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# Knowledge Stack Interface Contract
## Purpose
Provides semantic and graph-based retrieval augmented generation (RAG) for clinical guideline explanations, evidence arbitration, and diagnostic reasoning using vector embeddings (Qdrant) and ontology relationships (ladybugDB) with LLM grounding.
## Owner
Knowledge Engineering Team
## Provides
- guideline embeddings and vector similarity search (Qdrant)
- ontology relationships and graph traversal (ladybugDB)
- grounded explanation generation (retrieval + LLM + grounding)
- evidence arbitration and belief propagation for conflicting evidence
- knowledge versioning and temporal validity tracking
- hallucination detection and policy enforcement
## Consumes
- (none) – Knowledge stack provides foundational AI services; it does not consume other rooms' interfaces for its core purpose.
Note: Knowledge consumes internal storage (Qdrant, ladybugDB) and embedding/LLM services which are part of its boundary.
## Consumers
- frontend: consumes grounded explanations for UI display via `frontend:guideline-spec`.
- backend: consumes explanation generation for analysis reporting via `backend:api-spec`.
- ml: consumes model activation explanations via `ml:engine-spec`.
## Not Directly Consumable
- internal Qdrant collection names, vector dimensions, and indexing parameters
- ladybugDB schema details (predicate names, ontology version)
- embedding model specifics (model ID, tensor shapes)
- LLM prompt templates and decoding parameters
- knowledge curation pipeline details (source ingestion, validation)
## Breaking-change Policy
- Knowledge schema versioning via semantic versioning (MAJOR.MINOR.PATCH)
- Embedding model changes: require MAJOR version if dimension or architecture changes
- Ontology updates: backward compatible additions (MINOR), breaking changes require MAJOR
- LLM interface changes: versioned endpoints with deprecation windows
- Knowledge consumers must validate compatibility with new versions
- Deprecation notices for breaking changes 60 days in advance
- Automated migration tools for knowledge base version upgrades
## References
- NFR-3 (Explanation Latency ≤2s @ 95th percentile)
- NFR-6 (Guideline Coverage ≥95% of common synovitis queries)
- NFR-10 (Explanation Factuality Score ≥0.9)
- UC-25776 (Generate Grounded Explanation for Analysis)
- UC-65473 (Resolve Conflicting Evidence)
- SOLUTION_ARCHITECTURE_SPEC.md (Section 3.3)
- SOFTWARE_SYSTEM_DESIGN_FR_25.md (Section 4.3)
- GUIDELINE_SOURCES.md (Appendix B)

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# Knowledge Stack Specification
## Purpose
Provides semantic and graph-based retrieval augmented generation (RAG) for clinical guideline explanations, evidence arbitration, and diagnostic reasoning using vector embeddings (Qdrant) and ontology relationships (ladybugDB) with LLM grounding.
## Owner
Knowledge Engineering Team
## Boundary
Qdrant vector database instances, ladybugDB graph database instances, embedding model servers, LLM inference endpoints, knowledge curation pipelines, and validation/verification workflows.
## Internal Design
- Hybrid knowledge architecture: vector similarity search + graph traversal
- pgVec: stores guideline section embeddings (BioClinicalBERT, PubMedBERT) with payload metadata
- ladybugDB: stores ontology concepts (SNOMED-CT, LOINC, RadLex) and relational axioms
- EmbeddingGemma: generates 768-dimension vectors for text chunks
- GemmaE2B/MedGemma: LLM for answer generation with constrained decoding
- Retrieval pipeline: hybrid search (vector + BM25) → graph expansion → reranking
- Grounding module: verifies LLM outputs against source guidelines with citation extraction
- Arbitration engine: resolves conflicting evidence using belief propagation
- Continuous integration: automated guideline ingestion from trusted sources (NIH, CDC, radiology societies)
- Versioned knowledge bases with temporal validity tracking
- Monitoring: retrieval relevance, grounding accuracy, latency SLOs
## Interface Contract
See `bento/knowledge/spec/interface-contract.md`.
## Consumers
- frontend:guideline-spec (for displaying grounded explanations in UI)
- backend:api-spec (for analysis explanation generation)
- ml:engine-spec (for generating model activation explanations)
## Breaking-change Policy
- Knowledge schema versioning via semantic versioning
- Embedding model changes: require MAJOR version if dimension or architecture changes
- Ontology updates: backward compatible additions (MINOR), breaking changes require MAJOR
- LLM interface changes: versioned endpoints with deprecation windows
- Knowledge consumers must validate compatibility with new versions
- Deprecation notices for breaking changes 60 days in advance
- Automated migration tools for knowledge base version upgrades
## References
- NFR-3 (Explanation Latency ≤2s @ 95th percentile)
- NFR-6 (Guideline Coverage ≥95% of common synovitis queries)
- NFR-10 (Explanation Factuality Score ≥0.9)
- UC-25776 (Generate Grounded Explanation for Analysis)
- UC-65473 (Resolve Conflicting Evidence)
- SOLUTION_ARCHITECTURE_SPEC.md (Section 3.3)
- SOFTWARE_SYSTEM_DESIGN_FR_25.md (Section 4.3)
- GUIDELINE_SOURCES.md (Appendix B)