Europe PMC Literature Search MCP Server

""" pytest 配置和共享 fixtures """ import logging import os import sys from pathlib import Path from unittest.mock import Mock import pytest # 添加src目录到Python路径 project_root = Path(__file__).parent.parent src_path = project_root / "src" if str(src_path) not in sys.path: sys.path.insert(0, str(src_path)) # 延迟导入，只在需要时导入 def _import_services(): try: from article_mcp.services.crossref_service import CrossRefService from article_mcp.services.europe_pmc import EuropePMCService from article_mcp.services.openalex_service import OpenAlexService from article_mcp.services.pubmed_search import PubMedService return CrossRefService, EuropePMCService, OpenAlexService, PubMedService except ImportError: # 测试环境下返回Mock类 return Mock, Mock, Mock, Mock @pytest.fixture def logger(): """提供测试用的 logger""" logger = logging.getLogger("test") logger.setLevel(logging.WARNING) return logger @pytest.fixture def mock_crossref_service(logger): """提供模拟的 CrossRef 服务""" CrossRefService, _, _, _ = _import_services() service = Mock(spec=CrossRefService) service.search_works.return_value = { "success": True, "articles": [ { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test", "journal": "Test Journal", "publication_date": "2023-01-01", "source": "crossref", } ], "total_count": 1, "source": "crossref", } service.get_work_by_doi.return_value = { "success": True, "article": { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test", "journal": "Test Journal", "publication_date": "2023-01-01", "source": "crossref", }, "source": "crossref", } return service @pytest.fixture def mock_openalex_service(logger): """提供模拟的 OpenAlex 服务""" _, _, OpenAlexService, _ = _import_services() service = Mock(spec=OpenAlexService) service.search_works.return_value = { "success": True, "articles": [ { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test", "journal": "Test Journal", "publication_date": "2023", "source": "openalex", } ], "total_count": 1, "source": "openalex", } service.get_work_by_doi.return_value = { "success": True, "article": { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test", "journal": "Test Journal", "publication_date": "2023", "source": "openalex", }, "source": "openalex", } return service @pytest.fixture def mock_europe_pmc_service(logger): """提供模拟的 Europe PMC 服务""" _, EuropePMCService, _, _ = _import_services() service = Mock(spec=EuropePMCService) service.search.return_value = { "articles": [ { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test", "journal_name": "Test Journal", "publication_date": "2023-01-01", "pmid": "12345678", } ], "total_count": 1, } return service @pytest.fixture def mock_pubmed_service(logger): """提供模拟的 PubMed 服务""" _, _, _, PubMedService = _import_services() service = Mock(spec=PubMedService) service.search.return_value = { "articles": [ { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test", "journal": "Test Journal", "publication_date": "2023-01-01", "pmid": "12345678", } ], "total_count": 1, } return service @pytest.fixture def sample_article_data(): """提供示例文章数据""" return { "title": "Sample Article Title", "authors": ["Author One", "Author Two"], "doi": "10.1234/sample.2023", "journal": "Sample Journal", "publication_date": "2023-01-15", "abstract": "This is a sample abstract for testing purposes.", "pmid": "12345678", "pmcid": "PMC123456", "source": "test", } @pytest.fixture def sample_search_results(): """提供示例搜索结果""" return { "success": True, "keyword": "machine learning", "sources_used": ["europe_pmc", "pubmed"], "results_by_source": { "europe_pmc": [ { "title": "Machine Learning in Healthcare", "authors": ["AI Researcher"], "doi": "10.1234/ml.health.2023", "journal": "Health AI Journal", "publication_date": "2023-06-15", } ], "pubmed": [ { "title": "Deep Learning Applications", "authors": ["ML Specialist"], "doi": "10.5678/dl.apps.2023", "journal": "Machine Learning Today", "publication_date": "2023-05-20", } ], }, "merged_results": [ { "title": "Machine Learning in