Add CLAUDE.md - Comprehensive development guide for AI assistants
Content: - Project overview and MVP status - Architecture and performance metrics - Critical implementation details (batched embeddings, model paths) - Multi-account credential management - Common commands and code patterns - Performance optimization opportunities - Known issues and troubleshooting - Dependencies and git workflow - Recent changes and roadmap Key Sections: - Batched feature extraction (CRITICAL - 150x performance) - LLM-driven calibration (dynamic categories) - Threshold optimization (0.55 default) - Email provider credentials (3 accounts each) - Project structure reference - Important notes for AI assistants This document provides essential context for continuing development and ensures proper understanding of critical performance patterns.
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# Email Sorter - Claude Development Guide
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This document provides essential context for Claude (or other AI assistants) working on this project.
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## Project Overview
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**Email Sorter** is a hybrid ML/LLM email classification system designed to process large email backlogs (10k-100k+ emails) with high speed and accuracy.
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### Current MVP Status
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**✅ PROVEN WORKING** - 10,000 emails classified in ~24 seconds with 72.7% accuracy
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**Core Features:**
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- LLM-driven category discovery (no hardcoded categories)
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- ML model training on discovered categories (LightGBM)
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- Fast pure-ML classification with `--no-llm-fallback`
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- Category verification for new mailboxes with `--verify-categories`
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- Batched embedding extraction (512 emails/batch)
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- Multiple email provider support (Gmail, Outlook, IMAP, Enron)
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## Architecture
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### Three-Tier Classification Pipeline
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```
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Email → Rules Check → ML Classifier → LLM Fallback (optional)
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↓ ↓ ↓
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Definite High Confidence Low Confidence
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(5-10%) (70-80%) (10-20%)
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```
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### Key Technologies
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- **ML Model**: LightGBM (1.8MB, 11 categories, 28 threads)
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- **Embeddings**: all-minilm:l6-v2 via Ollama (384-dim, universal)
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- **LLM**: qwen3:4b-instruct-2507-q8_0 via Ollama (calibration only)
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- **Feature Extraction**: Embeddings + TF-IDF + pattern detection
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- **Thresholds**: 0.55 (optimized from 0.75 to reduce LLM fallback)
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### Performance Metrics
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| Emails | Time | Accuracy | LLM Calls | Throughput |
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|--------|------|----------|-----------|------------|
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| 10,000 | 24s | 72.7% | 0 | 423/sec |
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| 10,000 | 5min | 92.7% | 2,100 | 33/sec |
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## Project Structure
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```
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email-sorter/
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├── src/
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│ ├── cli.py # Main CLI interface
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│ ├── classification/ # Classification pipeline
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│ │ ├── adaptive_classifier.py # Rules → ML → LLM orchestration
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│ │ ├── ml_classifier.py # LightGBM classifier
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│ │ ├── llm_classifier.py # LLM fallback
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│ │ └── feature_extractor.py # Batched embedding extraction
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│ ├── calibration/ # LLM-driven calibration
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│ │ ├── workflow.py # Calibration orchestration
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│ │ ├── llm_analyzer.py # Batch category discovery (20 emails/call)
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│ │ ├── trainer.py # ML model training
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│ │ └── category_verifier.py # Category verification
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│ ├── email_providers/ # Email source connectors
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│ │ ├── gmail.py # Gmail API (OAuth 2.0)
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│ │ ├── outlook.py # Microsoft Graph API (OAuth 2.0)
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│ │ ├── imap.py # IMAP protocol
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│ │ └── enron.py # Enron dataset (testing)
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│ ├── llm/ # LLM provider interfaces
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│ │ ├── ollama.py # Ollama provider
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│ │ └── openai_compat.py # OpenAI-compatible provider
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│ └── models/ # Trained models
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│ ├── calibrated/ # User-calibrated models
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│ │ └── classifier.pkl # Current trained model (1.8MB)
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│ └── pretrained/ # Default models
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├── config/
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│ ├── default_config.yaml # System defaults
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│ ├── categories.yaml # Category definitions (thresholds: 0.55)
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│ └── llm_models.yaml # LLM configuration
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├── credentials/ # Email provider credentials (gitignored)
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│ ├── gmail/ # Gmail OAuth (3 accounts)
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│ ├── outlook/ # Outlook OAuth (3 accounts)
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│ └── imap/ # IMAP credentials (3 accounts)
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├── docs/ # Documentation
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├── scripts/ # Utility scripts
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└── logs/ # Log files (gitignored)
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```
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## Critical Implementation Details
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### 1. Batched Embedding Extraction (CRITICAL!)
