- Add 'databases' field to TemplateDefinition and TemplateMatchResult
- Support values: 'oxigraph' (SPARQL/KG), 'qdrant' (vector search)
- Add helper methods use_oxigraph() and use_qdrant()
- Default to both databases for backward compatibility
- Allows templates to skip vector search for factual/geographic queries
- Add ontology cache warming at startup in lifespan() function
- Add is_factual_query() detection in template_sparql.py (12 templates)
- Add factual_result and sparql_query fields to DSPyQueryResponse
- Skip LLM generation for factual templates (count, list, compare)
- Execute SPARQL directly and return results as table (~15s → ~2s latency)
- Update ConversationPanel.tsx to render factual results table
- Add CSS styling for factual results with green theme
For queries like 'hoeveel archieven zijn er in Den Haag', the SPARQL
results ARE the answer - no need for expensive LLM prose generation.
Major architectural changes based on Formica et al. (2023) research:
- Add TemplateClassifier for deterministic SPARQL template matching
- Add SlotExtractor with synonym resolution for slot values
- Add TemplateInstantiator using Jinja2 for query rendering
- Refactor dspy_heritage_rag.py to use template system
- Update main.py with streamlined pipeline
- Fix semantic_router.py ordering issues
- Add comprehensive metrics tracking
Template-based approach achieves 65% precision vs 10% LLM-only
per Formica et al. research on SPARQL generation.
- 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.
