"""Integration tests for Sprint 2 functionality.

Tests the integration between EmbeddingService and InMemoryKG
to ensure the full semantic search pipeline works correctly.
"""

from unittest.mock import Mock, patch

from kg_services.embedder import EmbeddingService
from kg_services.knowledge_graph import InMemoryKG
from kg_services.ontology import MCPTool


def test_sprint2_task2_1_embedding_service_integration():
    """Test Task 2.1: EmbeddingService generates embeddings."""
    # Mock the OpenAI client and environment for testing
    with (
        patch("kg_services.embedder.openai.OpenAI") as mock_openai,
        patch("kg_services.embedder.os.getenv") as mock_getenv,
    ):

        # Setup environment mock
        mock_getenv.return_value = "fake-api-key"

        # Setup mock response
        mock_client = Mock()
        mock_openai.return_value = mock_client

        mock_response = Mock()
        mock_response.data = [Mock()]
        mock_response.data[0].embedding = [0.1, 0.2, 0.3, 0.4, 0.5]
        mock_client.embeddings.create.return_value = mock_response

        # Test embedding generation
        embedder = EmbeddingService()
        embedding = embedder.get_embedding("Test text for embedding")

        assert embedding is not None
        assert len(embedding) == 5
        assert embedding == [0.1, 0.2, 0.3, 0.4, 0.5]

        # Verify OpenAI client was called correctly
        mock_client.embeddings.create.assert_called_once_with(
            model="text-embedding-3-small", input="Test text for embedding"
        )


def test_sprint2_task2_2_vector_index_building_integration():
    """Test Task 2.2: InMemoryKG builds vector index with real EmbeddingService."""
    kg = InMemoryKG()

    # Add test tools
    tool1 = MCPTool(
        tool_id="integration_tool_001",
        name="Test Tool 1",
        description="First test tool for integration testing.",
        tags=["test", "integration"],
    )

    tool2 = MCPTool(
        tool_id="integration_tool_002",
        name="Test Tool 2",
        description="Second test tool for integration testing.",
        tags=["test", "mock"],
    )

    kg.add_tool(tool1)
    kg.add_tool(tool2)

    # Mock embedder
    mock_embedder = Mock()
    mock_embedder.get_embedding.side_effect = [
        [0.1, 0.2, 0.3],  # For tool1
        [0.4, 0.5, 0.6],  # For tool2
    ]

    # Build vector index
    kg.build_vector_index(mock_embedder)

    # Verify integration
    assert len(kg.tool_embeddings) == 2
    assert len(kg.tool_ids_for_vectors) == 2
    assert mock_embedder.get_embedding.call_count == 2


def test_sprint2_task2_3_semantic_search_integration():
    """Test Task 2.3: End-to-end semantic search with cosine similarity."""
    kg = InMemoryKG()

    # Add test tools
    tool1 = MCPTool(
        tool_id="semantic_tool_001",
        name="Text Analyzer",
        description="Analyzes text for sentiment and entities.",
        tags=["nlp", "text"],
    )

    tool2 = MCPTool(
        tool_id="semantic_tool_002",
        name="Image Processor",
        description="Processes images for object detection.",
        tags=["vision", "image"],
    )

    tool3 = MCPTool(
        tool_id="semantic_tool_003",
        name="Text Summarizer",
        description="Summarizes long text documents.",
        tags=["nlp", "text"],
    )

    kg.add_tool(tool1)
    kg.add_tool(tool2)
    kg.add_tool(tool3)

    # Mock embedder with distinct, predictable embeddings
    mock_embedder = Mock()
    mock_embedder.get_embedding.side_effect = [
        [1.0, 0.0, 0.0],  # Text Analyzer - text-focused
        [0.0, 1.0, 0.0],  # Image Processor - vision-focused
        [0.9, 0.1, 0.0],  # Text Summarizer - similar to text analyzer
    ]

    # Build vector index
    kg.build_vector_index(mock_embedder)

    # Test semantic search with text-focused query
    text_query_embedding = [1.0, 0.0, 0.0]  # Similar to text tools
    similar_tools = kg.find_similar_tools(text_query_embedding, top_k=2)

    # Should find text-related tools first
    assert len(similar_tools) == 2
    assert "semantic_tool_001" in similar_tools  # Text Analyzer
    assert "semantic_tool_003" in similar_tools  # Text Summarizer

    # Test with vision-focused query
    vision_query_embedding = [0.0, 1.0, 0.0]  # Similar to vision tools
    similar_tools = kg.find_similar_tools(vision_query_embedding, top_k=1)

    # Should find vision tool first
    assert len(similar_tools) == 1
    assert similar_tools[0] == "semantic_tool_002"  # Image Processor


def test_sprint2_full_pipeline_integration():
    """Test complete Sprint 2 pipeline: embeddings → index → search."""
    # Mock the OpenAI client and environment for this test
    with (
        patch("kg_services.embedder.openai.OpenAI") as mock_openai,
        patch("kg_services.embedder.os.getenv") as mock_getenv,
    ):

        # Setup environment mock
        mock_getenv.return_value = "fake-api-key"

        # Setup mock OpenAI client
        mock_client = Mock()
        mock_openai.return_value = mock_client

        # Mock different embeddings for different tools
        def mock_embedding_response(call_kwargs):
            text = call_kwargs["input"]
            if "Text Analyzer" in text:
                return Mock(data=[Mock(embedding=[1.0, 0.1, 0.0])])
            if "Image Processor" in text:
                return Mock(data=[Mock(embedding=[0.1, 1.0, 0.0])])
            return Mock(data=[Mock(embedding=[0.5, 0.5, 0.5])])

        mock_client.embeddings.create.side_effect = (
            lambda **kwargs: mock_embedding_response(kwargs)
        )

        # Create real services
        embedder = EmbeddingService()
        kg = InMemoryKG()

        # Add tools
        kg.add_tool(
            MCPTool(
                tool_id="pipeline_tool_001",
                name="Text Analyzer",
                description="Analyzes text content.",
                tags=["nlp", "text"],
            )
        )

        kg.add_tool(
            MCPTool(
                tool_id="pipeline_tool_002",
                name="Image Processor",
                description="Processes image content.",
                tags=["vision", "image"],
            )
        )

        # Build vector index with real embedder
        kg.build_vector_index(embedder)

        # Verify index was built
        assert len(kg.tool_embeddings) == 2
        assert len(kg.tool_ids_for_vectors) == 2

        # Test semantic search
        # Query most similar to text analyzer
        text_focused_query = [1.0, 0.1, 0.0]
        results = kg.find_similar_tools(text_focused_query, top_k=1)

        assert len(results) == 1
        assert results[0] == "pipeline_tool_001"  # Text Analyzer should be most similar

        # Verify OpenAI was called for each tool
        assert mock_client.embeddings.create.call_count == 2