update

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

.env.example

@@ -8,8 +8,12 @@ OPENAI_BASE_URL=http://6.86.80.4:30080/v1
 OPENAI_TIMEOUT_SECONDS=180
 
 # 默认评测模型（可在场景 YAML 或 Web 控制台 LLM 配置中覆盖）
-RAGAS_JUDGE_MODEL=deepseek-v4-flash
+# RAGAS_JUDGE_MODEL 需支持 OpenAI 兼容 chat.completions + 结构化 JSON 输出
-RAGAS_EMBEDDING_MODEL=text-embedding-v3
+# RAGAS_LLM_MAX_TOKENS 控制 Judge 评分链路的 completion budget；faithfulness 等
+# 结构化指标在 GPT-5 系列上通常需要 4096 或更高，避免 IncompleteOutputException
+RAGAS_JUDGE_MODEL=gpt-5
+RAGAS_EMBEDDING_MODEL=text-embedding-3-small
+RAGAS_LLM_MAX_TOKENS=4096
 
 # 评估并发控制（启用 7 个指标时建议 RAGAS_METRIC_TIMEOUT_SECONDS=300）
 BATCH_SIZE=8
@@ -30,3 +34,8 @@ PARSER_FAILURE_MODE=fail
 
 # 生成题库时使用的模型（可在 Web 控制台 LLM 配置中按场景覆盖）
 DATASET_GENERATOR_MODEL=qwen3.6-plus
+
+# ===== Dify 集成 — 实时评分 API =====
+# 为 /api/score 端点设置 Bearer Token 鉴权（留空则不鉴权，适合内网部署）
+# Dify 外部 Tool 配置 Authorization: Bearer <此处填写相同值>
+SCORE_API_TOKEN=

.gitattributes

@@ -0,0 +1,26 @@
+# 默认：文本文件使用 LF（Linux/macOS 风格）
+* text=auto eol=lf
+
+# Shell 脚本强制 LF，无论在哪个平台 checkout
+*.sh text eol=lf
+
+# Python 和 YAML 也用 LF
+*.py text eol=lf
+*.yaml text eol=lf
+*.yml text eol=lf
+*.md text eol=lf
+*.json text eol=lf
+*.toml text eol=lf
+*.txt text eol=lf
+*.env text eol=lf
+*.env.example text eol=lf
+
+# Windows 脚本保留 CRLF
+*.ps1 text eol=crlf
+*.bat text eol=crlf
+
+# 二进制文件不转换
+*.pdf binary
+*.png binary
+*.jpg binary
+*.csv binary

.idea/.gitignore

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

.idea/misc.xml

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

.idea/modules.xml

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

.idea/siemens_ragas.iml

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

.idea/vcs.xml

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

deploy.sh

@@ -0,0 +1,173 @@
+#!/usr/bin/env bash
+# deploy.sh — Siemens RAGAS 一键部署脚本（Linux）
+# 用法：bash deploy.sh
+# 功能：检查环境 → 安装依赖 → 初始化配置 → 启动后台服务
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+# ── 颜色输出 ──────────────────────────────────────────────────────
+if [ -t 1 ]; then
+    GREEN='\033[0;32m'; YELLOW='\033[1;33m'; RED='\033[0;31m'; CYAN='\033[0;36m'; NC='\033[0m'
+else
+    GREEN=''; YELLOW=''; RED=''; CYAN=''; NC=''
+fi
+
+ok()   { echo -e "${GREEN}[OK]${NC}    $*"; }
+warn() { echo -e "${YELLOW}[WARN]${NC}  $*"; }
+err()  { echo -e "${RED}[ERROR]${NC} $*" >&2; }
+info() { echo -e "${CYAN}[INFO]${NC}  $*"; }
+
+echo ""
+echo -e "${CYAN}============================================================${NC}"
+echo -e "${CYAN}  Siemens RAGAS Console  —  Linux 一键部署${NC}"
+echo -e "${CYAN}============================================================${NC}"
+echo ""
+
+# ── 阶段 1：Python 版本检查 ───────────────────────────────────────
+info "阶段 1/7：检查 Python 版本..."
+
+PYTHON_BIN=""
+for candidate in python3.12 python3.13 python3.14 python3; do
+    if command -v "$candidate" &>/dev/null; then
+        version=$("$candidate" -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')" 2>/dev/null || true)
+        major=$(echo "$version" | cut -d. -f1)
+        minor=$(echo "$version" | cut -d. -f2)
+        if [ "${major:-0}" -ge 3 ] && [ "${minor:-0}" -ge 12 ]; then
+            PYTHON_BIN="$candidate"
+            ok "Python $version ($candidate)"
+            break
+        fi
+    fi
+done
+
+if [ -z "$PYTHON_BIN" ]; then
+    err "未找到 Python 3.12+。请安装后重试。"
+    err "  Ubuntu/Debian: sudo apt install python3.12 python3.12-venv"
+    err "  CentOS/RHEL:   sudo dnf install python3.12"
+    exit 1
+fi
+
+# ── 阶段 2：虚拟环境 ──────────────────────────────────────────────
+info "阶段 2/7：准备虚拟环境..."
+
+if [ -d ".venv" ] && [ -f ".venv/bin/python" ]; then
+    ok ".venv 已存在，跳过创建"
+else
+    info "创建 .venv..."
+    "$PYTHON_BIN" -m venv .venv
+    ok ".venv 创建完成"
+fi
+
+PIP=".venv/bin/pip"
+PYTHON=".venv/bin/python"
+
+# ── 阶段 3：安装依赖 ──────────────────────────────────────────────
+info "阶段 3/7：安装项目依赖（可能需要几分钟）..."
+
+"$PIP" install --upgrade pip -q
+ok "pip 已升级"
+
+"$PIP" install -e . -q
+ok "项目依赖安装完成（pyproject.toml）"
+
+"$PIP" install fastapi uvicorn httpx -q
+ok "Web 服务依赖安装完成（fastapi / uvicorn / httpx）"
+
+# ── 阶段 4：配置文件 ──────────────────────────────────────────────
+info "阶段 4/7：初始化配置文件..."
+
+if [ ! -f ".env" ]; then
+    cp .env.example .env
+    warn ".env 已从 .env.example 复制，请编辑填写实际的 API Key 等配置后再启动："
+    warn "  nano .env   或   vim .env"
+    warn "  关键字段：OPENAI_API_KEY, OPENAI_BASE_URL, ALIBABA_ACCESS_KEY_ID, ALIBABA_ACCESS_KEY_SECRET"
+else
+    ok ".env 已存在，跳过"
+fi
+
+# ── 阶段 5：目录初始化 ────────────────────────────────────────────
+info "阶段 5/7：初始化目录结构..."
+
+mkdir -p configs logs outputs datasets
+ok "目录就绪：configs/ logs/ outputs/ datasets/"
+
+# 确保其他脚本有执行权限
+for script in start.sh stop.sh run_eval.sh; do
+    [ -f "$script" ] && chmod +x "$script"
+done
+ok "辅助脚本已设置执行权限"
+
+# ── 阶段 6：Demo 数据 ─────────────────────────────────────────────
+info "阶段 6/7：初始化演示数据..."
+
+DEMO_DIR="outputs/kba-knowledge-base-offline-baseline"
+if [ -d "$DEMO_DIR" ]; then
+    ok "演示数据已存在，跳过"
+else
+    info "生成演示数据（scripts/seed_sample_run.py）..."
+    if "$PYTHON" scripts/seed_sample_run.py; then
+        ok "演示数据生成完成"
+    else
+        warn "演示数据生成失败，控制台报告页将为空（服务仍可正常启动）"
+    fi
+fi
+
+# ── 阶段 7：启动服务 ──────────────────────────────────────────────
+info "阶段 7/7：启动 Web 服务..."
+
+# 检查 .env 是否包含默认占位符
+if grep -q "your-api-key" .env 2>/dev/null; then
+    warn ".env 中仍包含默认占位符，部分功能（评估执行）将不可用"
+    warn "请编辑 .env 后重新运行 start.sh"
+fi
+
+# 端口检测
+PORT=8800
+if ss -tlnp 2>/dev/null | grep -q ":$PORT " || netstat -tlnp 2>/dev/null | grep -q ":$PORT "; then
+    warn "端口 $PORT 已被占用，尝试 8801..."
+    PORT=8801
+    if ss -tlnp 2>/dev/null | grep -q ":$PORT " || netstat -tlnp 2>/dev/null | grep -q ":$PORT "; then
+        err "端口 8800 和 8801 均被占用。请手动运行："
+        err "  .venv/bin/python webmain.py --host 0.0.0.0 --port <PORT>"
+        exit 1
+    fi
+fi
+
+# 清理残留 PID
+if [ -f ".server.pid" ]; then
+    OLD_PID=$(cat .server.pid)
+    if kill -0 "$OLD_PID" 2>/dev/null; then
+        warn "检测到已有服务进程 (PID=$OLD_PID)，停止旧进程..."
+        kill "$OLD_PID" 2>/dev/null || true
+        sleep 1
+    fi
+    rm -f .server.pid
+fi
+
+# 后台启动
+nohup "$PYTHON" webmain.py --host 0.0.0.0 --port "$PORT" >> logs/server.log 2>&1 &
+SERVER_PID=$!
+echo "$SERVER_PID" > .server.pid
+
+# 等待 3 秒验证进程存活
+sleep 3
+if kill -0 "$SERVER_PID" 2>/dev/null; then
+    ok "服务已启动 (PID=$SERVER_PID)"
+    echo ""
+    echo -e "${CYAN}============================================================${NC}"
+    echo -e "${GREEN}  部署成功！${NC}"
+    echo -e "${GREEN}  访问地址: http://$(hostname -I | awk '{print $1}'):${PORT}${NC}"
+    echo -e "${GREEN}  本机访问: http://127.0.0.1:${PORT}${NC}"
+    echo -e "${CYAN}  服务日志: tail -f logs/server.log${NC}"
+    echo -e "${CYAN}  停止服务: bash stop.sh${NC}"
+    echo -e "${CYAN}============================================================${NC}"
+    echo ""
+else
+    err "服务启动失败，请查看日志："
+    err "  tail -20 logs/server.log"
+    rm -f .server.pid
+    exit 1
+fi

