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docs: Archive working documents and organize doc structure
Archived Phase 2 implementation documents: Task 6 Archive (phase2-task6/): - PHASE2_TASK6_FINAL_REPORT.md - Complete benchmarking results - TASK6_COMPLETION_SUMMARY.md - Performance optimization summary - Key achievement: 93% accuracy with 5:1 chunk-to-token ratio - Optimal config: 4000/800/800 documented Task 7 Archive (phase2-task7/): - PHASE2_TASK7_PLAN.md - Overall implementation plan - PHASE2_TASK7_PHASE1-5_*.md - All 5 phase implementations - PHASE4_COMPLETE.md, PHASE5_COMPLETE.md - Completion summaries - TASK7_TAGGING_ENHANCEMENT.md - Tagging enhancements - Key achievement: 93% → 99-100% accuracy through prompt engineering - 10 documents total covering full implementation Advanced Tagging Archive (advanced-tagging/): - ADVANCED_TAGGING_ENHANCEMENTS.md - ML-based classification - DOCUMENT_TAGGING_SYSTEM.md - Core system architecture - IMPLEMENTATION_SUMMARY_ADVANCED_TAGGING.md - Implementation details - INTEGRATION_GUIDE.md - Integration instructions - Key achievement: 95%+ tag accuracy with hybrid approach Archive Organization: - Created README.md in each archive folder - Documents organized by feature/task - Cross-references to current documentation - Historical context preserved Integration Status: ✅ Task 6: Integrated into user-guide/configuration.md ✅ Task 7: Integrated into codeDocs/ (agents, prompt_engineering, pipelines) ✅ Tagging: Integrated into features/document-tagging.md Updated doc/README.md: - Added archive folder references - Updated historical documentation section - Noted 60+ working docs now properly archived Files moved: 16 Archive READMEs created: 3 Total lines archived: ~10,000+ All working documents now properly archived with full traceability
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‎doc/.archive/README.md‎

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- **phase2/**: `PHASE_2_COMPLETION_STATUS.md`, `PHASE_2_IMPLEMENTATION_SUMMARY.md`
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- **phase3/**: `PHASE_3_COMPLETE.md`, `PHASE_3_PLAN.md`
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### Task-Specific Archives
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Focused documentation from specific tasks and feature implementations:
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#### Phase 2 Task 6: Performance Optimization (`phase2-task6/`)
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Parameter optimization and benchmarking results:
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- **Key Achievement**: 93% accuracy with 5:1 chunk-to-token ratio
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- **Optimal Config**: 4000/800/800 (chunk_size/overlap/max_tokens)
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- **Documents**: `PHASE2_TASK6_FINAL_REPORT.md`, `TASK6_COMPLETION_SUMMARY.md`
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- **Status**: Production-ready configuration documented
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#### Phase 2 Task 7: Prompt Engineering (`phase2-task7/`)
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Quality enhancement implementation achieving 99-100% accuracy:
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- **Key Achievement**: 93% → 99-100% accuracy through 5 phases
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- **Components**: RequirementsPromptLibrary, FewShotManager, ExtractionInstructionsLibrary, MultiStageExtractor
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- **Documents**: 10 files covering planning, implementation, and completion
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- **Status**: Integrated into code documentation (doc/codeDocs/)
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#### Advanced Tagging System (`advanced-tagging/`)
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ML-based document classification and tag-aware processing:
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- **Key Achievement**: 95%+ tag accuracy with hybrid ML+rule-based approach
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- **Features**: Multi-label classification, tag hierarchies, A/B testing, custom tags
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- **Documents**: System architecture, enhancements, implementation summary, integration guide
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- **Status**: Integrated into features documentation (doc/features/document-tagging.md)
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### Working Documents (`working-docs/`)
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Operational documents created during development, including:

doc/ADVANCED_TAGGING_ENHANCEMENTS.md renamed to doc/.archive/advanced-tagging/ADVANCED_TAGGING_ENHANCEMENTS.md

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doc/DOCUMENT_TAGGING_SYSTEM.md renamed to doc/.archive/advanced-tagging/DOCUMENT_TAGGING_SYSTEM.md

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doc/IMPLEMENTATION_SUMMARY_ADVANCED_TAGGING.md renamed to doc/.archive/advanced-tagging/IMPLEMENTATION_SUMMARY_ADVANCED_TAGGING.md

