"""Synchronous embedding client using the OpenAI-compatible /v1/embeddings API.

Uses ``openai.OpenAI`` (sync) since Celery tasks run synchronously.
Handles connection failures gracefully — embedding is non-blocking for the pipeline.
"""

from __future__ import annotations

import logging

import openai

from config import Settings

logger = logging.getLogger(__name__)


class EmbeddingClient:
    """Sync embedding client backed by an OpenAI-compatible /v1/embeddings endpoint."""

    def __init__(self, settings: Settings) -> None:
        self.settings = settings
        self._client = openai.OpenAI(
            base_url=settings.embedding_api_url,
            api_key=settings.llm_api_key,
        )

    def embed(self, texts: list[str]) -> list[list[float]]:
        """Generate embedding vectors for a batch of texts.

        Parameters
        ----------
        texts:
            List of strings to embed.

        Returns
        -------
        list[list[float]]
            Embedding vectors. Returns empty list on connection/timeout errors
            so the pipeline can continue without embeddings.
        """
        if not texts:
            return []

        try:
            response = self._client.embeddings.create(
                model=self.settings.embedding_model,
                input=texts,
            )
        except (openai.APIConnectionError, openai.APITimeoutError) as exc:
            logger.warning(
                "Embedding API unavailable (%s: %s). Skipping %d texts.",
                type(exc).__name__,
                exc,
                len(texts),
            )
            return []
        except openai.APIError as exc:
            logger.warning(
                "Embedding API error (%s: %s). Skipping %d texts.",
                type(exc).__name__,
                exc,
                len(texts),
            )
            return []

        vectors = [item.embedding for item in response.data]

        # Validate dimensions
        expected_dim = self.settings.embedding_dimensions
        for i, vec in enumerate(vectors):
            if len(vec) != expected_dim:
                logger.warning(
                    "Embedding dimension mismatch at index %d: expected %d, got %d. "
                    "Returning empty list.",
                    i,
                    expected_dim,
                    len(vec),
                )
                return []

        logger.info(
            "Generated %d embeddings (dim=%d) using model=%s",
            len(vectors),
            expected_dim,
            self.settings.embedding_model,
        )
        return vectors