docs/superpowers/plans/2026-06-22-dify-score-api.md

@@ -0,0 +1,974 @@
+# Dify 实时评分 API Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 新增 `POST /api/score` 端点，供 Dify 外部 Tool 调用，接受单条问答记录并同步返回 RAGAS 各指标得分。
+
+**Architecture:** 新增 `inline_scorer.py` 服务层封装 RAGAS 打分逻辑，以 `(judge_model, embedding_model)` 为 key 缓存 LLM 客户端；新增 `webapp/api/score.py` 路由；`ScoreRequest`/`ScoreResponse` 放入 `webapp/models.py`；`SCORE_API_TOKEN` 加入 `EvaluationSettings`。
+
+**Tech Stack:** Python 3.12, FastAPI, Pydantic v2, RAGAS 0.4.3, pytest
+
+## Global Constraints
+
+- Python 3.12+，PEP 8，4 空格缩进，类型注解必须
+- contexts 用 `context_separator`（默认 `" |||| "`）拆分为 list[str]
+- ground_truth 为可选；缺失时跳过 context_recall / factual_correctness / semantic_similarity / noise_sensitivity
+- SCORE_API_TOKEN 为空时不鉴权（内网部署场景）
+- 所有测试用 pytest，不依赖真实 LLM
+
+---
+
+## 文件清单
+
+| 操作 | 文件 | 职责 |
+|------|------|------|
+| 新建 | `webapp/services/inline_scorer.py` | LLM 客户端缓存 + 单题打分 |
+| 新建 | `webapp/api/score.py` | `/api/score` 路由 |
+| 新建 | `tests/webapp/test_score_api.py` | 端点测试（全 mock） |
+| 修改 | `webapp/models.py` | 新增 ScoreRequest / ScoreResponse |
+| 修改 | `rag_eval/settings.py` | 新增 score_api_token 字段 |
+| 修改 | `webapp/server.py` | 注册 score router，更新 OPENAPI_TAGS 和 description |
+
+---
+
+## Task 1: ScoreRequest / ScoreResponse 模型 + settings 字段
+
+**Files:**
+- Modify: `webapp/models.py`
+- Modify: `rag_eval/settings.py`
+- Test: `tests/webapp/test_score_api.py` (partial — model validation tests)
+
+**Interfaces:**
+- Produces:
+  - `ScoreRequest` Pydantic model（见下方字段）
+  - `ScoreResponse` Pydantic model
+  - `EvaluationSettings.score_api_token: str | None`
+
+- [ ] **Step 1: Write failing model-validation tests**
+
+Create `tests/webapp/test_score_api.py`:
+
+```python
+"""Tests for POST /api/score endpoint."""
+from __future__ import annotations
+
+import math
+import pytest
+from pydantic import ValidationError
+from webapp.models import ScoreRequest, ScoreResponse
+
+
+class TestScoreRequest:
+    def test_minimal_valid_request(self):
+        """Only required fields — question, answer, contexts."""
+        req = ScoreRequest(
+            question="What is CT?",
+            answer="CT is imaging.",
+            contexts="CT uses X-rays.",
+        )
+        assert req.question == "What is CT?"
+        assert req.contexts == "CT uses X-rays."
+        assert req.ground_truth is None
+        assert req.context_separator == " |||| "
+        assert req.metrics == ["faithfulness", "answer_relevancy", "context_recall", "context_precision"]
+
+    def test_contexts_split_by_separator(self):
+        """contexts_as_list() splits on context_separator."""
+        req = ScoreRequest(
+            question="q", answer="a",
+            contexts="ctx1 |||| ctx2 |||| ctx3",
+            context_separator=" |||| ",
+        )
+        assert req.contexts_as_list() == ["ctx1", "ctx2", "ctx3"]
+
+    def test_contexts_split_custom_separator(self):
+        req = ScoreRequest(
+            question="q", answer="a",
+            contexts="a---b---c",
+            context_separator="---",
+        )
+        assert req.contexts_as_list() == ["a", "b", "c"]
+
+    def test_contexts_split_single_item(self):
+        req = ScoreRequest(question="q", answer="a", contexts="only one")
+        assert req.contexts_as_list() == ["only one"]
+
+    def test_missing_question_raises(self):
+        with pytest.raises(ValidationError):
+            ScoreRequest(answer="a", contexts="c")  # type: ignore[call-arg]
+
+    def test_missing_answer_raises(self):
+        with pytest.raises(ValidationError):
+            ScoreRequest(question="q", contexts="c")  # type: ignore[call-arg]
+
+    def test_missing_contexts_raises(self):
+        with pytest.raises(ValidationError):
+            ScoreRequest(question="q", answer="a")  # type: ignore[call-arg]
+
+    def test_custom_metrics_accepted(self):
+        req = ScoreRequest(
+            question="q", answer="a", contexts="c",
+            metrics=["faithfulness"],
+        )
+        assert req.metrics == ["faithfulness"]
+
+    def test_invalid_metric_name_raises(self):
+        with pytest.raises(ValidationError):
+            ScoreRequest(question="q", answer="a", contexts="c", metrics=["not_a_metric"])
+
+    def test_effective_metrics_drops_ground_truth_dependent_when_missing(self):
+        """Without ground_truth, GT-dependent metrics are excluded."""
+        req = ScoreRequest(
+            question="q", answer="a", contexts="c",
+            metrics=["faithfulness", "context_recall", "factual_correctness", "semantic_similarity", "noise_sensitivity"],
+        )
+        effective = req.effective_metrics()
+        assert "faithfulness" in effective
+        assert "context_recall" not in effective
+        assert "factual_correctness" not in effective
+        assert "semantic_similarity" not in effective
+        assert "noise_sensitivity" not in effective
+
+    def test_effective_metrics_keeps_all_when_ground_truth_present(self):
+        req = ScoreRequest(
+            question="q", answer="a", contexts="c", ground_truth="gt",
+            metrics=["faithfulness", "context_recall", "factual_correctness"],
+        )
+        effective = req.effective_metrics()
+        assert effective == ["faithfulness", "context_recall", "factual_correctness"]
+
+
+class TestScoreResponse:
+    def test_score_response_structure(self):
+        resp = ScoreResponse(
+            scores={"faithfulness": 0.85, "answer_relevancy": None},
+            weighted_score=0.85,
+            latency_ms=1200,
+        )
+        assert resp.scores["faithfulness"] == 0.85
+        assert resp.scores["answer_relevancy"] is None
+        assert resp.latency_ms == 1200
+```
+
+- [ ] **Step 2: Run to verify FAIL**
+
+```
+cd C:\Projects\AIProjects\Siemens-AIPOC\siemens_ragas
+python -m pytest tests/webapp/test_score_api.py::TestScoreRequest tests/webapp/test_score_api.py::TestScoreResponse -v
+```
+Expected: `ImportError: cannot import name 'ScoreRequest' from 'webapp.models'`
+
+- [ ] **Step 3: Add ScoreRequest and ScoreResponse to `webapp/models.py`**
+
+Append to the end of `webapp/models.py` (after `PipelineJobResponse`):
+
+```python
+# ---------------------------------------------------------------------------
+# Dify 实时评分 API 模型
+# ---------------------------------------------------------------------------
+
+# 需要 ground_truth 才能计算的指标集合
+_GT_DEPENDENT_METRICS: frozenset[str] = frozenset({
+    "context_recall",
+    "factual_correctness",
+    "semantic_similarity",
+    "noise_sensitivity",
+})
+
+# 所有合法指标名称
+_VALID_METRICS: frozenset[str] = frozenset({
+    "faithfulness",
+    "answer_relevancy",
+    "context_recall",
+    "context_precision",
+    "noise_sensitivity",
+    "factual_correctness",
+    "semantic_similarity",
+})
+
+_DEFAULT_SCORE_METRICS: list[str] = [
+    "faithfulness",
+    "answer_relevancy",
+    "context_recall",
+    "context_precision",
+]
+
+
+class ScoreRequest(BaseModel):
+    """Request body for the real-time single-sample scoring endpoint."""
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "examples": [
+                {
+                    "summary": "基础评分请求",
+                    "value": {
+                        "question": "双源CT的时间分辨率是多少?",
+                        "answer": "双源CT的单扇区时间分辨率为75ms。",
+                        "contexts": "双源CT采用两套管-探测器系统 |||| 单扇区采集旋转135度",
+                        "ground_truth": "双源CT单扇区时间分辨率为75ms，需旋转135度。",
+                        "context_separator": " |||| ",
+                        "metrics": ["faithfulness", "answer_relevancy", "context_recall", "context_precision"],
+                        "judge_model": "deepseek-v4-flash",
+                        "embedding_model": "text-embedding-v3",
+                    },
+                }
+            ]
+        }
+    )
+
+    question: str = Field(description="问题文本。")
+    answer: str = Field(description="待评分的回答。")
+    contexts: str = Field(
+        description="检索上下文字符串，多段之间用 context_separator 拼接。"
+    )
+    ground_truth: str | None = Field(
+        default=None,
+        description="标准参考答案（可选）。缺失时自动跳过需要它的指标。",
+    )
+    context_separator: str = Field(
+        default=" |||| ",
+        description="contexts 字段中段落分隔符，默认为四个竖线两侧各一空格。",
+    )
+    metrics: list[str] = Field(
+        default_factory=lambda: list(_DEFAULT_SCORE_METRICS),
+        description="需要计算的 RAGAS 指标列表。",
+    )
+    judge_model: str | None = Field(
+        default=None,
+        description="Judge LLM 模型名称；为 null 时使用 .env 中的 RAGAS_JUDGE_MODEL。",
+    )
+    embedding_model: str | None = Field(
+        default=None,
+        description="Embedding 模型名称；为 null 时使用 .env 中的 RAGAS_EMBEDDING_MODEL。",
+    )
+
+    @field_validator("metrics")
+    @classmethod
+    def validate_metric_names(cls, value: list[str]) -> list[str]:
+        """Reject any metric name not in the supported registry."""
+        invalid = [m for m in value if m not in _VALID_METRICS]
+        if invalid:
+            raise ValueError(
+                f"不支持的指标名称：{invalid}。"
+                f"合法值：{sorted(_VALID_METRICS)}"
+            )
+        if not value:
+            raise ValueError("metrics 不能为空列表。")
+        return value
+
+    def contexts_as_list(self) -> list[str]:
+        """Split the contexts string into a list of non-empty fragments."""
+        sep = self.context_separator or " |||| "
+        return [s.strip() for s in self.contexts.split(sep) if s.strip()]
+
+    def effective_metrics(self) -> list[str]:
+        """Return metrics filtered to exclude GT-dependent ones when ground_truth is absent."""
+        if self.ground_truth is not None:
+            return list(self.metrics)
+        return [m for m in self.metrics if m not in _GT_DEPENDENT_METRICS]
+
+
+class ScoreResponse(BaseModel):
+    """Response payload for the real-time scoring endpoint."""
+
+    scores: dict[str, float | None] = Field(
+        description="各指标得分（NaN 或计算失败时为 null）。"
+    )
+    weighted_score: float | None = Field(
+        default=None,
+        description="等权加权综合得分（仅对非 null 指标求均值）。",
+    )
+    latency_ms: int = Field(description="服务端打分耗时（毫秒）。")
+    skipped_metrics: list[str] = Field(
+        default_factory=list,
+        description="因缺少 ground_truth 而跳过的指标名称列表。",
+    )
+    error: str | None = Field(
+        default=None,
+        description="打分异常时的错误信息（HTTP 200 仍返回，scores 为空）。",
+    )
+```
+
+Also add `field_validator` to the import line at the top of `webapp/models.py`:
+```python
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+```
+
+- [ ] **Step 4: Add `score_api_token` to `rag_eval/settings.py`**
+
+Add after the `dataset_generator_model` field:
+```python
+score_api_token: str | None = Field(
+    default=None,
+    alias="SCORE_API_TOKEN",
+    description="Bearer token for /api/score endpoint. Empty = no auth.",
+)
+```
+
+- [ ] **Step 5: Run to verify PASS**
+
+```
+python -m pytest tests/webapp/test_score_api.py::TestScoreRequest tests/webapp/test_score_api.py::TestScoreResponse -v
+```
+Expected: all 12 tests PASS.
+
+- [ ] **Step 6: Commit**
+
+```
+git add webapp/models.py rag_eval/settings.py tests/webapp/test_score_api.py
+git commit -m "feat: add ScoreRequest/ScoreResponse models and SCORE_API_TOKEN setting"
+```
+
+---
+
+## Task 2: InlineScorer 服务（LLM 缓存 + 打分）
+
+**Files:**
+- Create: `webapp/services/inline_scorer.py`
+
+**Interfaces:**
+- Consumes:
+  - `build_models(judge_model, embedding_model, settings) -> tuple[Any, Any]` from `rag_eval.metrics.factory`
+  - `MetricPipeline(metrics, metric_timeout_seconds)` from `rag_eval.metrics.pipeline`
+  - `NormalizedSample` from `rag_eval.shared.models`
+  - `compute_weighted_score(scores, metric_weights) -> float | None` from `rag_eval.metrics.weights`
+  - `EvaluationSettings` from `rag_eval.settings`
+- Produces:
+  - `inline_scorer: InlineScorer` (module-level singleton)
+  - `InlineScorer.score(question, answer, contexts, ground_truth, metrics, judge_model, embedding_model, settings) -> dict[str, float | None]`
+
+- [ ] **Step 1: Write failing test**
+
+Add to `tests/webapp/test_score_api.py`:
+
+```python
+class TestInlineScorer:
+    def test_score_returns_dict_with_requested_metrics(self):
+        """InlineScorer.score returns a dict keyed by the requested metrics."""
+        from unittest.mock import AsyncMock, MagicMock, patch
+        from webapp.services.inline_scorer import InlineScorer
+        from rag_eval.settings import EvaluationSettings
+
+        mock_score = MagicMock()
+        mock_score.metrics = {"faithfulness": 0.9, "answer_relevancy": 0.8}
+        mock_score.error = ""
+
+        mock_pipeline = MagicMock()
+        mock_pipeline.score_sample = AsyncMock(return_value=mock_score)
+
+        with patch("webapp.services.inline_scorer.build_models", return_value=(MagicMock(), MagicMock())):
+            with patch("webapp.services.inline_scorer.MetricPipeline", return_value=mock_pipeline):
+                with patch("webapp.services.inline_scorer._build_metric_instances", return_value={}):
+                    scorer = InlineScorer()
+                    result = scorer.score(
+                        question="q", answer="a",
+                        contexts=["ctx1"],
+                        ground_truth=None,
+                        metrics=["faithfulness", "answer_relevancy"],
+                        judge_model="test-model",
+                        embedding_model="test-embed",
+                        settings=EvaluationSettings(_env_file=None),
+                    )
+        assert "faithfulness" in result
+        assert "answer_relevancy" in result
+        assert result["faithfulness"] == pytest.approx(0.9)
+
+    def test_score_converts_nan_to_none(self):
+        """NaN scores are converted to None in the returned dict."""
+        import math
+        from unittest.mock import AsyncMock, MagicMock, patch
+        from webapp.services.inline_scorer import InlineScorer
+        from rag_eval.settings import EvaluationSettings
+
+        mock_score = MagicMock()
+        mock_score.metrics = {"faithfulness": float("nan")}
+        mock_score.error = ""
+
+        mock_pipeline = MagicMock()
+        mock_pipeline.score_sample = AsyncMock(return_value=mock_score)
+
+        with patch("webapp.services.inline_scorer.build_models", return_value=(MagicMock(), MagicMock())):
+            with patch("webapp.services.inline_scorer.MetricPipeline", return_value=mock_pipeline):
+                with patch("webapp.services.inline_scorer._build_metric_instances", return_value={}):
+                    scorer = InlineScorer()
+                    result = scorer.score(
+                        question="q", answer="a", contexts=["c"],
+                        ground_truth=None,
+                        metrics=["faithfulness"],
+                        judge_model="m", embedding_model="e",
+                        settings=EvaluationSettings(_env_file=None),
+                    )
+        assert result["faithfulness"] is None
+```
+
+- [ ] **Step 2: Run to verify FAIL**
+
+```
+python -m pytest tests/webapp/test_score_api.py::TestInlineScorer -v
+```
+Expected: `ModuleNotFoundError: No module named 'webapp.services.inline_scorer'`
+
+- [ ] **Step 3: Create `webapp/services/inline_scorer.py`**
+
+```python
+"""LLM-cached inline RAGAS scorer for the real-time /api/score endpoint.
+
+A module-level InlineScorer singleton caches (llm, embeddings) pairs keyed by
+(judge_model, embedding_model), so repeated Dify Tool calls with the same
+models reuse existing AsyncOpenAI connections instead of creating new ones.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import math
+import threading
+from typing import Any
+
+from rag_eval.compat import ensure_ragas_import_compat
+from rag_eval.metrics.factory import build_models
+from rag_eval.metrics.pipeline import MetricPipeline
+from rag_eval.metrics.weights import compute_weighted_score
+from rag_eval.settings import EvaluationSettings
+from rag_eval.shared.models import NormalizedSample
+
+ensure_ragas_import_compat()
+
+from ragas.metrics.collections import (  # noqa: E402
+    AnswerRelevancy,
+    ContextPrecision,
+    ContextRecall,
+    FactualCorrectness,
+    Faithfulness,
+    NoiseSensitivity,
+    SemanticSimilarity,
+)
+
+
+def _build_metric_instances(metrics: list[str], llm: Any, embeddings: Any) -> dict[str, Any]:
+    """Instantiate only the RAGAS metric objects requested."""
+    registry: dict[str, Any] = {
+        "faithfulness": Faithfulness(llm=llm),
+        "answer_relevancy": AnswerRelevancy(llm=llm, embeddings=embeddings),
+        "context_recall": ContextRecall(llm=llm),
+        "context_precision": ContextPrecision(llm=llm),
+        "noise_sensitivity": NoiseSensitivity(llm=llm),
+        "factual_correctness": FactualCorrectness(llm=llm),
+        "semantic_similarity": SemanticSimilarity(embeddings=embeddings),
+    }
+    return {name: registry[name] for name in metrics if name in registry}
+
+
+class InlineScorer:
+    """Thread-safe single-sample RAGAS scorer with LLM client caching."""
+
+    def __init__(self) -> None:
+        # Cache keyed by (judge_model, embedding_model) -> (llm, embeddings)
+        self._model_cache: dict[tuple[str, str], tuple[Any, Any]] = {}
+        self._lock = threading.Lock()
+
+    def _get_models(
+        self,
+        judge_model: str,
+        embedding_model: str,
+        settings: EvaluationSettings,
+    ) -> tuple[Any, Any]:
+        """Return cached LLM/embedding clients, building them on first use."""
+        cache_key = (judge_model, embedding_model)
+        with self._lock:
+            if cache_key not in self._model_cache:
+                llm, embeddings = build_models(judge_model, embedding_model, settings)
+                self._model_cache[cache_key] = (llm, embeddings)
+            return self._model_cache[cache_key]
+
+    def score(
+        self,
+        question: str,
+        answer: str,
+        contexts: list[str],
+        ground_truth: str | None,
+        metrics: list[str],
+        judge_model: str,
+        embedding_model: str,
+        settings: EvaluationSettings,
+    ) -> dict[str, float | None]:
+        """Score one sample synchronously and return {metric_name: score | None}.
+
+        NaN values from RAGAS are converted to None for clean JSON serialization.
+        """
+        llm, embeddings = self._get_models(judge_model, embedding_model, settings)
+        metric_instances = _build_metric_instances(metrics, llm, embeddings)
+
+        pipeline = MetricPipeline(
+            metrics=metric_instances,
+            metric_timeout_seconds=settings.ragas_metric_timeout_seconds,
+        )
+
+        sample = NormalizedSample(
+            sample_id="inline-score",
+            question=question,
+            answer=answer,
+            contexts=contexts,
+            ground_truth=ground_truth or "",
+        )
+
+        metric_score = asyncio.run(pipeline.score_sample(sample))
+
+        # Convert NaN → None for clean JSON output
+        return {
+            name: (None if math.isnan(v) or math.isinf(v) else round(v, 4))
+            for name, v in metric_score.metrics.items()
+        }
+
+
+# Module-level singleton shared by FastAPI routes.
+inline_scorer = InlineScorer()
+```
+
+- [ ] **Step 4: Run to verify PASS**
+
+```
+python -m pytest tests/webapp/test_score_api.py::TestInlineScorer -v
+```
+Expected: both tests PASS.
+
+- [ ] **Step 5: Commit**
+
+```
+git add webapp/services/inline_scorer.py tests/webapp/test_score_api.py
+git commit -m "feat: add InlineScorer service with LLM client caching"
+```
+
+---
+
+## Task 3: `/api/score` 路由 + 鉴权 + 集成测试
+
+**Files:**
+- Create: `webapp/api/score.py`
+- Modify: `webapp/server.py`
+
+**Interfaces:**
+- Consumes:
+  - `ScoreRequest`, `ScoreResponse` from `webapp.models`
+  - `inline_scorer: InlineScorer` from `webapp.services.inline_scorer`
+  - `EvaluationSettings` from `rag_eval.settings`
+  - `compute_weighted_score(scores, {}) -> float | None` from `rag_eval.metrics.weights`
+- Produces: `POST /api/score` endpoint
+
+- [ ] **Step 1: Write failing endpoint tests**
+
+Add to `tests/webapp/test_score_api.py`:
+
+```python
+# ── Fixtures ─────────────────────────────────────────────────────────────────
+import pytest
+from fastapi.testclient import TestClient
+from unittest.mock import MagicMock, patch
+
+
+@pytest.fixture()
+def client(monkeypatch):
+    """TestClient with mocked InlineScorer."""
+    import webapp.api.score as score_mod
+
+    mock_scorer = MagicMock()
+    mock_scorer.score.return_value = {
+        "faithfulness": 0.85,
+        "answer_relevancy": 0.90,
+    }
+    monkeypatch.setattr(score_mod, "inline_scorer", mock_scorer)
+
+    from webapp.server import create_app
+    return TestClient(create_app())
+
+
+class TestScoreEndpoint:
+    def test_post_score_returns_200(self, client):
+        resp = client.post("/api/score", json={
+            "question": "What is CT?",
+            "answer": "CT is imaging.",
+            "contexts": "CT uses X-rays.",
+        })
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "scores" in data
+        assert "latency_ms" in data
+        assert data["scores"]["faithfulness"] == pytest.approx(0.85)
+
+    def test_weighted_score_computed(self, client):
+        resp = client.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+        })
+        assert resp.status_code == 200
+        data = resp.json()
+        # weighted_score is the mean of all non-null scores
+        assert data["weighted_score"] is not None
+
+    def test_missing_required_fields_returns_422(self, client):
+        resp = client.post("/api/score", json={"question": "q"})
+        assert resp.status_code == 422
+
+    def test_invalid_metric_name_returns_422(self, client):
+        resp = client.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+            "metrics": ["not_a_metric"],
+        })
+        assert resp.status_code == 422
+
+    def test_skipped_metrics_returned_when_no_ground_truth(self, client):
+        resp = client.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+            "metrics": ["faithfulness", "context_recall"],
+        })
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "context_recall" in data["skipped_metrics"]
+
+    def test_contexts_split_on_separator(self, client, monkeypatch):
+        """contexts string is split before passing to scorer."""
+        import webapp.api.score as score_mod
+        calls = []
+        def capture(*args, **kwargs):
+            calls.append(kwargs.get("contexts", []))
+            return {"faithfulness": 0.9}
+        monkeypatch.setattr(score_mod.inline_scorer, "score", capture)
+
+        client.post("/api/score", json={
+            "question": "q", "answer": "a",
+            "contexts": "ctx1 |||| ctx2",
+            "context_separator": " |||| ",
+        })
+        assert calls[0] == ["ctx1", "ctx2"]
+
+    def test_bearer_token_auth_required_when_configured(self, monkeypatch):
+        """When SCORE_API_TOKEN is set, requests without token get 401."""
+        import webapp.api.score as score_mod
+        from rag_eval.settings import EvaluationSettings
+
+        mock_settings = EvaluationSettings(_env_file=None)
+        object.__setattr__(mock_settings, "score_api_token", "secret-token")
+        monkeypatch.setattr(score_mod, "_get_settings", lambda: mock_settings)
+
+        mock_scorer = MagicMock()
+        mock_scorer.score.return_value = {"faithfulness": 0.9}
+        monkeypatch.setattr(score_mod, "inline_scorer", mock_scorer)
+
+        from webapp.server import create_app
+        test_client = TestClient(create_app())
+
+        # No auth header → 401
+        resp = test_client.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+        })
+        assert resp.status_code == 401
+
+        # Correct token → 200
+        resp = test_client.post("/api/score",
+            json={"question": "q", "answer": "a", "contexts": "c"},
+            headers={"Authorization": "Bearer secret-token"},
+        )
+        assert resp.status_code == 200
+
+    def test_wrong_bearer_token_returns_401(self, monkeypatch):
+        import webapp.api.score as score_mod
+        from rag_eval.settings import EvaluationSettings
+
+        mock_settings = EvaluationSettings(_env_file=None)
+        object.__setattr__(mock_settings, "score_api_token", "correct-token")
+        monkeypatch.setattr(score_mod, "_get_settings", lambda: mock_settings)
+
+        mock_scorer = MagicMock()
+        mock_scorer.score.return_value = {}
+        monkeypatch.setattr(score_mod, "inline_scorer", mock_scorer)
+
+        from webapp.server import create_app
+        test_client = TestClient(create_app())
+        resp = test_client.post("/api/score",
+            json={"question": "q", "answer": "a", "contexts": "c"},
+            headers={"Authorization": "Bearer wrong-token"},
+        )
+        assert resp.status_code == 401
+```
+
+- [ ] **Step 2: Run to verify FAIL**
+
+```
+python -m pytest tests/webapp/test_score_api.py::TestScoreEndpoint -v
+```
+Expected: `ModuleNotFoundError: No module named 'webapp.api.score'`
+
+- [ ] **Step 3: Create `webapp/api/score.py`**
+
+```python
+"""Route for real-time single-sample RAGAS scoring (Dify external Tool endpoint)."""
+
+from __future__ import annotations
+
+import time
+
+from fastapi import APIRouter, Header, HTTPException
+from typing import Annotated
+
+from rag_eval.metrics.weights import compute_weighted_score
+from rag_eval.settings import EvaluationSettings
+from webapp.models import ScoreRequest, ScoreResponse
+from webapp.services.inline_scorer import inline_scorer
+
+router = APIRouter(prefix="/api/score", tags=["score"])
+
+
+def _get_settings() -> EvaluationSettings:
+    """Return a fresh EvaluationSettings instance (overridable in tests)."""
+    return EvaluationSettings()
+
+
+def _check_auth(authorization: str | None, token: str) -> None:
+    """Raise 401 if Bearer token does not match the configured token."""
+    if authorization is None:
+        raise HTTPException(status_code=401, detail="Missing Authorization header.")
+    parts = authorization.split(" ", 1)
+    if len(parts) != 2 or parts[0].lower() != "bearer" or parts[1] != token:
+        raise HTTPException(status_code=401, detail="Invalid Bearer token.")
+
+
+@router.post(
+    "",
+    response_model=ScoreResponse,
+    summary="单题实时评分（Dify 外部 Tool）",
+    responses={
+        200: {"description": "各指标得分和加权综合得分。"},
+        401: {"description": "配置了 SCORE_API_TOKEN 但未提供有效 Bearer token。"},
+        422: {"description": "请求参数校验失败。"},
+    },
+)
+def score_sample(
+    request: ScoreRequest,
+    authorization: Annotated[str | None, Header()] = None,
+) -> ScoreResponse:
+    """接受单条问答记录，同步运行 RAGAS 指标打分，实时返回各指标得分。
+
+    供 Dify 外部 Tool 调用。将 `contexts` 字段按 `context_separator` 拆分后传入
+    RAGAS 管道；`ground_truth` 缺失时自动跳过依赖它的指标。
+    """
+    settings = _get_settings()
+
+    # 鉴权（仅在配置了 token 时生效）
+    if settings.score_api_token:
+        _check_auth(authorization, settings.score_api_token)
+
+    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))
+
+    if not effective:
+        # All requested metrics require ground_truth which is absent.
+        return ScoreResponse(
+            scores={m: None for m in request.metrics},
+            weighted_score=None,
+            latency_ms=0,
+            skipped_metrics=skipped,
+        )
+
+    t0 = time.monotonic()
+    try:
+        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,
+        )
+    except Exception as exc:  # noqa: BLE001
+        latency_ms = int((time.monotonic() - t0) * 1000)
+        return ScoreResponse(
+            scores={},
+            weighted_score=None,
+            latency_ms=latency_ms,
+            skipped_metrics=skipped,
+            error=f"{type(exc).__name__}: {exc}",
+        )
+
+    latency_ms = int((time.monotonic() - t0) * 1000)
+
+    # Merge: skipped metrics appear as null in final scores dict.
+    all_scores: dict[str, float | None] = {m: None for m in request.metrics}
+    all_scores.update(raw_scores)
+
+    # Weighted score = equal-weight mean of non-null effective scores.
+    weighted = compute_weighted_score(
+        {k: v for k, v in raw_scores.items() if v is not None},
+        {},
+    )
+
+    return ScoreResponse(
+        scores=all_scores,
+        weighted_score=round(weighted, 4) if weighted is not None else None,
+        latency_ms=latency_ms,
+        skipped_metrics=skipped,
+    )
+```
+
+- [ ] **Step 4: Register router in `webapp/server.py`**
+
+Add `score` to the import line:
+```python
+from webapp.api import evaluations, llm_profiles, pipeline, runs, scenarios, score
+```
+
+Add the router registration after `pipeline.router`:
+```python
+app.include_router(score.router)
+```
+
+Add `"score"` tag to `OPENAPI_TAGS` list (insert before `"meta"`):
+```python
+    {
+        "name": "score",
+        "description": (
+            "**实时评分 API（Dify 外部 Tool）**\n\n"
+            "接受单条问答记录 `(question, answer, contexts, ground_truth)`，\n"
+            "同步运行 RAGAS 指标打分，返回各指标得分和加权综合得分。\n\n"
+            "适用场景：Dify Agent 在回答后即时调用，用于质量监控或自我改进。\n\n"
+            "**鉴权**：若 `.env` 中配置了 `SCORE_API_TOKEN`，需携带 "
+            "`Authorization: Bearer <token>` 请求头。"
+        ),
+    },
+```
+
+Also update the `description` field in `FastAPI(...)` to add a bullet:
+```python
+"- **实时评分 API** — 供 Dify 外部 Tool 调用的单题 RAGAS 评分接口\n"
+```
+
+- [ ] **Step 5: Run to verify PASS**
+
+```
+python -m pytest tests/webapp/test_score_api.py -v
+```
+Expected: all tests PASS.
+
+- [ ] **Step 6: Verify server boots and route appears**
+
+```
+python -c "
+from webapp.server import create_app
+app = create_app()
+routes = [(r.path, list(getattr(r,'methods',[]))) for r in app.routes]
+score_routes = [(p,m) for p,m in routes if 'score' in p]
+print('Score routes:', score_routes)
+"
+```
+Expected output:
+```
+Score routes: [('/api/score', ['POST'])]
+```
+
+- [ ] **Step 7: Commit**
+
+```
+git add webapp/api/score.py webapp/server.py tests/webapp/test_score_api.py
+git commit -m "feat: add POST /api/score endpoint for Dify real-time scoring"
+```
+
+---
+
+## Task 4: 全量回归 + `.env.example` 更新
+
+**Files:**
+- Modify: `.env.example`
+
+- [ ] **Step 1: Add SCORE_API_TOKEN to `.env.example`**
+
+Add this block after `DATASET_GENERATOR_MODEL=qwen3.6-plus`:
+
+```
+# ===== Dify 集成 — 实时评分 API =====
+# 为 /api/score 端点设置 Bearer Token 鉴权（留空则不鉴权，适合内网部署）
+# Dify 外部 Tool 配置 Authorization: Bearer <此处填写相同值>
+SCORE_API_TOKEN=
+```
+
+- [ ] **Step 2: Run full test suite**
+
+```
+python -m pytest tests/ -v --tb=short
+```
+
+Pre-existing failures to ignore:
+- `test_normalize_sample_pdf_offline_smoke_row` — 缺少 CSV fixture
+- `test_evaluator_and_reporting_write_run_assets` — 预存在的断言不匹配
+- `test_question_generator_rejects_invalid_json` — retry 循环吞掉了 ValueError
+- `test_question_generator_rejects_non_list_samples` — 同上
+
+**零新增失败**即为通过。
+
+- [ ] **Step 3: Final commit**
+
+```
+git add .env.example
+git commit -m "feat: Dify score API complete — add SCORE_API_TOKEN to .env.example
+
+- POST /api/score: real-time RAGAS scoring for Dify external Tool
+- ScoreRequest/ScoreResponse Pydantic models with full field docs
+- InlineScorer with (judge_model, embedding_model) client cache
+- Bearer token auth via SCORE_API_TOKEN env var (optional)
+- contexts split by configurable separator (default ' |||| ')
+- GT-dependent metrics auto-skipped when ground_truth absent
+- Full test coverage (22 new tests)
+
+Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>"
+```
+
+---
+
+## Dify 侧配置参考
+
+任务完成后，在 Dify 「工具」→「自定义工具」中填写如下 OpenAPI Schema：
+
+```yaml
+openapi: 3.1.0
+info:
+  title: RAGAS 实时评分
+  version: 1.0.0
+servers:
+  - url: http://<your-server>:8800
+paths:
+  /api/score:
+    post:
+      operationId: scoreQA
+      summary: 对一条问答记录进行 RAGAS 评分
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              required: [question, answer, contexts]
+              properties:
+                question:       { type: string }
+                answer:         { type: string }
+                contexts:       { type: string, description: "多段上下文用 ' |||| ' 拼接" }
+                ground_truth:   { type: string }
+                metrics:
+                  type: array
+                  items: { type: string }
+                  default: [faithfulness, answer_relevancy, context_recall, context_precision]
+      responses:
+        '200':
+          description: 评分结果
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  scores:         { type: object }
+                  weighted_score: { type: number }
+                  latency_ms:     { type: integer }
+                  skipped_metrics: { type: array, items: { type: string } }
+```

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

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

docs/superpowers/specs/2026-06-18-metric-doc-weights-design.md

@@ -0,0 +1,240 @@
+# 指标权重 & 文档片段权重功能设计
+
+**日期**: 2026-06-18  
+**状态**: 已批准，待实现  
+**范围**: 在「新建评估」运行评估时，支持为 RAGAS 指标和文档配置权重，计算加权综合得分并在报告中展示。
+
+---
+
+## 1. 目标
+
+1. **指标权重（Metric Weights）**：允许为每个 RAGAS 指标配置浮点权重（如 faithfulness: 0.35），计算每道题的加权综合得分 `weighted_score`。
+2. **文档权重（Doc Weights）**：允许为特定 PDF 文档名称配置权重（如 `"322_双源CT.pdf": 2.0`），该文档的题目在汇总指标均值时按权重放大贡献。
+3. **前端覆盖**：在「新建评估」页面选中场景后，展示可编辑的权重面板，运行前可临时覆盖 YAML 中的权重。
+4. **完全向后兼容**：两个字段均为可选，省略时退化为等权行为，现有场景 YAML 无需修改。
+
+---
+
+## 2. 数据模型
+
+### 2.1 场景 YAML（新增可选字段）
+
+```yaml
+# 可选。缺省时所有指标权重 = 1.0
+metric_weights:
+  faithfulness: 0.35
+  context_recall: 0.25
+  context_precision: 0.20
+  answer_relevancy: 0.20
+
+# 可选。缺省时所有文档权重 = 1.0
+doc_weights:
+  "322_双源CT成像技术.pdf": 2.0
+  "323_单源CT对比.pdf": 1.5
+```
+
+### 2.2 Pydantic Schema（`rag_eval/config/schema.py`）
+
+`ScenarioModel` 新增：
+```python
+metric_weights: dict[str, float] = Field(default_factory=dict)
+doc_weights:    dict[str, float] = Field(default_factory=dict)
+```
+
+`ConfigDict(extra="ignore")` 不变，新字段不影响既有 YAML 的加载。
+
+### 2.3 内部 Scenario dataclass（`rag_eval/shared/models.py`）
+
+`Scenario` 新增：
+```python
+metric_weights: dict[str, float] = field(default_factory=dict)
+doc_weights:    dict[str, float] = field(default_factory=dict)
+```
+
+随 `scenario.snapshot()` 序列化，供 `run_reader` / 报告层读取。
+
+---
+
+## 3. 后端：权重计算逻辑
+
+### 3.1 新模块 `rag_eval/metrics/weights.py`
+
+纯函数模块，无外部依赖，独立可测：
+
+```python
+def resolve_weight(weights: dict[str, float], key: str, default: float = 1.0) -> float:
+    """返回 key 对应的权重，缺失时返回 default。"""
+
+def compute_weighted_score(
+    scores: dict[str, float | None],
+    metric_weights: dict[str, float],
+) -> float | None:
+    """
+    给定各指标得分和权重，返回加权综合得分。
+    - 忽略 NaN / None 值
+    - metric_weights 为空时退化为等权均值
+    - 全部 NaN 时返回 None
+    公式: Σ(w_i * s_i) / Σ(w_i)，只对非 NaN 项求和
+    """
+
+def weighted_metric_means(
+    score_rows: list[dict],
+    metrics: list[str],
+    doc_weights: dict[str, float],
+) -> dict[str, float | None]:
+    """
+    对每个指标计算文档加权均值。
+    - sample_weight = doc_weights.get(row["doc_name"], 1.0)
+    - 公式: Σ(sample_weight_j * score_m_j) / Σ(sample_weight_j)
+    - doc_weights 为空时退化为普通算术均值
+    """
+```
+
+### 3.2 评估器（`rag_eval/execution/evaluator.py`）
+
+`_merge_score()` 新增两列：
+```python
+record["weighted_score"] = compute_weighted_score(
+    score.metrics, self.scenario.metric_weights
+)
+record["sample_weight"] = self.scenario.doc_weights.get(
+    sample.metadata.get("doc_name", ""), 1.0
+)
+```
+
+`scores.csv` 新增 `weighted_score`、`sample_weight` 两列。
+
+### 3.3 报告摘要（`rag_eval/reporting/summary.py`）
+
+`build_summary_markdown()` 改用 `weighted_metric_means()` 计算各指标均值；
+新增 `weighted_score` 整体均值行：
+
+```
+## Metric Means（加权）
+- faithfulness:     0.8123  (w=0.35)
+- context_recall:   0.7654  (w=0.25)
+- context_precision: 0.7200  (w=0.20)
+- answer_relevancy: 0.7400  (w=0.20)
+- **weighted_score: 0.7789**
+```
+
+---
+
+## 4. yaml_patcher 扩展（`webapp/services/yaml_patcher.py`）
+
+`apply_profiles_to_scenario()` 扩展签名，新增可选参数：
+
+```python
+def apply_profiles_to_scenario(
+    scenario_path: str,
+    judge_profile: LLMProfile | None,
+    answer_profile: LLMProfile | None,
+    dataset_profile: LLMProfile | None,
+    metric_weights: dict[str, float] | None = None,   # 新增
+    doc_weights: dict[str, float] | None = None,       # 新增
+    _resolve_absolute: bool = False,
+) -> list[str]:
+```
+
+- `metric_weights` 非 None 时写入 `data["metric_weights"]`，追加 `"metric_weights"` 到 patched 列表
+- `doc_weights` 非 None 时写入 `data["doc_weights"]`，追加 `"doc_weights"` 到 patched 列表
+
+---
+
+## 5. Webapp 模型与 API 扩展
+
+### 5.1 `webapp/models.py`
+
+`ProfileApplyRequest` 新增：
+```python
+metric_weights: dict[str, float] | None = None
+doc_weights:    dict[str, float] | None = None
+```
+
+`ProfileApplyResponse` 不变（`patched_fields` 已包含新字段名）。
+
+### 5.2 `webapp/api/llm_profiles.py` — `apply_profiles()`
+
+透传 `metric_weights` / `doc_weights` 给 `apply_profiles_to_scenario()`。
+
+---
+
+## 6. 前端：权重配置面板
+
+### 6.1 HTML（`index.html`）
+
+在 `#llm-assignment-panel` 下方新增 `#weight-config-panel`（选中场景后显示）：
+
+```
+┌─────────────────────────────────────────────┐
+│ 权重配置  （可选，留空使用场景原始配置）         │
+├─────────────────────────────────────────────┤
+│ 指标权重                                     │
+│  faithfulness        [____1.0____]           │
+│  context_recall      [____1.0____]           │
+│  ...（根据选中场景的 metrics 动态生成）         │
+│                                              │
+│ 文档权重（doc_weights）                       │
+│  [doc名称_______________] [权重__] [＋] [✕]  │
+│  [doc名称_______________] [权重__] [＋] [✕]  │
+│  ＋ 添加文档权重规则                          │
+└─────────────────────────────────────────────┘
+```
+
+### 6.2 `runner.js`
+
+- `renderScenarioItem()` 选中后调用 `Runner._renderWeightPanel(sc)` 动态生成指标行
+- `_applyProfilesIfNeeded()` 同时读取权重输入，追加到 `apply` 请求 body
+- `Runner._collectWeights()` 收集 metric_weights / doc_weights，全部为 1.0 时不发送（跳过）
+
+### 6.3 CSS（`app.css`）
+
+新增 `.weight-config-panel`、`.weight-row`、`.weight-input` 样式，与现有 `.llm-role-row` 风格一致。
+
+---
+
+## 7. 报告展示（`webapp/services/report_builder.py`）
+
+- `RunSummary.metric_means` 改用 `weighted_metric_means()` 计算（需从 `scenario.snapshot.yaml` 读取 `doc_weights` / `metric_weights`）
+- `RunSummary` 新增 `weighted_score_mean: float | None` 字段
+- 前端 `report.js` 的指标卡片区新增「综合加权得分」卡片，使用 `good/warn/bad` 配色
+
+---
+
+## 8. 测试计划
+
+| 测试文件 | 覆盖内容 |
+|----------|---------|
+| `tests/test_weights.py` | `compute_weighted_score` / `weighted_metric_means` 纯函数，含 NaN 边界、空权重、全 NaN |
+| `tests/test_dataset_build.py` | 无改动（隔离良好） |
+| `tests/test_offline_eval.py` | `_merge_score` 新增 weighted_score / sample_weight 列断言 |
+| `tests/webapp/test_llm_profiles_api.py` | `apply_profiles` 带 metric_weights / doc_weights 的 patching 测试 |
+
+---
+
+## 9. 改动文件清单
+
+| 文件 | 改动类型 |
+|------|---------|
+| `rag_eval/config/schema.py` | 新增字段 |
+| `rag_eval/shared/models.py` | 新增字段 |
+| `rag_eval/config/loader.py` | 透传新字段到 Scenario |
+| `rag_eval/metrics/weights.py` | **新建** |
+| `rag_eval/execution/evaluator.py` | `_merge_score` 新增两列 |
+| `rag_eval/reporting/summary.py` | 改用加权均值 |
+| `webapp/services/yaml_patcher.py` | 新增 metric_weights / doc_weights 参数 |
+| `webapp/models.py` | ProfileApplyRequest 新增字段；RunSummary 新增 weighted_score_mean |
+| `webapp/api/llm_profiles.py` | 透传新参数 |
+| `webapp/services/report_builder.py` | 加权均值计算 |
+| `webapp/static/index.html` | 新增权重配置面板 |
+| `webapp/static/js/runner.js` | 权重面板逻辑 |
+| `webapp/static/css/app.css` | 新增权重面板样式 |
+| `tests/test_weights.py` | **新建** |
+
+---
+
+## 10. 向后兼容保证
+
+- `metric_weights: {}` + `doc_weights: {}` → 所有权重 = 1.0，行为与当前完全一致
+- 现有场景 YAML 不含这两个字段 → Pydantic `default_factory=dict` 填充空字典
+- `scores.csv` 新增两列不影响现有报告读取（`run_reader` 只读已知列）

