41c676a78e
Enterprise Features: - Hosted mode with remote sync for team pattern aggregation - Community sharing with privacy-preserving anonymization - LLM-based semantic claim extraction with Gemini integration - Pattern learning with promotion to declarative extractors - High-entropy secrets extractor with configurable thresholds - Auth bypass and insecure cookies extractors Module Refactoring: - Split oversized files to comply with 500-line limit - Config split: types/core.rs, types/extractors.rs, types/hosted.rs, etc. - Handlers split: scan.rs, policy.rs, report.rs modules - Extractors split: declarative/, high_entropy_secrets/, insecure_cookies/ - Learning split: store modules with metrics and persistence SDK & Ontology: - stemedb-ontology SDK with fluent builders and StemeDB client - Pharma domain extractors for FDA Orange Book data - Consumer health UAT test infrastructure Code Quality: - Fixed clippy warnings (needless_borrows_for_generic_args) - Added KVStore trait imports where needed - Fixed utoipa path re-exports for OpenAPI docs Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
81 lines
2.9 KiB
Rust
81 lines
2.9 KiB
Rust
//! Domain Ontology Layer for Episteme
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//!
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//! This crate defines how subjects are structured based on predicate type and domain.
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//! It ensures conflicts collide correctly when different sources report on the same thing.
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//!
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//! # Key Concepts
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//!
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//! - **Domain**: A vertical (pharma, finance, etc.) with entity types, predicate schemas, and source hierarchy
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//! - **PredicateSchema**: Defines how subjects are built for a predicate type
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//! - **SubjectBuilder**: Constructs canonical subject strings from entities
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//! - **MedicalExtractor**: Trait for extracting claims from medical sources
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//! - **StemeClient**: HTTP client for submitting assertions to StemeDB
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//!
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//! # Subject Patterns
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//!
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//! Different predicate types require different subject structures:
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//!
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//! | Predicate Type | Subject Pattern | Example |
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//! |----------------|-----------------|---------|
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//! | Efficacy | `{Drug}:{Indication}` | `Semaglutide:Type2Diabetes` |
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//! | Safety | `{Drug}` | `Semaglutide` |
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//! | Mechanism | `{Drug}:{Pathway}` | `Semaglutide:GLP1Receptor` |
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//!
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//! This ensures that efficacy claims for the same drug+indication collide,
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//! while safety claims for the same drug collide regardless of indication.
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//!
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//! # Example
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//!
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//! ```ignore
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//! use stemedb_ontology::{pharma, SubjectBuilder};
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//!
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//! let domain = pharma::definition();
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//! let schema = domain.get_schema("efficacy").unwrap();
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//!
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//! let mut entities = std::collections::HashMap::new();
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//! entities.insert("Drug".to_string(), "Semaglutide".to_string());
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//! entities.insert("Indication".to_string(), "Type2Diabetes".to_string());
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//!
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//! let subject = SubjectBuilder::build(schema, &entities).unwrap();
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//! assert_eq!(subject, "Semaglutide:Type2Diabetes");
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//! ```
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//!
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//! # StemeDB Integration
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//!
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//! ```ignore
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//! use stemedb_ontology::client::StemeClient;
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//! use stemedb_ontology::pharma::extractors::{FdaLabelExtractor, MedicalExtractor, SourceInput};
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//!
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//! let client = StemeClient::new("http://localhost:18180");
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//! let extractor = FdaLabelExtractor::new();
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//!
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//! // Extract claims from FDA
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//! let claims = extractor.extract(&SourceInput::DrugName("semaglutide".into())).await?;
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//!
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//! // Submit to StemeDB
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//! for claim in claims {
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//! let assertion = claim.to_assertion(&signing_key, agent_id, &hlc);
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//! let hash = client.assert(&assertion).await?;
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//! }
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//!
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//! // Query for conflicts
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//! let skeptic = client.skeptic("Semaglutide:Type2Diabetes", "hba1c_change_percent").await?;
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//! ```
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#![allow(clippy::print_stdout)] // CLI tool may use print
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pub mod client;
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pub mod domain;
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pub mod dto;
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pub mod pharma;
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pub mod subject;
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pub mod validator;
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pub use client::{ClientError, StemeClient};
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pub use domain::{Domain, EntityType, PredicateSchema, SourceTier};
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pub use subject::{SubjectBuilder, SubjectError};
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pub use validator::{ValidationError, Validator};
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// Re-export pharma domain for convenience
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pub use pharma::definition as pharma_domain;
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