"""Test demonstrating programmatic access with Langchain Ollama LLM and in-memory vector retriever."""

import pytest
from unittest.mock import patch, MagicMock
import requests
from langchain_ollama import OllamaEmbeddings, OllamaLLM
from langchain_community.vectorstores import FAISS
from langchain_core.retrievers import Document

from local_deep_research.api import (
    quick_summary,
    detailed_research,
    generate_report,
)
from local_deep_research.llm import clear_llm_registry


def _is_ollama_running():
    """Check if Ollama service is running."""
    try:
        response = requests.get("http://localhost:11434/api/tags", timeout=1)
        return response.status_code == 200
    except Exception:
        return False


# Skip entire module if Ollama is not running to avoid fixture initialization hangs
pytestmark = pytest.mark.skipif(
    not _is_ollama_running(),
    reason="Ollama is not running - skipping all Ollama integration tests",
)


@pytest.fixture(autouse=True)
def clear_registries():
    """Clear registries before and after each test."""
    clear_llm_registry()
    yield
    clear_llm_registry()


@pytest.fixture
def sample_documents():
    """Create sample documents for the vector store."""
    docs = [
        Document(
            page_content="Machine learning is a subset of artificial intelligence that enables systems to learn from data.",
            metadata={"source": "ml_basics.txt", "topic": "machine_learning"},
        ),
        Document(
            page_content="Deep learning uses neural networks with multiple layers to extract features from raw data.",
            metadata={"source": "dl_intro.txt", "topic": "deep_learning"},
        ),
        Document(
            page_content="Natural language processing allows computers to understand and generate human language.",
            metadata={"source": "nlp_guide.txt", "topic": "nlp"},
        ),
        Document(
            page_content="Computer vision enables machines to interpret and analyze visual information from images and videos.",
            metadata={"source": "cv_overview.txt", "topic": "computer_vision"},
        ),
        Document(
            page_content="Reinforcement learning trains agents to make decisions by rewarding desired behaviors.",
            metadata={
                "source": "rl_basics.txt",
                "topic": "reinforcement_learning",
            },
        ),
    ]
    return docs


@pytest.fixture
def ollama_llm():
    """Create an Ollama LLM instance."""
    # Using gemma3n:e4b as requested
    return OllamaLLM(
        model="gemma3n:e4b",
        temperature=0.7,
    )


@pytest.fixture
def memory_retriever(sample_documents):
    """Create an in-memory vector store retriever."""
    # Create embeddings using the specified multilingual model
    embeddings = OllamaEmbeddings(
        model="jeffh/intfloat-multilingual-e5-large-instruct:f16"
    )

    # Create FAISS vector store from documents
    vectorstore = FAISS.from_documents(
        documents=sample_documents, embedding=embeddings
    )

    # Create retriever from vector store
    retriever = vectorstore.as_retriever(
        search_kwargs={"k": 3}  # Return top 3 most relevant documents
    )

    return retriever


@pytest.fixture
def mock_search_system():
    """Create a mock search system for testing."""
    system = MagicMock()
    system.analyze_topic.return_value = {
        "current_knowledge": "Analysis using Ollama LLM and in-memory retriever",
        "findings": [
            "Successfully retrieved relevant documents from vector store",
            "Ollama LLM provided coherent responses",
        ],
        "iterations": 2,
        "questions": {
            "iteration_1": ["What is the main concept?"],
            "iteration_2": ["How is it applied in practice?"],
        },
        "formatted_findings": "## Research Summary\n- Vector retrieval worked effectively",
        "all_links_of_system": [],
    }
    system.model = MagicMock()
    return system


@pytest.mark.skipif(
    not _is_ollama_running(),
    reason="Ollama is not running - skipping integration test",
)
def test_quick_summary_with_ollama_and_memory_retriever(
    ollama_llm, memory_retriever, mock_search_system
):
    """Test quick_summary using Ollama LLM and in-memory vector retriever."""

    with patch(
        "local_deep_research.api.research_functions._init_search_system"
    ) as mock_init:
        mock_init.return_value = mock_search_system

        # Use programmatic API with Ollama and memory retriever
        result = quick_summary(
            query="What is deep learning and how does it relate to machine learning?",
            llms={"ollama_llm": ollama_llm},
            retrievers={"memory_docs": memory_retriever},
            provider="ollama_llm",
            search_tool="memory_docs",
            temperature=0.5,
        )

