"""OpenAI embedding service using text-embedding-3-small.

All strategies share the same embedding model (fixed, not configurable)
to ensure fair comparison.  Batch support up to 2048 texts per call.
"""

from __future__ import annotations

import logging

from openai import OpenAI

from src.core.config import settings
from src.core.dependencies import get_openai_client
from src.core.exceptions import EmbeddingError

logger = logging.getLogger(__name__)

# OpenAI batch limit for text-embedding-3-small
_BATCH_SIZE = 2048


def embed_texts(texts: list[str]) -> list[list[float]]:
    """Embed a list of texts and return their vectors.

    For the contextual_structure strategy, these are the enriched texts
    (not raw content) — this is by design (ADR 0011).

    Args:
        texts: List of strings to embed.

    Returns:
        List of embedding vectors (same order as input).

    Raises:
        EmbeddingError: If the OpenAI API call fails.
    """
    if not texts:
        return []

    client = get_openai_client()
    all_embeddings: list[list[float]] = []

    try:
        for start in range(0, len(texts), _BATCH_SIZE):
            batch = texts[start:start + _BATCH_SIZE]
            response = client.embeddings.create(
                model=settings.embedding_model,
                input=batch,
            )
            # Sort by index to guarantee order matches input
            sorted_data = sorted(response.data, key=lambda x: x.index)
            all_embeddings.extend([item.embedding for item in sorted_data])

            logger.debug(
                "Embedded batch %d-%d (%d texts)",
                start, start + len(batch), len(batch),
            )

        return all_embeddings
    except Exception as exc:
        raise EmbeddingError(f"Embedding failed: {exc}") from exc


def embed_single(text: str) -> list[float]:
    """Embed a single text (convenience wrapper)."""
    results = embed_texts([text])
    return results[0]