"""
公司知识提取器 — 从已完成项目自动生成结构化知识条目到 CompanyKnowledge 表
"""
from __future__ import annotations

import logging
from typing import List

from sqlalchemy.orm import Session
from app.models.company import CompanyProject
from app.models.company_knowledge import CompanyKnowledge

logger = logging.getLogger(__name__)


def extract_from_project(project: CompanyProject, db: Session) -> List[CompanyKnowledge]:
    """从已完成项目提取知识条目并持久化。"""
    entries = []

    ceo_plan = project.ceo_plan or {}
    review_scores = project.review_scores or []

    # 1. CEO 战略分析
    analysis = ceo_plan.get("analysis", "")
    if analysis:
        entries.append(CompanyKnowledge(
            company_id=project.company_id,
            project_id=project.id,
            title=f"战略分析: {project.name[:80]}",
            content=f"## 项目目标\n{project.description or project.name}\n\n## CEO 战略分析\n{analysis}",
            category="ceo_plan",
            tags=_extract_tags(analysis),
            source_dept="CEO",
        ))

    # 2. 部门计划和交付物
    dept_plans = ceo_plan.get("departments", [])
    for dp in dept_plans:
        dept_name = dp.get("department_name", "")
        goal = dp.get("goal", "")
        deliverables = dp.get("deliverables", [])
        if goal or deliverables:
            content = f"## 部门任务\n{goal}\n\n## 预期交付物\n" + "\n".join(f"- {d}" for d in deliverables)
            entries.append(CompanyKnowledge(
                company_id=project.company_id,
                project_id=project.id,
                title=f"部门计划: {dept_name} - {goal[:60]}",
                content=content,
                category="dept_output",
                tags=[dept_name],
                source_dept=dept_name,
            ))

    # 3. 审查打分结果
    if review_scores:
        score_lines = []
        for s in review_scores:
            name = s.get("name", "")
            score = s.get("score", 0)
            feedback = s.get("feedback", "")
            passed = s.get("pass", False)
            score_lines.append(f"### {name}: {score}/10 {'✓' if passed else '✗'}\n{feedback}")
        content = "## CEO 审查\n\n" + "\n\n".join(score_lines)
        entries.append(CompanyKnowledge(
            company_id=project.company_id,
            project_id=project.id,
            title=f"审查结果: {project.name[:80]}",
            content=content,
            category="review",
            tags=["review", "scoring"],
            source_dept="CEO",
        ))

    # 4. 风险和指标
    risks = ceo_plan.get("risks", [])
    metrics = ceo_plan.get("key_success_metrics", [])
    if risks or metrics:
        content = ""
        if risks:
            content += "## 风险点\n" + "\n".join(f"- {r}" for r in risks)
        if metrics:
            content += "\n\n## 关键指标\n" + "\n".join(f"- {m}" for m in metrics)
        entries.append(CompanyKnowledge(
            company_id=project.company_id,
            project_id=project.id,
            title=f"项目洞察: {project.name[:80]}",
            content=content,
            category="insight",
            tags=["risks", "metrics"],
            source_dept="CEO",
        ))

    for e in entries:
        db.add(e)

    logger.info("Extracted %d knowledge entries from project %s", len(entries), project.id)
    return entries


def _extract_tags(text: str, max_tags: int = 5) -> List[str]:
    keywords = ["市场", "产品", "技术", "营销", "销售", "运营", "风险", "增长", "用户", "创新"]
    found = [kw for kw in keywords if kw in text]
    return found[:max_tags] if found else ["通用"]