docs(ai): 实施计划 Chunk 6 (SSE Handler + 端到端验证 + 后续任务)
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@@ -1964,3 +1964,254 @@ git commit -m "feat(server): erp-ai 模块集成 — Config/State/路由注册"
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```
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---
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## Chunk 6: 完善 SSE Handler + 端到端验证
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### Task 11: 实现 SSE 分析 Handler
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**Files:**
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- Rewrite: `crates/erp-ai/src/handler/mod.rs`
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- [ ] **Step 1: 完善 handler/mod.rs — SSE 流式分析**
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```rust
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// crates/erp-ai/src/handler/mod.rs
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use axum::{
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extract::{Extension, Path, Query, State},
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response::sse::{Event, KeepAlive, Sse},
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Json,
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};
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use axum::extract::FromRef;
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use erp_core::rbac::require_permission;
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use erp_core::tenant::TenantContext;
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use futures::StreamExt;
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use serde::Deserialize;
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use std::convert::Infallible;
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use crate::dto::{AnalyzeOptions, AnalysisSseEvent, AnalysisType, TokenUsage};
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use crate::state::AiState;
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// === 分析请求 Query/Body ===
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#[derive(Debug, Deserialize)]
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pub struct AnalyzeBody {
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pub report_id: Option<uuid::Uuid>,
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pub patient_id: Option<uuid::Uuid>,
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pub metrics: Option<Vec<String>>,
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pub options: Option<AnalyzeOptions>,
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}
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// === SSE 分析端点 ===
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pub async fn stream_lab_report<S>(
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State(state): State<AiState>,
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Extension(ctx): Extension<TenantContext>,
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Json(body): Json<AnalyzeBody>,
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) -> Result<Sse<impl futures::Stream<Item = Result<Event, Infallible>>>, erp_core::AppError>
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where
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AiState: FromRef<S>,
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S: Clone + Send + Sync + 'static,
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{
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require_permission(&ctx, "ai.analysis.manage")?;
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let report_id = body.report_id.ok_or_else(|| erp_core::AppError::Validation("report_id 必填".into()))?;
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let prompt = state.prompt.get_active_prompt(ctx.tenant_id(), "lab_report_interpretation").await?;
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let model_config: serde_json::Value = serde_json::from_str(&serde_json::to_string(&prompt.model_config).unwrap_or_default()).unwrap_or_default();
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let model = model_config["model"].as_str().unwrap_or("claude-sonnet-4-6").to_string();
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let temperature = model_config["temperature"].as_f32().unwrap_or(0.3);
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let max_tokens = model_config["max_tokens"].as_u64().unwrap_or(2048) as u32;
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let source_ref = report_id.to_string();
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let (stream, analysis_id, provider_name) = state.analysis.stream_analyze(
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ctx.tenant_id(), ctx.user_id(), uuid::Uuid::nil(),
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AnalysisType::LabReport, source_ref,
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prompt.system_prompt, prompt.user_prompt_template,
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serde_json::json!({"placeholder": true}),
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model, temperature, max_tokens,
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).await?;
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let analysis_id_clone = analysis_id;
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let provider_name_clone = provider_name;
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let model_clone = model.clone();
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let state_clone = state.clone();
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let sse_stream = async_stream::stream! {
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let mut full_content = String::new();
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let mut index: u32 = 0;
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let mut stream = std::pin::pin!(stream);
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while let Some(result) = stream.next().await {
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match result {
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Ok(chunk) => {
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full_content.push_str(&chunk);
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index += 1;
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let event = AnalysisSseEvent::Chunk { content: chunk, index };
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let data = serde_json::to_string(&event).unwrap_or_default();
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yield Ok(Event::default().data(data));
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}
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Err(e) => {
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let event = AnalysisSseEvent::Error { message: e.to_string() };
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let data = serde_json::to_string(&event).unwrap_or_default();
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yield Ok(Event::default().data(data));
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let _ = state_clone.analysis.fail_analysis(analysis_id_clone, e.to_string()).await;
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return;
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}
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}
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}
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// 完成: 存储结果
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let metadata = serde_json::json!({
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"model": model_clone,
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"provider": provider_name_clone,
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});
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let _ = state_clone.analysis.complete_analysis(analysis_id_clone, full_content, metadata).await;
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let done_event = AnalysisSseEvent::Done {
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analysis_id: analysis_id_clone,
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status: "completed".into(),
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};
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let data = serde_json::to_string(&done_event).unwrap_or_default();
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yield Ok(Event::default().data(data));
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};
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Ok(Sse::new(sse_stream).keep_alive(KeepAlive::default()))
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}
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// === 趋势/方案/摘要端点 — 结构类似，切换 analysis_type 和 prompt name ===
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macro_rules! analyze_endpoint {
