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from __future__ import annotations
import argparse
from collections import Counter
import json
from pathlib import Path
import sys
from typing import Any
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from figment.focused_repair import build_focused_repair_prompts # noqa: E402
from figment.harness_evidence import build_harness_evidence # noqa: E402
from figment.observation_targets import required_observation_targets # noqa: E402
from figment.eval_metrics import score_expected_labels # noqa: E402
from figment.prompt_builder import build_prompt # noqa: E402
from figment.retrieval import known_card_ids # noqa: E402
from figment.retrieval import load_protocol_cards # noqa: E402
from figment.retrieval import query_from_intake # noqa: E402
from figment.retrieval import search_protocol_cards # noqa: E402
from figment.rules import run_red_flag_checks # noqa: E402
from figment.validators import urgency_floor_from_rules # noqa: E402
from figment.validators import validate_navigator_output # noqa: E402
from scripts.augment_finetune_repair_rows import _corrupt_output # noqa: E402
from scripts.augment_finetune_repair_rows import _extra_failures_for_scope # noqa: E402
from scripts.generate_finetune_data import forbidden_behavior_for_version # noqa: E402
from scripts.generate_finetune_data import ensure_retrieved_cards # noqa: E402
from scripts.generate_finetune_data import _required_retrieved_ids # noqa: E402
from scripts.generate_finetune_data import uses_v5_focused_policy # noqa: E402
from scripts.generate_finetune_data import uses_v6_observation_policy # noqa: E402
from scripts.generate_finetune_data import uses_v7_source_card_policy # noqa: E402
from scripts.generate_finetune_data import v2_policy_issues # noqa: E402
from scripts.generate_finetune_data import v3_policy_issues # noqa: E402
from scripts.generate_finetune_data import v5_policy_issues # noqa: E402
from scripts.generate_finetune_data import v6_policy_issues # noqa: E402
from scripts.generate_finetune_data import v7_source_card_closure_issues # noqa: E402
from scripts.generate_finetune_data import uses_v3_field_workflow_policy # noqa: E402
DEFAULT_DATASET = Path("data/finetune/figment_sft_v1.jsonl")
DEFAULT_CASE_SPECS = Path("data/finetune/figment_sft_v1_case_specs.jsonl")
ALLOWED_FACT_SOURCES = {"structured_field", "responder_note", "protocol_card"}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--dataset", type=Path, default=DEFAULT_DATASET)
parser.add_argument("--case-specs", type=Path, default=DEFAULT_CASE_SPECS)
args = parser.parse_args(argv)
summary = verify_rows(dataset_path=args.dataset, case_specs_path=args.case_specs)
print(json.dumps(summary, indent=2, sort_keys=True))
return 0 if summary["passed"] else 1
def verify_rows(*, dataset_path: Path, case_specs_path: Path) -> dict[str, Any]:
rows = _read_jsonl(dataset_path)
specs = {str(item["case_id"]): item for item in _read_jsonl(case_specs_path)}
rows_by_id = {str(row.get("case_id")): row for row in rows}
cards_by_id = {str(card["card_id"]): card for card in load_protocol_cards()}
issues: list[dict[str, Any]] = []
categories: Counter[str] = Counter()
task_types: Counter[str] = Counter()
for row_number, row in enumerate(rows, start=1):
case_id = str(row.get("case_id", ""))
categories[str(row.get("category", ""))] += 1
task_type = str(row.get("metadata", {}).get("task_type", "navigator_full"))
task_types[task_type] += 1
base_case_id = str(row.get("metadata", {}).get("base_case_id") or case_id)
spec = specs.get(base_case_id)
if spec is None:
issues.append(_issue(row_number, case_id, "missing_case_spec"))
continue
dataset_version = str(row.get("version") or spec.get("dataset_version") or "figment_sft_v1")
messages = row.get("messages")
if not isinstance(messages, list) or [item.get("role") for item in messages] != ["user", "assistant"]:
issues.append(_issue(row_number, case_id, "messages_must_match_llama_cpp_user_assistant_shape"))
continue
intake = spec["structured_intake"]
rule_results = [rule.to_dict() for rule in run_red_flag_checks(intake)]
floor = urgency_floor_from_rules(rule_results)
retrieved = search_protocol_cards(query_from_intake(intake), limit=6)
spec_dataset_version = str(spec.get("dataset_version") or dataset_version)
