“🍏Using Atlas Vector Search for RAG Applications In this unit, you’ll build a RAG application with LangChain and MongoDB First, you’ll learn what RAG is, and you’ll build up to creating a custom prompt and a full RAG application.
“Pretty excited about this new RAG technique I cooked up 🧑🍳 A top issue with RAG chunking is it splits the document into fragmented pieces, causing top-k retrieval to return partial context. Also most documents have multiple hierarchies of sections: top-level sections,
“Automatically generate cloud configurations with RAGformation! 🏗️ Describe your use case in natural language, get a tailored cloud architecture 🖼️ Visualize your setup with dynamically generated flow diagrams 💰 Receive pricing estimates for the generated architecture 🔄 Refine
“❓Limitations of Text Embeddings in RAG Applications Text embeddings are great for getting started with RAG, but can have some limitations on semi-structured data Learn how to overcome them using knowledge graphs and structured tools
“2023: RAG is all you need 2024: AI agents are all you need Now bringing you the fusion of AI agents and RAG pipelines. In our recent blog post @ecardenas300 and I lift the curtains of agentic RAG. We discuss: • What is agentic RAG • Architectures of agentic RAG • How to
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