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The article explores how smaller language models like the Meta 1 Billion model can be used for efficient summarization and indexing of large documents, improving the performance and scalability of Retrieval-Augmented Generation (RAG) systems.
The article explains semantic text chunking, a technique for automatically grouping similar pieces of text to be used in pre-processing stages for Retrieval Augmented Generation (RAG) or similar applications. It uses visualizations to understand the chunking process and explores extensions involving clustering and LLM-powered labeling.
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