if uses_v7_source_card_policy(spec_dataset_version):
synthetic_spec = type(
"SyntheticSpecForVerification",
(),
{
"target_protocol_card_id": str(spec.get("target_protocol_card_id") or ""),
"dataset_version": spec_dataset_version,
},
)()
retrieved = ensure_retrieved_cards(
retrieved,
required_ids=_required_retrieved_ids(synthetic_spec, rule_results),
cards_by_id=cards_by_id,
limit=6,
)
retrieved_ids = [str(item.get("card_id", "")) for item in retrieved if item.get("card_id")]
harness_prompt, prompt_hash = build_prompt(intake, retrieved, rule_results, floor)
expected_user_prompt = harness_prompt
try:
output = json.loads(str(messages[1].get("content", "")))
except json.JSONDecodeError as exc:
issues.append(_issue(row_number, case_id, "assistant_content_is_not_json", error=str(exc)))
continue
output_text = json.dumps(output, sort_keys=True)
lowered_output_text = output_text.lower()
if "<think" in lowered_output_text or "</think" in lowered_output_text:
issues.append(_issue(row_number, case_id, "assistant_contains_visible_reasoning_tag"))
if "teacher" in lowered_output_text:
issues.append(_issue(row_number, case_id, "assistant_contains_teacher_artifact"))
if dataset_version == "figment_sft_v2":
for issue in v2_policy_issues(
output,
failure_class=str(row.get("category") or row.get("metadata", {}).get("failure_class") or spec.get("failure_class") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
):
issue_type = "v2_forbidden_lexical_tripwire" if issue.startswith("forbidden_lexical_tripwire:") else f"v2_{issue}"
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
if uses_v7_source_card_policy(dataset_version) and task_type != "focused_repair":
for issue in v6_policy_issues(
output,
failure_class=str(row.get("category") or row.get("metadata", {}).get("failure_class") or spec.get("failure_class") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
rule_results=rule_results,
retrieved_cards=retrieved,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
dataset_version=dataset_version,
):
issue_type = "v6_forbidden_lexical_tripwire" if issue.startswith("forbidden_lexical_tripwire:") else f"v6_{issue}"
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
for issue in v7_source_card_closure_issues(
output,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
):
issues.append(_issue(row_number, case_id, f"v7_{issue}", policy_issue=issue))
elif uses_v6_observation_policy(dataset_version) and task_type != "focused_repair":
for issue in v6_policy_issues(
output,
failure_class=str(row.get("category") or row.get("metadata", {}).get("failure_class") or spec.get("failure_class") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
rule_results=rule_results,
retrieved_cards=retrieved,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
dataset_version=dataset_version,
):
issue_type = "v6_forbidden_lexical_tripwire" if issue.startswith("forbidden_lexical_tripwire:") else f"v6_{issue}"
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
_append_v6_metadata_issues(
issues,
row_number=row_number,
case_id=case_id,
row=row,
output=output,
)
elif uses_v5_focused_policy(dataset_version) and task_type != "focused_repair":
for issue in v5_policy_issues(
output,
failure_class=str(row.get("category") or row.get("metadata", {}).get("failure_class") or spec.get("failure_class") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
rule_results=rule_results,
retrieved_cards=retrieved,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
):
issue_type = "v5_forbidden_lexical_tripwire" if issue.startswith("forbidden_lexical_tripwire:") else f"v5_{issue}"
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
_append_v5_metadata_issues(
issues,
row_number=row_number,
case_id=case_id,
row=row,
output=output,
)
elif uses_v3_field_workflow_policy(dataset_version) and task_type != "focused_repair":
for issue in v3_policy_issues(
output,
failure_class=str(row.get("category") or row.get("metadata", {}).get("failure_class") or spec.get("failure_class") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
):
issue_type = "v3_forbidden_lexical_tripwire" if issue.startswith("forbidden_lexical_tripwire:") else f"v3_{issue}"
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
for fact in output.get("intake_facts", []):
if isinstance(fact, dict) and fact.get("source") not in ALLOWED_FACT_SOURCES:
