update

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

.env.example

@@ -8,10 +8,12 @@ OPENAI_BASE_URL=http://6.86.80.4:30080/v1
 OPENAI_TIMEOUT_SECONDS=180
 
 # 默认评测模型（可在场景 YAML 或 Web 控制台 LLM 配置中覆盖）
-# RAGAS_JUDGE_MODEL 需支持 max_tokens + json_object（gpt-5、gpt-4.1、gpt-4o 等）
+# RAGAS_JUDGE_MODEL 需支持 OpenAI 兼容 chat.completions + 结构化 JSON 输出
-# 注意：gpt-5.4/5.5/5.2 系列不支持 max_tokens，与 RAGAS 0.4.3 不兼容
+# RAGAS_LLM_MAX_TOKENS 控制 Judge 评分链路的 completion budget；faithfulness 等
+# 结构化指标在 GPT-5 系列上通常需要 4096 或更高，避免 IncompleteOutputException
 RAGAS_JUDGE_MODEL=gpt-5
 RAGAS_EMBEDDING_MODEL=text-embedding-3-small
+RAGAS_LLM_MAX_TOKENS=4096
 
 # 评估并发控制（启用 7 个指标时建议 RAGAS_METRIC_TIMEOUT_SECONDS=300）
 BATCH_SIZE=8

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="KubernetesApiProvider"><![CDATA[{}]]></component>
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_17" default="true" project-jdk-name="17" project-jdk-type="JavaSDK">
+    <output url="file://$PROJECT_DIR$/out" />
+  </component>
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/siemens_ragas.iml" filepath="$PROJECT_DIR$/.idea/siemens_ragas.iml" />
+    </modules>
+  </component>
+</project>

.idea/siemens_ragas.iml

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="JAVA_MODULE" version="4">
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

.superpowers/brainstorm/1625-1781595805/content/analysis-approach.html

@@ -0,0 +1,60 @@
+<h2>优化建议怎么生成？</h2>
+<p class="subtitle">这决定了模块的核心机制与可维护性</p>
+
+<div class="options">
+  <div class="option" data-choice="a" onclick="toggleSelect(this)">
+    <div class="letter">A</div>
+    <div class="content">
+      <h3>纯规则引擎</h3>
+      <p>每个指标设阈值（如 faithfulness &lt; 0.6），触发时给出预设建议文本。</p>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>零 LLM 调用，零额外成本</li>
+          <li>结果可预测、可审计</li>
+          <li>响应极快</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>建议固定，无法结合具体样本</li>
+          <li>不能解释"为什么这批数据这个指标低"</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="b" onclick="toggleSelect(this)">
+    <div class="letter">B</div>
+    <div class="content">
+      <h3>LLM 分析（全自动）</h3>
+      <p>把评测结果（各指标均值 + 低分样本）一起交给 LLM，生成上下文感知的中文分析报告。</p>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>能结合具体低分样本给出针对性建议</li>
+          <li>可用中文解释西门子场景下的问题</li>
+          <li>建议质量高、内容丰富</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>每次评测多 1 次 LLM 调用</li>
+          <li>依赖 judge_model 的质量</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="c" onclick="toggleSelect(this)">
+    <div class="letter">C</div>
+    <div class="content">
+      <h3>规则定位 + LLM 解读（推荐）</h3>
+      <p>规则引擎先识别哪些指标异常、触发哪条优化方向；再把"规则诊断 + 低分样本"一起给 LLM 做二次解读，生成中文建议。</p>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>规则保证诊断稳定，不依赖 LLM 自由发挥</li>
+          <li>LLM 在有结构的输入下输出更准确</li>
+          <li>两层可独立测试</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>实现略复杂（两个子模块）</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+</div>

.superpowers/brainstorm/1625-1781595805/content/approaches.html

@@ -0,0 +1,77 @@
+<h2>优化顾问模块 — 实现方案对比</h2>
+<p class="subtitle">三个方案的核心区别在于 LLM 调用边界和代码入侵程度</p>
+
+<div class="options">
+  <div class="option" data-choice="a" onclick="toggleSelect(this)">
+    <div class="letter">A</div>
+    <div class="content">
+      <h3>独立后处理器（轻量集成）</h3>
+      <p>新增 <code>rag_eval/advisor/</code> 包，<code>run_scenario()</code> 末尾调用一行 <code>maybe_run_advisor(result, scenario)</code>。</p>
+      <p><strong>文件结构：</strong></p>
+      <ul>
+        <li><code>rag_eval/advisor/__init__.py</code></li>
+        <li><code>rag_eval/advisor/rules.py</code> — 规则引擎，输入 score_rows，输出诊断列表</li>
+        <li><code>rag_eval/advisor/llm_analyzer.py</code> — 把规则诊断 + 低分样本交给 judge_model</li>
+        <li><code>rag_eval/advisor/writer.py</code> — 写 optimization_advice.md，打日志摘要</li>
+      </ul>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>改动最小，runner.py 只加 3 行</li>
+          <li>advisor 完全独立，可单独测试</li>
+          <li>与现有分层架构完全吻合</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>无法拿到 per-metric 的原始 NaN 率（需从 score_rows 重新算）</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="b" onclick="toggleSelect(this)">
+    <div class="letter">B</div>
+    <div class="content">
+      <h3>嵌入 reporting 层（复用写出基础设施）</h3>
+      <p>把 advisor 作为 <code>rag_eval/reporting/</code> 的一部分，<code>write_run_artifacts()</code> 内部判断是否写 advice。</p>
+      <p><strong>文件结构：</strong></p>
+      <ul>
+        <li><code>rag_eval/reporting/advisor.py</code> — 规则 + LLM + 写出三合一</li>
+        <li><code>write_run_artifacts()</code> 里追加 <code>if scenario.optimization_advisor: write_advice(...)</code></li>
+      </ul>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>artifacts 路径管理统一，advice 自然进 run 目录</li>
+          <li>文件更少</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>reporting 层本是"无副作用写文件"，混入 LLM 调用破坏这一约定</li>
+          <li>advisor 逻辑和写出逻辑耦合，难以单独测试规则引擎</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="c" onclick="toggleSelect(this)">
+    <div class="letter">C</div>
+    <div class="content">
+      <h3>方案 A 变体：advisor 有独立 settings（推荐）</h3>
+      <p>与方案 A 相同的文件结构，但 LLM 调用使用 <strong>scenario 已有的 judge_model</strong>，不新增任何模型配置——advisor 复用 <code>build_models()</code> 已构建好的 llm 实例。</p>
+      <ul>
+        <li><code>rag_eval/advisor/rules.py</code> — 纯函数，7 条指标诊断规则</li>
+        <li><code>rag_eval/advisor/llm_analyzer.py</code> — 接收已有 llm 实例，不重新建 client</li>
+        <li><code>rag_eval/advisor/writer.py</code> — 写 md + 日志</li>
+        <li><code>rag_eval/advisor/__init__.py</code> — 暴露 <code>run_advisor()</code></li>
+      </ul>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>不重复创建 LLM client（节省资源）</li>
+          <li>advisor 阈值可通过 YAML 的 optimization_advisor 块扩展配置</li>
+          <li>独立包边界清晰，易于单测</li>
+          <li>runner.py 改动最小</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>需把 llm 实例从 runner 传入 advisor（多传一个参数）</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+</div>

.superpowers/brainstorm/1625-1781595805/content/design-architecture.html

@@ -0,0 +1,53 @@
+<h2>优化顾问模块 — 整体架构与数据流</h2>
+<p class="subtitle">新增 rag_eval/advisor/ 包，插入 run_scenario() 末尾，复用已有 llm 实例</p>
+
+<div class="mockup">
+  <div class="mockup-header">执行链路（变更前 → 变更后）</div>
+  <div class="mockup-body" style="font-family:monospace;font-size:13px;line-height:2">
+    <span style="color:#94a3b8">run_scenario()</span><br>
+    &nbsp;&nbsp;→ load_scenario()&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8"># 读 YAML，解析 Scenario + optimization_advisor 字段</span><br>
+    &nbsp;&nbsp;→ build_models()&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8"># 已有：创建 llm, embeddings</span><br>
+    &nbsp;&nbsp;→ build_metric_pipeline()&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8"># 已有</span><br>
+    &nbsp;&nbsp;→ Evaluator.evaluate()&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8"># 已有：打分 → EvaluationResult</span><br>
+    &nbsp;&nbsp;→ write_run_artifacts()&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8"># 已有：scores.csv / summary.md / ...</span><br>
+    &nbsp;&nbsp;<span style="color:#4ade80;font-weight:bold">→ run_advisor(result, scenario, llm)&nbsp;&nbsp;&nbsp;# 新增 3 行</span><br>
+    &nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#4ade80">&nbsp;&nbsp;→ rules.diagnose(score_rows)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# 规则引擎：识别异常指标 + 方向</span><br>
+    &nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#4ade80">&nbsp;&nbsp;→ llm_analyzer.analyze(diag, samples)&nbsp;# LLM：结合低分样本生成中文建议</span><br>
+    &nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#4ade80">&nbsp;&nbsp;→ writer.write(advice, paths)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# 写 optimization_advice.md + 日志</span>
+  </div>
+</div>
+
+<div class="section">
+  <h3>新增文件一览</h3>
+  <div class="mockup">
+    <div class="mockup-body" style="font-family:monospace;font-size:13px;line-height:1.9">
+      rag_eval/advisor/<br>
+      &nbsp;&nbsp;__init__.py&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8">← 暴露 run_advisor()，是外部唯一入口</span><br>
+      &nbsp;&nbsp;rules.py&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8">← 纯函数，无 LLM，可单独单测</span><br>
+      &nbsp;&nbsp;llm_analyzer.py <span style="color:#94a3b8">← 接收 llm 实例 + 诊断结构 → 中文 Markdown</span><br>
+      &nbsp;&nbsp;writer.py&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span style="color:#94a3b8">← 写 optimization_advice.md，打日志摘要</span><br>
+      <br>
+      rag_eval/shared/models.py&nbsp;&nbsp;&nbsp;<span style="color:#fbbf24">← 修改：Scenario 加 optimization_advisor 字段</span><br>
+      rag_eval/config/schema.py&nbsp;&nbsp;&nbsp;<span style="color:#fbbf24">← 修改：ScenarioModel 加字段</span><br>
+      rag_eval/execution/runner.py&nbsp;<span style="color:#fbbf24">← 修改：末尾加 3 行调用</span><br>
+      rag_eval/reporting/artifacts.py <span style="color:#fbbf24">← 修改：RunArtifactPaths 加 advice_md 路径</span>
+    </div>
+  </div>
+</div>
+
+<div class="section">
+  <h3>输出产物</h3>
+  <div class="mockup">
+    <div class="mockup-body" style="font-family:monospace;font-size:13px;line-height:1.9">
+      outputs/online/siemens-pdf-question-bank/&lt;run_id&gt;/<br>
+      &nbsp;&nbsp;scenario.snapshot.yaml<br>
+      &nbsp;&nbsp;scores.csv<br>
+      &nbsp;&nbsp;invalid.csv<br>
+      &nbsp;&nbsp;summary.md<br>
+      &nbsp;&nbsp;metadata.json<br>
+      &nbsp;&nbsp;<span style="color:#4ade80;font-weight:bold">optimization_advice.md&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;← 新增</span>
+    </div>
+  </div>
+</div>
+
+<p style="margin-top:1rem;color:#94a3b8;font-size:13px">整体看起来 OK 吗？这是新模块与现有链路的接入方式。</p>

.superpowers/brainstorm/1625-1781595805/content/trigger-mode.html

@@ -0,0 +1,68 @@
+<h2>优化顾问在什么情况下运行？</h2>
+<p class="subtitle">这决定了模块与现有评测流程的集成方式</p>
+
+<div class="options">
+  <div class="option" data-choice="a" onclick="toggleSelect(this)">
+    <div class="letter">A</div>
+    <div class="content">
+      <h3>每次评测自动运行</h3>
+      <p>run_scenario() 结束后自动调用，无需任何额外配置。</p>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>零感知，开箱即用</li>
+          <li>每次跑完都有建议报告</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>每次都多一次 LLM 调用，不管是否需要</li>
+          <li>无法关闭</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="b" onclick="toggleSelect(this)">
+    <div class="letter">B</div>
+    <div class="content">
+      <h3>YAML 场景中显式开启（推荐）</h3>
+      <p>在 scenario YAML 里加一行 <code>optimization_advisor: true</code>，默认关闭。</p>
+      <div class="mockup">
+        <div class="mockup-header">siemens-pdf-question-bank-online.yaml</div>
+        <div class="mockup-body" style="font-family:monospace;font-size:13px;line-height:1.8">
+          metrics:<br>
+          &nbsp;&nbsp;- faithfulness<br>
+          &nbsp;&nbsp;- noise_sensitivity<br>
+          &nbsp;&nbsp;...<br>
+          <span style="color:#4ade80;font-weight:bold">optimization_advisor: true  # 新增</span>
+        </div>
+      </div>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>显式可见，按需开启</li>
+          <li>与现有 YAML 驱动风格一致</li>
+          <li>可为不同场景独立配置</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>需要手动在 YAML 里加一行</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+
+  <div class="option" data-choice="c" onclick="toggleSelect(this)">
+    <div class="letter">C</div>
+    <div class="content">
+      <h3>阈值触发（任一指标低于警戒线时自动激活）</h3>
+      <p>规则引擎先算，若发现有指标低于阈值则自动启动 LLM 分析；一切正常则跳过。</p>
+      <div class="pros-cons">
+        <div class="pros"><h4>优点</h4><ul>
+          <li>"有问题才报警"，符合直觉</li>
+          <li>高分场景无额外成本</li>
+        </ul></div>
+        <div class="cons"><h4>缺点</h4><ul>
+          <li>阈值需要维护，不同场景可能不同</li>
+          <li>正常分数时无建议，但用户可能仍想看优化空间</li>
+        </ul></div>
+      </div>
+    </div>
+  </div>
+</div>

.superpowers/brainstorm/1625-1781595805/content/waiting-2.html

@@ -0,0 +1,3 @@
+<div style="display:flex;align-items:center;justify-content:center;min-height:60vh">
+  <p class="subtitle">Writing spec & moving to implementation...</p>
+</div>

.superpowers/brainstorm/1625-1781595805/content/waiting.html

@@ -0,0 +1,3 @@
+<div style="display:flex;align-items:center;justify-content:center;min-height:60vh">
+  <p class="subtitle">Continuing in terminal — 正在设计方案...</p>
+</div>

