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Created January 8, 2024 21:07
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vector-search sql
-- Supabase AI is experimental and may produce incorrect answers
-- Always verify the output before executing
create
or replace function vector_search (
query_embedding vector (1536),
similarity_threshold float,
match_count int
) returns table (
id bigint,
first_name text,
last_name text,
image text,
address jsonb,
occupation text,
age int2,
hobbies text[],
relationship_status text,
country_of_origin text,
email text,
bio text,
similarity float
) language plpgsql as $$ begin return query select celebrities.id, celebrities.first_name, celebrities.last_name, celebrities.image, celebrities.address, celebrities.occupation, celebrities.age, celebrities.hobbies, celebrities.relationship_status, celebrities.country_of_origin, celebrities.email, celebrities.bio, 1 - (celebrities.embeddings <=> query_embedding) as similarity from celebrities where 1 - (celebrities.embeddings <=> query_embedding) > similarity_threshold order by celebrities.embeddings <=> query_embedding limit match_count; end; $$;
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