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

deepseek/deepseek-v4-pro · d2_pgvector · rep 1

pass

model

deepseek/deepseek-v4-pro

task

d2_pgvector

rep

day

steps

tokens

22,514

est. cost

$0.013

agent time

89s

wave

started

2026-07-24T06:11:49Z

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 — 12 commands

#1exit 0

echo "SUPABASE_URL: $SUPABASE_URL" && echo "SUPABASE_DB_URL set: $( [ -n "$SUPABASE_DB_URL" ] && echo yes || echo no )" && echo "SUPABASE_SERVICE_ROLE_KEY set: $( [ -n "$SUPABASE_SERVICE_ROLE_KEY" ] && echo yes || echo no )" && echo "SUPABASE_ANON_KEY set: $( [ -n "$SUPABASE_ANON_KEY" ] && echo yes || echo no )"

SUPABASE_URL: https://<ref>.supabase.co
SUPABASE_DB_URL set: yes
SUPABASE_SERVICE_ROLE_KEY set: yes
SUPABASE_ANON_KEY set: yes

#2exit 0

psql "$SUPABASE_DB_URL" -c "SELECT * FROM public.documents;"

 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)

#3exit 0

psql "$SUPABASE_DB_URL" -c "CREATE EXTENSION IF NOT EXISTS vector;"

CREATE EXTENSION

#4exit 0

psql "$SUPABASE_DB_URL" -c "ALTER TABLE public.documents ADD COLUMN embedding vector(4);"

ALTER TABLE

#5exit 0

psql "$SUPABASE_DB_URL" -c "UPDATE public.documents SET embedding = (embedding_json::text)::vector(4);"

UPDATE 6

#6exit 0

psql "$SUPABASE_DB_URL" -c "SELECT id, title, embedding FROM public.documents;"

 id |  title  |   embedding   
----+---------+---------------
  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)

#7exit 0

psql "$SUPABASE_DB_URL" -c "CREATE INDEX ON public.documents USING hnsw (embedding vector_cosine_ops);"

CREATE INDEX

#8exit 0

psql "$SUPABASE_DB_URL" << 'SQL'
CREATE OR REPLACE FUNCTION public.match_documents(
  query_embedding vector(4),
  match_count int
)
RETURNS TABLE (
  id int,
  title text,
  distance float8
)
LANGUAGE sql
STABLE
AS $$
  SELECT
    d.id,
    d.title,
    (d.embedding <=> query_embedding)::float8 AS distance
  FROM public.documents d
  ORDER BY d.embedding <=> query_embedding
  LIMIT match_count;
$$;
SQL

CREATE FUNCTION

#9exit 0

psql "$SUPABASE_DB_URL" -c "SELECT * FROM public.match_documents('[10,0.5,0,0]'::vector(4), 3);"

 id | title |      distance      
----+-------+--------------------
  1 | alpha |                  0
  2 | bravo | 0.0367087035723488
  3 | delta |  0.636791795933452
(3 rows)

#10exit 0

psql "$SUPABASE_DB_URL" -c "GRANT EXECUTE ON FUNCTION public.match_documents(vector(4), int) TO anon, authenticated;"

GRANT

#11exit 0

curl -s -X POST "${SUPABASE_URL}/rest/v1/rpc/match_documents" \
  -H "Content-Type: application/json" \
  -H "apikey: ${SUPABASE_ANON_KEY}" \
  -H "Authorization: Bearer ${SUPABASE_ANON_KEY}" \
  -d '{"query_embedding": [10, 0.5, 0, 0], "match_count": 3}' | python3 -m json.tool

[
    {
        "id": 1,
        "title": "alpha",
        "distance": 0
    },
    {
        "id": 2,
        "title": "bravo",
        "distance": 0.0367087035723488
    },
    {
        "id": 3,
        "title": "delta",
        "distance": 0.636791795933452
    }
]

#12exit 0

curl -s -X POST "${SUPABASE_URL}/rest/v1/rpc/match_documents" \
  -H "Content-Type: application/json" \
  -H "apikey: ${SUPABASE_ANON_KEY}" \
  -H "Authorization: Bearer ${SUPABASE_ANON_KEY}" \
  -d '{"query_embedding": [0.1, 0.1, 3, 4], "match_count": 4}' | python3 -m json.tool

[
    {
        "id": 6,
        "title": "golf",
        "distance": 0
    },
    {
        "id": 3,
        "title": "delta",
        "distance": 0.974711892170972
    },
    {
        "id": 2,
        "title": "bravo",
        "distance": 0.974711892401252
    },
    {
        "id": 1,
        "title": "alpha",
        "distance": 0.979034586516748
    }
]

What it said at the end

All four steps are complete and verified. Here's a summary: **1. pgvector extension**: Created with `CREATE EXTENSION IF NOT EXISTS vector`. **2. New column**: Added `embedding vector(4)` to `public.documents` and populated it from `embedding_json` (casting the JSONB text representation to `vector(4)`). All 6 rows converted successfully. **3. HNSW index**: Created with `CREATE INDEX ... USING hnsw (embedding vector_cosine_ops)`. **4. RPC function**: `match_documents(query_embedding vector(4), match_count int)` returns `TABLE(id int, title text, distance float8)`, using `<=>` (cosine distance). Granted execute to `anon` and `authenticated` roles. **Verification** — Two searches via the REST endpoint confirmed sensible results: - Query `[10, 0.5, 0, 0]` → `alpha` (distance 0 — exact match), then `bravo` (~0.037), then `delta` (~0.637). - Query `[0.1, 0.1, 3, 4]` → `golf` (distance 0 — exact match), with the others at ~0.97 cosine distance. Both return nearest neighbors first, as expected.

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