"""AI 调用工具封装

每个协程独立重试，失败后 sleep 200ms 再试，直到成功。
不再有全局门闸，互不影响。
"""

import asyncio
import re
from datetime import datetime
from typing import Any, Optional

from json_repair import repair_json
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import SystemMessage, HumanMessage
from snowflake import SnowflakeGenerator

from app.core.logger import log

# AI 日志专用雪花ID
_log_id_gen = SnowflakeGenerator(instance=2)

# 匹配 <think>任意内容</think>，用于剥离推理模型的思考过程
_THINK_RE = re.compile(r"<think>.*?</think>", re.DOTALL | re.IGNORECASE)

# 匹配 ```json ... ``` 代码块，提取中间的 JSON 内容
_CODE_BLOCK_RE = re.compile(r"```(?:json\w*)?\s*\n?(.*?)\n?\s*```", re.DOTALL | re.IGNORECASE)


def parse_llm_json(text: str) -> Any:
    """解析 AI 输出的 JSON，自动去除思考标签、markdown 代码块，容错处理"""
    # 1. 去掉 <think>...</think> 思考内容
    cleaned = _THINK_RE.sub("", text).strip()
    # 2. 如果有 ```json ... ``` 代码块，只取代码块里的内容
    match = _CODE_BLOCK_RE.search(cleaned)
    if match:
        cleaned = match.group(1).strip()
    # 3. repair_json 容错解析：修复不规范的 JSON（多余逗号、缺引号、非法转义等）
    return repair_json(cleaned, return_objects=True)


async def ai_chat(llm: BaseChatModel, system_prompt: str, user_message: str, scene: Optional[str] = None) -> str:
    """异步调用 LLM，返回原始文本。失败后独立重试直到成功。"""
    attempt = 0

    while True:
        attempt += 1
        try:
            messages = [
                SystemMessage(content=system_prompt),
                HumanMessage(content=user_message),
            ]
            response = await llm.ainvoke(messages)
            content = response.content
            # 记录 AI 调用日志（失败不影响主流程）
            if scene:
                await _save_call_log(scene, system_prompt, user_message, content)
            return content
        except Exception as e:
            log.warning("AI 调用失败(第{}次), 200ms 后重试: {}", attempt, e)
            await asyncio.sleep(0.2)


async def ai_chat_json(llm: BaseChatModel, system_prompt: str, user_message: str, scene: Optional[str] = None) -> Any:
    """异步调用 LLM，返回解析后的 JSON 对象"""
    raw = await ai_chat(llm, system_prompt, user_message, scene=scene)
    if not raw or not raw.strip():
        log.warning("AI 返回为空")
        return None
    try:
        return parse_llm_json(raw)
    except Exception as e:
        log.warning("AI JSON 解析失败: {}, raw={}", e, raw[:200])
        return None


async def _save_call_log(scene: str, system_prompt: str, user_message: str, response: Optional[str]) -> None:
    """异步写入 AI 调用日志到 PG，失败只 warning 不影响主流程"""
    try:
        from app.core.database import PgSession
        from sqlalchemy import insert
        from app.models.pg.ai_call_log import AiCallLog

        async with PgSession() as pg:
            await pg.execute(
                insert(AiCallLog).values(
                    id=next(_log_id_gen),
                    scene=scene,
                    system_prompt=system_prompt,
                    user_message=user_message,
                    response=response,
                    created_at=datetime.now(),
                )
            )
            await pg.commit()
    except Exception as e:
        log.warning("AI 调用日志写入失败: {}", e)