import asyncio
from typing import List, Iterable, AsyncGenerator, Optional
from sentence_transformers import SentenceTransformer


class JEmbeddingService:
    def __init__(self, model_name: str = "Qwen/Qwen3-Embedding-0.6B"):
        self.model_name = model_name
        self.model: Optional[SentenceTransformer] = None
        self._load_model()

    def _load_model(self) -> None:
        self.model = SentenceTransformer(self.model_name)

    async def embed(self, texts: List[str]) -> List[List[float]]:
        if not isinstance(texts, list) or any(not isinstance(t, str) for t in texts):
            raise ValueError("texts必须是字符串列表")
        loop = asyncio.get_running_loop()
        embeddings = await loop.run_in_executor(None, self.model.encode, texts)
        return [vec.tolist() if hasattr(vec, 'tolist') else vec for vec in embeddings]

    async def similarity(self, embeddings_a: List[List[float]], embeddings_b: List[List[float]]):
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(None, self.model.similarity, embeddings_a, embeddings_b)

    async def process_batch(self, items: Iterable[str]) -> AsyncGenerator[dict, None]:
        texts: List[str] = []
        indices: List[int] = []
        for idx, text in enumerate(items):
            try:
                if not isinstance(text, str):
                    raise ValueError("每个元素必须是字符串")
                texts.append(text)
                indices.append(idx)
            except Exception as e:
                yield {"index": idx, "status": "error", "message": str(e)}
            await asyncio.sleep(0)

        if not texts:
            return

        try:
            vectors = await self.embed(texts)
            for i, vec in zip(indices, vectors):
                yield {"index": i + 1, "status": "success", "embedding": vec}
        except Exception as e:
            for i in indices:
                yield {"index": i + 1, "status": "error", "message": str(e)}