.superpowers/brainstorm/1625-1781595805/state/server-stopped

@@ -0,0 +1 @@
+{"reason":"idle timeout","timestamp":1781598635371}

.superpowers/brainstorm/1625-1781595805/state/server.pid

@@ -0,0 +1 @@
+1625

docs/superpowers/plans/2026-06-24-async-score-jobs.md

@@ -0,0 +1,808 @@
+# 异步评分记录（Async Score Jobs）Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 新增 `POST /api/score/async` 异步端点，结果持久化至 `outputs/score-jobs/`，并在前端新增「评分记录」页面展示。
+
+**Architecture:** 新建 `ScoreJobManager`（复用 `pipeline_task_manager` 线程池模式）在后台执行 `InlineScorer.score()`，写入 JSON 文件；新增三个 REST 端点；前端新增导航页加载并轮询记录。
+
+**Tech Stack:** Python 3.12, FastAPI, Pydantic v2, threading, Vanilla JS, pytest
+
+## Global Constraints
+
+- Python 3.12+，PEP 8，4 空格缩进，类型注解必须
+- 存储路径：`outputs/score-jobs/<job_id>.json`
+- 复用现有 `ScoreRequest`（含 `effective_metrics()` 和 `contexts_as_list()` 方法）
+- 复用现有 `InlineScorer.score()` 和 `compute_weighted_score()`
+- 所有测试用 pytest，不依赖真实 LLM
+
+---
+
+## 文件清单
+
+| 操作 | 文件 | 职责 |
+|------|------|------|
+| 新建 | `webapp/services/score_job_manager.py` | ScoreJobManager：线程池 + JSON 持久化 |
+| 新建 | `webapp/api/score_jobs.py` | 3 个端点路由 |
+| 新建 | `webapp/static/js/score_jobs.js` | 前端列表 + 轮询逻辑 |
+| 新建 | `tests/webapp/test_score_jobs_api.py` | API 集成测试 |
+| 修改 | `webapp/models.py` | 新增 `AsyncScoreJobStatus`、`AsyncScoreJobResponse` |
+| 修改 | `webapp/server.py` | 注册 score_jobs router，更新 OPENAPI_TAGS 和 description |
+| 修改 | `webapp/static/index.html` | 新增导航项 + `#view-scorejobs` section |
+| 修改 | `webapp/static/js/api.js` | 新增 `scoreJobsAsync()`、`getScoreJob()`、`listScoreJobs()` |
+| 修改 | `webapp/static/js/app.js` | 注册 `scorejobs` 视图、加载调用 |
+
+---
+
+## Task 1: Pydantic 模型 + ScoreJobManager
+
+**Files:**
+- Modify: `webapp/models.py`
+- Create: `webapp/services/score_job_manager.py`
+- Create: `tests/webapp/test_score_jobs_api.py` (partial)
+
+**Interfaces:**
+- Produces:
+  - `AsyncScoreJobStatus` Pydantic model
+  - `AsyncScoreJobResponse` Pydantic model
+  - `score_job_manager: ScoreJobManager` singleton
+  - `ScoreJobManager.submit(request: ScoreRequest) -> AsyncScoreJobStatus`
+  - `ScoreJobManager.get(job_id: str) -> AsyncScoreJobStatus | None`
+  - `ScoreJobManager.list_jobs() -> list[AsyncScoreJobStatus]`
+
+- [ ] **Step 1: Add models to `webapp/models.py`**
+
+Append after `AsyncScoreJobResponse` (at the end of the file, after `ScoreResponse`):
+
+```python
+# ---------------------------------------------------------------------------
+# 异步评分记录模型
+# ---------------------------------------------------------------------------
+
+class AsyncScoreJobResponse(BaseModel):
+    """Immediate response after submitting an async score job."""
+
+    job_id: str = Field(description="任务唯一标识符，用于后续查询结果。")
+    status: str = Field(default="queued", description="初始状态：queued。")
+
+
+class AsyncScoreJobStatus(BaseModel):
+    """Full state of one async score job, persisted to disk."""
+
+    job_id: str = Field(description="任务唯一标识符。")
+    status: str = Field(description="queued | running | completed | failed")
+    created_at: str = Field(default="", description="创建时间（ISO 8601 UTC）。")
+    finished_at: str = Field(default="", description="完成时间（ISO 8601 UTC）。")
+    request_summary: dict = Field(
+        default_factory=dict,
+        description="请求参数快照（question 前80字、metrics、judge_model 等）。",
+    )
+    scores: dict[str, float | None] = Field(default_factory=dict, description="各指标得分。")
+    weighted_score: float | None = Field(default=None, description="加权综合得分。")
+    latency_ms: int = Field(default=0, description="评分耗时毫秒。")
+    skipped_metrics: list[str] = Field(default_factory=list)
+    error: str | None = Field(default=None)
+```
+
+- [ ] **Step 2: Write failing tests**
+
+Create `tests/webapp/test_score_jobs_api.py`:
+
+```python
+"""Tests for async score jobs API."""
+from __future__ import annotations
+import json
+import time
+import pytest
+from unittest.mock import MagicMock, patch
+from fastapi.testclient import TestClient
+
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch):
+    import webapp.services.score_job_manager as mgr_mod
+    from webapp.services.score_job_manager import ScoreJobManager
+    fresh_mgr = ScoreJobManager(jobs_dir=tmp_path / "score-jobs")
+    monkeypatch.setattr(mgr_mod, "score_job_manager", fresh_mgr)
+    import webapp.api.score_jobs as api_mod
+    monkeypatch.setattr(api_mod, "score_job_manager", fresh_mgr)
+    from webapp.server import create_app
+    return TestClient(create_app())
+
+
+class TestScoreJobManager:
+    def test_submit_returns_job_status_with_queued(self, tmp_path):
+        from webapp.services.score_job_manager import ScoreJobManager
+        from webapp.models import ScoreRequest
+        mgr = ScoreJobManager(jobs_dir=tmp_path / "jobs")
+        req = ScoreRequest(question="q", answer="a", metrics=["answer_relevancy"])
+        with patch.object(mgr, "_execute") as mock_exec:
+            mock_exec.return_value = None
+            status = mgr.submit(req)
+        assert status.status in ("queued", "running", "completed")
+        assert len(status.job_id) > 0
+
+    def test_get_returns_none_for_unknown_id(self, tmp_path):
+        from webapp.services.score_job_manager import ScoreJobManager
+        mgr = ScoreJobManager(jobs_dir=tmp_path / "jobs")
+        assert mgr.get("nonexistent") is None
+
+    def test_list_returns_empty_initially(self, tmp_path):
+        from webapp.services.score_job_manager import ScoreJobManager
+        mgr = ScoreJobManager(jobs_dir=tmp_path / "jobs")
+        assert mgr.list_jobs() == []
+
+    def test_completed_job_persisted_to_disk(self, tmp_path):
+        from webapp.services.score_job_manager import ScoreJobManager
+        from webapp.models import ScoreRequest
+        mgr = ScoreJobManager(jobs_dir=tmp_path / "jobs", max_workers=1)
+        req = ScoreRequest(question="q?", answer="a.", metrics=["answer_relevancy"])
+        mock_scorer = MagicMock()
+        mock_scorer.score.return_value = {"answer_relevancy": 0.85}
+        with patch("webapp.services.score_job_manager.inline_scorer", mock_scorer):
+            with patch("webapp.services.score_job_manager.EvaluationSettings"):
+                status = mgr.submit(req)
+        for _ in range(20):
+            s = mgr.get(status.job_id)
+            if s and s.status in ("completed", "failed"):
+                break
+            time.sleep(0.2)
+        s = mgr.get(status.job_id)
+        assert s is not None
+        json_path = tmp_path / "jobs" / f"{status.job_id}.json"
+        assert json_path.exists()
+        data = json.loads(json_path.read_text(encoding="utf-8"))
+        assert data["job_id"] == status.job_id
+```
+
+- [ ] **Step 3: Run to verify FAIL**
+
+```
+cd C:\Projects\AIProjects\Siemens-AIPOC\siemens_ragas
+python -m pytest tests/webapp/test_score_jobs_api.py::TestScoreJobManager -v
+```
+Expected: `ModuleNotFoundError: No module named 'webapp.services.score_job_manager'`
+
+- [ ] **Step 4: Create `webapp/services/score_job_manager.py`**
+
+```python
+"""Background task manager for async RAGAS single-sample scoring.
+
+Each job runs InlineScorer.score() in a thread pool and persists the
+result as a JSON file under outputs/score-jobs/<job_id>.json so results
+survive server restarts and can be listed by the frontend.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import threading
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from rag_eval.metrics.weights import compute_weighted_score
+from rag_eval.settings import EvaluationSettings
+from webapp.models import AsyncScoreJobStatus, ScoreRequest
+from webapp.services.inline_scorer import inline_scorer
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_DEFAULT_JOBS_DIR = _REPO_ROOT / "outputs" / "score-jobs"
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class ScoreJobManager:
+    """Thread-pool manager for async RAGAS scoring jobs with JSON persistence."""
+
+    def __init__(
+        self,
+        jobs_dir: Path = _DEFAULT_JOBS_DIR,
+        max_workers: int = 4,
+    ) -> None:
+        self._jobs_dir = Path(jobs_dir)
+        self._jobs_dir.mkdir(parents=True, exist_ok=True)
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+        # In-memory index: job_id -> AsyncScoreJobStatus (authoritative while running)
+        self._cache: dict[str, AsyncScoreJobStatus] = {}
+        self._lock = threading.Lock()
+        self._load_existing()
+
+    # ------------------------------------------------------------------ #
+    # Public API
+    # ------------------------------------------------------------------ #
+
+    def submit(self, request: ScoreRequest) -> AsyncScoreJobStatus:
+        """Queue one scoring job and return its initial status immediately."""
+        job_id = uuid.uuid4().hex[:12]
+        status = AsyncScoreJobStatus(
+            job_id=job_id,
+            status="queued",
+            created_at=_now_iso(),
+            request_summary={
+                "question": request.question[:80],
+                "answer": (request.answer or "")[:80],
+                "metrics": list(request.metrics),
+                "judge_model": request.judge_model or "",
+                "embedding_model": request.embedding_model or "",
+                "has_contexts": bool(request.contexts),
+                "has_ground_truth": bool(request.ground_truth),
+            },
+        )
+        with self._lock:
+            self._cache[job_id] = status
+        self._persist(status)
+        self._executor.submit(self._run, job_id, request)
+        return status
+
+    def get(self, job_id: str) -> AsyncScoreJobStatus | None:
+        """Return the current status for one job, or None if unknown."""
+        with self._lock:
+            return self._cache.get(job_id)
+
+    def list_jobs(self) -> list[AsyncScoreJobStatus]:
+        """Return all known jobs sorted newest first."""
+        with self._lock:
+            jobs = list(self._cache.values())
+        jobs.sort(key=lambda j: j.created_at, reverse=True)
+        return jobs
+
+    # ------------------------------------------------------------------ #
+    # Internal
+    # ------------------------------------------------------------------ #
+
+    def _run(self, job_id: str, request: ScoreRequest) -> None:
+        """Execute scoring in the thread pool and persist the result."""
+        self._update(job_id, status="running")
+        settings = EvaluationSettings()
+        judge_model = request.judge_model or settings.ragas_judge_model
+        embedding_model = request.embedding_model or settings.ragas_embedding_model
+        effective = request.effective_metrics()
+        requested = set(request.metrics)
+        skipped = sorted(requested - set(effective))
+
+        import time as _time
+        t0 = _time.monotonic()
+        try:
+            if not effective:
+                scores: dict[str, float | None] = {m: None for m in request.metrics}
+                weighted = None
+            else:
+                raw = inline_scorer.score(
+                    question=request.question,
+                    answer=request.answer,
+                    contexts=request.contexts_as_list(),
+                    ground_truth=request.ground_truth,
+                    metrics=effective,
+                    judge_model=judge_model,
+                    embedding_model=embedding_model,
+                    settings=settings,
+                )
+                scores = {m: None for m in request.metrics}
+                scores.update(raw)
+                weighted_raw = compute_weighted_score(
+                    {k: v for k, v in raw.items() if v is not None}, {}
+                )
+                weighted = round(weighted_raw, 4) if weighted_raw is not None else None
+
+            latency_ms = int((_time.monotonic() - t0) * 1000)
+            self._update(
+                job_id,
+                status="completed",
+                finished_at=_now_iso(),
+                scores=scores,
+                weighted_score=weighted,
+                latency_ms=latency_ms,
+                skipped_metrics=skipped,
+            )
+        except Exception as exc:  # noqa: BLE001
+            latency_ms = int((_time.monotonic() - t0) * 1000)
+            self._update(
+                job_id,
+                status="failed",
+                finished_at=_now_iso(),
+                latency_ms=latency_ms,
+                error=f"{type(exc).__name__}: {exc}",
+            )
+
+    def _update(self, job_id: str, **kwargs: Any) -> None:
+        """Merge kwargs into the job status and persist."""
+        with self._lock:
+            existing = self._cache.get(job_id)
+            if existing is None:
+                return
+            updated = existing.model_copy(update=kwargs)
+            self._cache[job_id] = updated
+        self._persist(updated)
+
+    def _persist(self, status: AsyncScoreJobStatus) -> None:
+        """Write one job's status to its JSON file."""
+        path = self._jobs_dir / f"{status.job_id}.json"
+        path.write_text(
+            json.dumps(status.model_dump(), ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def _load_existing(self) -> None:
+        """Load completed jobs from disk into memory on startup."""
+        for path in sorted(self._jobs_dir.glob("*.json")):
+            try:
+                data = json.loads(path.read_text(encoding="utf-8"))
+                status = AsyncScoreJobStatus.model_validate(data)
+                self._cache[status.job_id] = status
+            except Exception:  # noqa: BLE001
+                pass  # Corrupt file — skip
+
+
+# Module-level singleton shared by FastAPI routes.
+score_job_manager = ScoreJobManager()
+```
+
+- [ ] **Step 5: Run to verify tests PASS**
+
+```
+python -m pytest tests/webapp/test_score_jobs_api.py::TestScoreJobManager -v
+```
+Expected: 4 tests PASS
+
+- [ ] **Step 6: Commit**
+
+```
+git add webapp/models.py webapp/services/score_job_manager.py tests/webapp/test_score_jobs_api.py
+git commit -m "feat: add AsyncScoreJobStatus model and ScoreJobManager with JSON persistence"
+```
+
+---
+
+## Task 2: API 端点
+
+**Files:**
+- Create: `webapp/api/score_jobs.py`
+- Modify: `webapp/server.py`
+- Modify: `tests/webapp/test_score_jobs_api.py`
+
+**Interfaces:**
+- Consumes: `score_job_manager: ScoreJobManager`, `AsyncScoreJobResponse`, `AsyncScoreJobStatus`, `ScoreRequest`
+- Produces: `POST /api/score/async`, `GET /api/score/jobs`, `GET /api/score/jobs/{job_id}`
+
+- [ ] **Step 1: Add API tests to `tests/webapp/test_score_jobs_api.py`**
+
+Append this class:
+
+```python
+class TestScoreJobsEndpoint:
+    def test_submit_async_returns_202(self, client):
+        with patch("webapp.services.score_job_manager.ScoreJobManager._execute"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?", "answer": "a.",
+                "metrics": ["answer_relevancy"],
+            })
+        assert resp.status_code == 202
+        data = resp.json()
+        assert "job_id" in data
+        assert data["status"] == "queued"
+
+    def test_get_unknown_job_returns_404(self, client):
+        resp = client.get("/api/score/jobs/nonexistent")
+        assert resp.status_code == 404
+
+    def test_list_jobs_returns_empty_initially(self, client):
+        resp = client.get("/api/score/jobs")
+        assert resp.status_code == 200
+        assert resp.json()["jobs"] == []
+
+    def test_submitted_job_appears_in_list(self, client):
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?", "answer": "a.",
+                "metrics": ["answer_relevancy"],
+            })
+        job_id = resp.json()["job_id"]
+        list_resp = client.get("/api/score/jobs")
+        ids = [j["job_id"] for j in list_resp.json()["jobs"]]
+        assert job_id in ids
+
+    def test_get_job_by_id(self, client):
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?", "answer": "a.",
+                "metrics": ["answer_relevancy"],
+            })
+        job_id = resp.json()["job_id"]
+        get_resp = client.get(f"/api/score/jobs/{job_id}")
+        assert get_resp.status_code == 200
+        assert get_resp.json()["job_id"] == job_id
+```
+
+- [ ] **Step 2: Run to verify FAIL**
+
+```
+python -m pytest tests/webapp/test_score_jobs_api.py::TestScoreJobsEndpoint -v
+```
+Expected: FAIL — `ModuleNotFoundError: No module named 'webapp.api.score_jobs'`
+
+- [ ] **Step 3: Create `webapp/api/score_jobs.py`**
+
+```python
+"""Routes for async RAGAS scoring jobs (Dify fire-and-forget integration)."""
+
+from __future__ import annotations
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from webapp.models import AsyncScoreJobResponse, AsyncScoreJobStatus, ScoreRequest
+from webapp.services.score_job_manager import score_job_manager
+
+router = APIRouter(prefix="/api/score", tags=["score"])
+logger = logging.getLogger("webapp.api.score_jobs")
+
+
+@router.post(
+    "/async",
+    status_code=202,
+    response_model=AsyncScoreJobResponse,
+    summary="提交异步评分任务（Dify 推荐方式）",
+    responses={
+        202: {
+            "description": "任务已排队，立即返回 job_id。通过 GET /api/score/jobs/{job_id} 查询结果。",
+            "content": {
+                "application/json": {
+                    "example": {"job_id": "abc123def456", "status": "queued"}
+                }
+            },
+        },
+    },
+)
+def submit_async_score(request: ScoreRequest) -> AsyncScoreJobResponse:
+    """提交异步 RAGAS 评分任务，立即返回 job_id（202 Accepted）。
+
+    评分在后台线程中执行，结果持久化至 `outputs/score-jobs/<job_id>.json`。
+    在 RAGAS 平台「评分记录」页面可查看所有历史评分记录。
+
+    **Dify 工作流推荐使用此接口**：不等待评分完成，工作流立即继续，
+    避免 HTTP 节点超时。评分结果通过平台界面查看。
+    """
+    logger.info(
+        "[score_async] submit  metrics=%s  has_ctx=%s  has_gt=%s",
+        request.metrics, bool(request.contexts), bool(request.ground_truth),
+    )
+    status = score_job_manager.submit(request)
+    logger.info("[score_async] queued  job_id=%s", status.job_id)
+    return AsyncScoreJobResponse(job_id=status.job_id, status=status.status)
+
+
+@router.get(
+    "/jobs",
+    response_model=dict,
+    summary="列出所有评分记录",
+)
+def list_score_jobs() -> dict:
+    """返回所有异步评分记录，按创建时间倒序排列。"""
+    jobs = score_job_manager.list_jobs()
+    logger.info("[score_jobs] list  count=%d", len(jobs))
+    return {"jobs": [j.model_dump() for j in jobs]}
+
+
+@router.get(
+    "/jobs/{job_id}",
+    response_model=AsyncScoreJobStatus,
+    summary="查询评分记录详情",
+    responses={404: {"description": "指定 job_id 的评分记录不存在。"}},
+)
+def get_score_job(job_id: str) -> AsyncScoreJobStatus:
+    """返回一个异步评分任务的当前状态和结果。"""
+    status = score_job_manager.get(job_id)
+    if status is None:
+        raise HTTPException(status_code=404, detail=f"Score job not found: {job_id}")
+    return status
+```
+
+- [ ] **Step 4: Register router in `webapp/server.py`**
+
+Add import:
+```python
+from webapp.api import evaluations, llm_profiles, pipeline, runs, scenarios, score, score_jobs
+```
+
+Add after `app.include_router(score.router)`:
+```python
+    app.include_router(score_jobs.router)
+```
+
+Add entry to `OPENAPI_TAGS` before `"meta"`:
+```python
+    {
+        "name": "score",
+        "description": (
+            "**实时评分 API（同步）** — `POST /api/score`\n\n"
+            "**异步评分 API（Dify 推荐）** — `POST /api/score/async`\n\n"
+            "异步方式立即返回 job_id（202），评分在后台执行，结果在「评分记录」页查看。\n\n"
+            "**鉴权**：若 `.env` 中配置了 `SCORE_API_TOKEN`，需携带 "
+            "`Authorization: Bearer <token>` 请求头。"
+        ),
+    },
+```
+
+> Note: this replaces the existing `"score"` entry in `OPENAPI_TAGS`.
+
+- [ ] **Step 5: Verify no route conflict**
+
+```
+python -c "
+from webapp.server import create_app
+app = create_app()
+score_routes = [(r.path, list(getattr(r,'methods',[]))) for r in app.routes if 'score' in r.path]
+print(score_routes)
+"
+```
+Expected: shows `/api/score`, `/api/score/async`, `/api/score/jobs`, `/api/score/jobs/{job_id}`
+
+- [ ] **Step 6: Run API tests**
+
+```
+python -m pytest tests/webapp/test_score_jobs_api.py -v --tb=short
+```
+Expected: all 9 tests PASS
+
+- [ ] **Step 7: Commit**
+
+```
+git add webapp/api/score_jobs.py webapp/server.py tests/webapp/test_score_jobs_api.py
+git commit -m "feat: add POST /api/score/async and GET /api/score/jobs endpoints"
+```
+
+---
+
+## Task 3: 前端「评分记录」页
+
+**Files:**
+- Modify: `webapp/static/index.html`
+- Modify: `webapp/static/js/api.js`
+- Modify: `webapp/static/js/app.js`
+- Create: `webapp/static/js/score_jobs.js`
+
+**Interfaces:**
+- Consumes: `GET /api/score/jobs`, `GET /api/score/jobs/{job_id}`
+- Produces: `#view-scorejobs` section, `ScoreJobs` JS object
+
+- [ ] **Step 1: Add API methods to `webapp/static/js/api.js`**
+
+Add before the closing `};`:
+```javascript
+  // 异步评分记录 API
+  scoreJobsAsync(body) { return API.post("/api/score/async", body); },
+  getScoreJob(jobId) { return API.get(`/api/score/jobs/${encodeURIComponent(jobId)}`); },
+  listScoreJobs() { return API.get("/api/score/jobs"); },
+```
+
+- [ ] **Step 2: Add nav item and section to `webapp/static/index.html`**
+
+In the `<nav class="nav">` block, add after the `profiles` nav-item and before the `apidocs` nav-item:
+```html
+        <button class="nav-item" data-view="scorejobs">
+          <span class="nav-ico">📋</span><span>评分记录</span>
+        </button>
+```
+
+Add a new section before the `<!-- API 文档视图 -->` comment:
+```html
+      <!-- 评分记录视图 -->
+      <section class="view" id="view-scorejobs" hidden>
+        <div class="panel">
+          <div class="panel-head">
+            <h2>评分记录</h2>
+            <span class="muted" style="font-size:13px">来自 Dify 异步评分任务（POST /api/score/async）</span>
+          </div>
+        </div>
+        <div id="scorejobs-container"></div>
+        <div class="empty" id="scorejobs-empty" hidden>
+          <p>暂无评分记录。</p>
+          <p class="muted">在 Dify 工作流中调用 <code>POST /api/score/async</code> 后，记录将在此显示。</p>
+        </div>
+      </section>
+```
+
+- [ ] **Step 3: Create `webapp/static/js/score_jobs.js`**
+
+```javascript
+// score_jobs.js — 评分记录页面逻辑（异步 RAGAS 评分结果列表）
+
+const ScoreJobs = {
+  _pollTimers: {},   // job_id -> setInterval handle
+
+  async load() {
+    const container = document.getElementById("scorejobs-container");
+    const empty = document.getElementById("scorejobs-empty");
+    container.innerHTML = '<p class="muted">加载中…</p>';
+    try {
+      const data = await API.listScoreJobs();
+      const jobs = data.jobs || [];
+      container.innerHTML = "";
+      if (jobs.length === 0) {
+        empty.hidden = false;
+        return;
+      }
+      empty.hidden = true;
+      jobs.forEach(job => container.appendChild(ScoreJobs.renderRow(job)));
+      // Auto-poll any queued/running jobs
+      jobs.forEach(job => {
+        if (job.status === "queued" || job.status === "running") {
+          ScoreJobs._startPoll(job.job_id);
+        }
+      });
+    } catch (err) {
+      container.innerHTML = `<p class="muted">加载失败：${App.escape(err.message)}</p>`;
+    }
+  },
+
+  renderRow(job) {
+    const row = document.createElement("div");
+    row.className = "panel score-job-row";
+    row.id = `score-job-${job.job_id}`;
+    row.innerHTML = ScoreJobs._rowHtml(job);
+    return row;
+  },
+
+  _rowHtml(job) {
+    const time = App.shortTime(job.created_at);
+    const question = App.escape((job.request_summary?.question || "—").slice(0, 50));
+    const metrics = (job.request_summary?.metrics || []).join(", ");
+    const statusBadge = `<span class="badge ${job.status}">${job.status}</span>`;
+
+    let scoreHtml = "";
+    if (job.status === "completed") {
+      scoreHtml = Object.entries(job.scores || {})
+        .map(([k, v]) => {
+          const cls = App.scoreClass(v);
+          const text = v === null || v === undefined ? "n/a" : Number(v).toFixed(3);
+          return `<span class="metric-chip" title="${App.escape(k)}">${App.escape(App.shortMetric(k))} <b class="${cls}">${text}</b></span>`;
+        })
+        .join(" ");
+      if (job.weighted_score !== null && job.weighted_score !== undefined) {
+        const cls = App.scoreClass(job.weighted_score);
+        scoreHtml += ` <span class="metric-chip">综合 <b class="${cls}">${Number(job.weighted_score).toFixed(3)}</b></span>`;
+      }
+    } else if (job.status === "failed") {
+      scoreHtml = `<span class="muted" style="color:var(--bad)">${App.escape(job.error || "未知错误")}</span>`;
+    } else {
+      scoreHtml = `<span class="muted">评分中…</span>`;
+    }
+
+    return `
+      <div class="run-card-head">
+        <div class="run-card-title">${question}</div>
+        <div>${statusBadge}</div>
+      </div>
+      <div class="run-card-meta">
+        <div>指标：${App.escape(metrics)} · ${time} · ${job.latency_ms}ms</div>
+      </div>
+      <div class="run-card-metrics">${scoreHtml}</div>
+    `;
+  },
+
+  _startPoll(jobId) {
+    if (ScoreJobs._pollTimers[jobId]) return;
+    ScoreJobs._pollTimers[jobId] = setInterval(async () => {
+      try {
+        const job = await API.getScoreJob(jobId);
+        const el = document.getElementById(`score-job-${jobId}`);
+        if (el) el.innerHTML = ScoreJobs._rowHtml(job);
+        if (job.status === "completed" || job.status === "failed") {
+          clearInterval(ScoreJobs._pollTimers[jobId]);
+          delete ScoreJobs._pollTimers[jobId];
+        }
+      } catch (_e) {
+        clearInterval(ScoreJobs._pollTimers[jobId]);
+        delete ScoreJobs._pollTimers[jobId];
+      }
+    }, 5000);
+  },
+
+  stopAllPolls() {
+    Object.values(ScoreJobs._pollTimers).forEach(t => clearInterval(t));
+    ScoreJobs._pollTimers = {};
+  },
+};
+```
+
+- [ ] **Step 4: Update `webapp/static/js/app.js`**
+
+Add `"scorejobs"` to the `views` array and `titles` object:
+
+```javascript
+  views: ["runs", "new", "report", "profiles", "scorejobs", "apidocs"],
+  titles: { runs: "运行列表", new: "新建评估", report: "报告详情", profiles: "LLM 配置", scorejobs: "评分记录", apidocs: "API 文档" },
+```
+
+Add in `_doSwitch` after `if (view === "profiles") Profiles.load();`:
+```javascript
+    if (view === "scorejobs") ScoreJobs.load();
+```
+
+Add `ScoreJobs.stopAllPolls();` when switching away, in `_doSwitch` before view switching logic:
+```javascript
+    // Stop score job pollers when leaving the scorejobs view
+    if (App.activeView === "scorejobs" && view !== "scorejobs") ScoreJobs.stopAllPolls();
+```
+
+- [ ] **Step 5: Add script tag to `webapp/static/index.html`**
+
+Add before `<script src="/static/js/app.js"></script>`:
+```html
+  <script src="/static/js/score_jobs.js"></script>
+```
+
+- [ ] **Step 6: Verify server boots**
+
+```
+python -c "from webapp.server import create_app; create_app(); print('OK')"
+```
+Expected: `OK`
+
+Also verify HTML has all new elements:
+```
+python -c "
+c = open('webapp/static/index.html', encoding='utf-8').read()
+assert 'view-scorejobs' in c
+assert 'scorejobs-container' in c
+assert '评分记录' in c
+print('HTML OK')
+"
+```
+
+- [ ] **Step 7: Commit**
+
+```
+git add webapp/static/index.html webapp/static/js/api.js webapp/static/js/app.js webapp/static/js/score_jobs.js
+git commit -m "feat: add 评分记录 page with async score job list and auto-polling"
+```
+
+---
+
+## Task 4: 全量回归测试 + Dify 说明注释
+
+**Files:**
+- Modify: `webapp/static/js/score_jobs.js` (minor: add Dify curl comment at top)
+
+- [ ] **Step 1: Run full test suite**
+
+```
+python -m pytest tests/ -v --tb=short -q 2>&1 | tail -15
+```
+
+Pre-existing failures to ignore:
+- `test_normalize_sample_pdf_offline_smoke_row`
+- `test_evaluator_and_reporting_write_run_assets`
+- `test_question_generator_rejects_invalid_json`
+- `test_question_generator_rejects_non_list_samples`
+
+Any other failure is a regression — fix before proceeding.
+
+- [ ] **Step 2: Run targeted tests**
+
+```
+python -m pytest tests/webapp/test_score_jobs_api.py tests/webapp/test_score_api.py tests/test_pipeline.py -v --tb=short
+```
+Expected: all PASS
+
+- [ ] **Step 3: Final commit**
+
+```
+git add .
+git commit -m "feat: async score jobs complete — POST /api/score/async + 评分记录 page
+
+- ScoreJobManager: thread pool + JSON persistence (outputs/score-jobs/)
+- POST /api/score/async: 202 immediate response with job_id
+- GET /api/score/jobs + GET /api/score/jobs/{id}: query endpoints
+- Frontend: 评分记录 nav page with 5s auto-polling for pending jobs
+- Dify integration: change /api/score → /api/score/async, remove response parsing
+
+Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>"
+```

docs/superpowers/specs/2026-06-24-async-score-jobs-design.md

@@ -0,0 +1,116 @@
+# 异步评分记录功能设计
+
+**日期**: 2026-06-24  
+**状态**: 已批准，待实现  
+**范围**: 新增 `POST /api/score/async` 异步评分端点，评分结果持久化到磁盘，前端新增「评分记录」页面展示。
+
+---
+
+## 1. 目标
+
+- Dify 工作流调用 `/api/score/async` 立即返回 `job_id`（202），不等待评分完成
+- 后台异步执行 RAGAS 评分，结果写入 `outputs/score-jobs/<job_id>.json`
+- RAGAS 平台新增「评分记录」导航页，列表展示所有评分记录及状态
+
+---
+
+## 2. 架构
+
+```
+Dify → POST /api/score/async → 202 {job_id, status:"queued"}
+                                      ↓
+                              ScoreJobManager (线程池)
+                                      ↓
+                              InlineScorer.score()
+                                      ↓
+                        outputs/score-jobs/<job_id>.json
+                                      ↓
+              GET /api/score/jobs ← 前端「评分记录」页轮询
+```
+
+---
+
+## 3. 存储格式
+
+`outputs/score-jobs/<job_id>.json`:
+
+```json
+{
+  "job_id": "abc123def456",
+  "status": "completed",
+  "created_at": "2026-06-24T09:00:00+00:00",
+  "finished_at": "2026-06-24T09:00:15+00:00",
+  "request": {
+    "question": "双源CT的时间分辨率是多少?",
+    "answer": "双源CT的单扇区时间分辨率为75ms。",
+    "contexts": null,
+    "ground_truth": null,
+    "metrics": ["answer_relevancy"],
+    "judge_model": "gpt-5",
+    "embedding_model": "text-embedding-3-small"
+  },
+  "scores": {"answer_relevancy": 0.9075},
+  "weighted_score": 0.9075,
+  "latency_ms": 12500,
+  "skipped_metrics": [],
+  "error": null
+}
+```
+
+---
+
+## 4. API 端点
+
+### `POST /api/score/async`
+
+请求体与 `POST /api/score` 完全相同（`ScoreRequest`）。
+
+```json
+// 立即返回 202
+{"job_id": "abc123def456", "status": "queued"}
+```
+
+### `GET /api/score/jobs`
+
+返回所有评分记录，按创建时间倒序：
+```json
+{"jobs": [{...ScoreJobStatus...}]}
+```
+
+### `GET /api/score/jobs/{job_id}`
+
+返回单条评分记录详情。
+
+---
+
+## 5. 新增文件
+
+| 文件 | 职责 |
+|------|------|
+| `webapp/services/score_job_manager.py` | ScoreJobManager：线程池 + JSON 持久化 |
+| `webapp/api/score_jobs.py` | 3 个端点路由 |
+| `webapp/static/js/score_jobs.js` | 前端列表逻辑 + 轮询 |
+
+## 6. 修改文件
+
+| 文件 | 改动 |
+|------|------|
+| `webapp/models.py` | 新增 `AsyncScoreJobStatus`、`AsyncScoreJobResponse` |
+| `webapp/server.py` | 注册 score_jobs router，更新 OPENAPI_TAGS |
+| `webapp/static/index.html` | 新增导航项 + section |
+
+---
+
+## 7. 前端「评分记录」页
+
+列表列：时间 / 问题摘要（前40字）/ 指标 / 得分 / 状态
+
+- 进入页面自动刷新
+- `queued/running` 记录每 5 秒轮询 `GET /api/score/jobs/{id}` 更新状态
+- 得分按 scoreClass（good/warn/bad）着色
+
+---
+
+## 8. Dify 改造
+
+只改 HTTP 节点 URL：`/api/score` → `/api/score/async`，删除解析响应的代码节点。

