repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago
Lynavo/lynavo-drive
RAG-GPT, leveraging LLM and RAG technology, learns from user-customized knowledge bases to provide contextually relevant answers for a wide range of queries, ensuring rapid and accurate information retrieval.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 58%AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering →
- PossiblePossibly related (embedding) · 55%RAGless: Q-Q retrieval with score aggregation for closed-domain FAQ [P] →
- PossiblePossibly related (embedding) · 53%Little Brains, Big Feats: Exploring Compact Language Models →
- PossiblePossibly related (embedding) · 52%Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs →
- PossiblePossibly related (embedding) · 51%Hierarchical Evidence-Driven Reasoning for Long Document Understanding →
- PossiblePossibly related (embedding) · 48%Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval - towardsdatascience.com →
- PossiblePossibly related (embedding) · 50%Domain-tailored RAG framework improves industrial LLM question answering in new engineering study - EurekAlert! →
- PossiblePossibly related (embedding) · 50%I built an LLM benchmark harness that lets you browse and compare how models answered each question →
Implements
paperAB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question AnsweringpaperLittle Brains, Big Feats: Exploring Compact Language ModelspaperQuery-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge GraphspaperHierarchical Evidence-Driven Reasoning for Long Document Understanding
Covers
Covers (incoming)
newsLoop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval - towardsdatascience.comnewsDomain-tailored RAG framework improves industrial LLM question answering in new engineering study - EurekAlert!newsI built an LLM benchmark harness that lets you browse and compare how models answered each question
Related across the graph
paperLittle Brains, Big Feats: Exploring Compact Language ModelsnewsLoop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval - towardsdatascience.compaperAB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question AnsweringnewsRAGless: Q-Q retrieval with score aggregation for closed-domain FAQ [P]newsDomain-tailored RAG framework improves industrial LLM question answering in new engineering study - EurekAlert!paperHierarchical Evidence-Driven Reasoning for Long Document UnderstandingpaperQuery-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge GraphsnewsI built an LLM benchmark harness that lets you browse and compare how models answered each question