TL;DR Key Takeaways : GraphRAG uses knowledge graphs instead of traditional vector databases, allowing deeper insights and relationships within datasets through structured data representation.

Sivasubramanian further elaborated that the new service makes it easier for users to uncover hidden relationships across data – by storing the graph and vector data together. He also cited the example ...

“Since both graph analytics and vectors are all about uncovering the hidden relationships across our data, we thought to ourselves: ‘what if we combined vector search with the ability to ...

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 ...

The model consists of three parts: 1) The input representation layer, where the ALBERT module maps each word into a word vector through the Embedding layer and then uses the Transformer layer to ...