glam/SESSION-RESUME.md

# 🎯 Session 2 Summary - Ready for Next Session

## ✅ What We Accomplished

### Components Implemented
1. **ConversationParser** - Parse Claude conversation JSON files
2. **IdentifierExtractor** - Extract ISIL, Wikidata, VIAF, KvK, URLs using regex

### Test Coverage
- **60 tests, all passing** in 0.11 seconds
- Comprehensive edge case coverage
- Real-world data validation (Rijksmuseum, Nationaal Archief)

### Files Created/Modified

**Source Code** (4 files):
- `src/glam_extractor/parsers/conversation.py` - NEW
- `src/glam_extractor/parsers/__init__.py` - UPDATED  
- `src/glam_extractor/extractors/identifiers.py` - NEW
- `src/glam_extractor/extractors/__init__.py` - UPDATED

**Tests** (2 files):
- `tests/parsers/test_conversation.py` - NEW (25 tests)
- `tests/extractors/test_identifiers.py` - NEW (35 tests)

**Examples** (2 files):
- `examples/extract_identifiers.py` - NEW
- `examples/README.md` - NEW

**Documentation** (2 files):
- `docs/progress/session-02-summary.md` - NEW

**Test Fixtures** (previously created):
- `tests/fixtures/sample_conversation.json`
- `tests/fixtures/expected_extraction.json`

## 🚀 Quick Start for Next Session

### Running Tests
```bash
cd /Users/kempersc/Documents/claude/glam
PYTHONPATH=./src:$PYTHONPATH python -m pytest tests/ -v -o addopts=""
```

### Running Examples
```bash
cd /Users/kempersc/Documents/claude/glam
PYTHONPATH=./src:$PYTHONPATH python examples/extract_identifiers.py
```

## 📋 Next Priority Tasks

### Task 7: CSVParser (HIGH PRIORITY - Start Here!)
**Why**: Parse authoritative Dutch GLAM data (TIER_1)  
**Files to create**:
- `src/glam_extractor/parsers/isil_registry.py`
- `src/glam_extractor/parsers/dutch_orgs.py`
- `tests/parsers/test_csv_parsers.py`

**Data sources**:
- `data/ISIL-codes_2025-08-01.csv` (~300 Dutch institutions)
- `data/voorbeeld_lijst_organisaties_en_diensten-totaallijst_nederland.csv` (40+ columns)

**Output**: `HeritageCustodian` and `DutchHeritageCustodian` model instances

**Key points**:
- Use pandas for CSV parsing
- Map CSV columns to Pydantic models
- Set provenance: `data_source=ISIL_REGISTRY`, `data_tier=TIER_1_AUTHORITATIVE`
- Handle Dutch-specific fields: KvK, gemeente_code, provincie, etc.

### Task 9: LinkMLValidator (HIGH PRIORITY)
**Why**: Validate data quality against schema  
**File**: `src/glam_extractor/validators/linkml_validator.py`

**What to do**:
- Use `linkml` library (already in dependencies)
- Validate HeritageCustodian records against `schemas/heritage_custodian.yaml`
- Report validation errors with clear messages
- Test with both valid and invalid data

### Task 8: InstitutionExtractor (MEDIUM PRIORITY)
**Why**: Extract institution names using NER  
**File**: `src/glam_extractor/extractors/institutions.py`

**Key approach**:
- Use Task tool to launch coding subagent
- Subagent runs spaCy/transformers for NER
- Main code stays lightweight (no NLP dependencies)
- Return structured data with confidence scores

## 🔑 Key Architecture Points

1. **Pydantic v1**: Using v1.10.24 for compatibility
2. **No NLP in main code**: Use Task tool + subagents for NER
3. **Pattern matching**: ISIL, Wikidata, VIAF, KvK via regex
4. **Provenance tracking**: Every record tracks source, method, confidence
5. **Data tiers**: TIER_1 (CSV) > TIER_4 (conversation NLP)

## 📊 Current Status

| Component | Status | Tests | Priority |
|-----------|--------|-------|----------|
| Models (Pydantic v1) | ✅ Done | N/A | - |
| ConversationParser | ✅ Done | 25/25 ✅ | - |
| IdentifierExtractor | ✅ Done | 35/35 ✅ | - |
| CSVParser | ⏳ TODO | - | 🔴 HIGH |
| LinkMLValidator | ⏳ TODO | - | 🔴 HIGH |
| InstitutionExtractor | ⏳ TODO | - | 🟡 MEDIUM |
| JSON-LD Exporter | ⏳ TODO | - | 🟡 MEDIUM |

## 🎓 What You Learned

**ConversationParser**:
- Parse JSON with Pydantic validation
- Handle datetime parsing with multiple formats
- Extract and deduplicate text content
- Filter by sender (human/assistant)

**IdentifierExtractor**:
- Regex pattern matching for identifiers
- Country code validation (ISIL)
- Context-based filtering (KvK)
- URL extraction with domain filtering
- Deduplication strategies

**Integration**:
- Combining multiple extractors in a pipeline
- Pydantic models for data validation
- Test-driven development with pytest
- Fixture-based testing

## 🐛 Known Issues

None! All 60 tests passing.

## 🎯 Success Metrics This Session

- ✅ 2 major components implemented
- ✅ 60 comprehensive tests (100% passing)
- ✅ 0.11s test execution time
- ✅ Real-world data validation
- ✅ Working integration example
- ✅ Clear documentation

## 💡 Tips for Next Session

1. **Start with CSVParser**: High priority, no subagents needed, validates with real data
2. **Check CSV files exist**: Verify data files are at expected paths
3. **Test incrementally**: Parse one CSV first, then the other
4. **Use pandas**: Makes CSV parsing much easier
5. **Validate early**: Once CSVParser works, implement LinkMLValidator immediately

## 📂 Project Structure (Completed Parts)

```
glam/
├── src/glam_extractor/
│   ├── models.py                    ✅ (Pydantic v1 models)
│   ├── parsers/
│   │   ├── __init__.py              ✅
│   │   └── conversation.py          ✅ NEW
│   └── extractors/
│       ├── __init__.py              ✅
│       └── identifiers.py           ✅ NEW
├── tests/
│   ├── fixtures/
│   │   ├── sample_conversation.json ✅
│   │   └── expected_extraction.json ✅
│   ├── parsers/
│   │   └── test_conversation.py     ✅ NEW (25 tests)
│   └── extractors/
│       └── test_identifiers.py      ✅ NEW (35 tests)
├── examples/
│   ├── README.md                    ✅ NEW
│   └── extract_identifiers.py      ✅ NEW
└── docs/
    └── progress/
        └── session-02-summary.md    ✅ NEW
```

---

**Ready to continue!** Start with CSVParser next session. 🚀