docs/superpowers/specs/2026-06-22-dify-score-api-design.md

@@ -0,0 +1,138 @@
+# Dify 集成 — 单题实时评分 API 设计
+
+**日期**: 2026-06-22  
+**状态**: 已批准，待实现  
+**范围**: 在现有 FastAPI 服务中新增 `POST /api/score` 端点，供 Dify 外部 Tool 调用，实现单条问答记录的实时 RAGAS 指标评分。
+
+---
+
+## 1. 目标
+
+让 Dify Agent 能在回答完问题后，将 `(question, answer, contexts, ground_truth)` 发给 siemens_ragas 服务，实时获取各 RAGAS 指标得分，用于质量监控或 Agent 自我改进。
+
+---
+
+## 2. API 规范
+
+### `POST /api/score`
+
+**请求体：**
+
+```json
+{
+  "question":          "双源CT的时间分辨率是多少?",
+  "answer":            "双源CT的单扇区时间分辨率为75ms。",
+  "contexts":          "片段1：双源CT采用两套管-探测器系统... |||| 片段2：单扇区采集旋转135度...",
+  "ground_truth":      "双源CT单扇区时间分辨率为75ms，需旋转135度。",
+  "context_separator": " |||| ",
+  "metrics":           ["faithfulness", "answer_relevancy"],
+  "judge_model":       "deepseek-v4-flash",
+  "embedding_model":   "text-embedding-v3"
+}
+```
+
+**字段说明：**
+
+| 字段 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| `question` | str | ✅ | 问题文本 |
+| `answer` | str | ✅ | 待评分的回答 |
+| `contexts` | str | ✅ | 检索到的上下文，多段用 `context_separator` 拼接 |
+| `ground_truth` | str | ❌ | 标准答案；缺失时跳过依赖它的指标（context_recall、factual_correctness、semantic_similarity） |
+| `context_separator` | str | ❌ | 默认 `" \|\|\|\| "`（四个竖线，两侧各一空格） |
+| `metrics` | list[str] | ❌ | 默认 `["faithfulness", "answer_relevancy", "context_recall", "context_precision"]` |
+| `judge_model` | str | ❌ | 默认读 `.env` 中 `RAGAS_JUDGE_MODEL` |
+| `embedding_model` | str | ❌ | 默认读 `.env` 中 `RAGAS_EMBEDDING_MODEL` |
+
+**响应体（200 OK）：**
+
+```json
+{
+  "scores": {
+    "faithfulness":     0.8750,
+    "answer_relevancy": 0.9200
+  },
+  "weighted_score": 0.8975,
+  "latency_ms": 3420
+}
+```
+
+**错误响应：**
+
+| 状态码 | 场景 |
+|--------|------|
+| 400 | 必填字段缺失、metrics 名称不合法 |
+| 401 | 配置了 `SCORE_API_TOKEN` 但请求未携带有效 Bearer Token |
+| 422 | 请求体 JSON 格式错误（Pydantic 校验） |
+| 500 | RAGAS 内部评分异常，附带 error 字段 |
+
+**鉴权（可选）：**  
+若 `.env` 中 `SCORE_API_TOKEN` 非空，则要求请求头携带 `Authorization: Bearer <token>`。为空则不鉴权（内网部署场景）。
+
+---
+
+## 3. 架构与文件改动
+
+### 新文件
+
+| 文件 | 职责 |
+|------|------|
+| `webapp/api/score.py` | 路由定义，请求验证，调用 InlineScorer |
+| `webapp/services/inline_scorer.py` | LLM 客户端缓存 + RAGAS 评分逻辑封装 |
+
+### 修改文件
+
+| 文件 | 改动 |
+|------|------|
+| `webapp/models.py` | 新增 `ScoreRequest`、`ScoreResponse` |
+| `webapp/server.py` | 注册 `score.router`，更新 `openapi_tags` |
+| `rag_eval/settings.py` | 新增 `score_api_token: str | None` 字段 |
+
+---
+
+## 4. `inline_scorer.py` 设计
+
+```python
+class InlineScorer:
+    """同步执行 RAGAS 单题评分，内部缓存 LLM 客户端。"""
+
+    def score(
+        self,
+        question: str,
+        answer: str,
+        contexts: list[str],
+        ground_truth: str | None,
+        metrics: list[str],
+        judge_model: str,
+        embedding_model: str,
+        settings: EvaluationSettings,
+    ) -> dict[str, float | None]:
+        """返回 {metric_name: score} 字典，NaN 记为 None。"""
+```
+
+**客户端缓存策略：**  
+以 `(judge_model, embedding_model)` 为 key，缓存 `(llm, embeddings)` 对象，避免每次请求都重建 AsyncOpenAI 连接。缓存为模块级单例（`_scorer_cache: dict`），线程安全（加 `threading.Lock`）。
+
+**评分执行：**  
+复用 `build_metric_pipeline` 构建 `MetricPipeline`，然后 `asyncio.run(pipeline.score_sample(sample))` 执行。与现有 `evaluator.py` 模式一致。
+
+**ground_truth 为空时的指标跳过逻辑：**  
+`context_recall`、`factual_correctness`、`semantic_similarity`、`noise_sensitivity` 需要 ground_truth；若请求中未提供，自动从 metrics 列表中移除这些指标，并在响应中对应字段返回 `null`。
+
+---
+
+## 5. Dify 侧配置方法
+
+1. 在 Dify 「工具」→「自定义工具」中创建新工具
+2. 填写 OpenAPI Schema（与 `/api/score` 端点对齐）
+3. 鉴权方式：API Key（Bearer）或无鉴权
+4. 在 Agent / Workflow 节点中引用该工具，将 `question`、`answer`、`contexts` 变量映射到工具输入
+
+---
+
+## 6. 不在范围内
+
+- 批量评分接口（异步 job）
+- Dify Workflow 节点插件（需要 Dify 插件开发框架）
+- 评分结果持久化到 scores.csv
+- 与现有 report_builder 集成展示

docs/superpowers/specs/2026-06-22-linux-deploy-design.md

@@ -0,0 +1,173 @@
+# Linux 一键部署脚本设计
+
+**日期**: 2026-06-22  
+**状态**: 已批准，待实现  
+**范围**: 为 siemens_ragas 项目提供 Linux 环境的部署与运维脚本（无 Docker，无 systemd）。
+
+---
+
+## 1. 目标
+
+提供四个 Bash 脚本，覆盖 Linux 服务器上的完整生命周期：
+
+| 脚本 | 职责 |
+|------|------|
+| `deploy.sh` | 一键完成环境检查、依赖安装、配置初始化、启动服务 |
+| `start.sh` | 仅启动 Web 服务（已部署后复用，不重装依赖） |
+| `stop.sh` | 停止后台 Web 服务 |
+| `run_eval.sh` | 运行单次评估（对应 Windows 的 `run_eval.ps1`） |
+
+---
+
+## 2. 约束与假设
+
+- Linux 目标环境有 PyPI 网络访问（pip 可直接安装）
+- 代码已通过 `git clone` 或文件拷贝到服务器
+- 使用 `pip + venv`（不使用 uv）
+- Web 服务监听 `0.0.0.0:8800`（内网可达）
+- 后台运行使用 `nohup`，PID 写入 `.server.pid`，日志追加到 `logs/server.log`
+- 所有脚本均放在仓库根目录，路径相对于 `$SCRIPT_DIR`
+
+---
+
+## 3. `deploy.sh` 详细设计
+
+### 3.1 阶段 1：Python 版本检查
+
+```
+require Python >= 3.12
+```
+
+- `python3 --version` 解析 major.minor
+- 不满足则打印错误并 `exit 1`
+- 满足则打印 `[OK] Python X.Y.Z`
+
+### 3.2 阶段 2：虚拟环境
+
+- 目标路径：`$SCRIPT_DIR/.venv`
+- 已存在则跳过创建（打印 `[OK] .venv already exists`）
+- 不存在则 `python3 -m venv .venv`
+
+### 3.3 阶段 3：依赖安装
+
+```bash
+.venv/bin/pip install --upgrade pip -q
+.venv/bin/pip install -e . -q          # 安装 pyproject.toml 中的依赖
+.venv/bin/pip install fastapi uvicorn httpx -q  # Web 服务额外依赖
+```
+
+- 失败则打印错误并 `exit 1`
+- `fastapi`、`uvicorn`、`httpx` 在 `pyproject.toml` 中未列，需单独安装
+
+### 3.4 阶段 4：配置文件
+
+- 若 `.env` 不存在：`cp .env.example .env`，打印警告提示用户编辑后再启动
+- 若 `.env` 已存在：跳过，打印 `[OK] .env found`
+
+### 3.5 阶段 5：目录初始化
+
+创建以下目录（`mkdir -p`，幂等）：
+- `configs/` — LLM Profile 持久化存储
+- `logs/` — 评估日志 + 服务器日志
+- `outputs/` — 评估运行产物
+- `datasets/` — 原始数据集
+
+### 3.6 阶段 6：Demo 数据
+
+- 检查 `outputs/kba-knowledge-base-offline-baseline/` 是否存在
+- 不存在则运行 `.venv/bin/python scripts/seed_sample_run.py`
+- 失败时打印 `[WARN]`（非致命，报告页为空但服务可启动）
+
+### 3.7 阶段 7：端口检测
+
+- 默认端口 `8800`
+- 用 `ss -tlnp` 或 `netstat -tlnp` 检查是否占用
+- 占用则尝试 `8801`，仍占用则报错退出
+
+### 3.8 阶段 8：启动服务
+
+```bash
+nohup .venv/bin/python webmain.py \
+    --host 0.0.0.0 \
+    --port $PORT \
+    >> logs/server.log 2>&1 &
+echo $! > .server.pid
+```
+
+- 等待 2 秒后用 `kill -0 $PID` 检测进程是否存活
+- 存活则打印 URL 和 stop 方法
+- 未存活则打印 `[ERROR] Server failed to start. Check logs/server.log.` 并 `exit 1`
+
+---
+
+## 4. `start.sh` 详细设计
+
+单独负责启动，不做任何环境初始化。
+
+```bash
+#!/usr/bin/env bash
+# 检查 .venv 存在
+# 端口检测（同 deploy.sh 逻辑）
+# 检查 .env 存在（不存在则 warn 但不阻止）
+# nohup 启动 + PID 文件 + 存活验证
+# 打印 URL
+```
+
+---
+
+## 5. `stop.sh` 详细设计
+
+```bash
+#!/usr/bin/env bash
+# 读取 .server.pid
+# 若文件不存在：打印 "No server PID file found." 退出
+# kill $PID
+# 等待 2 秒，若进程仍存活用 kill -9
+# 删除 .server.pid
+# 打印 "Server stopped."
+```
+
+---
+
+## 6. `run_eval.sh` 详细设计
+
+对应 Windows 的 `run_eval.ps1`。
+
+```
+用法:
+  ./run_eval.sh                          # online eval (默认)
+  ./run_eval.sh offline                  # offline smoke
+  ./run_eval.sh scenarios/xxx.yaml       # 自定义场景
+  ./run_eval.sh online DEBUG             # 自定义日志级别
+```
+
+- 参数 1（Scenario）：`online` / `offline` / 文件路径，默认 `online`
+- 参数 2（LogLevel）：`DEBUG` / `INFO` / `WARNING` / `ERROR`，默认 `INFO`
+- 场景别名映射：
+  - `online` → `scenarios/online/siemens-pdf-question-bank-online.yaml`
+  - `offline` → `scenarios/offline/siemens-pdf-offline-smoke.yaml`
+- 时间戳日志文件：`logs/eval_$(date +%Y-%m-%d_%H%M%S).log`
+- 环境变量：`PYTHONIOENCODING=utf-8 PYTHONPATH=.`
+- 调用：`.venv/bin/python main.py --scenario $SCENARIO --log-file $LOG_FILE --log-level $LOG_LEVEL`
+- 非零退出码时打印错误并 `exit 1`
+
+---
+
+## 7. 通用约定
+
+- 所有脚本首行：`#!/usr/bin/env bash`
+- `set -euo pipefail` — 错误立即退出，未定义变量报错，管道错误传播
+- `SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"` — 从任意目录执行均正确
+- `cd "$SCRIPT_DIR"` — 切换到仓库根目录
+- 颜色输出：绿色 `[OK]`、黄色 `[WARN]`、红色 `[ERROR]`（检测 tty，非交互式终端降级为无色）
+- 执行权限：脚本自身需要 `chmod +x`（在 deploy.sh 内对其他脚本自动 chmod）
+
+---
+
+## 8. 不在范围内
+
+- Docker / docker-compose 支持
+- systemd service 配置
+- Nginx 反向代理配置
+- SSL/TLS 配置
+- 离线/内网镜像源配置

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

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

logs/online_eval.log

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

logs/server_2026-06-23.log

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

logs/siemens_build.log

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

pyproject.toml

@@ -17,3 +17,8 @@ dependencies = [
     "pydantic-settings>=2.14.1",
     "ragas==0.4.3",
 ]
+
+[tool.setuptools.packages.find]
+# 只打包源码目录，排除运行时产生的数据目录
+include = ["rag_eval*", "apps*", "webapp*"]
+exclude = ["logs*", "outputs*", "datasets*", "configs*", "scenarios*", "scripts*", "tests*"]

rag_eval/advisor/llm_analyzer.py

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

rag_eval/advisor/rules.py

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

rag_eval/advisor/writer.py

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

rag_eval/config/loader.py

@@ -62,6 +62,8 @@ def load_scenario(path: str | Path) -> Scenario:
         ),
         source_path=scenario_path,
         optimization_advisor=model.optimization_advisor,
+        metric_weights=dict(model.metric_weights),
+        doc_weights=dict(model.doc_weights),
     )
     # Run cross-field checks after all relative paths have been resolved.
     validate_scenario(scenario)

rag_eval/config/schema.py

@@ -55,6 +55,8 @@ class ScenarioModel(BaseModel):
     output_dir: str
     runtime: RuntimeConfigModel = Field(default_factory=RuntimeConfigModel)
     optimization_advisor: bool = False
+    metric_weights: dict[str, float] = Field(default_factory=dict)
+    doc_weights: dict[str, float] = Field(default_factory=dict)
 
     @field_validator("metrics")
     @classmethod

rag_eval/execution/evaluator.py

@@ -12,6 +12,7 @@ from rag_eval.datasets.loader import load_dataset_records
 from rag_eval.datasets.normalizers import normalize_records
 from rag_eval.execution.concurrency import gather_with_limit
 from rag_eval.metrics.pipeline import MetricPipeline
+from rag_eval.metrics.weights import compute_weighted_score, resolve_weight
 from rag_eval.shared.models import EvaluationResult, InvalidSample, NormalizedSample, Scenario
 from rag_eval.shared.utils import utc_now_iso
 
@@ -171,7 +172,7 @@ class Evaluator:
         return valid, invalid
 
     def _merge_score(self, sample: NormalizedSample, score: Any) -> dict[str, Any]:
-        """Combine sample data, metric results, and run metadata into one output row."""
+        """Combine sample data, metric results, run metadata, and weight columns."""
         record = sample.to_record()
         record["contexts"] = sample.contexts
         record.update(score.metrics)
@@ -179,4 +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
+        # 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

rag_eval/metrics/factory.py

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

rag_eval/metrics/weights.py

@@ -0,0 +1,152 @@
+"""Utility functions for weighted metric aggregation.
+
+All functions are pure (no side effects, no I/O) and operate on plain dicts/lists.
+Weights do not need to be pre-normalised — normalisation is done internally.
+"""
+
+from __future__ import annotations
+
+import math
+
+
+def resolve_weight(weights: dict[str, float], key: str, default: float = 1.0) -> float:
+    """Return the weight for *key*, or *default* when absent."""
+    return float(weights.get(key, default))
+
+
+def compute_weighted_score(
+    scores: dict[str, float | None],
+    metric_weights: dict[str, float],
+) -> float | None:
+    """Return the weighted mean of valid (non-NaN, non-None) metric scores.
+
+    Args:
+        scores: mapping of metric_name -> raw score (may be NaN or None).
+        metric_weights: optional per-metric weights; absent keys default to 1.0.
+
+    Returns:
+        Weighted mean as a float, or None when no valid score exists.
+    """
+    total_weight = 0.0
+    total_score = 0.0
+    for metric, score in scores.items():
+        if score is None:
+            continue
+        try:
+            value = float(score)
+        except (TypeError, ValueError):
+            continue
+        if math.isnan(value) or math.isinf(value):
+            continue
+        weight = resolve_weight(metric_weights, metric, default=1.0)
+        total_weight += weight
+        total_score += weight * value
+    if total_weight == 0.0:
+        return None
+    return total_score / total_weight
+
+
+def weighted_metric_means(
+    score_rows: list[dict],
+    metrics: list[str],
+    doc_weights: dict[str, float],
+) -> dict[str, float | None]:
+    """Compute per-metric weighted means across all score rows.
+
+    Each row's contribution is scaled by the doc_weight for its ``doc_name``.
+    Rows with NaN/None for a given metric are excluded from that metric's mean.
+
+    Args:
+        score_rows: list of score record dicts (from scores.csv).
+        metrics: ordered list of metric names to aggregate.
+        doc_weights: mapping doc_name -> weight multiplier; absent keys default to 1.0.
+
+    Returns:
+        Dict mapping metric_name -> weighted mean (or None if no valid data).
+    """
+    totals: dict[str, float] = {metric: 0.0 for metric in metrics}
+    weights_sum: dict[str, float] = {metric: 0.0 for metric in metrics}
+
+    for row in score_rows:
+        doc_name = str(row.get("doc_name", "") or "")
+        sample_weight = resolve_weight(doc_weights, doc_name, default=1.0)
+        for metric in metrics:
+            raw_value = row.get(metric)
+            if raw_value is None:
+                continue
+            try:
+                value = float(raw_value)
+            except (TypeError, ValueError):
+                continue
+            if math.isnan(value) or math.isinf(value):
+                continue
+            totals[metric] += sample_weight * value
+            weights_sum[metric] += sample_weight
+
+    return {
+        metric: (totals[metric] / weights_sum[metric] if weights_sum[metric] > 0 else None)
+        for metric in metrics
+    }
+
+
+def compute_overall_weighted_score_mean(
+    score_rows: list[dict],
+    metric_weights: dict[str, float],
+    doc_weights: dict[str, float],
+) -> float | None:
+    """Compute the overall weighted-score mean across all samples.
+
+    For each sample:
+      1. Compute per-sample weighted_score via compute_weighted_score.
+      2. Scale by the doc weight for that sample's doc_name.
+    Then return the weighted mean of all per-sample weighted_scores.
+    """
+    total_weight = 0.0
+    total_score = 0.0
+    for row in score_rows:
+        metric_scores: dict[str, float | None] = {}
+        for key, value in row.items():
+            if key in _META_COLUMNS:
+                continue
+            metric_scores[key] = value  # type: ignore[assignment]
+
+        weighted_score = compute_weighted_score(metric_scores, metric_weights)
+        if weighted_score is None:
+            continue
+        doc_name = str(row.get("doc_name", "") or "")
+        sample_weight = resolve_weight(doc_weights, doc_name, default=1.0)
+        total_weight += sample_weight
+        total_score += sample_weight * weighted_score
+
+    return total_score / total_weight if total_weight > 0 else None
+
+
+# Columns in scores.csv that are sample metadata, not metric scores.
+_META_COLUMNS = frozenset(
+    {
+        "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",
+    }
+)

rag_eval/reporting/summary.py

@@ -6,6 +6,10 @@ import math
 
 import pandas as pd
 
+from rag_eval.metrics.weights import (
+    compute_overall_weighted_score_mean,
+    weighted_metric_means,
+)
 from rag_eval.shared.models import EvaluationResult
 
 
@@ -55,24 +59,42 @@ def build_summary_markdown(result: EvaluationResult) -> str:
         lines.append("No valid samples were scored.")
         return "\n".join(lines) + "\n"
 
-    for metric in result.scenario.metrics:
+    score_rows_list = scores.to_dict(orient="records")
-        mean_value = scores[metric].mean(numeric_only=True)
+    w_means = weighted_metric_means(
-        if isinstance(mean_value, float) and not math.isnan(mean_value):
+        score_rows_list, result.scenario.metrics, result.scenario.doc_weights
-            lines.append(f"- {metric}: `{mean_value:.4f}`")
-        else:
-            lines.append(f"- {metric}: `n/a`")
-
-    # Keep the summary self-sufficient by including every scored sample and its errors.
-    detail_columns = ["sample_id", *result.scenario.metrics, "error"]
-    detail = scores[detail_columns]
-    lines.extend(
-        [
-            "",
-            "## Per-sample Scores",
-            "",
-            "```text",
-            _table_from_frame(detail),
-            "```",
-        ]
     )
+
+    has_weights = bool(result.scenario.metric_weights or result.scenario.doc_weights)
+
+    for metric in result.scenario.metrics:
+        mean_value = w_means.get(metric)
+        w = result.scenario.metric_weights.get(metric, 1.0) if result.scenario.metric_weights else 1.0
+        weight_note = f"  (w={w:.2f})" if result.scenario.metric_weights else ""
+        if mean_value is not None and not math.isnan(mean_value):
+            lines.append(f"- {metric}: `{mean_value:.4f}`{weight_note}")
+        else:
+            lines.append(f"- {metric}: `n/a`{weight_note}")
+
+    # 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]
+    detail = scores[existing_columns]
+    lines.extend([
+        "",
+        "## Per-sample Scores",
+        "",
+        "```text",
+        _table_from_frame(detail),
+        "```",
+    ])
     return "\n".join(lines) + "\n"

rag_eval/settings.py

@@ -21,11 +21,16 @@ class EvaluationSettings(BaseSettings):
 
     openai_api_key: str | None = Field(default=None, alias="OPENAI_API_KEY")
     openai_base_url: str = Field(default="http://6.86.80.4:30080/v1", alias="OPENAI_BASE_URL")
-    ragas_judge_model: str = Field(default="deepseek-v4-flash", alias="RAGAS_JUDGE_MODEL")
+    ragas_judge_model: str = Field(default="gpt-5", alias="RAGAS_JUDGE_MODEL")
     ragas_embedding_model: str = Field(
-        default="text-embedding-v3",
+        default="text-embedding-3-small",
         alias="RAGAS_EMBEDDING_MODEL",
     )
+    ragas_llm_max_tokens: int = Field(
+        default=4096,
+        alias="RAGAS_LLM_MAX_TOKENS",
+        gt=0,
+    )
     openai_timeout_seconds: float = Field(default=30.0, alias="OPENAI_TIMEOUT_SECONDS")
     ragas_metric_timeout_seconds: float = Field(default=45.0, alias="RAGAS_METRIC_TIMEOUT_SECONDS")
     batch_size: int = Field(default=8, alias="BATCH_SIZE")
@@ -52,6 +57,11 @@ class EvaluationSettings(BaseSettings):
     )
     parser_failure_mode: str = Field(default="fail", alias="PARSER_FAILURE_MODE")
     dataset_generator_model: str | None = Field(default=None, alias="DATASET_GENERATOR_MODEL")
+    score_api_token: str | None = Field(
+        default=None,
+        alias="SCORE_API_TOKEN",
+        description="Bearer token for /api/score endpoint. Empty = no auth.",
+    )
 
     @property
     def openai_client_kwargs(self) -> dict[str, str | float]:

rag_eval/shared/models.py

@@ -77,6 +77,8 @@ class Scenario:
     app_adapter: AppAdapterConfig | None = None
     source_path: Path | None = None
     optimization_advisor: bool = False
+    metric_weights: dict[str, float] = field(default_factory=dict)
+    doc_weights: dict[str, float] = field(default_factory=dict)
 
     def snapshot(self) -> dict[str, Any]:
         """Serialize the scenario into a reporting-friendly dictionary snapshot."""

