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Commit eb2ea03a eb2ea03a66e26c55b5c01d355cad180399c2a6e3 by cnb.bofCdSsphPA
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Add a runner for all planner validation entrypoints 

Constraint: The planner artifact had become executable, but future sessions still needed a reusable entrypoint instead of ad-hoc inline Python to consume it.
Rejected: Keep the execution proof as one-off shell snippets | That would not give the next session a durable command surface.
Confidence: high
Scope-risk: narrow
Directive: Use run_planner_validation_commands_live.py as the default preflight gate before attempting new Phase-1 worker changes on a host.
Tested: /usr/local/miniconda3/bin/python -m py_compile scripts/run_planner_validation_commands_live.py; git diff --check; /usr/local/miniconda3/bin/python scripts/run_planner_validation_commands_live.py --dsn 'postgres://d2:d2pass@127.0.0.1:5432/d2' --output data/pgvector_eval/music20/planner_validation_commands_runner_report.json
Not-tested: The runner only validates planner entrypoints; it does not unlock successful extraction on an environment-blocked host.
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Showing 5 changed files with 125 additions and 0 deletions
{
"prereq_audit": {
"command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" /usr/local/miniconda3/bin/python scripts/run_phase1_prereq_audit_live.py --dsn \"$PG_DSN\" --schema acr_test --output data/pgvector_eval/music20/phase1_prereq_audit_report.json",
"returncode": 0,
"stdout_tail": "\n },\n {\n \"extraction_job_id\": 4,\n \"model_name\": \"muq\",\n \"model_version\": \"large-msd-iter\",\n \"embedding_dim\": 768,\n \"target_scope\": \"reference_set:phase1_hot_reference_v1\",\n \"required_packages\": [\n \"numpy\",\n \"torch\",\n \"torchaudio\",\n \"transformers\"\n ],\n \"missing_packages\": [\n \"torch\",\n \"torchaudio\",\n \"transformers\"\n ],\n \"downloads_root_exists\": false,\n \"ready_for_live_worker\": false\n },\n {\n \"extraction_job_id\": 5,\n \"model_name\": \"ecapa\",\n \"model_version\": \"acr-baseline-v1\",\n \"embedding_dim\": 192,\n \"target_scope\": \"reference_set:phase1_hot_reference_v1\",\n \"required_packages\": [\n \"numpy\",\n \"torch\",\n \"torchaudio\",\n \"speechbrain\"\n ],\n \"missing_packages\": [\n \"torch\",\n \"torchaudio\",\n \"speechbrain\"\n ],\n \"downloads_root_exists\": false,\n \"ready_for_live_worker\": false\n }\n ],\n \"summary\": {\n \"total_jobs\": 5,\n \"ready_jobs\": 0,\n \"blocked_jobs\": 5,\n \"missing_packages_union\": [\n \"speechbrain\",\n \"torch\",\n \"torchaudio\",\n \"transformers\"\n ]\n }\n}\n",
"stderr_tail": "",
"passed": true
},
"worker_contract_smoke": {
"command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" /usr/local/miniconda3/bin/python scripts/run_phase1_worker_contract_smoke_live.py --dsn \"$PG_DSN\" --schema acr_test --output data/pgvector_eval/music20/phase1_worker_contract_smoke_report.json",
"returncode": 0,
"stdout_tail": "{\n \"schema\": \"acr_test\",\n \"dsn_redacted\": \"postgres://d2:***@127.0.0.1:5432/d2\",\n \"exact_lane\": {\n \"job_id\": 1,\n \"returncode\": 0,\n \"job_status\": \"failed\",\n \"failure_reason\": \"unreadable_audio_assets\",\n \"missing_asset_count\": 20,\n \"artifact\": \"data/pgvector_eval/music20/phase1_worker_contract_smoke_exact.json\"\n },\n \"semantic_lane\": {\n \"returncode\": 0,\n \"semantic_job_count\": 4,\n \"failed_jobs\": 4,\n \"unique_blockers\": [\n \"model_runtime_unavailable\",\n \"unreadable_audio_assets\"\n ],\n \"artifact\": \"data/pgvector_eval/music20/phase1_worker_contract_smoke_semantic_matrix.json\"\n },\n \"summary\": {\n \"exact_status\": \"failed\",\n \"semantic_failed_jobs\": 4,\n \"shared_environment_blockers\": [\n \"missing /workspace/downloads mount\",\n \"missing semantic model runtime dependencies\"\n ]\n }\n}\n",