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**ALWAYS use batched feature extraction:**
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```python
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# ✅ CORRECT - Batched (150x faster)
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all_features = feature_extractor.extract_batch(emails, batch_size=512)
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for email, features in zip(emails, all_features):
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result = adaptive_classifier.classify_with_features(email, features)
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# ❌ WRONG - Sequential (extremely slow)
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for email in emails:
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result = adaptive_classifier.classify(email) # Extracts features one-at-a-time
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```
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**Why this matters:**
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- Sequential: 10,000 emails × 15ms = 150 seconds just for embeddings
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- Batched: 20 batches × 1s = 20 seconds for embeddings
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- **150x performance difference**
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### 2. Model Paths
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**The model exists in TWO locations:**
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- `src/models/calibrated/classifier.pkl` - Created during calibration (authoritative)
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- `src/models/pretrained/classifier.pkl` - Loaded by default (copy of calibrated)
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**When calibration runs:**
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1. Saves model to `calibrated/classifier.pkl`
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2. MLClassifier loads from `pretrained/classifier.pkl` by default
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3. Need to copy or update path
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**Current status:** Both paths have the same 1.8MB model (Oct 25 02:54)
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### 3. LLM-Driven Calibration
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**NOT hardcoded categories** - categories are discovered by LLM:
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```python
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# Calibration process:
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1. Sample 300 emails (3% of 10k)
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2. Batch process in groups of 20 emails
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3. LLM discovers categories (not predefined)
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4. LLM labels each email
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5. Train LightGBM on discovered categories
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```
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**Result:** 11 categories discovered from Enron dataset:
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- Updates, Work, Meetings, External, Financial, Test, Administrative, Operational, Technical, Urgent, Requests
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### 4. Threshold Optimization
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**Default threshold: 0.55** (reduced from 0.75)
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**Impact:**
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- 0.75 threshold: 35% LLM fallback
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- 0.55 threshold: 21% LLM fallback
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- **40% reduction in LLM usage**
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All category thresholds in `config/categories.yaml` set to 0.55.
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### 5. Email Provider Credentials
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**Multi-account support:** 3 accounts per provider type
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**Credential files:**
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```
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credentials/
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├── gmail/
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│ ├── account1.json # Gmail OAuth credentials
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│ ├── account2.json
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│ └── account3.json
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├── outlook/
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│ ├── account1.json # Outlook OAuth credentials
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│ ├── account2.json
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│ └── account3.json
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└── imap/
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├── account1.json # IMAP username/password
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├── account2.json
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└── account3.json
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```
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**Security:** All `*.json` files in `credentials/` are gitignored (only `.example` files tracked).
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## Common Commands
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### Development
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```bash
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# Activate virtual environment
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source venv/bin/activate
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# Run classification (Enron dataset)
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python -m src.cli run --source enron --limit 10000 --output results/
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# Pure ML (no LLM fallback) - FAST
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python -m src.cli run --source enron --limit 10000 --output results/ --no-llm-fallback
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# With category verification
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python -m src.cli run --source enron --limit 10000 --output results/ --verify-categories
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# Gmail
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python -m src.cli run --source gmail --credentials credentials/gmail/account1.json --limit 1000
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# Outlook
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python -m src.cli run --source outlook --credentials credentials/outlook/account1.json --limit 1000
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```
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### Training
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```bash
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# Force recalibration (clears cached model)
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rm -rf src/models/calibrated/ src/models/pretrained/
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python -m src.cli run --source enron --limit 10000 --output results/
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```
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## Code Patterns
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### Adding New Features
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1. **Update CLI** ([src/cli.py](src/cli.py)):
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- Add click options
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- Pass to appropriate modules
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2. **Update Classifier** ([src/classification/adaptive_classifier.py](src/classification/adaptive_classifier.py)):
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- Add methods following existing pattern
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- Use `classify_with_features()` for batched processing
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3. **Update Feature Extractor** ([src/classification/feature_extractor.py](src/classification/feature_extractor.py)):
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- Always support batching (`extract_batch()`)
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- Keep `extract()` for backward compatibility
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### Testing
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```bash
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# Test imports
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python -c "from src.cli import cli; print('OK')"
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# Test providers
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python -c "from src.email_providers.gmail import GmailProvider; from src.email_providers.outlook import OutlookProvider; print('OK')"
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# Test classification