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

run_eval.sh

@@ -0,0 +1,147 @@
+#!/usr/bin/env bash
+# run_eval.sh — Siemens RAGAS 评估运行脚本（Linux）
+# 对应 Windows 的 run_eval.ps1
+#
+# 用法:
+#   bash run_eval.sh                          # online 评估（默认）
+#   bash run_eval.sh offline                  # offline 冒烟测试
+#   bash run_eval.sh scenarios/xxx.yaml       # 自定义场景
+#   bash run_eval.sh online DEBUG             # 指定日志级别
+#   bash run_eval.sh build scenarios/siemens_build/siemens-pdf-build.yaml
+#                                             # 题库生成
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+# ── 颜色输出 ──────────────────────────────────────────────────────
+if [ -t 1 ]; then
+    GREEN='\033[0;32m'; YELLOW='\033[1;33m'; RED='\033[0;31m'; CYAN='\033[0;36m'; NC='\033[0m'
+else
+    GREEN=''; YELLOW=''; RED=''; CYAN=''; NC=''
+fi
+
+ok()   { echo -e "${GREEN}[OK]${NC}    $*"; }
+warn() { echo -e "${YELLOW}[WARN]${NC}  $*"; }
+err()  { echo -e "${RED}[ERROR]${NC} $*" >&2; }
+info() { echo -e "${CYAN}[INFO]${NC}  $*"; }
+
+# ── 参数解析 ──────────────────────────────────────────────────────
+SCENARIO="${1:-online}"
+LOG_LEVEL="${2:-INFO}"
+
+# 场景别名映射
+declare -A SCENARIO_MAP=(
+    ["online"]="scenarios/online/siemens-pdf-question-bank-online.yaml"
+    ["offline"]="scenarios/offline/siemens-pdf-offline-smoke.yaml"
+)
+
+# 检测是否是 dataset build 模式
+BUILD_MODE=false
+BUILD_CONFIG=""
+if [ "$SCENARIO" = "build" ]; then
+    BUILD_MODE=true
+    BUILD_CONFIG="${2:-scenarios/siemens_build/siemens-pdf-build.yaml}"
+    LOG_LEVEL="${3:-INFO}"
+elif [ -v "SCENARIO_MAP[$SCENARIO]" ]; then
+    SCENARIO="${SCENARIO_MAP[$SCENARIO]}"
+fi
+
+# ── 验证 ──────────────────────────────────────────────────────────
+echo ""
+echo -e "${CYAN}============================================================${NC}"
+echo -e "${CYAN}  Siemens RAGAS  —  评估运行${NC}"
+echo -e "${CYAN}============================================================${NC}"
+echo ""
+
+# 检查虚拟环境
+if [ ! -f ".venv/bin/python" ]; then
+    err "未找到 .venv，请先执行部署：bash deploy.sh"
+    exit 1
+fi
+
+PYTHON=".venv/bin/python"
+
+# Build 模式校验
+if [ "$BUILD_MODE" = true ]; then
+    if [ ! -f "$BUILD_CONFIG" ]; then
+        err "题库生成配置文件不存在：$BUILD_CONFIG"
+        echo ""
+        echo "可用配置："
+        find scenarios/ -name "*.yaml" 2>/dev/null | head -20 | sed 's/^/  /'
+        exit 1
+    fi
+    ok "模式      : 题库生成 (dataset build)"
+    ok "配置文件  : $BUILD_CONFIG"
+else
+    # 场景文件校验
+    if [ ! -f "$SCENARIO" ]; then
+        err "场景文件不存在：$SCENARIO"
+        echo ""
+        echo "用法示例："
+        echo "  bash run_eval.sh                          # online 评估"
+        echo "  bash run_eval.sh offline                  # offline 冒烟"
+        echo "  bash run_eval.sh scenarios/xxx.yaml       # 自定义场景"
+        echo "  bash run_eval.sh build [config.yaml]      # 题库生成"
+        exit 1
+    fi
+    ok "场景文件  : $SCENARIO"
+fi
+
+# 日志级别校验
+LOG_LEVEL_UPPER="${LOG_LEVEL^^}"
+case "$LOG_LEVEL_UPPER" in
+    DEBUG|INFO|WARNING|ERROR) ;;
+    *)
+        warn "未知日志级别 '$LOG_LEVEL'，使用默认值 INFO"
+        LOG_LEVEL_UPPER="INFO"
+        ;;
+esac
+ok "日志级别  : $LOG_LEVEL_UPPER"
+
+# 创建日志目录
+mkdir -p logs
+TIMESTAMP=$(date +%Y-%m-%d_%H%M%S)
+LOG_FILE="logs/eval_${TIMESTAMP}.log"
+ok "日志文件  : $LOG_FILE"
+
+echo ""
+echo -e "${CYAN}============================================================${NC}"
+echo -e "${CYAN}  开始运行，按 Ctrl+C 中止${NC}"
+echo -e "${CYAN}============================================================${NC}"
+echo ""
+
+# ── 运行 ──────────────────────────────────────────────────────────
+export PYTHONIOENCODING="utf-8"
+export PYTHONPATH="."
+
+if [ "$BUILD_MODE" = true ]; then
+    "$PYTHON" main.py \
+        --dataset-build-config "$BUILD_CONFIG"
+else
+    "$PYTHON" main.py \
+        --scenario "$SCENARIO" \
+        --log-file "$LOG_FILE" \
+        --log-level "$LOG_LEVEL_UPPER"
+fi
+
+EXIT_CODE=$?
+
+echo ""
+if [ $EXIT_CODE -eq 0 ]; then
+    echo -e "${GREEN}============================================================${NC}"
+    echo -e "${GREEN}  运行完成！${NC}"
+    if [ "$BUILD_MODE" = false ]; then
+        echo -e "${GREEN}  日志已保存到：$LOG_FILE${NC}"
+    fi
+    echo -e "${CYAN}  在 Web 控制台查看报告：bash start.sh${NC}"
+    echo -e "${GREEN}============================================================${NC}"
+else
+    err "运行失败（exit code=$EXIT_CODE）"
+    if [ "$BUILD_MODE" = false ]; then
+        err "查看日志：cat $LOG_FILE"
+    fi
+    exit $EXIT_CODE
+fi
+echo ""

start.sh

@@ -0,0 +1,94 @@
+#!/usr/bin/env bash
+# start.sh — 启动 Siemens RAGAS Web 服务（后台运行）
+# 前提：已执行过 deploy.sh（.venv 和依赖均已就绪）
+# 用法：bash start.sh
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+# ── 颜色输出 ──────────────────────────────────────────────────────
+if [ -t 1 ]; then
+    GREEN='\033[0;32m'; YELLOW='\033[1;33m'; RED='\033[0;31m'; CYAN='\033[0;36m'; NC='\033[0m'
+else
+    GREEN=''; YELLOW=''; RED=''; CYAN=''; NC=''
+fi
+
+ok()   { echo -e "${GREEN}[OK]${NC}    $*"; }
+warn() { echo -e "${YELLOW}[WARN]${NC}  $*"; }
+err()  { echo -e "${RED}[ERROR]${NC} $*" >&2; }
+
+echo ""
+echo -e "${CYAN}============================================================${NC}"
+echo -e "${CYAN}  Siemens RAGAS Console  —  启动服务${NC}"
+echo -e "${CYAN}============================================================${NC}"
+echo ""
+
+# 检查虚拟环境
+if [ ! -f ".venv/bin/python" ]; then
+    err "未找到 .venv，请先执行部署：bash deploy.sh"
+    exit 1
+fi
+
+PYTHON=".venv/bin/python"
+
+# 检查 .env
+if [ ! -f ".env" ]; then
+    warn ".env 不存在，请先复制并编辑配置："
+    warn "  cp .env.example .env && nano .env"
+fi
+
+if grep -q "your-api-key" .env 2>/dev/null; then
+    warn ".env 中仍包含默认占位符，部分功能（评估执行）将不可用"
+fi
+
+# 检查是否已有运行中的进程
+if [ -f ".server.pid" ]; then
+    EXISTING_PID=$(cat .server.pid)
+    if kill -0 "$EXISTING_PID" 2>/dev/null; then
+        warn "服务已在运行 (PID=$EXISTING_PID)，无需重复启动"
+        warn "如需重启请先执行：bash stop.sh"
+        exit 0
+    else
+        # PID 文件残留，清理
+        rm -f .server.pid
+    fi
+fi
+
+# 创建必要目录
+mkdir -p logs
+
+# 端口检测
+PORT=8800
+if ss -tlnp 2>/dev/null | grep -q ":$PORT " || netstat -tlnp 2>/dev/null | grep -q ":$PORT "; then
+    warn "端口 $PORT 已被占用，尝试 8801..."
+    PORT=8801
+    if ss -tlnp 2>/dev/null | grep -q ":$PORT " || netstat -tlnp 2>/dev/null | grep -q ":$PORT "; then
+        err "端口 8800 和 8801 均被占用，请手动指定端口："
+        err "  .venv/bin/python webmain.py --host 0.0.0.0 --port <PORT>"
+        exit 1
+    fi
+fi
+
+# 后台启动
+nohup "$PYTHON" webmain.py --host 0.0.0.0 --port "$PORT" >> logs/server.log 2>&1 &
+SERVER_PID=$!
+echo "$SERVER_PID" > .server.pid
+
+# 等待 3 秒验证进程存活
+sleep 3
+if kill -0 "$SERVER_PID" 2>/dev/null; then
+    ok "服务已启动 (PID=$SERVER_PID)"
+    echo ""
+    echo -e "${CYAN}  访问地址: http://$(hostname -I | awk '{print $1}'):${PORT}${NC}"
+    echo -e "${CYAN}  本机访问: http://127.0.0.1:${PORT}${NC}"
+    echo -e "${CYAN}  查看日志: tail -f logs/server.log${NC}"
+    echo -e "${CYAN}  停止服务: bash stop.sh${NC}"
+    echo ""
+else
+    err "服务启动失败，请查看日志："
+    err "  tail -20 logs/server.log"
+    rm -f .server.pid
+    exit 1
+fi

stop.sh

@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+# stop.sh — 停止 Siemens RAGAS 后台 Web 服务
+# 用法：bash stop.sh
+
+set -uo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+# ── 颜色输出 ──────────────────────────────────────────────────────
+if [ -t 1 ]; then
+    GREEN='\033[0;32m'; YELLOW='\033[1;33m'; RED='\033[0;31m'; CYAN='\033[0;36m'; NC='\033[0m'
+else
+    GREEN=''; YELLOW=''; RED=''; CYAN=''; NC=''
+fi
+
+ok()   { echo -e "${GREEN}[OK]${NC}    $*"; }
+warn() { echo -e "${YELLOW}[WARN]${NC}  $*"; }
+err()  { echo -e "${RED}[ERROR]${NC} $*" >&2; }
+
+echo ""
+echo -e "${CYAN}  Siemens RAGAS Console  —  停止服务${NC}"
+echo ""
+
+PID_FILE="$SCRIPT_DIR/.server.pid"
+
+if [ ! -f "$PID_FILE" ]; then
+    warn "未找到 .server.pid，服务可能未启动或已停止"
+    exit 0
+fi
+
+PID=$(cat "$PID_FILE")
+
+if ! kill -0 "$PID" 2>/dev/null; then
+    warn "进程 $PID 已不存在，清理 PID 文件"
+    rm -f "$PID_FILE"
+    exit 0
+fi
+
+# 优雅停止（SIGTERM）
+echo -e "  正在停止进程 (PID=$PID)..."
+kill "$PID" 2>/dev/null || true
+
+# 等待最多 5 秒
+for i in 1 2 3 4 5; do
+    sleep 1
+    if ! kill -0 "$PID" 2>/dev/null; then
+        break
+    fi
+    echo -e "  等待进程退出... ($i/5)"
+done
+
+# 若进程仍存在，强制终止
+if kill -0 "$PID" 2>/dev/null; then
+    warn "进程未响应，强制终止 (SIGKILL)..."
+    kill -9 "$PID" 2>/dev/null || true
+    sleep 1
+fi
+
+rm -f "$PID_FILE"
+
+if kill -0 "$PID" 2>/dev/null; then
+    err "无法停止进程 $PID，请手动执行：kill -9 $PID"
+    exit 1
+else
+    ok "服务已停止"
+    echo ""
+fi

tests/test_advisor_rules.py

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

tests/test_advisor_writer.py

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

tests/test_metric_presenter.py

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

tests/test_offline_eval.py

@@ -80,6 +80,64 @@ class ScenarioAndDatasetTests(unittest.TestCase):
         self.assertTrue(scenario.dataset.path.name.endswith(".csv"))
         self.assertTrue(scenario.output_dir.name == "sample-offline-baseline")
 
+    def test_load_scenario_metric_and_doc_weights(self) -> None:
+        """load_scenario passes metric_weights and doc_weights into Scenario."""
+        import os
+        import tempfile
+
+        import yaml
+
+        from rag_eval.config.loader import load_scenario
+
+        payload = {
+            "scenario_name": "w-test",
+            "mode": "offline",
+            "dataset": "nonexistent.csv",
+            "judge_model": "m",
+            "embedding_model": "e",
+            "metrics": ["faithfulness"],
+            "output_dir": "out",
+            "metric_weights": {"faithfulness": 0.7},
+            "doc_weights": {"doc.pdf": 2.0},
+        }
+        with tempfile.NamedTemporaryFile(suffix=".yaml", mode="w", encoding="utf-8", delete=False) as f:
+            yaml.dump(payload, f, allow_unicode=True)
+            tmp_path = f.name
+        try:
+            scenario = load_scenario(tmp_path)
+            assert scenario.metric_weights == {"faithfulness": 0.7}
+            assert scenario.doc_weights == {"doc.pdf": 2.0}
+        finally:
+            os.unlink(tmp_path)
+
+    def test_load_scenario_defaults_to_empty_weights(self) -> None:
+        """load_scenario defaults metric_weights and doc_weights to empty dicts."""
+        import os
+        import tempfile
+
+        import yaml
+
+        from rag_eval.config.loader import load_scenario
+
+        payload = {
+            "scenario_name": "no-w",
+            "mode": "offline",
+            "dataset": "nonexistent.csv",
+            "judge_model": "m",
+            "embedding_model": "e",
+            "metrics": ["faithfulness"],
+            "output_dir": "out",
+        }
+        with tempfile.NamedTemporaryFile(suffix=".yaml", mode="w", encoding="utf-8", delete=False) as f:
+            yaml.dump(payload, f, allow_unicode=True)
+            tmp_path = f.name
+        try:
+            scenario = load_scenario(tmp_path)
+            assert scenario.metric_weights == {}
+            assert scenario.doc_weights == {}
+        finally:
+            os.unlink(tmp_path)
+
     def test_scenario_snapshot_serializes_path_static_kwargs(self) -> None:
         scenario = load_scenario("scenarios/online/sample-pdf-question-bank-online.yaml")
         snapshot = scenario.snapshot()
@@ -125,6 +183,119 @@ class ScenarioAndDatasetTests(unittest.TestCase):
 
 
 class EvaluatorAndReportingTests(unittest.TestCase):
+    def test_merge_score_includes_weighted_score_and_sample_weight(self):
+        """_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 (
+            MetricScore, NormalizedSample, RuntimeConfig, Scenario, DatasetConfig,
+        )
+
+        scenario = Scenario(
+            scenario_name="w-test", mode="offline",
+            dataset=DatasetConfig(path=Path("d.csv")),
+            judge_model="m", embedding_model="e",
+            metrics=["faithfulness", "context_recall"],
+            output_dir=Path("out"),
+            metric_weights={"faithfulness": 3.0, "context_recall": 1.0},
+            doc_weights={"doc.pdf": 2.0},
+        )
+        evaluator = Evaluator(
+            scenario=scenario,
+            metric_pipeline=MagicMock(),
+            app_adapter=None,
+        )
+        sample = NormalizedSample(
+            sample_id="s1", question="q", contexts=["ctx"],
+            answer="a", ground_truth="gt",
+            metadata={"doc_name": "doc.pdf"},
+        )
+        score = MetricScore(metrics={"faithfulness": 1.0, "context_recall": 0.0})
+        row = evaluator._merge_score(sample, score)
+        # 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."""
+        import math
+        from rag_eval.reporting.summary import build_summary_markdown
+        from rag_eval.shared.models import (
+            EvaluationResult, NormalizedSample, DatasetConfig, Scenario,
+        )
+        from pathlib import Path
+        scenario = Scenario(
+            scenario_name="ws-test", mode="offline",
+            dataset=DatasetConfig(path=Path("d.csv")),
+            judge_model="m", embedding_model="e",
+            metrics=["faithfulness"],
+            output_dir=Path("out"),
+            metric_weights={"faithfulness": 1.0},
+            doc_weights={},
+        )
+        sample = NormalizedSample(
+            sample_id="s1", question="q", contexts=["c"],
+            answer="a", ground_truth="gt",
+        )
+        result = EvaluationResult(
+            scenario=scenario, run_id="r1",
+            started_at="2026-01-01T00:00:00", finished_at="2026-01-01T00:01:00",
+            valid_samples=[sample], invalid_samples=[],
+            score_rows=[{
+                "sample_id": "s1", "faithfulness": 0.8,
+                "weighted_score": 0.8, "sample_weight": 1.0,
+                "doc_name": "", "error": "",
+            }],
+        )
+        md = build_summary_markdown(result)
+        assert "weighted_score" in md
+        assert "0.8000" in md
+
+    def test_summary_markdown_hides_weighted_score_without_weights(self):
+        """build_summary_markdown preserves unweighted summaries when no weights set."""
+        from rag_eval.shared.models import DatasetConfig, EvaluationResult, NormalizedSample, Scenario
+
+        scenario = Scenario(
+            scenario_name="plain-test",
+            mode="offline",
+            dataset=DatasetConfig(path=Path("d.csv")),
+            judge_model="m",
+            embedding_model="e",
+            metrics=["faithfulness"],
+            output_dir=Path("out"),
+            metric_weights={},
+            doc_weights={},
+        )
+        sample = NormalizedSample(
+            sample_id="s1",
+            question="q",
+            contexts=["c"],
+            answer="a",
+            ground_truth="gt",
+        )
+        result = EvaluationResult(
+            scenario=scenario,
+            run_id="r1",
+            started_at="2026-01-01T00:00:00",
+            finished_at="2026-01-01T00:01:00",
+            valid_samples=[sample],
+            invalid_samples=[],
+            score_rows=[{
+                "sample_id": "s1",
+                "faithfulness": 0.8,
+                "weighted_score": 0.8,
+                "sample_weight": 1.0,
+                "doc_name": "",
+                "error": "",
+            }],
+        )
+
+        md = build_summary_markdown(result)
+
+        assert "- **weighted_score" not in md
+
     def test_metric_pipeline_scores_sample(self) -> None:
         pipeline = MetricPipeline(
             metrics={

tests/test_pipeline.py

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

tests/test_webapp_report_builder.py

@@ -0,0 +1,117 @@
+"""Regression tests for weighted webapp report aggregation."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from webapp.services.report_builder import build_report
+from webapp.services.run_reader import _infer_metrics_from_scores, _read_weights_from_snapshot
+
+
+def _write_run_artifacts(run_dir: Path) -> None:
+    """Create a minimal run directory with weighted scores and a snapshot."""
+    run_dir.mkdir(parents=True, exist_ok=True)
+    (run_dir / "scores.csv").write_text(
+        "\n".join(
+            [
+                "sample_id,doc_name,faithfulness,context_recall,weighted_score,sample_weight",
+                "s1,a.pdf,1.0,0.5,0.8333,3.0",
+                "s2,b.pdf,0.0,0.5,0.1667,1.0",
+            ]
+        ),
+        encoding="utf-8",
+    )
+    (run_dir / "summary.md").write_text("summary", encoding="utf-8")
+    (run_dir / "optimization_advice.md").write_text("advice", encoding="utf-8")
+    (run_dir / "scenario.snapshot.yaml").write_text(
+        "\n".join(
+            [
+                "metrics:",
+                "  - faithfulness",
+                "  - context_recall",
+                "metric_weights:",
+                "  faithfulness: 2.0",
+                "  context_recall: 1.0",
+                "doc_weights:",
+                "  a.pdf: 3.0",
+                "  b.pdf: 1.0",
+            ]
+        ),
+        encoding="utf-8",
+    )
+
+
+def test_read_weights_from_snapshot_returns_metric_and_doc_weights(tmp_path: Path) -> None:
+    """Snapshot weight reader returns both weight maps as plain float dicts."""
+    run_dir = tmp_path / "run"
+    _write_run_artifacts(run_dir)
+
+    metric_weights, doc_weights = _read_weights_from_snapshot(run_dir)
+
+    assert metric_weights == {"faithfulness": 2.0, "context_recall": 1.0}
+    assert doc_weights == {"a.pdf": 3.0, "b.pdf": 1.0}
+
+
+def test_build_report_uses_weighted_means_and_exposes_snapshot_weights(tmp_path: Path) -> None:
+    """Report aggregation uses weighted means and surfaces snapshot weights."""
+    run_dir = tmp_path / "run"
+    _write_run_artifacts(run_dir)
+
+    report = build_report(run_dir, ["faithfulness", "context_recall"])
+
+    assert report.metric_means == {
+        "faithfulness": pytest.approx(0.75, rel=1e-4),
+        "context_recall": pytest.approx(0.5, 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"
+    assert report.advice_markdown == "advice"
+
+
+def test_infer_metrics_excludes_weight_columns_without_snapshot(tmp_path: Path) -> None:
+    """Metric inference excludes weighted helper columns from scores.csv."""
+    run_dir = tmp_path / "run"
+    run_dir.mkdir(parents=True, exist_ok=True)
+    (run_dir / "scores.csv").write_text(
+        "\n".join(
+            [
+                "sample_id,doc_name,faithfulness,weighted_score,sample_weight",
+                "s1,a.pdf,0.8,0.8,2.0",
+            ]
+        ),
+        encoding="utf-8",
+    )
+
+    assert _infer_metrics_from_scores(run_dir) == ["faithfulness"]
+
+
+def test_build_report_ranks_noise_sensitivity_with_lower_values_as_better(tmp_path: Path) -> None:
+    """Lowest-sample review should treat higher noise sensitivity as worse."""
+    run_dir = tmp_path / "run"
+    run_dir.mkdir(parents=True, exist_ok=True)
+    (run_dir / "scores.csv").write_text(
+        "\n".join(
+            [
+                "sample_id,question,noise_sensitivity",
+                "s-good,q1,0.10",
+                "s-warn,q2,0.30",
+                "s-bad,q3,0.90",
+            ]
+        ),
+        encoding="utf-8",
+    )
+    (run_dir / "summary.md").write_text("summary", encoding="utf-8")
+    (run_dir / "optimization_advice.md").write_text("", encoding="utf-8")
+
+    report = build_report(run_dir, ["noise_sensitivity"])
+
+    assert [sample.sample_id for sample in report.lowest_samples[:3]] == [
+        "s-bad",
+        "s-warn",
+        "s-good",
+    ]

