"""嵌入服务单元测试（mock httpx/openai，不发起真实网络请求）"""

from unittest.mock import AsyncMock, MagicMock, patch

import httpx

from app.config import settings
from app.core.embeddings import (
    EmbeddingService,
    LocalEmbeddingService,
    OpenAIEmbeddingService,
    create_embedding_service,
)


def _fake_openai_response(dim: int, n: int) -> MagicMock:
    """构造 OpenAI embeddings.create 的假响应"""
    resp = MagicMock()
    resp.data = [MagicMock(embedding=[0.1 * (i + 1)] * dim) for i in range(n)]
    return resp


class _FakeResponse:
    """httpx.Response 替代品"""

    def __init__(self, data: dict) -> None:
        self._data = data

    def raise_for_status(self) -> None:
        pass

    def json(self) -> dict:
        return self._data


class _FakeAsyncClient:
    """httpx.AsyncClient 替代品，记录请求参数"""

    def __init__(self, data: dict, captured: dict, **kwargs) -> None:
        self._data = data
        self._captured = captured
        captured["timeout"] = kwargs.get("timeout")

    async def __aenter__(self) -> "_FakeAsyncClient":
        return self

    async def __aexit__(self, *args) -> bool:
        return False

    async def post(self, url: str, json: dict | None = None) -> _FakeResponse:
        self._captured["url"] = url
        self._captured["json"] = json
        return _FakeResponse(self._data)


class TestOpenAIEmbeddingService:
    """OpenAI provider 行为"""

    async def test_embed_batch(self):
        """批量嵌入：模型与输入透传，返回与输入等长的向量"""
        with patch("app.core.embeddings.AsyncOpenAI") as mock_cls:
            client = mock_cls.return_value
            client.embeddings.create = AsyncMock(return_value=_fake_openai_response(dim=1536, n=2))
            service = OpenAIEmbeddingService(api_key="k", base_url="http://x/v1", model="m")
            vectors = await service.embed(["你好", "world"])

        assert len(vectors) == 2
        assert all(len(v) == 1536 for v in vectors)
        client.embeddings.create.assert_awaited_once_with(model="m", input=["你好", "world"])

    async def test_embed_empty(self):
        """空列表输入直接返回空列表，不调用 API"""
        with patch("app.core.embeddings.AsyncOpenAI") as mock_cls:
            client = mock_cls.return_value
            client.embeddings.create = AsyncMock()
            service = OpenAIEmbeddingService(api_key="k", base_url="http://x/v1", model="m")
            assert await service.embed([]) == []
        client.embeddings.create.assert_not_called()

    async def test_dimension_mismatch_warns(self):
        """返回维度与配置不一致时记录 warning，不抛错"""
        with (
            patch("app.core.embeddings.AsyncOpenAI") as mock_cls,
            patch("app.core.embeddings.logger") as mock_logger,
        ):
            client = mock_cls.return_value
            client.embeddings.create = AsyncMock(return_value=_fake_openai_response(dim=8, n=1))
            service = OpenAIEmbeddingService(api_key="k", base_url="http://x/v1", model="m")
            vectors = await service.embed(["a"])

        assert len(vectors[0]) == 8
        mock_logger.warning.assert_called_once()


class TestLocalEmbeddingService:
    """Ollama local provider 行为"""

    async def test_embed_batch(self, monkeypatch):
        """批量嵌入：POST /api/embed，payload 与 URL 正确，timeout 60s"""
        captured: dict = {}
        data = {"embeddings": [[0.1, 0.2], [0.3, 0.4]]}
        monkeypatch.setattr(httpx, "AsyncClient", lambda **kw: _FakeAsyncClient(data, captured, **kw))

        service = LocalEmbeddingService(base_url="http://localhost:11434/", model="bge-m3")
        vectors = await service.embed(["你好", "world"])

        assert vectors == [[0.1, 0.2], [0.3, 0.4]]
        assert captured["url"] == "http://localhost:11434/api/embed"
        assert captured["json"] == {"model": "bge-m3", "input": ["你好", "world"]}
        assert captured["timeout"] == 60.0

    async def test_embed_empty(self, monkeypatch):
        """空列表输入直接返回空列表，不发起请求"""
        called = []
        monkeypatch.setattr(httpx, "AsyncClient", lambda **kw: called.append(kw))

        service = LocalEmbeddingService(base_url="http://localhost:11434", model="bge-m3")
        assert await service.embed([]) == []
        assert called == []

    async def test_dimension_mismatch_warns(self, monkeypatch):
        """返回维度与配置不一致时记录 warning，不抛错"""
        data = {"embeddings": [[0.1] * 8]}
        monkeypatch.setattr(httpx, "AsyncClient", lambda **kw: _FakeAsyncClient(data, {}, **kw))

        with patch("app.core.embeddings.logger") as mock_logger:
            service = LocalEmbeddingService(base_url="http://localhost:11434", model="bge-m3")
            vectors = await service.embed(["a"])

        assert len(vectors[0]) == 8
        mock_logger.warning.assert_called_once()


class TestCreateEmbeddingService:
    """工厂函数选择逻辑"""

    def test_provider_openai(self, monkeypatch):
        monkeypatch.setattr(settings, "embedding_provider", "openai")
        monkeypatch.setattr(settings, "openai_api_key", "test-key")
        service = create_embedding_service()
        assert isinstance(service, OpenAIEmbeddingService)
        assert isinstance(service, EmbeddingService)

    def test_provider_local(self, monkeypatch):
        monkeypatch.setattr(settings, "embedding_provider", "local")
        monkeypatch.setattr(settings, "ollama_embedding_model", "bge-m3")
        service = create_embedding_service()
        assert isinstance(service, LocalEmbeddingService)
        assert service.model == "bge-m3"
        assert isinstance(service, EmbeddingService)