InterSystems Adds Vector Search to IRIS to Support Next-Generation AI Applications

InterSystems is adding vector search to the InterSystems IRIS data platform to enable efficient and accurate retrieval of relevant information from massive datasets when working with large language models.

By converting text and images into high-dimensional vectors, these techniques allow quick comparisons and searches, even when dealing with millions of files from disparate datasets across the organization.

The announcement was made by Scott Gnau, head of global data platforms, InterSystems in a blog post, stating “At InterSystems, we're always looking for ways to bring next generation data processing as close to our customers’ data as possible without having to transfer data to specialized systems.”

By adding vector search to the InterSystems IRIS data platform, the company is making the data platform searchable through vector embeddings to enhance the functionality of the software for tasks related to natural language processing (NLP), text, and image analysis.

This integration will make it easier for developers to create applications that use generative AI to complete complex tasks for a wide range of use cases and deliver up-to-date responses based on proprietary data processed by InterSystems. It also means they can do this with very curated data while being confident in keeping their internal proprietary intelligence secure.

This capability allows the InterSystems IRIS data platform to manage and query content and related dense vector embeddings, particularly as it enables Retrieval-Augmented Generation (RAG) integration to develop generative AI-based applications. With the rapidly evolving toolsets available, seamless RAG integration allows agile adoption for new models and use cases, according to the company.

“Our commitment to maintaining the highest standards of privacy, security, and responsibility will guide a thoughtful and just approach to AI that creates trust while accelerating innovation, ensuring customer success, and demonstrating a commitment to excellence. We believe transparency, responsibility and explainability are key to establishing trust in and driving innovation from AI systems,” said Gnau.

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