Published on October 22, 2023, 9:29 pm

Vector databases are becoming increasingly important in the era of generative AI. These databases store and retrieve high-dimensional vector representations, which are crucial for supporting large language models. Unlike traditional databases, vector databases are optimized for similarity searches. They are being used by organizations to enhance customer recommendations, enable real-time anomaly detection, and improve fraud detection capabilities. There are two types of vector databases: dedicated vector databases that offer seamless scalability for handling billions of vectors, and extended vector databases that support vectors through indexes and functions. The combination of generative AI and vector databases provides organizations with the opportunity to unlock new possibilities and extract valuable insights from their data assets.

In today’s era of generative AI (genAI), vector databases are gaining significance and emerging as a crucial element. These databases play a vital role in storing and retrieving high-dimensional vector representations, which are indispensable for supporting large language models (LLMs). Unlike traditional databases that are optimized for exact matches, vector databases are specifically designed to facilitate similarity searches. They excel in applications where the objective is to find data points similar to a given vector. For instance, a vector database can efficiently locate images similar to a provided image or identify text resembling a given piece of text. With the use of vectors, LLMs can rapidly process requests, providing the necessary performance required for conducting complex analyses.

While vector databases have been in existence for several decades now, their usage has been limited. According to Forrester’s estimates, the current adoption rate of vector databases stands at 6%, with an expected surge to 18% within the next year. In my opinion, the potential for vector databases is enormous, particularly when it comes to extracting insights from untapped data assets. Already, we see organizations leveraging vector databases to enhance customer recommendations, enable real-time anomaly detection with IoT data, and improve fraud detection capabilities.

There are different types of vector databases available in the market today. In addition to simply storing vectors, these databases offer various essential data management capabilities. This includes efficient metadata storage, real-time data changes management, granular access control, resource allocation for optimal performance, concurrency management, and elastic scalability. Vector databases possess built-in search capabilities that swiftly deliver optimized and relevant results, especially when dealing with complex data sets such as images, videos, or audio files. Moreover, these databases support pretrained embeddings of data like word or image embeddings to provide faster access and support for ML models. Their ability to store and process high-dimensional data efficiently allows them to identify patterns and relationships that may remain invisible when using non-vector databases.

Two primary types of vector databases can be distinguished:

1. Dedicated vector databases: These dedicated databases have the advantage of seamless scalability to handle billions of vectors. They offer optimized storage and query capabilities specifically designed for vector embeddings. Numerous organizations are currently utilizing these databases for genAI purposes, and feedback regarding their usage has been overwhelmingly positive.

2. Extended vector databases: These types of databases do not offer native support for vectors but instead support them through vector indexes and functions. We anticipate that most traditional databases will eventually incorporate some level of vector processing capabilities in the near future. Certain traditional database vendors already provide support for vector data, offering broader multimodal capabilities. Organizations are leveraging these extended vector databases to integrate traditional structured and unstructured data with high-dimensional vectors, effectively supporting semantically-driven LLMs.

The power of generative AI, empowered by vector databases, presents an immense opportunity for organizations across industries to unlock new possibilities and uncover valuable insights from their data assets.

– FutureCIO: [The key to unlocking the power of generative AI](


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