Awesome. I’ll begin the deep-dive into architecture, embeddings, indexing strategies, search techniques, and VSCode extension design for a local-first semantic search tool.
I’ll include:
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Comparative analysis of embedding models (OpenAI, Hugging Face, BGE, Instructor, etc.) for code and natural language
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Indexing strategies (chunking, AST, metadata) optimized for Python and JavaScript
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Vector store comparison with a focus on LanceDB, QDrant, and other developer-friendly local options