Thank you for the video, that was a great paper to go through. I find RAG research techniques have so much insight to how we can develop and identify our own cognitive impediments to our own judgement. The Comprehensiveness, Diversity of perspective, Empowerment and Directness is such a good mental model to use in our own human judgement.
great presentation Vivek. Some questions: - is graphRAG production ready ? if not, would it be difficult to upgrade RAG methods once we are in production ? - is there a RAG provider/stack that you prefer ? (datastax, pinecone, weaviate + a bunch of others who are all competing for attention) - what are your thoughts on LangChain vs LangGraph ?
same here brother. There are so many techniques, everyday I learn something new which is both good and terrifying ha. What stack are you thinking of using ? We are researching DataStax, Pinecone, Weaviate and are learning to build agents with LangGraph.
wouldn't it cut to the chase to train an llm on your own data? theres your graph use one of these OpenAI's GPT-3/4 Hugging Face Transformers (e.g., GPT-2, GPT-3 via third-party providers) Google's T5 (Text-to-Text Transfer Transformer) Meta's BART and BlenderBot Anthropic's Claude every week update the llm summarization is the death of real data, better off one level of summarization? Just a thought!
can you pontificate on the combination of upcoming transformer inference ASICs with deep agentic workflows employing GraphRAG style strategies? Seems like we will be close to our personal assistants writing a PhD thesis in the background whenever we ask a question. SOHU is reporting 500,000 tokens per second with Llama3 70B....