        # Verify result structure
        assert "summary" in result
        assert (
            result["summary"]
            == "Analysis using Ollama LLM and in-memory retriever"
        )
        assert len(result["findings"]) == 2
        assert "vector store" in result["findings"][0].lower()

        # Verify components were configured correctly
        init_kwargs = mock_init.call_args[1]
        assert init_kwargs["provider"] == "ollama_llm"
        assert init_kwargs["search_tool"] == "memory_docs"
        assert init_kwargs["temperature"] == 0.5


@pytest.mark.skipif(
    not _is_ollama_running(),
    reason="Ollama is not running - skipping integration test",
)
def test_detailed_research_with_ollama_and_memory_retriever(
    ollama_llm, memory_retriever, mock_search_system
):
    """Test detailed_research with Ollama and memory retriever."""

    with patch(
        "local_deep_research.api.research_functions._init_search_system"
    ) as mock_init:
        mock_init.return_value = mock_search_system

        result = detailed_research(
            query="Explain the differences between various machine learning approaches",
            llms={"ollama": ollama_llm},
            retrievers={"local_docs": memory_retriever},
            provider="ollama",
            search_tool="local_docs",
            iterations=3,
            questions_per_iteration=2,
        )

        # Verify detailed research results
        assert (
            result["query"]
            == "Explain the differences between various machine learning approaches"
        )
        assert (
            result["summary"]
            == "Analysis using Ollama LLM and in-memory retriever"
        )
        assert result["metadata"]["iterations_requested"] == 3
        assert result["metadata"]["search_tool"] == "local_docs"


@pytest.mark.skipif(
    not _is_ollama_running(),
    reason="Ollama is not running - skipping integration test",
)
def test_generate_report_with_ollama_and_memory_retriever(
    ollama_llm, memory_retriever
):
    """Test report generation using Ollama and memory retriever."""

    with patch(
        "local_deep_research.api.research_functions._init_search_system"
    ) as mock_init:
        with patch(
            "local_deep_research.api.research_functions.IntegratedReportGenerator"
        ) as mock_report_gen:
            # Setup mocks
            mock_system = MagicMock()
            mock_system.analyze_topic.return_value = {
                "findings": "Initial ML findings"
            }
            mock_init.return_value = mock_system

            mock_generator = MagicMock()
            mock_generator.generate_report.return_value = {
                "content": "# Machine Learning Overview\n\n## Introduction\nThis report covers key ML concepts from local documents.",
                "metadata": {
                    "query": "machine learning overview",
                    "sources_used": 5,
                },
            }
            mock_report_gen.return_value = mock_generator

            # Generate report
            result = generate_report(
                query="Create a comprehensive overview of machine learning concepts",
                llms={"ollama": ollama_llm},
                retrievers={"vector_store": memory_retriever},
                provider="ollama",
                search_tool="vector_store",
                searches_per_section=2,
            )

            # Verify report generation
            assert "content" in result
            assert "# Machine Learning Overview" in result["content"]
            assert "local documents" in result["content"]

            # Verify configuration
            init_kwargs = mock_init.call_args[1]
            assert init_kwargs["provider"] == "ollama"
            assert init_kwargs["search_tool"] == "vector_store"


@pytest.mark.skipif(
    not _is_ollama_running(),
    reason="Ollama is not running - skipping integration test",
)
def test_custom_vector_store_with_more_documents():
    """Test creating a larger in-memory vector store."""

    # Create more documents
    extended_docs = [
        Document(
            page_content="Transfer learning allows models trained on one task to be adapted for related tasks.",
            metadata={"source": "transfer_learning.txt"},
        ),
        Document(
            page_content="Attention mechanisms help models focus on relevant parts of the input data.",
            metadata={"source": "attention.txt"},
        ),
        Document(
            page_content="Gradient descent is an optimization algorithm used to train neural networks.",
            metadata={"source": "optimization.txt"},
        ),
        Document(
            page_content="Convolutional neural networks are particularly effective for image processing tasks.",
            metadata={"source": "cnn.txt"},
        ),
        Document(
            page_content="Recurrent neural networks can process sequential data like text or time series.",
            metadata={"source": "rnn.txt"},
        ),
    ]

    # Create vector store with extended documents
    embeddings = OllamaEmbeddings(
        model="jeffh/intfloat-multilingual-e5-large-instruct:f16"
    )
    vectorstore = FAISS.from_documents(
        documents=extended_docs, embedding=embeddings
    )

