Document server disk architecture, PyTorch CPU-only setup, service
management, and recovery procedures learned from disk space crisis.
- Document dual-disk architecture (/: root 75GB, /mnt/data: 49GB)
- PyTorch CPU-only installation via --index-url whl/cpu
- Custodian data symlink: /mnt/data/custodian → /var/lib/glam/api/data/
- Service restart procedures for Oxigraph, GLAM API, Qdrant, etc.
- Emergency recovery commands for disk space crises
Add final two chapters of the Person PID (PPID) design document:
- 08_implementation_guidelines.md: Database architecture, API design,
data ingestion pipeline, GHCID integration, security, performance,
technology stack, deployment, and monitoring specifications
- 09_governance_and_sustainability.md: Data governance policies,
quality assurance, sustainability planning, community engagement,
legal considerations, and long-term maintenance strategies
- Updated documentation to clarify integration points with existing components in the RAG pipeline and DSPy framework.
- Added detailed mapping of SPARQL templates to context templates for improved specificity filtering.
- Implemented wrapper patterns around existing classifiers to extend functionality without duplication.
- Introduced new tests for the SpecificityAwareClassifier and SPARQLToContextMapper to ensure proper integration and functionality.
- Enhanced the CustodianRDFConverter to include ISO country and subregion codes from GHCID for better geospatial data handling.
- Created deliverables_slot for expected or achieved deliverable outputs.
- Introduced event_id_slot for persistent unique event identifiers.
- Added follow_up_date_slot for scheduled follow-up action dates.
- Implemented object_ref_slot for references to heritage objects.
- Established price_slot for price information across entities.
- Added price_currency_slot for currency codes in price information.
- Created protocol_slot for API protocol specifications.
- Introduced provenance_text_slot for full provenance entry text.
- Added record_type_slot for classification of record types.
- Implemented response_formats_slot for supported API response formats.
- Established status_slot for current status of entities or activities.
- Added FactualCountDisplay component for displaying count query results.
- Introduced ReplyTypeIndicator component for visualizing reply types.
- Created approval_date_slot for formal approval dates.
- Added authentication_required_slot for API authentication status.
- Implemented capacity_items_slot for maximum storage capacity.
- Established conservation_lab_slot for conservation laboratory information.
- Added cost_usd_slot for API operation costs in USD.
- Add Rule 11 for Z.AI Coding Plan API usage (not BigModel)
- Add transliteration standards for non-Latin scripts
- Document GLM model options and Python implementation
- Implemented `generate_mermaid_with_instances.py` to create ER diagrams that include all classes, relationships, enum values, and instance data.
- Loaded instance data from YAML files and enriched enum definitions with meaningful annotations.
- Configured output paths for generated diagrams in both frontend and schema directories.
- Added support for excluding technical classes and limiting the number of displayed enum and instance values for readability.
- Created the Country class with ISO 3166-1 alpha-2 and alpha-3 codes, ensuring minimal design without additional metadata.
- Integrated the Country class into CustodianPlace and LegalForm schemas to support country-specific feature types and legal forms.
- Removed duplicate keys in FeatureTypeEnum.yaml, resulting in 294 unique feature types.
- Eliminated "Hypernyms:" text from FeatureTypeEnum descriptions, verifying that semantic relationships are now conveyed through ontology mappings.
- Created example instance file demonstrating integration of Country with CustodianPlace and LegalForm.
- Updated documentation to reflect the completion of the Country class implementation and hypernyms removal.
- Created SHACL shapes for validating temporal consistency and bidirectional relationships in custodial collections and staff observations.
- Implemented a Python script to validate RDF data against the defined SHACL shapes using the pyshacl library.
- Added command-line interface for validation with options for specifying data formats and output reports.
- Included detailed error handling and reporting for validation results.
- Implemented `owl_to_mermaid.py` to convert OWL/Turtle files into Mermaid class diagrams.
- Implemented `owl_to_plantuml.py` to convert OWL/Turtle files into PlantUML class diagrams.
- Added two new PlantUML files for custodian multi-aspect diagrams.
- Introduced custodian_hub_v3.mmd, custodian_hub_v4_final.mmd, and custodian_hub_v5_FINAL.mmd for Mermaid representation.
- Created custodian_hub_FINAL.puml and custodian_hub_v3.puml for PlantUML representation.
- Defined entities such as CustodianReconstruction, Identifier, TimeSpan, Agent, CustodianName, CustodianObservation, ReconstructionActivity, Appellation, ConfidenceMeasure, Custodian, LanguageCode, and SourceDocument.
- Established relationships and associations between entities, including temporal extents, observations, and reconstruction activities.
- Incorporated enumerations for various types, statuses, and classifications relevant to custodians and their activities.
