From 754a30ad593c9fcb9c797bc5ae4db19eec8b652a Mon Sep 17 00:00:00 2001
From: wangwei <Wei.Wang@t-systems.com>
Date: Fri, 26 Jun 2026 16:09:33 +0800
Subject: [PATCH] feat(session-async): add /api/score/session_async with
 incremental session report aggregation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- New POST /api/score/session_async endpoint: same session_id calls append to one shared report
- New GET /api/score/sessions/{session_id}: returns call_count, metric_means, all job records
- New GET /api/score/session/jobs/{job_id}: individual call status
- SessionScoreJobManager: deterministic run_id from session_id, per-session mutex for CSV append, advisor regenerated on every call
- SessionScoreRequest (extends ScoreRequest + session_id), SessionScoreJobResponse, SessionStatus models added
- 24 new tests, all passing

chore(weighted-score): comment out 综合加权得分 display and computation

- report.js: hide 综合加权得分 card in report detail page
- score_jobs.js: hide 综合 chip in async job list
- report_builder.py: overall_ws=None (computation disabled)
- summary.py: weighted_score summary line disabled
- evaluator.py: weighted_score/sample_weight columns no longer written to scores.csv
- score.py /api/score: weighted_score always returns null
- score_job_manager.py + session_score_manager.py: weighted=None
- Updated 3 tests to match new behaviour (6 pre-existing failures unchanged)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---
 .idea/.gitignore                              |   8 +
 .idea/misc.xml                                |   7 +
 .idea/modules.xml                             |   8 +
 .idea/siemens_ragas.iml                       |   9 +
 .idea/vcs.xml                                 |   6 +
 .../content/analysis-approach.html            |  60 +++
 .../1625-1781595805/content/approaches.html   |  77 +++
 .../content/design-architecture.html          |  53 ++
 .../1625-1781595805/content/trigger-mode.html |  68 +++
 .../1625-1781595805/content/waiting-2.html    |   3 +
 .../1625-1781595805/content/waiting.html      |   3 +
 .../1625-1781595805/state/server-stopped      |   1 +
 .../1625-1781595805/state/server.pid          |   1 +
 logs/online_eval.log                          |   1 +
 logs/server_2026-06-23.log                    |  24 +
 logs/siemens_build.log                        |  35 ++
 rag_eval/execution/evaluator.py               |  16 +-
 rag_eval/reporting/summary.py                 |  19 +-
 rag_eval/shared/profile_store.py              |  53 ++
 ...tml => siemens-ragas-project-overview.html |   0
 tests/test_offline_eval.py                    |  10 +-
 tests/test_pipeline.py                        | 280 +++++++++++
 tests/test_webapp_report_builder.py           |   3 +-
 tests/webapp/test_score_api.py                |   3 +-
 tests/webapp/test_session_score_jobs_api.py   | 299 ++++++++++++
 webapp/api/score.py                           |  11 +-
 webapp/api/session_score_jobs.py              | 171 +++++++
 webapp/models.py                              |  44 ++
 webapp/server.py                              |   7 +-
 webapp/services/pipeline_task_manager.py      | 257 ++++++++++
 webapp/services/report_builder.py             |   8 +-
 webapp/services/score_job_manager.py          |  10 +-
 webapp/services/session_score_manager.py      | 452 ++++++++++++++++++
 webapp/static/js/report.js                    |  22 +-
 webapp/static/js/score_jobs.js                |   9 +-
 webserver.log                                 |  17 +
 36 files changed, 2004 insertions(+), 51 deletions(-)
 create mode 100644 .idea/.gitignore
 create mode 100644 .idea/misc.xml
 create mode 100644 .idea/modules.xml
 create mode 100644 .idea/siemens_ragas.iml
 create mode 100644 .idea/vcs.xml
 create mode 100644 .superpowers/brainstorm/1625-1781595805/content/analysis-approach.html
 create mode 100644 .superpowers/brainstorm/1625-1781595805/content/approaches.html
 create mode 100644 .superpowers/brainstorm/1625-1781595805/content/design-architecture.html
 create mode 100644 .superpowers/brainstorm/1625-1781595805/content/trigger-mode.html
 create mode 100644 .superpowers/brainstorm/1625-1781595805/content/waiting-2.html
 create mode 100644 .superpowers/brainstorm/1625-1781595805/content/waiting.html
 create mode 100644 .superpowers/brainstorm/1625-1781595805/state/server-stopped
 create mode 100644 .superpowers/brainstorm/1625-1781595805/state/server.pid
 create mode 100644 logs/online_eval.log
 create mode 100644 logs/server_2026-06-23.log
 create mode 100644 logs/siemens_build.log
 create mode 100644 rag_eval/shared/profile_store.py
 rename project-overview.html => siemens-ragas-project-overview.html (100%)
 create mode 100644 tests/test_pipeline.py
 create mode 100644 tests/webapp/test_session_score_jobs_api.py
 create mode 100644 webapp/api/session_score_jobs.py
 create mode 100644 webapp/services/pipeline_task_manager.py
 create mode 100644 webapp/services/session_score_manager.py
 create mode 100644 webserver.log

diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..13566b8
--- /dev/null
+++ b/.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
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..1e305c1
--- /dev/null
+++ b/.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>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..76b7fe3
--- /dev/null
+++ b/.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>
\ No newline at end of file
diff --git a/.idea/siemens_ragas.iml b/.idea/siemens_ragas.iml
new file mode 100644
index 0000000..d6ebd48
--- /dev/null
+++ b/.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>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.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>
\ No newline at end of file
diff --git a/.superpowers/brainstorm/1625-1781595805/content/analysis-approach.html b/.superpowers/brainstorm/1625-1781595805/content/analysis-approach.html
new file mode 100644
index 0000000..85419b2
--- /dev/null
+++ b/.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>
diff --git a/.superpowers/brainstorm/1625-1781595805/content/approaches.html b/.superpowers/brainstorm/1625-1781595805/content/approaches.html
new file mode 100644
index 0000000..638bed6
--- /dev/null
+++ b/.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>
diff --git a/.superpowers/brainstorm/1625-1781595805/content/design-architecture.html b/.superpowers/brainstorm/1625-1781595805/content/design-architecture.html
new file mode 100644
index 0000000..acbde1b
--- /dev/null
+++ b/.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>
diff --git a/.superpowers/brainstorm/1625-1781595805/content/trigger-mode.html b/.superpowers/brainstorm/1625-1781595805/content/trigger-mode.html
new file mode 100644
index 0000000..617bddd
--- /dev/null
+++ b/.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>
diff --git a/.superpowers/brainstorm/1625-1781595805/content/waiting-2.html b/.superpowers/brainstorm/1625-1781595805/content/waiting-2.html
new file mode 100644
index 0000000..b5f5028
--- /dev/null
+++ b/.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>
diff --git a/.superpowers/brainstorm/1625-1781595805/content/waiting.html b/.superpowers/brainstorm/1625-1781595805/content/waiting.html
new file mode 100644
index 0000000..e588f77
--- /dev/null
+++ b/.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>
diff --git a/.superpowers/brainstorm/1625-1781595805/state/server-stopped b/.superpowers/brainstorm/1625-1781595805/state/server-stopped
new file mode 100644
index 0000000..c4e2401
--- /dev/null
+++ b/.superpowers/brainstorm/1625-1781595805/state/server-stopped
@@ -0,0 +1 @@
+{"reason":"idle timeout","timestamp":1781598635371}
diff --git a/.superpowers/brainstorm/1625-1781595805/state/server.pid b/.superpowers/brainstorm/1625-1781595805/state/server.pid
new file mode 100644
index 0000000..f71f46d
--- /dev/null
+++ b/.superpowers/brainstorm/1625-1781595805/state/server.pid
@@ -0,0 +1 @@
+1625
diff --git a/logs/online_eval.log b/logs/online_eval.log
new file mode 100644
index 0000000..061116f
--- /dev/null
+++ b/logs/online_eval.log
@@ -0,0 +1 @@
+Completed run: C:\Projects\AIProjects\Siemens-AIPOC\siemens_ragas\outputs\online\siemens-pdf-question-bank
diff --git a/logs/server_2026-06-23.log b/logs/server_2026-06-23.log
new file mode 100644
index 0000000..9005d33
--- /dev/null
+++ b/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
diff --git a/logs/siemens_build.log b/logs/siemens_build.log
new file mode 100644
index 0000000..47aaa36
--- /dev/null
+++ b/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
diff --git a/rag_eval/execution/evaluator.py b/rag_eval/execution/evaluator.py
index b2a84cc..5044730 100644
--- a/rag_eval/execution/evaluator.py
+++ b/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.
-        record["weighted_score"] = compute_weighted_score(
-            score.metrics, self.scenario.metric_weights
-        )
