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Leonid Boytsov authored and Leonid Boytsov committed Jul 8, 2018
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Expand Up @@ -5,7 +5,7 @@ We have Python notebooks for the following scenarios:
3. [The Euclidean space ofr for 8-bit integer SIFT vectors (the index is not optimized)](search_sift_uint8.ipynb);
4. [KL-divergence (non-optimized index)](search_vector_dense_kldiv.ipynb);
3. [Sparse cosine similarity (non-optimized index)](search_sparse_cosine.ipynb);
4. [Sparse Jaccard similarity (non-optimized index)](search_sparse_cosine.ipynb).
4. [Sparse Jaccard similarity (non-optimized index)](search_generic_sparse_jaccard.ipynb).

Note that for for the dense space, we have examples of the so-called optimized and non-optimized indices. Except HNSW, all the methods save meta-indices rather than real ones. Meta indices contain only index structure, but not the data. Hence, before a meta-index can be loaded, we need to re-load data. One example is a memory efficient space to search for SIFT vectors.

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