https://moonvalley.ai/

https://github.com/abgulati/LARS

This grounding helps increase accuracy and reduce the common issue of AI-generated inaccuracies or “hallucinations.” This technique is commonly known as “Retrieval Augmented Generation”, or RAG.

LARS aims to be the ultimate open-source RAG-centric LLM application. Towards this end, LARS takes the concept of RAG much further by adding detailed citations to every response, supplying you with specific document names, page numbers, text-highlighting, and images relevant to your question, and even presenting a document reader right within the response window. While all the citations are not always present for every response, the idea is to have at least some combination of citations brought up for every RAG response and that’s generally found to be the case.