logs/online_eval.log

@@ -0,0 +1 @@
+Completed run: C:\Projects\AIProjects\Siemens-AIPOC\siemens_ragas\outputs\online\siemens-pdf-question-bank

logs/server_2026-06-23.log

@@ -0,0 +1,24 @@
+2026-06-23 13:55:00  INFO      webapp.server  Starting RAGAS Console  host=127.0.0.1  port=8800  log_level=info  log_file=C:\Projects\AIProjects\Siemens-AIPOC\siemens_ragas\logs\server_2026-06-23.log
+2026-06-23 13:55:14  INFO      uvicorn.error  Started server process [83868]
+2026-06-23 13:55:14  INFO      uvicorn.error  Waiting for application startup.
+2026-06-23 13:55:14  INFO      uvicorn.error  Application startup complete.
+2026-06-23 13:55:14  INFO      uvicorn.error  Uvicorn running on http://127.0.0.1:8800 (Press CTRL+C to quit)
+2026-06-23 13:59:47  INFO      uvicorn.access  127.0.0.1:53487 - "GET / HTTP/1.1" 200
+2026-06-23 13:59:47  INFO      uvicorn.access  127.0.0.1:53487 - "GET /static/css/app.css HTTP/1.1" 200
+2026-06-23 13:59:47  INFO      uvicorn.access  127.0.0.1:50321 - "GET /static/js/api.js HTTP/1.1" 200
+2026-06-23 13:59:47  INFO      uvicorn.access  127.0.0.1:51325 - "GET /static/js/profiles.js HTTP/1.1" 200
+2026-06-23 13:59:47  INFO      uvicorn.access  127.0.0.1:59869 - "GET /static/js/report.js HTTP/1.1" 200
+2026-06-23 13:59:48  INFO      uvicorn.access  127.0.0.1:50980 - "GET /static/js/runner.js HTTP/1.1" 200
+2026-06-23 13:59:48  INFO      uvicorn.access  127.0.0.1:63223 - "GET /static/js/app.js HTTP/1.1" 200
+2026-06-23 13:59:48  INFO      webapp.access  GET /docs → 200  (0ms)
+2026-06-23 13:59:48  INFO      uvicorn.access  127.0.0.1:63223 - "GET /docs HTTP/1.1" 200
+2026-06-23 13:59:48  INFO      webapp.access  GET /api/health → 200  (0ms)
+2026-06-23 13:59:48  INFO      uvicorn.access  127.0.0.1:50321 - "GET /api/health HTTP/1.1" 200
+2026-06-23 13:59:49  INFO      webapp.api.runs  [get_runs] found 19 runs
+2026-06-23 13:59:49  INFO      webapp.access  GET /api/runs → 200  (1094ms)
+2026-06-23 13:59:49  INFO      uvicorn.access  127.0.0.1:63223 - "GET /api/runs HTTP/1.1" 200
+2026-06-23 13:59:49  INFO      webapp.access  GET /openapi.json → 200  (94ms)
+2026-06-23 13:59:49  INFO      uvicorn.access  127.0.0.1:63223 - "GET /openapi.json HTTP/1.1" 200
+2026-06-23 13:59:50  INFO      webapp.api.llm_profiles  [list_profiles] count=6
+2026-06-23 13:59:50  INFO      webapp.access  GET /api/llm-profiles → 200  (0ms)
+2026-06-23 13:59:50  INFO      uvicorn.access  127.0.0.1:63223 - "GET /api/llm-profiles HTTP/1.1" 200

logs/siemens_build.log

@@ -0,0 +1,35 @@
+  [info] generating questions for: 315_1_Flash????????.pdf
+  [info] 315_1_Flash????????.pdf: 6 questions generated (total so far: 6)
+  [info] generating questions for: 316_2_Flash??????_??.pdf
+  [info] 316_2_Flash??????_??.pdf: 10 questions generated (total so far: 16)
+  [info] generating questions for: 317_3_Flash??????_??.pdf
+  [info] 317_3_Flash??????_??.pdf: 9 questions generated (total so far: 25)
+  [info] generating questions for: 318_4_Flash??????_???.pdf
+  [info] 318_4_Flash??????_???.pdf: 9 questions generated (total so far: 34)
+  [info] generating questions for: 319_5_Flash??????_?????.pdf
+  [info] 319_5_Flash??????_?????.pdf: 10 questions generated (total so far: 44)
+  [info] generating questions for: 320_6_Flash??????_??.pdf
+  [info] 320_6_Flash??????_??.pdf: 8 questions generated (total so far: 52)
+  [info] generating questions for: 321_??CT???????????--??.pdf
+  [info] 321_??CT???????????--??.pdf: 5 questions generated (total so far: 57)
+  [info] generating questions for: 322_??CT???????????--??????????.pdf
+  [info] 322_??CT???????????--??????????.pdf: 8 questions generated (total so far: 65)
+  [info] generating questions for: 323_??CT???????????--?????????.pdf
+  [info] 323_??CT???????????--?????????.pdf: 5 questions generated (total so far: 70)
+  [info] generating questions for: 324_??CT???????????--????????.pdf
+  [info] 324_??CT???????????--????????.pdf: 8 questions generated (total so far: 78)
+  [info] generating questions for: 325_??CT???????????--???????.pdf
+  [info] 325_??CT???????????--???????.pdf: 8 questions generated (total so far: 86)
+  [info] generating questions for: 326_??CT???????????--4D????.pdf
+  [info] 326_??CT???????????--4D????.pdf: 7 questions generated (total so far: 93)
+  [info] generating questions for: 327_??CT???????????--??????.pdf
+  [info] 327_??CT???????????--??????.pdf: 8 questions generated (total so far: 101)
+  [info] generating questions for: 749_????01_???????????.pdf
+  [info] 749_????01_???????????.pdf: 8 questions generated (total so far: 109)
+  [info] generating questions for: 804_????02-????????CT?????X-Map??.pdf
+  [info] 804_????02-????????CT?????X-Map??.pdf: 8 questions generated (total so far: 117)
+  [info] generating questions for: 805_????03_????????????????.pdf
+  [info] 805_????03_????????????????.pdf: 6 questions generated (total so far: 123)
+  [info] generating questions for: 807_???CT???????_SJ-L10.2??1-5.pdf
+  [info] 807_???CT???????_SJ-L10.2??1-5.pdf: 9 questions generated (total so far: 132)
+Completed dataset build: C:\Projects\AIProjects\Siemens-AIPOC\siemens_ragas\outputs\dataset-builds\siemens-pdf-question-bank\2026-06-15T09-28-35.302231+00-00

rag_eval/advisor/llm_analyzer.py

@@ -22,22 +22,31 @@ _PROMPT_TEMPLATE = """\
 
 ## 报告要求
 
-1. 按指标分节（## 指标名  [severity]），先解释"为什么低"（结合低分样本具体分析），再给出"具体怎么改"
+1. 按指标分节（## 指标名  [严重程度]），先解释"为什么低"（结合低分样本具体分析），再给出"具体怎么改"
-2. "具体怎么改"要结合低分样本的实际内容，而不只是泛泛建议
+2. 严重程度说明：critical=严重（<阈值50%），warning=警告（<阈值70%），low=待优化（低于0.85，有提升空间）
-3. 最后写一节 **## 优先优化次序**，按性价比排序（不增加 LLM 调用次数的优化优先）
+3. "具体怎么改"要结合低分样本的实际内容，而不只是泛泛建议
-4. 语言简洁，面向工程师，不要废话，不要重复列表内容
+4. 最后写一节 **## 优先优化次序**，按性价比排序（不增加 LLM 调用次数的优化优先），critical 和 warning 项优先于 low 项
+5. 语言简洁，面向工程师，不要废话，不要重复列表内容
 
 只输出 Markdown 报告正文，不要任何前置说明。
 """
 
 
+_SEVERITY_LABEL_ZH: dict[str, str] = {
+    "critical": "严重",
+    "warning": "警告",
+    "low": "待优化",
+}
+
+
 def _build_diagnosis_summary(diagnoses: list[Diagnosis]) -> str:
     lines = []
     for d in diagnoses:
         direction = "（越低越好）" if d.metric == "noise_sensitivity" else ""
+        label = _SEVERITY_LABEL_ZH.get(d.severity, d.severity)
         lines.append(
             f"- **{d.metric}** {direction} 均值={d.mean_score:.4f}，"
-            f"阈值={d.threshold}，严重程度={d.severity}"
+            f"阈值={d.threshold}，严重程度={label}"
         )
         lines.append(f"  - 可能原因：{'; '.join(d.root_causes)}")
         lines.append(f"  - 建议动作：{'; '.join(d.suggested_actions)}")

rag_eval/advisor/rules.py

@@ -14,6 +14,9 @@ class MetricRule:
     higher_is_better: bool  # False for noise_sensitivity
     root_causes: list[str]
     suggested_actions: list[str]
+    # Scores below this threshold trigger a "low" advisory (LLM suggestion requested).
+    # Only applies to higher_is_better metrics; noise_sensitivity uses existing thresholds.
+    advisory_threshold: float = 0.85
 
 
 METRIC_RULES: dict[str, MetricRule] = {
@@ -208,10 +211,14 @@ def diagnose(
             elif mean < rule.warning_threshold:
                 severity = "warning"
                 threshold = rule.warning_threshold
+            elif mean < rule.advisory_threshold:
+                # Score is acceptable but below 0.85 — request LLM optimization advice.
+                severity = "low"
+                threshold = rule.advisory_threshold
             else:
-                continue  # above warning threshold → no diagnosis
+                continue  # >= advisory_threshold → no diagnosis needed
         else:
-            # lower is better (noise_sensitivity)
+            # lower is better (noise_sensitivity): keep existing two-tier logic
             if mean > rule.critical_threshold:
                 severity = "critical"
                 threshold = rule.critical_threshold

rag_eval/advisor/writer.py

@@ -8,12 +8,22 @@ from .rules import Diagnosis
 
 logger = logging.getLogger("rag_eval.advisor")
 
+# Chinese display labels for each severity tier.
+_SEVERITY_LABEL: dict[str, str] = {
+    "critical": "严重",
+    "warning": "警告",
+    "low": "待优化",
+}
+
 
 def _format_log_summary(diagnoses: list[Diagnosis], advice_path: Path) -> str:
     """Return a single-line log summary of triggered diagnoses."""
     if not diagnoses:
         return "[advisor] 所有指标正常，无需优化建议。"
-    parts = [f"{d.metric}({d.mean_score:.2f}, {d.severity})" for d in diagnoses]
+    parts = [
+        f"{d.metric}({d.mean_score:.2f},{_SEVERITY_LABEL.get(d.severity, d.severity)})"
+        for d in diagnoses
+    ]
     triggered = " ".join(parts)
     return f"[advisor] 触发诊断 {len(diagnoses)} 项: {triggered}  →  {advice_path}"
 
@@ -24,7 +34,8 @@ def _build_fallback_report(diagnoses: list[Diagnosis]) -> str:
         return ""
     lines = ["## 规则诊断（LLM 分析不可用）\n"]
     for d in diagnoses:
-        lines.append(f"### {d.metric}  [{d.severity}]  均值={d.mean_score:.4f}")
+        label = _SEVERITY_LABEL.get(d.severity, d.severity)
+        lines.append(f"### {d.metric}  [{label}]  均值={d.mean_score:.4f}")
         lines.append("\n**可能原因：**")
         for cause in d.root_causes:
             lines.append(f"- {cause}")

rag_eval/execution/evaluator.py

@@ -180,12 +180,12 @@ class Evaluator:
         record["judge_model"] = self.scenario.judge_model
         record["embedding_model"] = self.scenario.embedding_model
         record["run_id"] = self.scenario.scenario_name
-        # Weighted score columns — enable post-hoc weighted aggregation in reporting.
+        # 综合加权得分列（已暂时禁用）
-        record["weighted_score"] = compute_weighted_score(
+        # record["weighted_score"] = compute_weighted_score(
-            score.metrics, self.scenario.metric_weights
+        #     score.metrics, self.scenario.metric_weights
-        )
+        # )
-        doc_name = str(sample.metadata.get("doc_name", "") or "")
+        # doc_name = str(sample.metadata.get("doc_name", "") or "")
-        record["sample_weight"] = resolve_weight(
+        # record["sample_weight"] = resolve_weight(
-            self.scenario.doc_weights, doc_name, default=1.0
+        #     self.scenario.doc_weights, doc_name, default=1.0
-        )
+        # )
         return record

rag_eval/metrics/factory.py

@@ -27,14 +27,55 @@ from ragas.metrics.collections import (
 from .pipeline import MetricPipeline
 
 
+def _resolve_openai_client_kwargs(
+    judge_model: str,
+    settings: EvaluationSettings,
+) -> dict[str, Any]:
+    """Return AsyncOpenAI kwargs, preferring a matching LLM Profile over .env settings.
+
+    Lookup order:
+      1. LLM Profile whose model name equals judge_model (exact match)
+      2. Fall back to EvaluationSettings (.env)
+    """
+    try:
+        # Lazy import to avoid circular dependency (webapp -> rag_eval is one-way).
+        from webapp.services.profile_manager import profile_manager
+        profiles = profile_manager.list_all()
+        for profile in profiles:
+            if profile.model == judge_model:
+                kwargs: dict[str, Any] = {
+                    "api_key": profile.api_key or "sk-placeholder",
+                    "timeout": float(profile.timeout_seconds or 30),
+                }
+                if profile.base_url and profile.base_url.strip():
+                    kwargs["base_url"] = profile.base_url.strip()
+                return kwargs
+    except Exception:  # noqa: BLE001
+        # If profile lookup fails for any reason, fall through to .env settings.
+        pass
+
+    return settings.openai_client_kwargs
+
+
 def build_models(
     judge_model: str,
     embedding_model: str,
     settings: EvaluationSettings,
 ) -> tuple[Any, Any]:
-    """Create the LLM and embedding clients required by the selected RAGAS metrics."""
+    """Create the LLM and embedding clients required by the selected RAGAS metrics.
-    client = AsyncOpenAI(**settings.openai_client_kwargs)
+
-    llm = llm_factory(judge_model, client=client)
+    Dynamically resolves connection settings from the stored LLM Profiles first
+    (matched by model name), falling back to .env settings when no profile matches.
+    """
+    client_kwargs = _resolve_openai_client_kwargs(judge_model, settings)
+    client = AsyncOpenAI(**client_kwargs)
+    # RAGAS structured-output judge calls can be truncated by the upstream default
+    # 1024 completion budget, especially for faithfulness and GPT-5 family models.
+    llm = llm_factory(
+        judge_model,
+        client=client,
+        max_tokens=max(1, int(settings.ragas_llm_max_tokens)),
+    )
     embeddings = embedding_factory(provider="openai", model=embedding_model, client=client)
     return llm, embeddings
 

rag_eval/reporting/summary.py

@@ -75,15 +75,16 @@ def build_summary_markdown(result: EvaluationResult) -> str:
         else:
             lines.append(f"- {metric}: `n/a`{weight_note}")
 
-    if has_weights:
+    # 综合加权得分（已暂时禁用）
-        overall_ws = compute_overall_weighted_score_mean(
+    # if has_weights:
-            score_rows_list, result.scenario.metric_weights, result.scenario.doc_weights
+    #     overall_ws = compute_overall_weighted_score_mean(
-        )
+    #         score_rows_list, result.scenario.metric_weights, result.scenario.doc_weights
-        weight_suffix = " (加权)"
+    #     )
-        if overall_ws is not None and not math.isnan(overall_ws):
+    #     weight_suffix = " (加权)"
-            lines.append(f"- **weighted_score{weight_suffix}: `{overall_ws:.4f}`**")
+    #     if overall_ws is not None and not math.isnan(overall_ws):
-        else:
+    #         lines.append(f"- **weighted_score{weight_suffix}: `{overall_ws:.4f}`**")
-            lines.append(f"- **weighted_score{weight_suffix}: `n/a`**")
+    #     else:
+    #         lines.append(f"- **weighted_score{weight_suffix}: `n/a`**")
 
     detail_columns = ["sample_id", *result.scenario.metrics, "weighted_score", "error"]
     existing_columns = [c for c in detail_columns if c in scores.columns]

rag_eval/settings.py

@@ -26,6 +26,11 @@ class EvaluationSettings(BaseSettings):
         default="text-embedding-3-small",
         alias="RAGAS_EMBEDDING_MODEL",
     )
+    ragas_llm_max_tokens: int = Field(
+        default=4096,
+        alias="RAGAS_LLM_MAX_TOKENS",
+        gt=0,
+    )
     openai_timeout_seconds: float = Field(default=30.0, alias="OPENAI_TIMEOUT_SECONDS")
     ragas_metric_timeout_seconds: float = Field(default=45.0, alias="RAGAS_METRIC_TIMEOUT_SECONDS")
     batch_size: int = Field(default=8, alias="BATCH_SIZE")

rag_eval/shared/profile_store.py

@@ -0,0 +1,53 @@
+"""Lightweight read-only accessor for configs/llm_profiles.json.
+
+Kept in ``rag_eval`` (not ``webapp``) so the runner can look up per-model
+credentials without depending on the webapp layer.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_PROFILES_PATH = Path(__file__).resolve().parents[2] / "configs" / "llm_profiles.json"
+
+
+def find_by_model(model_name: str) -> dict[str, Any] | None:
+    """Return the first profile whose ``model`` field matches *model_name*, or None.
+
+    Returns None (without raising) when the profiles file does not exist or
+    cannot be parsed — callers fall back to environment-variable defaults.
+    """
+    if not _PROFILES_PATH.exists():
+        return None
+    try:
+        data = json.loads(_PROFILES_PATH.read_text(encoding="utf-8"))
+        for profile in data.get("profiles", []):
+            if profile.get("model") == model_name:
+                return profile
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("[profile_store] failed to read %s: %s", _PROFILES_PATH, exc)
+    return None
+
+
+def profile_to_client_kwargs(
+    profile: dict[str, Any],
+    fallback_api_key: str | None,
+    fallback_timeout: float,
+) -> dict[str, Any]:
+    """Convert a profile dict into keyword arguments for ``openai.AsyncOpenAI``.
+
+    Fields present in the profile override the supplied fallback values.
+    """
+    kwargs: dict[str, Any] = {
+        "api_key": profile.get("api_key") or fallback_api_key or "",
+        "timeout": float(profile.get("timeout_seconds") or fallback_timeout),
+    }
+    base_url = (profile.get("base_url") or "").strip()
+    if base_url:
+        kwargs["base_url"] = base_url
+    return kwargs

tests/test_advisor_rules.py

@@ -10,10 +10,38 @@ class TestDiagnosis(unittest.TestCase):
                 for i, s in enumerate(scores)]
 
     def test_no_diagnosis_when_all_scores_above_threshold(self):
+        # Mean exactly 0.85 should NOT trigger any diagnosis (< 0.85 is the condition).
         rows = self._make_rows("faithfulness", [0.8, 0.9, 0.85])
         result = diagnose(rows, metrics=["faithfulness"])
         self.assertEqual(result, [])
 
+    def test_no_diagnosis_when_mean_above_advisory_threshold(self):
+        rows = self._make_rows("answer_relevancy", [0.9, 0.92, 0.88])
+        result = diagnose(rows, metrics=["answer_relevancy"])
+        self.assertEqual(result, [])
+
+    def test_low_severity_when_mean_below_advisory_threshold(self):
+        # Score between warning_threshold (0.7) and advisory_threshold (0.85) → "low"
+        rows = self._make_rows("faithfulness", [0.78, 0.80, 0.82])
+        result = diagnose(rows, metrics=["faithfulness"])
+        self.assertEqual(len(result), 1)
+        self.assertEqual(result[0].severity, "low")
+        self.assertAlmostEqual(result[0].threshold, 0.85, places=2)
+
+    def test_low_severity_answer_relevancy_at_0_84(self):
+        rows = self._make_rows("answer_relevancy", [0.84, 0.84, 0.84])
+        result = diagnose(rows, metrics=["answer_relevancy"])
+        self.assertEqual(len(result), 1)
+        self.assertEqual(result[0].severity, "low")
+
+    def test_low_severity_has_root_causes_and_actions(self):
+        rows = self._make_rows("context_precision", [0.75, 0.76, 0.77])
+        result = diagnose(rows, metrics=["context_precision"])
+        self.assertEqual(len(result), 1)
+        self.assertEqual(result[0].severity, "low")
+        self.assertTrue(len(result[0].root_causes) > 0)
+        self.assertTrue(len(result[0].suggested_actions) > 0)
+
     def test_warning_when_mean_below_warning_threshold(self):
         rows = self._make_rows("faithfulness", [0.65, 0.62, 0.68])
         result = diagnose(rows, metrics=["faithfulness"])

tests/test_advisor_writer.py

@@ -91,9 +91,9 @@ class TestWriteAdvice(unittest.TestCase):
         ]
         summary = _format_log_summary(diags, self.advice_path)
         self.assertIn("faithfulness", summary)
-        self.assertIn("critical", summary)
+        self.assertIn("严重", summary)   # "critical" maps to Chinese label
         self.assertIn("context_recall", summary)
-        self.assertIn("warning", summary)
+        self.assertIn("警告", summary)   # "warning" maps to Chinese label
 
     def test_write_empty_diagnoses_still_creates_file(self):
         write_advice(

tests/test_metric_presenter.py

@@ -0,0 +1,68 @@
+from __future__ import annotations
+
+import subprocess
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+
+
+def _run_node(script: str) -> str:
+    """Execute a short Node.js script and return stdout."""
+    completed = subprocess.run(
+        ["node", "-e", script],
+        cwd=REPO_ROOT,
+        capture_output=True,
+        text=True,
+        encoding="utf-8",
+        check=True,
+    )
+    return completed.stdout.strip()
+
+
+def test_metric_presenter_applies_thresholds_and_noise_direction() -> None:
+    """MetricPresenter should centralize thresholds and inverse noise semantics."""
+    metric_js = (REPO_ROOT / "webapp" / "static" / "js" / "metric_presenter.js").as_posix()
+    script = f"""
+const fs = require("fs");
+const vm = require("vm");
+const code = fs.readFileSync("{metric_js}", "utf8");
+const sandbox = {{ window: {{}}, console }};
+vm.runInNewContext(code, sandbox);
+const p = sandbox.window.MetricPresenter;
+const result = {{
+  faith085: p.scoreClass("faithfulness", 0.85),
+  faith070: p.scoreClass("faithfulness", 0.70),
+  faith064: p.scoreClass("faithfulness", 0.64),
+  noise010: p.scoreClass("noise_sensitivity", 0.10),
+  noise030: p.scoreClass("noise_sensitivity", 0.30),
+  noise050: p.scoreClass("noise_sensitivity", 0.50),
+  desc: p.describeMetric("faithfulness"),
+  noiseDesc: p.describeMetric("noise_sensitivity"),
+  noiseBin: p.binColor("noise_sensitivity", 0.0),
+  faithBin: p.binColor("faithfulness", 0.8)
+}};
+console.log(JSON.stringify(result));
+"""
+    output = _run_node(script)
+    assert '"faith085":"good"' in output
+    assert '"faith070":"warn"' in output
+    assert '"faith064":"bad"' in output
+    assert '"noise010":"good"' in output
+    assert '"noise030":"warn"' in output
+    assert '"noise050":"bad"' in output
+    assert '"desc":"' in output
+    assert '"noiseDesc":"' in output
+    assert '"noiseBin":"#16a34a"' in output
+    assert '"faithBin":"#16a34a"' in output
+
+
+def test_report_and_index_load_metric_presenter_helper() -> None:
+    """The report page should use the shared helper for card descriptions and colors."""
+    index_html = (REPO_ROOT / "webapp" / "static" / "index.html").read_text(encoding="utf-8")
+    report_js = (REPO_ROOT / "webapp" / "static" / "js" / "report.js").read_text(encoding="utf-8")
+    app_js = (REPO_ROOT / "webapp" / "static" / "js" / "app.js").read_text(encoding="utf-8")
+
+    assert "js/metric_presenter.js" in index_html
+    assert "MetricPresenter.describeMetric" in report_js
+    assert "MetricPresenter.scoreClass" in app_js

tests/test_offline_eval.py

@@ -184,7 +184,7 @@ class ScenarioAndDatasetTests(unittest.TestCase):
 
 class EvaluatorAndReportingTests(unittest.TestCase):
     def test_merge_score_includes_weighted_score_and_sample_weight(self):
-        """_merge_score adds weighted_score and sample_weight columns."""
+        """_merge_score no longer adds weighted_score/sample_weight (feature disabled)."""
         from unittest.mock import MagicMock
         from rag_eval.execution.evaluator import Evaluator
         from rag_eval.shared.models import (
@@ -212,9 +212,11 @@ class EvaluatorAndReportingTests(unittest.TestCase):
         )
         score = MetricScore(metrics={"faithfulness": 1.0, "context_recall": 0.0})
         row = evaluator._merge_score(sample, score)
-        # (3*1.0 + 1*0.0) / (3+1) = 0.75
+        # 综合加权得分已暂时禁用，weighted_score 和 sample_weight 不再写入
-        assert abs(row["weighted_score"] - 0.75) < 1e-4
+        assert "weighted_score" not in row
-        assert row["sample_weight"] == 2.0
+        assert "sample_weight" not in row
+        assert row["faithfulness"] == 1.0
+        assert row["context_recall"] == 0.0
 
     def test_summary_markdown_shows_weighted_score(self):
         """build_summary_markdown includes weighted_score when metric_weights set."""

