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-- Enable the pgvector extension to work with embedding vectors
create extension vector;

-- Create a table to store your documents
create table documents (
  id bigserial primary key,
  content text, -- corresponds to Document.pageContent
  metadata jsonb, -- corresponds to Document.metadata
  embedding vector(1536), -- 1536 works for OpenAI embeddings, change if needed
  -- customize
  owner_sid uuid not null -- only users can query their documents
);

-- Create a function to search for documents
create function match_documents (
  query_embedding vector(1536),
  match_count int,
  -- owner
  document_owner uuid
) returns table (
  id bigint,
  content text,
  metadata jsonb,
  similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    1 - (documents.embedding <=> query_embedding) as similarity,
    -- owner
    owner_sid
  from documents
  order by documents.embedding <=> query_embedding
  limit match_count;
end;
$$;

-- Create an index to be used by the search function
create index on documents
  using ivfflat (embedding vector_cosine_ops)
  with (lists = 100);