VentureBeat

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a ...
Diginomica

Wikidata adds AI vectors to graph and knowledge bases. Here's why

Wikidata has built the semantic web backbone supporting knowledge cards in popular engines. Now, it's extending this foundation using a vector database to enhance its existing knowledge graph and ...
CIO

Vector Database vs. Knowledge Graph: Making the Right Choice When Implementing RAG

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 ...
dbta

TigerGraph Provides Comprehensive Solution with the Addition of Hybrid Search to its Graph Database

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 ...
TechCrunch

With Neptune Analytics, AWS combines the power of vector search and graph data

There’s been a debate of sorts in AI circles about which database is more important in finding truthful information in generative AI applications: graph or vector databases. AWS decided to leave the ...