"""Milvus 向量检索客户端的单元测试。"""

from __future__ import annotations

import asyncio
from unittest.mock import AsyncMock, MagicMock, patch

import pytest

from app.module.kg_graphrag.milvus_client import MilvusVectorRetriever


@pytest.fixture
def retriever() -> MilvusVectorRetriever:
    return MilvusVectorRetriever(
        uri="http://test-milvus:19530",
        embedding_model="text-embedding-test",
    )


def _run(coro):
    return asyncio.run(coro)


# ---------------------------------------------------------------------------
# has_collection 测试
# ---------------------------------------------------------------------------


class TestHasCollection:
    def test_collection_exists(self, retriever: MilvusVectorRetriever):
        mock_client = MagicMock()
        mock_client.has_collection = MagicMock(return_value=True)
        retriever._milvus_client = mock_client

        result = _run(retriever.has_collection("my_collection"))

        assert result is True

    def test_collection_not_exists(self, retriever: MilvusVectorRetriever):
        mock_client = MagicMock()
        mock_client.has_collection = MagicMock(return_value=False)
        retriever._milvus_client = mock_client

        result = _run(retriever.has_collection("nonexistent"))

        assert result is False

    def test_fail_open_on_error(self, retriever: MilvusVectorRetriever):
        mock_client = MagicMock()
        mock_client.has_collection = MagicMock(side_effect=Exception("connection error"))
        retriever._milvus_client = mock_client

        result = _run(retriever.has_collection("test"))

        assert result is False


# ---------------------------------------------------------------------------
# search 测试
# ---------------------------------------------------------------------------


class TestSearch:
    def test_successful_search(self, retriever: MilvusVectorRetriever):
        """正常搜索返回 VectorChunk 列表。"""
        mock_embeddings = AsyncMock()
        mock_embeddings.aembed_query = AsyncMock(return_value=[0.1, 0.2, 0.3])
        retriever._embeddings = mock_embeddings

        mock_milvus = MagicMock()
        mock_milvus.search = MagicMock(return_value=[
            [
                {"id": "doc1", "distance": 0.95, "entity": {"text": "文档片段一", "metadata": {"source": "kb1"}}},
                {"id": "doc2", "distance": 0.82, "entity": {"text": "文档片段二", "metadata": {}}},
            ]
        ])
        retriever._milvus_client = mock_milvus

        chunks = _run(retriever.search("my_collection", "用户数据", top_k=5))

        assert len(chunks) == 2
        assert chunks[0].id == "doc1"
        assert chunks[0].text == "文档片段一"
        assert chunks[0].score == 0.95
        assert chunks[0].metadata == {"source": "kb1"}
        assert chunks[1].id == "doc2"
        assert chunks[1].score == 0.82

    def test_empty_results(self, retriever: MilvusVectorRetriever):
        mock_embeddings = AsyncMock()
        mock_embeddings.aembed_query = AsyncMock(return_value=[0.1])
        retriever._embeddings = mock_embeddings

        mock_milvus = MagicMock()
        mock_milvus.search = MagicMock(return_value=[[]])
        retriever._milvus_client = mock_milvus

        chunks = _run(retriever.search("col", "query"))

        assert chunks == []

    def test_fail_open_on_embedding_error(self, retriever: MilvusVectorRetriever):
        """Embedding 失败时 fail-open 返回空列表。"""
        mock_embeddings = AsyncMock()
        mock_embeddings.aembed_query = AsyncMock(side_effect=Exception("API error"))
        retriever._embeddings = mock_embeddings

        chunks = _run(retriever.search("col", "query"))

        assert chunks == []

    def test_fail_open_on_milvus_error(self, retriever: MilvusVectorRetriever):
        """Milvus 搜索失败时 fail-open 返回空列表。"""
        mock_embeddings = AsyncMock()
        mock_embeddings.aembed_query = AsyncMock(return_value=[0.1])
        retriever._embeddings = mock_embeddings

        mock_milvus = MagicMock()
        mock_milvus.search = MagicMock(side_effect=Exception("Milvus down"))
        retriever._milvus_client = mock_milvus

        chunks = _run(retriever.search("col", "query"))

        assert chunks == []

    def test_search_uses_to_thread(self, retriever: MilvusVectorRetriever):
        """验证搜索通过 asyncio.to_thread 执行同步 Milvus I/O。"""
        mock_embeddings = AsyncMock()
        mock_embeddings.aembed_query = AsyncMock(return_value=[0.1])
        retriever._embeddings = mock_embeddings

        mock_milvus = MagicMock()
        mock_milvus.search = MagicMock(return_value=[[]])
        retriever._milvus_client = mock_milvus

        with patch("app.module.kg_graphrag.milvus_client.asyncio.to_thread", new_callable=AsyncMock) as mock_to_thread:
            mock_to_thread.return_value = [[]]

            chunks = _run(retriever.search("col", "query"))

        # asyncio.to_thread 应该被调用来包装同步 Milvus 调用
        mock_to_thread.assert_called_once()
        call_args = mock_to_thread.call_args
        assert call_args.args[0] == mock_milvus.search