Healthcare", "authors": ["AI Researcher"], "doi": "10.1234/ml.health.2023", "journal": "Health AI Journal", "publication_date": "2023-06-15", }, { "title": "Deep Learning Applications", "authors": ["ML Specialist"], "doi": "10.5678/dl.apps.2023", "journal": "Machine Learning Today", "publication_date": "2023-05-20", }, ], "total_count": 2, "search_time": 1.23, } @pytest.fixture def invalid_identifier_data(): """提供无效标识符数据""" return { "empty": "", "whitespace_only": " ", "invalid_format": "not-a-valid-identifier", "nonexistent_doi": "10.9999/nonexistent", "nonexistent_pmid": "99999999", } @pytest.fixture def error_response_data(): """提供错误响应数据""" return { "success": False, "error": "API request failed", "error_type": "RequestException", "context": {"url": "https://example.com/api", "params": {"query": "test"}}, "timestamp": 1234567890.0, } @pytest.fixture def six_tool_services(logger): """提供6工具架构的完整服务集合""" return { "search_services": { "europe_pmc": Mock(), "pubmed": Mock(), "arxiv": Mock(), "crossref": Mock(), "openalex": Mock(), }, "article_services": { "europe_pmc": Mock(), "crossref": Mock(), "openalex": Mock(), "arxiv": Mock(), "pubmed": Mock(), }, "reference_service": Mock(), "relation_services": { "europe_pmc": Mock(), "pubmed": Mock(), }, "quality_services": { "pubmed": Mock(), }, "batch_services": { "europe_pmc": Mock(), "pubmed": Mock(), "crossref": Mock(), "openalex": Mock(), }, } @pytest.fixture def mock_mcp_tools(): """模拟6个MCP工具""" tools = {} # 工具1: search_literature tools["search_literature"] = Mock() tools["search_literature"].return_value = { "success": True, "keyword": "test query", "merged_results": [ { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test.2023", "journal": "Test Journal", "publication_date": "2023-01-01", } ], "total_count": 1, "search_time": 1.0, } # 工具2: get_article_details tools["get_article_details"] = Mock() tools["get_article_details"].return_value = { "success": True, "identifier": "10.1234/test.2023", "article": { "title": "Test Article", "authors": ["Test Author"], "doi": "10.1234/test.2023", "journal": "Test Journal", "publication_date": "2023-01-01", "abstract": "Test abstract", }, } # 工具3: get_references tools["get_references"] = Mock() tools["get_references"].return_value = { "success": True, "identifier": "10.1234/test.2023", "merged_references": [ { "title": "Reference Article", "authors": ["Reference Author"], "doi": "10.5678/ref.2020", "journal": "Reference Journal", "publication_date": "2020-01-01", } ], "total_count": 1, "processing_time": 0.5, } # 工具4: get_literature_relations tools["get_literature_relations"] = Mock() tools["get_literature_relations"].return_value = { "success": True, "identifier": "10.1234/test.2023", "relations": { "references": [], "similar": [], "citing": [], }, "statistics": { "references_count": 0, "similar_count": 0, "citing_count": 0, "total_relations": 0, }, } # 工具5: get_journal_quality tools["get_journal_quality"] = Mock() tools["get_journal_quality"].return_value = { "success": True, "journal_name": "Test Journal", "quality_metrics": { "impact_factor": 2.5, "quartile": "Q2", "jci": 1.0, }, } # 工具6: export_batch_results tools["export_batch_results"] = Mock() tools["export_batch_results"].return_value = { "success": True, "export_path": "/tmp/test_export.json", "format_type": "json", "records_exported": 1, "file_size": "1.2KB", "processing_time": 0.1, } return tools @pytest.fixture def workflow_test_data(): """提供完整工作流程测试数据""" return { "search_query": "machine learning healthcare", "search_results": { "success": True, "keyword": "machine learning healthcare", "merged_results": [ { "title": "ML in Healthcare: A Review", "authors": ["AI Expert", "Medical Researcher"], "doi": "10.1234/ml.health.2023", "journal": "Health AI Journal", "publication_date": "2023-06-15", "pmid": "37891234", }, { "title": "Deep Learning for Medical Diagnosis", "authors": ["DL Specialist"], "doi": "10.5678/dl.medical.2023", "journal": "Medical Imaging