GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more. https://msft.it/6040l8lVO
Nate Jones
5d
Knowledge graphs + LLMs = 🔥🔥🔥 This combination, also used in the new HippoRAG paper, is shaping up to be a great multi-purpose RAG framework. Having an LLM create the knowledge graph and then using the graph for retrieval is just 🤌
We are looking forward to implementing this on our app.
Jia W.
4d
For question answering, factual questions must be already seen or trained with LLM. for temporal questions answering, if structured info can be extracted from new dataset and build a high quality KG, it could be directly used for answer retrieval. is there a benefit on building the context and feed into a LLM for answer generation? 🤔
We’ve been using graphs for a while, it’s a great approach for complex queries
Let's see if it was worth the wait.
Mike Cohan
4d
Can individual KG Communities be containerized while grooving shortest paths between two different and distinct communities? 🙏 #GraphRAG
J.Murat G.
5d
Muhammad Daniyal can you add this to our exel list please
looking forward to test this out
The wait is over
Co-Founder, Board Member at Aimped | AI Solutions Architect | Generative AI | NLP Certification, Master's Degree | Taught 3000+ ML Students Globally | Digital Marketing
5dThis is amazing news that we have been eagerly anticipating for a while at Aimped AI