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.

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.

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.

Graph database vendor Neo4j announced today new capabilities for vector search within its graph database.. Neo4j’s namesake database technology enables organizations to create a knowledge graph ...

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 ...

By generating a vector embedding of an inputted search query, we plot a point on the graph we want to target. Then, we can discover the embeddings that are the nearest to our search point.

Real-world architectures combining graphs, vectors and semantic layers for LLMs; Actionable guidance on tooling; Integration tips for your data lake, warehouse or cloud AI stack; Reserve your seat ...

As the saying goes, context is everything – and this is certainly the case with AI. To be useful, AI systems need to be able to understand nuance and deliver accurate, relevant results. Our ability to ...