Claude/implement research plan 01 wsn7n gi rhg ag qh mwa vi l9 q #2
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Implemented a comprehensive bidirectional diagramming system based on the research in misc/REASEARCH.md. This Phase 1 implementation includes: Core Architecture: - DiagramIR: AST-based intermediate representation for diagrams - Node and Edge types with extensible metadata - Validation and type-safe operations Features: - Text-to-Diagram conversion with simple rule-based parser - Mermaid renderer supporting multiple node/edge types - NetworkX integration for graph analysis (paths, cycles, topological sort) - CLI tool for command-line usage - Python API for programmatic access Components: - ij/core.py: Core data structures (DiagramIR, Node, Edge, enums) - ij/converters/text_to_ir.py: Text-to-diagram conversion - ij/renderers/mermaid.py: Mermaid syntax renderer - ij/graph_ops.py: NetworkX-based graph operations - ij/cli.py: Command-line interface Testing: - 26 comprehensive tests covering all modules - 100% test pass rate - Tests for core, renderers, converters, and graph operations Examples: - examples/basic_usage.py: 6 usage examples - Demonstrates text conversion, manual creation, graph analysis Documentation: - Comprehensive README with quick start, examples, roadmap - API documentation in docstrings - Examples directory with README Dependencies: - networkx>=3.0: Graph analysis foundation - graphviz>=0.20: Rendering support Following research recommendations: - AST-based IR for bidirectional conversion - Text-based DSL for version control - NetworkX for graph operations - Mermaid for GitHub-native diagrams - Extensible architecture for future formats (PlantUML, D2) All tests passing. Ready for Phase 2: Enhanced parsers and additional formats.
Added comprehensive bidirectional conversion capabilities and support for multiple diagram formats, completing Phase 2 of the roadmap. NEW FEATURES: 1. Mermaid Parser (Bidirectional Conversion): - Parse Mermaid flowchart syntax back to DiagramIR - Support for all node shapes (stadium, rhombus, cylinder, etc.) - Edge parsing with labels and types - Direction and title metadata extraction - Complete roundtrip validation (Mermaid → IR → Mermaid) 2. PlantUML Renderer: - Activity diagram syntax generation - Configurable skinparam styling - Support for all node types - Decision nodes with branches - Topological ordering for clean output 3. D2 Renderer (Terrastruct): - Modern, beautiful diagram syntax - Multiple layout engines (dagre, elk, tala) - Colored node styling by type - Direction conversion from Mermaid - Shape customization (oval, rectangle, diamond, cylinder) 4. Graphviz/DOT Renderer: - Classic graph visualization format - Multiple layout algorithms (dot, neato, fdp, circo, etc.) - Node coloring and styling - Edge type support (dashed, bidirectional) - Direct image rendering support 5. Enhanced Text Converter: - Conditional branches: "If condition: action, else: action" - Parallel flows: "[parallel: A, B, C]" - Loops: "while condition: action" - Better keyword detection and type inference - Sentence-style parsing support COMPONENTS ADDED: Parser Module: - ij/parsers/mermaid.py: Mermaid syntax parser - ij/parsers/__init__.py: Parser module exports Renderers: - ij/renderers/plantuml.py: PlantUML activity diagram renderer - ij/renderers/d2.py: D2 (Terrastruct) renderer - ij/renderers/graphviz.py: Graphviz/DOT renderer with image support - Updated ij/renderers/__init__.py: Export all renderers Converters: - ij/converters/enhanced_text.py: Advanced text parsing with NLP - Updated ij/converters/__init__.py: Export enhanced converter Tests (26 new tests): - tests/test_parsers.py: 7 parser tests including roundtrip validation - tests/test_new_renderers.py: 11 renderer tests for PlantUML, D2, Graphviz - tests/test_enhanced_converter.py: 8 enhanced converter tests Examples: - examples/phase2_features.py: 6 comprehensive examples demonstrating: * Bidirectional conversion workflow * Multi-format rendering * Enhanced text features * Format conversion pipelines * Complex workflows Documentation: - PHASE2.md: Complete Phase 2 feature documentation - Updated README.md: Phase 2 features, updated roadmap, test counts - Updated ij/__init__.py: Export all new classes TEST RESULTS: - 52 total tests (26 Phase 1 + 26 Phase 2) - 100% pass rate - All formats tested and validated - Roundtrip conversion verified CAPABILITIES: Multi-Format Workflow: Text → DiagramIR → Mermaid/PlantUML/D2/Graphviz Mermaid → DiagramIR → Any other format Supported Conversions: - Text → Mermaid (Phase 1) - Text → PlantUML/D2/Graphviz (Phase 2) - Mermaid → DiagramIR (Phase 2) - DiagramIR → Mermaid/PlantUML/D2/Graphviz (Phase 2) Enhanced Text Patterns: - Simple: "A -> B -> C" - Conditional: "If X: do Y, else: do Z" - Parallel: "[parallel: A, B, C]" - Loop: "while X: do Y" Following research recommendations: - Bidirectional editing (Mermaid ↔ IR) - Multiple format support (Mermaid, PlantUML, D2, Graphviz) - Enhanced natural language processing - Text-based DSL for version control - Extensible architecture for future parsers Ready for Phase 3: AI integration, visual editing, collaboration
Completed Phase 3 implementation with AI-powered diagram generation and code-to-diagram reverse engineering, achieving research-recommended features for modern diagramming systems. NEW FEATURES: 1. AI/LLM-Powered Diagram Generation: - OpenAI API integration for natural language → diagram - Uses gpt-4o-mini for cost-effective generation (~$0.00015/diagram) - Achieves 10-20x faster diagram creation (per research) - Intelligent node type inference - Clean, structured output - Temperature control for consistency 2. Iterative Diagram Refinement: - Conversational improvement with AI feedback - Add missing details to existing diagrams - Restructure flows based on feedback - Context-aware modifications - Preserves existing metadata 3. Python Code Analysis (AST-based): - Function flowchart generation from Python code - Control flow visualization (if/else, while, for) - Call graph generation showing dependencies - Class diagram generation with inheritance - Automatic node type inference - Support for nested conditions and loops 4. Hybrid Workflows: - Combine code analysis with AI enhancement - Analyze existing code → Generate diagram → Refine with AI - Supports iterative development - Preserves code structure while adding context COMPONENTS ADDED: Analyzers Module: - ij/analyzers/python_analyzer.py: Python AST analysis for diagrams * analyze_function() - function flowcharts * analyze_class() - class diagrams * analyze_module_calls() - call graphs - ij/analyzers/__init__.py: Analyzer exports Converters: - ij/converters/llm_converter.py: OpenAI API integration * convert() - natural language → diagram * refine() - iterative improvement * convert_with_examples() - few-shot learning - Updated ij/converters/__init__.py: Optional LLM export Tests (19 new tests): - tests/test_llm_converter.py: 9 tests (7 mocks + 2 optional real API) * Mock tests for all functionality (no API key required) * Optional real API tests (skip if OPENAI_API_KEY not set) * Uses gpt-4o-mini for cost-effective testing - tests/test_python_analyzer.py: 12 tests for code analysis * Function analysis with various control structures * Call graph generation * Class diagram generation * Error handling Examples: - examples/phase3_features.py: 7 comprehensive examples * Python function → flowchart * Call graph generation * Class diagram generation * AI-powered generation * Iterative refinement * Multi-format export from code * Hybrid workflows Documentation: - PHASE3.md: Complete Phase 3 documentation * AI model configuration * Cost estimation (~$1-40/month typical usage) * Python code analysis capabilities * Testing strategies * Best practices * Troubleshooting guide - Updated README.md: Phase 3 features, updated test counts - Updated ij/__init__.py: Export new classes Configuration: - Updated pyproject.toml: Added [ai] optional dependency group - Updated .github/workflows/ci.yml: OPENAI_API_KEY environment variable (Note: CI file uses composite actions, env set at test runner level) TEST RESULTS: - 71 total tests (26 Phase 1 + 26 Phase 2 + 19 Phase 3) - 100% pass rate with mocks - 2 additional optional tests with real OpenAI API - All tests skip gracefully if dependencies unavailable COST OPTIMIZATION: - Uses gpt-4o-mini by default (~10x cheaper than gpt-4o) - Temperature 0.3 for deterministic, consistent output - Mock tests for development (no API costs) - Optional real API tests (minimal cost: ~$0.0003/run) - Clear documentation on cost management PYTHON CODE ANALYSIS: Supported: - Control flow (if/elif/else, while, for) - Function calls and returns - Class definitions and inheritance - Method and attribute extraction - Nested conditions Capabilities: - Instant diagram generation from code - No manual drawing required - Accurate control flow representation - Handles complex nested structures AI/LLM CAPABILITIES: - Natural language understanding - Context-aware generation - Iterative refinement - Few-shot learning support - Error handling and validation - Code block cleanup INSTALLATION: ```bash # Core features (no AI) pip install ij # With AI support pip install ij[ai] export OPENAI_API_KEY=your-key ``` Following research recommendations: - AI-powered generation (10-20x speed improvement) - Code-to-diagram reverse engineering - Iterative refinement workflow - Cost-effective implementation - Production-ready with human review Ready for Phase 4: Visual editor, collaboration, multi-language support
Phase 4 completes the bidirectional diagramming system with advanced features for diagram manipulation, multi-format support, and sequence diagram generation. ## New Features ### 1. Bidirectional D2 Support - D2Parser: Parse D2 (Terrastruct) syntax to DiagramIR - Support for all node shapes (oval, rectangle, diamond, cylinder) - Edge types (direct, conditional, bidirectional) - Direction metadata handling - Automatic START/END node inference - Complete bidirectional D2 conversion ### 2. Sequence Diagram Generation - SequenceDiagramRenderer: Render DiagramIR as Mermaid sequence diagrams - Support for sync/async arrows (solid/dashed) - Notes and activations support - InteractionAnalyzer: Extract sequence diagrams from: * Python code (analyze method calls) * Natural language descriptions - Automatic participant detection ### 3. Diagram Transformations - DiagramTransforms class with powerful utilities: * simplify(): Remove isolated nodes and duplicate edges * filter_by_node_type(): Filter diagrams by node types * apply_node_filter(): Custom filtering with predicates * extract_subgraph(): Extract portions with BFS * merge_diagrams(): Combine multiple diagrams * reverse_edges(): Reverse all edge directions * find_cycles(): Detect circular dependencies * get_statistics(): Comprehensive metrics * merge_sequential_nodes(): Combine linear sequences ### 4. Multi-Format Workflows - Seamless conversion between all formats: * Mermaid (parse & render) * PlantUML (render) * D2 (parse & render) * Graphviz/DOT (render) ## Implementation Details New modules: - ij/parsers/d2.py: D2 parser (246 lines) - ij/renderers/sequence.py: Sequence diagrams (307 lines) - ij/transforms.py: Diagram transformations (400+ lines) New tests: - tests/test_d2_parser.py: 16 tests for D2 parsing - tests/test_sequence.py: 15 tests for sequence diagrams - tests/test_transforms.py: 20 tests for transformations Total: 51 new tests, all passing Overall: 124 tests (122 passing, 2 optional AI tests) ## Documentation - PHASE4.md: Comprehensive Phase 4 documentation - examples/phase4_features.py: 10 examples demonstrating all features - README.md: Updated with Phase 4 feature list ## Performance All operations optimized for efficiency: - Parsing: O(n) - Transformations: O(n + m) or better - No external dependencies beyond existing stack
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