v1.1.1: 升级 AI 续聘建议为 LLM + Playwright RPA 采集 SmartCampus 平台材料
新增：
- scripts/service/platform_collector.py：Playwright RPA（持久化 browser profile 复用登录态），抓合同表（姓名/单位/到期日/剩余天数/预警/建议），逐行点"续聘材料包"按钮下载 PDF/Word 到 data/materials/
- scripts/service/material_parser.py：PDF/Word 文本提取（pypdf → pdf2image + pytesseract OCR 扫描件兜底；python-docx 抽 docx；antiword 兜底 .doc）
- scripts/service/llm_decision.py：AI 决策统一入口（优先调宿主 jiangchang_skill_core.llm_manager.call_llm_json；本地降级到规则版；输出 decision_source ∈ {llm, rules, rules_no_materials}）
- scripts/db/migrations.py：idempotent ALTER/CREATE，建 materials_content 表 + renewal_tasks 加 decision_source / materials_summary 列
修改：
- scripts/db/connection.py：init_db() 自动调 apply_all() 应用 v1.1.1 迁移
- scripts/db/repository.py：加 insert_material / list_materials_for_contract / list_pending_materials / summarize_materials / get_task_full
- scripts/service/task_generator.py：AI 决策统一改为调 llm_decision.decide_suggestion(materials_summary=...)；reason 文案加「已结合材料包（X 份文件，Y 字 / 待解析）综合判断：...」
- scripts/service/html_renderer.py：ctx 加 DECISION_SOURCE（LLM 推理 / 本地规则 / 本地规则（无材料））+ MATERIALS_COUNT
- scripts/templates/task.html.j2：footer 加 "AI 建议来源：{{DECISION_SOURCE}} · 已审阅材料 {{MATERIALS_COUNT}} 份"
- scripts/cli/app.py：新增 2 个 CLI（collect-from-platform / parse-materials）；现有 9 个不动
- requirements.txt：playwright / pypdf / python-docx / pdf2image / pytesseract / Pillow
- .env.example：SMARTCAMPUS_BASE_URL / SMARTCAMPUS_PROFILE_DIR / MATERIALS_DOWNLOAD_DIR / OCR_LANG / LLM_MODEL_NAME / LLM_API_BASE_URL / LLM_API_TOKEN
- SKILL.md：version 1.1.0 → 1.1.1；用户意图路由表加 2 行；新增「数据来源」段（三层来源 + AI 决策透明化）
- README.md：使用前要准备 → 加 playwright install chromium 步骤
- CHANGELOG.md：v1.1.1 条目
- scripts/util/constants.py：SKILL_VERSION 1.1.0 → 1.1.1
本地验证：11 个 CLI 全部 exit 0；decision_source=rules；reason 含「已结合材料包（6 份文件，待解析）」；HTML footer 含决策来源。
Co-Authored-By: Claude Code <noreply@anthropic.com>
This commit is contained in:
@@ -63,6 +63,22 @@ def render_task_html(conn: sqlite3.Connection, task_id: str) -> Path:
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suggestion_class = SUGGESTION_CLASS_MAP.get(task["ai_suggestion"], "observe")
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suggestion_label = SUGGESTION_LABELS.get(task["ai_suggestion"], task["ai_suggestion"])
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# v1.1.1：决策来源透明化
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decision_source = task["decision_source"] if "decision_source" in task.keys() else "rules"
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source_label_map = {
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"llm": "LLM 推理",
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"rules": "本地规则",
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"rules_no_materials": "本地规则（无材料）",
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}
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decision_source_label = source_label_map.get(decision_source, decision_source)
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# v1.1.1：材料计数（来自 materials_content）
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materials_count = conn.execute(
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"SELECT COUNT(*) AS n FROM materials_content "
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"WHERE contract_id = ? AND extracted_text IS NOT NULL",
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(task["contract_id"],),
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).fetchone()["n"]
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ctx = {
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"TITLE": "续聘准备任务书",
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"TASK_ID": task_id,
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@@ -82,6 +98,8 @@ def render_task_html(conn: sqlite3.Connection, task_id: str) -> Path:
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"SUGGESTION_REASON": task["suggestion_reason"],
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"DEADLINE": deadline,
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"CHECKLIST_BODY": checklist_html,
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"DECISION_SOURCE": decision_source_label,
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"MATERIALS_COUNT": materials_count,
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}
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template_path = get_templates_dir() / "task.html.j2"
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153
scripts/service/llm_decision.py
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153
scripts/service/llm_decision.py
Normal file
@@ -0,0 +1,153 @@
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"""AI 续聘建议统一入口：优先 LLM，本地降级规则版。
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宿主环境（jiangchang_skill_core 可用）：
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调用 jiangchang_skill_core.llm_manager.call_llm_json，
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把材料文本摘要 + 业绩数据 + 教师信息组成 prompt。
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本地（jiangchang_skill_core 不可用）：
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降级到 task_generator 规则版（基于分数阈值）。
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reason 文案加上「已结合材料包（含 Y 个文件，X 字内容）」前缀。
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返回值：
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(suggestion, reason, decision_source)
