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

xiaomi/mimo-v2.5 · d2_pgvector · rep 2

pass
model
xiaomi/mimo-v2.5
task
d2_pgvector
rep
2
day
2
steps
14
tokens
36,279
est. cost
$0.007
agent time
35s
wave
s4c
started
2026-07-26T03:36:21Z

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 — 14 commands

#1exit 0
echo "SUPABASE_URL=$SUPABASE_URL"
echo "SUPABASE_PROJECT_REF=$SUPABASE_PROJECT_REF"
echo "SUPABASE_ANON_KEY=$SUPABASE_ANON_KEY"
echo "SUPABASE_SERVICE_ROLE_KEY=$SUPABASE_SERVICE_ROLE_KEY"
echo "SUPABASE_DB_URL=$SUPABASE_DB_URL"
echo "SUPABASE_DB_PASSWORD=$SUPABASE_DB_PASSWORD"
SUPABASE_URL=https://<ref>.supabase.co
SUPABASE_PROJECT_REF=<ref>
SUPABASE_ANON_KEY=<redacted:SUPABASE_ANON_KEY>
SUPABASE_SERVICE_ROLE_KEY=<redacted:SUPABASE_SERVICE_ROLE_KEY>
SUPABASE_DB_URL=<redacted:SUPABASE_DB_URL>
SUPABASE_DB_PASSWORD=<redacted:SUPABASE_DB_PASSWORD>
#2exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$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
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "CREATE EXTENSION IF NOT EXISTS vector SCHEMA extensions;"
CREATE EXTENSION
#4exit 1
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "
ALTER TABLE public.documents ADD COLUMN embedding vector(4);
UPDATE public.documents SET embedding = embedding_json::vector(4);
"
ALTER TABLE
ERROR:  cannot cast type jsonb to vector
LINE 3: ...TE public.documents SET embedding = embedding_json::vector(4...
                                                             ^
#5exit 1
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "
UPDATE public.documents SET embedding = embedding_json::text::vector(4);
"
ERROR:  column "embedding" of relation "documents" does not exist
LINE 2: UPDATE public.documents SET embedding = embedding_json::text...
                                    ^
#6exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "ALTER TABLE public.documents ADD COLUMN embedding vector(4);"
ALTER TABLE
#7exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "UPDATE public.documents SET embedding = embedding_json::text::vector(4);"
UPDATE 6
#8exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "SELECT id, title, embedding_json, embedding FROM public.documents;"
 id |  title  |  embedding_json  |   embedding   
----+---------+------------------+---------------
  1 | alpha   | [10, 0.5, 0, 0]  | [10,0.5,0,0]
  2 | bravo   | [0.9, 0.3, 0, 0] | [0.9,0.3,0,0]
  3 | delta   | [20, 60, 0, 0]   | [20,60,0,0]
  4 | echo    | [0, 1, 0, 0]     | [0,1,0,0]
  5 | foxtrot | [-5, 0.1, 0, 0]  | [-5,0.1,0,0]
  6 | golf    | [0.1, 0.1, 3, 4] | [0.1,0.1,3,4]
(6 rows)

#9exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "
CREATE INDEX documents_embedding_idx ON public.documents USING hnsw (embedding vector_cosine_ops);
"
CREATE INDEX
#10exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "
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 AS distance
  FROM public.documents d
  ORDER BY d.embedding <=> query_embedding
  LIMIT match_count;
\$\$;
"
CREATE FUNCTION
#11exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$SUPABASE_DB_URL" -c "
GRANT EXECUTE ON FUNCTION public.match_documents(vector(4), int) TO anon;
"
GRANT
#12exit 0
PGPASSWORD="$SUPABASE_DB_PASSWORD" psql "$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)

#13exit 0
curl -s -X POST \
  "$SUPABASE_URL/rest/v1/rpc/match_documents" \
  -H "apikey: $SUPABASE_ANON_KEY" \
  -H "Authorization: Bearer $SUPABASE_ANON_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query_embedding": [10, 0.5, 0, 0], "match_count": 3}' | python3 -m json.tool
[
    {
        "id": 1,
        "title": "alpha",
        "distance": 0
    },
    {
        "id": 2,
        "title": "bravo",
        "distance": 0.0367087035723488
    },
    {
        "id": 3,
        "title": "delta",
        "distance": 0.636791795933452
    }
]
#14exit 0
curl -s -X POST \
  "$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

Everything works correctly. Querying with `[0, 0, 3, 4]` returns **golf** (whose embedding `[0.1, 0.1, 3, 4]` is nearly identical) as the nearest neighbor with ~0 distance. --- ## Summary Here's what was built end-to-end: 1. **pgvector extension** — Enabled in the `extensions` schema. 2. **`embedding` column** — Added `vector(4)` to `public.documents` and populated all 6 rows from `embedding_json` (cast via `::text::vector(4)`). 3. **HNSW index** — Created `documents_embedding_idx` using `vector_cosine_ops` for efficient approximate nearest-neighbor search with cosine distance. 4. **`match_documents` function** — A `STABLE` SQL function taking `query_embedding vector(4)` and `match_count int`, returning the closest documents ordered by cosine distance (`<=>` operator). Granted `EXECUTE` to the `anon` role so PostgREST exposes it at `POST /rest/v1/rpc/match_documents`. 5. **Verified** — Both via direct SQL and via the REST API with the anon key. Queries return sensible nearest neighbors: - `[10, 0.5, 0, 0]` → **alpha** (exact match, dist=0), then **bravo** (dist≈0.037) - `[0, 0, 3, 4]` → **golf** (dist≈0.0004), the only document with non-trivial 3rd/4th dimensions

11 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