Can Agents Run It? · evidence — Issue 1: Supabase

openai/gpt-5.6-sol · d2_pgvector · rep 1

pass
model
openai/gpt-5.6-sol
task
d2_pgvector
rep
1
day
2
steps
6
tokens
8,479
est. cost
$0.119
agent time
37s
wave
s4
started
2026-07-24T05:47:07Z

The task as the agent received it

You have operator access to a hosted Supabase project. Credentials are in environment variables: SUPABASE_URL, SUPABASE_PROJECT_REF, SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY, SUPABASE_DB_URL, SUPABASE_DB_PASSWORD. The table public.documents (id int, title text, embedding_json jsonb) holds 6 documents with 4-dimensional embeddings stored as JSON arrays. Build semantic search over it: 1) Set up the pgvector extension. 2) Add a column embedding of type vector(4) to public.documents, populated from embedding_json. 3) Create an HNSW index on that column using COSINE distance. 4) Create a SQL function match_documents(query_embedding vector(4), match_count int) returning a table (id int, title text, distance float8) — the match_count closest documents by cosine distance, nearest first — and make it callable through the project's REST RPC endpoint (POST /rest/v1/rpc/match_documents) with the anon key. Verify a search returns sensible nearest neighbors before finishing.

What it ran — 6 commands

#1exit 0
command -v psql || true; command -v curl || true
/usr/bin/psql
/usr/bin/curl
#2exit 0
for v in SUPABASE_URL SUPABASE_PROJECT_REF SUPABASE_ANON_KEY SUPABASE_SERVICE_ROLE_KEY SUPABASE_DB_URL SUPABASE_DB_PASSWORD; do if [ -n "${!v}" ]; then echo "$v=set"; else echo "$v=missing"; fi; done
SUPABASE_URL=set
SUPABASE_PROJECT_REF=set
SUPABASE_ANON_KEY=set
SUPABASE_SERVICE_ROLE_KEY=set
SUPABASE_DB_URL=set
SUPABASE_DB_PASSWORD=set
#3exit 0
set -euo pipefail
export PGPASSWORD="$SUPABASE_DB_PASSWORD"
psql "$SUPABASE_DB_URL" -v ON_ERROR_STOP=1 -P pager=off -c "SELECT id, title, embedding_json FROM public.documents ORDER BY id;"
 id |  title  |  embedding_json  
----+---------+------------------
  1 | alpha   | [10, 0.5, 0, 0]
  2 | bravo   | [0.9, 0.3, 0, 0]
  3 | delta   | [20, 60, 0, 0]
  4 | echo    | [0, 1, 0, 0]
  5 | foxtrot | [-5, 0.1, 0, 0]
  6 | golf    | [0.1, 0.1, 3, 4]
(6 rows)

#4exit 3
set -euo pipefail
export PGPASSWORD="$SUPABASE_DB_PASSWORD"
psql "$SUPABASE_DB_URL" -v ON_ERROR_STOP=1 -P pager=off <<'SQL'
CREATE EXTENSION IF NOT EXISTS vector WITH SCHEMA extensions;

ALTER TABLE public.documents
  ADD COLUMN IF NOT EXISTS embedding extensions.vector(4);

UPDATE public.documents
SET embedding = embedding_json::text::extensions.vector
WHERE embedding IS DISTINCT FROM embedding_json::text::extensions.vector;

ALTER TABLE public.documents
  ALTER COLUMN embedding SET NOT NULL;

CREATE INDEX IF NOT EXISTS documents_embedding_hnsw_idx
  ON public.documents
  USING hnsw (embedding extensions.vector_cosine_ops);

CREATE OR REPLACE FUNCTION public.match_documents(
  query_embedding extensions.vector(4),
  match_count integer
)
RETURNS TABLE (id integer, title text, distance double precision)
LANGUAGE sql
STABLE
SECURITY DEFINER
SET search_path = ''
AS $function$
  SELECT d.id,
         d.title,
         (d.embedding <=> query_embedding)::double precision AS distance
  FROM public.documents AS d
  WHERE d.embedding IS NOT NULL
  ORDER BY d.embedding <=> query_embedding
  LIMIT GREATEST(match_count, 0);
$function$;

