TigerGraph Unveils Hybrid Search to Enhance AI Accuracy and Efficiency

TigerGraph, an enterprise AI infrastructure and graph database company, has further solidified its position in the AI infrastructure and graph database market with the launch of its next-gen hybrid solution that integrates vector search and graph search into a single platform.

According to TigerGraph, the vector search capabilities enable the detection of data anomalies through advanced pattern analysis. It also helps identify critical deviations from expected norms and provides actionable recommendations.

So, what does this mean for businesses? Hybrid search is becoming increasingly important for organizations as AI applications rely on both structured business data and unstructured content like text and images. Graph search helps users map relationships between data points. This helps with complex pattern recognition and a deeper contextual understanding of how different pieces of data are connected. On the other hand, a vector search translates information into numerical representations, making it easier to identify similarities and retrieve relevant results quickly.

Building on this, the combination of vector and graph search offers a comprehensive and powerful approach to data analysis. It allows businesses to process both structured and unstructured data within a single framework.

Additionally, users get quicker retrieval of relevant data while improving recall accuracy. This is especially useful for applications like recommendation systems, fraud detection, and AI-driven search queries.

TigerGraph aims to combine its core strengths – speed, accuracy, and scalability to ensure that vector search operates swiftly and accurately. “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.”

We know how important data has become for the modern-day organization. However, it is not just about the data, it is also about the ability of an organization to understand its data. Knowledge graphs are becoming increasingly popular with enterprises looking to make sense of their data by identifying relationships and context.

To take this concept further, enterprises are using GraphRAG, which is an integration of knowledge graphs with retrieval-augmented generation. This can help improve how AI understands and retrieves information.

While GraphRAG is still new, it is showing a dramatic improvement in LLM accuracy and reasoning capabilities. It is driving the next phase of GenAI, and organizations that can leverage its capabilities are set to gain a competitive edge.

Leveraging graphs for data representation, TigerGraph is integrating proprietary local knowledge with real-time data into its vector search framework. This includes using GraphRAG to deliver superior personalization and explainability.

The goal is to help AI systems retrieve more relevant information, make better connections between data points, and provide more accurate responses. This can also simplify AI development by reducing infrastructure complexity and provide unified enterprise support for security, access control, and reliability.

TigerGraph claims the vector search offers over five times faster vector searches with 23% higher recall than competitors while requiring 22.4x fewer resources. It also claims six times faster indexing with automatic incremental updates. Using graph-based indexing rather than vector search could explain the efficiency gains. However, TigerGraph has not shared any independent benchmarking yet. That would help add more weight to these claims.

As graph databases are inherently good at modeling relationships, TigerGraph expects the vector search to deliver support for complex relationships between entities and create sophisticated knowledge graphs. The company shared more technical details about TigerVector in a paper published on ArXiv.

TigerGraph has also introduced a free community edition that offers a graph database with 16 CPUs, 200 GB graph storage, and 100 GB vector storage. The company claims that this is the most powerful graph database that is free to use.

TigerGraph’s approach to vector search and graph-based analytics is promising. However, the value of this hybrid search will be evident in its real-world applications. It’s a competitive market with several key players, including Neo4j or Amazon Neptune, who offer graph-based analytics solutions. For TigerGraph to show its unique value, it would need to provide a strong enough reason for enterprises to choose a hybrid search approach.

Neo4j Partners with Google Cloud to Launch New GraphRAG Capabilities for GenAI Applications

AWS Launches New Analytics Engine That Combines the Power Of Vector Search And Graph Data

The post TigerGraph Unveils Hybrid Search to Enhance AI Accuracy and Efficiency appeared first on BigDATAwire.