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docs: update .docs summaries for all 11 workspace crates
Add comprehensive summary (summary.md) and individual per-crate summaries for previously missing crates (thesaurus, medical-learning, medical-roles). Update existing crate summaries with current architecture details. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎.docs/summary-crates-terraphim-medical-learning.md‎

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# terraphim-medical-learning - Case-Based Learning
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## Purpose
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Anonymized case-based learning for medical pattern recognition with clinician validation requirements.
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## Key Types
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- **MedicalCase**: Anonymized clinical case
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- **PatientProfile**: De-identified patient characteristics
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- **MedicalCaseLearner**: Learning engine
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- **LearnedPattern**: Recognized patterns
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- **AuditEntry/AuditAction**: Audit trail (Learn, Query, Validate)
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- **TreatmentOutcome**: Clinical outcomes
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- **AgeRange**: Age buckets for anonymization
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## Safety Requirements
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- NO PHI storage (anonymized profiles only)
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- Require clinician validation before learning
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- Audit trail for all patterns
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- Pattern recognition accuracy >85%
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- Learning inference latency <10ms
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## Integration
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Used by terraphim-medical-agents for case-based recommendations.

‎.docs/summary-crates-terraphim-medical-roles.md‎

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# terraphim-medical-roles - Specialist Role Abstractions
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## Purpose
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Defines specialist role abstractions and confidence thresholds for domain-specific medical agents.
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## Key Types
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- **SpecialistRole** trait: role_name, role_code, confidence_thresholds, can_handle_condition, priority_for_condition
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- **SpecialistRoleType**: Oncologist, Cardiologist, Pharmacogenomics, Neurologist, etc.
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- **ConfidenceThresholds**: min_for_treatment (0.75), min_for_trial (0.90), min_for_urgent (0.85), min_for_diagnosis (0.80)
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- **RoleConfig**: Complete role configuration
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- **RoleRegistry**: Central registry for managing and querying roles
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## Public API
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- `create_default_registry()` - Registry with all built-in roles
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## Integration
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Used by terraphim-medical-agents to determine which agent handles a given clinical task based on role priorities and confidence thresholds.

‎.docs/summary-crates-terraphim-thesaurus.md‎

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# terraphim-thesaurus - Domain-Specific Medical Thesaurus
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## Purpose
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Provides role-specific UMLS terminology slices for specialist roles (Oncologist, Cardiologist, Pharmacogenomics, Neurologist, General Practitioner).
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## Key Types
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- **RoleThesaurus**: Thesaurus for a specific role
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- **SpecialistRole**: Role definition enum
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- **UmlsSlice**: Terminology slice for a role
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- **SynonymLookup**: Lookup trait
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## Public API
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- `oncologist_data()`, `cardiologist_data()`, `pgx_data()`, `neurologist_data()`, `general_data()`, `additional_roles_data()` -- Embedded UMLS JSON slices
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## Integration
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Used by terraphim-medical-agents and terraphim-medical-roles for role-specific terminology. Fast lookup with ahash.