-        doc_name = str(sample.metadata.get("doc_name", "") or "")
-        record["sample_weight"] = resolve_weight(
-            self.scenario.doc_weights, doc_name, default=1.0
-        )
+        # 综合加权得分列（已暂时禁用）
+        # record["weighted_score"] = compute_weighted_score(
+        #     score.metrics, self.scenario.metric_weights
+        # )
+        # doc_name = str(sample.metadata.get("doc_name", "") or "")
+        # record["sample_weight"] = resolve_weight(
+        #     self.scenario.doc_weights, doc_name, default=1.0
+        # )
         return record
diff --git a/rag_eval/reporting/summary.py b/rag_eval/reporting/summary.py
index 13a76af..7953e7f 100644
--- a/rag_eval/reporting/summary.py
+++ b/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:
-        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):
-            lines.append(f"- **weighted_score{weight_suffix}: `{overall_ws:.4f}`**")
-        else:
-            lines.append(f"- **weighted_score{weight_suffix}: `n/a`**")
+    # 综合加权得分（已暂时禁用）
+    # if has_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):
+    #         lines.append(f"- **weighted_score{weight_suffix}: `{overall_ws:.4f}`**")
+    #     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]
diff --git a/rag_eval/shared/profile_store.py b/rag_eval/shared/profile_store.py
new file mode 100644
index 0000000..ccf93fc
--- /dev/null
+++ b/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
diff --git a/project-overview.html b/siemens-ragas-project-overview.html
similarity index 100%
rename from project-overview.html
rename to siemens-ragas-project-overview.html
diff --git a/tests/test_offline_eval.py b/tests/test_offline_eval.py
index 3f6ce90..aee354b 100644
--- a/tests/test_offline_eval.py
+++ b/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
-        assert abs(row["weighted_score"] - 0.75) < 1e-4
-        assert row["sample_weight"] == 2.0
+        # 综合加权得分已暂时禁用，weighted_score 和 sample_weight 不再写入
+        assert "weighted_score" not in row
+        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."""
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
new file mode 100644
index 0000000..9ed1b4c
--- /dev/null
+++ b/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()
diff --git a/tests/test_webapp_report_builder.py b/tests/test_webapp_report_builder.py
index 1ed0a35..8d492c1 100644
--- a/tests/test_webapp_report_builder.py
+++ b/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"
diff --git a/tests/webapp/test_score_api.py b/tests/webapp/test_score_api.py
index 1eb788c..a9ee7cc 100644
--- a/tests/webapp/test_score_api.py
+++ b/tests/webapp/test_score_api.py
@@ -241,7 +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"})
diff --git a/tests/webapp/test_session_score_jobs_api.py b/tests/webapp/test_session_score_jobs_api.py
new file mode 100644
index 0000000..22811ea
--- /dev/null
+++ b/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"],
+    )
diff --git a/webapp/api/score.py b/webapp/api/score.py
index 93cd512..dbddd98 100644
--- a/webapp/api/score.py
+++ b/webapp/api/score.py
@@ -156,10 +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(
-        {key: value for key, value in raw_scores.items() if value is not None},
-        {},
-    )
+    # 综合加权得分计算（已暂时禁用）
+    # 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",
@@ -169,7 +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,
     )
diff --git a/webapp/api/session_score_jobs.py b/webapp/api/session_score_jobs.py
new file mode 100644
index 0000000..d93b995
--- /dev/null
+++ b/webapp/api/session_score_jobs.py
@@ -0,0 +1,171 @@
+"""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）",
+    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
diff --git a/webapp/models.py b/webapp/models.py
index 604fc80..e124102 100644
--- a/webapp/models.py
+++ b/webapp/models.py
@@ -531,6 +531,50 @@ class AsyncScoreJobResponse(BaseModel):
     )
 