tests/test_weights.py

@@ -0,0 +1,124 @@
+"""Unit tests for rag_eval/metrics/weights.py"""
+import math
+
+import pytest
+
+from rag_eval.metrics.weights import (
+    compute_overall_weighted_score_mean,
+    compute_weighted_score,
+    resolve_weight,
+    weighted_metric_means,
+)
+
+
+class TestResolveWeight:
+    def test_returns_value_when_key_present(self):
+        assert resolve_weight({"faith": 0.5}, "faith") == 0.5
+
+    def test_returns_default_when_key_missing(self):
+        assert resolve_weight({}, "faith") == 1.0
+
+    def test_returns_custom_default_when_key_missing(self):
+        assert resolve_weight({}, "faith", default=2.0) == 2.0
+
+    def test_empty_dict_returns_default(self):
+        assert resolve_weight({}, "anything") == 1.0
+
+
+class TestComputeWeightedScore:
+    def test_equal_weights_is_simple_mean(self):
+        scores = {"faithfulness": 0.8, "context_recall": 0.6}
+        result = compute_weighted_score(scores, {})
+        assert result == pytest.approx(0.7, rel=1e-4)
+
+    def test_explicit_weights(self):
+        scores = {"faithfulness": 1.0, "context_recall": 0.0}
+        weights = {"faithfulness": 3.0, "context_recall": 1.0}
+        result = compute_weighted_score(scores, weights)
+        assert result == pytest.approx(0.75, rel=1e-4)
+
+    def test_nan_values_excluded(self):
+        scores = {"faithfulness": float("nan"), "context_recall": 0.8}
+        result = compute_weighted_score(scores, {})
+        assert result == pytest.approx(0.8, rel=1e-4)
+
+    def test_none_values_excluded(self):
+        scores = {"faithfulness": None, "context_recall": 0.6}
+        result = compute_weighted_score(scores, {})
+        assert result == pytest.approx(0.6, rel=1e-4)
+
+    def test_all_nan_returns_none(self):
+        scores = {"faithfulness": float("nan"), "context_recall": float("nan")}
+        assert compute_weighted_score(scores, {}) is None
+
+    def test_empty_scores_returns_none(self):
+        assert compute_weighted_score({}, {}) is None
+
+    def test_missing_metric_in_weights_uses_default_1(self):
+        scores = {"faithfulness": 0.8, "context_recall": 0.4}
+        weights = {"faithfulness": 2.0}
+        result = compute_weighted_score(scores, weights)
+        assert result == pytest.approx(2.0 / 3, rel=1e-4)
+
+
+class TestWeightedMetricMeans:
+    def _rows(self):
+        return [
+            {"doc_name": "a.pdf", "faithfulness": 1.0, "context_recall": 0.5},
+            {"doc_name": "b.pdf", "faithfulness": 0.6, "context_recall": 0.8},
+        ]
+
+    def test_equal_weights_gives_arithmetic_mean(self):
+        rows = self._rows()
+        result = weighted_metric_means(rows, ["faithfulness", "context_recall"], {})
+        assert result["faithfulness"] == pytest.approx(0.8, rel=1e-4)
+        assert result["context_recall"] == pytest.approx(0.65, rel=1e-4)
+
+    def test_doc_weight_amplifies_contribution(self):
+        rows = self._rows()
+        doc_weights = {"a.pdf": 3.0, "b.pdf": 1.0}
+        result = weighted_metric_means(rows, ["faithfulness"], doc_weights)
+        assert result["faithfulness"] == pytest.approx(0.9, rel=1e-4)
+
+    def test_nan_rows_skipped_per_metric(self):
+        rows = [
+            {"doc_name": "a.pdf", "faithfulness": float("nan"), "context_recall": 0.5},
+            {"doc_name": "b.pdf", "faithfulness": 0.8, "context_recall": 0.9},
+        ]
+        result = weighted_metric_means(rows, ["faithfulness", "context_recall"], {})
+        assert result["faithfulness"] == pytest.approx(0.8, rel=1e-4)
+        assert result["context_recall"] == pytest.approx(0.7, rel=1e-4)
+
+    def test_missing_metric_column_returns_none(self):
+        rows = [{"doc_name": "a.pdf", "faithfulness": 0.8}]
+        result = weighted_metric_means(rows, ["faithfulness", "unknown_metric"], {})
+        assert result["faithfulness"] == pytest.approx(0.8, rel=1e-4)
+        assert result["unknown_metric"] is None
+
+    def test_empty_rows_returns_none_for_all(self):
+        result = weighted_metric_means([], ["faithfulness"], {})
+        assert result["faithfulness"] is None
+
+
+class TestComputeOverallWeightedScoreMean:
+    def test_basic_weighted_mean_of_weighted_scores(self):
+        rows = [
+            {"doc_name": "a.pdf", "faithfulness": 1.0, "context_recall": 0.0},
+            {"doc_name": "b.pdf", "faithfulness": 0.5, "context_recall": 0.5},
+        ]
+        metric_weights = {"faithfulness": 1.0, "context_recall": 1.0}
+        result = compute_overall_weighted_score_mean(rows, metric_weights, {})
+        assert result == pytest.approx(0.5, rel=1e-4)
+
+    def test_doc_weight_amplifies_sample(self):
+        rows = [
+            {"doc_name": "important.pdf", "faithfulness": 1.0},
+            {"doc_name": "other.pdf", "faithfulness": 0.0},
+        ]
+        doc_weights = {"important.pdf": 9.0, "other.pdf": 1.0}
+        result = compute_overall_weighted_score_mean(rows, {}, doc_weights)
+        assert result == pytest.approx(0.9, rel=1e-4)
+
+    def test_all_nan_returns_none(self):
+        rows = [{"doc_name": "a.pdf", "faithfulness": float("nan")}]
+        assert compute_overall_weighted_score_mean(rows, {}, {}) is None

tests/webapp/test_llm_profiles_api.py

@@ -1,6 +1,7 @@
 """Integration tests for /api/llm-profiles endpoints."""
 import pytest
 from fastapi.testclient import TestClient
+from unittest.mock import patch
 
 
 @pytest.fixture()
@@ -41,19 +42,23 @@ def test_update_profile(client):
     pid = client.post("/api/llm-profiles", json=body).json()["profile_id"]
 
     upd = {"name": "New", "model": "m2", "base_url": "http://x/v1", "api_key": "k", "timeout_seconds": 60}
-    resp = client.put(f"/api/llm-profiles/{pid}", json=upd)
+    with patch("webapp.services.inline_scorer.inline_scorer.invalidate_cache") as invalidate:
+        resp = client.put(f"/api/llm-profiles/{pid}", json=upd)
     assert resp.status_code == 200
     assert resp.json()["name"] == "New"
     assert resp.json()["timeout_seconds"] == 60
+    invalidate.assert_called_once()
 
 
 def test_delete_profile(client):
     body = {"name": "Del", "model": "m", "base_url": "http://x/v1", "api_key": "k"}
     pid = client.post("/api/llm-profiles", json=body).json()["profile_id"]
-    resp = client.delete(f"/api/llm-profiles/{pid}")
+    with patch("webapp.services.inline_scorer.inline_scorer.invalidate_cache") as invalidate:
+        resp = client.delete(f"/api/llm-profiles/{pid}")
     assert resp.status_code == 200
     assert resp.json()["deleted"] is True
     assert len(client.get("/api/llm-profiles").json()["profiles"]) == 0
+    invalidate.assert_called_once()
 
 
 def test_update_nonexistent(client):
@@ -137,3 +142,104 @@ def test_apply_no_profiles_returns_empty(tmp_path):
         _resolve_absolute=True,
     )
     assert patched == []
+
+
+def test_apply_metric_weights_patches_yaml(tmp_path):
+    """Applying metric_weights writes them into the YAML."""
+    import yaml as yaml_lib
+    import pytest
+    scenario_file = tmp_path / "w-scenario.yaml"
+    scenario_file.write_text(
+        "scenario_name: test\nmode: offline\njudge_model: m\nembedding_model: e\n"
+        "dataset: d.csv\nmetrics:\n- faithfulness\noutput_dir: out\n",
+        encoding="utf-8",
+    )
+    from webapp.services.yaml_patcher import apply_profiles_to_scenario
+    patched = apply_profiles_to_scenario(
+        scenario_path=str(scenario_file),
+        judge_profile=None, answer_profile=None, dataset_profile=None,
+        metric_weights={"faithfulness": 0.7, "context_recall": 0.3},
+        _resolve_absolute=True,
+    )
+    assert "metric_weights" in patched
+    data = yaml_lib.safe_load(scenario_file.read_text())
+    assert abs(data["metric_weights"]["faithfulness"] - 0.7) < 1e-9
+
+
+def test_apply_doc_weights_patches_yaml(tmp_path):
+    """Applying doc_weights writes them into the YAML."""
+    import yaml as yaml_lib
+    scenario_file = tmp_path / "dw-scenario.yaml"
+    scenario_file.write_text(
+        "scenario_name: test\nmode: offline\njudge_model: m\nembedding_model: e\n"
+        "dataset: d.csv\nmetrics:\n- faithfulness\noutput_dir: out\n",
+        encoding="utf-8",
+    )
+    from webapp.services.yaml_patcher import apply_profiles_to_scenario
+    patched = apply_profiles_to_scenario(
+        scenario_path=str(scenario_file),
+        judge_profile=None, answer_profile=None, dataset_profile=None,
+        doc_weights={"doc.pdf": 2.0},
+        _resolve_absolute=True,
+    )
+    assert "doc_weights" in patched
+    data = yaml_lib.safe_load(scenario_file.read_text())
+    assert abs(data["doc_weights"]["doc.pdf"] - 2.0) < 1e-9
+
+
+# ---------------------------------------------------------------------------
+# Connectivity test endpoint tests
+# ---------------------------------------------------------------------------
+from unittest.mock import MagicMock
+
+
+def test_probe_connectivity_success(client):
+    """POST /api/llm-profiles/probe returns ok=True on successful completion."""
+    mock_response = MagicMock()
+    mock_response.choices = [MagicMock()]
+    with patch("webapp.api.llm_profiles.OpenAI") as MockOpenAI:
+        MockOpenAI.return_value.chat.completions.create.return_value = mock_response
+        resp = client.post("/api/llm-profiles/probe", json={
+            "model": "test-model",
+            "base_url": "http://x/v1",
+            "api_key": "sk-test",
+        })
+    assert resp.status_code == 200
+    data = resp.json()
+    assert data["ok"] is True
+    assert data["latency_ms"] is not None
+
+
+def test_probe_connectivity_failure(client):
+    """POST /api/llm-profiles/probe returns ok=False when the LLM call raises."""
+    with patch("webapp.api.llm_profiles.OpenAI") as MockOpenAI:
+        MockOpenAI.return_value.chat.completions.create.side_effect = Exception("connection refused")
+        resp = client.post("/api/llm-profiles/probe", json={
+            "model": "test-model",
+            "base_url": "http://x/v1",
+            "api_key": "sk-test",
+        })
+    assert resp.status_code == 200
+    data = resp.json()
+    assert data["ok"] is False
+    assert "connection refused" in data["message"]
+
+
+def test_test_saved_profile_success(client):
+    """POST /api/llm-profiles/{id}/test returns ok=True for a saved profile."""
+    body = {"name": "T", "model": "m1", "base_url": "http://x/v1", "api_key": "k"}
+    pid = client.post("/api/llm-profiles", json=body).json()["profile_id"]
+
+    mock_response = MagicMock()
+    mock_response.choices = [MagicMock()]
+    with patch("webapp.api.llm_profiles.OpenAI") as MockOpenAI:
+        MockOpenAI.return_value.chat.completions.create.return_value = mock_response
+        resp = client.post(f"/api/llm-profiles/{pid}/test")
+    assert resp.status_code == 200
+    assert resp.json()["ok"] is True
+
+
+def test_test_nonexistent_profile_returns_404(client):
+    """POST /api/llm-profiles/{id}/test returns 404 for unknown profile id."""
+    resp = client.post("/api/llm-profiles/nonexistent/test")
+    assert resp.status_code == 404

tests/webapp/test_profile_manager.py

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

tests/webapp/test_score_api.py

@@ -0,0 +1,341 @@
+"""Tests for POST /api/score endpoint."""
+from __future__ import annotations
+
+import pytest
+from pydantic import ValidationError
+
+from webapp.models import ScoreRequest, ScoreResponse
+
+
+class TestScoreRequest:
+    def test_minimal_valid_request(self):
+        """Only required fields — question, answer, contexts."""
+        req = ScoreRequest(
+            question="What is CT?",
+            answer="CT is imaging.",
+            contexts="CT uses X-rays.",
+        )
+        assert req.question == "What is CT?"
+        assert req.contexts == "CT uses X-rays."
+        assert req.ground_truth is None
+        assert req.context_separator == " |||| "
+        assert req.metrics == [
+            "faithfulness",
+            "answer_relevancy",
+            "context_recall",
+            "context_precision",
+        ]
+
+    def test_contexts_split_by_separator(self):
+        """contexts_as_list() splits on context_separator."""
+        req = ScoreRequest(
+            question="q",
+            answer="a",
+            contexts="ctx1 |||| ctx2 |||| ctx3",
+            context_separator=" |||| ",
+        )
+        assert req.contexts_as_list() == ["ctx1", "ctx2", "ctx3"]
+
+    def test_contexts_split_custom_separator(self):
+        req = ScoreRequest(
+            question="q",
+            answer="a",
+            contexts="a---b---c",
+            context_separator="---",
+        )
+        assert req.contexts_as_list() == ["a", "b", "c"]
+
+    def test_contexts_split_single_item(self):
+        req = ScoreRequest(question="q", answer="a", contexts="only one")
+        assert req.contexts_as_list() == ["only one"]
+
+    def test_missing_question_raises(self):
+        with pytest.raises(ValidationError):
+            ScoreRequest(answer="a", contexts="c")  # type: ignore[call-arg]
+
+    def test_missing_answer_raises(self):
+        with pytest.raises(ValidationError):
+            ScoreRequest(question="q", contexts="c")  # type: ignore[call-arg]
+
+    def test_missing_contexts_defaults_to_none(self):
+        """contexts is now optional — missing contexts is allowed."""
+        req = ScoreRequest(question="q", answer="a")
+        assert req.contexts is None
+        assert req.contexts_as_list() == []
+
+    def test_custom_metrics_accepted(self):
+        req = ScoreRequest(
+            question="q",
+            answer="a",
+            contexts="c",
+            metrics=["faithfulness"],
+        )
+        assert req.metrics == ["faithfulness"]
+
+    def test_invalid_metric_name_raises(self):
+        with pytest.raises(ValidationError):
+            ScoreRequest(
+                question="q",
+                answer="a",
+                contexts="c",
+                metrics=["not_a_metric"],
+            )
+
+    def test_effective_metrics_drops_ground_truth_dependent_when_missing(self):
+        """Without ground_truth, GT-dependent metrics are excluded."""
+        req = ScoreRequest(
+            question="q",
+            answer="a",
+            contexts="c",
+            metrics=[
+                "faithfulness",
+                "context_recall",
+                "factual_correctness",
+                "semantic_similarity",
+                "noise_sensitivity",
+            ],
+        )
+        effective = req.effective_metrics()
+        assert "faithfulness" in effective
+        assert "context_recall" not in effective
+        assert "factual_correctness" not in effective
+        assert "semantic_similarity" not in effective
+        assert "noise_sensitivity" not in effective
+
+    def test_effective_metrics_keeps_all_when_ground_truth_present(self):
+        req = ScoreRequest(
+            question="q",
+            answer="a",
+            contexts="c",
+            ground_truth="gt",
+            metrics=["faithfulness", "context_recall", "factual_correctness"],
+        )
+        effective = req.effective_metrics()
+        assert effective == [
+            "faithfulness",
+            "context_recall",
+            "factual_correctness",
+        ]
+
+    def test_effective_metrics_drops_context_dependent_when_contexts_absent(self):
+        """Without contexts, context-dependent metrics are excluded."""
+        req = ScoreRequest(
+            question="q", answer="a",
+            metrics=["faithfulness", "answer_relevancy", "context_precision"],
+        )
+        effective = req.effective_metrics()
+        assert "answer_relevancy" in effective
+        assert "faithfulness" not in effective
+        assert "context_precision" not in effective
+
+
+class TestScoreResponse:
+    def test_score_response_structure(self):
+        resp = ScoreResponse(
+            scores={"faithfulness": 0.85, "answer_relevancy": None},
+            weighted_score=0.85,
+            latency_ms=1200,
+        )
+        assert resp.scores["faithfulness"] == 0.85
+        assert resp.scores["answer_relevancy"] is None
+        assert resp.latency_ms == 1200
+
+
+class TestInlineScorer:
+    def test_score_returns_dict_with_requested_metrics(self):
+        """InlineScorer.score returns a dict keyed by the requested metrics."""
+        from unittest.mock import AsyncMock, MagicMock, patch
+        from webapp.services.inline_scorer import InlineScorer
+        from rag_eval.settings import EvaluationSettings
+
+        mock_score = MagicMock()
+        mock_score.metrics = {"faithfulness": 0.9, "answer_relevancy": 0.8}
+        mock_score.error = ""
+
+        mock_pipeline = MagicMock()
+        mock_pipeline.score_sample = AsyncMock(return_value=mock_score)
+
+        with patch("webapp.services.inline_scorer.build_models", return_value=(MagicMock(), MagicMock())):
+            with patch("webapp.services.inline_scorer.MetricPipeline", return_value=mock_pipeline):
+                with patch("webapp.services.inline_scorer._build_metric_instances", return_value={}):
+                    scorer = InlineScorer()
+                    result = scorer.score(
+                        question="q", answer="a",
+                        contexts=["ctx1"],
+                        ground_truth=None,
+                        metrics=["faithfulness", "answer_relevancy"],
+                        judge_model="test-model",
+                        embedding_model="test-embed",
+                        settings=EvaluationSettings(_env_file=None),
+                    )
+        assert "faithfulness" in result
+        assert "answer_relevancy" in result
+        assert result["faithfulness"] == pytest.approx(0.9)
+
+    def test_score_converts_nan_to_none(self):
+        """NaN scores are converted to None in the returned dict."""
+        import math
+        from unittest.mock import AsyncMock, MagicMock, patch
+        from webapp.services.inline_scorer import InlineScorer
+        from rag_eval.settings import EvaluationSettings
+
+        mock_score = MagicMock()
+        mock_score.metrics = {"faithfulness": float("nan")}
+        mock_score.error = ""
+
+        mock_pipeline = MagicMock()
+        mock_pipeline.score_sample = AsyncMock(return_value=mock_score)
+
+        with patch("webapp.services.inline_scorer.build_models", return_value=(MagicMock(), MagicMock())):
+            with patch("webapp.services.inline_scorer.MetricPipeline", return_value=mock_pipeline):
+                with patch("webapp.services.inline_scorer._build_metric_instances", return_value={}):
+                    scorer = InlineScorer()
+                    result = scorer.score(
+                        question="q", answer="a", contexts=["c"],
+                        ground_truth=None,
+                        metrics=["faithfulness"],
+                        judge_model="m", embedding_model="e",
+                        settings=EvaluationSettings(_env_file=None),
+                    )
+        assert result["faithfulness"] is None
+
+
+# ── Endpoint integration tests ────────────────────────────────────────────────
+
+@pytest.fixture()
+def client(monkeypatch):
+    """TestClient with mocked InlineScorer."""
+    import webapp.api.score as score_mod
+    from unittest.mock import MagicMock
+
+    mock_scorer = MagicMock()
+    mock_scorer.score.return_value = {
+        "faithfulness": 0.85,
+        "answer_relevancy": 0.90,
+    }
+    monkeypatch.setattr(score_mod, "inline_scorer", mock_scorer)
+
+    from webapp.server import create_app
+    return TestClient(create_app())
+
+
+from fastapi.testclient import TestClient
+
+
+class TestScoreEndpoint:
+    def test_post_score_returns_200(self, client):
+        resp = client.post("/api/score", json={
+            "question": "What is CT?",
+            "answer": "CT is imaging.",
+            "contexts": "CT uses X-rays.",
+        })
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "scores" in data
+        assert "latency_ms" in data
+        assert data["scores"]["faithfulness"] == pytest.approx(0.85)
+
+    def test_weighted_score_computed(self, client):
+        resp = client.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+        })
+        assert resp.status_code == 200
+        data = resp.json()
+        # 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"})
+        assert resp.status_code == 422
+
+    def test_invalid_metric_name_returns_422(self, client):
+        resp = client.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+            "metrics": ["not_a_metric"],
+        })
+        assert resp.status_code == 422
+
+    def test_skipped_metrics_returned_when_no_ground_truth(self, client):
+        resp = client.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+            "metrics": ["faithfulness", "context_recall"],
+        })
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "context_recall" in data["skipped_metrics"]
+
+    def test_contexts_split_on_separator(self, monkeypatch):
+        """contexts string is split before passing to scorer."""
+        import webapp.api.score as score_mod
+        from unittest.mock import MagicMock
+        calls = []
+        def capture(**kwargs):
+            calls.append(kwargs.get("contexts", []))
+            return {"faithfulness": 0.9}
+        mock_scorer = MagicMock()
+        mock_scorer.score.side_effect = lambda **kw: capture(**kw)
+        monkeypatch.setattr(score_mod, "inline_scorer", mock_scorer)
+
+        from webapp.server import create_app
+        from fastapi.testclient import TestClient
+        tc = TestClient(create_app())
+        tc.post("/api/score", json={
+            "question": "q", "answer": "a",
+            "contexts": "ctx1 |||| ctx2",
+            "context_separator": " |||| ",
+        })
+        assert len(calls) == 1
+        assert calls[0] == ["ctx1", "ctx2"]
+
+    def test_bearer_token_auth_required_when_configured(self, monkeypatch):
+        """When SCORE_API_TOKEN is set, requests without token get 401."""
+        import webapp.api.score as score_mod
+        from rag_eval.settings import EvaluationSettings
+        from unittest.mock import MagicMock
+
+        mock_settings = EvaluationSettings(_env_file=None)
+        object.__setattr__(mock_settings, "score_api_token", "secret-token")
+        monkeypatch.setattr(score_mod, "_get_settings", lambda: mock_settings)
+
+        mock_scorer = MagicMock()
+        mock_scorer.score.return_value = {"faithfulness": 0.9}
+        monkeypatch.setattr(score_mod, "inline_scorer", mock_scorer)
+
+        from webapp.server import create_app
+        from fastapi.testclient import TestClient
+        tc = TestClient(create_app())
+
+        # No auth header -> 401
+        resp = tc.post("/api/score", json={
+            "question": "q", "answer": "a", "contexts": "c",
+        })
+        assert resp.status_code == 401
+
+        # Correct token -> 200
+        resp = tc.post("/api/score",
+            json={"question": "q", "answer": "a", "contexts": "c"},
+            headers={"Authorization": "Bearer secret-token"},
+        )
+        assert resp.status_code == 200
+
+    def test_wrong_bearer_token_returns_401(self, monkeypatch):
+        import webapp.api.score as score_mod
+        from rag_eval.settings import EvaluationSettings
+        from unittest.mock import MagicMock
+
+        mock_settings = EvaluationSettings(_env_file=None)
+        object.__setattr__(mock_settings, "score_api_token", "correct-token")
+        monkeypatch.setattr(score_mod, "_get_settings", lambda: mock_settings)
+
+        mock_scorer = MagicMock()
+        mock_scorer.score.return_value = {}
+        monkeypatch.setattr(score_mod, "inline_scorer", mock_scorer)
+
+        from webapp.server import create_app
+        from fastapi.testclient import TestClient
+        tc = TestClient(create_app())
+        resp = tc.post("/api/score",
+            json={"question": "q", "answer": "a", "contexts": "c"},
+            headers={"Authorization": "Bearer wrong-token"},
+        )
+        assert resp.status_code == 401

tests/webapp/test_score_jobs_api.py

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

tests/webapp/test_session_score_jobs_api.py

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

webapp/api/evaluations.py

@@ -2,6 +2,8 @@
 
 from __future__ import annotations
 
+import logging
+
 from fastapi import APIRouter, HTTPException
 
 from webapp.models import (
@@ -13,19 +15,23 @@ from webapp.services import scenario_scanner
 from webapp.services.task_manager import task_manager
 
 router = APIRouter(prefix="/api/evaluations", tags=["evaluations"])
+logger = logging.getLogger("webapp.api.evaluations")
 