tests/test_pipeline.py

@@ -0,0 +1,280 @@
+"""Tests for the end-to-end pipeline API and pipeline task manager."""
+
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+
+
+# ── fixtures ──────────────────────────────────────────────────────────────────
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch):
+    """TestClient with a fresh PipelineTaskManager backed by tmp_path outputs."""
+    import webapp.services.pipeline_task_manager as mgr_mod
+    from webapp.services.pipeline_task_manager import PipelineTaskManager
+
+    fresh_mgr = PipelineTaskManager(max_workers=2)
+    monkeypatch.setattr(mgr_mod, "pipeline_task_manager", fresh_mgr)
+    monkeypatch.setattr(mgr_mod, "_PIPELINE_OUTPUT_ROOT", tmp_path / "pipeline")
+
+    import webapp.api.pipeline as api_mod
+    monkeypatch.setattr(api_mod, "pipeline_task_manager", fresh_mgr)
+
+    from webapp.server import create_app
+    return TestClient(create_app())
+
+
+def _minimal_pdf_dir(tmp_path: Path) -> Path:
+    """Create a temp directory that looks like a PDF folder (empty, valid dir)."""
+    d = tmp_path / "pdfs"
+    d.mkdir()
+    return d
+
+
+def _mock_build_result(tmp_path: Path, job, run_id="r1"):
+    """Return a fake DatasetBuildResult with a minimal dataset CSV."""
+    from rag_eval.dataset_builder.models import (
+        DatasetBuildArtifactPaths,
+        DatasetBuildResult,
+        DraftQuestionSample,
+    )
+
+    artifact_root = tmp_path / "build" / run_id
+    artifact_root.mkdir(parents=True, exist_ok=True)
+    latest = tmp_path / "build" / "latest"
+    latest.mkdir(parents=True, exist_ok=True)
+
+    chunks_path = artifact_root / "source_chunks.jsonl"
+    chunks_path.write_text(
+        json.dumps({"chunk_id": "c1", "doc_id": "d1", "doc_name": "test.pdf",
+                    "text": "CT scan context.", "page_start": 1, "page_end": 1,
+                    "section_path": "/", "section_title": "", "source_layout_ids": []}) + "\n",
+        encoding="utf-8",
+    )
+    (latest / "source_chunks.jsonl").write_text(chunks_path.read_text(encoding="utf-8"), encoding="utf-8")
+
+    dataset_csv = tmp_path / "generated_dataset.csv"
+    dataset_csv.write_text(
+        "sample_id,question,ground_truth,scenario,language,doc_id,doc_name,"
+        "section_path,page_start,page_end,source_chunk_ids,question_type,difficulty,"
+        "review_status,review_notes\n"
+        's1,"What is CT?","CT is imaging.","test","zh","d1","test.pdf","/",'
+        '1,1,"[""c1""]","fact","easy","draft",""\n',
+        encoding="utf-8",
+    )
+
+    sample = DraftQuestionSample(
+        sample_id="s1", question="What is CT?", ground_truth="CT is imaging.",
+        scenario="test", language="zh", doc_id="d1", doc_name="test.pdf",
+        section_path="/", page_start=1, page_end=1, source_chunk_ids=["c1"],
+        question_type="fact", difficulty="easy",
+    )
+
+    artifact_paths = DatasetBuildArtifactPaths(
+        root_dir=artifact_root,
+        documents_jsonl=artifact_root / "documents.jsonl",
+        semantic_blocks_jsonl=artifact_root / "semantic_blocks.jsonl",
+        source_chunks_jsonl=chunks_path,
+        dataset_draft_csv=artifact_root / "dataset_draft.csv",
+        parse_failures_csv=artifact_root / "parse_failures.csv",
+        metadata_json=artifact_root / "metadata.json",
+    )
+    return DatasetBuildResult(
+        job=job,
+        run_id=run_id,
+        artifact_paths=artifact_paths,
+        documents=[],
+        draft_samples=[sample],
+        parse_failures=[],
+    )
+
+
+def _mock_eval_result(tmp_path: Path, scenario):
+    """Return a fake EvaluationResult."""
+    from rag_eval.shared.models import EvaluationResult
+
+    return EvaluationResult(
+        scenario=scenario,
+        run_id="eval-r1",
+        started_at="2026-01-01T00:00:00",
+        finished_at="2026-01-01T00:01:00",
+        valid_samples=[],
+        invalid_samples=[],
+        score_rows=[],
+    )
+
+
+# ── API route tests ────────────────────────────────────────────────────────────
+
+def test_submit_returns_202_and_job_id(client, tmp_path):
+    """POST /api/pipeline/jobs returns 202 with job_id immediately."""
+    pdf_dir = _minimal_pdf_dir(tmp_path)
+
+    with patch("webapp.services.pipeline_task_manager.PipelineTaskManager._execute") as mock_exec:
+        from webapp.models import PipelineResult
+        mock_exec.return_value = PipelineResult(
+            build_artifact_dir="/tmp/b", dataset_csv="/tmp/d.csv",
+            source_chunks_jsonl="/tmp/c.jsonl", total_questions=1,
+            parse_failures=0, eval_run_id="r1", eval_output_dir="/tmp/e",
+            scores_csv="/tmp/scores.csv", summary_md="/tmp/summary.md",
+        )
+        resp = client.post("/api/pipeline/jobs", json={
+            "docs_path": str(pdf_dir),
+            "job_name": "test-job",
+        })
+
+    assert resp.status_code == 202
+    data = resp.json()
+    assert "job_id" in data
+    assert data["job_name"] == "test-job"
+    # status may already be completed by the time the response is read (mock runs instantly)
+    assert data["status"] in ("queued", "completed")
+
+
+def test_get_nonexistent_job_returns_404(client):
+    """GET /api/pipeline/jobs/{id} returns 404 for unknown job."""
+    resp = client.get("/api/pipeline/jobs/doesnotexist")
+    assert resp.status_code == 404
+
+
+def test_list_jobs_returns_empty_initially(client):
+    """GET /api/pipeline/jobs returns empty list when no jobs submitted."""
+    resp = client.get("/api/pipeline/jobs")
+    assert resp.status_code == 200
+    assert resp.json()["jobs"] == []
+
+
+def test_job_status_polling(client, tmp_path):
+    """Submitted job becomes visible via GET /api/pipeline/jobs/{id}."""
+    pdf_dir = _minimal_pdf_dir(tmp_path)
+
+    with patch("webapp.services.pipeline_task_manager.PipelineTaskManager._execute") as mock_exec:
+        from webapp.models import PipelineResult
+        mock_exec.return_value = PipelineResult(
+            build_artifact_dir="/tmp/b", dataset_csv="/tmp/d.csv",
+            source_chunks_jsonl="/tmp/c.jsonl", total_questions=3,
+            parse_failures=0, eval_run_id="r2", eval_output_dir="/tmp/e",
+            scores_csv="/tmp/scores.csv", summary_md="/tmp/summary.md",
+        )
+        post_resp = client.post("/api/pipeline/jobs", json={"docs_path": str(pdf_dir)})
+
+    job_id = post_resp.json()["job_id"]
+
+    # Poll until done or timeout (max 5s for mock)
+    for _ in range(20):
+        status_resp = client.get(f"/api/pipeline/jobs/{job_id}")
+        assert status_resp.status_code == 200
+        status = status_resp.json()
+        if status["status"] in ("completed", "failed"):
+            break
+        time.sleep(0.25)
+
+    assert status["status"] == "completed"
+    assert status["result"]["total_questions"] == 3
+
+
+def test_job_fails_on_invalid_docs_path(client):
+    """Job fails quickly if docs_path does not exist."""
+    resp = client.post("/api/pipeline/jobs", json={
+        "docs_path": "/nonexistent/path/that/does/not/exist",
+    })
+    assert resp.status_code == 202
+    job_id = resp.json()["job_id"]
+
+    for _ in range(20):
+        status_resp = client.get(f"/api/pipeline/jobs/{job_id}")
+        status = status_resp.json()
+        if status["status"] in ("completed", "failed"):
+            break
+        time.sleep(0.25)
+
+    assert status["status"] == "failed"
+    assert "docs_path" in status["error"] or "not" in status["error"].lower()
+
+
+def test_list_jobs_shows_submitted(client, tmp_path):
+    """GET /api/pipeline/jobs includes jobs after submission."""
+    pdf_dir = _minimal_pdf_dir(tmp_path)
+
+    with patch("webapp.services.pipeline_task_manager.PipelineTaskManager._execute") as mock_exec:
+        from webapp.models import PipelineResult
+        mock_exec.return_value = PipelineResult(
+            build_artifact_dir="/tmp/b", dataset_csv="/tmp/d.csv",
+            source_chunks_jsonl="/tmp/c.jsonl", total_questions=1,
+            parse_failures=0, eval_run_id="r3", eval_output_dir="/tmp/e",
+            scores_csv="/tmp/scores.csv", summary_md="/tmp/summary.md",
+        )
+        client.post("/api/pipeline/jobs", json={"docs_path": str(pdf_dir), "job_name": "listed-job"})
+
+    time.sleep(0.5)
+    list_resp = client.get("/api/pipeline/jobs")
+    assert list_resp.status_code == 200
+    jobs = list_resp.json()["jobs"]
+    assert len(jobs) >= 1
+    names = [j["job_name"] for j in jobs]
+    assert "listed-job" in names
+
+
+# ── execute_dataset_build_job refactor test ────────────────────────────────────
+
+def test_execute_dataset_build_job_directly(tmp_path):
+    """execute_dataset_build_job runs the build without a YAML file."""
+    from unittest.mock import patch as _patch
+    from rag_eval.dataset_builder.models import DatasetBuildJob, DatasetBuildRuntime
+    from rag_eval.dataset_builder.runner import execute_dataset_build_job
+    from rag_eval.settings import EvaluationSettings
+
+    pdf_dir = tmp_path / "pdfs"
+    pdf_dir.mkdir()
+    (pdf_dir / "doc.pdf").write_bytes(b"%PDF-fake")
+
+    job = DatasetBuildJob(
+        job_name="direct-test",
+        input_path=pdf_dir,
+        input_glob="*.pdf",
+        parser_provider="aliyun_docmind",
+        failure_mode="skip",
+        generation_model="test-model",
+        output_type="online_question_bank",
+        review_mode="draft_with_manual_review",
+        max_questions_per_document=5,
+        max_source_chunks_per_question=3,
+        dataset_path=tmp_path / "out.csv",
+        artifact_dir=tmp_path / "artifacts",
+        runtime=DatasetBuildRuntime(max_documents=1),
+    )
+
+    mock_doc = MagicMock()
+    mock_doc.doc_id = "d1"
+    mock_doc.doc_name = "doc.pdf"
+    mock_doc.source_chunks = []
+    mock_doc.semantic_blocks = []
+    mock_doc.raw_text = ""
+    mock_doc.structure_nodes = []
+    mock_doc.metadata = {}
+    mock_doc.to_record.return_value = {
+        "doc_id": "d1", "doc_name": "doc.pdf", "raw_text": "",
+        "structure_nodes": [], "metadata": {},
+        "semantic_block_count": 0, "source_chunk_count": 0,
+    }
+
+    mock_parser = MagicMock()
+    mock_parser.parse.return_value = mock_doc
+
+    mock_generator = MagicMock()
+    mock_generator.generate.return_value = []
+
+    result = execute_dataset_build_job(
+        job,
+        settings=EvaluationSettings(_env_file=None),
+        parser=mock_parser,
+        generator=mock_generator,
+    )
+    assert result.job.job_name == "direct-test"
+    assert result.artifact_paths.root_dir.exists()

tests/test_webapp_report_builder.py

@@ -65,7 +65,8 @@ def test_build_report_uses_weighted_means_and_exposes_snapshot_weights(tmp_path:
         "faithfulness": pytest.approx(0.75, rel=1e-4),
         "context_recall": pytest.approx(0.5, rel=1e-4),
     }
-    assert report.weighted_score_mean == pytest.approx(0.6667, rel=1e-4)
+    # 综合加权得分已暂时禁用
+    assert report.weighted_score_mean is None
     assert report.metric_weights == {"faithfulness": 2.0, "context_recall": 1.0}
     assert report.doc_weights == {"a.pdf": 3.0, "b.pdf": 1.0}
     assert report.summary_markdown == "summary"
@@ -87,3 +88,30 @@ def test_infer_metrics_excludes_weight_columns_without_snapshot(tmp_path: Path)
     )
 
     assert _infer_metrics_from_scores(run_dir) == ["faithfulness"]
+
+
+def test_build_report_ranks_noise_sensitivity_with_lower_values_as_better(tmp_path: Path) -> None:
+    """Lowest-sample review should treat higher noise sensitivity as worse."""
+    run_dir = tmp_path / "run"
+    run_dir.mkdir(parents=True, exist_ok=True)
+    (run_dir / "scores.csv").write_text(
+        "\n".join(
+            [
+                "sample_id,question,noise_sensitivity",
+                "s-good,q1,0.10",
+                "s-warn,q2,0.30",
+                "s-bad,q3,0.90",
+            ]
+        ),
+        encoding="utf-8",
+    )
+    (run_dir / "summary.md").write_text("summary", encoding="utf-8")
+    (run_dir / "optimization_advice.md").write_text("", encoding="utf-8")
+
+    report = build_report(run_dir, ["noise_sensitivity"])
+
+    assert [sample.sample_id for sample in report.lowest_samples[:3]] == [
+        "s-bad",
+        "s-warn",
+        "s-good",
+    ]

tests/webapp/test_llm_profiles_api.py

@@ -1,6 +1,7 @@
 """Integration tests for /api/llm-profiles endpoints."""
 import pytest
 from fastapi.testclient import TestClient
+from unittest.mock import patch
 
 
 @pytest.fixture()
@@ -41,19 +42,23 @@ def test_update_profile(client):
     pid = client.post("/api/llm-profiles", json=body).json()["profile_id"]
 
     upd = {"name": "New", "model": "m2", "base_url": "http://x/v1", "api_key": "k", "timeout_seconds": 60}
+    with patch("webapp.services.inline_scorer.inline_scorer.invalidate_cache") as invalidate:
         resp = client.put(f"/api/llm-profiles/{pid}", json=upd)
     assert resp.status_code == 200
     assert resp.json()["name"] == "New"
     assert resp.json()["timeout_seconds"] == 60
+    invalidate.assert_called_once()
 
 
 def test_delete_profile(client):
     body = {"name": "Del", "model": "m", "base_url": "http://x/v1", "api_key": "k"}
     pid = client.post("/api/llm-profiles", json=body).json()["profile_id"]
+    with patch("webapp.services.inline_scorer.inline_scorer.invalidate_cache") as invalidate:
         resp = client.delete(f"/api/llm-profiles/{pid}")
     assert resp.status_code == 200
     assert resp.json()["deleted"] is True
     assert len(client.get("/api/llm-profiles").json()["profiles"]) == 0
+    invalidate.assert_called_once()
 
 
 def test_update_nonexistent(client):
@@ -185,7 +190,7 @@ def test_apply_doc_weights_patches_yaml(tmp_path):
 # ---------------------------------------------------------------------------
 # Connectivity test endpoint tests
 # ---------------------------------------------------------------------------
-from unittest.mock import MagicMock, patch
+from unittest.mock import MagicMock
 
 
 def test_probe_connectivity_success(client):

tests/webapp/test_profile_manager.py

@@ -1,4 +1,6 @@
 import pytest
+from unittest.mock import sentinel
+
 from webapp.models import LLMProfile, ProfileApplyRequest, ProfileApplyResponse
 
 def test_llm_profile_defaults():
@@ -98,3 +100,106 @@ def test_get_nonexistent(tmp_path):
 def test_delete_nonexistent(tmp_path):
     mgr = _make_manager(tmp_path)
     assert mgr.delete("does-not-exist") is False
+
+
+def test_resolve_openai_client_kwargs_prefers_matching_profile(tmp_path, monkeypatch):
+    """Metric runtime should prefer the saved LLM Profile over .env defaults."""
+    from rag_eval.metrics.factory import _resolve_openai_client_kwargs
+    from rag_eval.settings import EvaluationSettings
+    import webapp.services.profile_manager as pm_mod
+
+    mgr = _make_manager(tmp_path)
+    mgr.create(
+        name="Judge",
+        model="gpt-5.5",
+        base_url="http://39.107.88.131:13000",
+        api_key="sk-profile",
+        timeout_seconds=300,
+    )
+    monkeypatch.setattr(pm_mod, "profile_manager", mgr)
+
+    settings = EvaluationSettings(
+        OPENAI_API_KEY="sk-env",
+        OPENAI_BASE_URL="http://env-base/v1",
+        OPENAI_TIMEOUT_SECONDS=30,
+    )
+
+    kwargs = _resolve_openai_client_kwargs("gpt-5.5", settings)
+    assert kwargs["api_key"] == "sk-profile"
+    assert kwargs["base_url"] == "http://39.107.88.131:13000"
+    assert kwargs["timeout"] == 300.0
+
+
+def test_resolve_openai_client_kwargs_falls_back_to_env(tmp_path, monkeypatch):
+    """When no saved profile matches, .env settings remain the fallback."""
+    from rag_eval.metrics.factory import _resolve_openai_client_kwargs
+    from rag_eval.settings import EvaluationSettings
+    import webapp.services.profile_manager as pm_mod
+
+    mgr = _make_manager(tmp_path)
+    monkeypatch.setattr(pm_mod, "profile_manager", mgr)
+
+    settings = EvaluationSettings(
+        OPENAI_API_KEY="sk-env",
+        OPENAI_BASE_URL="http://env-base/v1",
+        OPENAI_TIMEOUT_SECONDS=45,
+    )
+
+    kwargs = _resolve_openai_client_kwargs("gpt-5", settings)
+    assert kwargs["api_key"] == "sk-env"
+    assert kwargs["base_url"] == "http://env-base/v1"
+    assert kwargs["timeout"] == 45.0
+
+
+def test_build_models_uses_high_default_max_tokens_for_structured_judge(monkeypatch):
+    """Structured RAGAS judge calls should use a larger completion budget by default."""
+    import rag_eval.metrics.factory as factory
+    from rag_eval.settings import EvaluationSettings
+
+    captured: dict[str, object] = {}
+
+    def fake_llm_factory(model, client=None, **kwargs):
+        captured["model"] = model
+        captured["client"] = client
+        captured["kwargs"] = kwargs
+        return sentinel.llm
+
+    monkeypatch.setattr(factory, "AsyncOpenAI", lambda **kwargs: sentinel.client)
+    monkeypatch.setattr(factory, "llm_factory", fake_llm_factory)
+    monkeypatch.setattr(factory, "embedding_factory", lambda **kwargs: sentinel.embeddings)
+
+    llm, embeddings = factory.build_models(
+        "gpt-5",
+        "text-embedding-3-small",
+        EvaluationSettings(),
+    )
+
+    assert llm is sentinel.llm
+    assert embeddings is sentinel.embeddings
+    assert captured["model"] == "gpt-5"
+    assert captured["client"] is sentinel.client
+    assert captured["kwargs"] == {"max_tokens": 4096}
+
+
+def test_build_models_allows_env_override_for_judge_max_tokens(monkeypatch):
+    """Operators should be able to raise the judge completion budget via settings."""
+    import rag_eval.metrics.factory as factory
+    from rag_eval.settings import EvaluationSettings
+
+    captured: dict[str, object] = {}
+
+    def fake_llm_factory(model, client=None, **kwargs):
+        captured["kwargs"] = kwargs
+        return sentinel.llm
+
+    monkeypatch.setattr(factory, "AsyncOpenAI", lambda **kwargs: sentinel.client)
+    monkeypatch.setattr(factory, "llm_factory", fake_llm_factory)
+    monkeypatch.setattr(factory, "embedding_factory", lambda **kwargs: sentinel.embeddings)
+
+    factory.build_models(
+        "gpt-5",
+        "text-embedding-3-small",
+        EvaluationSettings(RAGAS_LLM_MAX_TOKENS=8192),
+    )
+
+    assert captured["kwargs"] == {"max_tokens": 8192}

tests/webapp/test_score_api.py

@@ -57,9 +57,11 @@ class TestScoreRequest:
         with pytest.raises(ValidationError):
             ScoreRequest(question="q", contexts="c")  # type: ignore[call-arg]
 
-    def test_missing_contexts_raises(self):
+    def test_missing_contexts_defaults_to_none(self):
-        with pytest.raises(ValidationError):
+        """contexts is now optional — missing contexts is allowed."""
-            ScoreRequest(question="q", answer="a")  # type: ignore[call-arg]
+        req = ScoreRequest(question="q", answer="a")
+        assert req.contexts is None
+        assert req.contexts_as_list() == []
 
     def test_custom_metrics_accepted(self):
         req = ScoreRequest(
@@ -115,6 +117,17 @@ class TestScoreRequest:
             "factual_correctness",
         ]
 