 
+# ---------------------------------------------------------------------------
+# 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.
+    """
+
+    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)."""
 
diff --git a/webapp/server.py b/webapp/server.py
index 5a9ad75..211545b 100644
--- a/webapp/server.py
+++ b/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, score_jobs
+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")
@@ -73,6 +73,10 @@ OPENAPI_TAGS = [
             "**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，完成后自动生成完整报告（含优化建议），"
             "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"
+            "通过 `GET /api/score/sessions/{session_id}` 查看 session 聚合状态。\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 "
@@ -111,6 +115,7 @@ def create_app() -> FastAPI:
     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):
diff --git a/webapp/services/pipeline_task_manager.py b/webapp/services/pipeline_task_manager.py
new file mode 100644
index 0000000..ced3ea6
--- /dev/null
+++ b/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()
diff --git a/webapp/services/report_builder.py b/webapp/services/report_builder.py
index 1082401..1f56f65 100644
--- a/webapp/services/report_builder.py
+++ b/webapp/services/report_builder.py
@@ -177,9 +177,11 @@ 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(
-        score_rows_list, metric_weights, doc_weights
-    )
+    # 综合加权得分计算（已暂时禁用）
+    # overall_ws = compute_overall_weighted_score_mean(
+    #     score_rows_list, metric_weights, doc_weights
+    # )
+    overall_ws = None
 
     distributions = {
         metric: _distribution(frame, metric)
diff --git a/webapp/services/score_job_manager.py b/webapp/services/score_job_manager.py
index 8805d5b..a3964a1 100644
--- a/webapp/services/score_job_manager.py
+++ b/webapp/services/score_job_manager.py
@@ -149,10 +149,12 @@ class ScoreJobManager:
             # Build full scores dict (skipped = None)
             all_scores: dict[str, float | None] = {m: None for m in request.metrics}
             all_scores.update(raw_scores)
-            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
+            # 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] = {
diff --git a/webapp/services/session_score_manager.py b/webapp/services/session_score_manager.py
new file mode 100644
index 0000000..d145975
--- /dev/null
+++ b/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()
diff --git a/webapp/static/js/report.js b/webapp/static/js/report.js
index abb7320..a76a7ab 100644
--- a/webapp/static/js/report.js
+++ b/webapp/static/js/report.js
@@ -128,17 +128,17 @@ const Report = {
       wrap.appendChild(card);
     });
 
-    // 综合加权得分卡片
-    const wsValue = (report && report.weighted_score_mean !== undefined) ? report.weighted_score_mean : null;
-    const wsCard = document.createElement("div");
-    wsCard.className = "metric-card weighted-score-card";
-    const wsCls = App.scoreClass(wsValue);
-    const wsText = wsValue === null || wsValue === undefined ? "n/a" : wsValue.toFixed(2);
-    wsCard.innerHTML = `
-      <div class="metric-value ${wsCls}">${wsText}</div>
-      <div class="metric-name">综合加权得分</div>
-    `;
-    wrap.appendChild(wsCard);
+    // 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 wsCard = document.createElement("div");
+    // wsCard.className = "metric-card weighted-score-card";
+    // const wsCls = App.scoreClass(wsValue);
+    // const wsText = wsValue === null || wsValue === undefined ? "n/a" : wsValue.toFixed(2);
+    // wsCard.innerHTML = `
+    //   <div class="metric-value ${wsCls}">${wsText}</div>
+    //   <div class="metric-name">综合加权得分</div>
+    // `;
+    // wrap.appendChild(wsCard);
   },
 
   // ② 分数分布直方图（可切换指标）。
diff --git a/webapp/static/js/score_jobs.js b/webapp/static/js/score_jobs.js
index cfe93e9..18f5d9c 100644
--- a/webapp/static/js/score_jobs.js
+++ b/webapp/static/js/score_jobs.js
@@ -55,10 +55,11 @@ const ScoreJobs = {
           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>`;
-      }
+      // 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 {
diff --git a/webserver.log b/webserver.log
new file mode 100644
index 0000000..399e996
--- /dev/null
+++ b/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