Neo4j Forms Strategic Alliance with AWS to Combat Hallucinations in Generative AI

Specializing in graph databases and analytics, Neo4j recently secured a multi-year strategic collaboration agreement with Amazon Web Services (AWS), aimed at refining generative artificial intelligence outcomes. This partnership will work on the integration with Amazon Bedrock to bolster accuracy, transparency, and clarity while lessening instances of AI hallucinations.

This collaborative initiative seeks to tackle a prevalent issue faced by developers utilizing large language models (LLMs), which often require persistent memory tied to specific enterprise data and contexts. By blending knowledge graphs and native vector searching, Neo4j aims to present a viable solution to this challenge.

According to Sudhir Hasbe, Chief Product Officer at Neo4j, 'The native vector searching capability of Neo4j allows generative AI apps using LLMs to perform semantic searches effortlessly on vectors saved as attributes within the same database housing the knowledge graph. This enables results to be supplemented by facts from neighboring nodes within the knowledge graph, granting a more comprehensive, precise, relevant, and explainable context for the answers.'

Accompanying this announcement, Neo4j introduced its fully managed graph database service, Neo4j Aura Professional , available on the AWS Marketplace which will streamline the experience for developers diving into generative AI by offering a quick-start platform.

AWS Marketplace serves as a digital showcase containing a myriad of software listings, making it easier to discover, test, purchase, and implement software that works seamlessly with AWS.

'The launch of Neo4j Aura Professional on the AWS Marketplace enables developers to build their first knowledge graph using their existing AWS accounts, simplifying signup, billing, and expenditure tracking,' remarked Hasbe from Neo4j.

Harnessing Native Vector Search for Improved LLM Outcomes

Neo4j claims that the functionality of its graph database has far-reaching capabilities that go beyond those of traditional competitors. By leveraging native vector search, it captures both overt and subtle relationships, leading to the formation of knowledge graphs. These graphs empower AI systems to effectively reason, infer, and retrieve relevant information.

As an enterprise database, Neo4j plays a crucial role in providing LLMs with long-term memory, ensuring the efficacy of generative AI systems.

The collaboration with Amazon Bedrock introduces several benefits, including the use of Retrieval Augmented Generation (RAG). In partnership with Langchain and Amazon Bedrock, Neo4j facilitates the development of virtual assistants embedded in enterprise knowledge, seeking to enhance hallucinations , resulting in outputs that are more precise, transparent, and explainable.

'RAG is considered a best practice in generative AI development, addressing the shortcomings of LLMs by grounding the models in external knowledge sources. This technique allows the LLM to act as a natural language gateway to access information beyond its internal database for generating answers,' Hasbe explained to Metaverse Post. 'With the integration of Neo4j and Amazon Bedrock, AWS developers can seamlessly implement RAG using a single API that connects to some of the world's leading LLMs.'

Moreover, when paired with", ', Neo4j’s knowledge graphs tap into a wide array of foundational models. This capability allows for the creation of highly personalized text and summaries for users.', "Developers can take advantage of Amazon Bedrock to generate vector embeddings from unstructured data, enhancing knowledge graphs through Neo4j’s newest vector search and storage functionality while also utilizing Amazon Bedrock’s generative AI features to process this unstructured data. Amazon Bedrock 'Blending the generative AI strength of Amazon Bedrock with the knowledge graphs of Neo4j can deliver institutional knowledge to every team member, plus it unlocks the potential from unstructured data, yielding thorough answers anchored in facts instead of inaccuracies,' Hasbe noted. 'The essence lies in augmenting Bedrock's features with Neo4j's factual knowledge graphs, facilitating precise and contextual responses rather than mere conjectures by LLMs.'

This strategic partnership marks a significant milestone in the integration of graph technology and cloud computing prowess, ushering businesses into a brand-new phase of utilizing connected data in an optimized manner.

'Neo4j provides long-term memory for large language models operational on AWS. With our Aura cloud offering available on AWS Marketplace and the powerful functionalities inherent in our graph database, we're simplifying the development pathway, enabling developers to start easily and effectively engage with practical applications of generative AI in the cloud,' Hasbe explains to Metaverse Post. 'Our joint efforts aim to unlock new opportunities and reshape the possibilities for enterprise innovation.'", 'Please be aware that the material on this page is not intended to serve as legal, tax, investment, financial, or any other form of advice. It's crucial to only invest what you can afford to lose and seek independent financial advice should any uncertainties arise. For additional information, we recommend reviewing the terms and conditions along with the help and support resources provided by the issuer or advertiser. MetaversePost endeavors to deliver accurate, unbiased news, but market conditions can change without prior notice.', "Victor serves as a Managing Tech Editor/Writer for Metaverse Post, covering topics such as artificial intelligence, cryptocurrency, data science, the metaverse, and cybersecurity in the corporate sector. With over five years of media and AI experience at prominent outlets including VentureBeat, DatatechVibe, and Analytics India Magazine, he is also a Media Mentor at esteemed institutions like Oxford and USC. Holding a Master’s degree in data science and analytics, Victor is dedicated to keeping pace with emerging industry trends.

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