Neo4j gens-up in Google Cloud LLM

Neo4j is a graph database & analytics company that says its mission is to help organisations ‘find hidden relationships and patterns’ across billions of data connections.

The company recently announced a new product integration with Google Cloud’s latest generative-AI features in Vertex AI, Google’s own Large Language Model (LLM) platform. 

The intention is to enable users to capture knowledge graphs built on Neo4j’s managed cloud offerings in Google Cloud Platform for generative-AI insights.

Neo4j’s graph database and analytics capabilities can be used to create knowledge graphs, which capture relationships between entities – and this is a function that can then be used to enable AI systems to reason, infer and retrieve relevant information.

The result (says the firm) ensures more accurate, explainable and transparent outcomes for LLMS and other generative AI systems.

Specific integrations with Google’s generative AI capabilities in Vertex AI enable enterprise customers to use 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.

“Neo4j’s partnership with Google represents a powerful union of graph technology and cloud computing excellence in a new era of AI,” said Emil Eifrem, co-founder and CEO, Neo4j. “Together, we empower enterprises seeking to leverage generative AI to better innovate, provide the best outcome for their customers and unlock the true power of their connected data at unprecedented speed.”

Developers also use 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 visualisation and query tools such as Bloom for business intelligence (BI) and Neo4j Graph Data Science.

“Businesses are undergoing data- and AI-driven transformations at an unprecedented rate,” said Nenshad Bardoliwalla, director of product management for Vertex AI, Google Cloud. “These new integrations between Neo4j and Vertex AI will help businesses create more value and impact with their data and LLMs through capabilities like real-time enrichments and grounding, pattern identification in large datasets and new abilities to explore their data with natural language.”

 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.

Neo4j can be leveraged to provide long-term memory for LLMs through support of vector embeddings. Neo4j’s Graph Data Science supports more than 60 algorithms, including efficient (approximate) nearest neighbour graphs and cosine similarity on embedding vectors to perform similarity searches. 

Google Cloud and Neo4j launched their strategic partnership in 2019. 

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