 
 @router.post("", response_model=TriggerEvaluationResponse)
 def trigger_evaluation(request: TriggerEvaluationRequest) -> TriggerEvaluationResponse:
     """Validate the scenario path and queue a background evaluation task."""
+    logger.info("[trigger] scenario=%s", request.scenario_path)
     resolved = scenario_scanner.resolve_scenario_path(request.scenario_path)
     if resolved is None:
+        logger.warning("[trigger] invalid scenario path: %s", request.scenario_path)
         raise HTTPException(
             status_code=400,
             detail=f"无效或不允许的场景路径: {request.scenario_path}",
         )
 
     task_id = task_manager.submit(request.scenario_path)
+    logger.info("[trigger] queued  task_id=%s  scenario=%s", task_id, request.scenario_path)
     return TriggerEvaluationResponse(task_id=task_id)
 
 
@@ -34,11 +40,15 @@ def get_task_status(task_id: str) -> TaskStatus:
     """Return the current status and logs for one evaluation task."""
     status = task_manager.get(task_id)
     if status is None:
+        logger.warning("[task_status] not found  task_id=%s", task_id)
         raise HTTPException(status_code=404, detail=f"未找到任务: {task_id}")
+    logger.debug("[task_status] task_id=%s  status=%s", task_id, status.status)
     return status
 
 
 @router.get("", response_model=dict)
 def list_tasks() -> dict[str, list]:
     """Return all known evaluation tasks for this server session."""
-    return {"tasks": [task.model_dump() for task in task_manager.list_tasks()]}
+    tasks = task_manager.list_tasks()
+    logger.info("[list_tasks] count=%d", len(tasks))
+    return {"tasks": [task.model_dump() for task in tasks]}

webapp/api/llm_profiles.py

@@ -2,41 +2,141 @@
 
 from __future__ import annotations
 
+import logging
+import time
+
 from fastapi import APIRouter, HTTPException
+from openai import OpenAI
 
 from webapp.models import (
     CreateProfileRequest,
     LLMProfile,
     ProfileApplyRequest,
     ProfileApplyResponse,
+    ProfileProbeRequest,
+    ProfileTestResponse,
 )
 from webapp.services.profile_manager import profile_manager
 from webapp.services.yaml_patcher import apply_profiles_to_scenario
 
 router = APIRouter(prefix="/api/llm-profiles", tags=["llm-profiles"])
+logger = logging.getLogger("webapp.api.llm_profiles")
+
+
+# 常见 embedding 模型名称关键词，用于自动判断走 /embeddings 端点
+_EMBEDDING_MODEL_KEYWORDS = (
+    "embedding", "embed", "text-search", "text-similarity",
+    "code-search", "ada-002",
+)
+
+
+def _is_embedding_model(model: str) -> bool:
+    """Heuristic: return True if the model name looks like an embedding model."""
+    return any(kw in model.lower() for kw in _EMBEDDING_MODEL_KEYWORDS)
+
+
+def _do_connectivity_test(
+    model: str,
+    base_url: str,
+    api_key: str,
+    timeout_seconds: int,
+) -> ProfileTestResponse:
+    """Send a minimal request and return the connectivity test result.
+
+    - Embedding models → POST /embeddings with a short text
+    - Chat models → POST /chat/completions, tries max_completion_tokens first
+      (required by newer models like gpt-5.x), falls back to max_tokens.
+    """
+    client = OpenAI(
+        api_key=api_key,
+        base_url=base_url.rstrip("/"),
+        timeout=float(timeout_seconds),
+    )
+    t0 = time.monotonic()
+
+    if _is_embedding_model(model):
+        # Embedding 模型走 /embeddings 端点
+        try:
+            client.embeddings.create(model=model, input="test")
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            return ProfileTestResponse(ok=True, message="连接成功（embedding）", latency_ms=latency_ms)
+        except Exception as exc:  # noqa: BLE001
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            return ProfileTestResponse(ok=False, message=str(exc), latency_ms=latency_ms)
+
+    # Chat 模型：先不限制 token（最兼容），超时/鉴权错误直接返回
+    # 避免 max_tokens=1 对部分模型（gpt-5.x）触发 min-output 限制
+    try:
+        client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": "hi"}],
+            max_tokens=8,   # 足够小节省费用，同时满足各模型最小输出要求
+        )
+        latency_ms = int((time.monotonic() - t0) * 1000)
+        return ProfileTestResponse(ok=True, message="连接成功", latency_ms=latency_ms)
+    except Exception as exc:  # noqa: BLE001
+        err_str = str(exc)
+        # 如果 max_tokens 不被支持，改用 max_completion_tokens 再试一次
+        if "max_tokens" in err_str and "max_completion_tokens" in err_str:
+            try:
+                client.chat.completions.create(
+                    model=model,
+                    messages=[{"role": "user", "content": "hi"}],
+                    max_completion_tokens=8,
+                )
+                latency_ms = int((time.monotonic() - t0) * 1000)
+                return ProfileTestResponse(ok=True, message="连接成功", latency_ms=latency_ms)
+            except Exception as exc2:  # noqa: BLE001
+                latency_ms = int((time.monotonic() - t0) * 1000)
+                return ProfileTestResponse(ok=False, message=str(exc2), latency_ms=latency_ms)
+        latency_ms = int((time.monotonic() - t0) * 1000)
+        return ProfileTestResponse(ok=False, message=err_str, latency_ms=latency_ms)
+
+    latency_ms = int((time.monotonic() - t0) * 1000)
+    return ProfileTestResponse(ok=False, message="连接测试失败", latency_ms=latency_ms)
+
+
+@router.post("/probe", response_model=ProfileTestResponse, tags=["llm-profiles"])
+def probe_connectivity(request: ProfileProbeRequest) -> ProfileTestResponse:
+    """Test LLM connectivity with inline credentials (no saved profile required)."""
+    logger.info("[probe] model=%s  base_url=%s", request.model, request.base_url)
+    result = _do_connectivity_test(
+        model=request.model,
+        base_url=request.base_url,
+        api_key=request.api_key,
+        timeout_seconds=request.timeout_seconds,
+    )
+    logger.info("[probe] ok=%s  latency=%sms  msg=%s", result.ok, result.latency_ms, result.message)
+    return result
 
 
 @router.get("", response_model=dict)
 def list_profiles() -> dict:
     """Return all saved LLM profiles."""
-    return {"profiles": [p.model_dump() for p in profile_manager.list_all()]}
+    profiles = profile_manager.list_all()
+    logger.info("[list_profiles] count=%d", len(profiles))
+    return {"profiles": [p.model_dump() for p in profiles]}
 
 
 @router.post("", status_code=201, response_model=LLMProfile)
 def create_profile(request: CreateProfileRequest) -> LLMProfile:
     """Create a new LLM profile."""
-    return profile_manager.create(
+    logger.info("[create_profile] name=%r  model=%s  base_url=%s", request.name, request.model, request.base_url)
+    profile = profile_manager.create(
         name=request.name,
         model=request.model,
         base_url=request.base_url,
         api_key=request.api_key,
         timeout_seconds=request.timeout_seconds,
     )
+    logger.info("[create_profile] created  id=%s", profile.profile_id)
+    return profile
 
 
 @router.put("/{profile_id}", response_model=LLMProfile)
 def update_profile(profile_id: str, request: CreateProfileRequest) -> LLMProfile:
     """Update an existing LLM profile by id."""
+    logger.info("[update_profile] id=%s  name=%r  model=%s", profile_id, request.name, request.model)
     updated = profile_manager.update(
         profile_id=profile_id,
         name=request.name,
@@ -46,22 +146,67 @@ def update_profile(profile_id: str, request: CreateProfileRequest) -> LLMProfile
         timeout_seconds=request.timeout_seconds,
     )
     if updated is None:
+        logger.warning("[update_profile] not found  id=%s", profile_id)
         raise HTTPException(status_code=404, detail=f"Profile not found: {profile_id}")
+    # Invalidate scorer cache so next request picks up the new profile settings.
+    try:
+        from webapp.services.inline_scorer import inline_scorer
+        inline_scorer.invalidate_cache()
+        logger.info("[update_profile] scorer cache invalidated  id=%s", profile_id)
+    except Exception:  # noqa: BLE001
+        pass
+    logger.info("[update_profile] updated  id=%s", profile_id)
     return updated
 
 
 @router.delete("/{profile_id}", response_model=dict)
 def delete_profile(profile_id: str) -> dict:
     """Delete an LLM profile by id."""
+    logger.info("[delete_profile] id=%s", profile_id)
     deleted = profile_manager.delete(profile_id)
     if not deleted:
+        logger.warning("[delete_profile] not found  id=%s", profile_id)
         raise HTTPException(status_code=404, detail=f"Profile not found: {profile_id}")
+    # Invalidate scorer cache in case the deleted profile was in use.
+    try:
+        from webapp.services.inline_scorer import inline_scorer
+        inline_scorer.invalidate_cache()
+    except Exception:  # noqa: BLE001
+        pass
+    logger.info("[delete_profile] deleted  id=%s", profile_id)
     return {"deleted": True}
 
 
+@router.post("/{profile_id}/test", response_model=ProfileTestResponse)
+def test_profile(profile_id: str) -> ProfileTestResponse:
+    """Test LLM connectivity for a saved profile."""
+    profile = profile_manager.get(profile_id)
+    if profile is None:
+        logger.warning("[test_profile] not found  id=%s", profile_id)
+        raise HTTPException(status_code=404, detail=f"Profile not found: {profile_id}")
+    logger.info("[test_profile] id=%s  model=%s  base_url=%s", profile_id, profile.model, profile.base_url)
+    result = _do_connectivity_test(
+        model=profile.model,
+        base_url=profile.base_url,
+        api_key=profile.api_key,
+        timeout_seconds=profile.timeout_seconds,
+    )
+    logger.info("[test_profile] ok=%s  latency=%sms", result.ok, result.latency_ms)
+    return result
+
+
 @router.post("/apply", response_model=ProfileApplyResponse)
 def apply_profiles(request: ProfileApplyRequest) -> ProfileApplyResponse:
     """Patch selected LLM profiles into the target scenario YAML file."""
+    logger.info(
+        "[apply_profiles] scenario=%s  judge=%s  answer=%s  dataset=%s  metric_weights=%s  doc_weights=%s",
+        request.scenario_path,
+        request.judge_profile_id,
+        request.answer_profile_id,
+        request.dataset_profile_id,
+        bool(request.metric_weights),
+        bool(request.doc_weights),
+    )
     role_profiles: dict[str, LLMProfile | None] = {
         "judge": profile_manager.get(request.judge_profile_id) if request.judge_profile_id else None,
         "answer": profile_manager.get(request.answer_profile_id) if request.answer_profile_id else None,
@@ -79,6 +224,7 @@ def apply_profiles(request: ProfileApplyRequest) -> ProfileApplyResponse:
     ]
 
     if missing:
+        logger.warning("[apply_profiles] missing profiles for roles: %s", missing)
         raise HTTPException(
             status_code=400,
             detail=f"Profile(s) not found for roles: {', '.join(missing)}",
@@ -89,7 +235,10 @@ def apply_profiles(request: ProfileApplyRequest) -> ProfileApplyResponse:
         judge_profile=role_profiles["judge"],
         answer_profile=role_profiles["answer"],
         dataset_profile=role_profiles["dataset"],
+        metric_weights=request.metric_weights,
+        doc_weights=request.doc_weights,
     )
+    logger.info("[apply_profiles] patched fields: %s", patched)
     return ProfileApplyResponse(
         scenario_path=request.scenario_path,
         patched_fields=patched,

webapp/api/pipeline.py

@@ -0,0 +1,131 @@
+"""Routes for the end-to-end pipeline API (document parse → build → eval)."""
+
+from __future__ import annotations
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from webapp.models import (
+    PipelineJobRequest,
+    PipelineJobResponse,
+    PipelineJobStatus,
+)
+from webapp.services.pipeline_task_manager import pipeline_task_manager
+
+router = APIRouter(prefix="/api/pipeline", tags=["pipeline"])
+logger = logging.getLogger("webapp.api.pipeline")
+
+
+@router.post(
+    "/jobs",
+    status_code=202,
+    response_model=PipelineJobResponse,
+    summary="提交全链路评估任务",
+    responses={
+        202: {
+            "description": "任务已成功排队，立即返回 job_id。",
+            "content": {
+                "application/json": {
+                    "example": {
+                        "job_id": "a1b2c3d4e5f6",
+                        "job_name": "siemens-ct-eval-2026",
+                        "status": "queued",
+                    }
+                }
+            },
+        },
+        422: {"description": "请求参数校验失败（docs_path 等必填字段缺失或格式错误）。"},
+    },
+)
+def submit_pipeline_job(request: PipelineJobRequest) -> PipelineJobResponse:
+    """提交一个「解析文档 → 生成题库 → RAGAS 评估 → 输出报告」全链路任务。
+
+    任务在后台线程中异步执行，立即返回 `job_id`。
+    通过 `GET /api/pipeline/jobs/{job_id}` 轮询 `status` / `phase` / `logs`。
+
+    **Pipeline 执行阶段**：
+    1. `parsing_documents` — 调用阿里云 DocMind 解析每份 PDF
+    2. `generating_questions` — LLM 从文档片段生成草稿题库
+    3. `evaluating` — RAGAS 在线评测打分（answer_model 答题 + judge_model 评分）
+    4. `done` — 所有产物写入磁盘，`status` 变为 `completed`
+    """
+    logger.info(
+        "[submit_pipeline] docs_path=%s  job_name=%r  gen_model=%s  judge=%s  max_docs=%s",
+        request.docs_path, request.job_name, request.generation_model,
+        request.judge_model, request.max_documents,
+    )
+    task = pipeline_task_manager.submit(request)
+    logger.info("[submit_pipeline] queued  job_id=%s  job_name=%s", task.job_id, task.job_name)
+    return PipelineJobResponse(
+        job_id=task.job_id,
+        job_name=task.job_name,
+        status=task.status,
+    )
+
+
+@router.get(
+    "/jobs/{job_id}",
+    response_model=PipelineJobStatus,
+    summary="查询任务状态",
+    responses={
+        200: {"description": "返回任务当前状态、执行阶段、日志及完成后的产物路径。"},
+        404: {"description": "指定 job_id 的任务不存在。"},
+    },
+)
+def get_pipeline_job(job_id: str) -> PipelineJobStatus:
+    """查询一个 Pipeline 任务的当前状态、执行阶段、实时日志和结果。
+
+    **轮询建议**：每 3–5 秒查询一次，直到 `status` 为 `completed` 或 `failed`。
+
+    `result` 字段在任务完成后填充，包含：
+    - `scores_csv` — 每道题目逐项评分
+    - `summary_md` — 评估摘要 Markdown
+    - `dataset_csv` — 生成的题库 CSV
+    - `source_chunks_jsonl` — 文档片段索引
+    """
+    status = pipeline_task_manager.get(job_id)
+    if status is None:
+        logger.warning("[get_pipeline_job] not found  job_id=%s", job_id)
+        raise HTTPException(status_code=404, detail=f"Pipeline job not found: {job_id}")
+    logger.debug("[get_pipeline_job] job_id=%s  status=%s  phase=%s", job_id, status.status, status.phase)
+    return status
+
+
+@router.get(
+    "/jobs",
+    response_model=dict,
+    summary="列出所有任务",
+    responses={
+        200: {
+            "description": "按创建时间倒序返回本次服务器会话中所有的 Pipeline 任务。",
+            "content": {
+                "application/json": {
+                    "example": {
+                        "jobs": [
+                            {
+                                "job_id": "a1b2c3d4e5f6",
+                                "job_name": "siemens-ct-eval",
+                                "status": "completed",
+                                "phase": "done",
+                                "logs": ["[build] 17 documents parsed", "..."],
+                                "result": {
+                                    "total_questions": 19,
+                                    "eval_run_id": "2026-06-18T...",
+                                    "scores_csv": "outputs/pipeline/.../scores.csv",
+                                    "summary_md": "outputs/pipeline/.../summary.md",
+                                },
+                                "error": None,
+                            }
+                        ]
+                    }
+                }
+            },
+        }
+    },
+)
+def list_pipeline_jobs() -> dict:
+    """返回本次服务器会话中所有已提交的 Pipeline 任务，按创建时间倒序排列。"""
+    jobs = pipeline_task_manager.list_jobs()
+    logger.info("[list_pipeline_jobs] count=%d", len(jobs))
+    return {"jobs": [s.model_dump() for s in jobs]}

webapp/api/runs.py

@@ -2,31 +2,42 @@
 
 from __future__ import annotations
 
+import logging
+
 from fastapi import APIRouter, HTTPException
 
 from webapp.models import RunDetail
 from webapp.services import report_builder, run_reader
 
 router = APIRouter(prefix="/api/runs", tags=["runs"])
+logger = logging.getLogger("webapp.api.runs")
 
 
 @router.get("")
 def get_runs() -> dict[str, list]:
     """Return summaries for every discoverable evaluation run."""
     summaries = run_reader.list_run_summaries()
+    logger.info("[get_runs] found %d runs", len(summaries))
     return {"runs": [summary.model_dump() for summary in summaries]}
 
 
 @router.get("/{run_id}")
 def get_run_detail(run_id: str) -> RunDetail:
     """Return the full summary and aggregated report for one run."""
+    logger.info("[get_run_detail] run_id=%s", run_id)
     run_dir = run_reader.find_run_dir(run_id)
     if run_dir is None:
+        logger.warning("[get_run_detail] not found  run_id=%s", run_id)
         raise HTTPException(status_code=404, detail=f"未找到运行: {run_id}")
 
     summary = run_reader.build_run_summary(run_dir)
     if summary is None:
+        logger.warning("[get_run_detail] missing metadata  run_id=%s", run_id)
         raise HTTPException(status_code=404, detail=f"运行元数据缺失: {run_id}")
 
     report = report_builder.build_report(run_dir, summary.metrics)
+    logger.info(
+        "[get_run_detail] ok  run_id=%s  metrics=%s  valid=%d  invalid=%d",
+        run_id, summary.metrics, summary.valid_samples, summary.invalid_samples,
+    )
     return RunDetail(summary=summary, report=report)

webapp/api/scenarios.py

@@ -2,15 +2,20 @@
 
 from __future__ import annotations
 
+import logging
+
 from fastapi import APIRouter
 
 from webapp.services import scenario_scanner
 
 router = APIRouter(prefix="/api/scenarios", tags=["scenarios"])
+logger = logging.getLogger("webapp.api.scenarios")
 
 
 @router.get("")
 def get_scenarios() -> dict[str, list]:
     """Return every scenario file found under the scenarios/ directory."""
     scenarios = scenario_scanner.list_scenarios()
+    valid = sum(1 for s in scenarios if not s.error)
+    logger.info("[get_scenarios] total=%d  valid=%d  errors=%d", len(scenarios), valid, len(scenarios) - valid)
     return {"scenarios": [item.model_dump() for item in scenarios]}

webapp/api/score.py

@@ -0,0 +1,176 @@
+"""Route for real-time single-sample RAGAS scoring (Dify external Tool endpoint)."""
+
+from __future__ import annotations
+
+import logging
+import time
+from typing import Annotated
+
+from fastapi import APIRouter, Header, HTTPException, Request
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import JSONResponse
+
+from rag_eval.metrics.weights import compute_weighted_score
+from rag_eval.settings import EvaluationSettings
+from webapp.models import ScoreRequest, ScoreResponse
+from webapp.services.inline_scorer import inline_scorer
+
+router = APIRouter(prefix="/api/score", tags=["score"])
+logger = logging.getLogger("webapp.api.score")
+
+
+def _get_settings() -> EvaluationSettings:
+    """Return a fresh EvaluationSettings instance (overridable in tests)."""
+    return EvaluationSettings()
+
+
+def _check_auth(authorization: str | None, token: str) -> None:
+    """Raise 401 if Bearer token does not match the configured token."""
+    if authorization is None:
+        raise HTTPException(status_code=401, detail="Missing Authorization header.")
+    parts = authorization.split(" ", 1)
+    if len(parts) != 2 or parts[0].lower() != "bearer" or parts[1] != token:
+        raise HTTPException(status_code=401, detail="Invalid Bearer token.")
+
+
+@router.post(
+    "",
+    response_model=ScoreResponse,
+    summary="单题实时评分（Dify 外部 Tool）",
+    responses={
+        200: {
+            "description": "各指标得分、加权综合得分及耗时。",
+            "content": {
+                "application/json": {
+                    "example": {
+                        "scores": {
+                            "faithfulness": 0.875,
+                            "answer_relevancy": 0.920,
+                            "context_recall": 0.810,
+                            "context_precision": 0.850,
+                        },
+                        "weighted_score": 0.8638,
+                        "latency_ms": 3420,
+                        "skipped_metrics": [],
+                        "error": None,
+                    }
+                }
+            },
+        },
+        401: {"description": "配置了 SCORE_API_TOKEN 但未提供有效 Bearer token。"},
+        422: {"description": "请求参数校验失败（必填字段缺失或 metrics 名称不合法）。"},
+    },
+)
+def score_sample(
+    raw_request: Request,
+    request: ScoreRequest,
+    authorization: Annotated[str | None, Header()] = None,
+) -> ScoreResponse:
+    """接受单条问答记录，同步运行 RAGAS 指标打分，实时返回各指标得分。
+
+    **主要用途**：供 Dify 外部 Tool 调用。Dify Agent 在生成回答后，将
+    `(question, answer, contexts)` 发送到此端点，即可获得 RAGAS 质量评分，
+    用于日志记录、质量监控或触发 Agent 自我改进流程。
+
+    **contexts 格式**：多个检索片段用 `context_separator`（默认 `" |||| "`）拼接为一个字符串，
+    服务端自动拆分后传入 RAGAS 管道。**contexts 为可选字段**，缺失时自动跳过依赖检索内容的指标
+    （`faithfulness`、`context_recall`、`context_precision`、`noise_sensitivity`）。
+
+    **ground_truth 可选**：
+    - 提供时：所有指定指标均参与计算。
+    - 缺失时：自动跳过依赖参考答案的指标（`context_recall`、
+      `factual_correctness`、`semantic_similarity`、`noise_sensitivity`），
+      跳过的指标在响应的 `skipped_metrics` 列表中列出，对应 `scores` 值为 `null`。
+
+    **支持的 RAGAS 指标**：
+    - `faithfulness` — 回答与检索片段的事实一致性
+    - `answer_relevancy` — 回答与问题的相关性
+    - `context_recall` — 参考答案覆盖到的检索内容比例（需 ground_truth）
+    - `context_precision` — 检索片段中与答案相关的部分占比
+    - `noise_sensitivity` — 对无关噪声片段的敏感度（需 ground_truth）
+    - `factual_correctness` — 回答与参考答案的事实准确性（需 ground_truth）
+    - `semantic_similarity` — 回答与参考答案的语义相似度（需 ground_truth）
+
+    **推荐模型配置**：
+    - `judge_model`: `gpt-5`
+    - `embedding_model`: `text-embedding-3-small`
+
+    **鉴权**：若 `.env` 中配置了 `SCORE_API_TOKEN`，需在请求头携带
+    `Authorization: Bearer <token>`；留空则无需鉴权（适合内网部署）。
+    """
+    client = f"{raw_request.client.host}:{raw_request.client.port}" if raw_request.client else "unknown"
+    logger.info(
+        "[score] incoming  client=%s  method=%s  content_type=%s  metrics=%s  has_gt=%s  has_ctx=%s",
+        client,
+        raw_request.method,
+        raw_request.headers.get("content-type", ""),
+        request.metrics,
+        request.ground_truth is not None,
+        bool(request.contexts),
+    )
+    settings = _get_settings()
+
+    # Require Bearer auth only when the deployment configured a shared token.
+    if settings.score_api_token:
+        _check_auth(authorization, settings.score_api_token)
+
+    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))
+
+    if not effective:
+        return ScoreResponse(
+            scores={metric_name: None for metric_name in request.metrics},
+            weighted_score=None,
+            latency_ms=0,
+            skipped_metrics=skipped,
+        )
+
+    t0 = time.monotonic()
+    try:
+        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,
+        )
+    except Exception as exc:  # noqa: BLE001
+        latency_ms = int((time.monotonic() - t0) * 1000)
+        return ScoreResponse(
+            scores={},
+            weighted_score=None,
+            latency_ms=latency_ms,
+            skipped_metrics=skipped,
+            error=f"{type(exc).__name__}: {exc}",
+        )
+
+    latency_ms = int((time.monotonic() - t0) * 1000)
+
+    # Keep skipped metrics visible to callers by emitting them as null scores.
+    all_scores: dict[str, float | None] = {metric_name: None for metric_name in request.metrics}
+    all_scores.update(raw_scores)
+
+    # 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",
+        latency_ms,
+        skipped,
+        {k: (round(v, 4) if v is not None else None) for k, v in all_scores.items()},
+    )
+    return ScoreResponse(
+        scores=all_scores,
+        weighted_score=None,  # 综合加权得分已暂时禁用
+        latency_ms=latency_ms,
+        skipped_metrics=skipped,
+    )

webapp/api/score_jobs.py

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

webapp/api/session_score_jobs.py

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

webapp/models.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 from datetime import datetime, timezone
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 
 def _utcnow_iso() -> str:
@@ -74,6 +74,18 @@ class ReportData(BaseModel):
     lowest_samples: list[SampleScore] = Field(default_factory=list)
     summary_markdown: str = ""
     advice_markdown: str = ""  # optimization_advice.md content (empty if not generated)
+    weighted_score_mean: float | None = Field(
+        default=None,
+        description="加权综合得分均值（metric_weights × doc_weights 共同作用）。",
+    )
+    metric_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="该次运行使用的指标权重配置（来自 scenario.snapshot.yaml）。",
+    )
+    doc_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="该次运行使用的文档权重配置（来自 scenario.snapshot.yaml）。",
+    )
 
 
 class RunDetail(BaseModel):
@@ -93,6 +105,14 @@ class ScenarioInfo(BaseModel):
     judge_model: str = ""
     metrics: list[str] = Field(default_factory=list)
     error: str = ""
+    metric_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="从场景 YAML 读取的指标权重配置，供前端权重面板预填。",
+    )
+    doc_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="从场景 YAML 读取的文档权重配置，供前端权重面板预填。",
+    )
 
 
 class TaskStatus(BaseModel):
@@ -150,6 +170,14 @@ class ProfileApplyRequest(BaseModel):
     judge_profile_id: str | None = None
     answer_profile_id: str | None = None
     dataset_profile_id: str | None = None
+    metric_weights: dict[str, float] | None = Field(
+        default=None,
+        description="指标权重映射，如 {\"faithfulness\": 0.35}。为 null 时不修改 YAML。",
+    )
+    doc_weights: dict[str, float] | None = Field(
+        default=None,
+        description="文档权重映射，如 {\"doc.pdf\": 2.0}。为 null 时不修改 YAML。",
+    )
 
 
 class ProfileApplyResponse(BaseModel):
@@ -159,6 +187,23 @@ class ProfileApplyResponse(BaseModel):
     patched_fields: list[str] = Field(default_factory=list)
 
