"""
Mem0 记忆层初始化与健康检查。
启动时验证 Ollama 可达和模型可用，失败则抛出 RuntimeError 阻止后端启动。
"""

import logging
import httpx
from mem0 import Memory

from app.config import (
    get_deepseek_api_key,
    get_ollama_base_url,
    get_mem0_embedding_model,
)

logger = logging.getLogger(__name__)

_memory: Memory | None = None


def _check_ollama_health() -> None:
    """检查 Ollama 服务是否可达，以及 embedding 模型是否已安装。"""
    base_url = get_ollama_base_url()
    model_name = get_mem0_embedding_model()

    try:
        resp = httpx.get(f"{base_url}/api/tags", timeout=5)
        resp.raise_for_status()
    except (httpx.ConnectError, httpx.TimeoutException, httpx.HTTPStatusError) as e:
        raise RuntimeError(
            f"无法连接 Ollama 服务（{base_url}）。"
            f"请确认 Ollama 已启动：启动方式参见 https://ollama.com\n"
            f"原始错误：{e}"
        ) from e

    models = resp.json().get("models", [])
    installed = [m.get("name", "").split(":")[0] for m in models]
    if model_name not in installed:
        raise RuntimeError(
            f"Ollama 已运行，但未找到 embedding 模型 '{model_name}'。\n"
            f"已安装的模型：{installed}\n"
            f"请运行：ollama pull {model_name}"
        )

    logger.info("Ollama 健康检查通过：%s 模型可用", model_name)


def init_memory() -> Memory:
    """初始化 Mem0 Memory 单例。首次调用时执行健康检查。"""
    global _memory
    if _memory is not None:
        return _memory

    _check_ollama_health()

    deepseek_key = get_deepseek_api_key()
    if not deepseek_key:
        raise RuntimeError(
            "DEEPSEEK_API_KEY 未配置。Mem0 需要该 key 进行事实提取。\n"
            "请在 .env 中设置 DEEPSEEK_API_KEY"
        )

    config = {
        "llm": {
            "provider": "deepseek",
            "config": {
                "model": "deepseek-chat",
                "temperature": 0.1,
                "max_tokens": 1500,
                "api_key": deepseek_key,
            },
        },
        "embedder": {
            "provider": "ollama",
            "config": {
                "model": get_mem0_embedding_model(),
                "ollama_base_url": get_ollama_base_url(),
            },
        },
        "vector_store": {
            "provider": "qdrant",
            "config": {
                "collection_name": "epeekit_memories",
                "embedding_model_dims": 768,
                "path": "./data/qdrant",
            },
        },
    }

    _memory = Memory.from_config(config)
    logger.info("Mem0 记忆层初始化成功")
    return _memory


def get_memory() -> Memory:
    """获取已初始化的 Memory 实例。未初始化时自动调用 init_memory()。"""
    if _memory is None:
        return init_memory()
    return _memory