Neo4j, the world’s leading graph database and analytics company, announced a new product integration with Google Cloud’s latest generative AI features in Vertex AI, Google’s leading large language model (LLM) platform. The result empowers enterprise customers to harness knowledge graphs built on Neo4j’s fully managed cloud offerings in Google Cloud Platform for generative AI insights and recommendations that are more accurate, transparent, and explainable.
Neo4j’s graph database and analytics capabilities can be used to create knowledge graphs, which capture relationships between entities, enabling AI systems to reason, infer, and retrieve relevant information effectively. The result ensures more accurate, explainable, and transparent outcomes for large language models (LLMs) and other generative AI systems.
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Specific integrations with Google’s generative AI capabilities in Vertex AI enable enterprise customers to:
- Leverage natural language to interact with knowledge graphs: Vertex AI’s generative AI capabilities can be used to provide a natural language interface to the knowledge graph. In this case, Cypher query language statements are generated from user input and used to query the database. This allows non-technical users unfamiliar with database query languages to access the knowledge graph. We will soon provide this capability to existing dashboarding and other user tools that are already used by most Neo4j customers.
- Transform unstructured data into knowledge graphs: Developers can leverage new generative AI capabilities in Vertex AI to process unstructured data, structure it, and load it into a knowledge graph. Once in a knowledge graph, users extract insights leveraging Neo4j data visualization and query tools such as Bloom for business intelligence (BI) and Neo4j Graph Data Science.
- Real-time GenAI enrichment: Neo4j databases now have the ability to call Vertex AI services in real-time to enrich knowledge graphs. The input to a generative model can be augmented from structured sources like knowledge graphs as requested context for guiding the model processing. The response can be post-processed for result verification, guard-railing, and enriched for correctly generated semantic entities.
Long-standing strategic partnership since 2019
Google Cloud and Neo4j launched their strategic partnership in 2019. Hundreds of large enterprises and SMBs have leveraged Neo4j on Google Cloud for AI use cases ranging from anti-money laundering to personalized recommendations, supply chain management, natural language generation, molecular design, digital twinning, and more.
“Businesses are undergoing data- and AI-driven transformations at an unprecedented rate,” said Nenshad Bardoliwalla, Director of Product Management for Vertex AI, Google Cloud.
SOURCE: PRNewswire