CXOToday has engaged in an exclusive interview with Deb Dutta, General Manager – Asia Pacific & Japan, DataStax
- Can you elaborate on the impact of DataStax’s vector search capability in Astra DB on the development of generative AI applications?
Astra DB allows for storing data as ‘vector embeddings’, a crucial component for building generative AI applications using Large Language Models (LLMs) like GPT-4. This capability enables efficient storage and retrieval of proprietory and complex data patterns, facilitating the training and generation of new content.
Secondly, Astra DB’s vector search feature outperforms other databases by remarkably improving data volume handling and latency. This means that developers working on generative AI applications can harness much larger datasets and experience significantly reduced response times, enabling faster and more accurate results compared to other databases.
Lastly, Astra DB’s support for semantic similarity search and distributed vector indexes ensures that generative AI use cases can be easily implemented. These features, coupled with DataStax’s proven and reliable database technology, significantly lower the entry barrier for new applications. This translates to faster time to market and effortless adoption for teams working on bringing generative AI applications to production and opening up new possibilities for innovation.
- What advantages does Astra DB offer as a vector database over other databases on the market for AI initiatives?
There is a common misconception among developers early in their generative AI application development projects that their semantically relevant database is small and doesn’t change frequently. This leads them to think they don’t require a scalable database for vector search.
However, in reality, producing generative AI applications generates significant amounts of data, often surpassing the vectorized data. For example, applications like chatbots need memory to track conversations, which creates substantial data volume proportional to the active user base.
This is where Astra DB trumps other databases in the market. It provides a high-write-throughput database that is required for these scenarios. Built on Cassandra’s speed and limitless scale, Astra DB provides exceptional performance and global scalability and availability.
Astra DB also supports the different kinds of query styles used by generative, predictive, and interpretive AI applications such as semantic search and primary key or index lookups. By supporting all these use cases, Astra DB reduces application complexity, simplifies architecture, and eases the learning curve for developers.
Overall, Astra DB stands out as a superior choice for AI initiatives due to its scalability, handling of high-write-throughput requirements, support for multiple query styles, and best-in-class performance leveraging Cassandra’s power. These advantages make it a compelling option for businesses seeking robust, efficient, and scalable database solutions for their AI-driven applications.
- As your services have expanded to include Microsoft Azure and Amazon Web Services in addition to Google Cloud, how does this broaden opportunities for businesses?
The availability of Astra DB’s vector capabilities on Microsoft Azure and Amazon Web Services (AWS) increases optionality for our customers. Astra DB’s availability across all major cloud providers allows Datastax’s customers the freedom to transact and consume from their preferred cloud platform. This flexibility ensures that businesses can leverage the specific features and benefits offered by Azure or AWS while still accessing the power of Astra DB and utilizing their committed spend.
Furthermore, the inclusion of Azure and AWS in Astra DB provides businesses with long-term flexibility. Organizations that adopt Astra DB can add another cloud provider if their requirements change or if they wish to pursue a multi-cloud strategy. By enabling zero-downtime cloud migration, businesses can seamlessly move their Astra DB-powered applications to a different cloud infrastructure, ensuring uninterrupted operations and minimizing any disruptions.
- Can you explain why databases supporting vectors are crucial to unlocking the potential of generative AI?
Without vector search capabilities, users often have to rely on complex and domain-specific techniques to construct search queries based on terms. This makes the process of dynamically searching for specific data in a database challenging. However, with vector search, the problem of finding what a user is looking for becomes simpler. Instead of dealing with language queries, vector search involves expressing the meaning of a given data type as a list of numerical dimensions or vectors.
When vectors are similar, their content and meaning are also similar. This means that users can easily find relevant data by identifying vectors that are closely related to their query. Various “embedding algorithms” are used to feed data into the algorithm, producing representative vectors that capture the essence of the data.
LLMs, which are integral to generative AI, communicate through a language of “embeddings.” These embeddings serve as the basis for how LLMs locate, retrieve, and provide data in their responses. Vector databases store and manage these embeddings efficiently, allowing LLMs to access, process, and correlate data swiftly, resulting in the contextual and responsive generation of information—an essential capability for effective generative AI
- It’s been said that trust in the outcomes of AI models is crucial for their widespread adoption. How does having vector search capabilities in a database help to build this trust?
When utilising language models (LLMs), such as chatbots or recommendation systems, users often pose queries like “What products do you think I like?” While LLMs can make educated guesses based on general data, providing relevant context significantly improves their accuracy. With vector search, this task becomes achievable, as it enables the LLMs to access and analyse vectors associated with specific user data or past purchases.
The power of vector search lies in its ability to provide LLMs with context to generate more informed and accurate responses. By feeding the LLMs vectors representing the user’s purchase history or product preferences, the LLMs can incorporate this relevant context and make more precise predictions. This means that when a user asks the question again, the LLM can provide a significantly better guess based on the prior context provided.
In essence, vector search enables LLMs to have a better understanding of a user’s preferences and make more reliable recommendations or responses. This ensures that the outcomes generated by AI models are not solely based on general assumptions, but rather on a user’s specific context and interactions captured by the vectors. By incorporating vector search capabilities, businesses can instill users with confidence in the AI models’ outputs, as they witness the models making more informed and contextually relevant predictions.