DataStax Introduces Astra DB Hybrid Search, Boosting AI Search Relevance by 45%

Powered by NVIDIA NeMo Retriever Text Reranking, Astra DB Hybrid Search Delivers Smarter, More Accurate AI Responses

SANTA CLARA, Calif.--(BUSINESS WIRE)--DataStax, a leading AI platform, today announced Astra DB Hybrid Search, a breakthrough capability that significantly enhances retrieval-augmented generation (RAG) systems by improving search relevance by 45%. Accelerated by the NVIDIA NeMo Retriever reranking microservices, part of NVIDIA AI Enterprise, Astra DB Hybrid Search seamlessly integrates vector search and lexical search to deliver highly accurate, AI-driven search and recommendation experiences.

“Retrieval is the most important part of RAG for delivering accuracy. We have heard from countless customers that attaining 95%+ accuracy is a non-negotiable when it comes to bringing enterprise AI into production. Astra DB Hybrid Search helps customers get there faster,” said Ed Anuff, Chief Product Officer, DataStax.

Hybrid search combines two powerful retrieval methods—vector search (for semantic understanding and contextual relevance) and Lexical search (for exact keyword matching to ensure critical terms aren’t overlooked)—to help ensure both contextual relevance and precise keyword matching. Increased relevance is a critical factor in AI-powered search, recommendations, and personalization. Poorly-ranked search results lead to irrelevant answers, frustrating users and ultimately compromising generative AI applications.

Combined with NVIDIA NeMo Retriever text reranking microservices, Astra DB automatically intelligently reorders search results using fine-tuned large language models (LLMs), providing state-of-the-art ranking for more relevant and meaningful responses. This AI-powered auto-reranking ensures that search responses are more accurate, significantly improving user experience in AI-driven applications.

For logistics software provider GoDash, Astra DB Hybrid Search will enable more efficient operations and faster, more relevant insights for its shipping customers, the company’s founder and CEO Aditya Swami said.

"Hybrid Search from DataStax will be a transformative solution for us. It seamlessly combines keyword and vector search, allowing us to instantly retrieve the most relevant shipment details, operational insights, and customer feedback. With AI-powered accuracy and real-time data retrieval, we can optimize logistics, reduce delays, and enhance the overall delivery experience—ensuring both operational efficiency and customer satisfaction at scale."

Developers can easily integrate this functionality using the Astra DB Python client and schema-less Data API, making it a powerful and intuitive solution to enhance AI search and recommendation systems.

Hybrid search is hosted on Astra DB with GPUs, enabling ultra-fast, cost-efficient AI workloads without the complexities of managed infrastructure. The new capability will be available in Langflow, the open-source tool and community for low-code AI application development, enabling developers to experiment quickly and optimize their search relevance effortlessly.

About DataStax

DataStax is the company that helps developers and companies successfully create a bold new world through GenAI. We offer a one-stop generative AI stack with everything needed for a faster, easier, path to production for relevant and responsive GenAI applications. DataStax delivers a RAG-first developer experience, with first-class integrations into leading AI ecosystem partners, so we work with developers’ existing stacks of choice. With DataStax, anyone can quickly build smart, high-growth AI applications at unlimited scale, on any cloud. Hundreds of the world’s leading enterprises, including Audi, Bud Financial, Capital One, Skypoint, and many more rely on DataStax. Learn more at DataStax.com.

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