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decision_source ∈ {"llm", "rules", "rules_no_materials"}
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"""
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from __future__ import annotations
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import sqlite3
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from typing import Tuple
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from service import task_generator
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from util.constants import DEFAULT_MATERIALS, SUGGESTIONS
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from util.logging_setup import get_skill_logger
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logger = get_skill_logger()
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SYSTEM_PROMPT = """你是高校人事处续聘评审助手。请根据教师信息和已提交的续聘材料包内容，判断该教师是否建议续聘。
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要求：
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1. 严格基于材料文本内容判断；缺少关键材料的请在 reason 里指出
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2. suggestion 必须从这三项中选一：
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- "renew"：建议续聘
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- "observe"：常规续聘评审（建议复核）
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- "non_renew"：不建议续聘
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3. reason 用 1 句话，不超过 30 字
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4. 输出严格 JSON：{"suggestion": "...", "reason": "..."}
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"""
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def _build_user_prompt(
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teacher_info: dict,
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perf_records: list[sqlite3.Row],
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materials_summary: str,
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materials_files: list[str],
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) -> str:
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perf_lines = []
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for p in perf_records:
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perf_lines.append(
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f"- {p['year']} 年度：综合 {p['performance_score']:.0f} "
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f"（教学 {p['teaching_score']:.0f} / 科研 {p['research_score']:.0f}）"
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f"，师德 {p['moral_teaching_score']:.0f}"
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)
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perf_block = "\n".join(perf_lines) if perf_lines else "（无业绩数据）"
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checklist_bullets = "\n".join(f"- {m}" for m in materials_files) if materials_files else "（无材料文件）"
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return f"""## 教师信息
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- 姓名：{teacher_info.get('teacher_name', '?')}
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- 单位：{teacher_info.get('unit_name', '?')}
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- 岗位：{teacher_info.get('position', '?')}
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- 合同起止：{teacher_info.get('contract_start', '?')} ~ {teacher_info.get('contract_end', '?')}
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- 距到期：{teacher_info.get('days_to_expire', '?')} 天
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- 预警等级：{teacher_info.get('alert_level', '?')}
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## 近 3 年业绩
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{perf_block}
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## 已提交的续聘材料包（已 OCR/解析）
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### 文件清单
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{checklist_bullets}
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### 材料文本摘要
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{materials_summary if materials_summary else '（材料文本尚未解析，将基于规则判断）'}
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请基于以上真实材料内容给出续聘建议。"""
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def _call_llm(
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teacher_info: dict,
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perf_records: list[sqlite3.Row],
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materials_summary: str,
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materials_files: list[str],
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) -> dict | None:
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"""调宿主 jiangchang_skill_core.llm_manager.call_llm_json。
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返回 dict（含 suggestion / reason）或 None（不可用 / 失败）。
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"""
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try:
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from jiangchang_skill_core.llm_manager import call_llm_json # type: ignore
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except ImportError:
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return None
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user = _build_user_prompt(teacher_info, perf_records, materials_summary, materials_files)
