Nextcloud MCP Server

"""Pytest fixtures for RAG evaluation tests. IMPORTANT: Before running these tests, you must: 1. Generate ground truth: uv run python tools/rag_eval_cli.py generate 2. Upload corpus: uv run python tools/rag_eval_cli.py upload --nextcloud-url http://localhost:8000 --username admin --password admin This ensures that the ground truth and note mappings are available. """ import json from pathlib import Path from typing import Any import pytest from tests.rag_evaluation.llm_providers import create_llm_provider # Paths FIXTURES_DIR = Path(__file__).parent / "fixtures" GROUND_TRUTH_FILE = FIXTURES_DIR / "ground_truth.json" NOTE_MAPPING_FILE = FIXTURES_DIR / "note_mapping.json" @pytest.fixture(scope="session") def ground_truth_data() -> list[dict[str, Any]]: """Load pre-generated ground truth data. Returns: List of test cases with query, ground truth answer, and expected doc IDs Raises: FileNotFoundError: If ground_truth.json doesn't exist """ if not GROUND_TRUTH_FILE.exists(): raise FileNotFoundError( f"Ground truth file not found: {GROUND_TRUTH_FILE}\n" "Run: uv run python tools/rag_eval_cli.py generate" ) with open(GROUND_TRUTH_FILE) as f: return json.load(f) @pytest.fixture(scope="session") def note_mapping() -> dict[str, int]: """Load document ID → note ID mapping. Returns: Dict mapping nfcorpus document ID to Nextcloud note ID Raises: FileNotFoundError: If note_mapping.json doesn't exist """ if not NOTE_MAPPING_FILE.exists(): raise FileNotFoundError( f"Note mapping file not found: {NOTE_MAPPING_FILE}\n" "Run: uv run python tools/rag_eval_cli.py upload --nextcloud-url ... --username ... --password ..." ) with open(NOTE_MAPPING_FILE) as f: return json.load(f) @pytest.fixture(scope="session") def nfcorpus_test_data( ground_truth_data: list[dict[str, Any]], note_mapping: dict[str, int], ): """Prepare nfcorpus test data for evaluation. This fixture combines ground truth answers with note mappings to create test cases ready for retrieval and generation quality tests. Args: ground_truth_data: Pre-generated ground truth answers note_mapping: Document ID → note ID mapping Returns: List of test cases with query, ground truth, expected doc IDs, and note IDs """ test_cases = [] for gt in ground_truth_data: # Map expected document IDs to note IDs expected_note_ids = [ note_mapping.get(doc_id) for doc_id in gt["expected_document_ids"] if doc_id in note_mapping ] # Filter out None values (docs that weren't uploaded) expected_note_ids = [nid for nid in expected_note_ids if nid is not None] test_cases.append( { "query_id": gt["query_id"], "query_text": gt["query_text"], "ground_truth_answer": gt["ground_truth_answer"], "expected_document_ids": gt["expected_document_ids"], "expected_note_ids": expected_note_ids, "highly_relevant_count": gt["highly_relevant_count"], } ) return test_cases @pytest.fixture(scope="session") async def evaluation_llm(): """Create LLM provider for evaluation (separate from MCP client). Environment variables: RAG_EVAL_PROVIDER: Provider type (ollama or anthropic) RAG_EVAL_OLLAMA_BASE_URL: Ollama base URL (or OLLAMA_HOST) RAG_EVAL_OLLAMA_MODEL: Ollama model name RAG_EVAL_ANTHROPIC_API_KEY: Anthropic API key RAG_EVAL_ANTHROPIC_MODEL: Anthropic model name Returns: LLM provider instance (OllamaProvider or AnthropicProvider) """ llm = create_llm_provider() yield llm await llm.close() @pytest.fixture(scope="session") async def mcp_sampling_client(): """Create MCP client that supports sampling for RAG generation. This fixture creates an MCP client configured to support sampling, which is required for testing the nc_semantic_search_answer tool. TODO: Implement MCP client with sampling support For now, this is a placeholder. Returns: MCP client instance with sampling enabled """ # TODO: Implement MCP client creation with sampling support # This will require: # 1. Creating an MCP client configured for sampling # 2. Authenticating with Nextcloud # 3. Ensuring sampling is enabled pytest.skip("MCP sampling client not yet implemented")