"stderr_tail": "",
"passed": true
},
"semantic_vector_negative_matrix": {
"command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" /usr/local/miniconda3/bin/python scripts/run_embedding_vector_table_negative_matrix_live.py --dsn \"$PG_DSN\" --output data/pgvector_eval/music20/embedding_vector_table_negative_matrix_report.json",
"returncode": 0,
"stdout_tail": "dding_vector_table_not_allowlisted_attempt.json\"\n },\n {\n \"case\": \"vector_table_missing_in_schema\",\n \"schema\": \"acr_vector_table_missing_test\",\n \"vector_table\": \"audio_embedding_vector_768\",\n \"job_status\": \"failed\",\n \"failure_reason\": \"preflight_failed\",\n \"preflight_blockers\": [\n \"unreadable_audio_assets\",\n \"vector_table_missing_in_schema\",\n \"model_runtime_unavailable\"\n ],\n \"vector_table_report\": {\n \"reason\": \"vector_table_missing_in_schema\",\n \"resolved\": false,\n \"expected_dim\": 768,\n \"table_exists\": false,\n \"allowed_vector_tables\": [\n \"audio_embedding_vector_192\",\n \"audio_embedding_vector_768\"\n ],\n \"requested_vector_table\": \"audio_embedding_vector_768\"\n },\n \"artifact\": \"data/pgvector_eval/music20/embedding_vector_table_missing_in_schema_attempt.json\"\n }\n ],\n \"summary\": {\n \"expected_reasons\": {\n \"vector_table_dim_mismatch\": \"vector_table_dim_mismatch\",\n \"vector_table_not_allowlisted\": \"vector_table_not_allowlisted\",\n \"vector_table_missing_in_schema\": \"vector_table_missing_in_schema\"\n },\n \"all_failed\": true\n }\n}\n",
"stderr_tail": "",
"passed": true
},
"asset_level_upsert_validation": {
"command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" /usr/local/miniconda3/bin/python scripts/validate_audio_embedding_asset_upsert_live.py --dsn \"$PG_DSN\" --schema acr_asset_upsert_test --output data/pgvector_eval/music20/audio_embedding_asset_upsert_live_report.json",
"returncode": 0,
"stdout_tail": "\n \"upsert_embedding_id\": 1,\n \"same_embedding_id_reused\": true,\n \"counts\": {\n \"audio_embedding\": 1,\n \"audio_embedding_vector_192\": 1\n },\n \"final_state\": {\n \"embedding_id\": 1,\n \"asset_id\": 1,\n \"window_id\": null,\n \"checksum\": \"checksum-v2\",\n \"embedding_uri\": \"inline://asset-probe-upsert\",\n \"metadata_json\": {\n \"probe\": \"asset_level_upsert_v2\"\n },\n \"vector_literal\": \"[0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2]\"\n },\n \"passed\": true\n}\n",
"stderr_tail": "",
"passed": true
},
"summary": {
"selected": [
"prereq_audit",
"worker_contract_smoke",
"semantic_vector_negative_matrix",
"asset_level_upsert_validation"
],
"executed_count": 4,
"all_passed": true
}
}
\ No newline at end of file
#!/usr/bin/env /usr/local/miniconda3/bin/python
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
import subprocess
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_PLAN = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'phase1_extraction_plan_report.json'
DEFAULT_OUTPUT = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'planner_validation_commands_runner_report.json'
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument('--plan', default=str(DEFAULT_PLAN))
ap.add_argument('--dsn', required=True)
ap.add_argument('--output', default=str(DEFAULT_OUTPUT))
ap.add_argument('--only', nargs='*')
args = ap.parse_args()
plan = json.loads(Path(args.plan).read_text(encoding='utf-8'))
commands: dict[str, str] = plan['validation_commands']
selected = args.only or list(commands.keys())
report: dict[str, Any] = {}
for key in selected:
if key not in commands:
raise SystemExit(f'unknown validation command: {key}')
