Graph databases are beneficial for applications like knowledge graphs, recommendation systems, and semantic search engines because they are excellent at capturing semantic context.

These include updates to its Spanner SQL database, which now features graph and vector search support, as well as extended full-text search capabilities.

New techniques make graph databases a powerful tool for grounding large language models in private data.

The Bulgarian graph database startup Graphwise today announced a major upgrade to its flagship GraphDB tool, adding new features aimed at boosting enterprise knowledge management and creating a ...

/PRNewswire/ -- Neo4j®, the world's leading graph database and analytics company, announced that it has integrated native vector search as part of its core ...

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

The addition of vectors provides context to the graph database for enhanced search and supports generative AI and large language models.

Banks, miners and police forces in Australia are among those using graph databases to provide the context and data relationships needed for more accurate and trustworthy AI, moving projects from ...

Async I/O and UUID v7 highlights of the September release, though some SQL features are delayed Users and developers can ...

Although databases that focus on the relational aspect of data analytics abound, few are as effective at revealing the hidden valuable insights as a graph database.