from __future__ import annotations

from .shared import *

class LLMTaskHelpersMixin:
    async def generate_task_breakdown_async(self, description: str, **kwargs: Any) -> list[dict]:
        if not description.strip():
            return []
        prompt = (
            "请将以下需求拆解为 5-8 条执行任务，返回 JSON 数组，"
            "每项格式: {\"task_name\":\"\",\"type\":\"backend|frontend|database|ai|devops\","
            "\"estimated_hours\":4,\"priority\":\"high|medium|low\"}。\n"
            f"需求：{description[:1200]}"
        )
        text = await self.generate(prompt, function_name="generate_task_breakdown", **kwargs)
        rows = self._parse_json_array(text)
        if rows:
            return rows
        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, **kwargs: Any) -> list[float]:
        db = kwargs.get("db") or kwargs.get("db_session")
        tenant_id = int(kwargs.get("tenant_id") or 1)
        if db is None:
            async with AsyncSessionLocal() as session:
                return await self.get_embedding_async(text, db=session, tenant_id=tenant_id)
        try:
            engine = await self._resolve_active_engine(db, tenant_id=tenant_id)
            provider = str(engine.get("provider") or "")
            if provider == "openai":
                endpoint = f"{str(engine.get('base_url') or '').rstrip('/')}/embeddings"
                headers = {
                    "Authorization": f"Bearer {str(engine.get('api_key') or '').strip()}",
                    "Content-Type": "application/json",
                }
                payload = {
                    "model": str(engine.get("model") or "text-embedding-3-small"),
                    "input": text,
                }
                async with httpx.AsyncClient(timeout=self.request_timeout, trust_env=False) as client:
                    resp = await client.post(endpoint, headers=headers, json=payload)
                    resp.raise_for_status()
                    data = resp.json()
                return list(data.get("data", [{}])[0].get("embedding") or [])
        except Exception:
            pass
        # DeepSeek/GLM 及失败场景：回退伪向量，保持功能可用。
        seed = hashlib.md5(text.encode("utf-8")).digest()
        return [((seed[i % 16] / 255.0) * 2 - 1) for i in range(64)]

    async def extract_context_insights_async(self, text: str, **kwargs: Any) -> dict:
        prompt = (
            "请提炼以下技术文档上下文的关键结论，返回 JSON："
            "{\"summary\":\"\",\"keywords\":[\"\",\"\"],\"risks\":[\"\",\"\"]}\n"
            f"上下文：{text[:2000]}"
        )
        raw = await self.generate(prompt, function_name="extract_context_insights", **kwargs)
        parsed = self._parse_json_object(raw)
        if parsed:
            return parsed
        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",
        }