<ul><li>Retrieval-Augmented Generation (RAG) is a powerful paradigm for grounding large language models in external knowledge sources.</li><li>This paper explores the use of Principal Component Analysis (PCA) to reduce the dimensionality of language model embeddings, addressing scalability challenges in processing large financial text corpora.</li><li>By reducing vectors from 3,072 to 110 dimensions, significant speedup in retrieval operations and reduction in index size are achieved, with moderate declines in correlation metrics.</li><li>The study highlights the practicality of leveraging classical dimensionality reduction techniques to optimize RAG architectures for knowledge-intensive applications in finance and trading.</li></ul>

PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation

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