"""LLM 服务兼容层。

在完整 AI 能力未接入时提供最小实现，保证 API 端点可运行。
"""

from __future__ import annotations

from typing import Any


class LLMService:
    async def generate(self, prompt: str, **_: Any) -> str:
        return f"[stub] {prompt[:200]}"

    async def generate_task_breakdown_async(self, description: str, **_: Any) -> list[dict]:
        if not description.strip():
            return []
        return [
            {
                "task_name": "需求分析",
                "type": "AI",
                "priority": "medium",
                "estimated_hours": 4,
                "description": description[:200],
            }
        ]

    async def get_embedding_async(self, text: str, **_: Any) -> list[float]:
        seed = float(len(text) % 10) / 10.0
        return [seed for _ in range(16)]

    async def extract_context_insights_async(self, text: str, **_: Any) -> dict:
        return {"summary": text[:120], "keywords": []}

    async def find_relevant_context(self, **_: Any) -> list[dict]:
        return []

    async def evaluate_with_voting_async(self, signal_content: str, impact_score: float, **_: Any) -> dict:
        is_strategic = impact_score >= 0.8
        return {
            "is_strategic": is_strategic,
            "confidence": min(1.0, max(0.2, impact_score)),
            "decision_proposal": {
                "summary": signal_content[:120],
                "action": "follow_up" if is_strategic else "observe",
            },
            "tasks": [],
            "_voting": {
                "enabled": False,
                "models_used": [],
                "strategic_votes": 0,
                "total_votes": 0,
                "unanimous": True,
            },
        }

    async def audit_code_change_async(
        self,
        task_name: str,
        commit_message: str,
        code_diff: str,
        **_: Any,
    ) -> dict:
        passed = bool(task_name and commit_message and code_diff is not None)
        return {
            "passed": passed,
            "reason": "basic_stub_check_passed" if passed else "invalid_input",
        }


llm_service = LLMService()