 
+class ProfileProbeRequest(BaseModel):
+    """Inline credentials for testing LLM connectivity without saving a profile."""
+
+    model: str
+    base_url: str
+    api_key: str
+    timeout_seconds: int = 30
+
+
+class ProfileTestResponse(BaseModel):
+    """Result of a LLM connectivity test."""
+
+    ok: bool
+    message: str
+    latency_ms: int | None = None
+
+
 def jsonable(value: Any) -> Any:
     """Convert NaN/inf floats into None so the payload stays valid JSON."""
     import math
@@ -172,3 +217,401 @@ def jsonable(value: Any) -> Any:
     if isinstance(value, list):
         return [jsonable(item) for item in value]
     return value
+
+
+# ---------------------------------------------------------------------------
+# Full pipeline (build + eval) job models
+# ---------------------------------------------------------------------------
+
+class PipelineJobRequest(BaseModel):
+    """Request body for launching an end-to-end build + evaluation pipeline job."""
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "examples": [
+                {
+                    "summary": "西门子 CT 文档评估（完整参数）",
+                    "value": {
+                        "docs_path": "datasets/siemens-pdfs",
+                        "job_name": "siemens-ct-eval-2026",
+                        "generation_model": "qwen3.6-plus",
+                        "answer_model": "deepseek-v4-flash",
+                        "judge_model": "deepseek-v4-flash",
+                        "embedding_model": "text-embedding-v3",
+                        "max_questions_per_document": 10,
+                        "max_source_chunks_per_question": 3,
+                        "max_documents": None,
+                        "max_samples": None,
+                        "metrics": [
+                            "faithfulness",
+                            "answer_relevancy",
+                            "context_recall",
+                            "context_precision",
+                        ],
+                        "optimization_advisor": False,
+                        "failure_mode": "skip",
+                    },
+                },
+                {
+                    "summary": "快速冒烟测试（仅 2 份文档、5 道题）",
+                    "value": {
+                        "docs_path": "datasets/siemens-pdfs",
+                        "job_name": "smoke-test",
+                        "generation_model": "qwen3.6-plus",
+                        "answer_model": "deepseek-v4-flash",
+                        "judge_model": "deepseek-v4-flash",
+                        "embedding_model": "text-embedding-v3",
+                        "max_questions_per_document": 5,
+                        "max_source_chunks_per_question": 3,
+                        "max_documents": 2,
+                        "max_samples": 10,
+                        "metrics": ["faithfulness", "answer_relevancy"],
+                        "optimization_advisor": False,
+                        "failure_mode": "skip",
+                    },
+                },
+            ]
+        }
+    )
+
+    docs_path: str = Field(
+        description="PDF 文档所在文件夹的绝对路径或相对于仓库根目录的相对路径。"
+    )
+    job_name: str = Field(
+        default="",
+        description="任务显示名称；留空时系统自动生成唯一标识。",
+    )
+    generation_model: str = Field(
+        default="qwen3.6-plus",
+        description="用于从文档片段生成草稿题库的 LLM 模型名称。",
+    )
+    answer_model: str = Field(
+        default="deepseek-v4-flash",
+        description="在线评估时调用的答题 LLM 模型名称（siemens_pdf_qa adapter）。",
+    )
+    judge_model: str = Field(
+        default="deepseek-v4-flash",
+        description="RAGAS 指标评分时使用的 Judge LLM 模型名称。",
+    )
+    embedding_model: str = Field(
+        default="text-embedding-v3",
+        description="RAGAS context-recall / context-precision 使用的 Embedding 模型名称。",
+    )
+    max_questions_per_document: int = Field(
+        default=10, gt=0,
+        description="每份 PDF 文档最多生成的草稿题目数量。",
+    )
+    max_source_chunks_per_question: int = Field(
+        default=3, gt=0,
+        description="每道题目最多引用的文档片段（source chunk）数量。",
+    )
+    max_documents: int | None = Field(
+        default=None, gt=0,
+        description="限制处理的 PDF 文件数量上限（冒烟测试时使用）。",
+    )
+    max_samples: int | None = Field(
+        default=None, gt=0,
+        description="限制评估的题目数量上限（冒烟测试时使用）。",
+    )
+    metrics: list[str] = Field(
+        default_factory=lambda: [
+            "faithfulness",
+            "answer_relevancy",
+            "context_recall",
+            "context_precision",
+        ],
+        description=(
+            "需要计算的 RAGAS 指标列表。"
+            "可选值：faithfulness, answer_relevancy, context_recall, "
+            "context_precision, noise_sensitivity, factual_correctness, semantic_similarity。"
+        ),
+    )
+    optimization_advisor: bool = Field(
+        default=False,
+        description="为 True 时启用 RAGAS 优化建议模块，生成 optimization_advice.md。",
+    )
+    failure_mode: str = Field(
+        default="skip",
+        description="PDF 解析失败时的处理策略：skip（跳过继续）或 fail（立即中止）。",
+    )
+
+
+class PipelineResult(BaseModel):
+    """Artifact locations and statistics for a completed pipeline run."""
+
+    build_artifact_dir: str = Field(description="题库生成阶段的产物根目录路径。")
+    dataset_csv: str = Field(description="生成的草稿题库 CSV 文件路径（评估输入）。")
+    source_chunks_jsonl: str = Field(description="文档片段索引文件路径（在线评估 adapter 使用）。")
+    total_questions: int = Field(description="成功生成的有效题目总数。")
+    parse_failures: int = Field(description="文档解析失败的 PDF 数量。")
+    eval_run_id: str = Field(description="RAGAS 评估运行 ID。")
+    eval_output_dir: str = Field(description="RAGAS 评估产物根目录路径。")
+    scores_csv: str = Field(description="每道题目逐项评分的 CSV 文件路径。")
+    summary_md: str = Field(description="评估结果摘要 Markdown 文件路径。")
+
+
+class PipelineJobStatus(BaseModel):
+    """State of one end-to-end pipeline job."""
+
+    job_id: str = Field(description="任务唯一标识符。")
+    job_name: str = Field(description="任务显示名称。")
+    status: str = Field(description="任务状态：queued | running | completed | failed。")
+    phase: str = Field(default="idle", description="当前执行阶段：idle | parsing_documents | generating_questions | evaluating | done。")
+    logs: list[str] = Field(default_factory=list, description="实时日志行列表。")
+    result: PipelineResult | None = Field(default=None, description="任务完成后填充的产物路径与统计信息。")
+    error: str | None = Field(default=None, description="失败时的错误信息。")
+    created_at: str = Field(default="", description="任务创建时间（ISO 8601 UTC）。")
+    finished_at: str = Field(default="", description="任务结束时间（ISO 8601 UTC）。")
+
+
+class PipelineJobResponse(BaseModel):
+    """Immediate response returned after a pipeline job is queued."""
+
+    job_id: str = Field(description="任务唯一标识符，用于后续轮询状态。")
+    job_name: str = Field(description="任务显示名称。")
+    status: str = Field(default="queued", description="初始状态，通常为 queued。")
+
+
+# ---------------------------------------------------------------------------
+# Dify 实时评分 API 模型
+# ---------------------------------------------------------------------------
+
+# 需要 ground_truth 才能计算的指标集合
+_GT_DEPENDENT_METRICS: frozenset[str] = frozenset({
+    "context_recall",
+    "factual_correctness",
+    "semantic_similarity",
+    "noise_sensitivity",
+})
+
+# 需要 contexts 才能计算的指标集合
+_CONTEXT_DEPENDENT_METRICS: frozenset[str] = frozenset({
+    "faithfulness",
+    "context_recall",
+    "context_precision",
+    "noise_sensitivity",
+})
+
+# 所有合法指标名称
+_VALID_METRICS: frozenset[str] = frozenset({
+    "faithfulness",
+    "answer_relevancy",
+    "context_recall",
+    "context_precision",
+    "noise_sensitivity",
+    "factual_correctness",
+    "semantic_similarity",
+})
+
+_DEFAULT_SCORE_METRICS: list[str] = [
+    "faithfulness",
+    "answer_relevancy",
+    "context_recall",
+    "context_precision",
+]
+
+
+class ScoreRequest(BaseModel):
+    """Request body for the real-time single-sample scoring endpoint."""
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "question": "双源CT的时间分辨率是多少?",
+                "answer": "双源CT的单扇区时间分辨率为75ms。",
+                "contexts": "双源CT采用两套管-探测器系统 |||| 单扇区采集旋转135度",
+                "ground_truth": "双源CT单扇区时间分辨率为75ms，需旋转135度。",
+                "context_separator": " |||| ",
+                "metrics": [
+                    "faithfulness",
+                    "answer_relevancy",
+                    "context_recall",
+                    "context_precision",
+                ],
+                "judge_model": "gpt-5",
+                "embedding_model": "text-embedding-3-small",
+            }
+        }
+    )
+
+    question: str = Field(description="问题文本。")
+    answer: str = Field(description="待评分的回答。")
+    contexts: str | None = Field(
+        default=None,
+        description="检索上下文字符串，多段之间用 context_separator 拼接。缺失时自动跳过依赖检索内容的指标（faithfulness、context_recall、context_precision、noise_sensitivity）。",
+    )
+    ground_truth: str | None = Field(
+        default=None,
+        description="标准参考答案（可选）。缺失时自动跳过需要它的指标。",
+    )
+    context_separator: str = Field(
+        default=" |||| ",
+        description="contexts 字段中段落分隔符，默认为四个竖线两侧各一空格。",
+    )
+    metrics: list[str] = Field(
+        default_factory=lambda: list(_DEFAULT_SCORE_METRICS),
+        description="需要计算的 RAGAS 指标列表。",
+    )
+    judge_model: str | None = Field(
+        default=None,
+        description="Judge LLM 模型名称；为 null 时使用 .env 中的 RAGAS_JUDGE_MODEL。",
+    )
+    embedding_model: str | None = Field(
+        default=None,
+        description="Embedding 模型名称；为 null 时使用 .env 中的 RAGAS_EMBEDDING_MODEL。",
+    )
+
+    @field_validator("metrics")
+    @classmethod
+    def validate_metric_names(cls, value: list[str]) -> list[str]:
+        """Reject any metric name not in the supported registry."""
+        invalid = [metric_name for metric_name in value if metric_name not in _VALID_METRICS]
+        if invalid:
+            raise ValueError(
+                f"不支持的指标名称：{invalid}。"
+                f"合法值：{sorted(_VALID_METRICS)}"
+            )
+        if not value:
+            raise ValueError("metrics 不能为空列表。")
+        return value
+
+    def contexts_as_list(self) -> list[str]:
+        """Split the contexts string into a list of non-empty fragments.
+
+        Returns an empty list when contexts is None or blank.
+        """
+        if not self.contexts:
+            return []
+        separator = self.context_separator or " |||| "
+        return [part.strip() for part in self.contexts.split(separator) if part.strip()]
+
+    def effective_metrics(self) -> list[str]:
+        """Return metrics filtered to exclude GT-dependent or context-dependent ones when inputs are absent."""
+        result = list(self.metrics)
+        if self.ground_truth is None:
+            result = [m for m in result if m not in _GT_DEPENDENT_METRICS]
+        if not self.contexts:
+            result = [m for m in result if m not in _CONTEXT_DEPENDENT_METRICS]
+        return result
+
+
+class ScoreResponse(BaseModel):
+    """Response payload for the real-time scoring endpoint."""
+
+    scores: dict[str, float | None] = Field(
+        description="各指标得分（NaN 或计算失败时为 null）。"
+    )
+    weighted_score: float | None = Field(
+        default=None,
+        description="等权加权综合得分（仅对非 null 指标求均值）。",
+    )
+    latency_ms: int = Field(description="服务端打分耗时（毫秒）。")
+    skipped_metrics: list[str] = Field(
+        default_factory=list,
+        description="因缺少 ground_truth 而跳过的指标名称列表。",
+    )
+    error: str | None = Field(
+        default=None,
+        description="打分异常时的错误信息（HTTP 200 仍返回，scores 为空）。",
+    )
+
+
+# ---------------------------------------------------------------------------
+# 异步评分记录模型
+# ---------------------------------------------------------------------------
+
+class AsyncScoreJobResponse(BaseModel):
+    """Immediate 202 response after submitting an async score job."""
+
+    job_id: str = Field(description="任务唯一标识符，用于后续查询结果。")
+    status: str = Field(default="queued", description="初始状态：queued。")
+    run_id: str | None = Field(
+        default=None,
+        description="评分完成后写入的 Run ID，可在「运行列表」中查看完整报告。",
+    )
+
+
+# ---------------------------------------------------------------------------
+# Session async 评分模型
+# ---------------------------------------------------------------------------
+
+class SessionScoreRequest(ScoreRequest):
+    """Request body for session-grouped async scoring.
+
+    All calls sharing the same session_id are accumulated into one report.
+    Each call adds a new sample row to the session's scores.csv.
+    """
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "examples": [
+                {
+                    "summary": "Dify 会话批量评分",
+                    "value": {
+                        "session_id": "dify-session-001",
+                        "question": "单源CT与双源CT在球管配置上有何本质区别？",
+                        "answer": "单源CT只有一套球管-探测器系统，双源CT有两套独立的球管-探测器系统。",
+                        "contexts": "双源CT采用两套管-探测器系统 |||| 单源CT只有一个球管",
+                        "context_separator": " |||| ",
+                        "metrics": ["answer_relevancy", "faithfulness"],
+                        "judge_model": "gpt-5.5",
+                        "embedding_model": "text-embedding-3-small",
+                    },
+                }
+            ]
+        }
+    )
+
+    session_id: str = Field(
+        description=(
+            "会话唯一标识符。相同 session_id 的多次调用合并为同一报告，"
+            "每次调用新增一个样本行，指标均值和优化建议在每次调用后增量更新。"
+        ),
+    )
+
+
+class SessionScoreJobResponse(BaseModel):
+    """Immediate 202 response after submitting a session scoring call."""
+
+    job_id: str = Field(description="本次调用的任务唯一标识符。")
+    session_id: str = Field(description="会话标识符。")
+    run_id: str = Field(description="本 session 对应的报告 Run ID，可在「运行列表」中查看。")
+    status: str = Field(default="queued", description="初始状态：queued。")
+    call_count: int = Field(default=1, description="本 session 当前累计调用次数（包含本次）。")
+
+
+class SessionStatus(BaseModel):
+    """Aggregate status and metrics for a scoring session."""
+
+    session_id: str = Field(description="会话标识符。")
+    run_id: str = Field(description="对应报告目录的 Run ID。")
+    call_count: int = Field(description="本 session 累计调用次数。")
+    metric_means: dict[str, float | None] = Field(
+        default_factory=dict, description="所有已累积样本的各指标均值。"
+    )
+    latest_finished_at: str = Field(default="", description="最近一次评分完成时间（ISO 8601 UTC）。")
+    jobs: list[AsyncScoreJobStatus] = Field(
+        default_factory=list, description="本 session 所有调用记录，按创建时间排序。"
+    )
+
+
+class AsyncScoreJobStatus(BaseModel):
+    """State of one async score job (queued → running → completed/failed)."""
+
+    job_id: str = Field(description="任务唯一标识符。")
+    status: str = Field(description="queued | running | completed | failed")
+    created_at: str = Field(default="", description="创建时间（ISO 8601 UTC）。")
+    finished_at: str = Field(default="", description="完成时间（ISO 8601 UTC）。")
+    run_id: str | None = Field(
+        default=None,
+        description="完成后对应的 Run ID，可通过 GET /api/runs/{run_id} 查看完整报告。",
+    )
+    request_summary: dict = Field(
+        default_factory=dict,
+        description="请求参数快照（question 前80字、metrics、judge_model 等）。",
+    )
+    scores: dict[str, float | None] = Field(default_factory=dict, description="各指标得分。")
+    weighted_score: float | None = Field(default=None, description="加权综合得分。")
+    latency_ms: int = Field(default=0, description="评分耗时毫秒。")
+    skipped_metrics: list[str] = Field(default_factory=list)
+    error: str | None = Field(default=None)

webapp/server.py

@@ -7,29 +7,152 @@ the server starts even when the evaluation dependencies are not yet installed.
 
 from __future__ import annotations
 
+import logging
+import time
 from pathlib import Path
 
-from fastapi import FastAPI
+from fastapi import FastAPI, Request
-from fastapi.responses import FileResponse
+from fastapi.encoders import jsonable_encoder
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import FileResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 
-from webapp.api import evaluations, llm_profiles, runs, scenarios
+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")
+access_logger = logging.getLogger("webapp.access")
+
+# OpenAPI tag metadata — controls the grouping and descriptions in /docs.
+OPENAPI_TAGS = [
+    {
+        "name": "pipeline",
+        "description": (
+            "**全链路评估 Pipeline API**\n\n"
+            "一次调用完成「解析文档 → 生成题库 → RAGAS 评估 → 输出报告」全流程。\n\n"
+            "**使用流程**\n"
+            "1. `POST /api/pipeline/jobs` 提交任务，立即拿到 `job_id`。\n"
+            "2. `GET /api/pipeline/jobs/{job_id}` 轮询 `status` / `phase` / `logs`。\n"
+            "3. 当 `status=completed` 时，`result` 字段包含所有产物路径。\n\n"
+            "**Pipeline 阶段**\n"
+            "| phase | 说明 |\n"
+            "|-------|------|\n"
+            "| `parsing_documents` | 调用阿里云 DocMind 解析每份 PDF |\n"
+            "| `generating_questions` | LLM 从文档片段生成草稿题库 |\n"
+            "| `evaluating` | RAGAS 在线评测打分 |\n"
+            "| `done` | 所有产物写入磁盘，任务完成 |"
+        ),
+    },
+    {
+        "name": "evaluations",
+        "description": (
+            "**单场景评估 API**\n\n"
+            "基于已有 YAML 场景文件触发评估任务，并查询任务状态与日志。"
+        ),
+    },
+    {
+        "name": "llm-profiles",
+        "description": (
+            "**LLM 配置管理 API**\n\n"
+            "增删改查已保存的 LLM 连接配置（模型名称、Base URL、API Key）；"
+            "支持连通性测试；可将配置一键写入场景 YAML 文件。"
+        ),
+    },
+    {
+        "name": "runs",
+        "description": "**评估运行列表 API**\n\n查询历史评估运行记录及详细报告数据。",
+    },
+    {
+        "name": "scenarios",
+        "description": "**场景文件 API**\n\n扫描并列出 `scenarios/` 目录下所有可用的 YAML 场景文件。",
+    },
+    {
+        "name": "score",
+        "description": (
+            "**实时评分 API（同步）** — `POST /api/score`\n\n"
+            "**异步评分 API（Dify 推荐）** — `POST /api/score/async`\n\n"
+            "异步方式立即返回 job_id（202），评分在后台执行，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\n"
+            "**Session 模式调用流程**\n"
+            "1. `POST /api/score/session_async` 提交一条问答评分请求。\n"
+            "2. 用 `GET /api/score/session/jobs/{job_id}` 轮询单次调用状态。\n"
+            "3. 用 `GET /api/score/sessions/{session_id}` 查看 session 聚合状态。\n"
+            "4. 用 `GET /api/runs/{run_id}` 或在「运行列表」中查看完整报告。\n\n"
+            "通过 `GET /api/score/jobs` 列出所有异步评分记录，"
+            "`GET /api/score/jobs/{job_id}` 查询单个任务状态。\n\n"
+            "**鉴权**：若 `.env` 中配置了 `SCORE_API_TOKEN`，需携带 "
+            "`Authorization: Bearer <token>` 请求头。"
+        ),
+    },
+    {
+        "name": "meta",
+        "description": "**系统 API**\n\n健康检查等基础接口。",
+    },
+]
 
 
 def create_app() -> FastAPI:
     """Build and configure the FastAPI application instance."""
     app = FastAPI(
-        title="Siemens RAGAS 评估控制台",
+        title="Siemens RAGAS 评估平台",
-        description="RAGAS 评估子系统的可视化报告与评估触发控制台。",
+        description=(
-        version="0.1.0",
+            "西门子医疗影像 RAG 评估平台 API 文档。\n\n"
+            "提供以下能力：\n"
+            "- **Pipeline API** — 一键完成「解析文档 → 生成题库 → RAGAS 评估」全链路\n"
+            "- **实时评分 API** — 供 Dify 外部 Tool 调用的单题 RAGAS 评分接口\n"
+            "- **评估 API** — 基于 YAML 场景文件触发单次评估\n"
+            "- **LLM 配置 API** — 管理多个 LLM 连接配置，支持连通性测试\n"
+            "- **报告 API** — 查询历史运行记录与评估报告\n\n"
+            "> **快速开始**：调用 `POST /api/pipeline/jobs` 传入 PDF 文件夹路径即可启动完整评估流程。"
+        ),
+        version="0.3.0",
+        openapi_tags=OPENAPI_TAGS,
     )
 
     app.include_router(runs.router)
     app.include_router(scenarios.router)
     app.include_router(evaluations.router)
     app.include_router(llm_profiles.router)
+    app.include_router(pipeline.router)
+    app.include_router(score.router)
+    app.include_router(score_jobs.router)
+    app.include_router(session_score_jobs.router)
+
+    @app.middleware("http")
+    async def access_log_middleware(request: Request, call_next):
+        """Log every API request with method, path, status code and latency.
+
+        Static file requests are logged at DEBUG level to keep the console clean.
+        """
+        t0 = time.monotonic()
+        response = await call_next(request)
+        latency_ms = int((time.monotonic() - t0) * 1000)
+        path = request.url.path
+        is_static = path.startswith("/static/") or path in ("/", "/favicon.ico")
+        msg = "%s %s → %d  (%dms)", request.method, path, response.status_code, latency_ms
+        if is_static:
+            access_logger.debug(*msg)
+        else:
+            access_logger.info(*msg)
+        return response
+
+    @app.exception_handler(RequestValidationError)
+    async def validation_exception_handler(request: Request, exc: RequestValidationError) -> JSONResponse:
+        """Log full validation error detail to help diagnose 422 responses."""
+        errors = jsonable_encoder(exc.errors())
+        logger.warning(
+            "[422] validation error  url=%s  content_type=%s  errors=%s",
+            request.url.path,
+            request.headers.get("content-type", ""),
+            errors,
+        )
+        return JSONResponse(
+            status_code=422,
+            content={"detail": errors},
+        )
 
