Whether IT leaders opt for the precision of a Knowledge Graph or the efficiency of a Vector DB, the goal remains clear—to harness the power of RAG systems and drive innovation, productivity, and ...

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

AWS announced a new capability today called Neptune Analytics that uses vector search to understand the relationships in a graph database.

Knowledge Graphs vs. Vector DBs: Similarities and Differences To help understand both the technology and the business impact, it’s important to understand what each of them do.

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 ...

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.

TigerGraph, the enterprise AI infrastructure and graph database leader, is releasing its next generation graph and vector hybrid search, delivering the industry's 'most advanced' solution for ...

Thus, knowledge graph advocates have made a strong case that their inherently knowledge-centric graph technology should be adopted—often standalone, sometimes in tandem with a vector store, so ...

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