Files
neta/supabase/migrations/0006_add_pgvector_and_embeddings.sql
T

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PL/PgSQL

-- 0006: Faz 8 - pgvector & RAG Embeddings
-- Enable the pgvector extension to work with embedding vectors
create extension if not exists vector;
-- Create a table to store document embeddings for RAG
create table if not exists public.document_embeddings (
id uuid default uuid_generate_v4() primary key,
user_id uuid references auth.users(id) on delete cascade not null,
content text not null,
metadata jsonb, -- e.g. { "source_type": "note", "source_id": "123" }
embedding vector(1536), -- 1536 works for OpenAI text-embedding-3-small and text-embedding-ada-002
created_at timestamp with time zone default timezone('utc'::text, now()) not null
);
-- Enable RLS
alter table public.document_embeddings enable row level security;
create policy "Users can view their own embeddings" on public.document_embeddings for select using (auth.uid() = user_id);
create policy "Users can insert their own embeddings" on public.document_embeddings for insert with check (auth.uid() = user_id);
create policy "Users can update their own embeddings" on public.document_embeddings for update using (auth.uid() = user_id);
create policy "Users can delete their own embeddings" on public.document_embeddings for delete using (auth.uid() = user_id);
-- Create a function to similarity search for embeddings
create or replace function match_documents (
query_embedding vector(1536),
match_count int default null,
filter_user_id uuid default null
) returns table (
id uuid,
content text,
metadata jsonb,
similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
return query
select
document_embeddings.id,
document_embeddings.content,
document_embeddings.metadata,
1 - (document_embeddings.embedding <=> query_embedding) as similarity
from document_embeddings
where document_embeddings.user_id = filter_user_id
order by document_embeddings.embedding <=> query_embedding
limit match_count;
end;
$$;