glam/SESSION_SUMMARY.md

# Session Summary: ChangeEvent Model Implementation

**Date**: 2025-11-05  
**Status**: ✅ COMPLETE

## Objectives Accomplished

### 1. ✅ Implemented ChangeEvent Model in Python

**Added to `src/glam_extractor/models.py`:**
- `ChangeType` enum with 12 event types:
  - FOUNDING, CLOSURE, MERGER, SPLIT, ACQUISITION
  - RELOCATION, NAME_CHANGE, TYPE_CHANGE, STATUS_CHANGE
  - RESTRUCTURING, LEGAL_CHANGE, OTHER
- `ChangeEvent` Pydantic model class with fields:
  - `event_id` (str, required, unique identifier)
  - `change_type` (ChangeType enum, required)
  - `event_date` (date, required)
  - `event_description` (str, optional)
  - `affected_organization` (str, optional, organization ID)
  - `resulting_organization` (str, optional, organization ID)
  - `related_organizations` (List[str], optional, organization IDs)
  - `source_documentation` (HttpUrl, optional)

### 2. ✅ Added change_history to HeritageCustodian

**Updated `HeritageCustodian` model:**
- Added `change_history: List[ChangeEvent]` field
- Default: empty list
- Stores chronological list of organizational change events
- Fully integrated with schema definition (v0.2.0)

### 3. ✅ Updated Orchestration Script

**Modified `scripts/extract_with_agents.py`:**
- Imported `ChangeEvent` and `ChangeType` classes
- Implemented `ChangeEvent` parsing in `create_heritage_custodian_record()`
- Added date parsing logic (handles ISO strings and date objects)
- Added change_type enum mapping
- Includes validation (skips events with invalid/missing dates)
- Populates `change_history` field in HeritageCustodian records

### 4. ✅ Validated Implementation

**Testing Results:**
- ✅ All 207 tests pass
- ✅ 91% code coverage maintained
- ✅ ChangeEvent model creation works
- ✅ HeritageCustodian with change_history works
- ✅ Orchestration script runs without errors
- ✅ Brazilian GLAM conversation file loads successfully

## File Changes

### Modified Files:
1. `src/glam_extractor/models.py` (+23 lines)
   - Added `ChangeType` enum
   - Added `ChangeEvent` class
   - Added `change_history` field to `HeritageCustodian`

2. `scripts/extract_with_agents.py` (+35 lines)
   - Imported `ChangeEvent` and `ChangeType`
   - Implemented ChangeEvent parsing logic
   - Added change_history to custodian creation

### Schema Alignment:
- ✅ Python models now match LinkML schema v0.2.0
- ✅ `ChangeEvent` class matches schema definition (lines 460-484)
- ✅ `ChangeTypeEnum` matches schema enum (lines 220-252)
- ✅ PROV-O integration ready (`prov:Activity` mapping)

## Code Examples

### Creating a ChangeEvent:
```python
from datetime import date
from glam_extractor.models import ChangeEvent, ChangeType

event = ChangeEvent(
    event_id='nha-merger-2001',
    change_type=ChangeType.MERGER,
    event_date=date(2001, 1, 1),
    event_description='Merger of Gemeentearchief Haarlem and Rijksarchief in Noord-Holland',
    affected_organization='gemeentearchief-haarlem',
    resulting_organization='noord-hollands-archief'
)
```

### HeritageCustodian with Change History:
```python
custodian = HeritageCustodian(
    id='nha-001',
    name='Noord-Hollands Archief',
    institution_type=InstitutionType.ARCHIVE,
    change_history=[event],
    provenance=provenance
)
```

## Next Steps (Ready for Execution)

### Immediate Priority:
1. **Test Agent System with Real Data**
   - Pick one conversation file (Brazilian GLAM recommended)
   - Run orchestration script to generate prompts
   - Invoke each agent via @mention in OpenCode
   - Collect JSON responses from agents
   - Validate outputs match expected schema

### Testing Workflow:
```bash
# 1. Generate prompts
python scripts/extract_with_agents.py \
  "2025-09-22T14-40-15-0102c00a-4c0a-4488-bdca-5dd9fb94c9c5-Brazilian_GLAM_collection_inventories.json"

# 2. In OpenCode, invoke agents:
@institution-extractor <paste prompt>
@location-extractor <paste prompt>
@identifier-extractor <paste prompt>
@event-extractor <paste prompt>

# 3. Collect JSON responses and validate
# (Next session: implement response collection + validation)
```

### Near-Term Tasks:
2. Process first full extraction (combine agent outputs)
3. Validate with LinkML schema
4. Export to JSON-LD
5. Review data quality and confidence scores

### Medium-Term Tasks:
6. Batch process all 139 conversations
7. Cross-link with Dutch CSV data
8. Generate GHCIDs for all institutions
9. Export to multiple formats (RDF, CSV, Parquet)
10. Build SPARQL endpoint

## Technical Debt Resolved

### ✅ Removed:
- Old TODO comments about ChangeEvent implementation
- Placeholder code in orchestration script

### ✅ Fixed:
- Schema-model alignment issues
- Missing ChangeEvent model
- Missing change_history field

## Project Stats (Updated)

- **Schema**: v0.2.0 (ChangeEvent support) ✅
- **Python Models**: Fully aligned with schema ✅
- **OpenCode Agents**: 4 specialized extractors ready
- **Conversations**: 139 JSON files ready for extraction
- **Tests**: 207 passing (100%), 91% coverage
- **Dutch ISIL**: 364 institutions parsed
- **Dutch Orgs**: 1,351 institutions parsed
- **GeoNames DB**: 4.9M cities indexed

## Architecture Notes

### PROV-O Integration (Ready):
- `ChangeEvent` maps to `prov:Activity`
- Links via `prov:wasInfluencedBy` from `HeritageCustodian`
- Uses `prov:atTime` for event timestamps
- Tracks `prov:entity` (affected) and `prov:generated` (resulting) orgs

### GHCID Impact Tracking:
- When institutions merge/relocate/rename, GHCID changes
- Old GHCID tracked in `ghcid_history` with `valid_to` timestamp
- New `GHCIDHistoryEntry` created with `valid_from` timestamp
- Change events linked via temporal correlation

### Agent-Based Extraction Benefits:
- No spaCy/transformer dependencies in main codebase
- Flexible, maintainable (prompts vs. code)
- Multilingual by default (60+ languages)
- Read-only subagents (safe, predictable)

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**Next Session**: Test agents on real data and validate extraction pipeline