A retrieval-augmented generation (RAG) application has exponentially higher utility if it can work with a wide variety of data types—tables, graphs, charts,…
A retrieval-augmented generation (RAG) application has exponentially higher utility if it can work with a wide variety of data types—tables, graphs, charts, and diagrams—and not just text. This requires a framework that can understand and generate responses by coherently interpreting textual, visual, and other forms of information. In this post, we discuss the challenges of tackling multiple…
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Source:: NVIDIA