Vector search queries

Vector search queries are built with the .like operator. This allows developers to combine standard database with vector-search queries. The philosophy is that developers do not need to convert their inputs into vector's themselves. Rather, this is taken care by the specified VectorIndex component.

The basic schematic for vector-search queries is:

table_or_collection
    .like(Document(<dict-to-search-with>), vector_index='<my-vector-index>')      # the operand is vectorized using registered models
    .filter_results(*args, **kwargs)            # the results of vector-search are filtered

or...

table_or_collection
    .filter_results(*args, **kwargs)            # the results of vector-search are filtered
    .like(Document(<dict-to-search-with>),
          vector_index='<my-vector-index>')      # the operand is vectorized using registered models

MongoDB

from superduper.ext.pillow import pil_image
from superduper import Document

my_image = PIL.Image.open('test/material/data/test_image.png')

q = db['my_collection'].find({'brand': 'Nike'}).like(Document({'img': pil_image(my_image)}), 
                                               vector_index='<my-vector-index>')

results = q.execute()

SQL

t = db['my_table']
t.filter(t.brand == 'Nike').like(Document({'img': pil_image(my_image)}))

results = db.execute(q)