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doc/INTEGRATION_GUIDE.md renamed to doc/.archive/advanced-tagging/INTEGRATION_GUIDE.md

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‎doc/.archive/advanced-tagging/README.md‎

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# Advanced Tagging System Archive
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**Feature:** Advanced Document Tagging Enhancements
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**Date:** October 2025
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**Status:** ✅ Complete and Integrated
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## Overview
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This archive contains documentation for the advanced document tagging system enhancements, which provide intelligent document classification and tag-aware processing strategies.
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## Features Implemented
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### 1. Machine Learning-Based Classification
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- TF-IDF vectorization with Random Forest
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- Multi-label classification support
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- Confidence scoring per label
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- Model persistence and retraining
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### 2. Multi-Label Document Support
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- Assign multiple tags per document
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- Hierarchical tag relationships
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- Tag inheritance and propagation
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### 3. Tag Hierarchies
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- Parent-child tag relationships
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- Automatic inheritance
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- Category-based organization
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### 4. A/B Testing Framework
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- Compare tagging strategies
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- Statistical significance testing
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- Performance metrics tracking
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### 5. Custom User-Defined Tags
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- YAML-based tag configuration
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- Custom detection rules
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- Extensible taxonomy
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### 6. Real-Time Monitoring
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- Tag accuracy metrics
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- Performance dashboards
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- Alert system for anomalies
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## Archived Documents
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| File | Purpose | Lines | Date |
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|------|---------|-------|------|
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| ADVANCED_TAGGING_ENHANCEMENTS.md | Advanced features documentation | 851 | Oct 2025 |
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| DOCUMENT_TAGGING_SYSTEM.md | Core system architecture | 751 | Oct 2025 |
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| IMPLEMENTATION_SUMMARY_ADVANCED_TAGGING.md | Implementation summary | TBD | Oct 2025 |
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| INTEGRATION_GUIDE.md | Integration instructions | TBD | Oct 2025 |
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## Integration into Main Documentation
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The tagging system documentation has been integrated into:
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### Feature Documentation
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- **doc/features/document-tagging.md** - Complete tagging system guide
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- Automatic categorization
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- Tag types and taxonomies
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- ML-based classification
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- Hybrid approaches
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- Custom tags and A/B testing
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### Developer Documentation
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- **doc/developer-guide/architecture.md** - Tagging system architecture
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- **doc/developer-guide/api-reference.md** - DocumentTagger API
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### Code Documentation
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- **doc/codeDocs/utils.rst** - MLDocumentTagger, HybridTagger, DocumentTagger classes
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- **doc/codeDocs/agents.rst** - TagAwareDocumentAgent
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## Key Components
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### Core Classes
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**DocumentTagger** (`src/utils/document_tagger.py`)
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- Rule-based tag classification
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- Filename and content analysis
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- Confidence scoring
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**MLDocumentTagger** (`src/utils/ml_tagger.py`)
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- Machine learning classification
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- Model training and persistence
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- Multi-label prediction
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**HybridTagger** (`src/utils/ml_tagger.py`)
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- Combines rule-based and ML approaches
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- Adaptive confidence thresholds
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- Performance optimization
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**TagAwareDocumentAgent** (`src/agents/tag_aware_agent.py`)
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- Tag-aware prompt selection
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- Document-type-specific processing
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- Enhanced extraction strategies
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### Configuration Files
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- **config/document_tags.yaml** - Tag definitions and detection rules
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- **config/custom_tags.yaml** - User-defined custom tags
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- **config/enhanced_prompts.yaml** - Tag-specific prompts
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## Usage Example
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```python
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from src.utils.document_tagger import DocumentTagger
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from src.utils.ml_tagger import MLDocumentTagger, HybridTagger
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# Rule-based tagging
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rule_tagger = DocumentTagger()
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tag, confidence = rule_tagger.tag_document("requirements.pdf")
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# ML-based tagging
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ml_tagger = MLDocumentTagger()
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ml_tagger.load_model("production_tagger")
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predictions = ml_tagger.predict(content, threshold=0.3)
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# Hybrid approach (best of both)
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hybrid = HybridTagger(rule_tagger, ml_tagger)
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result = hybrid.tag_document("document.pdf", content)
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```
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## Performance Metrics
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- **Tag Accuracy:** 95%+ for well-known document types
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- **ML Model Accuracy:** 92%+ after training on 100+ documents
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- **Hybrid Approach:** Best performance combining both methods
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- **Processing Time:** <100ms per document
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## References
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For current documentation, see:
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- Feature Guide: `doc/features/document-tagging.md`
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- Architecture: `doc/developer-guide/architecture.md`
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- API Reference: `doc/developer-guide/api-reference.md`
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- Code Docs: `doc/codeDocs/utils.rst` and `doc/codeDocs/agents.rst`
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---
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*Archive created: October 7, 2025*
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*Original implementation: October 2025*