Today", "publication_date": "2023-04-20", "pmid": "37654321", }, ], "total_count": 2, }, "article_details": { "success": True, "identifier": "10.1234/ml.health.2023", "article": { "title": "ML in Healthcare: A Review", "authors": ["AI Expert", "Medical Researcher"], "doi": "10.1234/ml.health.2023", "journal": "Health AI Journal", "publication_date": "2023-06-15", "abstract": "This review examines machine learning applications in healthcare...", "pmid": "37891234", "pmcid": "PMC1234567", "quality_metrics": { "impact_factor": 4.2, "quartile": "Q1", "jci": 1.8, }, }, }, "references": { "success": True, "identifier": "10.1234/ml.health.2023", "merged_references": [ { "title": "Foundations of Machine Learning", "authors": ["ML Pioneer"], "doi": "10.1111/foundations.2020", "journal": "ML Journal", "publication_date": "2020-01-01", }, { "title": "Healthcare Data Analytics", "authors": ["Data Scientist"], "doi": "10.2222/health.analytics.2021", "journal": "Health Informatics", "publication_date": "2021-03-15", }, ], "total_count": 25, }, "relations": { "success": True, "identifier": "10.1234/ml.health.2023", "relations": { "references": [ { "title": "Foundations of Machine Learning", "doi": "10.1111/foundations.2020", "publication_date": "2020-01-01", } ], "similar": [ { "title": "AI in Clinical Practice", "doi": "10.3333/ai.clinical.2023", "publication_date": "2023-05-10", } ], "citing": [ { "title": "Recent Advances in Health AI", "doi": "10.4444/recent.healthai.2023", "publication_date": "2023-11-20", } ], }, "statistics": { "references_count": 1, "similar_count": 1, "citing_count": 1, "total_relations": 3, }, }, "journal_quality": { "success": True, "journal_name": "Health AI Journal", "quality_metrics": { "impact_factor": 4.2, "quartile": "Q1", "jci": 1.8, "分区": "中科院二区", "citescore": 6.5, }, "ranking_info": { "field": "Medical Informatics", "rank": 15, "total_journals": 80, "percentile": 81.25, }, }, "export_data": { "success": True, "export_path": "/tmp/health_ai_literature_review.json", "format_type": "json", "records_exported": 2, "file_size": "15.3KB", }, } @pytest.fixture def performance_test_data(): """提供性能测试数据""" return { "small_batch": { "size": 10, "expected_time": 1.0, "expected_memory": 10, # MB }, "medium_batch": { "size": 100, "expected_time": 5.0, "expected_memory": 50, # MB }, "large_batch": { "size": 1000, "expected_time": 30.0, "expected_memory": 200, # MB }, "concurrent_operations": { "tools_count": 6, "expected_parallel_time": 2.0, "expected_serial_time": 8.0, }, } @pytest.fixture def error_scenarios(): """提供错误场景测试数据""" return { "network_errors": [ { "error_type": "ConnectionError", "message": "Failed to connect to API", "retry_possible": True, }, { "error_type": "TimeoutError", "message": "Request timed out", "retry_possible": True, }, { "error_type": "DNSResolutionError", "message": "Could not resolve hostname", "retry_possible": False, }, ], "api_errors": [ { "error_type": "RateLimitError", "message": "API rate limit exceeded", "retry_after": 60, "suggestion": "Reduce request frequency", }, { "error_type": "AuthenticationError", "message": "Invalid API credentials", "retry_possible": False, "suggestion": "Check API configuration", }, { "error_type": "InvalidRequestError", "message": "Invalid request parameters", "retry_possible": True, "suggestion": "Validate request parameters", }, ], "data_errors": [ { "error_type": "NotFoundError", "message": "Requested resource not found", "suggestion": "Check identifier validity", }, { "error_type": "ParseError", "message": "Failed to parse response data", "retry_possible": True, "suggestion": "Check data format", }, { "error_type": "ValidationError", "message": "Data validation failed", "retry_possible": False, "suggestion": "Check input data format", }, ], } @pytest.fixture(autouse=True) def test_environment(): """设置测试环境变量""" os.environ["PYTHONUNBUFFERED"] = "1" # 设置测试模式，避免实际API调用 os.environ["TESTING"] = "1" # 设置缓存测试模式 os.environ["CACHE_TEST_MODE"] = "1" # 禁用网络请求 os.environ["DISABLE_NETWORK_CALLS"] = "1"