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

google/gemini-3.1-pro-preview · d2_pgvector · rep 2

pass
model
google/gemini-3.1-pro-preview
task
d2_pgvector
rep
2
day
2
steps
7
tokens
13,037
est. cost
$0.078
agent time
40s
wave
s4
started
2026-07-24T04:58:06Z

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 — 7 commands

#1exit 0
env | grep SUPABASE
SUPABASE_DB_PASSWORD=<redacted:SUPABASE_DB_PASSWORD>
SUPABASE_DB_URL=<redacted:SUPABASE_DB_URL>
SUPABASE_URL=https://<ref>.supabase.co
SUPABASE_SERVICE_ROLE_KEY=<redacted:SUPABASE_SERVICE_ROLE_KEY>
SUPABASE_PROJECT_REF=<ref>
SUPABASE_ANON_KEY=<redacted:SUPABASE_ANON_KEY>
#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
cat << 'EOF' > run.sql
CREATE EXTENSION IF NOT EXISTS vector;
ALTER TABLE public.documents ADD COLUMN embedding vector(4);
UPDATE public.documents SET embedding = translate(embedding_json::text, '[]', '{}')::vector;
SELECT embedding FROM public.documents;
EOF
psql $SUPABASE_DB_URL -f run.sql
CREATE EXTENSION
ALTER TABLE
 embedding 
-----------
 
 
 
 
 
 
(6 rows)

psql:run.sql:3: ERROR:  invalid input syntax for type vector: "{10, 0.5, 0, 0}"
DETAIL:  Vector contents must start with "[".
#4exit 0
cat << 'EOF' > run.sql
SELECT embedding_json::text FROM public.documents;
EOF
psql $SUPABASE_DB_URL -f run.sql
  embedding_json  
------------------
 [10, 0.5, 0, 0]
 [0.9, 0.3, 0, 0]
 [20, 60, 0, 0]
 [0, 1, 0, 0]
 [-5, 0.1, 0, 0]
 [0.1, 0.1, 3, 4]
(6 rows)

#5exit 0
cat << 'EOF' > run.sql
UPDATE public.documents SET embedding = (embedding_json::text)::vector;
SELECT embedding FROM public.documents;
EOF
psql $SUPABASE_DB_URL -f run.sql
UPDATE 6
   embedding   
---------------
 [10,0.5,0,0]
 [0.9,0.3,0,0]
 [20,60,0,0]
 [0,1,0,0]
 [-5,0.1,0,0]
 [0.1,0.1,3,4]
(6 rows)

#6exit 0
cat << 'EOF' > run.sql
CREATE INDEX ON public.documents USING hnsw (embedding vector_cosine_ops);
CREATE OR REPLACE FUNCTION match_documents(
  query_embedding vector(4),
  match_count int DEFAULT null
)
RETURNS TABLE (
  id int,
  title text,
  distance float8
)
LANGUAGE sql
AS $$
  SELECT
    id,
    title,
    (embedding <=> query_embedding)::float8 AS distance
  FROM public.documents
  ORDER BY embedding <=> query_embedding
  LIMIT match_count;
$$;
GRANT EXECUTE ON FUNCTION match_documents(vector(4), integer) TO anon;
GRANT EXECUTE ON FUNCTION match_documents(vector(4), integer) TO authenticated;
EOF
psql $SUPABASE_DB_URL -f run.sql
CREATE INDEX
CREATE FUNCTION
GRANT
GRANT
#7exit 0
curl -s -X POST "${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, 0, 0]", "match_count": 3}'
[{"id":1,"title":"alpha","distance":0.00124766112215535}, 
 {"id":2,"title":"bravo","distance":0.0513166893837236}, 
 {"id":3,"title":"delta","distance":0.683772233983162}]

What it said at the end

I have successfully set up the semantic search functionality as requested. Here is a summary of the actions taken: 1. **Extension Setup:** Executed `CREATE EXTENSION IF NOT EXISTS vector;` to enable pgvector. 2. **Column Creation & Data Population:** Added a new column `embedding` of type `vector(4)` to the `public.documents` table. We then populated this column by casting the existing `embedding_json` strings directly into vectors. 3. **Index Creation:** Created an HNSW index on the new `embedding` column using the `vector_cosine_ops` operator class to optimize nearest-neighbor searches based on cosine distance. 4. **RPC Setup:** Created the `match_documents` SQL function to return the closely matching documents along with their `distance` (calculated with `<=>`). Execution privileges on this function were explicitly granted to the `anon` and `authenticated` roles, making it available as an RPC endpoint. 5. **Verification:** Executed a `cURL` POST request to the `/rest/v1/rpc/match_documents` endpoint as an anonymous user, using the `SUPABASE_ANON_KEY`, querying `[10, 0, 0, 0]` with a match count of 3. It returned accurate results with sensible distances, correctly ordering the nearest documents (`alpha` and `bravo`) to the queried embedding.

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