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‎doc/.archive/phase2-task6/README.md‎

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# Phase 2 Task 6 Archive
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**Task:** Performance Benchmarking and Parameter Optimization
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**Date:** October 5, 2025
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**Status:** ✅ Complete
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## Overview
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This archive contains documentation from Phase 2 Task 6, which focused on optimizing chunking and LLM parameters to achieve reproducible, high-accuracy requirements extraction.
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## Final Results
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- **Optimal Configuration:** TEST 4 (4000/800/800)
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- **Accuracy:** 93% (93/100 requirements)
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- **Reproducibility:** 100% (0% variance)
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- **Processing Time:** 13m 40s (23% faster than baseline)
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- **Key Discovery:** 5:1 chunk-to-token ratio is critical
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## Optimal Configuration
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```yaml
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Provider: ollama
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Model: qwen2.5:7b
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Temperature: 0.0
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Chunking:
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chunk_size: 4000 characters
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overlap: 800 characters (20%)
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LLM:
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max_tokens: 800
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chunk_to_token_ratio: 5:1
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```
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## Critical Discoveries
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1. **5:1 Chunk-to-Token Ratio:** The ratio of chunk size to max tokens is more important than absolute values
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2. **Higher Tokens Hurt:** Increasing max_tokens from 800 to 2048 decreased accuracy by 24%
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3. **Smaller Chunks Win:** 4000-character chunks outperform 6000-character chunks
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4. **20% Overlap Optimal:** Industry-standard overlap performs best
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5. **Temperature=0.0:** Enables 100% reproducibility
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## Test Results Summary
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| Test | Configuration | Accuracy | Time | Result |
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|------|--------------|----------|------|---------|
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| Baseline Run 1 | 6000/1200/1024 | 93% | 18m 4s | Inconsistent |
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| Baseline Run 2 | 6000/1200/1024 | 69% | 18m 4s | Inconsistent |
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| TEST 1 | 4000/1600/2048 | 73% | 32m 1s | Failed |
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| TEST 2 | 8000/3200/2048 | 75% | 21m 31s | Failed |
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| TEST 3 | 6000/1200/2048 | 69% | 16m 23s | Failed |
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| **TEST 4 Run 1** | **4000/800/800** | **93%** | **13m 51s** | **✅ OPTIMAL** |
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| **TEST 4 Run 2** | **4000/800/800** | **93%** | **13m 40s** | **✅ OPTIMAL** |
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## Archived Documents
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| File | Purpose | Date |
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|------|---------|------|
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| PHASE2_TASK6_FINAL_REPORT.md | Complete testing methodology and results | Oct 5, 2025 |
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| TASK6_COMPLETION_SUMMARY.md | Executive summary and recommendations | Oct 5, 2025 |
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## Integration into Main Documentation
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The key information has been integrated into:
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- **User Guide:** Configuration recommendations in `doc/user-guide/configuration.md`
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- **Developer Guide:** Parameter optimization insights in `doc/developer-guide/development-setup.md`
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- **Configuration Files:** `.env` and `.env.example` updated with optimal values
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## Key Achievements
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1. **Production-Ready Config:** Proven 93% accuracy with 0% variance
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2. **Performance Optimization:** 23% faster than baseline
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3. **Knowledge Base:** Complete testing methodology documented
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4. **Foundation for Task 7:** Fixed baseline for prompt engineering improvements
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## References
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For current documentation, see:
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- Configuration Guide: `doc/user-guide/configuration.md`
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- Development Setup: `doc/developer-guide/development-setup.md`
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- Environment Variables: `.env.example`
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---
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*Archive created: October 7, 2025*
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*Original implementation: October 5, 2025*

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