google/gemini-3.1-pro-preview · d2_pgvector · rep 1
pass
model
google/gemini-3.1-pro-preview
started
2026-07-24T04:57:06Z
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 — 5 commands
#1exit 0
psql "$SUPABASE_DB_URL" -c "\d public.documents; SELECT id, title, embedding_json FROM public.documents;"
Table "public.documents"
Column | Type | Collation | Nullable | Default
----------------+---------+-----------+----------+---------
id | integer | | not null |
title | text | | not null |
embedding_json | jsonb | | not null |
Indexes:
"documents_pkey" PRIMARY KEY, btree (id)
\d: extra argument "SELECT" ignored
\d: extra argument "id," ignored
\d: extra argument "title," ignored
\d: extra argument "embedding_json" ignored
\d: extra argument "FROM" ignored
\d: extra argument "public.documents;" ignored
#2exit 0
psql "$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)
#3exit 0
cat << 'EOF' > run_setup.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, '[]', '{}')::real[]::vector;
CREATE INDEX documents_embedding_hnsw_idx 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, authenticated;
EOF
psql "$SUPABASE_DB_URL" -f run_setup.sql
CREATE EXTENSION
ALTER TABLE
UPDATE 6
CREATE INDEX
CREATE FUNCTION
GRANT
#4exit 0
curl -s -X POST "$SUPABASE_URL/rest/v1/rpc/match_documents" \
-H "apikey: $SUPABASE_ANON_KEY" \
-H "Authorization: Bearer $SUPABASE_ANON_KEY" \
-H "Content-Type: application/json" \
-d '{"query_embedding": "[1,0,0,0]", "match_count": 3}'[{"id":1,"title":"alpha","distance":0.00124766112215546},
{"id":2,"title":"bravo","distance":0.0513167145152487},
{"id":3,"title":"delta","distance":0.683772233983162}]#5exit 0
psql "$SUPABASE_DB_URL" -c "SELECT id, embedding FROM public.documents limit 1;"
id | embedding
----+--------------
1 | [10,0.5,0,0]
(1 row)
What it said at the end
I have successfully set up semantic search over your `public.documents` table. Here is a summary of the steps taken:
1. **Set up `pgvector`**: Executed `CREATE EXTENSION IF NOT EXISTS vector;` to enable pgvector on the Supabase PostgreSQL database.
2. **Added the `embedding` column**: Added a new column `embedding vector(4)` to the `public.documents` table and populated it by parsing the JSON arrays stored in `embedding_json`.
3. **Created the HNSW index**: Addred a new index (`documents_embedding_hnsw_idx`) configured to use the `hnsw` index type with the `vector_cosine_ops` operator class to optimize nearest neighbor lookups utilizing cosine distance.
4. **Created the RPC Function**: Created the `match_documents` SQL function which computes the cosine distance using the `<=>` operator between the target `query_embedding` and the documents' embeddings, orders them by the nearest distance first, and limits the output to `match_count`. I also granted execute permissions on this function for the `anon` and `authenticated` roles to expose it to the PostgREST API limits securely.
5. **Verified via REST API**: Finally, I ran a test query requesting the nearest 3 neighbors for `[1,0,0,0]` via `curl -X POST "$SUPABASE_URL/rest/v1/rpc/match_documents"`.
The query successfully returned:
```json
[
{"distance":0.00124766112215546,"id":1,"title":"alpha"},
{"distance":0.0513167145152487,"id":2,"title":"bravo"},
{"distance":0.683772233983162,"id":3,"title":"delta"}
]
```
The results are sensible since `alpha` `[10,0.5,0,0]` has a direction nearly aligned with the exact principal X-axis `[1,0,0,0]`.
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
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