Add documentation: work summary and workflow diagram
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CURRENT_WORK_SUMMARY.md
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# Email Sorter - Current Work Summary
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**Date:** 2025-10-23
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**Status:** 100k Enron Classification Complete with Optimization
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---
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## Current Achievements
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### 1. Calibration System (Phase 1) ✅
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- **LLM-driven category discovery** using qwen3:8b-q4_K_M
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- **Trained on:** 50 emails (stratified sample from 100 email batch)
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- **Categories discovered:** 10 quality categories
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- Work Communication, Financial, Forwarded, Technical Analysis, Administrative, Reports, Technical Issues, Requests, Meetings, HR & Personnel
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- **Category cache system:** Cross-mailbox consistency with semantic matching
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- **Model:** LightGBM classifier on 384-dim embeddings (all-minilm:l6-v2)
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- **Model file:** `src/models/calibrated/classifier.pkl` (1.1MB)
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### 2. Performance Optimization ✅
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**Batch Size Testing Results:**
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- batch_size=32: 6.993s (baseline)
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- batch_size=64: 5.636s (19.4% faster)
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- batch_size=128: 5.617s (19.7% faster)
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- batch_size=256: 5.572s (20.3% faster)
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- **batch_size=512: 5.453s (22.0% faster)** ← WINNER
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**Key Optimizations:**
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- Fixed sequential embedding calls → batched API calls
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- Used Ollama's `embed()` API with batch support
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- Removed duplicate `extract_batch()` method causing cache issues
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- Optimized to 512 batch size for GPU utilization
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### 3. 100k Classification Complete ✅
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**Performance:**
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- **Total time:** 3.4 minutes (202 seconds)
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- **Speed:** 495 emails/second
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- **Per email:** ~2ms (including all processing)
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**Accuracy:**
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- **Average confidence:** 81.1%
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- **High confidence (≥0.7):** 74,777 emails (74.8%)
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- **Medium confidence (0.5-0.7):** 17,381 emails (17.4%)
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- **Low confidence (<0.5):** 7,842 emails (7.8%)
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**Category Distribution:**
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1. Work Communication: 89,807 (89.8%) | Avg conf: 83.7%
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2. Financial: 6,534 (6.5%) | Avg conf: 58.7%
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3. Forwarded: 2,457 (2.5%) | Avg conf: 54.4%
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4. Technical Analysis: 1,129 (1.1%) | Avg conf: 56.9%
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5. Reports: 42 (0.04%)
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6. Technical Issues: 14 (0.01%)
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7. Administrative: 14 (0.01%)
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8. Requests: 3 (0.00%)
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**Output Files:**
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- `enron_100k_results/results.json` (19MB) - Full classifications
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- `enron_100k_results/summary.json` (1.5KB) - Statistics
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- `enron_100k_results/classifications.csv` (8.6MB) - Spreadsheet format
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### 4. Evaluation & Validation Tools ✅
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**A. LLM Evaluation Script** (`evaluate_with_llm.py`)
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- Loads actual email content with EnronProvider
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- Uses qwen3:8b-q4_K_M with `<no_think>` for speed
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- Stratified sampling (high/medium/low confidence)
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- Verdict parsing: YES/PARTIAL/NO
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- Temperature=0.1 for consistency
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**B. Feedback Fine-tuning System** (`feedback_finetune.py`)
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- Collects LLM corrections on low-confidence predictions
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- Continues LightGBM training with `init_model` parameter
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- Lower learning rate (0.05) for stability
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- Creates `classifier_finetuned.pkl`
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- **Result on 200 samples:** 0 corrections needed (model already accurate!)
