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Explains how to implement semantic search on Linux: what embeddings are, why they outperform keyword search, and how to build a local pipeline with open-source models and a vector index. Covers indexing docs and code, command-line integration, hardware and performance tips, privacy benefits of running locally, and practical workflows for fast, meaning-aware retrieval.