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

z-ai/glm-5.2 · d2_pgvector · rep 1

pass

model

z-ai/glm-5.2

task

d2_pgvector

rep

day

steps

tokens

23,213

est. cost

$0.035

agent time

80s

wave

started

2026-07-24T16:04:14Z

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 — 10 commands

#1exit 0

echo "SUPABASE_URL=$SUPABASE_URL"; echo "SUPABASE_PROJECT_REF=$SUPABASE_PROJECT_REF"; echo "SUPABASE_DB_URL=$SUPABASE_DB_URL"; echo "Keys set: anon=${SUPABASE_ANON_KEY:0:10}... service=${SUPABASE_SERVICE_ROLE_KEY:0:10}..."; echo "DB password set: ${SUPABASE_DB_PASSWORD:+yes}"

SUPABASE_URL=https://<ref>.supabase.co
SUPABASE_PROJECT_REF=<ref>
SUPABASE_DB_URL=<redacted:SUPABASE_DB_URL>
Keys set: anon=eyJhbGciOi... service=eyJhbGciOi...
DB password set: yes

#2exit 0

which psql curl jq python3 2>&1; echo "---"; psql --version 2>&1

/usr/bin/psql
/usr/bin/curl
/usr/bin/jq
/usr/bin/python3
---
psql (PostgreSQL) 18.4 (Debian 18.4-1.pgdg12+1)

#3exit 0

export PGPASSWORD='<redacted:SUPABASE_DB_PASSWORD>'; psql "<redacted:SUPABASE_DB_URL>" -c "SELECT version();" 2>&1

                                   version                                    
------------------------------------------------------------------------------
 PostgreSQL 17.6 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 15.2.0, 64-bit
(1 row)

#4exit 0

export PGPASSWORD='<redacted:SUPABASE_DB_PASSWORD>'; psql "<redacted:SUPABASE_DB_URL>" -c "\d public.documents" 2>&1

                 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)

#5exit 0

export PGPASSWORD='<redacted:SUPABASE_DB_PASSWORD>'; psql "<redacted:SUPABASE_DB_URL>" -c "SELECT id, title, embedding_json FROM public.documents ORDER BY id;" 2>&1

 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)

#6exit 0

export PGPASSWORD='<redacted:SUPABASE_DB_PASSWORD>'; psql "<redacted:SUPABASE_DB_URL>" <<'SQL' 2>&1
-- 1) pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- 2) Add embedding vector(4) column and populate from embedding_json
ALTER TABLE public.documents ADD COLUMN IF NOT EXISTS embedding vector(4);

UPDATE public.documents
SET embedding = (embedding_json::text)::vector(4);

-- verify
SELECT id, title, embedding_json, embedding FROM public.documents ORDER BY id;
SQL

CREATE EXTENSION
ALTER TABLE
UPDATE 6
 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)

#7exit 0

export PGPASSWORD='<redacted:SUPABASE_DB_PASSWORD>'; psql "<redacted:SUPABASE_DB_URL>" <<'SQL' 2>&1
-- 3) HNSW index using cosine distance
CREATE INDEX IF NOT EXISTS documents_embedding_hnsw_idx
ON public.documents
USING hnsw (embedding vector_cosine_ops);

-- 4) SQL function returning nearest neighbors by cosine distance
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 $$
    SELECT
        d.id,
        d.title,
        (d.embedding <=> query_embedding)::float8 AS distance
    FROM public.documents d
    ORDER BY d.embedding <=> query_embedding
    LIMIT match_count;
$$;

-- Allow anon role to call via REST RPC
GRANT EXECUTE ON FUNCTION public.match_documents(vector(4), int) TO anon, authenticated;

-- Verify
\df+ public.match_documents
SQL

CREATE INDEX
CREATE FUNCTION
GRANT
                                                                                                                            List of functions
 Schema |      Name       |                     Result data type                     |             Argument data types             | Type | Volatility | Parallel |  Owner   | Security | Leakproof? |    Access privileges     | Language | Internal name | Description 
--------+-----------------+----------------------------------------------------------+---------------------------------------------+------+------------+----------+----------+----------+------------+--------------------------+----------+---------------+-------------
 public | match_documents | TABLE(id integer, title text, distance double precision) | query_embedding vector, match_count integer | func | stable     | unsafe   | postgres | invoker  | no         | =X/postgres             +| sql      |               | 
        |                 |                                                          |                                             |      |            |          |          |          |            | postgres=X/postgres     +|          |               | 
        |                 |                                                          |                                             |      |            |          |          |          |            | anon=X/postgres         +|          |               | 
        |                 |                                                          |                                             |      |            |          |          |          |            | authenticated=X/postgres+|          |               | 
        |                 |                                                          |                                             |      |            |          |          |          |            | service_role=X/postgres  |          |               | 
(1 row)

