"""嵌入服务 — Embedding 适配器外观。

用法::

    from app.ai_provider.embedding import EmbeddingService

    service = EmbeddingService(manager)
    result = await service.embed("要嵌入的文本")
    results = await service.embed(["文本1", "文本2"])
"""

from __future__ import annotations

from app.ai_provider.embedding.base import (
    EmbeddingProtocol,
    EmbeddingRequest,
    EmbeddingResponse,
    EmbeddingResult,
    EmbeddingServiceError,
)
from app.ai_provider.types.errors import AIProviderError


class EmbeddingService:
    """外观类，负责解析 Embedding 模型并委托给 ProviderManager。

    接受 ProviderManager 或实现了以下接口的适配器：
    - ``.model_registry`` → 具有 ``list_by_capability(capability)`` 方法
    - ``.create_embedding(name)`` → 返回 EmbeddingProtocol 实例
    """

    def __init__(self, manager) -> None:
        self._manager = manager
        self._adapter: EmbeddingProtocol | None = None

    # ── Registry 兼容适配 ─────────────────────────────────────────

    @property
    def _registry(self):
        mgr = self._manager
        if hasattr(mgr, "model_registry"):
            return mgr.model_registry
        return mgr

    @property
    def _factory(self):
        mgr = self._manager
        if hasattr(mgr, "create_embedding"):
            return mgr
        return None

    # ── Embedding ──────────────────────────────────────────────────

    @property
    def adapter(self) -> EmbeddingProtocol:
        """解析并缓存 Embedding 适配器。"""
        if self._adapter is None:
            self._adapter = self._resolve()
        return self._adapter

    async def embed(
        self,
        input: str | list[str],
        model_hint: str | None = None,
        dimensions: int | None = None,
    ) -> EmbeddingResponse:
        """快捷方法：构造请求并调用 Embedding 适配器。"""
        model_name = self._resolve_model_name(model_hint)
        # 优先从模型配置读 dimensions
        effective_dimensions = dimensions
        if effective_dimensions is None:
            effective_dimensions = self._resolve_model_dimensions(model_hint)
        request = EmbeddingRequest(
            input=input if isinstance(input, list) else [input],
            model=model_name,
            dimensions=effective_dimensions,
        )
        return await self.adapter.embed(request)

    # ── 解析 ───────────────────────────────────────────────────────

    def _resolve(self) -> EmbeddingProtocol:
        """查找第一个活跃的 Embedding 模型并通过工厂创建。"""
        models = self._list_capability_models("embedding")
        if not models:
            raise AIProviderError(
                status_code=503,
                code="embedding_no_model",
                message="No embedding model configured in the model registry.",
            )
        factory = self._factory
        if factory is None:
            raise AIProviderError(
                status_code=501,
                code="embedding_no_factory",
                message="Embedding adapter factory not available (provider manager missing create_embedding).",
            )
        return factory.create_embedding(models[0].name)

    def _resolve_model_name(self, model_hint: str | None) -> str:
        models = self._list_capability_models("embedding")
        if model_hint:
            return model_hint
        if models:
            return models[0].name
        return ""

    def _resolve_model_dimensions(self, model_hint: str | None) -> int | None:
        """从模型配置读取向量维度。"""
        models = self._list_capability_models("embedding")
        if model_hint:
            for m in models:
                if m.name == model_hint:
                    return m.dimensions
        if models:
            return models[0].dimensions
        return None

    def _list_capability_models(self, tag: str):
        reg = self._registry
        if hasattr(reg, "list_capability_models"):
            return reg.list_capability_models(tag)
        if hasattr(reg, "list_by_capability"):
            return reg.list_by_capability(tag)
        return []

    # ── 生命周期 ────────────────────────────────────────────────────

    async def close(self) -> None:
        if self._adapter is not None:
            close = getattr(self._adapter, "close", None)
            if close is not None:
                await close()

    async def __aenter__(self) -> EmbeddingService:
        return self

    async def __aexit__(self, *exc_info: object) -> None:
        await self.close()


__all__ = [
    "EmbeddingProtocol",
    "EmbeddingRequest",
    "EmbeddingResponse",
    "EmbeddingResult",
    "EmbeddingService",
    "EmbeddingServiceError",
]