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Commit 5e43f28b 5e43f28b8371048aad36a7919b16fb7afe8ead55 by cnb.bofCdSsphPA
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Bootstrap the fused song-centric schema with repeatable live seed data 

Constraint: Keep all new initialization logic on top of the current 4-table song-centric schema and validate it against the user PostgreSQL instead of synthetic-only assumptions.
Rejected: Stop at one-row smoke evidence | It does not prove the schema is practical for repeated Phase-1 bootstrap workflows.
Confidence: high
Scope-risk: narrow
Directive: Use bootstrap_songcentric_phase1_live.py as the default seed/bootstrap path when demonstrating or validating the fused schema on live PostgreSQL.
Tested: /usr/local/miniconda3/bin/python acr-engine/scripts/bootstrap_songcentric_phase1_live.py --dsn postgres://d2:d2pass@127.0.0.1:5432/d2 --schema acr_songcentric_test; git diff --check; /usr/local/miniconda3/bin/python scripts/check_markdown_links.py --root docs returned OK for 11 active markdown files
Not-tested: large-batch bootstrap and conflict handling under concurrent writers
1 parent 3ce36679
Inline Side-by-side
Showing 4 changed files with 277 additions and 0 deletions
{
"schema": "acr_songcentric_test",
"songs": [
{
"song_id": 2,
"asset_id": 3,
"window_id": 4,
"fingerprint_feature_id": 3,
"embedding_feature_id": 4,
"membership_id": 2
},
{
"song_id": 3,
"asset_id": 5,
"window_id": 6,
"fingerprint_feature_id": 5,
"embedding_feature_id": 6,
"membership_id": 3
}
],
"counts": {
"media_entity": 3,
"audio_object": 6,
"feature_fact": 6,
"set_membership": 3
},
"lineage_sample": {
"feature_type": "embedding",
"model_name": "mert",
"window_id": 2,
"asset_id": 1,
"song_id": 1,
"title": "Smoke Song"
}
}
\ No newline at end of file
#!/usr/bin/env /usr/local/miniconda3/bin/python
from __future__ import annotations
import argparse
import json
from pathlib import Path
import psycopg
from psycopg.rows import dict_row
def quote_ident(name: str) -> str:
return '"' + name.replace('"', '""') + '"'
def ensure_song(cur, biz_key: str, title: str, artist_name: str) -> int:
row = cur.execute(
"""
select entity_id from media_entity
where entity_type = 'song' and biz_key = %s
""",
(biz_key,),
).fetchone()
if row:
return row['entity_id']
return cur.execute(
"""
insert into media_entity (entity_type, biz_key, title, artist_name)
values ('song', %s, %s, %s)
returning entity_id
""",
(biz_key, title, artist_name),
).fetchone()['entity_id']
def ensure_asset(cur, song_id: int, source_type: str, storage_uri: str, checksum: str, duration_ms: int) -> int:
row = cur.execute(
"""
select object_id from audio_object
where object_type = 'asset' and song_id = %s and checksum = %s
""",
(song_id, checksum),
).fetchone()
if row:
return row['object_id']
return cur.execute(
"""
insert into audio_object (
object_type, song_id, source_type, storage_uri, storage_scheme,
checksum, codec, sample_rate, channels, duration_ms
) values (
'asset', %s, %s, %s, 'file', %s, 'wav', 16000, 1, %s
) returning object_id
""",
(song_id, source_type, storage_uri, checksum, duration_ms),
).fetchone()['object_id']
def ensure_window(cur, song_id: int, asset_id: int, start_ms: int, end_ms: int) -> int:
row = cur.execute(
"""
select object_id from audio_object
where object_type = 'window' and parent_object_id = %s and start_ms = %s and end_ms = %s
""",
(asset_id, start_ms, end_ms),
).fetchone()
if row:
return row['object_id']
return cur.execute(
"""
insert into audio_object (
object_type, song_id, parent_object_id, start_ms, end_ms, duration_ms
) values ('window', %s, %s, %s, %s, %s)
returning object_id
""",
(song_id, asset_id, start_ms, end_ms, end_ms - start_ms),
).fetchone()['object_id']
def ensure_feature(cur, feature_type: str, object_id: int, song_id: int, model_name: str, model_version: str,
feature_set_name: str, payload: dict) -> int:
row = cur.execute(
"""
select feature_id from feature_fact
where object_id = %s and model_name = %s and model_version = %s
and feature_set_name = %s and feature_type = %s
""",
(object_id, model_name, model_version, feature_set_name, feature_type),
).fetchone()
if row:
return row['feature_id']
if feature_type == 'fingerprint':
return cur.execute(
"""
insert into feature_fact (
feature_type, object_id, song_id, model_name, model_version,
feature_set_name, fingerprint_value, checksum, metadata_json
) values (%s, %s, %s, %s, %s, %s, %s, %s, %s::jsonb)
returning feature_id
""",
(
feature_type, object_id, song_id, model_name, model_version,
feature_set_name, payload['fingerprint_value'], payload['checksum'], json.dumps(payload.get('metadata_json', {})),
),
).fetchone()['feature_id']
return cur.execute(
"""
insert into feature_fact (
feature_type, object_id, song_id, model_name, model_version,
feature_set_name, embedding_dim, embedding_uri, vector_table_name, checksum, metadata_json
) values (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s::jsonb)
returning feature_id
