823d69a3c3
- KnowledgeV2Source: 实现 KnowledgeSource trait，自动搜索所有启用的知识库 - AnalysisService.knowledge_sources: 改 Option → Vec 支持多知识源 - 最佳匹配策略：遍历所有知识源取最高 confidence 的上下文注入 system prompt - main.rs 共享 EmbeddingService + KnowledgeV2Service 实例 Phase 2 Task 12-15
122 lines
3.5 KiB
Rust
122 lines
3.5 KiB
Rust
pub mod structured_source;
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pub mod v2_source;
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pub mod vector_search;
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pub mod vector_source;
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use async_trait::async_trait;
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use serde::{Deserialize, Serialize};
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use uuid::Uuid;
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use crate::error::AiResult;
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/// 知识源 trait — 统一结构化和未来向量检索的知识获取接口
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#[async_trait]
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pub trait KnowledgeSource: Send + Sync {
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/// 根据查询获取知识上下文
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async fn get_context(&self, query: &KnowledgeQuery) -> AiResult<KnowledgeContext>;
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/// 知识源类型标识
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fn source_type(&self) -> &str;
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/// 健康检查
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async fn health_check(&self) -> AiResult<bool>;
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}
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/// 知识查询参数
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct KnowledgeQuery {
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pub tenant_id: Uuid,
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pub analysis_type: String,
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pub patient_context: Option<PatientSummary>,
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pub query_text: Option<String>,
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}
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/// 脱敏患者摘要（不含 PII）
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct PatientSummary {
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pub age: Option<i32>,
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pub gender: Option<String>,
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pub tags: Vec<String>,
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}
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/// 知识上下文（返回给 Prompt 注入）
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct KnowledgeContext {
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pub source: String,
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pub context_text: String,
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pub references: Vec<Reference>,
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pub confidence: f32,
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}
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/// 参考引用
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct Reference {
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pub title: String,
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pub source: String,
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pub relevance_score: f32,
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn knowledge_query_construction() {
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let query = KnowledgeQuery {
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tenant_id: Uuid::now_v7(),
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analysis_type: "lab_report".into(),
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patient_context: Some(PatientSummary {
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age: Some(65),
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gender: Some("male".into()),
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tags: vec!["高血压".into(), "糖尿病".into()],
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}),
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query_text: Some("血红蛋白偏低".into()),
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};
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assert_eq!(query.analysis_type, "lab_report");
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assert_eq!(query.patient_context.as_ref().unwrap().tags.len(), 2);
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}
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#[test]
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fn knowledge_context_serde_roundtrip() {
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let ctx = KnowledgeContext {
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source: "structured".into(),
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context_text: "【规则】血压 >140 需关注".into(),
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references: vec![Reference {
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title: "高血压指南".into(),
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source: "system".into(),
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relevance_score: 0.95,
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}],
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confidence: 0.9,
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};
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let json = serde_json::to_string(&ctx).unwrap();
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let back: KnowledgeContext = serde_json::from_str(&json).unwrap();
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assert_eq!(back.source, "structured");
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assert_eq!(back.references.len(), 1);
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assert!((back.confidence - 0.9).abs() < 0.01);
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}
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#[test]
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fn patient_summary_serde() {
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let summary = PatientSummary {
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age: Some(70),
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gender: Some("female".into()),
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tags: vec![],
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};
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let json = serde_json::to_string(&summary).unwrap();
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let back: PatientSummary = serde_json::from_str(&json).unwrap();
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assert_eq!(back.age, Some(70));
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}
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#[test]
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fn truncate_context_text() {
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let long_text: String = "x".repeat(10000);
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let max_chars = 8000;
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let truncated = if long_text.len() > max_chars {
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long_text[..max_chars].to_string()
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} else {
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long_text
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};
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assert_eq!(truncated.len(), max_chars);
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}
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}
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