#8exit 0

export PGPASSWORD='<redacted:SUPABASE_DB_PASSWORD>'; psql "<redacted:SUPABASE_DB_URL>" <<'SQL' 2>&1
-- Verify sensible neighbors from SQL directly
SELECT * FROM public.match_documents('[10,0.5,0,0]'::vector(4), 5);
SQL

 id | title |      distance      
----+-------+--------------------
  1 | alpha |                  0
  2 | bravo | 0.0367087035723488
  3 | delta |  0.636791795933452
  4 | echo  |  0.950062383056108
  6 | golf  |  0.979034586516748
(5 rows)

#9exit 0

curl -s -X POST "https://<ref>.supabase.co/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": 5}' | jq . 2>&1

[
  {
    "id": 1,
    "title": "alpha",
    "distance": 0
  },
  {
    "id": 2,
    "title": "bravo",
    "distance": 0.0367087035723488
  },
  {
    "id": 3,
    "title": "delta",
    "distance": 0.636791795933452
  },
  {
    "id": 4,
    "title": "echo",
    "distance": 0.950062383056108
  },
  {
    "id": 6,
    "title": "golf",
    "distance": 0.979034586516748
  }
]

#10exit 0

# Second verification: query near "bravo" / small vectors
curl -s -X POST "https://<ref>.supabase.co/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, 0.3, 0, 0], "match_count": 3}' | jq . 2>&1
echo "---"
# Third: query near "golf"
curl -s -X POST "https://<ref>.supabase.co/rest/v1/rpc/match_documents" \
  -H "apikey: ${SUPABASE_ANON_KEY}" \
  -H "Authorization: Bearer ${SUPABASE_ANON_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"query_embedding": [0.1, 0.1, 3, 4], "match_count": 2}' | jq . 2>&1
echo "---"
# Confirm index exists
export PGPASSWORD='<redacted:SUPABASE_DB_PASSWORD>'; psql "<redacted:SUPABASE_DB_URL>" -c "\di public.documents_embedding_hnsw_idx" 2>&1

[
  {
    "id": 2,
    "title": "bravo",
    "distance": 0.000458813290824645
  },
  {
    "id": 1,
    "title": "alpha",
    "distance": 0.0290193401171952
  },
  {
    "id": 3,
    "title": "delta",
    "distance": 0.42450660263693
  }
]
---
[
  {
    "id": 6,
    "title": "golf",
    "distance": 0
  },
  {
    "id": 3,
    "title": "delta",
    "distance": 0.974711892170972
  }
]
---
                           List of indexes
 Schema |             Name             | Type  |  Owner   |   Table   
--------+------------------------------+-------+----------+-----------
 public | documents_embedding_hnsw_idx | index | postgres | documents
(1 row)

What it said at the end

All steps are complete and verified. Here's a summary of what was done: ## What was built 1. **pgvector extension** — `CREATE EXTENSION IF NOT EXISTS vector` on the hosted Postgres 17 project. 2. **`embedding vector(4)` column** — Added to `public.documents` and populated from `embedding_json` by casting the JSON array text to `vector(4)`. All 6 rows migrated correctly (e.g., `[10, 0.5, 0, 0]` → `[10,0.5,0,0]`). 3. **HNSW index** — Created `documents_embedding_hnsw_idx` using `USING hnsw (embedding vector_cosine_ops)` for cosine distance. 4. **`match_documents(query_embedding vector(4), match_count int)`** — A `STABLE` SQL function returning `(id int, title text, distance float8)` ordered by cosine distance (`<=>`) ascending, with `LIMIT match_count`. Granted `EXECUTE` to `anon` and `authenticated` roles so it's callable through PostgREST with the anon key. ## Verification The REST RPC endpoint `POST /rest/v1/rpc/match_documents` was tested with the anon key and returns sensible nearest neighbors: - **Query `[10, 0.5, 0, 0]`** (exact match of "alpha") → alpha (d=0), bravo (d=0.037), delta (d=0.637), echo (d=0.950), golf (d=0.979) — foxtrot (a negative-x outlier) correctly excluded from top 5. - **Query `[1, 0.3, 0, 0]`** → bravo (d=0.0005), alpha (d=0.029), delta (d=0.425) — bravo is nearest as expected. - **Query `[0.1, 0.1, 3, 4]`** (golf's embedding) → golf (d=0), delta (d=0.975). All distances are sensible cosine distances (0 = identical, approaching 1 = orthogonal), ordered nearest-first, and the HNSW index is in place.

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