cmd = commands[key]
proc = subprocess.run(
cmd,
cwd=ROOT,
shell=True,
text=True,
capture_output=True,
env={**os.environ, 'PG_DSN': args.dsn},
)
report[key] = {
'command': cmd,
'returncode': proc.returncode,
'stdout_tail': proc.stdout[-1200:],
'stderr_tail': proc.stderr[-1200:],
'passed': proc.returncode == 0,
}
report['summary'] = {
'selected': selected,
'executed_count': len(selected),
'all_passed': all(item['passed'] for key, item in report.items() if key != 'summary'),
}
out = Path(args.output)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding='utf-8')
print(json.dumps(report, ensure_ascii=False, indent=2))
if __name__ == '__main__':
main()
## 2026-06-04
- 新增 `scripts/run_planner_validation_commands_live.py` 与 `planner_validation_commands_runner_report.json`，可直接读取 `phase1_extraction_plan_report.json` 中的 `validation_commands` 并批量执行；当前 4 条 entrypoints 已全部执行成功，`executed_count=4`、`all_passed=true`。
- 更新 `phase1_validation_commands_execution_report.json`，补齐 planner 中剩余两条 validation commands 的直接执行证据：`semantic_vector_negative_matrix` 与 `asset_level_upsert_validation` 也已 `returncode=0`，当前 4 条 validation entrypoints 已全部验证可被脚本直接消费。
- 新增 `phase1_validation_commands_execution_report.json`，直接从 `phase1_extraction_plan_report.json` 读取并执行 `validation_commands.prereq_audit` 与 `validation_commands.worker_contract_smoke`，两条命令均返回 `0`，证明 planner 产物可被脚本化直接消费。
- 更新 `scripts/plan_phase1_extraction_jobs_live.py` 与 `phase1_extraction_plan_report.json`，除了 per-job `command_suggestions` 之外，又补充了 `validation_commands`：`prereq_audit`、`worker_contract_smoke`、`semantic_vector_negative_matrix`、`asset_level_upsert_validation`，使 planner 本身也成为下次 session 的执行入口。
......
......@@ -240,3 +240,25 @@ flowchart TD
- `validation_commands.asset_level_upsert_validation`
这意味着下次启动时可以先跑“全局验证入口”，再决定是否执行具体 job，而不必手工拼测试命令。
## 6.2 当前推荐的一键验证入口
如果只是想先确认当前 host 是否具备继续推进 Phase-1 的条件，推荐优先执行：
```bash
cd /workspace/acr-engine
/usr/local/miniconda3/bin/python scripts/run_planner_validation_commands_live.py --dsn 'postgres://d2:d2pass@127.0.0.1:5432/d2' --output data/pgvector_eval/music20/planner_validation_commands_runner_report.json
```
它会直接读取 `phase1_extraction_plan_report.json` 的 `validation_commands`，并批量执行：
- `prereq_audit`
- `worker_contract_smoke`
- `semantic_vector_negative_matrix`
- `asset_level_upsert_validation`
当前 live 结果：
- `executed_count = 4`
- `all_passed = true`
......
......@@ -198,6 +198,7 @@ sed -n '1,320p' acr-engine/sql/acr_pg_schema_v2.sql
- `scripts/run_phase1_prereq_audit_live.py` 已给出当前 host 的先决条件审计：`downloads_root_exists=false`、`ready_jobs=0/5`，并把 `torch/torchaudio/transformers/speechbrain` 的缺失状态按 job 落成 JSON 报告
- `phase1_extraction_plan_report.json` 现已附带 `validation_commands`，下次 session 可以直接从 planner 复制 `prereq_audit / worker_contract_smoke / semantic_vector_negative_matrix / asset_level_upsert_validation` 四类命令
- `phase1_validation_commands_execution_report.json` 已证明 planner 里的 4 条 validation commands 都可以被直接脚本消费且 `returncode=0`：`prereq_audit`、`worker_contract_smoke`、`semantic_vector_negative_matrix`、`asset_level_upsert_validation`
- `scripts/run_planner_validation_commands_live.py` 已把这 4 条 validation commands 收敛成通用 runner；当前 `planner_validation_commands_runner_report.json` 显示 `executed_count=4` 且 `all_passed=true`
- `phase1_hot_reference_v1` 在 `acr_test` 里已经真实补齐 `20` 个 reference members，因此 worker dry-run 当前看到的 scope 已是 `20 recordings / 20 assets / 20 windows`
- worker contract 现在已有基础前置状态保护；重复执行同一 chromaprint dry-run job 会被 `expected_status=pending` 明确拒绝，证据见 `phase1_worker_double_claim_guard_report.json`
- exact lane 的 `run_chromaprint_job.py` 已具备非 dry-run 写入路径；当前在 `acr_test` 的 live 结果是因为 `/workspace/downloads/...` 缺失而明确 `failed`，不是继续假装 `completed`
......