When to choose Knowledge Graphs vs. Vector DBs. Specific use cases where Vector DBs excel are in RAG systems designed to assist customer service representatives.

One of the best-known graph databases is Neo4j, which recently announced support for the enterprise version of its cloud-hosted service, Aura, on Azure.Available in the Azure Marketplace, it’s a ...

While graph data is great at representing and analyzing complex relationships and connections, vector data is optimized for efficient search capabilities and calculations in high-dimensional spaces.

The intersection of large language models and graph databases is one that’s rich with possibilities. The folks at property graph database maker Neo4j today took a first step in realizing those ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

He explained that the new service helps customers analyze existing Neptune graph data or data lakes on top of S3 storage, taking advantage of vector search to find key insights.

Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable.

Neo4j is targeting GenAI workloads in the fast-growing APAC market by leveraging knowledge graphs to improve the accuracy and explainability of large language models.