TigerGraph offers enhanced hybrid search with its graph database to power AI at scale

TigerGraph, the enterprise AI infrastructure and graph database leader, is releasing its next generation graph and vector hybrid search, delivering the industry's “most advanced” solution for detecting data anomalies through sophisticated pattern analysis, identifying critical deviations from expected norms, and providing actionable recommendations.

According to the company, the integration of graph and vector search on a single high-performance, scalable platform offers businesses a comprehensive solution for developing significantly more accurate AI systems for fraud and anti-money laundering detection, real-time personalized recommendations, and image and multimedia matches among others.

Simultaneously, TigerGraph is releasing a Community Edition of its graph database that offers significant compute power and storage capacity, the company said.

By leveraging graphs to represent proprietary local knowledge and real-time data, including their interrelationships, graph-enhanced AI and GraphRAG deliver superior personalization and explainability.

This multi-modal approach simplifies the design and operation of complex AI use cases, dramatically reducing infrastructure complexity and code requirements while providing enterprise-grade security, access controls, and reliability, the company said.

TigerGraph vector search benefits include:

• Advanced hybrid search of structured and unstructured data – enhances discoverability and contextually rich understanding for ML and AI systems, significantly improving their analytical capabilities.
• Rich relationship modeling – delivers support for complex relationships between entities and creates sophisticated knowledge graphs.
• Integrated query language – express hybrid graph+vector queries in GSQL, harmonically achieve structured and unstructured query composition. The Python library also supports vector database operations.

TigerGraph's Community Edition is a powerful graph database that's free to use, even in production:

• 16 CPUs of compute power for significantly higher performance.
• 200 GB graph storage and 100 GB of vector storage to enable AI-driven applications.
• Extensive AI/ML open-source library, simplifying the development of graph + vector applications, including GraphRAG.
• GSQL, OpenCypher, and ISO GQL for the widest and most powerful query language support.

“We’re continuing to lead the way in delivering the industry’s fastest, most scalable analytics for AI and machine learning users,” said Rajeev Shrivastava, CEO of TigerGraph. “The engineer in me is excited to put these solutions directly into the hands of developers who are building mission critical, AI dependent products that improve their customers’ lives.”

For more information about this news, visit www.tigergraph.com.