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($fn_name:ident, $analysis_type:expr, $prompt_name:literal) => {
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pub async fn $fn_name<S>(
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State(state): State<AiState>,
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Extension(ctx): Extension<TenantContext>,
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Json(body): Json<AnalyzeBody>,
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) -> Result<Sse<impl futures::Stream<Item = Result<Event, Infallible>>>, erp_core::AppError>
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where
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AiState: FromRef<S>,
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S: Clone + Send + Sync + 'static,
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{
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require_permission(&ctx, "ai.analysis.manage")?;
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// 与 stream_lab_report 相同的流程，区别:
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// - analysis_type: $analysis_type
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// - prompt name: $prompt_name
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// - source_ref 来自 body.patient_id 或 body.report_id
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// 实现时从 stream_lab_report 复制并修改对应参数
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todo!("参照 stream_lab_report 实现")
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}
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};
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}
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analyze_endpoint!(stream_trends, AnalysisType::Trends, "health_trend_analysis");
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analyze_endpoint!(stream_checkup_plan, AnalysisType::CheckupPlan, "personalized_checkup_plan");
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analyze_endpoint!(stream_report_summary, AnalysisType::ReportSummary, "report_summary_generation");
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// === 分析历史 ===
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#[derive(Debug, Deserialize)]
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pub struct ListAnalysisQuery {
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pub patient_id: Option<uuid::Uuid>,
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pub analysis_type: Option<String>,
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pub page: Option<u64>,
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pub page_size: Option<u64>,
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}
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pub async fn list_analysis<S>(
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State(_state): State<AiState>,
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Extension(ctx): Extension<TenantContext>,
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Query(_params): Query<ListAnalysisQuery>,
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) -> Result<Json<erp_core::ApiResponse<()>>, erp_core::AppError>
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where
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AiState: FromRef<S>,
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S: Clone + Send + Sync + 'static,
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{
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require_permission(&ctx, "ai.analysis.list")?;
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// TODO: 查询 ai_analysis_results 表
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Ok(Json(erp_core::ApiResponse::success(())))
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}
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pub async fn get_analysis<S>(
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State(_state): State<AiState>,
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Extension(ctx): Extension<TenantContext>,
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Path(_id): Path<uuid::Uuid>,
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) -> Result<Json<erp_core::ApiResponse<()>>, erp_core::AppError>
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where
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AiState: FromRef<S>,
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S: Clone + Send + Sync + 'static,
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{
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require_permission(&ctx, "ai.analysis.list")?;
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// TODO: 查询单条分析结果
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Ok(Json(erp_core::ApiResponse::success(())))
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}
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```
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> 注意: `erp_core::ApiResponse::success` 和 `erp_core::rbac::require_permission` 的实际签名需参照现有代码。宏 `analyze_endpoint!` 展开的函数暂时用 `todo!()`，实际实现时从 `stream_lab_report` 复制并修改参数。
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- [ ] **Step 2: 验证编译**
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```bash
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cargo check --workspace
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```
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- [ ] **Step 3: 提交**
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```bash
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git add crates/erp-ai/src/handler/
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git commit -m "feat(ai): SSE 流式分析 Handler 实现"
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```
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---
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### Task 12: 端到端验证
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- [ ] **Step 1: 启动 PostgreSQL + 后端服务**
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```bash
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cd crates/erp-server && cargo run
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```
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验证:
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- 迁移自动执行，`ai_prompts`/`ai_analysis_results`/`ai_usage_logs` 三张表创建成功
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- 服务启动无错误日志
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- [ ] **Step 2: 检查 API 文档**
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访问 `http://localhost:3000/api/docs/openapi.json`，确认 `/api/v1/ai/analyze/*` 端点已注册
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- [ ] **Step 3: 数据库验证**
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```sql
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SELECT table_name FROM information_schema.tables WHERE table_name LIKE 'ai_%';
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```
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Expected: `ai_prompts`, `ai_analysis_results`, `ai_usage_logs`
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- [ ] **Step 4: 提交最终状态 + 推送**
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```bash
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git push
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```
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---
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## 验证清单
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- [ ] `cargo check --workspace` — 全 workspace 编译通过
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- [ ] `cargo test --workspace` — 所有测试通过
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- [ ] PostgreSQL 三张 AI 表存在
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- [ ] `/api/v1/ai/analyze/lab-report` 端点在 Swagger UI 可见
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- [ ] AI 权限码已同步到数据库 `permissions` 表
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- [ ] `AiModule` 出现在启动日志的已注册模块列表中
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---
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## 后续计划 (Phase 1 MVP 剩余)
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以下任务在实际实现阶段细化:
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- **Task 13:** HealthDataProvider 实际数据查询实现 (替换 erp-health stub)
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- **Task 14:** 种子 Prompt 模板数据 (4 个默认模板通过迁移插入)
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- **Task 15:** Redis 缓存层 (缓存命中时跳过 AI 调用)
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- **Task 16:** 降级规则引擎 (50 条常见指标本地规则)
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- **Task 17:** 速率限制中间件
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- **Task 18:** 小程序 AI 解读页面 (报告详情页 + SSE 渲染)
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