issues.append(
_issue(
row_number,
case_id,
"intake_fact_source_not_in_harness_schema",
source=fact.get("source"),
)
)
if task_type == "focused_repair":
base_row = rows_by_id.get(base_case_id)
if base_row is None:
metadata = row.get("metadata", {}) if isinstance(row.get("metadata"), dict) else {}
replay_audit = metadata.get("v7_replay_audit") or metadata.get("v6_replay_audit")
if isinstance(replay_audit, dict) and replay_audit.get("accepted") is True:
if not isinstance(output, dict) or not output:
issues.append(_issue(row_number, case_id, "replay_focused_repair_output_empty"))
if "focused field repair only" not in str(messages[0].get("content", "")).lower():
issues.append(_issue(row_number, case_id, "replay_focused_repair_prompt_not_self_contained"))
continue
issues.append(_issue(row_number, case_id, "focused_repair_missing_base_row", base_case_id=base_case_id))
continue
repair_scope = str(row.get("metadata", {}).get("repair_scope", ""))
base_gold = json.loads(base_row["messages"][1]["content"])
previous_output = _corrupt_output(base_gold, repair_scope, floor)
if previous_output is None:
issues.append(_issue(row_number, case_id, "focused_repair_previous_output_could_not_be_rebuilt"))
continue
previous_validation = validate_navigator_output(
previous_output,
known_card_ids=known_card_ids(),
urgency_floor=floor,
confirmed_intake=intake,
rule_results=rule_results,
retrieved_card_ids=set(retrieved_ids),
retrieved_cards=retrieved,
strict_schema=True,
).to_dict()
focused_prompt = next(
(
item
for item in build_focused_repair_prompts(
original_prompt=harness_prompt,
previous_output=previous_output,
failures=list(previous_validation.get("failures", []))
+ _extra_failures_for_scope(previous_output, spec, repair_scope),
urgency_floor=floor,
required_observation_targets=required_observation_targets(retrieved),
)
if item.scope.name == repair_scope
),
None,
)
if focused_prompt is None:
issues.append(_issue(row_number, case_id, "focused_repair_prompt_not_generated", repair_scope=repair_scope))
continue
expected_user_prompt = focused_prompt.prompt
expected_fields = set(focused_prompt.scope.fields)
if set(output) != expected_fields:
issues.append(
_issue(
row_number,
case_id,
"focused_repair_output_keys_do_not_match_scope",
expected=sorted(expected_fields),
actual=sorted(output),
)
)
for field in expected_fields:
if output.get(field) != base_gold.get(field):
issues.append(_issue(row_number, case_id, "focused_repair_field_not_from_base_gold", field=field))
if uses_v7_source_card_policy(dataset_version):
reconstructed = json.loads(json.dumps(base_gold))
reconstructed.update(output)
for issue in v6_policy_issues(
reconstructed,
failure_class=str(spec.get("failure_class") or base_row.get("category") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
rule_results=rule_results,
retrieved_cards=retrieved,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
dataset_version=dataset_version,
):
issue_type = (
"v6_forbidden_lexical_tripwire"
if issue.startswith("forbidden_lexical_tripwire:")
else f"v6_{issue}"
)
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
for issue in v7_source_card_closure_issues(
reconstructed,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
):
issues.append(_issue(row_number, case_id, f"v7_{issue}", policy_issue=issue))
elif uses_v6_observation_policy(dataset_version):
reconstructed = json.loads(json.dumps(base_gold))
reconstructed.update(output)
for issue in v6_policy_issues(
reconstructed,
failure_class=str(spec.get("failure_class") or base_row.get("category") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
rule_results=rule_results,
retrieved_cards=retrieved,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
dataset_version=dataset_version,
):
issue_type = (
"v6_forbidden_lexical_tripwire"
if issue.startswith("forbidden_lexical_tripwire:")
else f"v6_{issue}"
)
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
elif uses_v5_focused_policy(dataset_version):
reconstructed = json.loads(json.dumps(base_gold))
reconstructed.update(output)
for issue in v5_policy_issues(
reconstructed,
failure_class=str(spec.get("failure_class") or base_row.get("category") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
rule_results=rule_results,
retrieved_cards=retrieved,
