Annoy vector search#
scikitplot.annoy wraps Spotify [1] Annoy-style approximate nearest-neighbor
search with a higher-level Index facade. It is designed for immutable or
build-once vector indexes where memory-mapped persistence, predictable query
semantics, and process sharing matter more than online mutation.
Important
Approximate nearest-neighbor search is a retrieval strategy, not a proof of semantic relevance. Vector dimensionality and metric are index invariants; add items before building, and treat saved indexes as data artifacts with their own lifecycle.
Build a small index, add vectors, build trees, and query neighbors.
Understand the compiled backend, high-level Index facade, and mixins.
Choose native files, bundles, bytes, metadata, or pickle deliberately.
Tune tree count, search effort, metrics, and build concurrency.
Map the high-level public surface and advanced mixins.
Compiled-backend requirements, mmap lifetime, vector rules, and build caveats.
Deep dives#
Vector Similarity Search and Vector Database – vector-database concepts and similarity search.
Annoy Index class Inheritance Diagrams (MRO) – wrapper/backend inheritance diagrams.
ANNoy Cython – private Cython-backend notes for advanced maintainers.
Examples#
See Annoy for executable examples that accompany this guide.
See also#
Further reading#
See also
See also
Nearest neighbor search (background): https://en.wikipedia.org/wiki/Nearest_neighbor_search
https://www.researchgate.net/publication/363234433_Analysis_of_Image_Similarity_Using_CNN_and_ANNOY
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/XboxInnerProduct.pdf
https://link.springer.com/chapter/10.1007/978-981-97-7831-7_2