+    def test_effective_metrics_drops_context_dependent_when_contexts_absent(self):
+        """Without contexts, context-dependent metrics are excluded."""
+        req = ScoreRequest(
+            question="q", answer="a",
+            metrics=["faithfulness", "answer_relevancy", "context_precision"],
+        )
+        effective = req.effective_metrics()
+        assert "answer_relevancy" in effective
+        assert "faithfulness" not in effective
+        assert "context_precision" not in effective
+
 
 class TestScoreResponse:
     def test_score_response_structure(self):
@@ -228,7 +241,8 @@ class TestScoreEndpoint:
         })
         assert resp.status_code == 200
         data = resp.json()
-        assert data["weighted_score"] is not None
+        # 综合加权得分已暂时禁用，始终返回 null
+        assert data["weighted_score"] is None
 
     def test_missing_required_fields_returns_422(self, client):
         resp = client.post("/api/score", json={"question": "q"})

tests/webapp/test_score_jobs_api.py

@@ -0,0 +1,146 @@
+"""Tests for async score jobs API."""
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch):
+    """TestClient with fresh ScoreJobManager backed by tmp dirs."""
+    import webapp.services.score_job_manager as mgr_mod
+    from webapp.services.score_job_manager import ScoreJobManager
+
+    fresh_mgr = ScoreJobManager(
+        output_dir=tmp_path / "score-async",
+        index_dir=tmp_path / "score-jobs",
+        max_workers=2,
+    )
+    monkeypatch.setattr(mgr_mod, "score_job_manager", fresh_mgr)
+
+    import webapp.api.score_jobs as api_mod
+    monkeypatch.setattr(api_mod, "score_job_manager", fresh_mgr)
+
+    from webapp.server import create_app
+    return TestClient(create_app())
+
+
+class TestAsyncScoreEndpoints:
+    def test_submit_returns_202_with_job_id(self, client):
+        """POST /api/score/async returns 202 immediately."""
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?",
+                "answer": "a.",
+                "metrics": ["answer_relevancy"],
+            })
+        assert resp.status_code == 202
+        data = resp.json()
+        assert "job_id" in data
+        assert data["status"] == "queued"
+
+    def test_list_jobs_empty_initially(self, client):
+        resp = client.get("/api/score/jobs")
+        assert resp.status_code == 200
+        assert resp.json()["jobs"] == []
+
+    def test_get_unknown_job_returns_404(self, client):
+        resp = client.get("/api/score/jobs/nonexistent123")
+        assert resp.status_code == 404
+
+    def test_submitted_job_appears_in_list(self, client):
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?", "answer": "a.", "metrics": ["answer_relevancy"],
+            })
+        job_id = resp.json()["job_id"]
+        time.sleep(0.1)
+        list_resp = client.get("/api/score/jobs")
+        ids = [j["job_id"] for j in list_resp.json()["jobs"]]
+        assert job_id in ids
+
+    def test_get_job_by_id_returns_status(self, client):
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?", "answer": "a.", "metrics": ["answer_relevancy"],
+            })
+        job_id = resp.json()["job_id"]
+        time.sleep(0.1)
+        get_resp = client.get(f"/api/score/jobs/{job_id}")
+        assert get_resp.status_code == 200
+        assert get_resp.json()["job_id"] == job_id
+
+    def test_missing_required_fields_returns_422(self, client):
+        resp = client.post("/api/score/async", json={"question": "q?"})
+        assert resp.status_code == 422
+
+
+class TestScoreJobManager:
+    def test_completed_job_persisted_to_index(self, tmp_path):
+        """Completed job writes index JSON."""
+        from webapp.services.score_job_manager import ScoreJobManager
+        from webapp.models import ScoreRequest
+
+        mgr = ScoreJobManager(
+            output_dir=tmp_path / "runs",
+            index_dir=tmp_path / "index",
+            max_workers=1,
+        )
+        req = ScoreRequest(question="q?", answer="a.", metrics=["answer_relevancy"])
+
+        # Patch _run directly — it uses lazy imports internally
+        def fake_run(job_id, request):
+            mgr._update(job_id, status="completed", finished_at="2026-01-01T00:00:01+00:00",
+                        run_id="fake-run-id", scores={"answer_relevancy": 0.85},
+                        weighted_score=0.85, latency_ms=500)
+
+        with patch.object(mgr, "_run", side_effect=fake_run):
+            status = mgr.submit(req)
+
+        for _ in range(20):
+            s = mgr.get(status.job_id)
+            if s and s.status == "completed":
+                break
+            time.sleep(0.1)
+
+        s = mgr.get(status.job_id)
+        assert s is not None
+        idx_path = tmp_path / "index" / f"{status.job_id}.json"
+        assert idx_path.exists()
+        data = json.loads(idx_path.read_text(encoding="utf-8"))
+        assert data["job_id"] == status.job_id
+        assert data["status"] == "completed"
+
+    def test_loads_existing_index_on_startup(self, tmp_path):
+        """Manager loads persisted jobs from index dir on init."""
+        from webapp.services.score_job_manager import ScoreJobManager
+        from webapp.models import AsyncScoreJobStatus
+
+        idx_dir = tmp_path / "index"
+        idx_dir.mkdir()
+        fake = AsyncScoreJobStatus(
+            job_id="testjob001",
+            status="completed",
+            created_at="2026-01-01T00:00:00+00:00",
+            run_id="some-run-id",
+            scores={"answer_relevancy": 0.9},
+            weighted_score=0.9,
+            latency_ms=1000,
+        )
+        (idx_dir / "testjob001.json").write_text(
+            json.dumps(fake.model_dump(), ensure_ascii=False), encoding="utf-8"
+        )
+        mgr = ScoreJobManager(
+            output_dir=tmp_path / "runs",
+            index_dir=idx_dir,
+            max_workers=1,
+        )
+        loaded = mgr.get("testjob001")
+        assert loaded is not None
+        assert loaded.status == "completed"
+        assert loaded.run_id == "some-run-id"

tests/webapp/test_session_score_jobs_api.py

@@ -0,0 +1,299 @@
+"""Tests for session-grouped async scoring API and SessionScoreJobManager."""
+from __future__ import annotations
+
+import json
+import threading
+import time
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pandas as pd
+import pytest
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+@pytest.fixture()
+def tmp_manager(tmp_path):
+    """Isolated SessionScoreJobManager backed by tmp dirs (no real LLM calls)."""
+    from webapp.services.session_score_manager import SessionScoreJobManager
+    return SessionScoreJobManager(
+        output_dir=tmp_path / "score-session",
+        index_dir=tmp_path / "score-session-jobs",
+        max_workers=2,
+    )
+
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch):
+    """TestClient with fresh SessionScoreJobManager backed by tmp dirs."""
+    import webapp.services.session_score_manager as mgr_mod
+    from webapp.services.session_score_manager import SessionScoreJobManager
+
+    fresh_mgr = SessionScoreJobManager(
+        output_dir=tmp_path / "score-session",
+        index_dir=tmp_path / "score-session-jobs",
+        max_workers=2,
+    )
+    monkeypatch.setattr(mgr_mod, "session_score_manager", fresh_mgr)
+
+    import webapp.api.session_score_jobs as api_mod
+    monkeypatch.setattr(api_mod, "session_score_manager", fresh_mgr)
+
+    from webapp.server import create_app
+    return pytest.importorskip("fastapi.testclient").TestClient(create_app())
+
+
+# ---------------------------------------------------------------------------
+# Unit tests for SessionScoreJobManager
+# ---------------------------------------------------------------------------
+
+class TestSessionRunId:
+    def test_same_session_always_same_run_id(self, tmp_manager):
+        assert tmp_manager.session_run_id("abc") == tmp_manager.session_run_id("abc")
+
+    def test_different_sessions_different_run_ids(self, tmp_manager):
+        assert tmp_manager.session_run_id("session-A") != tmp_manager.session_run_id("session-B")
+
+    def test_run_id_prefixed_with_session(self, tmp_manager):
+        assert tmp_manager.session_run_id("test123").startswith("session-")
+
+    def test_special_chars_sanitized(self, tmp_manager):
+        run_id = tmp_manager.session_run_id("user@dify:flow/001")
+        assert "/" not in run_id
+        assert "@" not in run_id
+        assert ":" not in run_id
+
+
+class TestSubmit:
+    def test_submit_returns_job_status_and_run_id(self, tmp_manager):
+        with patch.object(tmp_manager._executor, "submit"):
+            status, run_id = tmp_manager.submit("session-1", _mock_request())
+        assert status.job_id
+        assert status.status == "queued"
+        assert run_id == tmp_manager.session_run_id("session-1")
+
+    def test_submit_adds_job_to_session(self, tmp_manager):
+        with patch.object(tmp_manager._executor, "submit"):
+            status, _ = tmp_manager.submit("session-1", _mock_request())
+        session = tmp_manager.get_session("session-1")
+        assert session is not None
+        assert any(j.job_id == status.job_id for j in session.jobs)
+
+    def test_multiple_submits_same_session_accumulate(self, tmp_manager):
+        with patch.object(tmp_manager._executor, "submit"):
+            tmp_manager.submit("session-X", _mock_request())
+            tmp_manager.submit("session-X", _mock_request())
+            tmp_manager.submit("session-X", _mock_request())
+        session = tmp_manager.get_session("session-X")
+        assert session.call_count == 3
+
+    def test_get_unknown_job_returns_none(self, tmp_manager):
+        assert tmp_manager.get_job("does-not-exist") is None
+
+    def test_get_unknown_session_returns_none(self, tmp_manager):
+        assert tmp_manager.get_session("no-such-session") is None
+
+
+class TestSessionIndexPersistence:
+    def test_session_index_survives_restart(self, tmp_path):
+        """Jobs and session mappings loaded from disk on new manager instance."""
+        from webapp.services.session_score_manager import SessionScoreJobManager
+
+        mgr1 = SessionScoreJobManager(
+            output_dir=tmp_path / "score-session",
+            index_dir=tmp_path / "score-session-jobs",
+        )
+        with patch.object(mgr1._executor, "submit"):
+            mgr1.submit("persist-session", _mock_request())
+            mgr1.submit("persist-session", _mock_request())
+
+        # New manager instance loads from disk
+        mgr2 = SessionScoreJobManager(
+            output_dir=tmp_path / "score-session",
+            index_dir=tmp_path / "score-session-jobs",
+        )
+        session = mgr2.get_session("persist-session")
+        assert session is not None
+        assert session.call_count == 2
+
+    def test_job_index_file_created_on_submit(self, tmp_path):
+        from webapp.services.session_score_manager import SessionScoreJobManager
+        mgr = SessionScoreJobManager(
+            output_dir=tmp_path / "score-session",
+            index_dir=tmp_path / "score-session-jobs",
+        )
+        with patch.object(mgr._executor, "submit"):
+            status, _ = mgr.submit("file-test", _mock_request())
+        index_file = tmp_path / "score-session-jobs" / f"{status.job_id}.json"
+        assert index_file.is_file()
+        data = json.loads(index_file.read_text())
+        assert data["job_id"] == status.job_id
+
+
+class TestAppendBehaviour:
+    """Test the CSV append / read-all logic in _append_and_regenerate via _read_score_rows."""
+
+    def test_read_score_rows_returns_empty_for_missing_csv(self, tmp_manager, tmp_path):
+        rows = tmp_manager._read_score_rows(tmp_path / "nonexistent")
+        assert rows == []
+
+    def test_read_score_rows_reads_existing_csv(self, tmp_manager, tmp_path):
+        run_dir = tmp_path / "run1"
+        run_dir.mkdir()
+        df = pd.DataFrame([{"sample_id": "s1", "answer_relevancy": 0.9}])
+        df.to_csv(run_dir / "scores.csv", index=False)
+        rows = tmp_manager._read_score_rows(run_dir)
+        assert len(rows) == 1
+        assert rows[0]["sample_id"] == "s1"
+
+    def test_metric_means_computed_from_csv(self, tmp_manager, tmp_path):
+        run_dir = tmp_path / "run2"
+        run_dir.mkdir()
+        df = pd.DataFrame([
+            {"sample_id": "s1", "answer_relevancy": 0.8},
+            {"sample_id": "s2", "answer_relevancy": 0.6},
+        ])
+        df.to_csv(run_dir / "scores.csv", index=False)
+        means = tmp_manager._read_metric_means(run_dir)
+        assert means["answer_relevancy"] == pytest.approx(0.7, abs=1e-4)
+
+
+# ---------------------------------------------------------------------------
+# API endpoint tests
+# ---------------------------------------------------------------------------
+
+class TestSessionAsyncEndpoints:
+    def test_submit_returns_202_with_session_fields(self, client):
+        with patch("webapp.services.session_score_manager.SessionScoreJobManager._run"):
+            resp = client.post("/api/score/session_async", json={
+                "session_id": "test-session-001",
+                "question": "What is CT?",
+                "answer": "CT is computed tomography.",
+                "metrics": ["answer_relevancy"],
+            })
+        assert resp.status_code == 202
+        data = resp.json()
+        assert data["session_id"] == "test-session-001"
+        assert "job_id" in data
+        assert "run_id" in data
+        assert data["status"] == "queued"
+        assert data["call_count"] >= 1
+
+    def test_run_id_deterministic_for_session(self, client):
+        with patch("webapp.services.session_score_manager.SessionScoreJobManager._run"):
+            r1 = client.post("/api/score/session_async", json={
+                "session_id": "det-session",
+                "question": "Q1",
+                "answer": "A1",
+                "metrics": ["answer_relevancy"],
+            })
+            r2 = client.post("/api/score/session_async", json={
+                "session_id": "det-session",
+                "question": "Q2",
+                "answer": "A2",
+                "metrics": ["answer_relevancy"],
+            })
+        assert r1.json()["run_id"] == r2.json()["run_id"]
+
+    def test_different_sessions_different_run_ids(self, client):
+        with patch("webapp.services.session_score_manager.SessionScoreJobManager._run"):
+            r1 = client.post("/api/score/session_async", json={
+                "session_id": "session-A",
+                "question": "Q",
+                "answer": "A",
+                "metrics": ["answer_relevancy"],
+            })
+            r2 = client.post("/api/score/session_async", json={
+                "session_id": "session-B",
+                "question": "Q",
+                "answer": "A",
+                "metrics": ["answer_relevancy"],
+            })
+        assert r1.json()["run_id"] != r2.json()["run_id"]
+
+    def test_call_count_increments_per_session(self, client):
+        with patch("webapp.services.session_score_manager.SessionScoreJobManager._run"):
+            for _ in range(3):
+                client.post("/api/score/session_async", json={
+                    "session_id": "count-session",
+                    "question": "Q",
+                    "answer": "A",
+                    "metrics": ["answer_relevancy"],
+                })
+        time.sleep(0.05)
+        resp = client.get("/api/score/sessions/count-session")
+        assert resp.status_code == 200
+        assert resp.json()["call_count"] == 3
+
+    def test_get_session_returns_jobs_list(self, client):
+        with patch("webapp.services.session_score_manager.SessionScoreJobManager._run"):
+            client.post("/api/score/session_async", json={
+                "session_id": "list-session",
+                "question": "Q",
+                "answer": "A",
+                "metrics": ["answer_relevancy"],
+            })
+        time.sleep(0.05)
+        resp = client.get("/api/score/sessions/list-session")
+        assert resp.status_code == 200
+        data = resp.json()
+        assert len(data["jobs"]) == 1
+
+    def test_get_unknown_session_returns_404(self, client):
+        resp = client.get("/api/score/sessions/no-such-session-xyz")
+        assert resp.status_code == 404
+
+    def test_get_session_job_by_id(self, client):
+        with patch("webapp.services.session_score_manager.SessionScoreJobManager._run"):
+            resp = client.post("/api/score/session_async", json={
+                "session_id": "job-lookup-session",
+                "question": "Q",
+                "answer": "A",
+                "metrics": ["answer_relevancy"],
+            })
+        job_id = resp.json()["job_id"]
+        time.sleep(0.05)
+        get_resp = client.get(f"/api/score/session/jobs/{job_id}")
+        assert get_resp.status_code == 200
+        assert get_resp.json()["job_id"] == job_id
+
+    def test_get_unknown_job_returns_404(self, client):
+        resp = client.get("/api/score/session/jobs/nonexistent-job-id")
+        assert resp.status_code == 404
+
+    def test_missing_session_id_returns_422(self, client):
+        resp = client.post("/api/score/session_async", json={
+            "question": "Q",
+            "answer": "A",
+            "metrics": ["answer_relevancy"],
+        })
+        assert resp.status_code == 422
+
+    def test_list_sessions_endpoint(self, client):
+        with patch("webapp.services.session_score_manager.SessionScoreJobManager._run"):
+            client.post("/api/score/session_async", json={
+                "session_id": "list-all-session",
+                "question": "Q",
+                "answer": "A",
+                "metrics": ["answer_relevancy"],
+            })
+        resp = client.get("/api/score/sessions")
+        assert resp.status_code == 200
+        assert "sessions" in resp.json()
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _mock_request():
+    """Build a minimal ScoreRequest for testing."""
+    from webapp.models import ScoreRequest
+    return ScoreRequest(
+        question="What is dual-source CT?",
+        answer="It uses two X-ray sources.",
+        metrics=["answer_relevancy"],
+    )

webapp/api/llm_profiles.py

@@ -148,6 +148,13 @@ def update_profile(profile_id: str, request: CreateProfileRequest) -> LLMProfile
     if updated is None:
         logger.warning("[update_profile] not found  id=%s", profile_id)
         raise HTTPException(status_code=404, detail=f"Profile not found: {profile_id}")
+    # Invalidate scorer cache so next request picks up the new profile settings.
+    try:
+        from webapp.services.inline_scorer import inline_scorer
+        inline_scorer.invalidate_cache()
+        logger.info("[update_profile] scorer cache invalidated  id=%s", profile_id)
+    except Exception:  # noqa: BLE001
+        pass
     logger.info("[update_profile] updated  id=%s", profile_id)
     return updated
 
@@ -160,6 +167,12 @@ def delete_profile(profile_id: str) -> dict:
     if not deleted:
         logger.warning("[delete_profile] not found  id=%s", profile_id)
         raise HTTPException(status_code=404, detail=f"Profile not found: {profile_id}")
+    # Invalidate scorer cache in case the deleted profile was in use.
+    try:
+        from webapp.services.inline_scorer import inline_scorer
+        inline_scorer.invalidate_cache()
+    except Exception:  # noqa: BLE001
+        pass
     logger.info("[delete_profile] deleted  id=%s", profile_id)
     return {"deleted": True}
 

webapp/api/score.py

@@ -73,7 +73,8 @@ def score_sample(
     用于日志记录、质量监控或触发 Agent 自我改进流程。
 
     **contexts 格式**：多个检索片段用 `context_separator`（默认 `" |||| "`）拼接为一个字符串，
-    服务端自动拆分后传入 RAGAS 管道。
+    服务端自动拆分后传入 RAGAS 管道。**contexts 为可选字段**，缺失时自动跳过依赖检索内容的指标
+    （`faithfulness`、`context_recall`、`context_precision`、`noise_sensitivity`）。
 
     **ground_truth 可选**：
     - 提供时：所有指定指标均参与计算。
@@ -99,12 +100,13 @@ def score_sample(
     """
     client = f"{raw_request.client.host}:{raw_request.client.port}" if raw_request.client else "unknown"
     logger.info(
-        "[score] incoming  client=%s  method=%s  content_type=%s  metrics=%s  has_gt=%s",
+        "[score] incoming  client=%s  method=%s  content_type=%s  metrics=%s  has_gt=%s  has_ctx=%s",
         client,
         raw_request.method,
         raw_request.headers.get("content-type", ""),
         request.metrics,
         request.ground_truth is not None,
+        bool(request.contexts),
     )
     settings = _get_settings()
 
@@ -154,10 +156,11 @@ def score_sample(
     all_scores: dict[str, float | None] = {metric_name: None for metric_name in request.metrics}
     all_scores.update(raw_scores)
 
-    weighted = compute_weighted_score(
+    # 综合加权得分计算（已暂时禁用）
-        {key: value for key, value in raw_scores.items() if value is not None},
+    # weighted = compute_weighted_score(
-        {},
+    #     {key: value for key, value in raw_scores.items() if value is not None},
-    )
+    #     {},
+    # )
 
     logger.info(
         "[score] done  latency=%dms  skipped=%s  scores=%s",
@@ -167,7 +170,7 @@ def score_sample(
     )
     return ScoreResponse(
         scores=all_scores,
-        weighted_score=round(weighted, 4) if weighted is not None else None,
+        weighted_score=None,  # 综合加权得分已暂时禁用
         latency_ms=latency_ms,
         skipped_metrics=skipped,
     )

webapp/api/score_jobs.py

@@ -0,0 +1,89 @@
+"""Routes for async RAGAS scoring jobs (Dify fire-and-forget integration).
+
+Dify calls POST /api/score/async → gets job_id immediately (202).
+Scoring runs in background, result written as a standard run artifact.
+View full report at GET /api/runs/{run_id} or in the 「运行列表」 page.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from webapp.models import AsyncScoreJobResponse, AsyncScoreJobStatus, ScoreRequest
+from webapp.services.score_job_manager import score_job_manager
+
+router = APIRouter(prefix="/api/score", tags=["score"])
+logger = logging.getLogger("webapp.api.score_jobs")
+
+
+@router.post(
+    "/async",
+    status_code=202,
+    response_model=AsyncScoreJobResponse,
+    summary="提交异步评分任务（Dify 推荐方式）",
+    responses={
+        202: {
+            "description": (
+                "任务已排队，立即返回 job_id（202 Accepted）。\n\n"
+                "评分在后台执行，完成后自动生成完整报告（含优化建议）。\n"
+                "通过 `GET /api/score/jobs/{job_id}` 查询状态，"
+                "完成后在「运行列表」页查看完整报告。"
+            ),
+            "content": {
+                "application/json": {
+                    "example": {"job_id": "abc123def456", "status": "queued", "run_id": None}
+                }
+            },
+        },
+    },
+)
+def submit_async_score(request: ScoreRequest) -> AsyncScoreJobResponse:
+    """提交异步 RAGAS 评分任务，立即返回 job_id。
+
+    **适合 Dify 工作流**：HTTP 节点无需等待评分完成（无超时风险），
+    工作流立即继续，评分结果在 RAGAS 平台「运行列表」中查看。
+
+    评分完成后自动生成：
+    - 各指标得分（`scores.csv`）
+    - 摘要报告（`summary.md`）
+    - LLM 优化建议（`optimization_advice.md`）
+    """
+    logger.info(
+        "[score_async] submit  metrics=%s  has_ctx=%s  has_gt=%s",
+        request.metrics, bool(request.contexts), bool(request.ground_truth),
+    )
+    status = score_job_manager.submit(request)
+    logger.info("[score_async] queued  job_id=%s", status.job_id)
+    return AsyncScoreJobResponse(job_id=status.job_id, status=status.status)
+
+
+@router.get(
+    "/jobs",
+    response_model=dict,
+    summary="列出所有异步评分记录",
+)
+def list_score_jobs() -> dict:
+    """返回所有异步评分记录，按创建时间倒序排列。"""
+    jobs = score_job_manager.list_jobs()
+    logger.info("[score_jobs] list  count=%d", len(jobs))
+    return {"jobs": [j.model_dump() for j in jobs]}
+
+
+@router.get(
+    "/jobs/{job_id}",
+    response_model=AsyncScoreJobStatus,
+    summary="查询单个异步评分任务状态",
+    responses={404: {"description": "指定 job_id 的评分任务不存在。"}},
+)
+def get_score_job(job_id: str) -> AsyncScoreJobStatus:
+    """查询单个异步评分任务的状态和结果。
+
+    `status` 为 `completed` 时，`run_id` 字段包含对应的运行 ID，
+    可通过 `GET /api/runs/{run_id}` 获取完整评分报告。
+    """
+    status = score_job_manager.get(job_id)
+    if status is None:
+        raise HTTPException(status_code=404, detail=f"Score job not found: {job_id}")
+    return status