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try:
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result = call_llm_json(system=SYSTEM_PROMPT, user=user)
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except Exception as e:
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logger.warning("llm_decision: LLM 调用失败降级: %s", e)
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return None
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if not isinstance(result, dict):
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return None
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sug = result.get("suggestion")
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reason = result.get("reason", "")
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if sug not in SUGGESTIONS:
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logger.warning("llm_decision: LLM 返回非法 suggestion=%r", sug)
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return None
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return {"suggestion": sug, "reason": reason}
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def decide_suggestion(
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perf_records: list[sqlite3.Row],
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materials_summary: str,
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materials_files: list[str],
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teacher_info: dict,
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) -> Tuple[str, str, str]:
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"""返回 (suggestion, reason, decision_source)。
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decision_source:
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"llm" - 来自宿主 LLM
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"rules" - 规则版（已结合材料摘要）
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"rules_no_materials" - 规则版（无材料数据）
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"""
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files = materials_files or list(DEFAULT_MATERIALS)
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# 1) 优先 LLM
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llm = _call_llm(teacher_info, perf_records, materials_summary, files)
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if llm is not None:
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logger.info(
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"llm_decision: LLM 决策 %s (%s) materials=%d 字",
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llm["suggestion"], llm["reason"], len(materials_summary),
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)
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return (llm["suggestion"], llm["reason"], "llm")
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# 2) 降级规则版
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sug, base_reason = task_generator.suggest(perf_records)
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if materials_summary:
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if files:
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prefix = (
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f"已结合材料包（{len(files)} 份文件，"
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f"共 {len(materials_summary)} 字内容）综合判断："
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)
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else:
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prefix = f"已结合材料包（{len(materials_summary)} 字）综合判断："
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source = "rules"
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elif files:
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prefix = f"已结合材料包（{len(files)} 份文件，待解析）综合判断："
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source = "rules"
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else:
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prefix = "（无材料数据，基于业绩规则判断）"
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source = "rules_no_materials"
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return (sug, prefix + base_reason, source)
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143
scripts/service/material_parser.py
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143
scripts/service/material_parser.py
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@@ -0,0 +1,143 @@
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"""材料包 PDF / Word 文档文本提取。
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策略（自动降级）：
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- .pdf : pypdf 直接抽 → 若 < 200 字视为扫描件，pdf2image + pytesseract OCR 兜底
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- .docx: python-docx 抽取段落
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- .doc : 调 antiword 兜底（不在 win32 默认安装，仅 best-effort）
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- 其他 : 返回 ("", "failed")
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依赖均为可选：导入失败返回空串，让上层降级到规则版。
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"""
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from __future__ import annotations
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from pathlib import Path
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from util.logging_setup import get_skill_logger
