Add USearch as Jaccard backend, update README and noteboooks#20
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Add USearch as Jaccard backend, update README and noteboooks#20
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USearchIndex.build_from_binary() builds an HNSW index using native bit-vector Jaccard distance (b1x8 dtype). No MinHash encoding needed. Estimator auto-selects backend based on input type: - Dense binary matrix (ndarray/DataFrame) → USearch Jaccard - Sparse matrix (scipy CSR) → MinHash+LSH (avoids densification OOM) - Sets/strings → MinHash+LSH (variable-length data) Also switches transform/add_points to batch queries (was per-point loop). Validation: 0/1 enforcement on build, add, and query; width checks on query; eager failure on load when binary vectors sidecar is missing.
- 13 new USearchIndex binary tests: build, kNN contract, query, add, validation (non-0/1 rejection, wrong width), persistence, pickle - Update estimator tests for USearch routing: relax tie-order assertions, verify sparse CSR stays on LSH path, use set input for MinHash seed test
- docs/usearch_backend.md: how it works, when it's used, performance tables (recall, build time, query time, memory), tuning guide - notebooks/06_metric_guide.ipynb: update summary table for USearch routing - notebooks/12_usearch_jaccard.ipynb: standalone USearch usage + estimator
- README: update pipeline diagram, add USearchIndex standalone examples - api_reference: add kneighbors(), update property docs for index_/lsh_forest_ - notebook 11: kernel metadata update
- Ruff check
Removed unused images from the README.
…utputs when building the KNN
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Add USearch as Jaccard backend, update README and noteboooks