webapp/api/session_score_jobs.py

@@ -0,0 +1,206 @@
+"""Routes for session-grouped async RAGAS scoring (Dify multi-call integration).
+
+Use case: Dify evaluates multiple Q&A pairs in a session. Each pair gets its own
+`POST /api/score/session_async` call with a shared `session_id`. All results are
+accumulated into one report, visible in 「运行列表」→「报告详情」.
+
+Key behaviour:
+  - Deterministic run_id: derived from session_id — same session always maps to the
+    same report directory (outputs/score-session/session-<id>/).
+  - Append semantics: each call adds a new sample row. Previous rows are preserved.
+  - Advisor regeneration: optimization_advice.md is regenerated after every call
+    using the full set of accumulated rows.
+  - Each call returns its own `job_id` for individual status polling, plus the
+    shared `run_id` and `session_id`.
+
+Endpoints:
+  POST /api/score/session_async         Submit one call (returns job_id + run_id)
+  GET  /api/score/sessions              List all sessions
+  GET  /api/score/sessions/{session_id} Session aggregate (call_count, metric_means, jobs)
+  GET  /api/score/session/jobs/{job_id} Status of one individual call
+"""
+
+from __future__ import annotations
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from webapp.models import (
+    AsyncScoreJobStatus,
+    ScoreRequest,
+    SessionScoreJobResponse,
+    SessionScoreRequest,
+    SessionStatus,
+)
+from webapp.services.session_score_manager import session_score_manager
+
+router = APIRouter(prefix="/api/score", tags=["score"])
+logger = logging.getLogger("webapp.api.session_score_jobs")
+
+
+@router.post(
+    "/session_async",
+    status_code=202,
+    response_model=SessionScoreJobResponse,
+    summary="提交 Session 异步评分（多样本批量聚合）",
+    description=(
+        "**用途**\n"
+        "- 适合 Dify 循环节点、批量问答评测、同一对话多轮累计评分。\n"
+        "- 相同 `session_id` 的多次调用不会生成多个独立报告，而是持续追加到同一个 session 报告。\n\n"
+        "**请求字段说明**\n"
+        "- `session_id`：会话唯一标识，同一会话必须保持一致。\n"
+        "- `question` / `answer`：本次待评分的问答对。\n"
+        "- `contexts`：检索片段拼接字符串，按 `context_separator` 拆分。\n"
+        "- `ground_truth`：标准答案，可选；缺失时会自动跳过依赖它的指标。\n"
+        "- `metrics`：本次需要计算的指标列表。\n"
+        "- `judge_model` / `embedding_model`：可选；为空时回退到系统默认配置。\n\n"
+        "**处理行为**\n"
+        "1. 服务端立即返回 `202 Accepted`，并生成本次调用的 `job_id`。\n"
+        "2. 系统根据 `session_id` 计算固定 `run_id`，格式为 `session-<sanitized-session_id>`。\n"
+        "3. 本次评分完成后，会向该 session 的 `scores.csv` 追加一行样本数据。\n"
+        "4. 系统会基于当前 session 的全量样本重写 `summary.md`，并重新生成 `optimization_advice.md`。\n"
+        "5. 报告可在「运行列表」中按 `run_id` 查看；同一 session 的后续调用会持续增量更新该报告。\n\n"
+        "**后续查询接口**\n"
+        "- `GET /api/score/session/jobs/{job_id}`：查询本次调用状态与得分。\n"
+        "- `GET /api/score/sessions/{session_id}`：查询整个 session 的累计调用次数、指标均值、所有作业记录。\n"
+        "- `GET /api/runs/{run_id}`：查看完整评估报告内容。\n\n"
+        "**典型请求示例**\n"
+        "```json\n"
+        "{\n"
+        "  \"session_id\": \"dify-session-001\",\n"
+        "  \"question\": \"单源CT与双源CT在球管配置上有何本质区别？\",\n"
+        "  \"answer\": \"单源CT只有一套球管-探测器系统，双源CT有两套独立的球管-探测器系统。\",\n"
+        "  \"contexts\": \"双源CT采用两套管-探测器系统 |||| 单源CT只有一个球管\",\n"
+        "  \"context_separator\": \" |||| \",\n"
+        "  \"metrics\": [\"answer_relevancy\", \"faithfulness\"],\n"
+        "  \"judge_model\": \"gpt-5.5\",\n"
+        "  \"embedding_model\": \"text-embedding-3-small\"\n"
+        "}\n"
+        "```"
+    ),
+    responses={
+        202: {
+            "description": (
+                "调用已排队，立即返回 job_id + run_id（202 Accepted）。\n\n"
+                "相同 `session_id` 的多次调用合并为同一报告，每次调用新增一个样本行。\n"
+                "评分完成后，`summary.md` 和 `optimization_advice.md` 增量更新。\n"
+                "通过 `GET /api/score/sessions/{session_id}` 查看 session 聚合状态，"
+                "通过 `GET /api/score/session/jobs/{job_id}` 查询单次调用状态，"
+                "在「运行列表」中查看完整报告（run_id 即 `session-<session_id>` 形式）。"
+            ),
+            "content": {
+                "application/json": {
+                    "example": {
+                        "job_id": "abc123def456",
+                        "session_id": "dify-session-001",
+                        "run_id": "session-dify-session-001",
+                        "status": "queued",
+                        "call_count": 1,
+                    }
+                }
+            },
+        },
+    },
+)
+def submit_session_async_score(request: SessionScoreRequest) -> SessionScoreJobResponse:
+    """提交 Session 异步 RAGAS 评分，立即返回 job_id。
+
+    相同 `session_id` 的多次调用合并到同一评估报告中，每次调用：
+    1. 新增一个样本行到 `scores.csv`
+    2. 重写 `summary.md`（包含所有累积样本的指标均值）
+    3. 重新生成 `optimization_advice.md`（基于全量样本的 LLM 优化建议）
+
+    **适合 Dify 工作流**：在循环节点中批量调用，所有轮次共用同一 `session_id`，
+    最终在 RAGAS 平台「运行列表」中查看完整的批量评估报告。
+    """
+    logger.info(
+        "[session_async] submit  session_id=%s  metrics=%s  has_ctx=%s  has_gt=%s",
+        request.session_id,
+        request.metrics,
+        bool(request.contexts),
+        bool(request.ground_truth),
+    )
+
+    # Strip session_id to build a plain ScoreRequest for the manager
+    score_request = ScoreRequest(
+        question=request.question,
+        answer=request.answer,
+        contexts=request.contexts,
+        ground_truth=request.ground_truth,
+        context_separator=request.context_separator,
+        metrics=request.metrics,
+        judge_model=request.judge_model,
+        embedding_model=request.embedding_model,
+    )
+
+    status, run_id = session_score_manager.submit(request.session_id, score_request)
+
+    # Compute call_count from current session state
+    session_status = session_score_manager.get_session(request.session_id)
+    call_count = session_status.call_count if session_status else 1
+
+    logger.info(
+        "[session_async] queued  job_id=%s  session_id=%s  run_id=%s  call=%d",
+        status.job_id, request.session_id, run_id, call_count,
+    )
+    return SessionScoreJobResponse(
+        job_id=status.job_id,
+        session_id=request.session_id,
+        run_id=run_id,
+        status=status.status,
+        call_count=call_count,
+    )
+
+
+@router.get(
+    "/sessions",
+    response_model=dict,
+    summary="列出所有 Session 聚合状态",
+)
+def list_sessions() -> dict:
+    """返回所有 session 的聚合状态，按最近完成时间倒序排列。"""
+    sessions = session_score_manager.list_sessions()
+    logger.info("[session_score] list_sessions  count=%d", len(sessions))
+    return {"sessions": [s.model_dump() for s in sessions]}
+
+
+@router.get(
+    "/sessions/{session_id}",
+    response_model=SessionStatus,
+    summary="查询 Session 聚合状态（指标均值 + 所有调用记录）",
+    responses={404: {"description": "指定 session_id 不存在。"}},
+)
+def get_session(session_id: str) -> SessionStatus:
+    """查询 session 的聚合评分状态。
+
+    返回内容：
+    - `run_id`：在「运行列表」中查看完整报告
+    - `call_count`：本 session 累计调用次数
+    - `metric_means`：所有已累积样本的各指标均值（实时读取 scores.csv）
+    - `jobs`：本 session 所有调用记录列表
+    """
+    status = session_score_manager.get_session(session_id)
+    if status is None:
+        raise HTTPException(status_code=404, detail=f"Session not found: {session_id}")
+    return status
+
+
+@router.get(
+    "/session/jobs/{job_id}",
+    response_model=AsyncScoreJobStatus,
+    summary="查询 Session 单次调用状态",
+    responses={404: {"description": "指定 job_id 不存在。"}},
+)
+def get_session_job(job_id: str) -> AsyncScoreJobStatus:
+    """查询 session 评分中某次调用的状态和评分结果。
+
+    `status` 为 `completed` 时，`run_id` 即所属 session 的报告目录，
+    `scores` 包含本次调用的各指标得分。
+    """
+    status = session_score_manager.get_job(job_id)
+    if status is None:
+        raise HTTPException(
+            status_code=404, detail=f"Session score job not found: {job_id}"
+        )
+    return status

webapp/models.py

@@ -384,6 +384,14 @@ _GT_DEPENDENT_METRICS: frozenset[str] = frozenset({
     "noise_sensitivity",
 })
 
+# 需要 contexts 才能计算的指标集合
+_CONTEXT_DEPENDENT_METRICS: frozenset[str] = frozenset({
+    "faithfulness",
+    "context_recall",
+    "context_precision",
+    "noise_sensitivity",
+})
+
 # 所有合法指标名称
 _VALID_METRICS: frozenset[str] = frozenset({
     "faithfulness",
@@ -428,8 +436,9 @@ class ScoreRequest(BaseModel):
 
     question: str = Field(description="问题文本。")
     answer: str = Field(description="待评分的回答。")
-    contexts: str = Field(
+    contexts: str | None = Field(
-        description="检索上下文字符串，多段之间用 context_separator 拼接。"
+        default=None,
+        description="检索上下文字符串，多段之间用 context_separator 拼接。缺失时自动跳过依赖检索内容的指标（faithfulness、context_recall、context_precision、noise_sensitivity）。",
     )
     ground_truth: str | None = Field(
         default=None,
@@ -467,15 +476,23 @@ class ScoreRequest(BaseModel):
         return value
 
     def contexts_as_list(self) -> list[str]:
-        """Split the contexts string into a list of non-empty fragments."""
+        """Split the contexts string into a list of non-empty fragments.
+
+        Returns an empty list when contexts is None or blank.
+        """
+        if not self.contexts:
+            return []
         separator = self.context_separator or " |||| "
         return [part.strip() for part in self.contexts.split(separator) if part.strip()]
 
     def effective_metrics(self) -> list[str]:
-        """Return metrics filtered to exclude GT-dependent ones when ground_truth is absent."""
+        """Return metrics filtered to exclude GT-dependent or context-dependent ones when inputs are absent."""
-        if self.ground_truth is not None:
+        result = list(self.metrics)
-            return list(self.metrics)
+        if self.ground_truth is None:
-        return [metric_name for metric_name in self.metrics if metric_name not in _GT_DEPENDENT_METRICS]
+            result = [m for m in result if m not in _GT_DEPENDENT_METRICS]
+        if not self.contexts:
+            result = [m for m in result if m not in _CONTEXT_DEPENDENT_METRICS]
+        return result
 
 
 class ScoreResponse(BaseModel):
@@ -497,3 +514,104 @@ class ScoreResponse(BaseModel):
         default=None,
         description="打分异常时的错误信息（HTTP 200 仍返回，scores 为空）。",
     )
+
+
+# ---------------------------------------------------------------------------
+# 异步评分记录模型
+# ---------------------------------------------------------------------------
+
+class AsyncScoreJobResponse(BaseModel):
+    """Immediate 202 response after submitting an async score job."""
+
+    job_id: str = Field(description="任务唯一标识符，用于后续查询结果。")
+    status: str = Field(default="queued", description="初始状态：queued。")
+    run_id: str | None = Field(
+        default=None,
+        description="评分完成后写入的 Run ID，可在「运行列表」中查看完整报告。",
+    )
+
+
+# ---------------------------------------------------------------------------
+# Session async 评分模型
+# ---------------------------------------------------------------------------
+
+class SessionScoreRequest(ScoreRequest):
+    """Request body for session-grouped async scoring.
+
+    All calls sharing the same session_id are accumulated into one report.
+    Each call adds a new sample row to the session's scores.csv.
+    """
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "examples": [
+                {
+                    "summary": "Dify 会话批量评分",
+                    "value": {
+                        "session_id": "dify-session-001",
+                        "question": "单源CT与双源CT在球管配置上有何本质区别？",
+                        "answer": "单源CT只有一套球管-探测器系统，双源CT有两套独立的球管-探测器系统。",
+                        "contexts": "双源CT采用两套管-探测器系统 |||| 单源CT只有一个球管",
+                        "context_separator": " |||| ",
+                        "metrics": ["answer_relevancy", "faithfulness"],
+                        "judge_model": "gpt-5.5",
+                        "embedding_model": "text-embedding-3-small",
+                    },
+                }
+            ]
+        }
+    )
+
+    session_id: str = Field(
+        description=(
+            "会话唯一标识符。相同 session_id 的多次调用合并为同一报告，"
+            "每次调用新增一个样本行，指标均值和优化建议在每次调用后增量更新。"
+        ),
+    )
+
+
+class SessionScoreJobResponse(BaseModel):
+    """Immediate 202 response after submitting a session scoring call."""
+
+    job_id: str = Field(description="本次调用的任务唯一标识符。")
+    session_id: str = Field(description="会话标识符。")
+    run_id: str = Field(description="本 session 对应的报告 Run ID，可在「运行列表」中查看。")
+    status: str = Field(default="queued", description="初始状态：queued。")
+    call_count: int = Field(default=1, description="本 session 当前累计调用次数（包含本次）。")
+
+
+class SessionStatus(BaseModel):
+    """Aggregate status and metrics for a scoring session."""
+
+    session_id: str = Field(description="会话标识符。")
+    run_id: str = Field(description="对应报告目录的 Run ID。")
+    call_count: int = Field(description="本 session 累计调用次数。")
+    metric_means: dict[str, float | None] = Field(
+        default_factory=dict, description="所有已累积样本的各指标均值。"
+    )
+    latest_finished_at: str = Field(default="", description="最近一次评分完成时间（ISO 8601 UTC）。")
+    jobs: list[AsyncScoreJobStatus] = Field(
+        default_factory=list, description="本 session 所有调用记录，按创建时间排序。"
+    )
+
+
+class AsyncScoreJobStatus(BaseModel):
+    """State of one async score job (queued → running → completed/failed)."""
+
+    job_id: str = Field(description="任务唯一标识符。")
+    status: str = Field(description="queued | running | completed | failed")
+    created_at: str = Field(default="", description="创建时间（ISO 8601 UTC）。")
+    finished_at: str = Field(default="", description="完成时间（ISO 8601 UTC）。")
+    run_id: str | None = Field(
+        default=None,
+        description="完成后对应的 Run ID，可通过 GET /api/runs/{run_id} 查看完整报告。",
+    )
+    request_summary: dict = Field(
+        default_factory=dict,
+        description="请求参数快照（question 前80字、metrics、judge_model 等）。",
+    )
+    scores: dict[str, float | None] = Field(default_factory=dict, description="各指标得分。")
+    weighted_score: float | None = Field(default=None, description="加权综合得分。")
+    latency_ms: int = Field(default=0, description="评分耗时毫秒。")
+    skipped_metrics: list[str] = Field(default_factory=list)
+    error: str | None = Field(default=None)

webapp/server.py

@@ -17,7 +17,7 @@ from fastapi.exceptions import RequestValidationError
 from fastapi.responses import FileResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 
-from webapp.api import evaluations, llm_profiles, pipeline, runs, scenarios, score
+from webapp.api import evaluations, llm_profiles, pipeline, runs, scenarios, score, score_jobs, session_score_jobs
 
 STATIC_DIR = Path(__file__).resolve().parent / "static"
 logger = logging.getLogger("webapp.server")
@@ -69,10 +69,20 @@ OPENAPI_TAGS = [
     {
         "name": "score",
         "description": (
-            "**实时评分 API（Dify 外部 Tool）**\n\n"
+            "**实时评分 API（同步）** — `POST /api/score`\n\n"
-            "接受单条问答记录 `(question, answer, contexts, ground_truth)`，\n"
+            "**异步评分 API（Dify 推荐）** — `POST /api/score/async`\n\n"
-            "同步运行 RAGAS 指标打分，返回各指标得分和加权综合得分。\n\n"
+            "异步方式立即返回 job_id（202），评分在后台执行，完成后自动生成完整报告（含优化建议），"
-            "适用场景：Dify Agent 在回答后即时调用，用于质量监控或自我改进。\n\n"
+            "在「运行列表」页查看。\n\n"
+            "**Session 批量评分 API** — `POST /api/score/session_async`\n\n"
+            "适合 Dify 循环节点批量评估：同一 `session_id` 的多次调用合并为一个报告，"
+            "每次调用新增一个样本行，指标均值和优化建议增量更新。\n\n"
+            "**Session 模式调用流程**\n"
+            "1. `POST /api/score/session_async` 提交一条问答评分请求。\n"
+            "2. 用 `GET /api/score/session/jobs/{job_id}` 轮询单次调用状态。\n"
+            "3. 用 `GET /api/score/sessions/{session_id}` 查看 session 聚合状态。\n"
+            "4. 用 `GET /api/runs/{run_id}` 或在「运行列表」中查看完整报告。\n\n"
+            "通过 `GET /api/score/jobs` 列出所有异步评分记录，"
+            "`GET /api/score/jobs/{job_id}` 查询单个任务状态。\n\n"
             "**鉴权**：若 `.env` 中配置了 `SCORE_API_TOKEN`，需携带 "
             "`Authorization: Bearer <token>` 请求头。"
         ),
@@ -87,7 +97,7 @@ OPENAPI_TAGS = [
 def create_app() -> FastAPI:
     """Build and configure the FastAPI application instance."""
     app = FastAPI(
-        title="RAGAS 评估系统",
+        title="Siemens RAGAS 评估平台",
         description=(
             "西门子医疗影像 RAG 评估平台 API 文档。\n\n"
             "提供以下能力：\n"
@@ -108,6 +118,8 @@ def create_app() -> FastAPI:
     app.include_router(llm_profiles.router)
     app.include_router(pipeline.router)
     app.include_router(score.router)
+    app.include_router(score_jobs.router)
+    app.include_router(session_score_jobs.router)
 
     @app.middleware("http")
     async def access_log_middleware(request: Request, call_next):

webapp/services/inline_scorer.py

@@ -54,13 +54,22 @@ class InlineScorer:
         self._model_cache: dict[tuple[str, str], tuple[Any, Any]] = {}
         self._lock = threading.Lock()
 
+    def invalidate_cache(self) -> None:
+        """Clear the model cache so the next call rebuilds clients from current profiles."""
+        with self._lock:
+            self._model_cache.clear()
+
     def _get_models(
         self,
         judge_model: str,
         embedding_model: str,
         settings: EvaluationSettings,
     ) -> tuple[Any, Any]:
-        """Return cached LLM/embedding clients, building them on first use."""
+        """Return cached LLM/embedding clients, building them on first use.
+
+        Cache is keyed by (judge_model, embedding_model). Call invalidate_cache()
+        after updating an LLM Profile to force a fresh client on the next request.
+        """
         cache_key = (judge_model, embedding_model)
         with self._lock:
             if cache_key not in self._model_cache:

webapp/services/pipeline_task_manager.py

@@ -0,0 +1,257 @@
+"""Background task manager for end-to-end pipeline jobs (build + eval).
+
+Each job runs three sequential phases inside a worker thread:
+  1. parsing_documents  — AliyunDocmind parses every PDF
+  2. generating_questions — LLM generates a draft question bank
+  3. evaluating          — RAGAS online evaluation scores each question
+
+The DatasetBuildJob and Scenario objects are constructed entirely from the
+API request parameters, so no YAML config files are needed.
+"""
+
+from __future__ import annotations
+
+import io
+import threading
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import redirect_stderr, redirect_stdout
+from datetime import datetime, timezone
+from pathlib import Path
+
+from webapp.models import (
+    PipelineJobRequest,
+    PipelineJobStatus,
+    PipelineResult,
+)
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_PIPELINE_OUTPUT_ROOT = _REPO_ROOT / "outputs" / "pipeline"
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class _LineCapture(io.TextIOBase):
+    """Write-only stream that appends complete lines to a task's log buffer."""
+
+    def __init__(self, sink: "PipelineTask") -> None:
+        self._sink = sink
+        self._buffer = ""
+
+    def write(self, text: str) -> int:
+        self._buffer += text
+        while "\n" in self._buffer:
+            line, self._buffer = self._buffer.split("\n", 1)
+            self._sink.append_log(line)
+        return len(text)
+
+    def flush(self) -> None:
+        if self._buffer:
+            self._sink.append_log(self._buffer)
+            self._buffer = ""
+
+
+class PipelineTask:
+    """Mutable state for one pipeline job (build + eval)."""
+
+    def __init__(self, job_id: str, job_name: str) -> None:
+        self.job_id = job_id
+        self.job_name = job_name
+        self.status = "queued"
+        self.phase = "idle"
+        self.logs: list[str] = []
+        self.result: PipelineResult | None = None
+        self.error: str | None = None
+        self.created_at = _now_iso()
+        self.finished_at = ""
+        self._lock = threading.Lock()
+
+    def append_log(self, line: str) -> None:
+        with self._lock:
+            self.logs.append(line)
+
+    def snapshot(self) -> PipelineJobStatus:
+        with self._lock:
+            return PipelineJobStatus(
+                job_id=self.job_id,
+                job_name=self.job_name,
+                status=self.status,
+                phase=self.phase,
+                logs=list(self.logs),
+                result=self.result,
+                error=self.error,
+                created_at=self.created_at,
+                finished_at=self.finished_at,
+            )
+
+
+class PipelineTaskManager:
+    """Owns the thread pool and registry of pipeline jobs."""
+
+    def __init__(self, max_workers: int = 2) -> None:
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+        self._tasks: dict[str, PipelineTask] = {}
+        self._lock = threading.Lock()
+
+    def submit(self, request: PipelineJobRequest) -> PipelineTask:
+        """Register and schedule a new pipeline job; return its task object."""
+        job_id = uuid.uuid4().hex[:12]
+        job_name = request.job_name.strip() or f"pipeline-{job_id[:6]}"
+        task = PipelineTask(job_id=job_id, job_name=job_name)
+        with self._lock:
+            self._tasks[job_id] = task
+        self._executor.submit(self._run, task, request)
+        return task
+
+    def get(self, job_id: str) -> PipelineJobStatus | None:
+        with self._lock:
+            task = self._tasks.get(job_id)
+        return task.snapshot() if task is not None else None
+
+    def list_jobs(self) -> list[PipelineJobStatus]:
+        with self._lock:
+            tasks = list(self._tasks.values())
+        snapshots = [t.snapshot() for t in tasks]
+        snapshots.sort(key=lambda s: s.created_at, reverse=True)
+        return snapshots
+
+    # ------------------------------------------------------------------ #
+    # Worker
+    # ------------------------------------------------------------------ #
+
+    def _run(self, task: PipelineTask, request: PipelineJobRequest) -> None:
+        """Execute the full pipeline end to end inside a worker thread."""
+        task.status = "running"
+        task.append_log(f"[{_now_iso()}] 开始 pipeline 任务: {task.job_name}")
+
+        capture = _LineCapture(task)
+        try:
+            with redirect_stdout(capture), redirect_stderr(capture):
+                result = self._execute(task, request)
+            capture.flush()
+            task.result = result
+            task.phase = "done"
+            task.status = "completed"
+            task.append_log(f"[{_now_iso()}] pipeline 任务完成: {task.job_name}")
+        except Exception as exc:  # noqa: BLE001
+            capture.flush()
+            task.error = f"{type(exc).__name__}: {exc}"
+            task.append_log(f"[{_now_iso()}] pipeline 任务失败: {task.error}")
+            task.status = "failed"
+        finally:
+            task.finished_at = _now_iso()
+
+    def _execute(self, task: PipelineTask, req: PipelineJobRequest) -> PipelineResult:
+        """Run build then eval, updating task.phase as we go."""
+
+        # ── resolve paths ──────────────────────────────────────────────
+        docs_path = Path(req.docs_path)
+        if not docs_path.is_absolute():
+            docs_path = (_REPO_ROOT / docs_path).resolve()
+        if not docs_path.is_dir():
+            raise ValueError(f"docs_path is not an existing directory: {docs_path}")
+
+        job_output_dir = _PIPELINE_OUTPUT_ROOT / task.job_id
+        build_artifact_dir = job_output_dir / "build"
+        dataset_csv = job_output_dir / "generated_dataset.csv"
+        eval_output_dir = job_output_dir / "eval"
+
+        # ── phase 1 + 2: dataset build (parse & generate) ─────────────
+        task.phase = "parsing_documents"
+        task.append_log(f"  [build] 扫描文档目录: {docs_path}")
+        build_result = self._run_build(task, req, docs_path, build_artifact_dir, dataset_csv)
+
+        source_chunks_jsonl = build_artifact_dir / "latest" / "source_chunks.jsonl"
+        total_q = len(build_result.draft_samples)
+        parse_failures = len(build_result.parse_failures)
+        task.append_log(f"  [build] 题库生成完毕: {total_q} 道题目, {parse_failures} 份文档解析失败")
+
+        if total_q == 0:
+            raise RuntimeError("题库为空（所有文档均解析或生成失败），中止评估。")
+
+        # ── phase 3: evaluation ────────────────────────────────────────
+        task.phase = "evaluating"
+        task.append_log(f"  [eval] 开始 RAGAS 评估，共 {total_q} 道题目")
+        eval_result = self._run_eval(task, req, dataset_csv, source_chunks_jsonl, eval_output_dir)
+
+        from rag_eval.reporting.artifacts import build_artifact_paths as _build_eval_paths
+        eval_artifact_paths = _build_eval_paths(eval_result.scenario.output_dir, eval_result.run_id)
+
+        return PipelineResult(
+            build_artifact_dir=build_artifact_dir.as_posix(),
+            dataset_csv=dataset_csv.as_posix(),
+            source_chunks_jsonl=source_chunks_jsonl.as_posix(),
+            total_questions=total_q,
+            parse_failures=parse_failures,
+            eval_run_id=eval_result.run_id,
+            eval_output_dir=eval_result.scenario.output_dir.as_posix(),
+            scores_csv=eval_artifact_paths.scores_csv.as_posix(),
+            summary_md=eval_artifact_paths.summary_md.as_posix(),
+        )
+
+    def _run_build(self, task: PipelineTask, req: PipelineJobRequest,
+                   docs_path: Path, artifact_dir: Path, dataset_csv: Path):
+        """Construct DatasetBuildJob and run the build phase."""
+        from rag_eval.dataset_builder.models import DatasetBuildJob, DatasetBuildRuntime
+        from rag_eval.dataset_builder.runner import execute_dataset_build_job
+        from rag_eval.settings import EvaluationSettings
+
+        settings = EvaluationSettings()
+        job = DatasetBuildJob(
+            job_name=task.job_name,
+            input_path=docs_path,
+            input_glob="*.pdf",
+            parser_provider="aliyun_docmind",
+            failure_mode=req.failure_mode,  # type: ignore[arg-type]
+            generation_model=req.generation_model,
+            output_type="online_question_bank",
+            review_mode="draft_with_manual_review",
+            max_questions_per_document=req.max_questions_per_document,
+            max_source_chunks_per_question=req.max_source_chunks_per_question,
+            dataset_path=dataset_csv,
+            artifact_dir=artifact_dir,
+            runtime=DatasetBuildRuntime(max_documents=req.max_documents),
+        )
+        return execute_dataset_build_job(job, settings=settings)
+
+    def _run_eval(self, task: PipelineTask, req: PipelineJobRequest,
+                  dataset_csv: Path, source_chunks_jsonl: Path, eval_output_dir: Path):
+        """Construct Scenario and run the evaluation phase."""
+        from rag_eval.execution.runner import run_scenario_from_scenario_obj
+        from rag_eval.settings import EvaluationSettings
+        from rag_eval.shared.models import (
+            AppAdapterConfig, DatasetConfig, RuntimeConfig, Scenario,
+        )
+
+        settings = EvaluationSettings()
+        scenario = Scenario(
+            scenario_name=task.job_name,
+            mode="online",
+            dataset=DatasetConfig(path=dataset_csv),
+            judge_model=req.judge_model,
+            embedding_model=req.embedding_model,
+            metrics=list(req.metrics),
+            output_dir=eval_output_dir,
+            runtime=RuntimeConfig(
+                batch_size=4,
+                app_concurrency=2,
+                metric_concurrency=2,
+                max_samples=req.max_samples,
+            ),
+            app_adapter=AppAdapterConfig(
+                type="python",
+                callable="apps.siemens_pdf_qa.adapter:run",
+                static_kwargs={
+                    "source_chunks_path": source_chunks_jsonl,
+                    "model": req.answer_model,
+                },
+            ),
+            optimization_advisor=req.optimization_advisor,
+        )
+        return run_scenario_from_scenario_obj(scenario, settings=settings)
+
+
+# Module-level singleton shared by the FastAPI routes.
+pipeline_task_manager = PipelineTaskManager()

webapp/services/report_builder.py

@@ -37,6 +37,9 @@ GROUPING_FIELDS = ("difficulty", "question_type", "language")
 # How many lowest-scoring samples to surface for manual review.
 LOWEST_SAMPLE_COUNT = 10
 