‎.docs/summary.md‎

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# Terraphim MedGemma Competition - Comprehensive Project Summary
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**Last Updated**: 2026-02-22
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## Project Overview
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Terraphim MedGemma Competition is a Rust workspace implementing a **personalized medicine clinical decision support system** for the Google MedGemma Impact Challenge. It combines medical knowledge graphs (SNOMED CT, UMLS, PrimeKG), pharmacogenomic safety validation (CPIC), and Google's MedGemma foundation models through an Erlang/OTP-inspired multi-agent architecture.
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## Workspace Structure (11 crates)
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```
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Cargo.toml (workspace root, edition 2021, MIT license)
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crates/
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medgemma-client/ # MedGemma model inference (multi-backend)
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terraphim-automata/ # Entity extraction (Aho-Corasick, <50us)
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terraphim-kg/ # Knowledge graph (SNOMED CT + PrimeKG)
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terraphim-pgx/ # Pharmacogenomics (CPIC guidelines)
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terraphim-medical-agents/ # Multi-agent orchestration (OTP patterns)
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terraphim-api/ # REST API (Axum)
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terraphim-demo/ # Full clinical workflow demo
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terraphim-evaluation/ # Evaluation harness (10-case smoke suite)
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terraphim-thesaurus/ # Role-specific UMLS terminology
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terraphim-medical-learning/ # Case-based learning (anonymized)
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terraphim-medical-roles/ # Specialist role abstractions
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```
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## Architecture
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### End-to-End Pipeline
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```
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Clinical Text Input
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|
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v
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[Entity Extraction] -- terraphim-automata (Aho-Corasick, 48.9M UMLS terms)
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|
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v
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[Knowledge Graph Grounding] -- terraphim-kg (SNOMED CT + PrimeKG)
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| \
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| [PGx Safety Check] -- terraphim-pgx (CPIC)
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v
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[MedGemma Inference] -- medgemma-client (local GGUF / HF / mock)
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|
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v
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[Safety Validation] -- SafetyValidationAgent (hard-gate)
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|
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v
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Treatment Recommendation Output
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```
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### Multi-Agent System (terraphim-medical-agents)
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Erlang/OTP-inspired architecture with:
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- **6 Specialized Agents**: ClinicalReasoning, Imaging, Pharmacogenomics, KnowledgeGraph, TreatmentPlanning, SafetyValidation
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- **Messaging**: Mailboxes with at-least-once/at-most-once/exactly-once delivery
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- **Supervision**: OneForOne, OneForAll, RestForOne restart strategies
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- **Task Orchestration**: Topological sort for dependency-aware parallel execution
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- **Two-Layer Validation**: Coherence (graph connectivity) + Completeness (category coverage)
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### Clinical Pipeline Phases
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1. **Extraction**: Entity extraction from clinical text
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2. **Parallel Analysis**: KG grounding + Imaging + PGx (concurrent)
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3. **Synthesis**: Treatment plan generation
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4. **Validation**: Safety gate (hard-block authority)
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## Key Performance Characteristics
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| Component | Metric | Target | Achieved |
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|-----------|--------|--------|----------|
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| Entity Extraction | Latency | <2ms | <50us (40x better) |
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| Knowledge Graph | Ancestor query | <10ms | <1us (O(1) cache) |
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| KG Artifact Load | Startup | <500ms | <100ms |
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| UMLS Artifact Load | Startup | <1s | <100ms |
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| MedGemma (GPU) | Inference | <30s | 5-10s |
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| MedGemma (CPU Q4) | Inference | <60s | 30-60s |
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## Data Sources
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| Source | Size | Format | Purpose |
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|--------|------|--------|---------|
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| UMLS | 48.9M terms, 4.2M CUIs | TSV | Entity extraction |
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| SNOMED CT | 1.5M concepts, 4.5M descriptions | RF2 | Medical hierarchy |
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| PrimeKG | 100K+ nodes, 4M+ edges | CSV | Drug-disease relationships |
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| CPIC | Guidelines per gene-drug pair | JSON | Pharmacogenomic safety |
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| MedGemma GGUF | 2.3GB (Q4_K_M) | GGUF | LLM inference |
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## Crate Dependency Graph
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```
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terraphim-api (HTTP REST)
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+-- terraphim-medical-agents (orchestration)
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| +-- medgemma-client (LLM inference)
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| +-- terraphim-pgx (PGx safety)
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| +-- terraphim-kg (knowledge graph)
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| +-- terraphim-automata (entity extraction)
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| +-- terraphim-medical-learning (case learning)
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| +-- terraphim-thesaurus (role terminology)
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| +-- terraphim-medical-roles (role abstractions)
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+-- terraphim-automata
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+-- terraphim-pgx
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terraphim-demo (full workflow demo)
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+-- (all of the above + HTTP proxy)
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terraphim-evaluation (testing harness)
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+-- generic gate interface
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```
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## Individual Crate Summaries
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- [medgemma-client](summary-crates-medgemma-client.md) - Multi-backend MedGemma inference
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- [terraphim-automata](summary-crates-terraphim-automata.md) - Aho-Corasick entity extraction
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- [terraphim-kg](summary-crates-terraphim-kg.md) - SNOMED CT + PrimeKG knowledge graph
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- [terraphim-medical-agents](summary-crates-terraphim-medical-agents.md) - OTP-style multi-agent system
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- [terraphim-pgx](summary-crates-terraphim-pgx.md) - CPIC pharmacogenomics validator
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- [terraphim-api](summary-crates-terraphim-api.md) - Axum REST API
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- [terraphim-demo](summary-crates-terraphim-demo.md) - Clinical workflow demo
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- [terraphim-evaluation](summary-crates-terraphim-evaluation.md) - Evaluation harness
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- [terraphim-thesaurus](summary-crates-terraphim-thesaurus.md) - Role-specific UMLS terminology
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- [terraphim-medical-learning](summary-crates-terraphim-medical-learning.md) - Case-based learning
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- [terraphim-medical-roles](summary-crates-terraphim-medical-roles.md) - Specialist role abstractions
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## Key Architectural Patterns
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1. **Artifact-Based Loading**: One-time build of binary artifacts (bincode + zstd) for instant production startup
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2. **Sharded Aho-Corasick**: Overcomes 2M pattern limit by distributing across 59 automaton shards
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3. **OTP Supervision**: Erlang-style process management with restart strategies and intensity limits
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4. **Circuit Breaker Fallback**: Multiple inference backends with health-based routing
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5. **Two-Layer Validation**: Coherence + completeness ensures quality clinical responses
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6. **Safety Hard-Gate**: SafetyValidationAgent as non-bypassable final authority
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7. **Role-Based Confidence**: Different confidence thresholds per medical specialty
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## Technology Stack
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- **Language**: Rust (edition 2021)
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- **Async Runtime**: Tokio
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- **Web Framework**: Axum 0.7
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- **Graph Library**: petgraph
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- **Pattern Matching**: daachorse (double-array Aho-Corasick)
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- **Serialization**: serde + bincode + zstd
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- **HTTP Client**: reqwest with rustls-tls
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- **LLM Inference**: Python subprocess (transformers) + llama-cpp-2 (planned)
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- **Python Bindings**: PyO3/maturin
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- **Testing**: criterion (benchmarks), built-in test framework
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## Documentation Structure
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```
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.docs/
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summary.md # This file
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summary-crates-*.md # Per-crate summaries
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define/ # Business scenarios, domain model
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design/ # Architecture, TDRs, implementation plans
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develop/ # Code architecture, roadmap, test plan
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discovery/ # Personas, risk scan, Wardley map, SOTA
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research/ # Competitive landscape, model evaluation
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validation/ # Phase 5 validation reports
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verification/ # Phase 4 verification reports
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docs/
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ARCHITECTURE.md # Mermaid diagrams and API reference
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submission/technical-writeup.md
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```

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