Code-Index-MCP

# Comprehensive Test Suite Implementation Summary ## Overview Implemented comprehensive test cases for 5 key document processing features, with each test file containing 6-8 test methods covering the main functionality. ## Test Files Implemented ### 1. test_natural_language_queries.py **Purpose**: Test natural language query processing capabilities **Test Methods**: 1. `test_query_parsing_basic` - Basic query parsing and tokenization 2. `test_intent_detection_types` - Query intent detection (symbol_search, documentation_search, etc.) 3. `test_query_expansion_synonyms` - Query expansion with synonyms and related terms 4. `test_contextual_query_understanding` - Understanding queries in context 5. `test_fuzzy_matching_tolerance` - Fuzzy matching for misspellings and variations 6. `test_semantic_query_matching` - Semantic understanding of queries 7. `test_ranking_algorithm_accuracy` - Result ranking based on relevance 8. `test_multi_language_query_support` - Queries spanning multiple programming languages **Key Features Tested**: - Query parsing and tokenization - Intent detection with confidence scores - Query expansion with synonyms - Fuzzy matching with tolerance - Semantic search capabilities - Ranking algorithms - Cross-language search ### 2. test_document_structure_extraction.py (Enhanced) **Purpose**: Test document structure extraction and navigation **Existing Test Methods** (preserved): - `test_heading_hierarchy_extraction` - `test_code_block_extraction` - `test_metadata_extraction` - `test_table_extraction` - `test_section_depth_search` - `test_list_content_extraction` - `test_cross_reference_extraction` - `test_structured_search_queries` - `test_document_outline_generation` **Key Features**: - Heading hierarchy extraction - Code block detection with language tags - Frontmatter metadata parsing - Table content extraction - Multi-level section navigation - Cross-reference detection - Document outline generation ### 3. test_cross_document_search.py **Purpose**: Test multi-document search and linking capabilities **Test Methods**: 1. `test_multi_document_basic_search` - Search across multiple documents 2. `test_document_linking_discovery` - Discover links between documents 3. `test_citation_tracking` - Track citations and references 4. `test_semantic_document_clustering` - Group documents by semantic similarity 5. `test_cross_reference_resolution` - Resolve cross-references between docs 6. `test_document_similarity_search` - Find similar documents 7. `test_topic_based_document_discovery` - Discover documents by topic **Key Features Tested**: - Multi-document search with relevance scoring - Document graph building with nodes and edges - Citation extraction and co-citation patterns - Semantic clustering by topic - Cross-reference resolution with anchors - Document similarity scoring - Topic-based discovery ### 4. test_metadata_search.py (Enhanced) **Purpose**: Test metadata-based search and filtering **Test Methods**: 1. `test_frontmatter_parsing_yaml` - Parse YAML frontmatter 2. `test_author_filtering` - Filter documents by author 3. `test_date_range_filtering` - Filter by date ranges 4. `test_tag_based_search` - Search by tags (AND/OR modes) 5. `test_custom_metadata_fields` - Handle custom metadata fields 6. `test_metadata_combination_queries` - Combine multiple metadata criteria 7. `test_metadata_validation_extraction` - Handle edge cases 8. `test_metadata_indexing_performance` - Performance with many documents **Key Features Tested**: - YAML frontmatter parsing - Author and multi-author filtering - Date range queries - Tag-based search with Boolean logic - Custom field support (nested fields) - Complex metadata queries - Edge case handling (invalid YAML, missing metadata) - Performance testing ### 5. test_section_search.py (Enhanced) **Purpose**: Test section-specific search capabilities **Test Methods**: 1. `test_section_specific_search` - Search within specific sections 2. `test_heading_based_search` - Search by heading patterns 3. `test_nested_section_queries` - Queries in nested sections 4. `test_section_range_search` - Search within section ranges 5. `test_section_context_awareness` - Maintain section context 6. `test_section_metadata_search` - Search sections with metadata 7. `test_hierarchical_section_search` - Hierarchical section awareness 8. `test_section_boundary_detection` - Accurate boundary detection **Key Features Tested**: - Section-specific search - Heading pattern matching - Nested section navigation - Section range queries - Context preservation in results - Section metadata (tags, attributes) - Hierarchical search with exclusions - Section boundary accuracy ## Common Test Patterns ### Base Test Class Usage All test classes inherit from `BaseDocumentTest` which provides: - Automatic setup/teardown - SQLite store initialization - Plugin initialization (Markdown, PlainText) - Test file creation utilities - Validation helpers ### Test Data Creation Each test creates realistic documents with: - Rich metadata (frontmatter) - Nested structure (headings, sections) - Code blocks with language tags - Cross-references and links - Tables and lists - Various content types ### Assertion Patterns - Result count validation - Content verification - Metadata field checking - Score/ranking validation - Performance benchmarks - Edge case handling ## Integration Points ### Dispatcher Methods Tested - `parse_query()` - Query parsing - `detect_intent()` - Intent detection - `expand_query()` - Query expansion - `search_with_context()` - Contextual search - `fuzzy_search()` - Fuzzy matching - `search_semantic()` - Semantic search - `ranked_search()` - Ranked results - `search_cross_document()` - Multi-doc search - `build_document_graph()` - Document linking - `search_by_metadata()` - Metadata filtering - `search_in_section()` - Section search ### Plugin Integration - MarkdownPlugin for document parsing - SectionExtractor for structure extraction - DocumentParser for content parsing - FrontmatterParser for metadata - EnhancedDispatcher for advanced features ## Performance Considerations ### Test Performance - Indexing performance tested with 50+ documents - Query performance benchmarked < 1 second - Memory usage monitored - Parallel processing support ### Optimization Areas - Batch indexing for multiple documents - Caching for repeated queries - Efficient section boundary detection - Optimized metadata indexing ## Usage Examples ### Running Tests ```bash # Run all document feature tests pytest test_natural_language_queries.py test_document_structure_extraction.py test_cross_document_search.py test_metadata_search.py test_section_search.py -v # Run specific test pytest test_natural_language_queries.py::TestNaturalLanguageQueries::test_fuzzy_matching_tolerance -v # Run with coverage pytest test_*.py --cov=mcp_server --cov-report=html ``` ### Example Test Output ``` test_natural_language_queries.py::TestNaturalLanguageQueries::test_query_parsing_basic PASSED test_natural_language_queries.py::TestNaturalLanguageQueries::test_intent_detection_types PASSED test_natural_language_queries.py::TestNaturalLanguageQueries::test_query_expansion_synonyms PASSED ... ``` ## Future Enhancements ### Potential Additional Tests 1. Multi-language document support (i18n) 2. Real-time document updates 3. Concurrent search operations 4. Advanced query syntax (boolean, proximity) 5. Machine learning model integration 6. Graph-based navigation 7. Version control integration 8. Collaborative filtering ### Performance Improvements 1. Indexed metadata storage 2. Query result caching 3. Parallel document processing 4. Incremental indexing 5. Distributed search ## Conclusion This comprehensive test suite provides thorough coverage of document processing features including: - Natural language understanding - Document structure extraction - Cross-document search - Metadata-based filtering - Section-specific search Each test file contains 6-8 focused test methods that validate both basic functionality and advanced features, ensuring the system meets user requirements for intelligent document search and navigation.