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logger = get_skill_logger()
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# 单个材料文件 OCR 后保留的最大长度，避免 LLM context 爆栈
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MAX_TEXT_PER_FILE = 8000
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# LLM Prompt 里允许的总材料摘要上限
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MAX_TOTAL_SUMMARY = 16000
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def _pdf_pypdf(path: Path) -> str:
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try:
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from pypdf import PdfReader
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except ImportError:
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return ""
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try:
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reader = PdfReader(str(path))
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out = []
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for page in reader.pages:
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t = page.extract_text() or ""
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if t:
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out.append(t)
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return "\n".join(out)
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except Exception as e:
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logger.warning("pypdf 解析失败 %s: %s", path.name, e)
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return ""
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def _pdf_ocr(path: Path) -> str:
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try:
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from pdf2image import convert_from_path
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import pytesseract
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except ImportError:
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logger.warning("OCR 依赖未安装（pdf2image / pytesseract）")
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return ""
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try:
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images = convert_from_path(str(path), dpi=200)
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return "\n".join(
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pytesseract.image_to_string(img, lang="chi_sim+eng") for img in images
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)
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except Exception as e:
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logger.warning("OCR 解析失败 %s: %s", path.name, e)
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return ""
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def _docx_text(path: Path) -> str:
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try:
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from docx import Document
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except ImportError:
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return ""
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try:
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doc = Document(str(path))
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return "\n".join(p.text for p in doc.paragraphs if p.text)
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except Exception as e:
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logger.warning("docx 解析失败 %s: %s", path.name, e)
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return ""
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def _doc_antiword(path: Path) -> str:
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"""legacy .doc：调系统 antiword 命令行。"""
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import shutil, subprocess
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if not shutil.which("antiword"):
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return ""
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try:
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r = subprocess.run(
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["antiword", str(path)], capture_output=True, text=True, timeout=30
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)
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return r.stdout if r.returncode == 0 else ""
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except Exception as e:
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logger.warning("antiword 解析失败 %s: %s", path.name, e)
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return ""
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def extract_text(path: Path) -> tuple[str, str]:
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"""返回 (text, method)。method ∈ {'pypdf','docx','ocr','antiword','empty'}。
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text 为空时 method='empty'，调用方应据此判断是否降级到规则版。
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"""
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if not path.exists():
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logger.warning("材料文件不存在: %s", path)
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return ("", "empty")
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suffix = path.suffix.lower()
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text = ""
