import asyncio
import logging
from functools import lru_cache

from ollama import AsyncClient

from app.config import get_settings


logger = logging.getLogger(__name__)


_BACKOFF_SECONDS: tuple[int, ...] = (1, 2, 4)


class EmbeddingError(Exception):
    pass


@lru_cache(maxsize=1)
def _client() -> AsyncClient:
    return AsyncClient(host=get_settings().ollama_url)


async def embed_texts(texts: list[str], model: str) -> list[list[float]]:
    """Embed each text via Ollama. Retries individual calls 3x with backoff."""
    vectors: list[list[float]] = []
    for text in texts:
        vec = await _embed_one(text, model)
        vectors.append(vec)
    return vectors


async def _embed_one(text: str, model: str) -> list[float]:
    last_err: Exception | None = None
    client = _client()
    for attempt in range(len(_BACKOFF_SECONDS) + 1):
        try:
            response = await client.embeddings(model=model, prompt=text)
            return list(response["embedding"])
        except Exception as exc:
            last_err = exc
            if attempt < len(_BACKOFF_SECONDS):
                wait = _BACKOFF_SECONDS[attempt]
                logger.warning(
                    "ollama embed retry",
                    extra={"event": "embed_retry", "attempt": attempt + 1, "wait_s": wait, "error": str(exc)},
                )
                await asyncio.sleep(wait)
    raise EmbeddingError("embed failed after retries") from last_err


async def embedding_dimension(model: str) -> int:
    """Probe a single embedding to discover the model's vector dimension."""
    vec = await _embed_one("dimension probe", model)
    return len(vec)