Merge pull request #13481 from supabase/thor/add-new-vercel-integration-example

feat: add next.js openai doc search starter template.

studio/components/interfaces/SQLEditor/SQLEditor.constants.ts

@@ -1070,5 +1070,91 @@ select
     'table hit rate' as name,
     sum(heap_blks_hit) / nullif(sum(heap_blks_hit) + sum(heap_blks_read),0) as ratio
   from pg_statio_user_tables;`,
-  }
+  },
+  {
+    id: 20,
+    type: 'quickstart',
+    title: 'OpenAI Vector Search',
+    description: 'Template for the Next.js OpenAI Doc Search Starter.',
+    sql: `
+-- Enable pg_vector extension
+create extension if not exists vector with schema public;
+
+-- Create tables 
+create table "public"."nods_page" (
+  id bigserial primary key,
+  parent_page_id bigint references public.nods_page,
+  path text not null unique,
+  checksum text,
+  meta jsonb,
+  type text,
+  source text
+);
+alter table "public"."nods_page" enable row level security;
+
+create table "public"."nods_page_section" (
+  id bigserial primary key,
+  page_id bigint not null references public.nods_page on delete cascade,
+  content text,
+  token_count int,
+  embedding vector(1536),
+  slug text,
+  heading text
+);
+alter table "public"."nods_page_section" enable row level security;
+
+-- Create embedding similarity search functions
+create or replace function match_page_sections(embedding vector(1536), match_threshold float, match_count int, min_content_length int)
+returns table (id bigint, page_id bigint, slug text, heading text, content text, similarity float)
+language plpgsql
+as $$
+#variable_conflict use_variable
+begin
+  return query
+  select
+    nods_page_section.id,
+    nods_page_section.page_id,
+    nods_page_section.slug,
+    nods_page_section.heading,
+    nods_page_section.content,
+    (nods_page_section.embedding <#> embedding) * -1 as similarity
+  from nods_page_section
+
+  -- We only care about sections that have a useful amount of content
+  where length(nods_page_section.content) >= min_content_length
+
+  -- The dot product is negative because of a Postgres limitation, so we negate it
+  and (nods_page_section.embedding <#> embedding) * -1 > match_threshold
+
+  -- OpenAI embeddings are normalized to length 1, so
+  -- cosine similarity and dot product will produce the same results.
+  -- Using dot product which can be computed slightly faster.
+  --
+  -- For the different syntaxes, see https://github.com/pgvector/pgvector
+  order by nods_page_section.embedding <#> embedding
+  
+  limit match_count;
+end;
+$$;
+
+create or replace function get_page_parents(page_id bigint)
+returns table (id bigint, parent_page_id bigint, path text, meta jsonb)
+language sql
+as $$
+  with recursive chain as (
+    select *
+    from nods_page 
+    where id = page_id
+
+    union all
+
+    select child.*
+      from nods_page as child
+      join chain on chain.parent_page_id = child.id 
+  )
+  select id, parent_page_id, path, meta
+  from chain;
+$$;
+`.trim(),
+  },
 ]

studio/lib/vercelConfigs.ts

@@ -64,6 +64,28 @@ export const VERCEL_INTEGRATION_CONFIGS = [
       },
     ],
   },
+  {
+    id: 'nextjs-open-ai-doc-search',
+    name: 'Next.js OpenAI Doc Search Starter',
+    template: QuickStart.find((x) => x.title == 'OpenAI Vector Search'),
+    envs: [
+      {
+        key: 'NEXT_PUBLIC_SUPABASE_URL',
+        alias: INTEGRATION_ENVS_ALIAS.ENDPOINT,
+        type: 'encrypted',
+      },
+      {
+        key: 'NEXT_PUBLIC_SUPABASE_ANON_KEY',
+        alias: INTEGRATION_ENVS_ALIAS.ANONKEY,
+        type: 'encrypted',
+      },
+      {
+        key: 'SUPABASE_SERVICE_ROLE_KEY',
+        alias: INTEGRATION_ENVS_ALIAS.SERVICEKEY,
+        type: 'encrypted',
+      },
+    ],
+  },
   {
     id: 'supabase-todo-list',
     name: 'Todo List Database',