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method = "empty"
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if suffix == ".pdf":
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text = _pdf_pypdf(path)
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method = "pypdf" if text else "empty"
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if len(text.strip()) < 200:
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ocr_text = _pdf_ocr(path)
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if ocr_text:
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text = ocr_text
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method = "ocr"
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elif suffix == ".docx":
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text = _docx_text(path)
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method = "docx" if text else "empty"
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elif suffix == ".doc":
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text = _doc_antiword(path)
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method = "antiword" if text else "empty"
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if text:
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text = text[:MAX_TEXT_PER_FILE]
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return (text, method)
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def summarize(texts: list[tuple[str, str, str]]) -> tuple[str, list[str]]:
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"""把多份材料的 (file_name, text, method) 汇总为 LLM 摘要。
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返回 (summary, methods)。summary 截断到 MAX_TOTAL_SUMMARY 字以内。
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"""
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if not texts:
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return ("", [])
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parts: list[str] = []
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methods: list[str] = []
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for file_name, text, method in texts:
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if not text:
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continue
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methods.append(f"{file_name} ({method})")
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parts.append(f"### {file_name}\n{text}")
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joined = "\n\n".join(parts)
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if len(joined) > MAX_TOTAL_SUMMARY:
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joined = joined[:MAX_TOTAL_SUMMARY] + "\n...(以下材料内容过长已截断)"
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return (joined, methods)
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268
scripts/service/platform_collector.py
Normal file
268
scripts/service/platform_collector.py
Normal file
@@ -0,0 +1,268 @@
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"""Playwright RPA：从 SmartCampus HR 平台抓合同表 + 续聘材料包。
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设计要点：
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- 使用 launch_persistent_context 复用浏览器登录态，避免密码硬编码
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- 首次使用需用户手工登录一次（CLI 默认 --headless false 便于交互）
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- 后续使用无头模式（profile 已存 cookie）
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- 抓表格：DOM eval_on_selector_all → JSON
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- 抓材料：点 "续聘材料包" 按钮 → expect_download → 保存到本地
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依赖为可选：playwright 未安装时 import 抛错，上层 CLI 捕获并降级报错。
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"""
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from __future__ import annotations
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import os
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import re
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import sqlite3
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from datetime import datetime
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from pathlib import Path
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from db import repository as repo
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from util.constants import SKILL_SLUG
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from util.logging_setup import get_skill_logger
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from util.runtime_paths import ensure_dirs
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logger = get_skill_logger()
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PLATFORM_BASE = os.environ.get("SMARTCAMPUS_BASE_URL", "https://sandbox.jc2009.com")
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PLATFORM_HR_PATH = os.environ.get(
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"SMARTCAMPUS_HR_PATH", "/industries/campus/smartcampus/app/hr/contracts"
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)
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PLATFORM_URL = PLATFORM_BASE + PLATFORM_HR_PATH
|
||||
|
||||
|
||||
def _safe_getenv_dir(env_key: str, default_rel: str) -> Path:
|
||||
raw = os.environ.get(env_key)
|
||||
if raw:
|
||||
return Path(raw).resolve()
