A Retrieval-Augmented Generation (RAG) system can be built from scratch using pure Python and basic libraries like 'requests' and 'numpy,' without relying on extensive frameworks. The core architecture involves embedding user queries, searching a vector store for relevant context, and then using a large language model to generate an answer based on that context. This approach provides a transparent understanding of RAG's internal workings, enabling developers to customize and deploy production-ready systems efficiently.
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