Supercharging India’s AI Innovation with Vector Search and Retrieval Augmented Generation (RAG)

If data is the currency of innovation, then artificial intelligence (AI) is its most powerful tool. As businesses generate and collect unprecedented amounts of data, the challenge isn’t just in storing or processing it—it’s in tapping into the full potential of data and elevating their AI capabilities for an experience that’s faster and better able to meet the needs of customers or end users.

Enter Elastic, a Search AI company that brings together the precision of search and the intelligence of AI. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, enables organisations to accelerate the results that matter. By taking advantage of all structured and unstructured data—securing and protecting private information more effectively—Elastic’s complete, cloud-based solutions for search, security, and observability help organisations deliver on the promise of AI.

Changing the game with Elastic and Search AI

Elastic’s innovations go beyond the ordinary. Its advanced search AI capabilities, like vector search and Elastic for Retrieval Augment Generation (RAG) workflows, redefine how information is retrieved, processed, and utilised for generative AI use cases.

For developers, vector search and RAG open up a world of possibilities. These technologies enable them to build applications that are not only more intelligent but also more responsive to user needs. As demand for smarter, more intuitive AI solutions grows, developers need an understanding of how to better use data to push the boundaries of innovation.

Vector Search: Searching beyond keywords

Traditional search engines have limitations—they rely on keyword matching, which often falls short when queries are complex or data is unstructured. Enter vector search, a technology that transcends these limitations by understanding the context and relationships between different pieces of information.

Vector search leverages machine learning (ML) to capture the meaning and context of unstructured data, including text and images, transforming it into a numeric representation, yielding more relevant results and executing faster. A search engine can thus deliver results that are not just based on keywords but on the underlying meaning of the query. Using the unstructured data beyond text ranging from videos, images, to audio, vector search can provide a more intuitive e-commerce search experience, retrieve videos and images to recommend films, movies, and games, or even analyse genetic and chemical sequences to accelerate drug discovery.

Vector search combined with traditional search techniques will be key to unlocking the next generation of generative AI experiences with capabilities including semantic search, which is based on the interpreted meaning of words and phrases instead of keywords; multimodal search to find visually similar images, video, and audio that match a specific sample; and enrich search experiences through natural language processing to answer highly-specific questions or determine emotional nuance for a better chatbot customer-facing experience.

​​Cisco’s re-imagined Enterprise Search Platform powered by Elasticsearch ensures Cisco.com users receive detailed, easy-to-consume results, keeping them engaged. Cisco uses AI and Elastic vector search to automatically generate queries from a customer support ticket that are then sent back through Elastic search for content recommendations which are immediately provided to the customer. Cisco has achieved 73% faster search queries with 90% of support requests resolved with the new platform, and 5,000 support engineer hours saved per month.

RAG: A new frontier in AI

It is important to acknowledge that while the promised benefits of generative AI are compelling — improved customer and employee experiences, increased workforce productivity—there is still much work to do to ensure that a user gets quality, accurate information from a query or prompt.

While today, a large language model (LLM) might mistakenly suggest putting glue on pizza, or that rocks are edible, RAG adds a crucial layer of control and context to make sure an LLM’s responses are grounded with control and context to become more reliable and relevant. RAG is a technique that enables enterprises to search proprietary data sources and provide context that grounds large language models for more accurate, real-time responses. This is particularly crucial in industries like healthcare, finance, and education, where precision and up-to-date knowledge are non-negotiable.

In effect, RAG optimises an LLM’s output by providing authoritative information from sources that lie beyond the LLM’s training data. These sources could be from an organisation’s systems or knowledge bases or trusted third-party providers. The information they contain may include current data that the LLM was not previously trained on, proprietary internal knowledge, recent conversations, or updated personalisation data.

Basic RAG architecture begins with user questions, using vector databases to retrieve relevant data like documents, images, and audio. However, for an advanced RAG workflow, several layers are involved. These include the data layer which determines the type and storage of information; the model layer which incorporates foundational LLMs and embedding models; the application layer, which manages retrieval, prompts, and application logic; and finally the analysis and deployment layers, which ensure the solution is fit for its purpose, efficiently deployed, and can adapt to new data.

For an organisation to fully reap the benefits of RAG, IT teams and developers would need to procure quality data and secure said data. An organisation would also need to consider the right generative AI tool or LLM for their use case, and have an effective means of search to retrieve data for RAG workflows. Organisations are also not locked into specific LLMs – search and RAG workflows can work with different LLMs to fit an organisation’s changing needs and priorities.

For example, Consensus is a pioneering search engine that uses advanced artificial intelligence (AI) and LLMs to aggregate and distill insights from more than 200 million peer-reviewed papers from the Semantic Scholar database. Using Elastic for RAG workflows has helped Consensus increase search accuracy by 30% and reduce search latency by 75%. Consensus plans to expand beyond academic research and include high quality data sets and expert knowledge outside of peer-reviewed journals.

Push the boundaries of AI innovation at ElasticON

Learn more about vector search, RAG, and many more innovations at the ElasticON Tour Bengaluru, an exclusive event designed to provide practical knowledge, hands-on demonstrations, and real-world use cases of searchI-powered AI solutions. Whether you’re a developer, architect, IT professional, or business leader, this is your opportunity to learn directly from the experts who are shaping the future of search-powered AI.

For those ready to dive deeper into the AI-driven future, the ElasticON Tour Bengaluru event on September 25, 2024 is a must-attend. Held at Conrad Bengaluru, this one-day event will showcase how Elastic’s cutting-edge technologies can help businesses harness the full potential of their data, using AI and advanced search capabilities to drive tangible results. At ElasticON, you’ll get to hear directly from Elastic leaders about the industry’s first Search AI Lake, and what Search AI-powered solutions are on the horizon.

Register now and secure your spot at ElasticON. Discover how you can use the precision of search with the intelligence of AI to unlock new possibilities for your business with the Elastic Search AI Platform.

Take your AI initiatives to the next level with Elastic. Learn from industry experts, network with peers, and discover game-changing solutions at ElasticON Tour Bengaluru—where data meets innovation.

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