Neo4j®, the global leader in graph databases and analytics, today announced the availability of new native integrations with the Google Cloud platform with the aim of accelerating the development and deployment of graph applications. Generative AI. A boon for companies that face complexity and hallucinations when creating and deploying these types of applications, requiring, more often than not, real-time contextual data and precise, explainable results.
Knowledge graphs identify relationships between data, allowing LLMs (Large Language Models) to better anchor facts, allowing them to reason, infer and retrieve the most relevant information accurately and efficiently. In its latest AI Design Patterns for Knowledge Graphs and Generative AI report, Gartner® announces that “data and analytics leaders must leverage the power of large language models (LLM) with the robustness of knowledge graphs to stronger, more fault-tolerant AI applications.”
Retrieval Augmented Generation (RAG) is the method by which LLMs are fed with external data. Combining knowledge graphs with RAG, known as GraphRAG, allows LLMs to access external datasets and ensures more accurate, explainable and transparent generative AI results.
GraphRAG with Google Cloud: capabilities and benefits
Developers can now apply GraphRAG to knowledge graphs to improve the accuracy, context, and explainability of LLMs. Among the advantages of GraphRAG we find:
1. Rapid development of knowledge graphs and more accurate results. Developers can now easily create knowledge graphs with Gemini, VertexAI, LangChain and Neo4j from unstructured data such as PDFs, web pages and other documents, including those loaded directly or from Google Cloud Storage ( GCS).
2. Data ingestion, processing and analysis in seconds. Developers can use Flex models in Dataflow to create reproducible and secure data pipelines that ingest, process, and analyze data across Google BigQuery, Google Cloud Storage, and Neo4j. An approach to populating knowledge graphs with real-time information and enabling generative AI applications to provide relevant and timely insights.
3. Design of generative AI applications powered by knowledge graphs stored on Google Cloud. Customers can use Gemini on Google Workspace and Vertex AI’s Reasoning Engine to deploy, monitor and scale generative AI applications and APIs on Google Cloud Run. Gemini Workspace uses Neo4j training data to automatically translate any code snippet into Neo4j’s Cypher query language. Through this process, application development becomes faster and easier, while facilitating collaboration through the integration of natural language understanding and generation capabilities across varied applications and environments. Developers can also use Cypher with any Integrated Development Environment supported by Gemini Workspace 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 on Google Cloud.
A long journey of innovation: additional stages of the partnership
Google Distribution Cloud (GDC) Hosted, available since last March, is a private cloud infrastructure stored in an edge environment and isolated from any other computer network. It enables customers to meet strict regulatory requirements for data hosting and security. Neo4j is GDC’s preferred partner providing reliable graph database and analytics capabilities.
Customers can also perform in-memory graph analyzes of hidden and complex data models using the Neo4j catalog of over 70 graph data science functions directly on BigQuery data and from BigQuery SQL using Apache Spark stored procedures.
Additionally, this month Neo4j won Google Cloud’s Technology Partner of the Year in the Data Management category for the second year in a row. In 2023, Neo4j became the only native graph provider to integrate their products with generative AI capabilities into Google Cloud Vertex AI, following a strategic partnership between the two players that began in 2019. Also in 2023 , Neo4j has integrated native vector capabilities into its graph database, allowing it to serve as a long-term memory for LLMs.
Jeff Dalgliesh, Chief Technology Officer, at Data²
“At Data², we combine the strength of generative AI with the robustness of knowledge graphs to optimize the potential of our clients’ data. The launch of new native Neo4j integrations with Google Cloud Platform, including the ability to enhance generative AI applications with Neo4j knowledge graphs, marks a significant milestone for the industry. With Neo4j’s GraphRAG approach and Google Cloud’s robust infrastructure, we will be able to provide our customers with even more powerful and explainable AI insights, helping them make critical decisions with confidence and speed. »
Moheesh Raj, Senior Engineering Manager at Dun & Bradstreet
“At Dun & Bradstreet, we are committed to helping businesses leverage data and analytical insights to drive intelligent decisions that give them a competitive advantage. This announcement gives us new opportunities to streamline and accelerate our compliance services, setting a new industry standard for data-driven due diligence. »
Ritika Suri, Director of Technology Partnerships, Google Cloud
“Generative AI can significantly increase the value customers derive from their critical data. Using Google Cloud’s Gemini and Vertex AI models, Neo4j can increase the speed and accuracy of generative AI application development. »
Sudhir Hasbe, Chief Product Officer at Neo4j
“The GraphRAG, Neo4j and Google Cloud alliance will enable companies to move from development to deployment of generative AI at record speed. This milestone highlights the full potential of graph technology, generative AI, and the excellence of cloud computing, which offer businesses the opportunity to leverage their connected data while innovating. »
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