A conversational search experience for retail: Elasticsearch Relevance Engine with Google Cloud’s generative AI

Previously published on the Google Cloud blog

For retailers, having a powerful search function for their website and mobile applications is crucial. However, most current search experiences are somewhat basic and limited, primarily returning a list of results ranked based on keyword relevance. And typically, customers only have a vague idea of what they want, rather than a specific item in mind. For instance, they might be searching for a nice dining room table but aren't sure about the style, size, shape, or other details. Not surprisingly, many customers leave a retail website after unsuccessful search attempts. This not only deprives them of a potentially satisfying shopping experience but also represents a lost opportunity for retailers.

Today, we are excited to present a new search experience for retailers using generative AI with Vertex AI and Elasticsearch®. This enhanced interface offers users an interactive, conversational experience that summarizes pertinent data and tailors responses based on each customer's unique needs, all while drawing upon the retailer's public and internal knowledge. That can include, for example, public knowledge from the retailer's domain expertise:

• Guidelines and common scenarios for solving your specific need
• Summary of the reviews for similar products matching your request
• Public references on how to build, combine, or use the products

Or, the knowledge can be private to the retailer:

• A user’s general location and local regulations that may apply to them
• Real-time updated product catalog, stock nearby, and prices
• Private documentation and knowledge

Instead of merely generating a simple list of relevant items, this advanced search experience functions much like a concierge sales representative with in-depth domain knowledge. It assists and guides customers throughout their entire purchase journey, creating a more engaging and efficient shopping experience.

In this architecture, a search query happens in two broad phases.

But when the context window size is limited, common models only allow processing of just a few thousands tokens. It’s therefore impossible to pass the whole enterprise data set to each query. Moreover, the bigger the query the user submits, the more performance and cost are impacted. You need to filter and find relevant context even before sending the query to generative AI.

The Elasticsearch Relevance Engine^TM (ESRE) is a package built on years of R&D and expertise from Elastic in the search and AI space to help customers leverage the power of Elasticsearch with high-end machine learning features. ESRE enables relevant results retrieval from huge, heterogeneous data sets quickly using advanced vector search features on text, images, audio, and video, all while combining with classic keyword text-search on Elasticsearch indices. With ESRE, not only is every information retrieved relevant, but also secured thanks to document- and field-level security applicable to all content that Elastic handles.

With Elastic, you have the freedom of choosing and running your own machine learning (ML) models to create embeddings or for feature extraction, thus allowing full control and customization depending on your specific domain, language, and data type. Vertex AI can then help you create your transformer models to import into the Elastic platform. No worries if you want to get started quickly instead, with no internal ML skills: the out-of-the-box ELSER retrieval model lets you be up and running with just a few clicks.

Model customization: Retailers may prefer to interact with foundational models to adapt them to their unique needs. Fine-tuning is an efficient and economical approach to enhance a model's task-specific performance or comply with distinct output requirements when instructions aren't adequate. But developing and training new foundational models from the ground up can be costly. Techniques such as efficient parameters tuning reduce overhead while implementing adapter layers atop existing LLMs. These layers supplement the models with your specific missing knowledge, maintaining their already established skills.

Security and privacy: While consumer AI assistants are typically robust, controlling their features and security isn’t always in your hands. Having complete insight into your data's usage and how it is stored is vital to compliance, privacy, and maintaining a competitive edge. Google’s Secure AI Framework provides clear industry standards for building and deploying AI technology in a responsible manner. This is why adopting generative AI for enterprises via Google’s Vertex AI capabilities is so smart.

Enterprise-grade solution: Vertex AI Generative AI Studio provides access to cutting-edge models and allows for navigation through various releases and modifications. Once customized, you can test, deploy, and integrate them with other applications and platforms hosted on Google Cloud. This can be done via the Vertex AI SDK or directly through the Google Cloud console, making it user friendly for almost anyone, even without specialized data-science expertise.

Responsible AI: With great power comes great responsibility. LLMs have the capacity to translate languages, condense text, and generate creative writing, code, and images, among many other tasks. However, as an emerging technology, its evolving abilities and applications may lead to potential misuse, misapplication, and unexpected consequences. These models can sometimes generate outputs that are unpredictable, including text that might be offensive, insensitive, or factually incorrect. Google is dedicated to promoting responsible use of this technology, providing safety features and filters to protect users and ensure compliance with its AI principles and commitment to responsible AI.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

Elastic, Elasticsearch, ESRE, Elasticsearch Relevance Engine and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.