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

deepseek/deepseek-v4-pro · d2_pgvector · rep 3

pass
model
deepseek/deepseek-v4-pro
task
d2_pgvector
rep
3
day
2
steps
11
tokens
20,932
est. cost
$0.013
agent time
64s
wave
s4
started
2026-07-24T06:14:42Z

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 — 11 commands

#1exit 0
echo "SUPABASE_URL=$SUPABASE_URL" && echo "SUPABASE_PROJECT_REF=$SUPABASE_PROJECT_REF" && echo "SUPABASE_DB_URL=$SUPABASE_DB_URL" | sed 's/:[^:]*@/:****@/' && echo "Keys present:" && echo "ANON_KEY: ${#SUPABASE_ANON_KEY} chars" && echo "SERVICE_ROLE: ${#SUPABASE_SERVICE_ROLE_KEY} chars"
SUPABASE_URL=https://<ref>.supabase.co
SUPABASE_PROJECT_REF=<ref>
SUPABASE_DB_URL=postgresql://postgres.<ref>:<redacted>@aws-0-us-east-1.pooler.supabase.com:5432/postgres
Keys present:
ANON_KEY: 208 chars
SERVICE_ROLE: 219 chars
#2exit 0
psql "$SUPABASE_DB_URL" -c "CREATE EXTENSION IF NOT EXISTS vector;"
CREATE EXTENSION
#3exit 0
psql "$SUPABASE_DB_URL" -c "ALTER TABLE public.documents ADD COLUMN embedding vector(4);" 2>&1
ALTER TABLE
#4exit 0
psql "$SUPABASE_DB_URL" -c "SELECT id, title, embedding_json 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)

#5exit 0
psql "$SUPABASE_DB_URL" -c "UPDATE public.documents SET embedding = (SELECT array_agg(elem::float8)::vector(4) FROM jsonb_array_elements_text(embedding_json) AS elem);"
UPDATE 6
#6exit 0
psql "$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" <<'EOF'
CREATE OR REPLACE FUNCTION match_documents(query_embedding vector(4), match_count int)
RETURNS TABLE (id int, title text, distance float8)
LANGUAGE sql
AS $$
  SELECT
    documents.id,
    documents.title,
    (documents.embedding <=> query_embedding)::float8 AS distance
  FROM public.documents
  ORDER BY documents.embedding <=> query_embedding
  LIMIT match_count;
$$;
EOF
CREATE FUNCTION
#9exit 0
psql "$SUPABASE_DB_URL" -c "SELECT * FROM match_documents('[1, 0, 0, 0]'::vector(4), 3);"
 id | title |      distance       
----+-------+---------------------
  1 | alpha | 0.00124766112215546
  2 | bravo |  0.0513167145152487
  3 | delta |   0.683772233983162
(3 rows)

#10exit 0
curl -s -X POST "${SUPABASE_URL}/rest/v1/rpc/match_documents" \
  -H "apikey: ${SUPABASE_ANON_KEY}" \
  -H "Authorization: Bearer ${SUPABASE_ANON_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"query_embedding": [1, 0, 0, 0], "match_count": 3}' | python3 -m json.tool
[
    {
        "id": 1,
        "title": "alpha",
        "distance": 0.00124766112215546
    },
    {
        "id": 2,
        "title": "bravo",
        "distance": 0.0513167145152487
    },
    {
        "id": 3,
        "title": "delta",
        "distance": 0.683772233983162
    }
]
#11exit 0
curl -s -X POST "${SUPABASE_URL}/rest/v1/rpc/match_documents" \
  -H "apikey: ${SUPABASE_ANON_KEY}" \
  -H "Authorization: Bearer ${SUPABASE_ANON_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"query_embedding": [0, 0, 3, 4], "match_count": 3}' | python3 -m json.tool
[
    {
        "id": 6,
        "title": "golf",
        "distance": 0.000399769304185948
    },
    {
        "id": 1,
        "title": "alpha",
        "distance": 1
    },
    {
        "id": 2,
        "title": "bravo",
        "distance": 1
    }
]

What it said at the end

All done. Here's a summary of what was accomplished: --- ### Summary 1. **pgvector extension** — Enabled via `CREATE EXTENSION IF NOT EXISTS vector;` 2. **New column** — Added `embedding vector(4)` to `public.documents` and populated it from `embedding_json` by extracting JSON array elements and casting. 3. **HNSW index** — Created on `embedding` with `vector_cosine_ops` (cosine distance operator). 4. **`match_documents` function** — Takes `query_embedding vector(4)` and `match_count int`, returns `TABLE(id int, title text, distance float8)` ordered by cosine distance (nearest first using `<=>`). Accessible via `POST /rest/v1/rpc/match_documents` with the anon key. ### Verification - **SQL**: `SELECT * FROM match_documents('[1,0,0,0]', 3)` → alpha (0.0012), bravo (0.051), delta (0.68) — sensible results. - **REST**: `POST /rest/v1/rpc/match_documents` with `{"query_embedding": [0,0,3,4], "match_count": 3}` → golf (≈0.0004) returned first, exactly matching its embedding `[0.1,0.1,3,4]`. Everything works.

3 earlier narration turn(s) are in the raw transcript.

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