supabase-schema.sql

-- Docs chatbot: run in Supabase → SQL Editor.
create extension if not exists vector with schema extensions;

create table public.documents (
  id bigint generated always as identity primary key,
  source text not null,              -- file path or URL the chunk came from
  chunk_index int not null,
  content text not null,
  embedding extensions.vector(1536) not null,   -- text-embedding-3-small
  unique (source, chunk_index)
);

create index documents_embedding_idx
  on public.documents using hnsw (embedding extensions.vector_cosine_ops);

-- RLS on, no policies: only the server (service role key) reads and writes.
alter table public.documents enable row level security;

create or replace function public.match_documents(
  query_embedding extensions.vector(1536),
  match_count int default 6,
  min_similarity float default 0.3
)
returns table (id bigint, source text, content text, similarity float)
language sql stable
set search_path = public, extensions
as $$
  select d.id, d.source, d.content, 1 - (d.embedding <=> query_embedding) as similarity
  from public.documents d
  where 1 - (d.embedding <=> query_embedding) > min_similarity
  order by d.embedding <=> query_embedding
  limit match_count;
$$;