In March, an incident in an open-source library caused OpenAI's ChatGPT to go offline, inadvertently allowing some users to access other users' chat titles and ChatGPT Plus subscribers' payment information. Redis quickly intervened to troubleshoot the issue for OpenAI, swiftly restoring service and communication to ensure the continuity of conversations. As we all know, Redis has traditionally been known for its simple key-value data processing. Now, it has expanded its capabilities to include vector search functionality in the latest Redis 7.2 version. "This addition addresses the inefficiency of using key-value pairs for vector embedding, as users often need to search across these embeddings to analyze the relative distances between queries and stored data," said Tim Hall, Chief Product Officer, in an exclusive interaction with AIM. Therefore, Redis now not only supports storage but also enables efficient searching of vector data. "We have also been advancing our ecosystem integration, including collaboration with LangChain. This has resulted in Harrison's OpenGPT on Rediscloud, leveraging our vector embedding and search capabilities," emphasized Hall. Rediscloud showcases how developers can build generative AI applications and recommendation systems, especially when real-time and interactive responses are required. LangChain's open-source initiative, OpenGPT, offers a flexible approach to generative AI, allowing users to choose models, control data retrieval, and manage data storage. Redis has a range of high-profile clients such as X (formerly Twitter), Stack Overflow, Snapchat, and Craigslist, who favor it for its versatility, high performance, ease of use, and custom options specifically suited for AI applications. Its enterprise-grade solution provides robust security authentication and reliably handles new data structures like vector embeddings. While discussing the same topic, Hall emphasized the practicality and flexibility of Redis for AI applications, particularly when dealing with retrieval tasks involving vector embeddings, using an example. The Retrieval Augmented Generation (RAG) framework shown in the diagram illustrates this, with Redis being used alongside OpenAI's embedding layer. Regardless of the type of data, it is transformed into vector embeddings and stored in Redis's vector database, which is then queried to find relevant information during questioning. This efficient and real-time process eliminates the need for fine-tuning with sensitive data, highlighting Redis's capabilities in fast and secure AI development, making it highly suitable for use cases like document analysis and chatbot interactions. In the field of generative AI, which is rapidly becoming the norm in numerous applications, there is a critical need for databases capable of managing complex and real-time data. Redis is particularly well-suited for this real-time requirement, especially for storing and searching vector embeddings. Redis addresses the subtle requirements of adopting Large Language Models (LLMs) in conversations with customers. Enterprises are increasingly interested in leveraging LLMs to automate customer support and enhance interactive retail experiences. Such AI implementations can efficiently handle complex queries, simplify customer interactions, and potentially increase sales while reducing costs associated with human support personnel. However, deploying LLMs also presents its challenges. "One major concern among customers is the illusion that AI provides irrelevant or incorrect answers. To address this, we have created a hybrid search mechanism that combines vector embeddings with metadata, improving search results and maintaining response relevance," explained Hall. Further concerns revolve around controlling and protecting the privacy of proprietary data during the embedding process. Customers worry that their unique data may be used for other model training without consent. Redis's solution is a vector database that allows the construction of customized, private AI systems, similar to having a personal ChatGPT. This empowers users with control over their own data, ensuring privacy and preventing unauthorized usage. Regarding the evolution of the database solutions market, Hall foresees a trend towards more unified and flexible systems. Over the past decade, there has been a transition from traditional relational databases to specialized solutions designed for specific data types or tasks, such as JSON documents and time series data. Vector databases have recently emerged as an important trend in the rise of AI and machine learning. However, Hall points out that the field has reached a critical moment where the operational complexity of running multiple specialized databases is no longer manageable. Slower databases may be suitable if real-time interaction is optional. "The key is to determine if a database can meet the majority of your use cases, especially if you are dealing with one, two, or three different types of data. Everyone is trying to find the sweet spot for their specific use case and the problem they are trying to solve, considering that there are approximately 450 different data platforms globally. Our specific focus at Redis is real-time solutions," explained Hall. Hall predicts an integration and consolidation for the future of the database market, marking a shift from the rapid expansion and specialization of the past few years. This trend reflects a broader industry movement towards improving operational efficiency and reducing complexity, particularly as generative AI matures and becomes more deeply integrated into technological infrastructure. In India, Redis has doubled its workforce since 2020, expanding its presence nationwide in Delhi, Mumbai, and Pune, and opening its first dedicated regional office in Bangalore in 2019. Groww, Apna, AngelOne, Purplle, Motilal Oswal, and Zee are among the company's clients in India. While sharing a personal anecdote about economic growth, Hall humorously compared India's soaring GDP to the early days of the internet in the United States when the digital wave was just beginning to light up screens. He fondly recalled how his British friends were once perplexed by the concept of "24/7." Fast forward to the present, it is now India's turn, driven by the explosive growth of mobile devices, to bring forth a 24/7 economy where services never cease. Redis seizes the opportunity, powering numerous mobile applications in India and leveraging the country's digital transformation to strengthen its growth in this vibrant market. "India presents a tremendous opportunity for interactive applications, especially those that Redis can support. We are collaborating with various organizations in India, particularly in the banking and entertainment industries, to support their mobile applications. India is now an exciting and critical market for us," concluded Hall.