We have Python notebooks for the following scenarios:

1. The Euclidean space with the optimized index;
2. The Euclidean space with the non-optimized index;
3. The Euclidean space for for Uint8 SIFT vectors (the index is not optimized);
4. KL-divergence (non-optimized index);
5. Sparse cosine similarity (non-optimized index);
6. Sparse Jaccard similarity (non-optimized index).

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. If the index was saved with the parameter save_data set to True, then reloading the data can be achieved by specifying load_data=True in a call to the loadIndex function.

For ease of reproduction, examples use very small corpora. Thus, used search methods do not necessarily outperform brute-force search. Typically, the larger is the corpora, the larger is the improvement in efficiency over the brute-force search.

• The sparse Jaccard space example is interesting because data set objects are represented by strings. In the case of the Jaccard space, a string is simply a list of sorted space-separated IDs. Other non-vector spaces can use different formats.
• The KL-divergence example is interesting, because it is a non-metric data set with non-symmetric distance function.
• The SIFT vector space is interesting, because it uses a compact storage for data (each vector element occuppies exactly one byte).