EuConquisto Composer MCP

--- document: Task Specification - Advanced NLP Content Processor version: 1.0.0 status: active author: Claude Code created: 2025-06-28 last_updated: 2025-06-28 --- # TASK-004: Advanced Natural Language Processing Content Processor ## 📋 Task Overview **Task ID**: TASK-004 **Title**: Advanced NLP Content Processor **Status**: pending **Owner**: Claude Desktop **Priority**: medium **Dependencies**: TASK-003 (enhanced widget system) **Created**: 2025-06-28 13:52 EST **Updated**: 2025-06-28 13:52 EST ## 🎯 Objective Develop an advanced natural language processing system that intelligently converts educational content into appropriate widget structures, with support for Portuguese (pt_br) and English, intent recognition, and content optimization. ## 📊 Current Context ### Current NLP Capabilities - ✅ Basic markdown-to-widget conversion - ✅ Simple header detection (# patterns) - ✅ Text widget creation from paragraphs - ⚠️ Limited intent recognition - ❌ No content structure analysis - ❌ No educational content optimization ### Target Enhancement Areas ``` NLP Processing Pipeline: ├── Content Analysis │ ├── Language detection (pt_br/en) │ ├── Educational content classification │ └── Structure recognition ├── Intent Recognition │ ├── Learning objective extraction │ ├── Content type identification │ └── Interaction pattern detection ├── Widget Mapping Intelligence │ ├── Content-to-widget optimization │ ├── Educational flow design │ └── Engagement enhancement └── Content Optimization ├── Readability analysis ├── Learning path creation └── Accessibility improvements ``` ## 🏗️ 4-Phase Execution Plan ### Phase 1: Understand Scope, Plan Implementation, Define Deliverables #### Scope Analysis ``` Advanced NLP System Components: ├── Language Detection Service │ ├── Portuguese (Brazil) support │ ├── English language support │ └── Mixed content handling ├── Educational Content Classifier │ ├── Learning objective extraction │ ├── Content difficulty assessment │ └── Pedagogical pattern recognition ├── Intent Recognition Engine │ ├── User goal identification │ ├── Content purpose analysis │ └── Interaction requirement detection ├── Smart Widget Mapper │ ├── Content-to-widget optimization │ ├── Educational flow design │ └── Engagement pattern application ├── Content Structure Analyzer │ ├── Document hierarchy detection │ ├── Section relationship mapping │ └── Cross-reference identification └── Optimization Engine ├── Readability enhancement ├── Learning path creation └── Accessibility compliance ``` #### Implementation Plan ``` 1. Language Detection & Analysis - Implement pt_br/en detection - Content classification algorithms - Educational pattern recognition 2. Intent Recognition System - Learning objective extraction - Content purpose identification - Interaction requirement analysis 3. Smart Widget Mapping - Advanced content-to-widget algorithms - Educational flow optimization - Engagement enhancement rules 4. Content Optimization - Readability analysis - Learning path generation - Accessibility improvements 5. Integration & Testing - Composition manager integration - Performance optimization - Accuracy validation ``` #### Deliverables ``` Primary Artifacts: ├── /src/nlp/ │ ├── language-detector.ts │ ├── content-classifier.ts │ ├── intent-recognizer.ts │ ├── widget-mapper.ts │ ├── structure-analyzer.ts │ └── optimization-engine.ts ├── /src/nlp/models/ │ ├── educational-patterns.json │ ├── intent-patterns.json │ └── widget-mapping-rules.json ├── /src/nlp/processors/ │ ├── portuguese-processor.ts │ ├── english-processor.ts │ └── mixed-content-processor.ts └── /tests/nlp/ ├── language-detection.test.js ├── intent-recognition.test.js ├── widget-mapping.test.js └── content-optimization.test.js Configuration: ├── /config/nlp/ │ ├── language-models.json │ ├── educational-taxonomies.json │ ├── intent-patterns.json │ └── optimization-rules.json Documentation: ├── /docs/guides/nlp-usage.md ├── /docs/api/nlp-api.md ├── /docs/examples/nlp-examples.md └── /docs/analysis/nlp-performance.md ``` **STOP AND WAIT** - Do not proceed to implementation **DO NOT** update knowledge graph **PAUSE** for explicit next-phase instructions ### Phase 2: Implementation #### Step 1: Create Artifacts ``` Implementation Order: 1. Language Detection Service (/src/nlp/language-detector.ts) - Portuguese (Brazil) detection - English language detection - Mixed content analysis - Confidence scoring 2. Educational Content Classifier (/src/nlp/content-classifier.ts) - Learning objective extraction - Content type identification - Difficulty level assessment - Pedagogical pattern recognition 3. Intent Recognition Engine (/src/nlp/intent-recognizer.ts) - User goal identification - Content purpose analysis - Interaction requirement detection - Context understanding 4. Smart Widget Mapper (/src/nlp/widget-mapper.ts) - Content-to-widget optimization - Educational flow design - Engagement pattern application - Widget sequence optimization 5. Structure Analyzer (/src/nlp/structure-analyzer.ts) - Document hierarchy detection - Section relationship mapping - Cross-reference identification - Navigation structure creation 6. Optimization Engine (/src/nlp/optimization-engine.ts) - Readability analysis and enhancement - Learning path creation - Accessibility compliance checking - Performance optimization 7. Language-Specific Processors - Portuguese processor with Brazilian patterns - English processor with educational focus - Mixed content handling 8. Model and Pattern Files - Educational pattern recognition models - Intent recognition patterns - Widget