. This makes them the ideal database for emerging applications across all industries. For example, in healthcare, vector databases are aiding drug discovery by analyzing molecular structures for potential therapeutic properties. In the financial sector, they can assist in anomaly detection, with similarity searches being used to spot unusual patterns that might indicate fraudulent activities. As technology develops, so does the amount of data collected, which in turn leads to an increased demand for vector databases that allow industries to harness and use the data.
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