GitHub / ksm26 / Retrieval-Optimization-From-Tokenization-to-Vector-Quantization

The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.

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Topics: data-science, embeddingmodels, hnsw, machine-learning, machinelearning, natural-language-processing, rag, rag-systems, ragsystems, retrieval-augmented-generation, retrievaloptimization, search-algorithm, search-optimization, searchoptimization, tokenization, vectorquantization, vectorsearch

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