import hashlib
import math
from dataclasses import dataclass

import httpx

from app.core.config import settings
from app.core.exceptions import AppError


@dataclass(frozen=True)
class EmbeddingUsage:
    """Embedding 调用指标：记录批量向量化的模型和 token 用量。"""

    model: str
    total_tokens: int | None = None


class OpenAICompatibleEmbeddingClient:
    """Embedding Adapter：封装 OpenAI-compatible embeddings 接口，并提供稳定 mock。"""

    @property
    def is_mock_mode(self) -> bool:
        """模式判断：没有 API Key 或显式 mock provider 时使用确定性本地向量。"""
        return settings.embedding_provider.lower() == "mock" or not settings.embedding_api_key

    async def embed_texts(self, texts: list[str]) -> tuple[list[list[float]], EmbeddingUsage]:
        """文本向量化：对文本批量生成 embedding，返回与输入顺序一致的向量列表。"""
        if not texts:
            return [], EmbeddingUsage(model=settings.embedding_model, total_tokens=0)
        if self.is_mock_mode:
            return [self._mock_vector(text) for text in texts], EmbeddingUsage(model=f"mock-{settings.embedding_model}")

        try:
            async with httpx.AsyncClient(timeout=settings.embedding_timeout_seconds) as client:
                resp = await client.post(
                    self._embeddings_url(),
                    headers={"Authorization": f"Bearer {settings.embedding_api_key}"},
                    json={"model": settings.embedding_model, "input": texts},
                )
                resp.raise_for_status()
                payload = resp.json()
        except (httpx.TimeoutException, httpx.HTTPError, ValueError) as exc:
            raise AppError("EMBEDDING_CALL_FAILED", "embedding service call failed", 502) from exc

        try:
            vectors = [item["embedding"] for item in sorted(payload["data"], key=lambda item: item.get("index", 0))]
            self._validate_vectors(vectors)
            usage = payload.get("usage") or {}
            return vectors, EmbeddingUsage(model=settings.embedding_model, total_tokens=usage.get("total_tokens"))
        except (KeyError, TypeError, ValueError) as exc:
            raise AppError("EMBEDDING_RESPONSE_INVALID", "embedding response format invalid", 502) from exc

    def _embeddings_url(self) -> str:
        """接口地址：兼容 base URL 和完整 /embeddings URL 两种写法。"""
        base_url = settings.embedding_base_url.rstrip("/")
        if base_url.endswith("/embeddings"):
            return base_url
        return f"{base_url}/embeddings"

    def _validate_vectors(self, vectors: list[list[float]]) -> None:
        """向量校验：确保向量维度与 Milvus collection 维度一致。"""
        for vector in vectors:
            if len(vector) != settings.embedding_dim:
                raise ValueError(f"embedding dimension mismatch: {len(vector)} != {settings.embedding_dim}")

    def _mock_vector(self, text: str) -> list[float]:
        """Mock向量：基于文本哈希生成稳定归一化向量，便于本地和CI测试。"""
        values: list[float] = []
        seed = hashlib.sha256(f"{settings.embedding_model}:{text}".encode("utf-8")).digest()
        current = seed
        while len(values) < settings.embedding_dim:
            current = hashlib.sha256(current).digest()
            values.extend((byte / 127.5) - 1.0 for byte in current)
        vector = values[: settings.embedding_dim]
        norm = math.sqrt(sum(item * item for item in vector)) or 1.0
        return [item / norm for item in vector]