target_protocol_card_id=str(spec.get("target_protocol_card_id") or ""),
):
issue_type = (
"v5_forbidden_lexical_tripwire"
if issue.startswith("forbidden_lexical_tripwire:")
else f"v5_{issue}"
)
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
elif uses_v3_field_workflow_policy(dataset_version):
reconstructed = json.loads(json.dumps(base_gold))
reconstructed.update(output)
for issue in v3_policy_issues(
reconstructed,
failure_class=str(spec.get("failure_class") or base_row.get("category") or ""),
expected_red_flag_rule_ids=[str(item) for item in spec.get("expected_red_flag_rule_ids", [])],
expected_candidate_pathway_card_ids=[
str(item) for item in spec.get("expected_candidate_pathway_card_ids", [])
],
structured_intake=intake,
):
issue_type = (
"v3_forbidden_lexical_tripwire"
if issue.startswith("forbidden_lexical_tripwire:")
else f"v3_{issue}"
)
issues.append(_issue(row_number, case_id, issue_type, policy_issue=issue))
if row.get("metadata", {}).get("prompt_hash") != _stable_hash_content(expected_user_prompt):
issues.append(_issue(row_number, case_id, "metadata_prompt_hash_mismatch"))
if messages[0].get("content") != expected_user_prompt:
issues.append(_issue(row_number, case_id, "focused_repair_prompt_does_not_match_harness"))
continue
if messages[0].get("content") != expected_user_prompt:
issues.append(
_issue(
row_number,
case_id,
"user_prompt_does_not_equal_harness_build_prompt",
retrieved_card_ids=retrieved_ids,
)
)
validation = validate_navigator_output(
output,
known_card_ids=known_card_ids(),
urgency_floor=floor,
confirmed_intake=intake,
rule_results=rule_results,
retrieved_card_ids=set(retrieved_ids),
retrieved_cards=retrieved,
strict_schema=True,
).to_dict()
if validation.get("passed") is not True:
issues.append(_issue(row_number, case_id, "validator_failed_under_harness", validation=validation))
expected_score = score_expected_labels(
{
"case_id": case_id,
"structured_intake": intake,
"target_protocol_card_id": spec.get("target_protocol_card_id"),
"expected_min_protocol_urgency": spec.get("expected_min_protocol_urgency"),
"expected_red_flag_rule_ids": spec.get("expected_red_flag_rule_ids", []),
"expected_source_card_ids": spec.get("expected_source_card_ids", []),
"expected_candidate_pathway_card_ids": spec.get("expected_candidate_pathway_card_ids", []),
"expected_model_observation_cues": spec.get("expected_model_observation_cues", []),
"expected_handoff_cues": spec.get("expected_handoff_cues", []),
"expected_harness_evidence_cues": spec.get("expected_harness_evidence_cues", []),
"expected_missing_observations": spec.get("expected_missing_observations", []),
"forbidden_behavior": _forbidden_behavior_for_dataset_version(dataset_version),
"actual_red_flag_rule_ids": [str(rule["rule_id"]) for rule in rule_results],
"actual_protocol_urgency": output.get("protocol_urgency"),
"actual_source_card_ids": _string_list(output.get("source_cards")),
"actual_candidate_pathway_card_ids": _candidate_ids(output.get("candidate_protocol_pathways")),
"retrieved_card_ids": retrieved_ids,
"harness_evidence": build_harness_evidence(
confirmed_intake=intake,
retrieved_card_ids=retrieved_ids,
rule_results=rule_results,
urgency_floor=floor,
validator_result=validation,
final_output=output,
),
"final_output": output,
"final_validation": validation,
}
)
if expected_score.get("all_expected_labels_passed") is not True:
issues.append(_issue(row_number, case_id, "expected_label_score_failed", expected_score=expected_score))
metadata = row.get("metadata", {})
if metadata.get("prompt_hash") != _stable_hash_content(harness_prompt):
issues.append(_issue(row_number, case_id, "metadata_prompt_hash_mismatch"))
if metadata.get("prompt_template_hash") != prompt_hash:
issues.append(_issue(row_number, case_id, "metadata_prompt_template_hash_mismatch"))
if "harness" not in str(metadata.get("teacher_label_mode", "")):
issues.append(_issue(row_number, case_id, "teacher_label_mode_does_not_record_harness_alignment"))
return {
"rows": len(rows),
"case_specs": len(specs),
"passed": not issues,
"issue_count": len(issues),
"issue_types": dict(Counter(item["type"] for item in issues)),
"categories": dict(sorted(categories.items())),
"task_types": dict(sorted(task_types.items())),
"issues_sample": issues[:20],
}
def _read_jsonl(path: Path) -> list[dict[str, Any]]:
