124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
from __future__ import annotations
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import argparse
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import json
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import sys
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from datetime import UTC, datetime
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from pathlib import Path
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from typing import Any
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REPO_ROOT = Path(__file__).resolve().parents[1]
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SRC_ROOT = REPO_ROOT / "src"
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OUTPUT_ROOT = REPO_ROOT / "outputs"
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REFERENCE_OUTPUT_ROOT = OUTPUT_ROOT / "reference"
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if str(SRC_ROOT) not in sys.path:
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sys.path.insert(0, str(SRC_ROOT))
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from summary_mcp.models.article_candidate import (
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CandidateMetadata,
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CandidateSourceRefs,
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build_article_candidate_record,
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build_openclaw_candidate_input,
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)
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from summary_mcp.models.document import ExtractedArticle
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from summary_mcp.models.filtering import FilterDecisionResult
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from summary_mcp.models.item import Item
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from summary_mcp.models.llm_result import LlmSummaryResult
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def _load_json(path: Path) -> dict[str, Any]:
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return json.loads(path.read_text(encoding="utf-8-sig"))
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def _save_json(path: Path, payload: dict[str, Any]) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
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def main() -> None:
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parser = argparse.ArgumentParser(
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description="Build an internal ArticleCandidateRecord and a slim OpenClawCandidateInput from pipeline outputs."
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)
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parser.add_argument("--summary", type=Path, required=True, help="Structured LLM summary JSON file")
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parser.add_argument("--extracted", type=Path, required=True, help="Extracted article JSON file")
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parser.add_argument("--filter", type=Path, required=True, help="Filter decision JSON file")
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parser.add_argument("--item", type=Path, default=None, help="Optional normalized item JSON file")
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parser.add_argument(
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"--section-hint",
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default=None,
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choices=[
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"top_news",
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"tools_and_workflows",
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"risk_and_security",
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"open_source",
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"insights",
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"deep_dive",
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],
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help="Optional digest section hint for downstream aggregation",
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)
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parser.add_argument(
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"--rank",
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type=int,
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default=None,
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help="Optional digest rank override. Defaults to filter priority when omitted.",
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)
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parser.add_argument(
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"--rendered-markdown",
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type=Path,
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default=None,
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help="Optional markdown file to embed as rendered_markdown",
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)
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parser.add_argument(
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"--output",
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dest="record_output",
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type=Path,
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default=REFERENCE_OUTPUT_ROOT / "candidates" / "article-candidate-record.json",
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help="Where to save the internal ArticleCandidateRecord payload",
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)
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parser.add_argument(
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"--openclaw-output",
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type=Path,
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default=REFERENCE_OUTPUT_ROOT / "candidates" / "openclaw-candidate-input.json",
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help="Where to save the slim OpenClawCandidateInput payload",
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)
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args = parser.parse_args()
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summary = LlmSummaryResult.model_validate(_load_json(args.summary))
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extracted_payload = _load_json(args.extracted)
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article = ExtractedArticle.model_validate(extracted_payload.get("article", extracted_payload))
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decision = FilterDecisionResult.model_validate(_load_json(args.filter))
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item = Item.model_validate(_load_json(args.item)) if args.item else None
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rendered_markdown = args.rendered_markdown.read_text(encoding="utf-8-sig") if args.rendered_markdown else None
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record = build_article_candidate_record(
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summary=summary,
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article=article,
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filter_result=decision,
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item=item,
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digest_section_hint=args.section_hint,
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digest_rank=args.rank,
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rendered_markdown=rendered_markdown,
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source_refs=CandidateSourceRefs(
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item_path=str(args.item) if args.item else None,
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extracted_path=str(args.extracted),
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summary_path=str(args.summary),
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filter_path=str(args.filter),
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),
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metadata=CandidateMetadata(
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generated_at=datetime.now(tz=UTC),
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producer="run_article_candidate.py",
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run_id=datetime.now(tz=UTC).strftime("candidate-%Y%m%d-%H%M%S"),
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),
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)
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openclaw_input = build_openclaw_candidate_input(record)
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_save_json(args.record_output, record.model_dump(mode="json"))
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_save_json(args.openclaw_output, openclaw_input.model_dump(mode="json"))
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print(f"Saved article candidate record to {args.record_output}")
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print(f"Saved OpenClaw candidate input to {args.openclaw_output}")
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if __name__ == "__main__":
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main() |