"""Integration tests for RAG pipeline with OpenAI/GitHub Models API.

These tests validate the complete semantic search and MCP sampling flow using:
1. OpenAI embeddings for semantic search
2. MCP sampling for answer generation
3. Pre-indexed Nextcloud User Manual as the knowledge base

Environment Variables:
    OPENAI_API_KEY: OpenAI API key or GitHub token for models.github.ai
    OPENAI_BASE_URL: Base URL override (e.g., "https://models.github.ai/inference")
    OPENAI_EMBEDDING_MODEL: Embedding model (default: "text-embedding-3-small")
    OPENAI_GENERATION_MODEL: Generation model for sampling (default: "gpt-4o-mini")
    RAG_MANUAL_PATH: Path to manual PDF in Nextcloud (default: "Nextcloud_User_Manual.pdf")

For GitHub CI, set:
    OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
    OPENAI_BASE_URL: https://models.github.ai/inference
    OPENAI_EMBEDDING_MODEL: openai/text-embedding-3-small
    OPENAI_GENERATION_MODEL: openai/gpt-4o-mini

Prerequisites:
    - Nextcloud User Manual PDF uploaded to Nextcloud
    - VECTOR_SYNC_ENABLED=true on the MCP server
"""

import json
import logging
import os
from pathlib import Path
from typing import Any, AsyncGenerator

import anyio
import pytest
from mcp import ClientSession

from nextcloud_mcp_server.providers.openai import OpenAIProvider
from tests.conftest import create_mcp_client_session
from tests.integration.sampling_support import create_openai_sampling_callback

logger = logging.getLogger(__name__)

# Default path to the Nextcloud User Manual PDF
DEFAULT_MANUAL_PATH = "Nextcloud Manual.pdf"


async def llm_judge(
    provider: "OpenAIProvider",
    ground_truth: str,
    system_output: str,
) -> bool:
    """Use LLM to judge if system output aligns with ground truth.

    Args:
        provider: OpenAI provider with generation capability
        ground_truth: The expected/reference answer
        system_output: The system's actual output to evaluate

    Returns:
        True if output aligns with ground truth, False otherwise
    """
    prompt = f"""GROUND TRUTH: {ground_truth}

SYSTEM OUTPUT: {system_output}

Does the system output contain the key facts from the ground truth?

Answer: TRUE or FALSE"""

    response = await provider.generate(prompt, max_tokens=10)
    return "TRUE" in response.upper()


# Skip all tests if OpenAI API key not configured
pytestmark = [
    pytest.mark.integration,
    pytest.mark.skipif(
        not os.getenv("OPENAI_API_KEY"),
        reason="OPENAI_API_KEY not set - skipping OpenAI RAG tests",
    ),
]

# Ground truth fixture path
FIXTURES_DIR = Path(__file__).parent / "fixtures"
GROUND_TRUTH_FILE = FIXTURES_DIR / "nextcloud_manual_ground_truth.json"


@pytest.fixture(scope="module")
def ground_truth_qa():
    """Load ground truth Q&A pairs for the Nextcloud manual."""
    if not GROUND_TRUTH_FILE.exists():
        pytest.skip(f"Ground truth file not found: {GROUND_TRUTH_FILE}")

    with open(GROUND_TRUTH_FILE) as f:
        return json.load(f)


@pytest.fixture(scope="module")
async def indexed_manual_pdf(nc_client, nc_mcp_client):
    """Ensure the Nextcloud User Manual PDF is tagged and indexed for vector search.

    This fixture:
    1. Gets file info for the manual PDF
    2. Creates/gets the 'vector-index' tag
    3. Assigns the tag to the file
    4. Waits for vector sync to complete indexing

    Environment Variables:
        RAG_MANUAL_PATH: Path to manual PDF in Nextcloud (default: Nextcloud Manual.pdf)
    """
    manual_path = os.getenv("RAG_MANUAL_PATH", DEFAULT_MANUAL_PATH)

    logger.info(f"Setting up indexed manual PDF: {manual_path}")

    # Get file info to verify file exists and get file ID
    file_info = await nc_client.webdav.get_file_info(manual_path)
    if not file_info:
        pytest.skip(f"Manual PDF not found at '{manual_path}'")

    file_id = file_info["id"]
    logger.info(f"Found manual PDF: {manual_path} (file_id={file_id})")

    # Create or get the vector-index tag
    tag = await nc_client.webdav.get_or_create_tag("vector-index")
    tag_id = tag["id"]
    logger.info(f"Using tag 'vector-index' (tag_id={tag_id})")