return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]
def _issue(row_number: int, case_id: str, issue_type: str, **details: Any) -> dict[str, Any]:
return {"row_number": row_number, "case_id": case_id, "type": issue_type, **details}
def _append_v5_metadata_issues(
issues: list[dict[str, Any]],
*,
row_number: int,
case_id: str,
row: dict[str, Any],
output: dict[str, Any],
) -> None:
metadata = row.get("metadata") if isinstance(row.get("metadata"), dict) else {}
if metadata.get("dataset_version") != "figment_sft_v5":
issues.append(_issue(row_number, case_id, "v5_metadata_dataset_version_missing"))
if metadata.get("training_focus") != row.get("category"):
issues.append(
_issue(
row_number,
case_id,
"v5_metadata_training_focus_mismatch",
training_focus=metadata.get("training_focus"),
category=row.get("category"),
)
)
excluded = metadata.get("excluded_eval_case_ids")
if not isinstance(excluded, list) or not {
"field_workflow_holdout_v1-000054",
"field_workflow_holdout_v1-000099",
} <= {str(item) for item in excluded}:
issues.append(_issue(row_number, case_id, "v5_metadata_excluded_eval_case_ids_missing"))
source_cards = set(_string_list(output.get("source_cards")))
missing_source = [card_id for card_id in _string_list(metadata.get("must_include_source_cards")) if card_id not in source_cards]
if missing_source:
issues.append(_issue(row_number, case_id, "v5_metadata_must_include_source_cards_missing", missing=missing_source))
required_selected_ids = _string_list(metadata.get("must_include_selected_required_observation_ids"))
selected_ids = set(_string_list(output.get("selected_required_observation_ids")))
missing_selected = [target_id for target_id in required_selected_ids if target_id not in selected_ids]
if missing_selected:
issues.append(
_issue(
row_number,
case_id,
"v5_metadata_must_include_selected_required_observation_ids_missing",
missing=missing_selected,
)
)
def _append_v6_metadata_issues(
issues: list[dict[str, Any]],
*,
row_number: int,
case_id: str,
row: dict[str, Any],
output: dict[str, Any],
) -> None:
metadata = row.get("metadata") if isinstance(row.get("metadata"), dict) else {}
dataset_version = str(metadata.get("dataset_version") or row.get("version") or "")
if not dataset_version.startswith("figment_sft_v6"):
issues.append(_issue(row_number, case_id, "v6_metadata_dataset_version_missing"))
if metadata.get("training_focus") != row.get("category"):
issues.append(
_issue(
row_number,
case_id,
"v6_metadata_training_focus_mismatch",
training_focus=metadata.get("training_focus"),
category=row.get("category"),
)
)
if metadata.get("v6_training_policy_version") != 1:
issues.append(_issue(row_number, case_id, "v6_metadata_policy_version_missing"))
required_targets = metadata.get("required_observation_targets")
if not isinstance(required_targets, list):
issues.append(_issue(row_number, case_id, "v6_metadata_required_observation_targets_missing"))
forbidden_cues = metadata.get("harness_metadata_cues_not_observations")
if not isinstance(forbidden_cues, list) or "source card ids" not in {str(item).lower() for item in forbidden_cues}:
issues.append(_issue(row_number, case_id, "v6_metadata_harness_cues_missing"))
required_selected_ids = _string_list(metadata.get("must_include_selected_required_observation_ids"))
selected_ids = set(_string_list(output.get("selected_required_observation_ids")))
missing_selected = [target_id for target_id in required_selected_ids if target_id not in selected_ids]
if missing_selected:
issues.append(
_issue(
row_number,
case_id,
"v6_metadata_must_include_selected_required_observation_ids_missing",
missing=missing_selected,
)
)
def _string_list(value: Any) -> list[str]:
if isinstance(value, list):
return [str(item) for item in value if str(item)]
return []
def _candidate_ids(value: Any) -> list[str]:
if not isinstance(value, list):
return []
ids = []
for item in value:
if isinstance(item, dict) and item.get("card_id"):
ids.append(str(item["card_id"]))
return ids
def _forbidden_behavior_for_dataset_version(dataset_version: str) -> list[str]:
return forbidden_behavior_for_version(dataset_version)
def _stable_hash_content(value: str) -> str:
from figment.trace import stable_hash
return stable_hash(value)
if __name__ == "__main__":
raise SystemExit(main())
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