""",
(
feature_type, object_id, song_id, model_name, model_version,
feature_set_name, payload['embedding_dim'], payload['embedding_uri'], payload['vector_table_name'],
payload['checksum'], json.dumps(payload.get('metadata_json', {})),
),
).fetchone()['feature_id']
def ensure_membership(cur, set_type: str, set_name: str, member_type: str, member_id: int, song_id: int, priority: int) -> int:
row = cur.execute(
"""
select membership_id from set_membership
where set_type = %s and set_name = %s and member_type = %s and member_id = %s
""",
(set_type, set_name, member_type, member_id),
).fetchone()
if row:
return row['membership_id']
return cur.execute(
"""
insert into set_membership (set_type, set_name, member_type, member_id, song_id, priority)
values (%s, %s, %s, %s, %s, %s)
returning membership_id
""",
(set_type, set_name, member_type, member_id, song_id, priority),
).fetchone()['membership_id']
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument('--dsn', required=True)
parser.add_argument('--schema', default='acr_songcentric_test')
parser.add_argument('--output', default='acr-engine/data/pgvector_eval/music20/songcentric_phase1_bootstrap_report.json')
args = parser.parse_args()
output_path = Path('/workspace') / args.output
output_path.parent.mkdir(parents=True, exist_ok=True)
qschema = quote_ident(args.schema)
report = {'schema': args.schema, 'songs': []}
songs = [
{'biz_key': 'song-10001', 'title': 'Song 10001', 'artist_name': 'Artist A'},
{'biz_key': 'song-10002', 'title': 'Song 10002', 'artist_name': 'Artist B'},
]
with psycopg.connect(args.dsn, row_factory=dict_row) as conn:
with conn.cursor() as cur:
cur.execute(f'set search_path to {qschema}, public')
for idx, song in enumerate(songs, start=1):
song_id = ensure_song(cur, **song)
asset_id = ensure_asset(
cur, song_id, 'official', f'/workspace/downloads/{song["biz_key"]}/master.wav',
f'sha256:{song["biz_key"]}', 180000 + idx * 1000,
)
window_id = ensure_window(cur, song_id, asset_id, 30000, 35000)
fingerprint_id = ensure_feature(
cur, 'fingerprint', window_id, song_id,
'chromaprint', 'phase1', 'chromaprint_5s',
{'fingerprint_value': f'fp-{song["biz_key"]}', 'checksum': f'fpchk-{song["biz_key"]}', 'metadata_json': {'lane': 'exact'}},
)
embedding_id = ensure_feature(
cur, 'embedding', window_id, song_id,
'mert', 'v1-95m', 'mert_5s_hop2.5_meanpool',
{
'embedding_dim': 768,
'embedding_uri': f's3://bucket/{song["biz_key"]}/win0001.npy',
'vector_table_name': 'audio_embedding_vector_768',
'checksum': f'embchk-{song["biz_key"]}',
'metadata_json': {'lane': 'semantic'},
},
)
membership_id = ensure_membership(cur, 'reference_set', 'phase1_hot_reference_v1', 'asset', asset_id, song_id, 100)
report['songs'].append({
'song_id': song_id,
'asset_id': asset_id,
'window_id': window_id,
'fingerprint_feature_id': fingerprint_id,
'embedding_feature_id': embedding_id,
'membership_id': membership_id,
})
counts = {}
for table in ['media_entity', 'audio_object', 'feature_fact', 'set_membership']:
counts[table] = cur.execute(f'select count(*) as c from {table}').fetchone()['c']
report['counts'] = counts
report['lineage_sample'] = cur.execute(
"""
select ff.feature_type,
ff.model_name,
win.object_id as window_id,
ast.object_id as asset_id,
song.entity_id as song_id,
song.title
from feature_fact ff
join audio_object win on win.object_id = ff.object_id and win.object_type = 'window'
join audio_object ast on ast.object_id = win.parent_object_id and ast.object_type = 'asset'
join media_entity song on song.entity_id = ff.song_id and song.entity_type = 'song'
where ff.feature_type = 'embedding'
order by ff.feature_id asc
limit 1
"""
).fetchone()
conn.commit()
output_path.write_text(json.dumps(report, ensure_ascii=False, indent=2))
print(json.dumps(report, ensure_ascii=False, indent=2))
return 0
if __name__ == '__main__':
raise SystemExit(main())
## 2026-06-04
- 新增 `acr-engine/scripts/bootstrap_songcentric_phase1_live.py`，把当前 4 表 schema 从“单条 smoke 写入”推进到“可重复 Phase-1 bootstrap”；并准备对 `acr_songcentric_test` 做 fresh live 初始化验证。
- 新增正式 SQL 文件 `acr-engine/sql/acr_pg_schema_songcentric_v1.sql` 与 live smoke 脚本 `acr-engine/scripts/smoke_songcentric_schema_live.py`，把 4 张融合优先表从文档草案推进到可执行 schema，并准备在用户 PostgreSQL 上做 fresh 验证。
- 重写 `docs/postgres_db_schema_samples.md` 为当前 song-centric 融合优先方案的 DDL 草案，补齐 4 张核心表（`media_entity` / `audio_object` / `feature_fact` / `set_membership`）、落表说明、流程图与常用 SQL 样例。
......
......@@ -237,6 +237,21 @@ sequenceDiagram
ING->>DB: insert set_membership(reference_set)
```
### 4.4 Phase-1 bootstrap 流程
```mermaid
flowchart TD
A[bootstrap_songcentric_phase1_live.py] --> B[media_entity song x N]
B --> C[audio_object asset x N]
C --> D[audio_object window x N]
D --> E1[feature_fact fingerprint x N]
D --> E2[feature_fact embedding x N]
C --> F[set_membership reference_set x N]
```
当前 live bootstrap 脚本：[`acr-engine/scripts/bootstrap_songcentric_phase1_live.py`](../acr-engine/scripts/bootstrap_songcentric_phase1_live.py)
---
## 5. 最常用 SQL 样例
......