Databases? Yeah, They're Cool Again (Thanks to GenAI)

Generative AI (GenAI) is the talk of the tech world. From crafting marketing copy to generating eerily realistic images, these AI systems seem capable of near-magic.

But as the models become more complex, their hunger for data grows. This places a new demand on traditional databases, forcing engineers to rethink how they store and manage the massive data pools needed to train and refine GenAI models.

That's where the rise of vector databases is changing the game.

Vectors: The language of GenAI

Imagine trying to teach someone a new language by limiting them solely to flashcards. That's akin to how most databases work with keywords and exact matches.

Vector databases offer a richer form of database communication. They convert data—text, images, code, and more—into mathematical representations called embeddings. They represent all the data as vectors so that real-world information, semantic relationships and context are inherently understood. This is vital for GenAI.

“A key requirement for GenAI databases is vector search, which assists organizations in discovering linked concepts in search responses, rather than solely focusing on keywords. Vector search is a method in artificial intelligence and data retrieval that uses mathematical vectors to represent and efficiently search through complex, unstructured data,” says William McLane, chief technology officer for cloud at DataStax.

Take the query, "Find images of puppies playing at the beach." A vector database doesn't just look for photos tagged with those precise words. It understands concepts like "playful" or "outdoors" and can even match images containing relevant objects even if "puppy" or "beach" aren't explicitly mentioned.

It's an area where DataStax is making great strides with its Astra DB. McLane claims it now outperforms its nearest rivals by 20%, a huge increase in accuracy and precision.

The result is that GenAI models can be trained with far more nuanced data. This leads to applications that understand and generate content in surprisingly human-like ways.

Speed and scalability: Keeping up with AI's appetite

Accuracy is only one side of the equation.

GenAI models are notoriously data-hungry, needing to process vast amounts of real-time information to generate responses.

Vector databases, like Astra DB often built on distributed architectures, excel at this. They can scale quickly and handle large volumes of data without performance bottlenecks.

“This heightened throughput enables generative AI applications to function more efficiently and handle larger workloads, providing developers and organizations with a competitive edge,” says McLane.

The high throughput and the ability to integrate real-time data streams are essential to powering the interactive GenAI applications that are starting to emerge.

RAG and the power of context

By now, everyone understands GenAI's hallucination problem. This is where the GenAI models make up responses for the prompt with unsubstantiated information.

This has put Retrieval-Augmented Generation (RAG) in the spotlight; it is also where vector databases truly shine.

"For RAG-based architectures, rapidly retrieving contextual information from a known source of truth is critical, and vector databases have the native ability to store and retrieve information structured for generative AI applications efficiently," says McLane.

Let's say your organization has a vast documentation library. A RAG-powered AI assistant could query this library using vector search, finding the most relevant sections based on the user's intent rather than just keywords. This contextual awareness allows models to provide smarter answers and complete complex tasks, minimizing the chance of 'hallucinations'.

“By providing a streamlined approach to utilizing past, present, and future information, the data being used by generative AI models is constantly updated, validated, and contextualized properly,” says McLane.

RAG architectures powered by a vector database can also allow GenAI applications to tap into "multiple different modalities that have been stored and generate contextual, relevant outputs across multiple different domains such as text, audio, and images," McLane adds.

Navigating the cloud and open source

Another area where vector databases are making their mark is in the cloud.

Cloud-based offerings often feature the ease of deployment and scalability needed to handle GenAI workloads. Many nextgen databases are also built on open-source foundations like Apache Cassandra.

"With the help of a nextgen database, users can easily deploy a single distributed database that spans multiple clouds, ensuring seamless access through one or more microservices," McLane explains.

This means that IT teams can utilize microservices that do not require knowledge of the presence of multiple clouds and are integrated through a single API.

"Essentially, nextgen databases allow for the effortless addition of new clouds without the need for substantial modifications to the microservices," says McLane, improving agility and reducing the workload (and chances of any error).

Another advantage of utilizing a distributed database with a peer-to-peer design is the absence of a single point of failure. Rather than relying on a primary-secondary architecture commonly found in relational and most NoSQL databases, nextgen databases implement an active-everywhere architecture.

“If the primary becomes unavailable, a new primary would need to be elected in traditional primary/secondary environments, resulting in a temporary suspension of write requests,” says McLane.

With a peer-to-peer architecture, requests can be directed to any available database node, eliminating the need for a designated primary. "In cases where a server becomes unavailable, requests will simply not be sent to that node," McLane adds.

This helped Skypoint, a data, analytics, and AI SaaS platform for the healthcare sector, use AI to streamline the time-consuming and labor-intensive process of collecting and analyzing vast amounts of data for use cases such as developing and customizing individual patient policies.

DataStax's solutions, particularly the vector search capabilities and Astra DB, allowed Skypoint to create a powerful search functionality within their AI stack.

“With the ability to access real-time data from documents and unstructured sources, Skypoint could ensure the recency of information and connect disparate data sources seamlessly,” says McLane.

In addition, DataStax's solutions also offered a memory component to Skypoint's AI stack. It allowed the company to integrate various open-source orchestration technologies to build a complete AI stack.

Data privacy in the GenAI era

One of the most pressing concerns with GenAI is ensuring the responsible use of data.

Vector databases, when integrated into a robust LLMOps (Large Language Model Operations) framework, can play a crucial role in mitigating privacy risks, McLane observes

“Privacy issues such as Personally Identifiable Information (PII) leakage can have severe consequences, including violating regulatory requirements and compromising user trust,” says McLane. “Nextgen databases should facilitate robust validation processes that detect and prevent PII leakage, ensuring compliance with privacy regulations.”

By constantly monitoring data feeds for bias and PII leakage, developers can take a proactive approach to ethical AI.

McLane also notes that correctness metrics play a vital role in ensuring the ethical deployment of AI models. Metrics like faithfulness and hallucinations can assess the accuracy and reliability of AI predictions and help identify instances where the model deviates from expected behavior.

“By monitoring and analyzing these metrics, nextgen databases empower users to take proactive measures to rectify any ethical concerns arising from AI deployment,” he adds.

The road ahead

The integration of vector databases and GenAI is still in its early stages, but the potential is vast.

Soon, we'll see AI systems that seamlessly blend knowledge from internal data stores, code repositories, and the wider world. This will drive breakthroughs in everything from customer service to scientific research, with databases acting as the intelligent backbone.

The vector databases of today are just the start. As GenAI leaps forward, those who design and manage these nextgen databases will be the architects of a truly transformative era.

Image credit: iStockphoto/master1305