“Build a multi-agent LLM app with RAG using Llama 3.2 and vector database without writing a single line of Python Code (100% free and without internet):
“Astute RAG Proposes a novel RAG approach to deal with the imperfect retrieval augmentation and knowledge conflicts of LLMs. Astute RAG adaptively elicits essential information from LLMs’ internal knowledge. Then it iteratively consolidates internal and external knowledge with
New technique makes RAG systems much better at retrieving the right documents | VentureBeat
“Previously, RAG systems were the standard method for retrieving information from documents. However, if you are not repeatedly querying the same document, it may be more convenient and effective to just use long-context LLMs. For example, Llama 3.1 8B and Llama 3.2 1B/3B now
“Everyone talks about agents. At Zapier, we’ve built the most advanced agent on the market—with 7,000+ app integrations and live dynamic RAG. Whether you want to search your data in Notion or automate complex workflows, we’ve got you covered.
“After running these additional experiments, we were impressed by a few things: 1) OpenAI o1 models show a consistent improvement over Anthropic and Google models on our long context RAG Benchmark up to 128k tokens. (3/5)” / X
“2) Despite lower performance than the SOTA OpenAI and Anthropic models, Google Gemini 1.5 models have consistent RAG performance at extreme context lengths of up to 2 million tokens. (4/5)” / X
GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem – YouTube
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