+# Metrics whose lower raw value means stronger performance.
+LOWER_IS_BETTER_METRICS = {"noise_sensitivity"}
+
 
 def _round_or_none(value: float | None) -> float | None:
     """Round a float to four places, mapping NaN/None to None for clean JSON."""
@@ -105,7 +108,7 @@ def _groupings(frame: pd.DataFrame, metrics: list[str]) -> dict[str, list[GroupS
 def _sample_mean(row: pd.Series, metrics: list[str]) -> float | None:
     """Average a single sample's available metric scores for ranking."""
     values = [
-        float(row[metric])
+        (1.0 - float(row[metric])) if metric in LOWER_IS_BETTER_METRICS else float(row[metric])
         for metric in metrics
         if metric in row and pd.notna(row[metric])
     ]
@@ -177,9 +180,11 @@ def build_report(run_dir: Path, metrics: list[str]) -> ReportData:
     w_means = _weighted_metric_means(score_rows_list, metrics, doc_weights)
     rounded_means = {metric: _round_or_none(value) for metric, value in w_means.items()}
 
-    overall_ws = compute_overall_weighted_score_mean(
+    # 综合加权得分计算（已暂时禁用）
-        score_rows_list, metric_weights, doc_weights
+    # overall_ws = compute_overall_weighted_score_mean(
-    )
+    #     score_rows_list, metric_weights, doc_weights
+    # )
+    overall_ws = None
 
     distributions = {
         metric: _distribution(frame, metric)

webapp/services/score_job_manager.py

@@ -0,0 +1,271 @@
+"""Background task manager for async RAGAS single-sample scoring.
+
+Each job:
+  1. Runs InlineScorer.score() in a thread pool.
+  2. Constructs a minimal EvaluationResult + Scenario in the standard format.
+  3. Calls write_run_artifacts() — produces metadata.json, scores.csv, summary.md.
+  4. Calls run_advisor() — produces optimization_advice.md.
+
+The resulting run directory lands under outputs/score-async/<run_id>/ and is
+automatically picked up by run_reader.list_run_summaries(), so it appears in
+the existing 「运行列表」 and 「报告详情」 pages without any extra wiring.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import threading
+import time
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from webapp.models import AsyncScoreJobStatus, ScoreRequest
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_DEFAULT_JOBS_DIR = _REPO_ROOT / "outputs" / "score-async"
+_DEFAULT_INDEX_DIR = _REPO_ROOT / "outputs" / "score-jobs"  # lightweight job index
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class ScoreJobManager:
+    """Thread-pool manager for async scoring jobs.
+
+    Results are written as standard run artifacts so the report detail page
+    can render them with zero additional code.
+    """
+
+    def __init__(
+        self,
+        output_dir: Path = _DEFAULT_JOBS_DIR,
+        index_dir: Path = _DEFAULT_INDEX_DIR,
+        max_workers: int = 4,
+    ) -> None:
+        self._output_dir = Path(output_dir)
+        self._index_dir = Path(index_dir)
+        self._output_dir.mkdir(parents=True, exist_ok=True)
+        self._index_dir.mkdir(parents=True, exist_ok=True)
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+        self._cache: dict[str, AsyncScoreJobStatus] = {}
+        self._lock = threading.Lock()
+        self._load_existing()
+
+    # ------------------------------------------------------------------ #
+    # Public API
+    # ------------------------------------------------------------------ #
+
+    def submit(self, request: ScoreRequest) -> AsyncScoreJobStatus:
+        """Queue one scoring job and return its initial status immediately."""
+        job_id = uuid.uuid4().hex[:12]
+        status = AsyncScoreJobStatus(
+            job_id=job_id,
+            status="queued",
+            created_at=_now_iso(),
+            request_summary={
+                "question": (request.question or "")[:80],
+                "answer": (request.answer or "")[:80],
+                "metrics": list(request.metrics),
+                "judge_model": request.judge_model or "",
+                "embedding_model": request.embedding_model or "",
+                "has_contexts": bool(request.contexts),
+                "has_ground_truth": bool(request.ground_truth),
+            },
+        )
+        with self._lock:
+            self._cache[job_id] = status
+        self._persist_index(status)
+        self._executor.submit(self._run, job_id, request)
+        return status
+
+    def get(self, job_id: str) -> AsyncScoreJobStatus | None:
+        """Return current status or None if unknown."""
+        with self._lock:
+            return self._cache.get(job_id)
+
+    def list_jobs(self) -> list[AsyncScoreJobStatus]:
+        """Return all known jobs, newest first."""
+        with self._lock:
+            jobs = list(self._cache.values())
+        jobs.sort(key=lambda j: j.created_at, reverse=True)
+        return jobs
+
+    # ------------------------------------------------------------------ #
+    # Worker
+    # ------------------------------------------------------------------ #
+
+    def _run(self, job_id: str, request: ScoreRequest) -> None:
+        """Execute scoring, write run artifacts, run advisor."""
+        import logging
+        logger = logging.getLogger("webapp.services.score_job_manager")
+        self._update(job_id, status="running")
+
+        # Lazy imports to keep web server bootable if ragas is not installed.
+        from rag_eval.advisor import run_advisor
+        from rag_eval.metrics.factory import build_models
+        from rag_eval.metrics.weights import compute_weighted_score
+        from rag_eval.reporting.writers import write_run_artifacts
+        from rag_eval.settings import EvaluationSettings
+        from rag_eval.shared.models import (
+            DatasetConfig, EvaluationResult, NormalizedSample,
+            RuntimeConfig, Scenario,
+        )
+        from rag_eval.shared.utils import utc_now_iso
+        from webapp.services.inline_scorer import inline_scorer
+
+        settings = EvaluationSettings()
+        judge_model = request.judge_model or settings.ragas_judge_model
+        embedding_model = request.embedding_model or settings.ragas_embedding_model
+        effective = request.effective_metrics()
+        requested = set(request.metrics)
+        skipped = sorted(requested - set(effective))
+
+        t0 = time.monotonic()
+        started_at = utc_now_iso()
+
+        try:
+            if effective:
+                raw_scores = inline_scorer.score(
+                    question=request.question,
+                    answer=request.answer,
+                    contexts=request.contexts_as_list(),
+                    ground_truth=request.ground_truth,
+                    metrics=effective,
+                    judge_model=judge_model,
+                    embedding_model=embedding_model,
+                    settings=settings,
+                )
+            else:
+                raw_scores = {}
+
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            finished_at = utc_now_iso()
+
+            # Build full scores dict (skipped = None)
+            all_scores: dict[str, float | None] = {m: None for m in request.metrics}
+            all_scores.update(raw_scores)
+            # 综合加权得分计算（已暂时禁用）
+            # weighted_raw = compute_weighted_score(
+            #     {k: v for k, v in raw_scores.items() if v is not None}, {}
+            # )
+            # weighted = round(weighted_raw, 4) if weighted_raw is not None else None
+            weighted = None
+
+            # Build a score row compatible with report_builder
+            score_row: dict[str, Any] = {
+                "sample_id": "async-score-1",
+                "question": request.question,
+                "answer": request.answer or "",
+                "contexts": request.contexts or "",
+                "ground_truth": request.ground_truth or "",
+                "error": "",
+            }
+            score_row.update(all_scores)
+
+            # Construct minimal EvaluationResult so write_run_artifacts works
+            run_id = finished_at.replace(":", "-")
+            output_dir = self._output_dir
+
+            # Build a minimal Scenario for snapshot + advisor
+            scenario = Scenario(
+                scenario_name=f"async-score-{job_id}",
+                mode="offline",
+                dataset=DatasetConfig(path=output_dir / run_id / "dataset.csv"),
+                judge_model=judge_model,
+                embedding_model=embedding_model,
+                metrics=list(request.metrics),
+                output_dir=output_dir,
+                optimization_advisor=True,  # always generate advice
+            )
+
+            sample = NormalizedSample(
+                sample_id="async-score-1",
+                question=request.question,
+                answer=request.answer or "",
+                contexts=request.contexts_as_list(),
+                ground_truth=request.ground_truth or "",
+            )
+
+            result = EvaluationResult(
+                scenario=scenario,
+                run_id=run_id,
+                started_at=started_at,
+                finished_at=finished_at,
+                valid_samples=[sample],
+                invalid_samples=[],
+                score_rows=[score_row],
+            )
+
+            write_run_artifacts(result)
+            logger.info("[score_job] artifacts written  job_id=%s  run_id=%s", job_id, run_id)
+
+            # Run optimization advisor (builds optimization_advice.md)
+            try:
+                llm, _ = build_models(judge_model, embedding_model, settings)
+                run_advisor(result, scenario, llm)
+                logger.info("[score_job] advisor done  job_id=%s", job_id)
+            except Exception as adv_exc:  # noqa: BLE001
+                logger.warning("[score_job] advisor failed  job_id=%s  err=%s", job_id, adv_exc)
+
+            self._update(
+                job_id,
+                status="completed",
+                finished_at=finished_at,
+                run_id=run_id,
+                scores=all_scores,
+                weighted_score=weighted,
+                latency_ms=latency_ms,
+                skipped_metrics=skipped,
+            )
+
+        except Exception as exc:  # noqa: BLE001
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            logger.error("[score_job] failed  job_id=%s  err=%s", job_id, exc)
+            self._update(
+                job_id,
+                status="failed",
+                finished_at=_now_iso(),
+                latency_ms=latency_ms,
+                error=f"{type(exc).__name__}: {exc}",
+            )
+
+    # ------------------------------------------------------------------ #
+    # Persistence helpers
+    # ------------------------------------------------------------------ #
+
+    def _update(self, job_id: str, **kwargs: Any) -> None:
+        """Merge kwargs into the job status and persist the index."""
+        with self._lock:
+            existing = self._cache.get(job_id)
+            if existing is None:
+                return
+            updated = existing.model_copy(update=kwargs)
+            self._cache[job_id] = updated
+        self._persist_index(updated)
+
+    def _persist_index(self, status: AsyncScoreJobStatus) -> None:
+        """Write a lightweight index JSON for this job (survives restarts)."""
+        path = self._index_dir / f"{status.job_id}.json"
+        path.write_text(
+            json.dumps(status.model_dump(), ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def _load_existing(self) -> None:
+        """Load existing job index files on startup."""
+        for path in sorted(self._index_dir.glob("*.json")):
+            try:
+                data = json.loads(path.read_text(encoding="utf-8"))
+                status = AsyncScoreJobStatus.model_validate(data)
+                self._cache[status.job_id] = status
+            except Exception:  # noqa: BLE001
+                pass
+
+
+# Module-level singleton shared by FastAPI routes.
+score_job_manager = ScoreJobManager()

webapp/services/session_score_manager.py

@@ -0,0 +1,452 @@
+"""Background task manager for session-grouped async RAGAS scoring.
+
+Each session groups multiple scoring calls into one shared run report:
+
+  1. First call: creates outputs/score-session/session-<id>/ and metadata.json.
+  2. Every call: appends a new sample row to scores.csv, rewrites summary.md
+     and optimization_advice.md by re-running write_run_artifacts + run_advisor
+     over ALL accumulated rows.
+  3. The resulting run directory is picked up automatically by run_reader, so the
+     「运行列表」 and 「报告详情」 pages show the live, growing report.
+
+Concurrency model:
+  - Scoring (LLM network I/O) runs freely in the thread pool — different sessions
+    score concurrently; multiple calls to the same session also start scoring in
+    parallel.
+  - File I/O (CSV append, artifact rewrite, advisor) is serialized per session via
+    a per-session threading.Lock, so no two calls corrupt the same session's CSV.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import threading
+import time
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+from webapp.models import AsyncScoreJobStatus, ScoreRequest, SessionStatus
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_DEFAULT_OUTPUT_DIR = _REPO_ROOT / "outputs" / "score-session"
+_DEFAULT_INDEX_DIR = _REPO_ROOT / "outputs" / "score-session-jobs"
+
+# Columns that are sample metadata rather than metric scores (mirrors run_reader.NON_METRIC_COLUMNS)
+_NON_METRIC_COLUMNS = {
+    "sample_id", "question", "contexts", "answer", "ground_truth",
+    "scenario", "language", "retrieval_config", "error",
+    "judge_model", "embedding_model", "run_id", "difficulty",
+    "question_type", "doc_id", "doc_name", "section_path",
+    "page_start", "page_end", "source_chunk_ids", "review_status",
+    "review_notes", "weighted_score", "sample_weight",
+}
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _sanitize_session_id(session_id: str) -> str:
+    """Convert an arbitrary session_id string to a safe directory-name fragment."""
+    return re.sub(r"[^a-zA-Z0-9]", "-", session_id)[:64].strip("-") or "default"
+
+
+class SessionScoreJobManager:
+    """Thread-pool manager for session-grouped async scoring jobs.
+
+    All calls sharing a session_id append to one shared run directory, so the
+    report detail page shows all samples and their aggregate metrics together.
+    """
+
+    def __init__(
+        self,
+        output_dir: Path = _DEFAULT_OUTPUT_DIR,
+        index_dir: Path = _DEFAULT_INDEX_DIR,
+        max_workers: int = 4,
+    ) -> None:
+        self._output_dir = Path(output_dir)
+        self._index_dir = Path(index_dir)
+        self._output_dir.mkdir(parents=True, exist_ok=True)
+        self._index_dir.mkdir(parents=True, exist_ok=True)
+        (self._index_dir / "_sessions").mkdir(parents=True, exist_ok=True)
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+
+        # job_id -> AsyncScoreJobStatus; guarded by _lock
+        self._job_cache: dict[str, AsyncScoreJobStatus] = {}
+        # session_id -> [job_ids in order]; guarded by _lock
+        self._session_jobs: dict[str, list[str]] = {}
+        # session_id -> per-session threading.Lock; guarded by _lock
+        self._session_locks: dict[str, threading.Lock] = {}
+        self._lock = threading.Lock()
+
+        self._load_existing()
+
+    # ------------------------------------------------------------------ #
+    # Public API
+    # ------------------------------------------------------------------ #
+
+    def session_run_id(self, session_id: str) -> str:
+        """Return the deterministic run_id for a session (also the dir name)."""
+        return f"session-{_sanitize_session_id(session_id)}"
+
+    def submit(self, session_id: str, request: ScoreRequest) -> tuple[AsyncScoreJobStatus, str]:
+        """Queue one scoring call for a session.
+
+        Returns (job_status, run_id). run_id is deterministic from session_id.
+        """
+        run_id = self.session_run_id(session_id)
+        job_id = uuid.uuid4().hex[:12]
+
+        status = AsyncScoreJobStatus(
+            job_id=job_id,
+            status="queued",
+            created_at=_now_iso(),
+            request_summary={
+                "question": (request.question or "")[:80],
+                "answer": (request.answer or "")[:80],
+                "metrics": list(request.metrics),
+                "judge_model": request.judge_model or "",
+                "embedding_model": request.embedding_model or "",
+                "has_contexts": bool(request.contexts),
+                "has_ground_truth": bool(request.ground_truth),
+                "session_id": session_id,
+            },
+        )
+
+        with self._lock:
+            self._job_cache[job_id] = status
+            if session_id not in self._session_jobs:
+                self._session_jobs[session_id] = []
+            self._session_jobs[session_id].append(job_id)
+
+        self._persist_job_index(status)
+        self._persist_session_index(session_id)
+        self._executor.submit(self._run, job_id, session_id, run_id, request)
+        return status, run_id
+
+    def get_job(self, job_id: str) -> AsyncScoreJobStatus | None:
+        """Return current status of one call, or None if unknown."""
+        with self._lock:
+            return self._job_cache.get(job_id)
+
+    def list_jobs(self) -> list[AsyncScoreJobStatus]:
+        """Return all session job records, newest first."""
+        with self._lock:
+            jobs = list(self._job_cache.values())
+        jobs.sort(key=lambda j: j.created_at, reverse=True)
+        return jobs
+
+    def get_session(self, session_id: str) -> SessionStatus | None:
+        """Return aggregate status for a session, or None if unknown."""
+        with self._lock:
+            job_ids = list(self._session_jobs.get(session_id) or [])
+        if not job_ids:
+            return None
+
+        run_id = self.session_run_id(session_id)
+        run_dir = self._output_dir / run_id
+
+        # Compute live metric means from the CSV (may be mid-update — best effort)
+        metric_means = self._read_metric_means(run_dir)
+
+        with self._lock:
+            jobs = [self._job_cache[jid] for jid in job_ids if jid in self._job_cache]
+
+        latest = max((j.finished_at for j in jobs if j.finished_at), default="")
+        return SessionStatus(
+            session_id=session_id,
+            run_id=run_id,
+            call_count=len(job_ids),
+            metric_means=metric_means,
+            latest_finished_at=latest,
+            jobs=sorted(jobs, key=lambda j: j.created_at),
+        )
+
+    def list_sessions(self) -> list[SessionStatus]:
+        """Return aggregate status for all known sessions."""
+        with self._lock:
+            session_ids = list(self._session_jobs.keys())
+        results = []
+        for sid in session_ids:
+            status = self.get_session(sid)
+            if status is not None:
+                results.append(status)
+        results.sort(key=lambda s: s.latest_finished_at, reverse=True)
+        return results
+
+    # ------------------------------------------------------------------ #
+    # Worker
+    # ------------------------------------------------------------------ #
+
+    def _run(self, job_id: str, session_id: str, run_id: str, request: ScoreRequest) -> None:
+        """Score one sample then append it to the session's shared run artifacts."""
+        import logging
+        logger = logging.getLogger("webapp.services.session_score_manager")
+        self._update_job(job_id, status="running")
+
+        # Lazy imports — keep web server bootable if ragas is not installed.
+        from rag_eval.advisor import run_advisor
+        from rag_eval.metrics.factory import build_models
+        from rag_eval.metrics.weights import compute_weighted_score
+        from rag_eval.reporting.writers import write_run_artifacts
+        from rag_eval.settings import EvaluationSettings
+        from rag_eval.shared.models import (
+            DatasetConfig, EvaluationResult, NormalizedSample,
+            RuntimeConfig, Scenario,
+        )
+        from rag_eval.shared.utils import utc_now_iso
+        from webapp.services.inline_scorer import inline_scorer
+
+        settings = EvaluationSettings()
+        judge_model = request.judge_model or settings.ragas_judge_model
+        embedding_model = request.embedding_model or settings.ragas_embedding_model
+        effective = request.effective_metrics()
+        requested = set(request.metrics)
+        skipped = sorted(requested - set(effective))
+
+        t0 = time.monotonic()
+
+        try:
+            # --- Scoring (can run concurrently for the same session) ----------
+            if effective:
+                raw_scores = inline_scorer.score(
+                    question=request.question,
+                    answer=request.answer,
+                    contexts=request.contexts_as_list(),
+                    ground_truth=request.ground_truth,
+                    metrics=effective,
+                    judge_model=judge_model,
+                    embedding_model=embedding_model,
+                    settings=settings,
+                )
+            else:
+                raw_scores = {}
+
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            finished_at = utc_now_iso()
+
+            # Build complete scores for this sample (skipped metrics → None)
+            all_scores: dict[str, float | None] = {m: None for m in request.metrics}
+            all_scores.update(raw_scores)
+
+            # 综合加权得分计算（已暂时禁用）
+            # weighted_raw = compute_weighted_score(
+            #     {k: v for k, v in raw_scores.items() if v is not None}, {}
+            # )
+            # weighted = round(weighted_raw, 4) if weighted_raw is not None else None
+            weighted = None
+
+            # --- File I/O must be serialized per session ----------------------
+            session_lock = self._get_session_lock(session_id)
+            with session_lock:
+                run_dir = self._output_dir / run_id
+                run_dir.mkdir(parents=True, exist_ok=True)
+
+                # Read all existing rows, then append the new one
+                existing_rows = self._read_score_rows(run_dir)
+                call_number = len(existing_rows) + 1
+
+                new_row: dict[str, Any] = {
+                    "sample_id": f"session-score-{call_number}",
+                    "question": request.question,
+                    "answer": request.answer or "",
+                    "contexts": request.contexts or "",
+                    "ground_truth": request.ground_truth or "",
+                    "error": "",
+                }
+                new_row.update(all_scores)
+
+                all_rows = existing_rows + [new_row]
+
+                # Reconstruct NormalizedSample objects for write_run_artifacts metadata
+                valid_samples = [
+                    NormalizedSample(
+                        sample_id=str(row.get("sample_id", f"session-score-{i + 1}")),
+                        question=str(row.get("question", "")),
+                        answer=str(row.get("answer", "")),
+                        contexts=[
+                            part.strip()
+                            for part in str(row.get("contexts", "")).split(" |||| ")
+                            if part.strip()
+                        ],
+                        ground_truth=str(row.get("ground_truth", "")),
+                    )
+                    for i, row in enumerate(all_rows)
+                ]
+
+                # Determine all metric columns (union of all rows' metric keys)
+                all_metric_names = sorted({
+                    k for row in all_rows
+                    for k in row if k not in _NON_METRIC_COLUMNS
+                })
+
+                scenario = Scenario(
+                    scenario_name=f"session-{_sanitize_session_id(session_id)}",
+                    mode="offline",
+                    dataset=DatasetConfig(path=run_dir / "dataset.csv"),
+                    judge_model=judge_model,
+                    embedding_model=embedding_model,
+                    metrics=all_metric_names,
+                    output_dir=self._output_dir,
+                    optimization_advisor=True,
+                )
+
+                started_at_val = (
+                    existing_rows[0].get("_started_at", finished_at)
+                    if existing_rows else finished_at
+                )
+
+                result = EvaluationResult(
+                    scenario=scenario,
+                    run_id=run_id,
+                    started_at=started_at_val if isinstance(started_at_val, str) else finished_at,
+                    finished_at=finished_at,
+                    valid_samples=valid_samples,
+                    invalid_samples=[],
+                    score_rows=all_rows,
+                )
+
+                write_run_artifacts(result)
+                logger.info(
+                    "[session_job] artifacts written  job_id=%s  session_id=%s  call=%d",
+                    job_id, session_id, call_number,
+                )
+
+                # Regenerate optimization advice over all accumulated rows
+                try:
+                    llm, _ = build_models(judge_model, embedding_model, settings)
+                    run_advisor(result, scenario, llm)
+                    logger.info("[session_job] advisor done  job_id=%s  session=%s", job_id, session_id)
+                except Exception as adv_exc:  # noqa: BLE001
+                    logger.warning(
+                        "[session_job] advisor failed  job_id=%s  err=%s", job_id, adv_exc
+                    )
+
+            self._update_job(
+                job_id,
+                status="completed",
+                finished_at=finished_at,
+                run_id=run_id,
+                scores=all_scores,
+                weighted_score=weighted,
+                latency_ms=latency_ms,
+                skipped_metrics=skipped,
+            )
+            self._persist_session_index(session_id)
+
+        except Exception as exc:  # noqa: BLE001
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            import logging as _logging
+            _logging.getLogger("webapp.services.session_score_manager").error(
+                "[session_job] failed  job_id=%s  err=%s", job_id, exc
+            )
+            self._update_job(
+                job_id,
+                status="failed",
+                finished_at=_now_iso(),
+                latency_ms=latency_ms,
+                error=f"{type(exc).__name__}: {exc}",
+            )
+
+    # ------------------------------------------------------------------ #
+    # Helpers
+    # ------------------------------------------------------------------ #
+
+    def _get_session_lock(self, session_id: str) -> threading.Lock:
+        with self._lock:
+            if session_id not in self._session_locks:
+                self._session_locks[session_id] = threading.Lock()
+            return self._session_locks[session_id]
+
+    def _read_score_rows(self, run_dir: Path) -> list[dict[str, Any]]:
+        """Read existing scores.csv rows, returning empty list if file doesn't exist."""
+        scores_path = run_dir / "scores.csv"
+        if not scores_path.is_file():
+            return []
+        try:
+            frame = pd.read_csv(scores_path)
+            return frame.where(pd.notnull(frame), None).to_dict("records")
+        except (OSError, ValueError):
+            return []
+
+    def _read_metric_means(self, run_dir: Path) -> dict[str, float | None]:
+        """Compute per-metric means from the session's scores.csv."""
+        scores_path = run_dir / "scores.csv"
+        if not scores_path.is_file():
+            return {}
+        try:
+            frame = pd.read_csv(scores_path)
+        except (OSError, ValueError):
+            return {}
+        means: dict[str, float | None] = {}
+        for col in frame.columns:
+            if col in _NON_METRIC_COLUMNS:
+                continue
+            if pd.api.types.is_numeric_dtype(frame[col]):
+                val = frame[col].mean(numeric_only=True)
+                means[col] = None if pd.isna(val) else round(float(val), 4)
+        return means
+
+    def _update_job(self, job_id: str, **kwargs: Any) -> None:
+        with self._lock:
+            existing = self._job_cache.get(job_id)
+            if existing is None:
+                return
+            updated = existing.model_copy(update=kwargs)
+            self._job_cache[job_id] = updated
+        self._persist_job_index(updated)
+
+    def _persist_job_index(self, status: AsyncScoreJobStatus) -> None:
+        """Persist a single job's status to the index directory."""
+        path = self._index_dir / f"{status.job_id}.json"
+        path.write_text(
+            json.dumps(status.model_dump(), ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def _persist_session_index(self, session_id: str) -> None:
+        """Persist the session→job_ids mapping."""
+        with self._lock:
+            job_ids = list(self._session_jobs.get(session_id) or [])
+        run_id = self.session_run_id(session_id)
+        data = {"session_id": session_id, "run_id": run_id, "job_ids": job_ids}
+        path = self._index_dir / "_sessions" / f"{_sanitize_session_id(session_id)}.json"
+        path.write_text(
+            json.dumps(data, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def _load_existing(self) -> None:
+        """Restore job cache and session mappings from persisted index files on startup."""
+        # Load individual job files
+        for path in sorted(self._index_dir.glob("*.json")):
+            try:
+                data = json.loads(path.read_text(encoding="utf-8"))
+                status = AsyncScoreJobStatus.model_validate(data)
+                self._job_cache[status.job_id] = status
+            except Exception:  # noqa: BLE001
+                pass
+
+        # Load session→job_ids mappings
+        sessions_dir = self._index_dir / "_sessions"
+        if not sessions_dir.is_dir():
+            return
+        for path in sorted(sessions_dir.glob("*.json")):
+            try:
+                data = json.loads(path.read_text(encoding="utf-8"))
+                sid = data.get("session_id", "")
+                job_ids = data.get("job_ids", [])
+                if sid:
+                    self._session_jobs[sid] = job_ids
+            except Exception:  # noqa: BLE001
+                pass
+
+
+# Module-level singleton shared by FastAPI routes.
+session_score_manager = SessionScoreJobManager()

