openai/gpt-5.6-luna · d2_pgvector · rep 2

set -e
printf 'DB URL present: '; [ -n "$SUPABASE_DB_URL" ] && echo yes || echo no
command -v psql || true
python - <<'PY'
import os
for k in ['SUPABASE_URL','SUPABASE_PROJECT_REF','SUPABASE_ANON_KEY','SUPABASE_SERVICE_ROLE_KEY','SUPABASE_DB_URL']:
 print(k, 'set' if os.getenv(k) else 'missing')
PY

DB URL present: yes
/usr/bin/psql

bash: line 4: python: command not found

set -e
psql "$SUPABASE_DB_URL" -v ON_ERROR_STOP=1 <<'SQL'
CREATE EXTENSION IF NOT EXISTS vector;
ALTER TABLE public.documents ADD COLUMN IF NOT EXISTS embedding vector(4);
UPDATE public.documents
SET embedding = embedding_json::text::vector
WHERE embedding IS NULL AND embedding_json IS NOT NULL;
CREATE INDEX IF NOT EXISTS documents_embedding_hnsw_cosine_idx
ON public.documents USING hnsw (embedding vector_cosine_ops);
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 $$
  SELECT d.id, d.title, (d.embedding <=> query_embedding)::float8 AS distance
  FROM public.documents AS d
  WHERE d.embedding IS NOT NULL
  ORDER BY d.embedding <=> query_embedding
  LIMIT match_count;
$$;
GRANT EXECUTE ON FUNCTION public.match_documents(vector(4), integer) TO anon, authenticated;
SQL

echo '--- catalog verification ---'
psql "$SUPABASE_DB_URL" -v ON_ERROR_STOP=1 -P pager=off -c "SELECT extname FROM pg_extension WHERE extname='vector'; SELECT column_name, data_type, udt_name FROM information_schema.columns WHERE table_schema='public' AND table_name='documents' AND column_name='embedding'; SELECT indexname, indexdef FROM pg_indexes WHERE schemaname='public' AND tablename='documents' AND indexname='documents_embedding_hnsw_cosine_idx';"
echo '--- sample search ---'
psql "$SUPABASE_DB_URL" -v ON_ERROR_STOP=1 -P pager=off -c "SELECT * FROM public.match_documents((SELECT embedding FROM public.documents WHERE embedding IS NOT NULL LIMIT 1), 3);"

CREATE EXTENSION
ALTER TABLE
UPDATE 6
CREATE INDEX
CREATE FUNCTION
GRANT
--- catalog verification ---
 extname 
---------
 vector
(1 row)

 column_name |  data_type   | udt_name 
-------------+--------------+----------
 embedding   | USER-DEFINED | vector
(1 row)

              indexname              |                                                   indexdef                                                    
-------------------------------------+---------------------------------------------------------------------------------------------------------------
 documents_embedding_hnsw_cosine_idx | CREATE INDEX documents_embedding_hnsw_cosine_idx ON public.documents USING hnsw (embedding vector_cosine_ops)
(1 row)

--- sample search ---
 id | title |      distance      
----+-------+--------------------
  1 | alpha |                  0
  2 | bravo | 0.0367087035723488
  3 | delta |  0.636791795933452
(3 rows)

set -e
read -r EMB < <(psql "$SUPABASE_DB_URL" -At -c "SELECT embedding_json::text FROM public.documents WHERE embedding IS NOT NULL ORDER BY id LIMIT 1")
echo "Query embedding: $EMB"
RESP=$(curl --fail-with-body -sS -X POST "$SUPABASE_URL/rest/v1/rpc/match_documents" \
  -H "apikey: $SUPABASE_ANON_KEY" \
  -H "Authorization: Bearer $SUPABASE_ANON_KEY" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  --data "{\"query_embedding\":$EMB,\"match_count\":3}")
printf '%s\n' "$RESP"
case "$RESP" in *'"id":1'*'"title":"alpha"'*) echo 'REST RPC verification: passed';; *) echo 'REST RPC verification: unexpected response' >&2; exit 1;; esac

Query embedding: [10, 0.5, 0, 0]
[{"id":1,"title":"alpha","distance":0}, 
 {"id":2,"title":"bravo","distance":0.0367087035723488}, 
 {"id":3,"title":"delta","distance":0.636791795933452}]
REST RPC verification: passed