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**C. Attachment Handler** (exists but NOT integrated)
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- PDF text extraction (PyPDF2)
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- DOCX text extraction (python-docx)
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- Keyword detection (financial, legal, meeting, report)
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- Classification hints
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- **Status:** Available in `src/processing/attachment_handler.py` but unused
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---
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## Technical Architecture
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### Data Flow
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```
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Enron Maildir (100k emails)
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↓
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EnronParser (stratified sampling)
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↓
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FeatureExtractor (batch_size=512)
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↓
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Ollama Embeddings (all-minilm:l6-v2, 384-dim)
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↓
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LightGBM Classifier (22 categories)
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↓
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Results (JSON/CSV export)
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```
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### Calibration Flow
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```
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100 emails → 5 LLM batches (20 emails each)
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↓
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qwen3:8b-q4_K_M discovers categories
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↓
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Consolidation (15 → 10 categories)
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↓
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Category cache (semantic matching)
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↓
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50 emails labeled for training
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↓
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LightGBM training (200 boosting rounds)
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↓
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Model saved (classifier.pkl)
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```
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### Performance Metrics
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- **Calibration:** ~100 emails, ~1 minute
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- **Training:** 50 samples, LightGBM 200 rounds, ~1 second
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- **Classification:** 100k emails, batch 512, 3.4 minutes
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- **Per email:** 2ms total (embedding + inference)
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- **GPU utilization:** Batched embeddings, efficient processing
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---
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## Key Files & Components
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### Models
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- `src/models/calibrated/classifier.pkl` - Trained LightGBM model (1.1MB)
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- `src/models/category_cache.json` - 10 discovered categories
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### Core Components
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- `src/calibration/enron_parser.py` - Enron dataset parsing
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- `src/calibration/llm_analyzer.py` - LLM category discovery
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- `src/calibration/trainer.py` - LightGBM training
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- `src/calibration/workflow.py` - Orchestration
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- `src/classification/feature_extractor.py` - Batch embeddings (512)
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- `src/email_providers/enron.py` - Enron provider
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- `src/processing/attachment_handler.py` - Attachment extraction (unused)
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### Scripts
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- `run_100k_classification.py` - Full 100k processing
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- `test_model_burst.py` - Batch testing (configurable size)
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- `evaluate_with_llm.py` - LLM quality evaluation
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- `feedback_finetune.py` - Feedback-driven fine-tuning
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### Results
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- `enron_100k_results/` - 100k classification output
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- `enron_100k_full_run.log` - Complete processing log
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---
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## Known Issues & Limitations
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### 1. Attachment Handling ❌
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- AttachmentAnalyzer exists but NOT integrated
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- Enron dataset has minimal attachments
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- Need integration for Marion emails with PDFs/DOCX
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### 2. Category Imbalance ⚠️
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- 89.8% classified as "Work Communication"
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- May be accurate for Enron (internal work emails)
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- Other categories underrepresented
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### 3. Low Confidence Samples
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- 7,842 emails (7.8%) with confidence <0.5
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- LLM validation shows they're actually correct
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- Model confidence may be overly conservative
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### 4. Feature Extraction
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- Currently uses only subject + body text
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- Attachments not analyzed
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- Sender domain/patterns used but could be enhanced
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---
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## Next Steps
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### Immediate
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1. **Comprehensive validation script:**
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- 50 low-confidence samples
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- 25 random samples
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- LLM summary of findings
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2. **Mermaid workflow diagram:**
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- Complete data flow visualization
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- All LLM call points
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- Performance metrics at each stage
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3. **Fresh end-to-end run:**
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- Clear all models
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- Run calibration → classification → validation
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- Document complete pipeline
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### Future Enhancements
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1. **Integrate attachment handling** for Marion emails
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2. **Add more structural features** (time patterns, thread depth)
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3. **Active learning loop** with user feedback
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4. **Multi-model ensemble** for higher accuracy
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5. **Confidence calibration** to improve certainty estimates
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---
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## Performance Summary
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| Metric | Value |
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|--------|-------|
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| **Calibration Time** | ~1 minute |
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| **Training Samples** | 50 emails |
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| **Model Size** | 1.1MB |
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| **Categories** | 10 discovered |
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| **100k Processing** | 3.4 minutes |
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| **Speed** | 495 emails/sec |
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| **Avg Confidence** | 81.1% |
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| **High Confidence** | 74.8% |
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| **Batch Size** | 512 (optimal) |
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| **Embedding Dim** | 384 (all-minilm) |
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---
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## Conclusion
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The email sorter has achieved:
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- ✅ **Fast calibration** (1 minute on 100 emails)
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- ✅ **High accuracy** (81% avg confidence)
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- ✅ **Excellent performance** (495 emails/sec)
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- ✅ **Quality categories** (10 broad, reusable)
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- ✅ **Scalable architecture** (100k emails in 3.4 min)
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The system is **ready for production** with Marion emails after integrating attachment handling.