    # Assign tag to file
    await nc_client.webdav.assign_tag_to_file(file_id, tag_id)
    logger.info(f"Tagged file {file_id} with vector-index tag")

    # Wait for vector sync to complete indexing
    max_attempts = 60
    poll_interval = 10

    logger.info("Waiting for vector sync to index the manual...")

    for attempt in range(1, max_attempts + 1):
        try:
            # Call the MCP tool via the existing client session
            result = await nc_mcp_client.call_tool(
                "nc_get_vector_sync_status",
                arguments={},
            )

            if not result.isError:
                content = result.structuredContent or {}
                indexed = content.get("indexed_count", 0)
                pending = content.get("pending_count", 1)

                logger.info(
                    f"Attempt {attempt}/{max_attempts}: "
                    f"indexed={indexed}, pending={pending}"
                )

                if indexed > 0 and pending == 0:
                    logger.info(
                        f"Vector indexing complete: {indexed} documents indexed"
                    )
                    break
        except Exception as e:
            logger.warning(f"Attempt {attempt}: Error checking status: {e}")

        if attempt < max_attempts:
            await anyio.sleep(poll_interval)
    else:
        logger.warning(
            f"Vector indexing may not be complete after {max_attempts} attempts"
        )

    yield {
        "path": manual_path,
        "file_id": file_id,
        "tag_id": tag_id,
    }


@pytest.fixture(scope="module")
async def openai_provider():
    """OpenAI provider configured from environment (embeddings only)."""
    api_key = os.getenv("OPENAI_API_KEY")
    base_url = os.getenv("OPENAI_BASE_URL")
    embedding_model = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-3-small")

    provider = OpenAIProvider(
        api_key=api_key,
        base_url=base_url,
        embedding_model=embedding_model,
        generation_model=None,  # Embeddings only
    )

    yield provider
    await provider.close()


@pytest.fixture(scope="module")
async def openai_generation_provider():
    """OpenAI provider configured for text generation (for sampling callback)."""
    api_key = os.getenv("OPENAI_API_KEY")
    base_url = os.getenv("OPENAI_BASE_URL")
    generation_model = os.getenv("OPENAI_GENERATION_MODEL", "gpt-4o-mini")

    # For GitHub Models API, use the prefixed model name
    if base_url and "models.github.ai" in base_url:
        if not generation_model.startswith("openai/"):
            generation_model = f"openai/{generation_model}"

    provider = OpenAIProvider(
        api_key=api_key,
        base_url=base_url,
        embedding_model=None,  # Generation only
        generation_model=generation_model,
    )

    yield provider
    await provider.close()


@pytest.fixture(scope="module")
async def nc_mcp_client_with_sampling(
    anyio_backend, openai_generation_provider
) -> AsyncGenerator[ClientSession, Any]:
    """MCP client with OpenAI-based sampling support.

    This fixture creates an MCP client that can handle sampling requests
    from the server using OpenAI for text generation.
    """
    sampling_callback = create_openai_sampling_callback(openai_generation_provider)

    async for session in create_mcp_client_session(
        url="http://localhost:8000/mcp",
        client_name="OpenAI Sampling MCP",
        sampling_callback=sampling_callback,
    ):
        yield session


async def test_openai_embeddings_work(openai_provider: OpenAIProvider):
    """Test that OpenAI embeddings can be generated."""
    embedding = await openai_provider.embed("test query about Nextcloud")

    assert isinstance(embedding, list)
    assert len(embedding) > 0
    assert all(isinstance(x, float) for x in embedding)
    # OpenAI embedding dimensions: 1536 (small) or 3072 (large)
    assert len(embedding) in [1536, 3072]


async def test_semantic_search_retrieval(
    nc_mcp_client, ground_truth_qa, indexed_manual_pdf, openai_generation_provider
):
    """Test that semantic search retrieves relevant documents from the manual.

    This tests the retrieval component of RAG - ensuring that queries
    return relevant chunks from the indexed Nextcloud User Manual.
    """
    # Use first query from ground truth
    test_case = ground_truth_qa[0]  # 2FA question
    query = test_case["query"]

    # Perform semantic search via MCP tool
    result = await nc_mcp_client.call_tool(
        "nc_semantic_search",
        arguments={
            "query": query,
            "limit": 5,
            "score_threshold": 0.0,
        },
    )

    assert result.isError is False, f"Tool call failed: {result}"
    data = result.structuredContent

    # Verify we got results
    assert data["success"] is True
    assert data["total_found"] > 0, f"No results for query: {query}"
    assert len(data["results"]) > 0

    # Use LLM judge to evaluate if excerpts are relevant to ground truth
    all_excerpts = " ".join([r["excerpt"] for r in data["results"]])
    is_relevant = await llm_judge(
        openai_generation_provider,
        test_case["ground_truth"],
        all_excerpts,
    )
    assert is_relevant, f"LLM judge: excerpts not relevant to query: {query}"


async def test_semantic_search_answer_with_sampling(
    nc_mcp_client_with_sampling,
    ground_truth_qa,
    indexed_manual_pdf,
    openai_generation_provider,
):
    """Test semantic search with MCP sampling for answer generation.

