pgvector

create extension if not exists vector;

create table documents (
  id bigserial primary key,
  content text,
  embedding vector(1536)
);

import OpenAI from 'openai';
import { createClient } from '@insforge/sdk';

const openai = new OpenAI({
  baseURL: 'https://openrouter.ai/api/v1',
  apiKey: process.env.OPENROUTER_API_KEY,
});
const insforge = createClient({ projectId: process.env.INSFORGE_PROJECT_ID });

const { data } = await openai.embeddings.create({
  model: 'openai/text-embedding-3-small',
  input: 'hello world',
});

await insforge.database.from('documents').insert({
  content: 'hello world',
  embedding: data[0].embedding,
});

select id, content
from documents
order by embedding <=> $1
limit 5;

create or replace function match_documents(
  query_embedding vector(1536),
  match_count int default 5,
  match_threshold float default 0
)
returns table (id bigint, content text, similarity float)
language sql stable
as $$
  select id, content, 1 - (embedding <=> query_embedding) as similarity
  from documents
  where 1 - (embedding <=> query_embedding) > match_threshold
  order by embedding <=> query_embedding
  limit match_count;
$$;

create index on documents using hnsw (embedding vector_cosine_ops);