mapping rules and algorithms ``` #### Step 2: Validate ``` Testing Protocol: 1. Language Detection Testing - Portuguese content accuracy - English content accuracy - Mixed language handling - Performance benchmarking 2. Educational Classification Testing - Learning objective extraction accuracy - Content type identification precision - Difficulty assessment validation - Pattern recognition reliability 3. Intent Recognition Testing - Goal identification accuracy - Purpose analysis precision - Context understanding validation - Multi-intent content handling 4. Widget Mapping Testing - Content-to-widget optimization accuracy - Educational flow effectiveness - Engagement pattern application - Sequence optimization validation 5. Integration Testing - Composition manager integration - End-to-end workflow validation - Performance impact assessment - Error handling verification ``` **STOP AND WAIT** - Do not proceed to Phase 3 **DO NOT** update knowledge graph **PAUSE** for explicit next-phase instructions ### Phase 3: Documentation #### Step 1: Knowledge Graph Updates ``` Entities to Create: ├── Advanced NLP System Entity ├── Language Detection Service Entity ├── Content Classifier Entity ├── Intent Recognition Engine Entity ├── Widget Mapper Entity ├── Structure Analyzer Entity └── Optimization Engine Entity Relations to Establish: ├── NLP System → Uses → Language Detection ├── NLP System → Uses → Content Classifier ├── NLP System → Uses → Intent Recognition ├── Widget Mapper → Optimizes → Widget Creation ├── Structure Analyzer → Analyzes → Content Structure └── Optimization Engine → Enhances → Content Quality ``` #### Step 2: Progress Tracking ``` Documentation Updates: ├── /docs/progress/2025-06-28.md (update completion) ├── /docs/architecture/nlp-system.md (new) ├── /docs/guides/nlp-usage.md (comprehensive guide) ├── /docs/api/nlp-api.md (API documentation) └── /docs/examples/nlp-examples.md (usage examples) Status Updates: ├── Mark TASK-004 as COMPLETED ├── Document created files ├── Update NLP capabilities └── Synchronize all documentation ``` **STOP AND WAIT** - Do not proceed to Phase 4 **DO NOT** update knowledge graph **PAUSE** for explicit next-phase instructions ### Phase 4: Thorough Verification #### Validation Protocol ``` 1. Implementation Completeness Check ├── Verify all NLP components implemented ├── Check language support functional └── Validate optimization algorithms 2. System Validation ├── Test educational content processing ├── Validate intent recognition accuracy └── Confirm widget mapping optimization 3. Performance Validation ├── Processing speed benchmarks ├── Memory usage optimization └── Accuracy measurements 4. Documentation Validation ├── API documentation completeness ├── Usage guide accuracy └── Example validation ``` #### Verification Checklist ``` Per Component Verification: □ Language Detection - pt_br/en support □ Content Classifier - educational patterns □ Intent Recognition - goal identification □ Widget Mapper - optimization algorithms □ Structure Analyzer - hierarchy detection □ Optimization Engine - readability enhancement □ Portuguese Processor - Brazilian patterns □ English Processor - educational focus □ Mixed Content Handler - multi-language □ Performance benchmarks - acceptable □ Documentation complete - comprehensive ``` ## 🔗 Related Files ### Dependencies - `/src/composition-manager.ts` - Current NLP implementation - `/src/widgets/widget-factory.ts` - Widget creation system - Educational content analysis from previous sessions ### Analysis References - Widget analysis (6 types) for mapping rules - Educational content patterns - Portuguese language educational standards ## 📈 Success Criteria ### Primary Goals 1. **Language Support**: Accurate pt_br and English processing 2. **Intent Recognition**: >85% accuracy in goal identification 3. **Widget Optimization**: Improved content-to-widget mapping 4. **Educational Enhancement**: Learning-focused content structuring ### Secondary Goals 1. **Performance**: <500ms processing time for typical content 2. **Accessibility**: WCAG 2.1 compliance suggestions 3. **Extensibility**: Easy addition of new languages/patterns 4. **Documentation**: Comprehensive guides and examples ## 🧠 NLP Processing Pipeline ### Content Analysis Flow ``` Input Content ↓ Language Detection ↓ Content Classification ↓ Intent Recognition ↓ Structure Analysis ↓ Widget Mapping ↓ Optimization ↓ Output Widgets ``` ### Educational Pattern Recognition ``` Learning Objectives: ├── Knowledge Transfer │ ├── Factual information → Text widgets │ ├── Conceptual explanation → Header + Text │ └── Procedural steps → List widgets ├── Skill Development │ ├── Interactive exercises → Hotspot widgets │ ├── Visual examples → Image/Gallery widgets │ └── Practice scenarios → Mixed widget sequences └── Assessment ├── Questions → Interactive widgets ├── Self-evaluation → List widgets └── Reflection prompts → Text widgets ``` ### Portuguese Language Specifics ``` Brazilian Portuguese Patterns: ├── Educational Terminology │ ├── Learning objectives ("objetivos de aprendizagem") │ ├── Activities ("atividades") │ └── Assessments ("avaliações") ├── Content Structure │ ├── Introduction patterns │ ├── Development sections │ └── Conclusion markers └── Interaction Cues ├── Call-to-action phrases ├── Question indicators └── Reflection prompts ``` --- **Note**: This task creates a sophisticated NLP system specifically designed for educational content, with strong support for Portuguese (Brazilian) and English languages, enabling intelligent and pedagogically-sound content structuring.