    This tests the full RAG pipeline:
    1. Semantic search retrieves relevant documents
    2. MCP sampling generates an answer from the retrieved context
    3. OpenAI generates the answer via the sampling callback

    Uses nc_mcp_client_with_sampling which has OpenAI-based sampling enabled.
    """
    # Use the 2FA question - has clear expected answer
    test_case = ground_truth_qa[0]
    query = test_case["query"]

    result = await nc_mcp_client_with_sampling.call_tool(
        "nc_semantic_search_answer",
        arguments={
            "query": query,
            "limit": 5,
            "score_threshold": 0.0,
            "max_answer_tokens": 300,
        },
    )

    assert result.isError is False, f"Tool call failed: {result}"
    data = result.structuredContent

    # Verify response structure
    assert data["success"] is True
    assert "query" in data
    assert "generated_answer" in data
    assert "sources" in data
    assert "search_method" in data

    # Check for either successful sampling or graceful fallback
    fallback_methods = {
        "semantic_sampling_unsupported",
        "semantic_sampling_user_declined",
        "semantic_sampling_timeout",
        "semantic_sampling_mcp_error",
        "semantic_sampling_fallback",
    }

    if data["search_method"] in fallback_methods:
        # Fallback mode - verify sources still returned
        assert len(data["sources"]) > 0, "Expected sources even in fallback mode"
        pytest.skip(
            f"MCP sampling not available (method: {data['search_method']}), "
            f"but retrieval succeeded with {len(data['sources'])} sources"
        )
    else:
        # Successful sampling - verify answer quality
        assert data["search_method"] == "semantic_sampling"
        assert data["generated_answer"] is not None
        assert len(data["generated_answer"]) > 50  # Non-trivial answer

        # Use LLM judge to evaluate answer relevance
        is_relevant = await llm_judge(
            openai_generation_provider,
            test_case["ground_truth"],
            data["generated_answer"],
        )
        assert is_relevant, f"LLM judge: answer not relevant to query: {query}"


@pytest.mark.parametrize(
    "qa_index,min_expected_results",
    [
        (0, 1),  # 2FA question
        (1, 1),  # File quotas question
        (2, 1),  # Linux installation question
        (3, 1),  # Windows requirements question
        (4, 1),  # Client apps with 2FA question
    ],
)
async def test_retrieval_quality_all_queries(
    nc_mcp_client, ground_truth_qa, indexed_manual_pdf, qa_index, min_expected_results
):
    """Test retrieval quality for all ground truth queries.

    Validates that each query returns at least the minimum expected
    number of relevant results from the Nextcloud manual.
    """
    if qa_index >= len(ground_truth_qa):
        pytest.skip(f"Ground truth index {qa_index} not available")

    test_case = ground_truth_qa[qa_index]
    query = test_case["query"]

    result = await nc_mcp_client.call_tool(
        "nc_semantic_search",
        arguments={
            "query": query,
            "limit": 5,
            "score_threshold": 0.0,
        },
    )

    assert result.isError is False
    data = result.structuredContent

    assert data["total_found"] >= min_expected_results, (
        f"Query '{query}' returned {data['total_found']} results, "
        f"expected at least {min_expected_results}"
    )


async def test_no_results_for_unrelated_query(nc_mcp_client, indexed_manual_pdf):
    """Test that completely unrelated queries return low/no scores.

    The Nextcloud manual shouldn't have relevant content for
    quantum physics queries.
    """
    result = await nc_mcp_client.call_tool(
        "nc_semantic_search",
        arguments={
            "query": "quantum entanglement hadron collider particle physics",
            "limit": 5,
            "score_threshold": 0.5,  # Higher threshold to filter irrelevant
        },
    )

    assert result.isError is False
    data = result.structuredContent

    # Should have few or no high-scoring results
    # Low score threshold means we might get some results, but they should be low quality
    if data["total_found"] > 0:
        # If results exist, they should have low scores
        max_score = max(r["score"] for r in data["results"])
        assert max_score < 0.8, f"Unexpected high score {max_score} for unrelated query"