import logging

from langchain_openai import OpenAIEmbeddings

from kbqa.api.errors import AppError
from kbqa.config import Settings, validate_live_settings

logger = logging.getLogger(__name__)


class EmbeddingProvider:
    def __init__(self, settings: Settings) -> None:
        self.settings = settings
        self._client: OpenAIEmbeddings | None = None

    def _get_client(self) -> OpenAIEmbeddings:
        if self._client is None:
            validate_live_settings(self.settings)
            self._client = OpenAIEmbeddings(
                api_key=self.settings.dashscope_api_key,
                base_url=self.settings.dashscope_base_url,
                model=self.settings.embedding_model,
                dimensions=self.settings.embedding_dim,
                chunk_size=10,
                max_retries=2,
                timeout=60.0,
                check_embedding_ctx_length=False,
            )
        return self._client

    def _validate_vectors(self, vectors: list[list[float]]) -> list[list[float]]:
        if any(len(vector) != self.settings.embedding_dim for vector in vectors):
            raise AppError(
                "EMBEDDING_DIMENSION_MISMATCH",
                "Embedding 返回的向量维度不正确",
                502,
                retryable=True,
            )
        return vectors

    async def embed_documents(self, texts: list[str]) -> list[list[float]]:
        try:
            vectors = await self._get_client().aembed_documents(texts)
        except AppError:
            raise
        except Exception as exc:
            logger.exception("Embedding document request failed error_type=%s", type(exc).__name__)
            raise AppError(
                "EMBEDDING_UPSTREAM_ERROR",
                "文档向量化失败，请稍后重试",
                502,
                retryable=True,
            ) from exc
        return self._validate_vectors(vectors)

    async def embed_query(self, text: str) -> list[float]:
        try:
            vector = await self._get_client().aembed_query(text)
        except AppError:
            raise
        except Exception as exc:
            logger.exception("Embedding query request failed error_type=%s", type(exc).__name__)
            raise AppError(
                "EMBEDDING_UPSTREAM_ERROR",
                "查询向量化失败，请稍后重试",
                502,
                retryable=True,
            ) from exc
        return self._validate_vectors([vector])[0]