from dataclasses import dataclass
import json
import os
from urllib.error import URLError
from urllib.request import Request, urlopen


class LLMConfigurationError(ValueError):
    pass


class EmbeddingConfigurationError(ValueError):
    pass


@dataclass
class LLMResponse:
    content: str = ""
    model_name: str = ""
    success: bool = True
    error: Exception | None = None


class MockLLMProvider:
    def __init__(self, model_name: str = "mock-model"):
        self.model_name = model_name or "mock-model"

    def generate(self, messages: list[dict], response_format: dict | None = None) -> LLMResponse:
        # Mock Provider 的职责是让页面和测试在未接入真实模型时也能闭环。
        # 因此这里直接返回稳定 JSON，方便后续统一走结构化解析逻辑。
        user_content = ""
        for message in reversed(messages):
            if message.get("role") == "user":
                user_content = message.get("content", "")
                break
        return LLMResponse(
            content=json.dumps(
                {
                    "answer": f"模拟回答：{user_content}",
                    "confidence": "medium",
                    "references": [],
                },
                ensure_ascii=False,
            ),
            model_name=self.model_name,
            success=True,
        )


class OpenAICompatibleProvider:
    def __init__(self, api_key: str, base_url: str, model_name: str):
        self.api_key = api_key
        self.base_url = base_url
        self.model_name = model_name

    def generate(self, messages: list[dict], response_format: dict | None = None) -> LLMResponse:
        if not self.api_key:
            return LLMResponse(
                model_name=self.model_name,
                success=False,
                error=LLMConfigurationError("LLM_API_KEY 未配置，无法调用 OpenAI 兼容模型接口"),
            )
        payload = {
            "model": self.model_name,
            "messages": messages,
        }
        if response_format:
            payload["response_format"] = response_format
        try:
            data = _post_json(
                base_url=self.base_url,
                endpoint="chat/completions",
                api_key=self.api_key,
                payload=payload,
            )
            choice = data.get("choices", [{}])[0]
            content = choice.get("message", {}).get("content", "")
            return LLMResponse(
                content=content,
                model_name=data.get("model", self.model_name),
                success=True,
            )
        except Exception as exc:
            return LLMResponse(model_name=self.model_name, success=False, error=exc)


class OpenAICompatibleEmbeddingProvider:
    def __init__(self, api_key: str, base_url: str, model_name: str):
        self.api_key = api_key
        self.base_url = base_url
        self.model_name = model_name

    def embed_texts(self, texts: list[str]) -> list[list[float]]:
        if not self.api_key:
            raise EmbeddingConfigurationError("EMBEDDING_API_KEY 未配置，无法调用 OpenAI 兼容 Embedding 接口")
        data = _post_json(
            base_url=self.base_url,
            endpoint="embeddings",
            api_key=self.api_key,
            payload={"model": self.model_name, "input": texts},
        )
        return [item.get("embedding", []) for item in data.get("data", [])]


def _post_json(base_url: str, endpoint: str, api_key: str, payload: dict) -> dict:
    url = f"{base_url.rstrip('/')}/{endpoint}"
    request = Request(
        url,
        data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json",
        },
        method="POST",
    )
    try:
        with urlopen(request, timeout=60) as response:
            return json.loads(response.read().decode("utf-8"))
    except URLError as exc:
        raise RuntimeError(f"OpenAI 兼容接口调用失败：{exc}") from exc


def create_llm_provider(config: dict | None = None):
    config = config or {}
    provider_name = config.get("LLM_PROVIDER")
    if not provider_name:
        provider_name = "openai_compatible" if config.get("LLM_API_KEY") else "mock"
    model_name = config.get("LLM_MODEL", "mock-model")
    if provider_name == "mock":
        return MockLLMProvider(model_name=model_name)
    return OpenAICompatibleProvider(
        api_key=config.get("LLM_API_KEY", ""),
        base_url=config.get("LLM_BASE_URL", "https://api.openai.com/v1"),
        model_name=model_name,
    )


def create_embedding_provider(config: dict | None = None):
    config = config or {}
    return OpenAICompatibleEmbeddingProvider(
        api_key=config.get("EMBEDDING_API_KEY", config.get("LLM_API_KEY", "")),
        base_url=config.get("EMBEDDING_BASE_URL", config.get("LLM_BASE_URL", "https://api.openai.com/v1")),
        model_name=config.get("EMBEDDING_MODEL", "text-embedding-3-small"),
    )


def get_runtime_llm_config(overrides: dict | None = None) -> dict:
    """
    从环境变量读取运行时配置。

    Agent Core 通过这层读取模型配置，避免直接依赖 Django settings，
    这样本模块在独立脚本、测试和 Django 中都能复用。
    """
    config = {
        "LLM_PROVIDER": os.environ.get("LLM_PROVIDER", ""),
        "LLM_API_KEY": os.environ.get("LLM_API_KEY", ""),
        "LLM_BASE_URL": os.environ.get("LLM_BASE_URL", "https://api.openai.com/v1"),
        "LLM_MODEL": os.environ.get("LLM_MODEL", "mock-model"),
    }
    if overrides:
        config.update(overrides)
    return config