google/gemini-3.1-pro-preview · d2_pgvector · rep 3
pass
model
google/gemini-3.1-pro-preview
started
2026-07-24T04:59:01Z
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 — 5 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 "$SUPABASE_DB_URL" -c "SELECT * FROM public.documents;"
id | title | embedding_json
----+---------+------------------
1 | alpha | [10, 0.5, 0, 0]
2 | bravo | [0.9, 0.3, 0, 0]
3 | delta | [20, 60, 0, 0]
4 | echo | [0, 1, 0, 0]
5 | foxtrot | [-5, 0.1, 0, 0]
6 | golf | [0.1, 0.1, 3, 4]
(6 rows)
#3exit 0
cat << 'EOF' > run.sql
CREATE EXTENSION IF NOT EXISTS vector;
ALTER TABLE public.documents ADD COLUMN embedding vector(4);
UPDATE public.documents SET embedding = embedding_json::text::vector;
EOF
psql "$SUPABASE_DB_URL" -f run.sql
CREATE EXTENSION
ALTER TABLE
UPDATE 6
#4exit 0
cat << 'EOF' > run2.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)
RETURNS TABLE (id int, title text, distance float8)
LANGUAGE sql
AS $$
SELECT id, title, (embedding <=> query_embedding) AS distance
FROM public.documents
ORDER BY embedding <=> query_embedding
LIMIT match_count;
$$;
GRANT EXECUTE ON FUNCTION match_documents TO anon;
GRANT SELECT ON public.documents TO anon;
EOF
psql "$SUPABASE_DB_URL" -f run2.sql
CREATE INDEX
CREATE FUNCTION
GRANT
GRANT
#5exit 0
curl -s "$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}'[{"id":1,"title":"alpha","distance":0},
{"id":2,"title":"bravo","distance":0.0367087035723488},
{"id":3,"title":"delta","distance":0.636791795933452}]What it said at the end
I have completed the semantic search setup required for your Supabase project. Here is a summary of the steps taken:
1. **Set up `pgvector`:** Enabled the `vector` extension in the PostgreSQL database.
2. **Added `embedding` column:** Created an `embedding vector(4)` column in `public.documents` and populated it by casting the contents of the `embedding_json` column.
3. **Created HNSW index:** Added an HNSW index on `public.documents(embedding)` utilizing `vector_cosine_ops` to enable efficient cosine distance queries.
4. **Created RPC function:** Created the `match_documents` SQL function which takes a `query_embedding vector(4)` and `match_count int`, returning the closest documents based on the cosine distance operator (`<=>`).
5. **Configured API Access:** Granted the `anon` role `EXECUTE` privileges on the `match_documents` function and `SELECT` privileges on the `public.documents` table making it successfully callable through PostgREST.
I verified the functionality using a `curl` request making a POST to `/rest/v1/rpc/match_documents` with the query vector `[10, 0.5, 0, 0]` and `match_count` of 3. The search successfully returned sensible neighbors, correctly retrieving document `1` at a distance of `0`, document `2` at a distance of `~0.037`, and document `3` at a distance of `~0.637`.
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