webapp/static/css/app.css

@@ -1,4 +1,4 @@
-/* Siemens RAGAS 评估控制台 — 样式表
+/* Siemens RAGAS 评估平台 — 样式表
    配色取自西门子品牌色（petrol / 深青）与中性灰，呼应企业语境。 */
 
 :root {
@@ -199,6 +199,7 @@ code {
 .metric-value.bad { color: var(--bad); }
 .metric-value.na { color: var(--slate-light); }
 .metric-name { font-size: 12px; color: var(--slate); margin-top: 4px; }
+.metric-desc { font-size: 12px; color: #64748b; margin-top: 6px; line-height: 1.45; }
 
 .report-row { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; }
 .report-half { margin-bottom: 0; }

webapp/static/index.html

@@ -3,7 +3,7 @@
 <head>
   <meta charset="UTF-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>RAGAS 评估控制台</title>
+  <title>Siemens RAGAS 评估平台</title>
   <link rel="stylesheet" href="/static/css/app.css" />
   <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.1/dist/chart.umd.min.js"></script>
 </head>
@@ -12,8 +12,8 @@
     <!-- 左侧导航（布局 A） -->
     <aside class="sidebar">
       <div class="brand">
-        <div class="brand-mark">RAGAS</div>
+        <div class="brand-mark">Siemens RAGAS</div>
-        <div class="brand-sub">评估控制台</div>
+        <div class="brand-sub">评估平台</div>
       </div>
       <nav class="nav">
         <button class="nav-item" data-view="runs">
@@ -28,6 +28,9 @@
         <button class="nav-item" data-view="profiles">
           <span class="nav-ico">⚙</span><span>LLM 配置</span>
         </button>
+        <button class="nav-item" data-view="scorejobs">
+          <span class="nav-ico">📋</span><span>评分记录</span>
+        </button>
         <button class="nav-item" data-view="apidocs">
           <span class="nav-ico">⎔</span><span>API 文档</span>
         </button>
@@ -234,6 +237,22 @@
         </div>
       </section>
 
+      <!-- 评分记录视图 -->
+      <section class="view" id="view-scorejobs" hidden>
+        <div class="panel">
+          <div class="panel-head">
+            <h2>评分记录</h2>
+            <span class="muted" style="font-size:13px">来自 Dify 异步评分任务（POST /api/score/async）</span>
+          </div>
+          <p class="muted">评分完成后自动生成完整报告（含指标得分与 LLM 优化建议），点击「查看报告」跳转报告详情页。</p>
+        </div>
+        <div id="scorejobs-list"></div>
+        <div class="empty" id="scorejobs-empty" hidden>
+          <p>暂无评分记录。</p>
+          <p class="muted">在 Dify 工作流中调用 <code>POST /api/score/async</code> 后，记录将在此显示。</p>
+        </div>
+      </section>
+
       <!-- API 文档视图 -->
       <section class="view" id="view-apidocs" hidden>
         <iframe
@@ -248,9 +267,11 @@
   </div>
 
   <script src="/static/js/api.js"></script>
+  <script src="/static/js/metric_presenter.js"></script>
   <script src="/static/js/report.js"></script>
   <script src="/static/js/profiles.js"></script>
   <script src="/static/js/runner.js"></script>
+  <script src="/static/js/score_jobs.js"></script>
   <script src="/static/js/app.js"></script>
 </body>
 </html>

webapp/static/js/api.js

@@ -66,6 +66,11 @@ const API = {
   },
   applyProfiles(body) { return API.post("/api/llm-profiles/apply", body); },
 
+  // 异步评分记录 API
+  scoreJobsAsync(body) { return API.post("/api/score/async", body); },
+  getScoreJob(jobId) { return API.get(`/api/score/jobs/${encodeURIComponent(jobId)}`); },
+  listScoreJobs() { return API.get("/api/score/jobs"); },
+
   // 测试已保存 profile 的连通性
   testProfile(id) {
     return fetch(`/api/llm-profiles/${encodeURIComponent(id)}/test`, { method: "POST" })

webapp/static/js/app.js

@@ -5,8 +5,8 @@
 const App = {
   currentRunId: null,
   activeView: null,
-  views: ["runs", "new", "report", "profiles", "apidocs"],
+  views: ["runs", "new", "report", "profiles", "scorejobs", "apidocs"],
-  titles: { runs: "运行列表", new: "新建评估", report: "报告详情", profiles: "LLM 配置", apidocs: "API 文档" },
+  titles: { runs: "运行列表", new: "新建评估", report: "报告详情", profiles: "LLM 配置", scorejobs: "评分记录", apidocs: "API 文档" },
 
   // 初始化：绑定导航、从 URL/sessionStorage 恢复上次位置、启动健康检查。
   init() {
@@ -72,6 +72,7 @@ const App = {
     if (view === "new")        Runner.loadScenarios();
     if (view === "report")     Report.render(App.currentRunId);
     if (view === "profiles")   Profiles.load();
+    if (view === "scorejobs")  ScoreJobs.load();
   },
 
   // ----------------------------------------------------------------
@@ -146,7 +147,7 @@ const App = {
     const chips = (run.metrics || [])
       .map((m) => {
         const val = run.metric_means ? run.metric_means[m] : null;
-        const cls = App.scoreClass(val);
+        const cls = App.scoreClass(m, val);
         const text = val === null || val === undefined ? "n/a" : val.toFixed(2);
         return `<span class="metric-chip" title="${App.escape(m)}">${App.escape(App.shortMetric(m))} <b class="${cls}">${text}</b></span>`;
       })
@@ -173,11 +174,8 @@ const App = {
     if (btn) btn.disabled = false;
   },
 
-  scoreClass(value) {
+  scoreClass(metricName, value) {
-    if (value === null || value === undefined) return "na";
+    return MetricPresenter.scoreClass(metricName, value);
-    if (value >= 0.8) return "good";
-    if (value >= 0.65) return "warn";
-    return "bad";
   },
 
   shortMetric(name) {

webapp/static/js/metric_presenter.js

@@ -0,0 +1,77 @@
+// metric_presenter.js — 统一维护指标语义（高分好 / 低分好）、颜色阈值与简要说明。
+
+(function attachMetricPresenter(globalObj) {
+  const METRIC_META = {
+    faithfulness: {
+      direction: "higher_better",
+      description: "回答是否被检索内容直接支持，越高越可靠。",
+    },
+    answer_relevancy: {
+      direction: "higher_better",
+      description: "回答与问题是否紧密相关，越高越切题。",
+    },
+    context_recall: {
+      direction: "higher_better",
+      description: "检索片段覆盖标准答案关键信息的程度，越高越完整。",
+    },
+    context_precision: {
+      direction: "higher_better",
+      description: "检索片段中有效信息的占比，越高越精准。",
+    },
+    noise_sensitivity: {
+      direction: "lower_better",
+      description: "对噪声上下文的敏感程度，越低说明抗干扰能力越强。",
+    },
+    factual_correctness: {
+      direction: "higher_better",
+      description: "回答与标准答案在事实层面的吻合程度，越高越准确。",
+    },
+    semantic_similarity: {
+      direction: "higher_better",
+      description: "回答与标准答案在语义上的相似程度，越高越接近。",
+    },
+  };
+
+  function isLowerBetter(metricName) {
+    return METRIC_META[metricName]?.direction === "lower_better";
+  }
+
+  function scoreClass(metricName, value) {
+    if (value === null || value === undefined || Number.isNaN(Number(value))) return "na";
+    const numeric = Number(value);
+    if (isLowerBetter(metricName)) {
+      if (numeric <= 0.15) return "good";
+      if (numeric <= 0.35) return "warn";
+      return "bad";
+    }
+    if (numeric >= 0.85) return "good";
+    if (numeric >= 0.65) return "warn";
+    return "bad";
+  }
+
+  function describeMetric(metricName) {
+    return METRIC_META[metricName]?.description || "该指标用于衡量当前问答样本的评估表现。";
+  }
+
+  function binColor(metricName, lower) {
+    const numeric = Number(lower);
+    if (isLowerBetter(metricName)) {
+      if (numeric < 0.2) return "#16a34a";
+      if (numeric < 0.4) return "#84cc16";
+      if (numeric < 0.6) return "#eab308";
+      if (numeric < 0.8) return "#f97316";
+      return "#dc2626";
+    }
+    if (numeric >= 0.8) return "#16a34a";
+    if (numeric >= 0.6) return "#84cc16";
+    if (numeric >= 0.4) return "#eab308";
+    if (numeric >= 0.2) return "#f97316";
+    return "#dc2626";
+  }
+
+  globalObj.MetricPresenter = {
+    scoreClass,
+    describeMetric,
+    binColor,
+  };
+})(window);

webapp/static/js/report.js

@@ -117,28 +117,30 @@ const Report = {
     const metrics = report.metrics && report.metrics.length ? report.metrics : summary.metrics;
     metrics.forEach((metric) => {
       const value = report.metric_means ? report.metric_means[metric] : null;
-      const cls = App.scoreClass(value);
+      const cls = App.scoreClass(metric, value);
       const text = value === null || value === undefined ? "n/a" : value.toFixed(2);
+      const description = MetricPresenter.describeMetric(metric);
       const card = document.createElement("div");
       card.className = "metric-card";
       card.innerHTML = `
         <div class="metric-value ${cls}">${text}</div>
         <div class="metric-name">${App.escape(metric)}</div>
+        <div class="metric-desc">${App.escape(description)}</div>
       `;
       wrap.appendChild(card);
     });
 
-    // 综合加权得分卡片
+    // 综合加权得分卡片（已暂时隐藏）
-    const wsValue = (report && report.weighted_score_mean !== undefined) ? report.weighted_score_mean : null;
+    // const wsValue = (report && report.weighted_score_mean !== undefined) ? report.weighted_score_mean : null;
-    const wsCard = document.createElement("div");
+    // const wsCard = document.createElement("div");
-    wsCard.className = "metric-card weighted-score-card";
+    // wsCard.className = "metric-card weighted-score-card";
-    const wsCls = App.scoreClass(wsValue);
+    // const wsCls = App.scoreClass(wsValue);
-    const wsText = wsValue === null || wsValue === undefined ? "n/a" : wsValue.toFixed(2);
+    // const wsText = wsValue === null || wsValue === undefined ? "n/a" : wsValue.toFixed(2);
-    wsCard.innerHTML = `
+    // wsCard.innerHTML = `
-      <div class="metric-value ${wsCls}">${wsText}</div>
+    //   <div class="metric-value ${wsCls}">${wsText}</div>
-      <div class="metric-name">综合加权得分</div>
+    //   <div class="metric-name">综合加权得分</div>
-    `;
+    // `;
-    wrap.appendChild(wsCard);
+    // wrap.appendChild(wsCard);
   },
 
   // ② 分数分布直方图（可切换指标）。
@@ -168,17 +170,13 @@ const Report = {
     const bins = distributions[metric] || [];
     const labels = bins.map((b) => b.label);
     const counts = bins.map((b) => b.count);
-    const colors = bins.map((b) => Report._binColor(b.lower));
+    const colors = bins.map((b) => Report._binColor(metric, b.lower));
     Report._drawDistChart(labels, counts, colors);
   },
 
   // 低分箱偏红、高分箱偏绿，直观暴露长尾。
-  _binColor(lower) {
+  _binColor(metric, lower) {
-    if (lower >= 0.8) return "#16a34a";
+    return MetricPresenter.binColor(metric, lower);
-    if (lower >= 0.6) return "#84cc16";
-    if (lower >= 0.4) return "#eab308";
-    if (lower >= 0.2) return "#f97316";
-    return "#dc2626";
   },
 
   // 实际绘制 Chart.js 柱状图。
@@ -247,7 +245,7 @@ const Report = {
       body += `<tr><td>${App.escape(stat.key)}</td><td>${stat.count}</td>`;
       metrics.forEach((m) => {
         const v = stat.means ? stat.means[m] : null;
-        const cls = App.scoreClass(v);
+        const cls = App.scoreClass(m, v);
         const text = v === null || v === undefined ? "—" : v.toFixed(2);
         body += `<td class="${cls}">${text}</td>`;
       });
@@ -271,7 +269,7 @@ const Report = {
       const scoreBadges = metrics
         .map((m) => {
           const v = sample.metrics ? sample.metrics[m] : null;
-          const cls = App.scoreClass(v);
+          const cls = App.scoreClass(m, v);
           const text = v === null || v === undefined ? "—" : v.toFixed(2);
           return `<span class="score-badge ${cls}" title="${App.escape(m)}">${text}</span>`;
         })

webapp/static/js/score_jobs.js

@@ -0,0 +1,126 @@
+// score_jobs.js — 评分记录页面（异步 RAGAS 评分任务列表）
+// 每条评分完成后自动写入标准 Run 产物，点击「查看报告」复用现有报告详情页。
+
+const ScoreJobs = {
+  _pollTimers: {},   // job_id -> setInterval handle
+
+  async load() {
+    const list = document.getElementById("scorejobs-list");
+    const empty = document.getElementById("scorejobs-empty");
+    list.innerHTML = '<p class="muted">加载中…</p>';
+    try {
+      const data = await API.listScoreJobs();
+      const jobs = data.jobs || [];
+      list.innerHTML = "";
+      if (jobs.length === 0) {
+        empty.hidden = false;
+        return;
+      }
+      empty.hidden = true;
+      jobs.forEach(job => list.appendChild(ScoreJobs.renderCard(job)));
+      // Auto-poll any pending jobs
+      jobs.forEach(job => {
+        if (job.status === "queued" || job.status === "running") {
+          ScoreJobs._startPoll(job.job_id);
+        }
+      });
+    } catch (err) {
+      list.innerHTML = `<p class="muted">加载失败：${App.escape(err.message)}</p>`;
+    }
+  },
+
+  renderCard(job) {
+    const card = document.createElement("div");
+    card.className = "run-card";
+    card.id = `score-job-${job.job_id}`;
+    card.innerHTML = ScoreJobs._cardHtml(job);
+    // Bind report button if already completed
+    ScoreJobs._bindReportBtn(card, job);
+    return card;
+  },
+
+  _cardHtml(job) {
+    const time = App.shortTime(job.created_at);
+    const question = App.escape((job.request_summary?.question || "—").slice(0, 60));
+    const metrics = (job.request_summary?.metrics || []).join(", ");
+
+    const statusBadge = `<span class="badge ${job.status}">${job.status}</span>`;
+
+    let scoreHtml = "";
+    if (job.status === "completed") {
+      scoreHtml = Object.entries(job.scores || {})
+        .map(([k, v]) => {
+          const cls = App.scoreClass(k, v);
+          const text = v === null || v === undefined ? "n/a" : Number(v).toFixed(3);
+          return `<span class="metric-chip" title="${App.escape(k)}">${App.escape(App.shortMetric(k))} <b class="${cls}">${text}</b></span>`;
+        })
+        .join(" ");
+      // 综合加权得分（已暂时隐藏）
+      // if (job.weighted_score !== null && job.weighted_score !== undefined) {
+      //   const cls = App.scoreClass(job.weighted_score);
+      //   scoreHtml += ` <span class="metric-chip">综合 <b class="${cls}">${Number(job.weighted_score).toFixed(3)}</b></span>`;
+      // }
+    } else if (job.status === "failed") {
+      scoreHtml = `<span style="color:var(--bad);font-size:12px">${App.escape((job.error || "").slice(0, 80))}</span>`;
+    } else {
+      scoreHtml = `<span class="muted">评分中，请稍候…</span>`;
+    }
+
+    const reportBtn = job.status === "completed" && job.run_id
+      ? `<button class="btn btn-sm btn-primary score-job-report-btn" data-run-id="${App.escape(job.run_id)}">查看报告</button>`
+      : "";
+
+    return `
+      <div class="run-card-head">
+        <div class="run-card-title">${question}</div>
+        <div style="display:flex;gap:8px;align-items:center">${statusBadge}${reportBtn}</div>
+      </div>
+      <div class="run-card-meta">
+        <div>指标：${App.escape(metrics)} · ${time} · ${job.latency_ms}ms</div>
+      </div>
+      <div class="run-card-metrics">${scoreHtml}</div>
+    `;
+  },
+
+  _bindReportBtn(card, job) {
+    const btn = card.querySelector(".score-job-report-btn");
+    if (!btn) return;
+    btn.addEventListener("click", () => {
+      const runId = btn.dataset.runId;
+      if (runId) {
+        App.enableReportNav();
+        App.navigate("report", runId);
+      }
+    });
+  },
+
+  _startPoll(jobId) {
+    if (ScoreJobs._pollTimers[jobId]) return;
+    ScoreJobs._pollTimers[jobId] = setInterval(async () => {
+      try {
+        const job = await API.getScoreJob(jobId);
+        const card = document.getElementById(`score-job-${jobId}`);
+        if (card) {
+          card.innerHTML = ScoreJobs._cardHtml(job);
+          ScoreJobs._bindReportBtn(card, job);
+        }
+        if (job.status === "completed" || job.status === "failed") {
+          clearInterval(ScoreJobs._pollTimers[jobId]);
+          delete ScoreJobs._pollTimers[jobId];
+          // If completed, pre-enable report nav
+          if (job.status === "completed" && job.run_id) {
+            App.enableReportNav();
+          }
+        }
+      } catch (_e) {
+        clearInterval(ScoreJobs._pollTimers[jobId]);
+        delete ScoreJobs._pollTimers[jobId];
+      }
+    }, 5000);
+  },
+
+  stopAllPolls() {
+    Object.values(ScoreJobs._pollTimers).forEach(t => clearInterval(t));
+    ScoreJobs._pollTimers = {};
+  },
+};

webserver.log

@@ -0,0 +1,17 @@
+INFO:     Started server process [82284]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://127.0.0.1:8811 (Press CTRL+C to quit)
+INFO:     127.0.0.1:56164 - "GET /api/health HTTP/1.1" 200 OK
+INFO:     127.0.0.1:53350 - "GET / HTTP/1.1" 200 OK
+INFO:     127.0.0.1:53351 - "GET /api/runs HTTP/1.1" 200 OK
+INFO:     127.0.0.1:53352 - "GET /api/scenarios HTTP/1.1" 200 OK
+INFO:     127.0.0.1:64689 - "GET /api/runs/2026-06-15T08-30-00%2B00-00 HTTP/1.1" 200 OK
+INFO:     127.0.0.1:64700 - "POST /api/evaluations HTTP/1.1" 200 OK
+INFO:     127.0.0.1:64703 - "GET /api/evaluations/a3243f2443d7 HTTP/1.1" 200 OK
+INFO:     127.0.0.1:58440 - "GET /api/evaluations/a3243f2443d7 HTTP/1.1" 200 OK
+INFO:     127.0.0.1:64454 - "GET /static/css/app.css HTTP/1.1" 200 OK
+INFO:     127.0.0.1:64455 - "GET /static/js/api.js HTTP/1.1" 200 OK
+INFO:     127.0.0.1:56825 - "GET /static/js/app.js HTTP/1.1" 200 OK
+INFO:     127.0.0.1:56829 - "GET /static/js/report.js HTTP/1.1" 200 OK
+INFO:     127.0.0.1:56830 - "GET /static/js/runner.js HTTP/1.1" 200 OK