generated from kgod/ai-review-template
hypandClaude
ae2d9b128d
自内部仓库剥离深度优化与 RAG 知识库后的交付版本： - Builder 对话式简历生成（FSM + 意图路由 LLM 兜底增强） - 条目级轻度优化：事实覆盖门禁 + STAR/bullet 修复链，功能/简介/成果与技术栈同级保护 - 简历导入：DOCX/PDF 解析、结构归一、手机号脱敏 - PostgreSQL 运行时 + Alembic 迁移链 Co-Authored-By: Claude <noreply@anthropic.com>
123 lines
4.8 KiB
Python
123 lines
4.8 KiB
Python
from __future__ import annotations
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from copy import deepcopy
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from datetime import UTC, datetime
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from typing import Any, Protocol
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from pydantic import BaseModel, ConfigDict, Field
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from .llm_services import OpenAICompatibleStructuredClient
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from .settings import Settings
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class ProfileSummaryOutput(BaseModel):
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model_config = ConfigDict(extra="forbid")
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content: str = Field(min_length=20, max_length=600)
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class ProfileSummaryGenerator(Protocol):
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def generate(self, content: dict[str, Any]) -> str: ...
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class RuleBasedProfileSummaryGenerator:
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"""Deterministic Chinese summary for tests and configured rule fallback."""
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def generate(self, content: dict[str, Any]) -> str:
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basics = content.get("basics") if isinstance(content.get("basics"), dict) else {}
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target = content.get("target") if isinstance(content.get("target"), dict) else {}
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position = str(target.get("position") or target.get("target_position") or "目标岗位").strip()
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groups = content.get("skill_groups") if isinstance(content.get("skill_groups"), list) else []
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skills = [
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str(skill).strip()
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for group in groups
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if isinstance(group, dict)
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for skill in group.get("skills") or []
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if str(skill).strip()
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][:5]
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first_entry: dict[str, Any] = {}
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for section in content.get("sections") or []:
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if isinstance(section, dict) and section.get("items"):
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candidate = section["items"][0]
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if isinstance(candidate, dict):
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first_entry = candidate
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break
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major = str(first_entry.get("major") or basics.get("major") or "").strip()
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focus = str(
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first_entry.get("company")
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or first_entry.get("project_name")
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or first_entry.get("school")
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or "相关实践"
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).strip()
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skill_text = "、".join(dict.fromkeys(skills)) or "相关技术与实践能力"
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major_text = f"，具备{major}相关学习背景" if major else ""
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return f"面向{position}{major_text}，具备{skill_text}等能力，拥有{focus}相关经历，能够结合已完成的项目与实践持续提升岗位匹配度。"
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class OpenAIProfileSummaryGenerator:
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def __init__(self, completion: OpenAICompatibleStructuredClient) -> None:
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self.completion = completion
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def generate(self, content: dict[str, Any]) -> str:
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output: ProfileSummaryOutput = self.completion.complete(
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schema=ProfileSummaryOutput,
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schema_name="profile_summary",
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system_prompt=(
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"你是中文简历个人总结撰写助手。仅返回 JSON。根据用户已确认的简历内容，"
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"写一段 80 到 180 字、适合置于中文简历开头的个人总结。"
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"只概括目标岗位、教育/经历、项目和技能中的已有事实；不得包含手机、邮箱等隐私信息，"
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"不得编造公司、学校、项目、学历、奖项、证书或量化数字。"
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"内容应自然连贯，不使用标题、列表、Markdown 或解释。"
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),
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payload={"resume": _summary_source(content)},
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)
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return _validate_summary(output.content)
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class FallbackProfileSummaryGenerator:
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def __init__(self, primary: ProfileSummaryGenerator, fallback: ProfileSummaryGenerator) -> None:
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self.primary = primary
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self.fallback = fallback
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def generate(self, content: dict[str, Any]) -> str:
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try:
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return self.primary.generate(content)
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except Exception:
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return self.fallback.generate(content)
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def build_profile_summary_generator(
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settings: Settings, client: Any | None = None
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) -> ProfileSummaryGenerator:
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rules = RuleBasedProfileSummaryGenerator()
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if not settings.use_openai:
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return rules
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primary = OpenAIProfileSummaryGenerator(OpenAICompatibleStructuredClient(settings, client))
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return FallbackProfileSummaryGenerator(primary, rules) if settings.fallback_to_rules else primary
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def generated_summary(content: str) -> dict[str, Any]:
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return {
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"content": _validate_summary(content),
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"source": "ai_generated",
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"generated_at": datetime.now(UTC).isoformat(),
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"stale": False,
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}
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def _validate_summary(value: str) -> str:
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clean = " ".join(str(value or "").split())
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if not 20 <= len(clean) <= 600:
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raise ValueError("profile_summary_invalid")
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return clean
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def _summary_source(content: dict[str, Any]) -> dict[str, Any]:
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result = deepcopy(content)
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basics = result.get("basics")
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if isinstance(basics, dict):
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for field in ("phone", "email", "masked_phone"):
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basics.pop(field, None)
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result.pop("profile_summary", None)
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return result
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