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We'll release vector indexing feature very soon. See ongoing development: #568#571 .
We use LSH to compute and store binary encoding for embedding vectors. And vector similarity search can be done using normal SQL together with other filters, e.g.
selectv.id, v.attribute,
calculateHammingDistance(query_embedding, v.embedding) AS hamming_distance
from vector_table v
order by hamming_distance limit10;
The vector indexing feature is designed for large scale analytical usage for unstructured data on data lake.
In the AI+Data scenario,How does LakeSoul store and use unstructured data? Are there any examples?
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