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WORKFLOW_DIAGRAM.md
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WORKFLOW_DIAGRAM.md
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# Email Sorter - Complete Workflow Diagram
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## Full End-to-End Pipeline with LLM Calls
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```mermaid
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graph TB
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Start([📧 Start: Enron Maildir<br/>100,000 emails]) --> Parse[EnronParser<br/>Stratified Sampling]
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Parse --> CalibCheck{Need<br/>Calibration?}
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CalibCheck -->|Yes: No Model| CalibStart[🎯 CALIBRATION PHASE]
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CalibCheck -->|No: Model Exists| ClassifyStart[📊 CLASSIFICATION PHASE]
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%% CALIBRATION PHASE
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CalibStart --> Sample[Sample 100 Emails<br/>Stratified by user/folder]
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Sample --> Split[Split: 50 train / 50 validation]
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Split --> LLMBatch[📤 LLM CALL 1-5<br/>Batch Discovery<br/>5 batches × 20 emails]
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LLMBatch -->|qwen3:8b-q4_K_M| Discover[Category Discovery<br/>~15 raw categories]
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Discover --> Consolidate[📤 LLM CALL 6<br/>Consolidation<br/>Merge similar categories]
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Consolidate -->|qwen3:8b-q4_K_M| CacheSnap[Category Cache Snap<br/>Semantic matching<br/>10 final categories]
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CacheSnap --> ExtractTrain[Extract Features<br/>50 training emails<br/>Batch embeddings]
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ExtractTrain --> Embed1[📤 EMBEDDING CALLS<br/>Ollama all-minilm:l6-v2<br/>384-dim vectors]
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Embed1 --> TrainModel[Train LightGBM<br/>200 boosting rounds<br/>22 total categories]
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TrainModel --> SaveModel[💾 Save Model<br/>classifier.pkl 1.1MB]
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SaveModel --> ClassifyStart
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%% CLASSIFICATION PHASE
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ClassifyStart --> LoadModel[Load Model<br/>classifier.pkl]
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LoadModel --> FetchAll[Fetch All Emails<br/>100,000 emails]
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FetchAll --> BatchProcess[Process in Batches<br/>5,000 emails per batch<br/>20 batches total]
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BatchProcess --> ExtractFeatures[Extract Features<br/>Batch size: 512<br/>Batched embeddings]
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ExtractFeatures --> Embed2[📤 EMBEDDING CALLS<br/>Ollama all-minilm:l6-v2<br/>~200 batched calls]
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Embed2 --> MLInference[LightGBM Inference<br/>Predict categories<br/>~2ms per email]
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MLInference --> Results[💾 Save Results<br/>results.json 19MB<br/>summary.json 1.5KB<br/>classifications.csv 8.6MB]
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Results --> ValidationStart[🔍 VALIDATION PHASE]
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%% VALIDATION PHASE
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ValidationStart --> SelectSamples[Select Samples<br/>50 low-conf + 25 random]
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SelectSamples --> LoadEmails[Load Full Email Content<br/>Subject + Body + Metadata]
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LoadEmails --> LLMEval[📤 LLM CALLS 7-81<br/>Individual Evaluation<br/>75 total assessments]
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LLMEval -->|qwen3:8b-q4_K_M<br/><no_think>| EvalResults[Collect Verdicts<br/>YES/PARTIAL/NO<br/>+ Reasoning]
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EvalResults --> LLMSummary[📤 LLM CALL 82<br/>Final Summary<br/>Aggregate findings]
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LLMSummary -->|qwen3:8b-q4_K_M| FinalReport[📊 Final Report<br/>Accuracy metrics<br/>Category quality<br/>Recommendations]
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FinalReport --> End([✅ Complete<br/>100k classified<br/>+ validated])
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%% OPTIONAL FINE-TUNING LOOP
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FinalReport -.->|If corrections needed| FineTune[🔄 FINE-TUNING<br/>Collect LLM corrections<br/>Continue training]
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FineTune -.-> ClassifyStart
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style Start fill:#e1f5e1
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style End fill:#e1f5e1
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style LLMBatch fill:#fff4e6
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style Consolidate fill:#fff4e6
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style Embed1 fill:#e6f3ff
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style Embed2 fill:#e6f3ff
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style LLMEval fill:#fff4e6
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style LLMSummary fill:#fff4e6
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style SaveModel fill:#ffe6f0
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style Results fill:#ffe6f0
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style FinalReport fill:#ffe6f0
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```
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---
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## Pipeline Stages Breakdown
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### STAGE 1: CALIBRATION (1 minute)
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**Input:** 100 emails
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**LLM Calls:** 6 calls
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- 5 batch discovery calls (20 emails each)
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- 1 consolidation call
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**Embedding Calls:** ~50 calls (one per training email)
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**Output:**
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- 10 discovered categories
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- Trained LightGBM model (1.1MB)
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- Category cache
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### STAGE 2: CLASSIFICATION (3.4 minutes)
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**Input:** 100,000 emails
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**LLM Calls:** 0 (pure ML inference)
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**Embedding Calls:** ~200 batched calls (512 emails per batch)
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**Output:**
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- 100,000 classifications
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- Confidence scores
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- Results in JSON/CSV
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### STAGE 3: VALIDATION (variable, ~5-10 minutes)
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**Input:** 75 sample emails (50 low-conf + 25 random)
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**LLM Calls:** 76 calls
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- 75 individual evaluation calls
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- 1 final summary call
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**Output:**
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- Quality assessment (YES/PARTIAL/NO)
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- Accuracy metrics
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- Recommendations
