These kind of vector search similarity queries are rather obscure and
rare in practice. They require the user to specify a maximum distance
which is not intuitive to obtain. Furthermore, these queries are not
natively supported in USearch, so the vector search index had to emulate
these queries.
Therefore simplifying the code base and restricting vector search to
ORDER-BY queries only.
Indexes for approximate nearest neighbourhood (ANN) search (USearch) can
be build on columns of type Array(Float32) or Tuple(Float32[, Float32[, ...]]).
In practice, Arrays(Float32) is the only relevant data type.
Arrays store high-dimensional embeddings consecutively (--> cache
locality) and the additional flexibility of different data types in a
tuple is not needed for vector search.
Therefore removing support for ANN indexes over tuple columns to
simplify the code, tests and docs.
Annoy indexes fell out of favor in the community, at least when it comes
to vector databases. Such indexes work okay-ish low dimensions but they
suffers badly from a curse of dimensionality which makes them inapt for
a high number of dimensions.
Now that Annoy is gone, issue (*) also disappears and we can drop
'no-ubsan', 'no-cpu-aarch64', and 'no-asan' from tests.
(*) spotify/annoy#456
Registers usearch and annoy properly via configure_config.cmake and
config.h.in like all other 3rd party libs, instead of (mis)using
target_compile_definitions.
No directory 'SimSIMD-map' exists, the build only worked because SimSIMD
support in usearch was (accidentally?) disabled. This commit corrects
the build description. SimSIMD support in usearch will be enabled by a
later commit.
- Don't allow random settings that affect the memory usage
- Run two queries and compare the memory usage, rather than
having an arbitrary hardcoded value
