generated from kgod/ai-review-template
97 lines
4.8 KiB
Python
97 lines
4.8 KiB
Python
"""Candidate rewrite for Builder entries (light STAR optimization)."""
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from __future__ import annotations
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from copy import deepcopy
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import re
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from typing import Any
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from .state import _dedupe_strings
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from ..experience_optimizer import _fact_text_is_preserved, split_description_parts
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def _candidate_rewrite(
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agent: Any, profile: dict[str, Any], entry: dict[str, Any], section: str, *, instruction: str | None = None,
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ensure_facts: bool = False,
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) -> dict[str, Any]:
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try:
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proposal = agent.expander.expand(
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deepcopy(entry),
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context={
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"job_type": profile.get("job_type"),
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"target_position": profile.get("target_position"),
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"entry_type": section,
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"instruction": instruction,
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},
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)
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except Exception:
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proposal = {}
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original = str(entry.get("description") or "").strip()
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optimized = str(proposal.get("optimized_description") or "").strip() or original
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if ensure_facts:
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# Explicit user-requested revision: still-missing material facts are folded
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# back in (the user asked for them; this is not a silent auto-append).
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missing = _uncovered_material_facts(optimized, original)
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if missing:
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if "• " in optimized:
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optimized = optimized + "".join(f"\n• {fact}" for fact in missing)
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else:
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optimized = f"{optimized.rstrip('。')}；{'；'.join(missing)}。"
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return {
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"optimized_description": optimized,
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"changes": proposal.get("changes") or [],
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"source": proposal.get("source") or "ai_expanded",
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"uncovered_facts": _uncovered_material_facts(optimized, original),
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**({"generation_source": proposal["generation_source"]} if proposal.get("generation_source") else {}),
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}
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def _uncovered_material_facts(candidate: str, original: str) -> list[str]:
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"""Material user facts the candidate dropped. Reported, never auto-appended."""
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uncovered = [fact for fact in _material_fact_fragments(original) if not _fact_is_preserved(fact, candidate)]
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fragments = split_description_parts(original)
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if len(fragments) >= 2:
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# Structured descriptions (feature lists, tech stack, outcomes) are checked
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# fragment by fragment, so a dropped feature module is reported even when the
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# tech stack survived. Single-sentence descriptions keep the regex-only path.
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ledger = [
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{"id": f"fragment_{index}", "source": "user_form", "field": "description_part", "text": fragment}
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for index, fragment in enumerate(fragments, start=1)
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]
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uncovered.extend(
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fragment
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for index, fragment in enumerate(fragments, start=1)
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if not _fact_text_is_preserved(f"fragment_{index}", ledger, candidate)
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)
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return _dedupe_strings(uncovered)
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def _material_fact_fragments(text: str) -> list[str]:
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facts: list[str] = []
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patterns = (
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r"gpa\s*[:：]?\s*\d+(?:\.\d+)?\s*/\s*\d+(?:\.\d+)?",
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r"(?:排名\s*)?(?:前\s*百分之\s*\d+(?:\.\d+)?|前\s*\d+(?:\.\d+)?\s*%|top\s*\d+(?:\.\d+)?\s*%)",
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r"(?:专业|年级)?(?:排名)?前(?:十|二十|三十|五十)",
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r"(?:获得|荣获|获评|获奖|取得)[^。；;\n]{0,30}(?:奖学金|奖项|荣誉|一等奖|二等奖|三等奖|优秀[^。；;\n]{0,12})",
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r"(?:完成|参与|负责|主导|开发|设计|实现|搭建|推进|开展)[^。；;\n]{0,40}(?:课程项目|课程设计|项目|竞赛|实验室|实践|实训|研究|论文)",
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r"(?:服务|覆盖|面向|参与|支持|管理|处理|完成|交付|提升|降低|增长)[^。；;\n]{0,20}?\d+(?:\.\d+)?\s*(?:%|人|名(?:学生|用户|客户|参与者)?|次|天|周|月|小时|万元|万|千|个|项|篇|场)",
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)
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for pattern in patterns:
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facts.extend(match.group(0).strip(" \t,，") for match in re.finditer(pattern, text, flags=re.IGNORECASE))
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tool_pattern = r"\b(?:python|sql|java|javascript|typescript|vue|react|excel|power\s*bi|tableau|pandas|tensorflow|pytorch|docker|git|linux)\b"
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facts.extend(match.group(0).strip() for match in re.finditer(tool_pattern, text, flags=re.IGNORECASE))
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return _dedupe_strings([fact for fact in facts if fact])
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def _fact_is_preserved(fact: str, candidate: str) -> bool:
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normalized_fact = _normalize_material_fact(fact)
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normalized_candidate = _normalize_material_fact(candidate)
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return bool(normalized_fact) and normalized_fact in normalized_candidate
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def _normalize_material_fact(value: str) -> str:
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normalized = value.casefold().replace("百分之", "%")
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normalized = re.sub(r"(?:排名|专业排名|年级排名)?前\s*(\d+(?:\.\d+)?)\s*%?", r"top\1", normalized)
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normalized = re.sub(r"top\s*(\d+(?:\.\d+)?)\s*%?", r"top\1", normalized)
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return re.sub(r"[\s,，。；;：:]", "", normalized)
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