generated from kgod/ai-review-template
105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
"""Injectable entry expansion protocol and deterministic P0 implementation."""
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from __future__ import annotations
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import re
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from typing import Any, Protocol
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_BULLET_PREFIX = re.compile(r"^(?:[•●▪◦]\s*|[-*]\s+|\d+[.)、]\s*)")
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class EntryExpander(Protocol):
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"""Produce an optimization proposal without mutating the source entry."""
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def expand(self, entry: dict[str, Any], *, context: dict[str, Any]) -> dict[str, Any]: ...
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class RuleBasedEntryExpander:
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"""Conservative local fallback used when no model is configured or available."""
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def expand(self, entry: dict[str, Any], *, context: dict[str, Any]) -> dict[str, Any]:
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entry_type = str(context.get("entry_type") or "")
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description = str(entry.get("description") or "").strip()
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highlights = [
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str(value).strip()
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for value in entry.get("highlights") or []
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if str(value).strip()
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]
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material = description or "；".join(highlights)
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if material:
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optimized = _polish_text(material)
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else:
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optimized = _description_from_structured_facts(entry, entry_type)
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if not optimized:
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return {"optimized_description": "", "changes": [], "source": "rule_polish"}
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if entry_type != "education":
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optimized = normalize_bullet_description(optimized)
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changes = ["统一为简洁、正式的简历表达"]
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if not description and not highlights:
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changes = ["根据已填写的结构化事实补充经历描述"]
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return {
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"optimized_description": optimized,
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"changes": changes,
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"source": "rule_polish",
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}
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def normalize_bullet_description(text: str) -> str:
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"""Normalize existing lines into resume bullets without rewriting their text."""
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bullets: list[str] = []
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for raw_line in text.splitlines() or [text]:
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line = raw_line.strip()
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if not line:
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continue
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line = _BULLET_PREFIX.sub("", line).strip()
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if line:
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bullets.append(f"• {line}")
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return "\n".join(bullets)
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def _polish_text(text: str) -> str:
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replacements = (
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(r"^做过", "完成"),
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(r"^做了", "完成"),
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(r"^拿了奖(?:项)?", "获得奖项"),
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(r"^参与了", "参与"),
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(r"^使用了?\s*(?=[A-Za-z0-9])", "基于 "),
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(r"^帮忙", "协助"),
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(r"^负责", "承担"),
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(r"^参加", "参与"),
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(r"，将", "，推动"),
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(r"，获得", "，并获得"),
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(r"降低了", "降低"),
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(r"提升了", "提升"),
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(r"优化了", "优化"),
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)
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parts: list[str] = []
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for raw in re.split(r"[。；;\n]+", text):
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part = raw.strip(" ，,。；;")
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if not part:
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continue
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for pattern, replacement in replacements:
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part = re.sub(pattern, replacement, part)
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parts.append(part)
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return "\n".join(parts)
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def _description_from_structured_facts(entry: dict[str, Any], entry_type: str) -> str:
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if entry_type in {"work_experience", "internship_experience"}:
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company = str(entry.get("company") or "").strip()
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position = str(entry.get("position") or "").strip()
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return f"在{company}担任{position}。" if company and position else ""
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if entry_type == "project_experience":
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name = str(entry.get("project_name") or "").strip()
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role = str(entry.get("project_role") or "").strip()
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return f"参与{name}，担任{role}。" if name and role else ""
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if entry_type == "competition":
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name = str(entry.get("name") or "").strip()
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award = str(entry.get("award") or "").strip()
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return f"参加{name}并获得{award}。" if name and award else ""
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if entry_type == "campus_experience":
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organization = str(entry.get("organization") or "").strip()
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role = str(entry.get("role") or "").strip()
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return f"在{organization}担任{role}。" if organization and role else ""
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return ""
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