File size: 3,649 Bytes
0a55f0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | from __future__ import annotations
import json
from pathlib import Path
import typer
from .paper_package import load_paper_package
from .pipeline import TwoPassAnnotationPipeline
app = typer.Typer(help="Run step 8: derive target contributions, enabling contributions, and groundings.")
def _default_output_root() -> Path:
return Path("runs/two_pass_outputs")
@app.command()
def run(
paper_dir: Path = typer.Option(..., exists=True, file_okay=False, dir_okay=True),
provider: str = typer.Option("openai", help="Provider family: openai or gemini."),
model: str = typer.Option("openai/gpt-5", help="Reasoning model used for target-contribution derivation and annotation."),
formatter_model: str | None = typer.Option(
None,
help="Optional model override for pass 2 formatting, e.g. openai/gpt-5-mini or openai/gpt-5.4-pro.",
),
judge_model: str | None = typer.Option(
None,
help="Optional model override for pass 1 candidate ranking. Ignored when --candidate-count=1.",
),
candidate_count: int = typer.Option(
1,
help="Number of reasoning candidates to generate. If set to 1, no judge call is made.",
),
formatter_max_attempts: int = typer.Option(
3,
help="Formatter-only retry attempts after pass 1 has succeeded.",
),
include_reference_examples: bool = typer.Option(
True,
"--include-reference-examples/--no-include-reference-examples",
help="Include the built-in reference examples in the pass-1 reasoning prompt.",
),
prompt_profile: str = typer.Option(
"full",
help="Reasoning prompt profile: full or generic.",
),
output_root: Path = typer.Option(
_default_output_root(),
help="Directory to store run outputs.",
),
run_label: str | None = typer.Option(None, help="Optional label to include in the saved run directory name."),
annotator_id: str = typer.Option("llm", help="Annotator id to embed in the final UI payload."),
extracted_claim: str | None = typer.Option(None, help="Optional override for the extracted target contribution."),
) -> None:
paper = load_paper_package(paper_dir, extracted_claim_override=extracted_claim)
pipeline = TwoPassAnnotationPipeline(
provider=provider,
model=model,
formatter_model=formatter_model,
judge_model=judge_model,
output_root=output_root,
run_label=run_label,
annotator_id=annotator_id,
candidate_count=candidate_count,
formatter_max_attempts=formatter_max_attempts,
include_reference_examples=include_reference_examples,
prompt_profile=prompt_profile,
progress_callback=typer.echo,
)
result = pipeline.run(paper)
typer.echo(str(result.run_dir / "run_output.json"))
@app.command()
def summarize(run_output: Path = typer.Option(..., exists=True, dir_okay=False, file_okay=True)) -> None:
data = json.loads(run_output.read_text())
payload = data.get("ui_payload") or {}
claims = payload.get("claims") or []
summary = {
"paper_id": data.get("paper_id"),
"target_contribution_count": len(claims),
"target_contributions": [
{
"claim_id": claim.get("claim_id"),
"rewritten_claim": claim.get("rewritten_claim"),
"decision": claim.get("decision"),
"enabling_contribution_count": len(claim.get("ingredients") or []),
}
for claim in claims
],
}
typer.echo(json.dumps(summary, indent=2))
if __name__ == "__main__":
app()
|