    # Create retriever with different search parameters
    retriever = vectorstore.as_retriever(
        search_type="similarity",
        search_kwargs={"k": 5},  # Return top 5 documents
    )

    # Create Ollama LLM
    llm = OllamaLLM(model="gemma3n:e4b", temperature=0.3)

    with patch(
        "local_deep_research.api.research_functions._init_search_system"
    ) as mock_init:
        mock_system = MagicMock()
        mock_system.analyze_topic.return_value = {
            "current_knowledge": "Extended document analysis complete",
            "findings": ["Found relevant information about neural networks"],
        }
        mock_init.return_value = mock_system

        result = quick_summary(
            query="How do different types of neural networks work?",
            llms={"ollama": llm},
            retrievers={"extended_docs": retriever},
            provider="ollama",
            search_tool="extended_docs",
        )

        assert "summary" in result
        assert result["summary"] == "Extended document analysis complete"


@pytest.mark.skipif(
    not _is_ollama_running(),
    reason="Ollama is not running - skipping integration test",
)
def test_multiple_retrievers_with_ollama():
    """Test using multiple in-memory retrievers with Ollama."""

    # Create first retriever for ML topics
    ml_docs = [
        Document(
            page_content="Supervised learning uses labeled data for training."
        ),
        Document(
            page_content="Unsupervised learning finds patterns in unlabeled data."
        ),
    ]

    # Create second retriever for application topics
    app_docs = [
        Document(
            page_content="ML is used in recommendation systems for personalized content."
        ),
        Document(
            page_content="ML powers autonomous vehicles through computer vision and sensor fusion."
        ),
    ]

    embeddings = OllamaEmbeddings(
        model="jeffh/intfloat-multilingual-e5-large-instruct:f16"
    )

    ml_vectorstore = FAISS.from_documents(ml_docs, embeddings)
    app_vectorstore = FAISS.from_documents(app_docs, embeddings)

    ml_retriever = ml_vectorstore.as_retriever()
    app_retriever = app_vectorstore.as_retriever()

    ollama_llm = OllamaLLM(model="gemma3n:e4b")

    with patch(
        "local_deep_research.api.research_functions._init_search_system"
    ) as mock_init:
        mock_system = MagicMock()
        mock_system.analyze_topic.return_value = {
            "current_knowledge": "Analysis from multiple vector stores",
            "findings": ["ML concepts retrieved", "Applications identified"],
        }
        mock_init.return_value = mock_system

        result = quick_summary(
            query="What are ML techniques and their applications?",
            llms={"ollama": ollama_llm},
            retrievers={
                "ml_concepts": ml_retriever,
                "ml_applications": app_retriever,
            },
            provider="ollama",
            search_tool="ml_concepts",  # Use a registered retriever
        )

        assert "summary" in result
        assert "multiple vector stores" in result["summary"]


@pytest.mark.skipif(
    not _is_ollama_running(),
    reason="Ollama is not running - skipping integration test",
)
def test_simple_ollama_factory_pattern():
    """Test using a factory function to create Ollama instances."""

    def create_ollama_llm(model_name="gemma3n:e4b", temperature=0.7, **kwargs):
        """Factory function for creating configured Ollama instances."""
        return OllamaLLM(
            model=model_name,
            temperature=temperature,
            num_predict=kwargs.get("max_tokens", 256),
        )

    # Create simple in-memory retriever
    docs = [Document(page_content="Test content for factory pattern demo.")]
    embeddings = OllamaEmbeddings(
        model="jeffh/intfloat-multilingual-e5-large-instruct:f16"
    )
    vectorstore = FAISS.from_documents(docs, embeddings)
    retriever = vectorstore.as_retriever()

    with patch(
        "local_deep_research.api.research_functions._init_search_system"
    ) as mock_init:
        mock_system = MagicMock()
        mock_system.analyze_topic.return_value = {
            "current_knowledge": "Factory pattern test successful",
            "findings": [],
        }
        mock_init.return_value = mock_system

        result = quick_summary(
            query="Test factory pattern",
            llms={"ollama_factory": create_ollama_llm},
            retrievers={"test_docs": retriever},
            provider="ollama_factory",
            search_tool="test_docs",
            model_name="gemma3n:e4b",
            temperature=0.2,
            max_tokens=512,
        )

        assert "summary" in result
        assert "Factory pattern test successful" in result["summary"]