"""AI Agent 分析模块 - 调用 OpenAI 兼容接口分析诊断结果 (支持流式输出)"""
import json
import httpx
from backend.config import AI_API_BASE, AI_API_KEY, AI_MODEL

SYSTEM_PROMPT = """你是一位资深的 Kubernetes 运维专家和 SRE 工程师。你的任务是分析 K8S 集群诊断报告，找出根本原因并给出修复建议。

分析要求：
1. **问题定位**：明确指出每个问题的根本原因（Root Cause），不要只描述表面现象
2. **影响评估**：说明每个问题对业务的潜在影响
3. **修复方案**：给出具体的修复命令或操作步骤（kubectl 命令、YAML 修改等）
4. **优先级排序**：按紧急程度排序，先处理影响最大的问题
5. **预防建议**：给出避免类似问题再次发生的建议

输出格式：使用清晰的 Markdown，包含标题、列表、代码块。语言使用中文。"""

_HEADERS = {
    "Authorization": f"Bearer {AI_API_KEY}",
    "Content-Type": "application/json",
}


async def _stream_chat(messages: list[dict]):
    """通用流式请求 - yield 文本 chunk"""
    # connect=10s 快速失败; read=90s 防止 AI 服务无响应时前端无限卡死
    timeout = httpx.Timeout(90, connect=10)
    async with httpx.AsyncClient(timeout=timeout) as client:
        async with client.stream(
            "POST",
            f"{AI_API_BASE}/chat/completions",
            headers=_HEADERS,
            json={
                "model": AI_MODEL,
                "messages": messages,
                "temperature": 0.3,
                "max_tokens": 4096,
                "stream": True,
            },
        ) as resp:
            # 非 2xx 直接抛异常 (由上层转成 error 事件)
            resp.raise_for_status()

            # 有些服务认证失败时返回 200 + JSON 错误体 (非 SSE)，需要识别
            ctype = resp.headers.get("content-type", "")
            if "text/event-stream" not in ctype and "application/json" in ctype:
                body = await resp.aread()
                try:
                    err = json.loads(body.decode())
                    raise httpx.HTTPStatusError(
                        f"AI 服务返回错误: {err.get('error', body.decode()[:300])}",
                        request=resp.request,
                        response=resp,
                    )
                except (json.JSONDecodeError, UnicodeDecodeError):
                    raise httpx.HTTPStatusError(
                        f"AI 服务返回非流式响应: {body.decode()[:300]}",
                        request=resp.request,
                        response=resp,
                    )

            async for line in resp.aiter_lines():
                if not line.startswith("data: "):
                    continue
                data_str = line[6:].strip()
                if data_str == "[DONE]":
                    break
                try:
                    chunk = json.loads(data_str)
                    delta = chunk["choices"][0].get("delta", {})
                    content = delta.get("content", "")
                    if content:
                        yield content
                except (json.JSONDecodeError, KeyError, IndexError):
                    continue


async def analyze_with_ai_stream(report: str, question: str = ""):
    """流式 AI 分析诊断报告 - yield 文本 chunk"""
    user_msg = f"以下是 K8S 集群诊断报告，请进行详细分析：\n\n{report}"
    if question:
        user_msg += f"\n\n用户额外问题：{question}"

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_msg},
    ]
    async for chunk in _stream_chat(messages):
        yield chunk


async def chat_with_ai_stream(messages: list[dict], context: str = ""):
    """流式多轮对话 - yield 文本 chunk"""
    msgs = [{"role": "system", "content": SYSTEM_PROMPT}]
    if context:
        msgs.append({"role": "system", "content": f"当前诊断上下文：\n{context}"})
    msgs.extend(messages)
    async for chunk in _stream_chat(msgs):
        yield chunk


async def analyze_with_ai(report: str, question: str = "") -> dict:
    """非流式 AI 分析 (兼容旧接口)"""
    try:
        content = ""
        async for chunk in analyze_with_ai_stream(report, question):
            content += chunk
        return {"success": True, "analysis": content, "model": AI_MODEL}
    except httpx.HTTPStatusError as e:
        return {"success": False, "analysis": f"AI 接口返回错误: {e.response.status_code} - {e.response.text[:500]}", "model": AI_MODEL}
    except httpx.ConnectError:
        return {"success": False, "analysis": f"无法连接 AI 服务 ({AI_API_BASE})，请检查 .env 中的 AI_API_BASE 配置", "model": AI_MODEL}
    except Exception as e:
        return {"success": False, "analysis": f"AI 分析失败: {str(e)}", "model": AI_MODEL}


async def chat_with_ai(messages: list[dict], context: str = "") -> dict:
    """非流式多轮对话 (兼容旧接口)"""
    try:
        content = ""
        async for chunk in chat_with_ai_stream(messages, context):
            content += chunk
        return {"success": True, "reply": content}
    except Exception as e:
        return {"success": False, "reply": f"AI 对话失败: {str(e)}"}