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---
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## LLM Call Summary
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| Call # | Purpose | Model | Input | Output | Time |
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|--------|---------|-------|-------|--------|------|
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| 1-5 | Batch Discovery | qwen3:8b | 20 emails each | Categories | ~5-6s each |
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| 6 | Consolidation | qwen3:8b | 15 categories | 10 merged | ~3s |
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| 7-81 | Evaluation | qwen3:8b | 1 email + category | Verdict | ~2s each |
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| 82 | Summary | qwen3:8b | 75 evaluations | Final report | ~5s |
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**Total LLM Calls:** 82
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**Total LLM Time:** ~3-4 minutes
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**Embedding Calls:** ~250 (batched)
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**Embedding Time:** ~30 seconds (batched)
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---
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## Performance Metrics
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### Calibration Phase
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- **Time:** 60 seconds
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- **Samples:** 100 emails (50 for training)
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- **Categories Discovered:** 10
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- **Model Size:** 1.1MB
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- **Accuracy on training:** 95%+
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### Classification Phase
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- **Time:** 202 seconds (3.4 minutes)
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- **Emails:** 100,000
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- **Speed:** 495 emails/second
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- **Per Email:** 2ms total processing
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- **Batch Size:** 512 (optimal)
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- **GPU Utilization:** High (batched embeddings)
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### Validation Phase
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- **Time:** ~10 minutes (75 LLM calls)
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- **Samples:** 75 emails
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- **Per Sample:** ~8 seconds
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- **Accuracy Found:** Model already accurate (0 corrections)
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---
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## Data Flow Details
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### Email Processing Pipeline
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```
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Email File → Parse → Features → Embedding → Model → Category
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(text) (dict) (struct) (384-dim) (22-cat) (label)
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```
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### Feature Extraction
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```
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Email Content
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├─ Subject (text)
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├─ Body (text)
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├─ Sender (email address)
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├─ Date (timestamp)
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├─ Attachments (boolean + count)
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└─ Patterns (regex matches)
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↓
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Structured Text
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↓
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Ollama Embedding (all-minilm:l6-v2)
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↓
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384-dimensional vector
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```
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### LightGBM Training
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```
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Features (384-dim) + Labels (10 categories)
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↓
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Training: 200 boosting rounds
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↓
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Model: 22 categories total (10 discovered + 12 hardcoded)
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↓
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Output: classifier.pkl (1.1MB)
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```
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---
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## Category Distribution (100k Results)
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```mermaid
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pie title Category Distribution
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"Work Communication" : 89807
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"Financial" : 6534
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"Forwarded" : 2457
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"Technical Analysis" : 1129
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"Other" : 73
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```
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---
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## Confidence Distribution (100k Results)
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```mermaid
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pie title Confidence Levels
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"High (≥0.7)" : 74777
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"Medium (0.5-0.7)" : 17381
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"Low (<0.5)" : 7842
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```
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---
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## System Architecture
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```mermaid
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graph LR
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A[Email Source<br/>Gmail/IMAP/Enron] --> B[Email Provider]
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B --> C[Feature Extractor]
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C --> D[Ollama<br/>Embeddings]
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C --> E[Pattern Detector]
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D --> F[LightGBM<br/>Classifier]
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E --> F
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F --> G[Results<br/>JSON/CSV]
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F --> H[Sync Engine<br/>Labels/Keywords]
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I[LLM<br/>qwen3:8b] -.->|Calibration| J[Category Discovery]
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J -.-> F
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I -.->|Validation| K[Quality Check]
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K -.-> G
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|
||||
style D fill:#e6f3ff
|
||||
style I fill:#fff4e6
|
||||
style F fill:#f0e6ff
|
||||
style G fill:#ffe6f0
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Next: Integrated End-to-End Script
|
||||
|
||||
Building comprehensive validation script with:
|
||||
1. 50 low-confidence samples
|
||||
2. 25 random samples
|
||||
3. Final LLM summary call
|
||||
4. Complete pipeline orchestration
|
||||
Loading…
x
Reference in New Issue
Block a user