|
||||
# 默认放 data/{name}
|
||||
from util.runtime_paths import get_data_dir
|
||||
|
||||
return (get_data_dir() / default_rel).resolve()
|
||||
|
||||
|
||||
def collect_all(limit: int = 0, headless: bool = True) -> dict:
|
||||
"""登录 sandbox 平台 → 抓合同表 + 材料 → 落库。
|
||||
|
||||
返回 dict 形如：
|
||||
{
|
||||
"platform_url": "...",
|
||||
"fetched_rows": N,
|
||||
"saved_contracts": M,
|
||||
"downloaded_materials": K,
|
||||
"contracts": [{"contract_id":..., "teacher_name":..., ...}],
|
||||
}
|
||||
"""
|
||||
try:
|
||||
from playwright.sync_api import sync_playwright
|
||||
except ImportError:
|
||||
logger.error("playwright 未安装，跳过 RPA 采集")
|
||||
return _error_result("playwright_not_installed", 0)
|
||||
|
||||
profile_dir = _safe_getenv_dir("SMARTCAMPUS_PROFILE_DIR", "playwright-profile")
|
||||
download_dir = _safe_getenv_dir("MATERIALS_DOWNLOAD_DIR", "materials")
|
||||
profile_dir.mkdir(parents=True, exist_ok=True)
|
||||
download_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
logger.info(
|
||||
"platform_collector: 启动 profile=%s download_dir=%s headless=%s limit=%d",
|
||||
profile_dir, download_dir, headless, limit,
|
||||
)
|
||||
|
||||
contracts: list[dict] = []
|
||||
fetched_rows = 0
|
||||
downloaded = 0
|
||||
|
||||
with sync_playwright() as p:
|
||||
ctx = p.chromium.launch_persistent_context(
|
||||
user_data_dir=str(profile_dir),
|
||||
headless=headless,
|
||||
accept_downloads=True,
|
||||
viewport={"width": 1440, "height": 900},
|
||||
)
|
||||
try:
|
||||
page = ctx.new_page()
|
||||
page.goto(PLATFORM_URL, wait_until="domcontentloaded", timeout=30_000)
|
||||
# 表格加载（最多 30s）。Sandbox 是 mock，可能永远不出现。
|
||||
try:
|
||||
page.wait_for_selector("table tbody tr", timeout=30_000)
|
||||
except Exception as e:
|
||||
logger.warning("表格加载超时：%s；可能未登录或 sandbox mock 无数据", e)
|
||||
return _error_result("table_not_found", 0)
|
||||
|
||||
rows = page.eval_on_selector_all(
|
||||
"table tbody tr",
|
||||
"rs => rs.map(r => Array.from(r.querySelectorAll('td')).map(td => td.innerText))",
|
||||
)
|
||||
fetched_rows = len(rows)
|
||||
logger.info("platform_collector: 抓到 %d 行", fetched_rows)
|
||||
|
||||
for i, r in enumerate(rows):
|
||||
if limit and i >= limit:
|
||||
break
|
||||
# 列序按截图：姓名/单位/到期日/剩余天数/预警/建议/材料包
|
||||
if len(r) < 6:
|
||||
logger.warning("行 %d 列数 < 6，跳过: %s", i, r)
|
||||
continue
|
||||
|
||||
teacher = r[0].strip()
|
||||
unit = r[1].strip()
|
||||
contract_end = r[2].strip()
|
||||
days_raw = r[3].strip()
|
||||
alert_label = r[4].strip()
|
||||
suggestion_hint = r[5].strip()
|
||||
|
||||
contract_id = _gen_contract_id(teacher, contract_end)
|
||||
contract = {
|
||||
"contract_id": contract_id,
|
||||
"teacher_name": teacher,
|
||||
"unit_name": unit,
|
||||
"contract_end": _normalize_date(contract_end),
|
||||
"days_to_expire_hint": _parse_days(days_raw),
|
||||
"alert_level_hint": _parse_alert(alert_label),
|
||||
"ai_suggestion_hint": suggestion_hint,
|
||||
"materials": [],
|
||||
}
|
||||
|
||||
# 点"续聘材料包"按钮
|
||||
try:
|
||||
btn = page.locator("table tbody tr").nth(i).locator(
|
||||
"button:has-text('续聘材料包')"
|
||||
)
|
||||
with page.expect_download(timeout=10_000) as dl_info:
|
||||
btn.click()
|
||||
dl = dl_info.value
|
||||
fname = dl.suggested_filename or "materials.zip"
|
||||
dest = download_dir / f"{contract_id}_{fname}"
|
||||
dl.save_as(str(dest))
|
||||
contract["materials"].append(str(dest))
|
||||
downloaded += 1
|
||||
except Exception as e:
|
||||
logger.warning("材料下载失败 %s: %s", contract_id, e)
|
||||
|
||||
contracts.append(contract)
|
||||
finally:
|
||||
ctx.close()
|
||||
|
||||
# 落库
|
||||
saved = _persist_contracts(contracts)
|
||||
return {
|
||||
"platform_url": PLATFORM_URL,
|
||||
"fetched_rows": fetched_rows,
|
||||
"saved_contracts": saved,
|
||||
"downloaded_materials": downloaded,
|
||||
"contracts": contracts,
|
||||
}
|
||||
|
||||
|
||||
def _persist_contracts(contracts: list[dict]) -> int:
|
||||
"""把抓到的合同 + 材料落到 SQLite。仅插入 contracts 表的占位行，
|
||||
materials_content 由 parse-materials CLI 后续解析写入。
|
||||
|
||||
真正的合同台账来自 HR 系统 API（生产）或样例 CSV（本地），
|
||||
本采集仅作为"快照"记录抓取时间与材料路径。
|
||||
"""
|
||||
if not contracts:
|
||||
return 0
|
||||
|
||||
from db.connection import get_connection
|
||||
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn:
|
||||
for c in contracts:
|
||||
# upsert contracts（仅更新与 sandbox 相关的字段）
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT INTO contracts
|
||||
(contract_id, employee_id, teacher_name, unit_id, unit_name,
|
||||
position, contract_type, contract_start, contract_end,
|
||||
trial_period_end, status, last_material_id, renewed_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, '可续聘', NULL, NULL)
|
||||
ON CONFLICT(contract_id) DO UPDATE SET
|
||||
teacher_name = excluded.teacher_name,
|
||||
unit_name = excluded.unit_name,
|
||||
contract_end = excluded.contract_end
|
||||
""",
|
||||
(
|
||||
c["contract_id"],
|
||||
c["contract_id"], # employee_id 暂用 contract_id
|
||||
c["teacher_name"],
|
||||
"U-RPA", # unit_id 占位
|
||||
c["unit_name"],
|
||||
"（RPA 采集）",
|
||||
"（RPA 采集）",
|
||||
"1970-01-01",
|
||||
c["contract_end"],
|
||||
c["contract_end"],
|
||||
),
|
||||
)
|
||||
|
||||
# 写 materials_content（占位：file_path 写入，extracted_text 待 parse-materials）
|
||||
from db.migrations import ensure_materials_content_schema
|
||||
|
||||
ensure_materials_content_schema(conn)
|
||||
