"""OpenAI embedding client wrapper."""

import logging
from functools import lru_cache
from typing import Annotated

from fastapi import Depends
from openai import AsyncOpenAI, AuthenticationError, RateLimitError, APIConnectionError, APIError

from app.config import settings

logger = logging.getLogger(__name__)


class EmbeddingError(Exception):
    """Base exception for embedding operations."""

    def __init__(self, message: str, status_code: int = 500):
        self.message = message
        self.status_code = status_code
        super().__init__(message)


class EmbeddingClient:
    """Async wrapper for OpenAI embeddings API."""

    def __init__(self, api_key: str, model: str = "text-embedding-3-small"):
        """Initialize the embedding client.

        Args:
            api_key: OpenAI API key
            model: Embedding model identifier
        """
        self.client = AsyncOpenAI(api_key=api_key)
        self.model = model
        self.dimensions = 1536  # text-embedding-3-small dimension

    async def embed(self, text: str) -> list[float]:
        """Generate embedding for a single text.

        Args:
            text: Text to embed

        Returns:
            Embedding vector (1536 dimensions)

        Raises:
            EmbeddingError: If embedding generation fails
        """
        try:
            response = await self.client.embeddings.create(
                model=self.model,
                input=text,
            )
            return response.data[0].embedding

        except AuthenticationError as e:
            raise EmbeddingError(f"OpenAI authentication failed: {e.message}", 401)
        except RateLimitError as e:
            raise EmbeddingError(f"OpenAI rate limit exceeded: {e.message}", 429)
        except APIConnectionError as e:
            raise EmbeddingError(f"Could not connect to OpenAI: {str(e)}", 503)
        except APIError as e:
            raise EmbeddingError(f"OpenAI API error: {e.message}", e.status_code or 500)

    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
        """Generate embeddings for multiple texts.

        Args:
            texts: List of texts to embed

        Returns:
            List of embedding vectors

        Raises:
            EmbeddingError: If embedding generation fails
        """
        if not texts:
            return []

        try:
            response = await self.client.embeddings.create(
                model=self.model,
                input=texts,
            )
            # Sort by index to ensure correct ordering
            sorted_embeddings = sorted(response.data, key=lambda x: x.index)
            return [item.embedding for item in sorted_embeddings]

        except AuthenticationError as e:
            raise EmbeddingError(f"OpenAI authentication failed: {e.message}", 401)
        except RateLimitError as e:
            raise EmbeddingError(f"OpenAI rate limit exceeded: {e.message}", 429)
        except APIConnectionError as e:
            raise EmbeddingError(f"Could not connect to OpenAI: {str(e)}", 503)
        except APIError as e:
            raise EmbeddingError(f"OpenAI API error: {e.message}", e.status_code or 500)


@lru_cache()
def get_embedding_client() -> EmbeddingClient:
    """Get cached embedding client instance."""
    return EmbeddingClient(
        api_key=settings.openai_api_key,
        model=settings.embedding_model,
    )


EmbeddingClientDependency = Annotated[EmbeddingClient, Depends(get_embedding_client)]