from dataclasses import dataclass

import httpx

from app.application.retrieval import build_hash_embedding
from app.bootstrap.settings import KnowledgeServiceSettings


class EmbeddingProviderError(Exception):
    pass


@dataclass(frozen=True)
class EmbeddingResult:
    embedding: list[float]
    model: str
    provider: str


class EmbeddingService:
    def __init__(self, *, settings: KnowledgeServiceSettings) -> None:
        self.settings = settings

    def embed_text(self, text: str) -> EmbeddingResult:
        if self.settings.embedding_provider == "http":
            try:
                return self._embed_with_http(text)
            except EmbeddingProviderError:
                if not self.settings.embedding_fallback_to_local:
                    raise
        return self._embed_with_local_hash(text)

    def _embed_with_local_hash(self, text: str) -> EmbeddingResult:
        return EmbeddingResult(
            embedding=build_hash_embedding(
                text,
                dimensions=self.settings.embedding_dimensions),
            model=self.settings.embedding_model,
            provider="local-hash")

    def _embed_with_http(self, text: str) -> EmbeddingResult:
        if not self.settings.embedding_base_url:
            raise EmbeddingProviderError("embedding_base_url is required for http provider")

        headers: dict[str, str] = {}
        if self.settings.embedding_api_key:
            headers["Authorization"] = f"Bearer {self.settings.embedding_api_key}"

        try:
            with httpx.Client(timeout=self.settings.embedding_timeout_seconds) as client:
                response = client.post(
                    f"{self.settings.embedding_base_url.rstrip('/')}/embeddings",
                    headers=headers,
                    json={"model": self.settings.embedding_model, "input": text})
                response.raise_for_status()
                payload = response.json()
        except (httpx.HTTPError, ValueError) as exc:
            raise EmbeddingProviderError(f"http embedding request failed: {exc}") from exc

        embedding = _read_openai_embedding(payload)
        if embedding is None:
            raise EmbeddingProviderError("embedding response missing data[0].embedding")
        return EmbeddingResult(
            embedding=embedding,
            model=self.settings.embedding_model,
            provider="http")


def _read_openai_embedding(payload: object) -> list[float] | None:
    if not isinstance(payload, dict):
        return None
    data = payload.get("data")
    if not isinstance(data, list) or not data:
        return None
    first_item = data[0]
    if not isinstance(first_item, dict):
        return None
    embedding = first_item.get("embedding")
    if not isinstance(embedding, list):
        return None
    values: list[float] = []
    for item in embedding:
        if not isinstance(item, (int, float)) or isinstance(item, bool):
            return None
        values.append(float(item))
    return values