PMFlow/backend/app/api/sstc_api/tasks_ai_breakdown.py

from __future__ import annotations

import re

from .shared import *

from ...services.sstc.entity_lookup import _ensure_charter, _ensure_project
from ...services.sstc.governance import _assert_charter_aligned, _require_manager
from ...services.sstc.signal_core import _as_dict, _parse_node_id, _parse_project_id, _sync_pmflow_rag_async

router = APIRouter(tags=["sstc"])

_TITLE_NORMALIZE_RE = re.compile(r"[\s\-_]+")


def _task_title_key(value: str) -> str:
    text = str(value or "").strip().lower()
    return _TITLE_NORMALIZE_RE.sub("", text)

@router.post("/projects/{project_id}/charters/{charter_id}/tasks/ai-breakdown")
async def ai_breakdown_tasks(
    project_id: str,
    charter_id: str,
    permission_user: User = Depends(require_project_member_or_manager),
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    _ = permission_user
    _require_manager(current_user)
    pid = _parse_project_id(project_id)
    cid = _parse_node_id(charter_id, "charter_id")
    tenant_id = int(current_user.tenant_id)
    await _ensure_project(SSTCService(db, tenant_id=tenant_id), pid)
    charter = await _ensure_charter(db, tenant_id=tenant_id, project_id=pid, charter_id=cid)
    _assert_charter_aligned(charter)

    prompt = f"""请基于以下需求宪章拆解 5-8 条任务，返回 JSON 数组：
[
  {{"task_name":"", "type":"backend/frontend/ai/devops", "estimated_hours": 4.0, "priority":"P0/P1/P2"}}
]

宪章标题：{charter.title}
宪章内容：{_as_dict(charter.content)}
"""
    try:
        rows = await llm_service.generate_task_breakdown_async(
            prompt,
            db=db,
            tenant_id=tenant_id,
            project_id=pid,
        )
    except RuntimeError as exc:
        raise HTTPException(status_code=403, detail=str(exc)) from exc
    except Exception as exc:
        raise HTTPException(status_code=500, detail=f"AI 拆解失败: {exc}") from exc

    existing_task_title_result = await db.execute(
        select(Task.title).where(
            and_(
                Task.truth_node_id == cid,
                Task.project_id == pid,
                Task.tenant_id == tenant_id,
            )
        )
    )
    existing_title_keys = {
        _task_title_key(str(title))
        for title in existing_task_title_result.scalars().all()
        if str(title or "").strip()
    }
    seen_title_keys = set(existing_title_keys)

    created = []
    skipped_duplicates = 0
    prompt_tokens = max(len(prompt) // 4, 1)
    completion_tokens = 0
    for item in rows or []:
        title = compact_title((item.get("task_name") or item.get("title") or ""), max_len=32)
        if not title:
            continue
        title_key = _task_title_key(title)
        if not title_key:
            continue
        if title_key in seen_title_keys:
            skipped_duplicates += 1
            continue
        seen_title_keys.add(title_key)

        priority = str(item.get("priority") or "P1").lower()
        mapped_priority = "high" if priority == "p0" else "medium" if priority == "p1" else "low"
        task = await persist_task(
            db,
            tenant_id=tenant_id,
            truth_node_id=cid,
            project_id=pid,
            draft=build_task_create_draft(
                title=title,
                description=item.get("description"),
                required_skill_tag_ids=[],
                priority=mapped_priority,
                status="todo",
                estimated_hours=float(item.get("estimated_hours") or 0) or None,
            ),
            actor_user_id=int(current_user.id),
            action="task.create",
            remark="sstc.ai_breakdown",
            commit=False,
            refresh_workload=False,
        )
        created.append(task)
        completion_tokens += max(len(title) // 2, 1)

    db.add(
        AICallLog(
            tenant_id=tenant_id,
            user_id=int(current_user.id),
            function_name="sstc_ai_breakdown",
            prompt=prompt,
            response=f"created_count={len(created)},skipped_duplicates={skipped_duplicates}",
            tokens_used=prompt_tokens + completion_tokens,
            cost=((prompt_tokens * 0.00015 + completion_tokens * 0.0006) / 1000),
            status="success",
        )
    )
    await log_operation(
        db,
        tenant_id=tenant_id,
        user_id=int(current_user.id),
        project_id=pid,
        entity_type="task",
        entity_id=str(cid),
        action="ai_breakdown",
        after_data={"created_count": len(created), "skipped_duplicates": skipped_duplicates},
    )
    await db.commit()
    for task in created:
        _sync_pmflow_rag_async(
            tenant_id=tenant_id,
            record_type="task",
            record_id=int(task.id),
            project_id=pid,
            title=task.title,
            content=task.description or "",
        )
    await context_cache_service.invalidate_system_context(tenant_id=tenant_id)
    return success_response(
        {
            "created_count": len(created),
            "skipped_duplicates": skipped_duplicates,
            "items": [
                {"id": str(t.id), "title": t.title, "priority": t.priority, "status": t.status}
                for t in created
            ],
        }
    )