obsidian-plugin/lllin000_PaperForge
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fix: P0 exception order, status sync, retryable transition, P1 scorer warnings

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Research Assistant 2026-06-08 12:40:57 +08:00
parent 84112c3817
commit 05daf752ea
2 changed files with 25 additions and 6 deletions
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24
paperforge/worker/ocr.py
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@ -94,7 +94,10 @@ OCR_QUEUE_STATUSES = {
def apply_ocr_error_state(row: dict, meta: dict, error_state: dict) -> None:
row["queue_status"] = error_state["status"]
status = error_state["status"]
row["queue_status"] = status
meta["ocr_status"] = status
meta["error"] = error_state["last_error"]
meta["error_type"] = error_state["error_type"]
meta["error_stage"] = error_state["error_stage"]
meta["retryable"] = error_state["retryable"]
@ -2137,6 +2140,19 @@ def run_ocr(vault: Path, verbose: bool = False, no_progress: bool = False, selec
meta["ocr_finished_at"] = ""
meta["retry_count"] = 0
write_json(paths["ocr"] / key / "meta.json", meta)
elif current == "retryable_error":
retry_count = int(meta.get("retry_count", 0)) + 1
meta["retry_count"] = retry_count
if retry_count > 3:
meta["ocr_status"] = "fatal_error"
meta["error"] = meta.get("last_error", "Max retries exceeded")
else:
meta["ocr_status"] = "pending"
meta["ocr_job_id"] = ""
meta["error"] = meta.get("last_error", "")
write_json(paths["ocr"] / key / "meta.json", meta)
elif current == "fatal_error":
pass
elif current == "nopdf":
meta["ocr_status"] = "pending"
meta["error"] = ""
@ -2246,11 +2262,7 @@ def run_ocr(vault: Path, verbose: bool = False, no_progress: bool = False, selec
page_num, markdown_path, json_path, fulltext_md_path = postprocess_ocr_result(
vault, key, all_results
)
except KeyboardInterrupt:
raise
except SystemExit:
raise
except BaseException:
except (KeyboardInterrupt, SystemExit):
raise
except (OCRPDFResolveError, OCRArtifactIntegrityError, OCRPostprocessError) as exc:
error_state = classify_ocr_error(exc, stage="postprocess")

7
paperforge/worker/ocr_health.py
View file

@ -143,6 +143,13 @@ def build_ocr_health(
report["figure_match_confidence_distribution"] = _score_distribution(fig_scores)
report["table_match_confidence_distribution"] = _score_distribution(table_scores)
low_confidence_figures = [s for s in fig_scores if s < 0.4]
if low_confidence_figures:
degraded_reasons.append(f"low figure caption confidence ({len(low_confidence_figures)} figures)")
low_confidence_tables = [s for s in table_scores if s < 0.4]
if low_confidence_tables:
degraded_reasons.append(f"low table match confidence ({len(low_confidence_tables)} tables)")
return report