We will return later to the integration of tool APIs, but for the rest, we see the elements of a RAG approach, along with the knowledge graph. Note the presence of a “prompt processing” component.

For example, with LLMs and RAG systems, “using graph databases to map relationships within vast datasets can uncover deeper insights—such as better recommendations or enhanced search results—making ...

To keep an SLM relevant and accurate, you still need to feed it fresh, contextual data. That’s where graph technology comes ...

By retrieving real, relevant information, RAG helps AI stay accurate, current, and context-aware. AWS, for example, ...

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, is releasing AllegroGraph 8.2 with ...

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 exper ...

Powerful hardware is also required for the final phase of RAG, text generation. The example from neuml uses a Mistral 7B OpenOrca model for this.

I like to think of a knowledge graph as a cross between an encyclopedia and a database. Each article is called an “ Entity ” by developers, or a “Topic” in Google’s customer-facing articles.

GraphPath’s claims that as it is delivered as a SaaS, it enables a faster and reliable implementation when compared with in-house solutions, and clients of the platform will also have the ...