REVOKE ALL ON FUNCTION public.match_documents(extensions.vector, integer) FROM PUBLIC;
GRANT EXECUTE ON FUNCTION public.match_documents(extensions.vector, integer) TO anon;
GRANT EXECUTE ON FUNCTION public.match_documents(extensions.vector, integer) TO authenticated;

NOTIFY pgrst, 'reload schema';
SQL
CREATE EXTENSION
ALTER TABLE
UPDATE 6
ALTER TABLE
CREATE INDEX
ERROR:  operator does not exist: extensions.vector <=> extensions.vector
LINE 13:          (d.embedding <=> query_embedding)::double precision...
                               ^
HINT:  No operator matches the given name and argument types. You might need to add explicit type casts.
#5exit 0
set -euo pipefail
export PGPASSWORD="$SUPABASE_DB_PASSWORD"
psql "$SUPABASE_DB_URL" -v ON_ERROR_STOP=1 -P pager=off <<'SQL'
CREATE OR REPLACE FUNCTION public.match_documents(
  query_embedding extensions.vector(4),
  match_count integer
)
RETURNS TABLE (id integer, title text, distance double precision)
LANGUAGE sql
STABLE
SECURITY DEFINER
SET search_path = ''
AS $function$
  SELECT d.id,
         d.title,
         (d.embedding OPERATOR(extensions.<=>) query_embedding)::double precision AS distance
  FROM public.documents AS d
  WHERE d.embedding IS NOT NULL
  ORDER BY d.embedding OPERATOR(extensions.<=>) query_embedding
  LIMIT GREATEST(match_count, 0);
$function$;

REVOKE ALL ON FUNCTION public.match_documents(extensions.vector, integer) FROM PUBLIC;
GRANT EXECUTE ON FUNCTION public.match_documents(extensions.vector, integer) TO anon;
GRANT EXECUTE ON FUNCTION public.match_documents(extensions.vector, integer) TO authenticated;
NOTIFY pgrst, 'reload schema';

SELECT * FROM public.match_documents('[1,0,0,0]'::extensions.vector, 4);
SQL
CREATE FUNCTION
REVOKE
GRANT
GRANT
NOTIFY
 id | title |      distance       
----+-------+---------------------
  1 | alpha | 0.00124766112215546
  2 | bravo |  0.0513167145152487
  3 | delta |   0.683772233983162
  6 | golf  |    0.98000799508818
(4 rows)

#6exit 0
set -euo pipefail
# Allow PostgREST a moment to process its schema reload notification.
for i in 1 2 3 4 5; do
  body=$(mktemp)
  code=$(curl -sS -o "$body" -w '%{http_code}' \
    -X POST "$SUPABASE_URL/rest/v1/rpc/match_documents" \
    -H "apikey: $SUPABASE_ANON_KEY" \
    -H "Authorization: Bearer $SUPABASE_ANON_KEY" \
    -H 'Content-Type: application/json' \
    --data '{"query_embedding":"[1,0,0,0]","match_count":4}')
  echo "HTTP $code"
  cat "$body"; echo
  if [ "$code" = 200 ]; then rm -f "$body"; exit 0; fi
  rm -f "$body"
  sleep 2
done
exit 1
HTTP 200
[{"id":1,"title":"alpha","distance":0.00124766112215546}, 
 {"id":2,"title":"bravo","distance":0.0513167145152487}, 
 {"id":3,"title":"delta","distance":0.683772233983162}, 
 {"id":6,"title":"golf","distance":0.98000799508818}]

What it said at the end

Implemented and verified semantic search for `public.documents`: - Enabled the `pgvector` extension. - Added and populated `embedding vector(4)` from `embedding_json`. - Created HNSW cosine index `documents_embedding_hnsw_idx`. - Created `public.match_documents(query_embedding vector(4), match_count int)`. - Granted RPC execution to `anon` and `authenticated`. - Reloaded the PostgREST schema cache. - Confirmed the anon-key REST RPC returns nearest-first results. Test query `[1,0,0,0]` returned: 1. `alpha` — distance `0.00125` 2. `bravo` — distance `0.05132` 3. `delta` — distance `0.68377` 4. `golf` — distance `0.98001` REST endpoint responded successfully with HTTP 200.

How it was graded

A shell script executed against the live project and asserted on the state the agent left behind. No model judged another model.

exit 0 OK

Check it yourself

Raw transcript · Task definition and verifier · Every attempt