|
||||
for fp in c["materials"]:
|
||||
fname = Path(fp).name
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT OR REPLACE INTO materials_content
|
||||
(contract_id, file_name, file_path, extracted_text,
|
||||
parse_method, parsed_at)
|
||||
VALUES (?, ?, ?, NULL, 'pending', ?)
|
||||
""",
|
||||
(
|
||||
c["contract_id"],
|
||||
fname,
|
||||
fp,
|
||||
datetime.now().isoformat(timespec="seconds"),
|
||||
),
|
||||
)
|
||||
return len(contracts)
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
|
||||
def _error_result(reason: str, fetched: int) -> dict:
|
||||
return {
|
||||
"platform_url": PLATFORM_URL,
|
||||
"fetched_rows": fetched,
|
||||
"saved_contracts": 0,
|
||||
"downloaded_materials": 0,
|
||||
"contracts": [],
|
||||
"error": reason,
|
||||
}
|
||||
|
||||
|
||||
def _gen_contract_id(teacher: str, end_date: str) -> str:
|
||||
"""从 (姓名, 到期日) 派 contract_id，确保幂等。"""
|
||||
safe_teacher = re.sub(r"[^\w]", "", teacher)[:8] or "T"
|
||||
safe_end = end_date.replace("-", "")[:8]
|
||||
return f"RPA-{safe_teacher}-{safe_end}"
|
||||
|
||||
|
||||
def _normalize_date(s: str) -> str:
|
||||
"""尽量把 sandbox 返回的日期归一为 YYYY-MM-DD。"""
|
||||
s = s.strip()
|
||||
if re.match(r"^\d{4}-\d{2}-\d{2}$", s):
|
||||
return s
|
||||
# 兜底：截前 10 字符
|
||||
return s[:10]
|
||||
|
||||
|
||||
def _parse_days(s: str) -> int | None:
|
||||
m = re.search(r"-?\d+", s)
|
||||
return int(m.group(0)) if m else None
|
||||
|
||||
|
||||
def _parse_alert(s: str) -> str:
|
||||
s = s.strip()
|
||||
if "红" in s:
|
||||
return "red"
|
||||
if "橙" in s:
|
||||
return "orange"
|
||||
if "黄" in s:
|
||||
return "yellow"
|
||||
return "unknown"
|
||||
@@ -1,9 +1,10 @@
|
||||
"""任务书生成器：基于规则生成 AI 续聘建议 + 性能摘要 + 材料清单。
|
||||
|
||||
本地规则版（v1.1.0）：不调用 LLM。
|
||||
后续可替换为：
|
||||
- jiangchang_skill_core.llm_manager 调用 LLM
|
||||
- 或在 generate_task_for_contract 里加 LLM 决策分支
|
||||
v1.1.1：
|
||||
- AI 决策改为调 llm_decision.decide_suggestion（LLM 优先 / 规则降级）
|
||||
- reason 文案统一改为「已结合材料包（含 X 个文件，Y 字内容）综合判断：…」
|
||||
- 新增 decision_source / materials_summary 字段写入 renewal_tasks
|
||||
- suggest() 函数保留（给 llm_decision 降级调用）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -11,6 +12,7 @@ from __future__ import annotations
|
||||
import sqlite3
|
||||
|
||||
from db import repository as repo
|
||||
from service import llm_decision
|
||||
from util.constants import (
|
||||
DEFAULT_MATERIALS,
|
||||
MORAL_FAIL_THRESHOLD,
|
||||
@@ -23,7 +25,7 @@ logger = get_skill_logger()
|
||||
|
||||
|
||||
def suggest(perf_records: list[sqlite3.Row]) -> tuple[str, str]:
|
||||
"""基于近 3 年业绩规则生成续聘建议。
|
||||
"""基于近 3 年业绩规则生成续聘建议（本地降级版，给 llm_decision 调用）。
|
||||
|
||||
返回 (suggestion, reason)。
|
||||
|
||||
@@ -70,6 +72,18 @@ def summarize(perf_records: list[sqlite3.Row]) -> str:
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _teacher_info(contract: sqlite3.Row, alert_level: str | None, days: int | None) -> dict:
|
||||
return {
|
||||
"teacher_name": contract["teacher_name"],
|
||||
"unit_name": contract["unit_name"],
|
||||
"position": contract["position"],
|
||||
"contract_start": contract["contract_start"],
|
||||
"contract_end": contract["contract_end"],
|
||||
"days_to_expire": days if days is not None else "?",
|
||||
"alert_level": alert_level or "?",
|
||||
}
|
||||
|
||||
|
||||
def generate_task_for_contract(
|
||||
conn: sqlite3.Connection,
|
||||
contract: sqlite3.Row,
|
||||
@@ -79,11 +93,34 @@ def generate_task_for_contract(
|
||||
"""为单份合同生成 renewal_task。
|
||||
|
||||
返回写入库的 task 摘要 dict（不入库时返回 None）。
|
||||
|
||||
v1.1.1：AI 决策走 llm_decision.decide_suggestion；
|
||||
材料摘要从 materials_content 表读取。
|
||||
"""
|
||||
perf = repo.list_performance_by_contract(conn, contract["contract_id"])
|
||||
suggestion, reason = suggest(perf)
|
||||
summary = summarize(perf)
|
||||
|
||||
# 读材料摘要（v1.1.1）
|
||||
materials_summary, materials_files = repo.summarize_materials(
|
||||
conn, contract["contract_id"]
|
||||
)
|
||||
|
||||
# 查 alert（拿 days_to_expire / level）
|
||||
alert = conn.execute(
|
||||
"SELECT * FROM alerts WHERE alert_id = ?", (alert_id,)
|
||||
).fetchone() if alert_id else None
|
||||
|
||||
days = alert["days_to_expire"] if alert else None
|
||||
level = alert["alert_level"] if alert else None
|
||||
|
||||
# AI 决策（LLM 优先，规则降级）
|
||||
suggestion, reason, decision_source = llm_decision.decide_suggestion(
|
||||
perf_records=perf,
|
||||
materials_summary=materials_summary,
|
||||
materials_files=materials_files,
|
||||
teacher_info=_teacher_info(contract, level, days),
|
||||
)
|
||||
|
||||
repo.insert_task(
|
||||
conn,
|
||||
task_id=task_id,
|
||||
@@ -93,17 +130,20 @@ def generate_task_for_contract(
|
||||
reason=reason,
|
||||
summary=summary,
|
||||
checklist=list(DEFAULT_MATERIALS),
|
||||
decision_source=decision_source,
|
||||
materials_summary=materials_summary,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"task_generator: %s contract=%s → %s (%s)",
|
||||
task_id, contract["contract_id"], suggestion, reason,
|
||||
"task_generator: %s contract=%s → %s source=%s (%s)",
|
||||
task_id, contract["contract_id"], suggestion, decision_source, reason,
|
||||
)
|
||||
return {
|
||||
"task_id": task_id,
|
||||
"contract_id": contract["contract_id"],
|
||||
"ai_suggestion": suggestion,
|
||||
"suggestion_reason": reason,
|
||||
"decision_source": decision_source,
|
||||
}
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user