Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integration.

Abstract: This paper presents a secure question answering framework for financial compliance using a graph-based retrieval-augmented generation (Graph-RAG) model. The system constructs a multi-layer ...

According to @godofprompt, graph retrieval-augmented generation (Graph RAG) significantly surpasses traditional vector search by enabling multi-hop reasoning across 3-4 levels of data relationships, ...

According to @godofprompt, Microsoft has reported a 40% improvement in answer quality when utilizing graph-based Retrieval-Augmented Generation (RAG) compared to pure vector search, citing significant ...

GraphRAG (Graphs + Retrieval Augmented Generation) is a technique for richly understanding text datasets by combining text extraction, network analysis, and LLM prompting and summarization into a ...

What if your AI agent could not only answer your questions but also truly understand them, navigating complex queries with precision and speed? While the rise of vector search has transformed how AI ...

oracle_graph_entities: 384-dim entity embeddings from Graph RAG oracle_document_chunks: 384-dim document chunk embeddings from Grok RAG ...

Abstract: This study introduces Knowledge Augmented Question Generation (KAQG), an educational assessment framework that integrates Item Response Theory (IRT), Bloom’s Taxonomy, and knowledge graphs ...

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