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

SAN MATEO, Calif., April 10, 2024 — Neo4j has announced new native integrations with Google Cloud that dramatically speed up Generative AI application development and deployment across several crucial stages. The results solve a problem for enterprises that struggle with complexity and hallucinations when building and deploying successful GenAI applications requiring real-time, contextually rich data and accurate, explainable results. The integrations are available now.

Knowledge graphs capture relationships between entities, ground LLMs in facts, and enable LLMs to reason, infer, and retrieve relevant information accurately and effectively. According to Gartner, “Data and analytics leaders must leverage the power of large language models (LLMs) with the robustness of knowledge graphs for fault-tolerant AI applications,” in Gartner’s November 2023 report AI Design Patterns for Knowledge Graphs and Generative AI.

Retrieval Augmented Generation (RAG) is the technique by which LLMs access external datasets. Combining knowledge graphs with RAG, known as GraphRAG, ensures that GenAI outcomes are accurate, explainable, and transparent, including with real-time data.

GraphRAG with Google Cloud: Capabilities and Benefits

Developers can easily apply GraphRAG techniques with knowledge graphs to ground LLMs for accuracy, context, and explainability, enhancing GenAI innovation. Specifically, they can:

• Quickly create knowledge graphs for accurate, explainable results. Developers can easily create knowledge graphs with Gemini models, Google Cloud VertexAI, LangChain, and Neo4j from unstructured data like PDFs, web pages, and documents – either directly or loaded from Google Cloud Storage buckets.
• Ingest, process, and analyze real-time data in seconds. Developers can use Flex templates in Dataflow to create repeatable, secure data pipelines that ingest, process, and analyze data across Google BigQuery, Google Cloud Storage, and Neo4j—supplying knowledge graphs with real-time information and enabling GenAI applications to provide relevant, timely insights.
• Build GenAI applications powered by knowledge graphs on Google Cloud. Customers can use Gemini for Google Workspace and Reasoning Engine from Vertex AI platform to easily deploy, monitor, and scale GenAI apps and APIs onto Google Cloud Run. Gemini models are trained on Neo4j’s training data to automatically turn any language code snippets to Neo4j’s Cypher query language. The result makes application development faster, easier, and more collaborative by integrating natural language understanding and generation capabilities within various applications and environments. Developers can also use Cypher with any Integrated Development Environment (IDE) supported by Gemini models for more efficient querying and visualization of graph data. Neo4j’s vector search, GraphRAG, and conversational memory capabilities integrate seamlessly through LangChain and Neo4j AuraDB with Google Cloud.

“Generative AI can significantly increase the value customers get from critical business data,” said Ritika Suri, Director of Technology Partnerships at Google Cloud. “By utilizing Google Cloud’s Gemini models and Vertex AI, Neo4j can increase the speed and accuracy of generative AI application development.”

Neo4j Chief Product Officer Sudhir Hasbe commented: “GraphRAG with Neo4j and Google Cloud enables enterprises to move from GenAI development to deployment much faster and see value from their production use cases. Our latest milestone combines the power of graph technology, GenAI, and cloud computing excellence, enabling enterprises to achieve better results faster from their connected data, and innovate with GenAI.”

These capabilities are available now. For more information, read the blog post here.

About Neo4j

Neo4j, the Graph Database & Analytics leader, helps organizations find hidden relationships and patterns across billions of data connections deeply, easily and quickly. Customers leverage the structure of their connected data to reveal new ways of solving their most pressing business problems, from fraud detection, customer 360, knowledge graphs, supply chain, personalization, IoT, network management, and more – even as their data grows. Neo4j’s full graph stack delivers powerful native graph storage with native vector search capability, data science, advanced analytics, and visualization, with enterprise-grade security controls, scalable architecture and ACID compliance. Neo4j’s dynamic open-source community brings together over 250,000 developers, data scientists, and architects across hundreds of Fortune 500 companies, government agencies and NGOs.

Source: Neo4j