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python -m src.cli run --source enron --limit 100 --output test/
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```
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## Performance Optimization
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### Current Bottlenecks
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1. **Embedding generation** - 20s for 10k emails (batched)
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- Optimized with batch_size=512
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- Could use local sentence-transformers for 5-10x speedup
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2. **Email parsing** - 0.5s for 10k emails (fast)
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3. **ML inference** - 0.7s for 10k emails (very fast)
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### Optimization Opportunities
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1. **Local embeddings** - Replace Ollama API with sentence-transformers
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- Current: 20 API calls, ~20 seconds
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- With local: Direct GPU, ~2-5 seconds
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- Trade-off: More dependencies, larger memory footprint
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2. **Embedding cache** - Pre-compute and cache to disk
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- One-time cost: 20 seconds
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- Subsequent runs: 2-3 seconds to load from disk
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- Perfect for development/testing
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3. **Larger batches** - Tested 512, 1024, 2048
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- 512: 23.6s (chosen for balance)
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- 1024: 22.1s (6.6% faster)
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- 2048: 21.9s (7.5% faster, diminishing returns)
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## Known Issues
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### 1. Background Processes
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There are stale background bash processes from previous sessions:
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- These can be safely ignored
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- Do NOT try to kill them (per user's CLAUDE.md instructions)
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### 2. Model Path Confusion
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- Calibration saves to `src/models/calibrated/`
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- Default loads from `src/models/pretrained/`
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- Both currently have the same model (synced)
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### 3. Category Cache
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- `src/models/category_cache.json` stores discovered categories
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- Can become polluted if different datasets used
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- Clear with `rm src/models/category_cache.json` if issues
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## Dependencies
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### Required
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```bash
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pip install click pyyaml lightgbm numpy scikit-learn ollama
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```
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### Email Providers
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```bash
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# Gmail
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pip install google-api-python-client google-auth-oauthlib google-auth-httplib2
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# Outlook
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pip install msal requests
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# IMAP - no additional dependencies (Python stdlib)
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```
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### Optional
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```bash
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# For faster local embeddings
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pip install sentence-transformers
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# For development
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pip install pytest black mypy
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```
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## Git Workflow
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### What's Gitignored
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- `credentials/` (except `.example` files)
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- `logs/`
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- `results/`
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- `src/models/calibrated/` (trained models)
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- `*.log`
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- `debug_*.txt`
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- Test directories
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### What's Tracked
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- All source code
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- Configuration files
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- Documentation
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- Example credential files
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- Pretrained model (if present)
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## Important Notes for AI Assistants
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1. **NEVER create files unless necessary** - Always prefer editing existing files
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2. **ALWAYS use batching** - Feature extraction MUST be batched (512 emails/batch)
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3. **Read before writing** - Use Read tool before any Edit operations
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4. **Verify paths** - Model paths can be confusing (calibrated vs pretrained)
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5. **No emoji in commits** - Per user's CLAUDE.md preferences
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6. **Test before committing** - Verify imports and CLI work
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7. **Security** - Never commit actual credentials, only `.example` files
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8. **Performance matters** - 10x performance differences are common, always batch
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9. **LLM is optional** - System works without LLM (pure ML mode with --no-llm-fallback)
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10. **Categories are dynamic** - They're discovered by LLM, not hardcoded
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## Recent Changes (Last Session)
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1. **Fixed embedding bottleneck** - Changed from sequential to batched feature extraction (10x speedup)
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2. **Added Outlook provider** - Full Microsoft Graph API integration
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3. **Added credentials system** - Support for 3 accounts per provider type
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4. **Optimized thresholds** - Reduced from 0.75 to 0.55 (40% less LLM usage)
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5. **Added category verifier** - Optional single LLM call to verify model fit
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6. **Project reorganization** - Clean docs/, scripts/, logs/ structure
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## Next Steps (Roadmap)
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See [docs/PROJECT_STATUS_AND_NEXT_STEPS.html](docs/PROJECT_STATUS_AND_NEXT_STEPS.html) for complete roadmap.
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**Immediate priorities:**
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1. Test Gmail provider with real credentials
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2. Test Outlook provider with real credentials
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3. Implement email syncing (apply labels back to mailbox)
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4. Add incremental classification (process only new emails)
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5. Create web dashboard for results visualization
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---
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**Remember:** This is an MVP with proven performance. Don't over-engineer. Keep it fast and simple.
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