How Redis is Fueling the Generative AI Wave
Back in March, a glitch within an open-source library knocked OpenAI’s ChatGPT offline and inadvertently gave a few users access to others’ chat titles and ChatGPT Plus subscribers’ payment information. Redis stepped in to troubleshoot for OpenAI, quickly restoring service and communication and keeping the conversation going. Traditionally known for its simple key-value pair data handling, the company has expanded its capabilities to include vector search functionality in the latest Redis 7.2 release. “This addition addresses the inefficiency of using key-value pairs for vector embeddings, as users often need to search across these embeddings to analyse the relative distance between a query and the stored data,” said Tim Hall, chief product officer, in an exclusive interaction with AIM. Thus, Redis Cloud now supports not just the storage but also the effective searching of vector data. Relevance of Vector Db in the Generative AI Era “We’ve also been advancing our ecosystem integration, including collaborating with LangChain. This led to Harrison’s OpenGPT on Redis Cloud, leveraging our vector embedding and search capabilities,” said Hall, highlighting that Redis Cloud exemplifies how developers can build generative AI applications and recommendation systems, especially when real-time and interactive responsiveness is needed. OpenGPTs, an open-source initiative by LangChain, offers a flexible approach to generative AI, allowing users to select models, control data retrieval, and manage data storage. Redis has a suite of premium customers like AngelOne, British Airways, and Raymond James, among others, for its versatility, high performance, ease of use, and customisation options, particularly suited for AI applications. Its enterprise-grade solutions offer robust security certifications and reliable handling of new data structures, like vector embeddings. Discussing the same, Hall emphasised the practicality and flexibility of Redis for AI applications, particularly in handling vector embeddings for retrieval tasks with an example. The Retrieval Augmented Generation (RAG) framework showcased in the image embodies this by using Redis Cloud alongside OpenAI’s embedding layer. Regardless of type, data is converted into vector embeddings and stored in Redis’s vector database, which is then queried to find relevant information on asking a question. This efficient, real-time process, which eliminates the need for fine-tuning with sensitive data, underscores Redis’s capabilities for rapid and secure AI development, perfectly aligning with use cases like document analysis and chatbot interaction. In the space of generative AI, which is swiftly becoming a staple in numerous applications, the necessity for databases capable of managing intricate and real-time data is paramount. Redis Cloud is ideal for such real-time requirements, particularly storing and searching vector embeddings in real-time applications. So, will vector databases be irrelevant soon because of peaking generative AI models? Short answer: no. “I don’t think vector databases will become irrelevant quickly. It’s typical for vendors to expand their capabilities, but they will still need to have these under your control. You should be wary of ceding too much control to someone else, especially in this space,” he commented. Solving Multiple Problems at One Go In dialogue with customers, Redis addresses the nuanced demands of adopting LLMs. Businesses are increasingly interested in utilising LLMs to automate customer support and enhance interactive retail experiences. Such AI implementations can efficiently handle complex queries, streamlining customer interactions and potentially boosting sales while reducing costs associated with human support staff. However, the deployment of LLMs has its challenges. “A primary concern among customers is hallucinations, where the AI provides irrelevant or incorrect responses for which we have created a hybrid search mechanism that merges vector embeddings with metadata, which refines search results and keeps responses relevant,” Hall explained. A further concern is the control and privacy of proprietary data during the embedding process. Customers fear their unique data might be utilised without consent for other model training. Redis’s solution is a vector database that allows for constructing bespoke, private AI systems akin to having a personal ChatGPT. This empowers users with the autonomy to control their data, ensuring privacy and preventing unauthorised usage. Growth of Database Solutions Market As for the evolution of the database solutions market, Hall foresees a shift towards more unified and flexible systems. The past decade has transitioned from conventional relational databases to specialised solutions for specific data types or tasks, such as JSON documents and time series data. The inclusion of vector databases for vector data management is a recent trend, becoming increasingly significant with the rise of AI and machine learning. However, Hall notes that the sector has reached a critical juncture where the operational complexity of juggling multiple specialised databases for a singular application is no longer tenable. There are slower databases that might be suitable if real-time interactivity is optional. “The key is determining whether one database can satisfy most of your use cases, especially if you deal with one, two, or three different kinds of data. Everyone is trying to find the sweet spot for their specific use case and the problem they’re trying to solve, considering the vast array of data platforms available—around 450 globally. Our specific focus at Redis is on real-time solutions,” Hall explained. Hall’s prognosis for the future of the database market is one of consolidation and integration, a departure from the previous era of rapid expansion and specialisation. This trend reflects a broader industry movement towards enhancing operational efficiency and diminishing complexity, especially as generative AI matures and integrates more deeply into the technological fabric. India as a Market Since 2020, Redis has doubled its headcount within India, expanded its presence across the country (Delhi, Mumbai, and Pune), and opened its first dedicated regional office in Bangalore in 2019. Groww, Apna, AngelOne, Purplle, Motilal Oswal and Zee are among the company’s customers in India. Sharing a personal anecdote on economic growth, all humorously compared India’s surging GDP to the early internet days in the U.S., where the digital boom was just beginning to light up screens. He fondly recounts how his English friends were once mystified by the concept of ’24/7’ Fast forward, and it’s India’s turn, with its tech leapfrog driven by a mobile device explosion, ushering in a 24/7 economy where services never sleep. Redis, seizing the moment, is powering a myriad of India’s mobile applications, banking on the country’s digital transformation to fortify its own growth in this dynamic market. “India presents a tremendous opportunity for interactive applications, particularly those that Redis can power. We’re working with various organisations in India, especially in the banking and entertainment sectors, to power their mobile applications. India is an exciting and vital market for us right now,” concluded Hall.
“We’ve also been advancing our ecosystem integrations, including collaborating with LangChain. This led to Harrison’s OpenGPT on Redis Cloud, leveraging our vector embedding and search capabilities,” Tim Hall, chief product officer, told AIM.
"We've also been advancing our ecosystem integrations, including collaborating with LangChain. This led to Harrison's OpenGPT on Redis cloud, leveraging our vector embedding and search capabilities,"Tim Hall, chief product officer, told AIM.
["Interviews and Discussions"]
["ChatGPT", "machine learning", "OpenAI", "AI", "ML", "Ray", "LangChain", "Aim", "generative AI", "retrieval augmented generation", "Interviews and Discussions"]
["AI", "machine learning", "ML", "generative AI", "ChatGPT", "OpenAI", "LangChain", "Aim", "Ray", "retrieval augmented generation"]
Shritama (she/her) is a technology journalist at AIM who is passionate to explore generative AI with a special focus on big techs, database, healthcare, DE&amp;I, hiring in tech and more.