#ZigSmartSearch: Fast Semantic Search in Zig

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hybrid kestrel
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๐Ÿ” Explore concept-based search for JSON datasets with these key advantages:

  • ๐Ÿ“– Dictionary-Based Expansion: Understands meaning via a custom JSON dictionary of weighted related terms.
  • ๐Ÿง  Lemmatized Matching: Captures word variations using base-form mapping for better relevance.
  • ๐ŸŽฏ Precision Scoring: Rewards exact/phrase matches, penalizes irrelevant terms, and normalizes for fairness.
  • โšก Fast Performance: Powered by Zig for quick, memory-efficient searching.
  • ๐Ÿ”Œ No Dependencies: Pure Zig implementation, keeping it lightweight and self-contained.

โญ Please Star the repo to support the project: https://github.com/RohanVashisht1234/ZigSmartSearch

GitHub

Contribute to RohanVashisht1234/ZigSmartSearch development by creating an account on GitHub.

marble forum
lime burrow
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  1. Too much vibe-coded.
  2. SOTA approach is to use neural network/autoencoders/BERT to get text embedding and use similarity metrics like dot product between query and the database.
hybrid kestrel
hybrid kestrel
lime burrow
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Zig should be able to implement universal function approximators, otherwise you are dealing with your delusions

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About first point - try to Google for "aistudio Google"