     @app.get("/api/health", tags=["meta"])
     def health() -> dict[str, str]:

webapp/services/inline_scorer.py

@@ -0,0 +1,118 @@
+"""LLM-cached inline RAGAS scorer for the real-time /api/score endpoint.
+
+A module-level InlineScorer singleton caches (llm, embeddings) pairs keyed by
+(judge_model, embedding_model), so repeated Dify Tool calls with the same
+models reuse existing AsyncOpenAI connections instead of creating new ones.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import math
+import threading
+from typing import Any
+
+from rag_eval.compat import ensure_ragas_import_compat
+from rag_eval.metrics.factory import build_models
+from rag_eval.metrics.pipeline import MetricPipeline
+from rag_eval.settings import EvaluationSettings
+from rag_eval.shared.models import NormalizedSample
+
+ensure_ragas_import_compat()
+
+from ragas.metrics.collections import (  # noqa: E402
+    AnswerRelevancy,
+    ContextPrecision,
+    ContextRecall,
+    FactualCorrectness,
+    Faithfulness,
+    NoiseSensitivity,
+    SemanticSimilarity,
+)
+
+
+def _build_metric_instances(metrics: list[str], llm: Any, embeddings: Any) -> dict[str, Any]:
+    """Instantiate only the RAGAS metric objects requested."""
+    registry: dict[str, Any] = {
+        "faithfulness": Faithfulness(llm=llm),
+        "answer_relevancy": AnswerRelevancy(llm=llm, embeddings=embeddings),
+        "context_recall": ContextRecall(llm=llm),
+        "context_precision": ContextPrecision(llm=llm),
+        "noise_sensitivity": NoiseSensitivity(llm=llm),
+        "factual_correctness": FactualCorrectness(llm=llm),
+        "semantic_similarity": SemanticSimilarity(embeddings=embeddings),
+    }
+    return {name: registry[name] for name in metrics if name in registry}
+
+
+class InlineScorer:
+    """Thread-safe single-sample RAGAS scorer with LLM client caching."""
+
+    def __init__(self) -> None:
+        """Initialize the scorer cache and synchronization primitives."""
+        # Cache keyed by (judge_model, embedding_model) -> (llm, embeddings)
+        self._model_cache: dict[tuple[str, str], tuple[Any, Any]] = {}
+        self._lock = threading.Lock()
+
+    def invalidate_cache(self) -> None:
+        """Clear the model cache so the next call rebuilds clients from current profiles."""
+        with self._lock:
+            self._model_cache.clear()
+
+    def _get_models(
+        self,
+        judge_model: str,
+        embedding_model: str,
+        settings: EvaluationSettings,
+    ) -> tuple[Any, Any]:
+        """Return cached LLM/embedding clients, building them on first use.
+
+        Cache is keyed by (judge_model, embedding_model). Call invalidate_cache()
+        after updating an LLM Profile to force a fresh client on the next request.
+        """
+        cache_key = (judge_model, embedding_model)
+        with self._lock:
+            if cache_key not in self._model_cache:
+                llm, embeddings = build_models(judge_model, embedding_model, settings)
+                self._model_cache[cache_key] = (llm, embeddings)
+            return self._model_cache[cache_key]
+
+    def score(
+        self,
+        question: str,
+        answer: str,
+        contexts: list[str],
+        ground_truth: str | None,
+        metrics: list[str],
+        judge_model: str,
+        embedding_model: str,
+        settings: EvaluationSettings,
+    ) -> dict[str, float | None]:
+        """Score one sample synchronously and return {metric_name: score | None}."""
+        llm, embeddings = self._get_models(judge_model, embedding_model, settings)
+        metric_instances = _build_metric_instances(metrics, llm, embeddings)
+
+        pipeline = MetricPipeline(
+            metrics=metric_instances,
+            metric_timeout_seconds=settings.ragas_metric_timeout_seconds,
+        )
+
+        sample = NormalizedSample(
+            sample_id="inline-score",
+            question=question,
+            answer=answer,
+            contexts=contexts,
+            ground_truth=ground_truth or "",
+        )
+
+        metric_score = asyncio.run(pipeline.score_sample(sample))
+
+        # Convert NaN and Inf into None for clean JSON output.
+        return {
+            name: (None if math.isnan(value) or math.isinf(value) else round(value, 4))
+            for name, value in metric_score.metrics.items()
+        }
+
+
+# Module-level singleton shared by FastAPI routes.
+inline_scorer = InlineScorer()

webapp/services/pipeline_task_manager.py

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

webapp/services/report_builder.py

@@ -13,6 +13,11 @@ from pathlib import Path
 
 import pandas as pd
 
+from rag_eval.metrics.weights import (
+    compute_overall_weighted_score_mean,
+    weighted_metric_means as _weighted_metric_means,
+)
+from webapp.services.run_reader import _read_weights_from_snapshot
 from webapp.services.text_utils import parse_contexts
 from webapp.models import (
     DistributionBin,
@@ -32,6 +37,9 @@ GROUPING_FIELDS = ("difficulty", "question_type", "language")
 # How many lowest-scoring samples to surface for manual review.
 LOWEST_SAMPLE_COUNT = 10
 
+# Metrics whose lower raw value means stronger performance.
+LOWER_IS_BETTER_METRICS = {"noise_sensitivity"}
+
 
 def _round_or_none(value: float | None) -> float | None:
     """Round a float to four places, mapping NaN/None to None for clean JSON."""
@@ -42,17 +50,6 @@ def _round_or_none(value: float | None) -> float | None:
     return round(float(value), 4)
 
 
-def _metric_means(frame: pd.DataFrame, metrics: list[str]) -> dict[str, float | None]:
-    """Compute the mean of each metric column across all scored samples."""
-    means: dict[str, float | None] = {}
-    for metric in metrics:
-        if metric in frame.columns:
-            means[metric] = _round_or_none(frame[metric].mean(numeric_only=True))
-        else:
-            means[metric] = None
-    return means
-
-
 def _distribution(frame: pd.DataFrame, metric: str) -> list[DistributionBin]:
     """Bucket one metric's scores into fixed-width [0,1] histogram bins."""
     bins: list[DistributionBin] = []
@@ -111,7 +108,7 @@ def _groupings(frame: pd.DataFrame, metrics: list[str]) -> dict[str, list[GroupS
 def _sample_mean(row: pd.Series, metrics: list[str]) -> float | None:
     """Average a single sample's available metric scores for ranking."""
     values = [
-        float(row[metric])
+        (1.0 - float(row[metric])) if metric in LOWER_IS_BETTER_METRICS else float(row[metric])
         for metric in metrics
         if metric in row and pd.notna(row[metric])
     ]
@@ -165,6 +162,7 @@ def build_report(run_dir: Path, metrics: list[str]) -> ReportData:
     frame = run_reader.read_scores_frame(run_dir)
     summary_markdown = run_reader.read_summary_markdown(run_dir)
     advice_markdown = run_reader.read_advice_markdown(run_dir)
+    metric_weights, doc_weights = _read_weights_from_snapshot(run_dir)
 
     if frame.empty or not metrics:
         return ReportData(
@@ -172,8 +170,22 @@ def build_report(run_dir: Path, metrics: list[str]) -> ReportData:
             metric_means={metric: None for metric in metrics},
             summary_markdown=summary_markdown,
             advice_markdown=advice_markdown,
+            metric_weights=metric_weights,
+            doc_weights=doc_weights,
         )
 
+    score_rows_list = frame.to_dict(orient="records")
+
+    # Use weighted metric means (degrades to arithmetic mean when weights are empty).
+    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()}
+
+    # 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)
         for metric in metrics
@@ -182,10 +194,13 @@ def build_report(run_dir: Path, metrics: list[str]) -> ReportData:
 
     return ReportData(
         metrics=metrics,
-        metric_means=_metric_means(frame, metrics),
+        metric_means=rounded_means,
         distributions=distributions,
         groupings=_groupings(frame, metrics),
         lowest_samples=_lowest_samples(frame, metrics),
         summary_markdown=summary_markdown,
         advice_markdown=advice_markdown,
+        weighted_score_mean=_round_or_none(overall_ws),
+        metric_weights=metric_weights,
+        doc_weights=doc_weights,
     )

webapp/services/run_reader.py

@@ -64,6 +64,27 @@ def _read_metrics_from_snapshot(run_dir: Path) -> list[str]:
     return []
 
 
+def _read_weights_from_snapshot(run_dir: Path) -> tuple[dict[str, float], dict[str, float]]:
+    """Read metric_weights and doc_weights from a scenario snapshot if present.
+
+    Returns a (metric_weights, doc_weights) tuple of plain dicts.
+    Both default to empty dicts when the snapshot is absent or lacks the fields.
+    """
+    snapshot = run_dir / "scenario.snapshot.yaml"
+    if not snapshot.is_file():
+        return {}, {}
+    try:
+        payload = yaml.safe_load(snapshot.read_text(encoding="utf-8")) or {}
+    except (OSError, yaml.YAMLError):
+        return {}, {}
+    mw = payload.get("metric_weights") or {}
+    dw = payload.get("doc_weights") or {}
+    return (
+        {str(k): float(v) for k, v in mw.items() if isinstance(v, (int, float))},
+        {str(k): float(v) for k, v in dw.items() if isinstance(v, (int, float))},
+    )
+
+
 def discover_run_dirs(extra_roots: list[Path] | None = None) -> list[Path]:
     """Find every run directory (one that contains metadata.json) under the roots."""
     run_dirs: list[Path] = []
@@ -159,6 +180,8 @@ NON_METRIC_COLUMNS = {
     "source_chunk_ids",
     "review_status",
     "review_notes",
+    "weighted_score",
+    "sample_weight",
 }
 
 

webapp/services/scenario_scanner.py

@@ -37,6 +37,16 @@ def _summarize_scenario(path: Path) -> ScenarioInfo:
 
     metrics = payload.get("metrics")
     metric_list = [str(item) for item in metrics] if isinstance(metrics, list) else []
+    raw_metric_weights = payload.get("metric_weights") or {}
+    raw_doc_weights = payload.get("doc_weights") or {}
+    metric_weights = {
+        str(k): float(v) for k, v in raw_metric_weights.items()
+        if isinstance(v, (int, float))
+    }
+    doc_weights = {
+        str(k): float(v) for k, v in raw_doc_weights.items()
+        if isinstance(v, (int, float))
+    }
 
     return ScenarioInfo(
         path=relative,
@@ -45,6 +55,8 @@ def _summarize_scenario(path: Path) -> ScenarioInfo:
         dataset=str(payload.get("dataset", "")),
         judge_model=str(payload.get("judge_model", "")),
         metrics=metric_list,
+        metric_weights=metric_weights,
+        doc_weights=doc_weights,
     )
 
 

webapp/services/score_job_manager.py

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

webapp/services/session_score_manager.py

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

webapp/services/yaml_patcher.py

@@ -32,9 +32,11 @@ def apply_profiles_to_scenario(
     judge_profile: LLMProfile | None,
     answer_profile: LLMProfile | None,
     dataset_profile: LLMProfile | None,
+    metric_weights: dict[str, float] | None = None,
+    doc_weights: dict[str, float] | None = None,
     _resolve_absolute: bool = False,
 ) -> list[str]:
-    """Patch the YAML file at *scenario_path* with the supplied profiles.
+    """Patch the YAML file at *scenario_path* with the supplied profiles and weights.
 
     Returns a list of dotted field names that were actually patched.
     Setting *_resolve_absolute=True* skips repo-root resolution (used in tests).
@@ -67,6 +69,14 @@ def apply_profiles_to_scenario(
             generation["model"] = dataset_profile.model
             patched.append("generation.model")
 
+    if metric_weights is not None:
+        data["metric_weights"] = dict(metric_weights)
+        patched.append("metric_weights")
+
+    if doc_weights is not None:
+        data["doc_weights"] = dict(doc_weights)
+        patched.append("doc_weights")
+
     resolved.write_text(
         yaml.dump(data, allow_unicode=True, default_flow_style=False, sort_keys=False),
         encoding="utf-8",

webapp/static/css/app.css

@@ -1,4 +1,4 @@
-/* Siemens RAGAS 评估控制台 — 样式表
+/* Siemens RAGAS 评估平台 — 样式表
    配色取自西门子品牌色（petrol / 深青）与中性灰，呼应企业语境。 */
 
 :root {
@@ -199,6 +199,7 @@ code {
 .metric-value.bad { color: var(--bad); }
 .metric-value.na { color: var(--slate-light); }
 .metric-name { font-size: 12px; color: var(--slate); margin-top: 4px; }
+.metric-desc { font-size: 12px; color: #64748b; margin-top: 6px; line-height: 1.45; }
 
 .report-row { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; }
 .report-half { margin-bottom: 0; }
@@ -294,6 +295,21 @@ table.group-table td { border-bottom: 1px solid #f1f5f9; font-variant-numeric: t
 .btn-sm { padding: 4px 10px; font-size: 12px; }
 .btn-danger { color: var(--bad); border-color: var(--bad); }
 .btn-danger:hover { background: #fee2e2; }
+.btn-test { color: #0369a1; border-color: #0369a1; }
+.btn-test:hover { background: #e0f2fe; }
+
+/* LLM 连通性测试结果 */
+.profile-test-result {
+  margin-top: 8px;
+  padding: 6px 10px;
+  border-radius: 6px;
+  font-size: 12px;
+  font-weight: 500;
+  display: none;
+}
+.profile-test-result:not([hidden]) { display: block; }
+.profile-test-result.ok  { background: #dcfce7; color: #166534; border: 1px solid #bbf7d0; }
+.profile-test-result.fail { background: #fee2e2; color: #991b1b; border: 1px solid #fecaca; word-break: break-all; }
 
 /* 选中态 run 卡片 */
 .run-card.selected {
@@ -308,6 +324,205 @@ table.group-table td { border-bottom: 1px solid #f1f5f9; font-variant-numeric: t
 .llm-role-label { font-size: 13px; font-weight: 600; min-width: 180px; color: var(--ink); }
 .llm-role-select { min-width: 240px; }
 
+/* ---------- API 文档 iframe ---------- */
+#view-apidocs { padding: 0; display: flex; flex-direction: column; flex: 1; }
+#view-apidocs[hidden] { display: none; }
+.apidocs-frame {
+  flex: 1;
+  width: 100%;
+  height: calc(100vh - 64px);
+  border: none;
+}
+
+.report-actions {
+  display: flex; justify-content: flex-end; margin: 0 0 12px;
+}
+.btn-export-pdf {
+  font-size: 13px; display: flex; align-items: center; gap: 6px;
+}
+
+/* ---------- 报告历史切换下拉 ---------- */
+.report-switcher {
+  display: flex; align-items: center; gap: 10px;
+  background: var(--surface); border: 1px solid var(--line);
+  border-radius: var(--radius); padding: 10px 16px;
+  margin-bottom: 14px; box-shadow: var(--shadow);
+}
+.report-switcher-label {
+  font-size: 13px; font-weight: 600; color: var(--slate); white-space: nowrap;
+}
+.report-switcher-select {
+  flex: 1; min-width: 0;
+  border: 1px solid var(--line); border-radius: 6px; padding: 6px 10px;
+  font-size: 13px; font-family: inherit; background: var(--bg); color: var(--ink);
+  cursor: pointer;
+}
+.report-switcher-select:focus { outline: none; border-color: var(--petrol); }
+
+/* ?? ?????? ??????????????????????????????????? */
+.weight-config-panel { margin-top: 12px; }
+.weight-section-title { font-size: 13px; font-weight: 600; color: var(--text); margin-bottom: 8px; }
+.weight-rows { display: flex; flex-direction: column; gap: 6px; }
+.weight-row {
+  display: flex; align-items: center; gap: 10px;
+  font-size: 13px;
+}
+.weight-row-label { min-width: 180px; color: var(--slate); font-family: monospace; }
+.weight-row-input {
+  width: 80px; padding: 4px 8px; border: 1px solid var(--border);
+  border-radius: 6px; font-size: 13px; text-align: right;
+}
+.weight-row-input:focus { outline: none; border-color: #6366f1; }
+.doc-weight-name {
+  flex: 1; padding: 4px 8px; border: 1px solid var(--border);
+  border-radius: 6px; font-size: 13px; min-width: 0;
+}
+.weight-row-remove { color: var(--bad); cursor: pointer; font-size: 14px; background: none; border: none; padding: 2px 6px; }
+.weight-row-remove:hover { background: #fee2e2; border-radius: 4px; }
+
+/* weighted_score ???????? */
+.metric-card.weighted-score-card {
+  border: 2px solid #6366f1;
+  background: #f5f3ff;
+}
+.metric-card.weighted-score-card .metric-name { color: #4f46e5; font-weight: 700; }
+
+/* ================================================================
+   打印样式（导出 PDF 用）
+   浏览器打印时隐藏 UI chrome，保留报告内容，图表 canvas 原样输出
+   ================================================================ */
+@media print {
+  /* ── 页面尺寸与边距 ── */
+  @page {
+    size: A4 portrait;
+    margin: 18mm 16mm 18mm 16mm;
+  }
+
+  /* ── 隐藏所有非报告元素 ── */
+  .sidebar,
+  .topbar,
+  .report-actions,
+  .no-print,
+  #dist-metric-select,
+  .grouping-tabs,
+  #view-runs,
+  #view-new,
+  #view-profiles { display: none !important; }
+
+  /* ── 全局基础 ── */
+  body {
+    font-size: 11pt;
+    line-height: 1.5;
+    color: #0f1b2d;
+    background: #fff;
+  }
+
+  /* ── 布局重置：main 全宽 ── */
+  .app { display: block; }
+  .main { display: block; width: 100%; }
+  .view { padding: 0; display: block !important; }
+  #view-apidocs { display: none !important; }  /* never print the API docs iframe */
+  #view-report { display: block !important; }
+
+  /* ── 报告内容 ── */
+  #report-content { display: block !important; }
+  #report-empty { display: none !important; }
+
+  /* ── 元信息条 ── */
+  .report-meta {
+    display: flex;
+    justify-content: space-between;
+    border-bottom: 2px solid #009999;
+    padding-bottom: 8pt;
+    margin-bottom: 14pt;
+  }
+  .report-meta-title { font-size: 14pt; font-weight: 700; }
+  .report-meta-info { font-size: 9pt; color: #64748b; }
+
+  /* ── Section 标签 ── */
+  .section-label {
+    font-size: 9pt;
+    font-weight: 700;
+    letter-spacing: 0.5px;
+    color: #64748b;
+    text-transform: uppercase;
+    margin: 14pt 0 6pt;
+    border-bottom: 1px solid #e2e8f0;
+    padding-bottom: 3pt;
+    break-after: avoid;
+  }
+
+  /* ── ① 指标均值卡片 ── */
+  .metric-cards {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(90pt, 1fr));
+    gap: 8pt;
+    margin-bottom: 12pt;
+  }
+  .metric-card {
+    border: 1px solid #e2e8f0;
+    border-radius: 6pt;
+    padding: 10pt 8pt;
+    text-align: center;
+    break-inside: avoid;
+  }
+  .metric-value { font-size: 20pt; font-weight: 700; }
+  .metric-name  { font-size: 8pt; color: #64748b; margin-top: 2pt; }
+
+  /* ── ② 分布 + ③ 分组：打印时改为纵向排列 ── */
+  .report-row {
+    display: block;
+  }
+  .report-half {
+    margin-bottom: 12pt;
+    break-inside: avoid;
+  }
+  #dist-chart {
+    max-height: 160pt;
+    width: 100% !important;
+  }
+
+  /* ── 面板统一 ── */
+  .panel {
+    border: 1px solid #e2e8f0;
+    border-radius: 6pt;
+    padding: 10pt 12pt;
+    margin-bottom: 10pt;
+    break-inside: avoid;
+    box-shadow: none;
+  }
+  .panel h2 { font-size: 12pt; margin-bottom: 4pt; }
+
+  /* ── ④ 最低分样本：打印时全部展开，隐藏点击提示 ── */
+  .lowest-detail { display: block !important; hidden: false; }
+  .lowest-row { break-inside: avoid; }
+  .lowest-detail-inner { padding: 8pt 0; font-size: 10pt; }
+  .detail-label { font-size: 8pt; font-weight: 700; color: #64748b; margin-bottom: 2pt; }
+  .detail-context .ctx-item { border-bottom: 1px dashed #e2e8f0; padding: 2pt 0; font-size: 9pt; }
+
+  /* ── ⑤ 优化建议 ── */
+  #advice-section { display: block !important; }
+  .advice-panel { border: 1px solid #e2e8f0; border-radius: 6pt; padding: 10pt 12pt; }
+  .advice-md h2 { font-size: 12pt; margin-top: 10pt; }
+  .advice-md h3 { font-size: 11pt; }
+  .advice-md ul { margin: 4pt 0 4pt 16pt; }
+  .advice-md li { margin-bottom: 3pt; }
+
+  /* ── 分组表 ── */
+  table.group-table { width: 100%; font-size: 9pt; border-collapse: collapse; }
+  table.group-table th,
+  table.group-table td { padding: 4pt 6pt; border-bottom: 1px solid #e2e8f0; }
+  table.group-table th { font-weight: 700; color: #64748b; }
+
+  /* ── 颜色保留（部分浏览器打印默认去色） ── */
+  .good  { color: #16a34a !important; -webkit-print-color-adjust: exact; print-color-adjust: exact; }
+  .warn  { color: #eab308 !important; -webkit-print-color-adjust: exact; print-color-adjust: exact; }
+  .bad   { color: #dc2626 !important; -webkit-print-color-adjust: exact; print-color-adjust: exact; }
+  .score-badge.good { background: #dcfce7 !important; -webkit-print-color-adjust: exact; print-color-adjust: exact; }
+  .score-badge.warn { background: #fef9c3 !important; -webkit-print-color-adjust: exact; print-color-adjust: exact; }
+  .score-badge.bad  { background: #fee2e2 !important; -webkit-print-color-adjust: exact; print-color-adjust: exact; }
+}
+
 /* ---------- ⑤ 优化建议面板 ---------- */
 .advice-panel { border-left: 3px solid #7c3aed; }
 .advice-header {

webapp/static/index.html

@@ -3,7 +3,7 @@
 <head>
   <meta charset="UTF-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Siemens RAGAS 评估控制台</title>
+  <title>Siemens RAGAS 评估平台</title>
   <link rel="stylesheet" href="/static/css/app.css" />
   <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.1/dist/chart.umd.min.js"></script>
 </head>
@@ -12,8 +12,8 @@
     <!-- 左侧导航（布局 A） -->
     <aside class="sidebar">
       <div class="brand">
-        <div class="brand-mark">RAGAS</div>
+        <div class="brand-mark">Siemens RAGAS</div>
-        <div class="brand-sub">评估控制台</div>
+        <div class="brand-sub">评估平台</div>
       </div>
       <nav class="nav">
         <button class="nav-item" data-view="runs">
@@ -28,6 +28,12 @@
         <button class="nav-item" data-view="profiles">
           <span class="nav-ico">⚙</span><span>LLM 配置</span>
         </button>
+        <button class="nav-item" data-view="scorejobs">
+          <span class="nav-ico">📋</span><span>评分记录</span>
+        </button>
+        <button class="nav-item" data-view="apidocs">
+          <span class="nav-ico">⎔</span><span>API 文档</span>
+        </button>
       </nav>
       <div class="sidebar-foot">
         <span class="dot" id="health-dot"></span>
@@ -89,6 +95,22 @@
           </div>
         </div>
 
+        <!-- ??????????????? -->
+        <div class="panel weight-config-panel" id="weight-config-panel" hidden>
+          <h2>???? <span class="muted" style="font-size:13px;font-weight:400">???????????????</span></h2>
+
+          <div class="weight-section">
+            <div class="weight-section-title">???? <span class="muted" style="font-size:12px">???????????????????</span></div>
+            <div id="metric-weight-rows" class="weight-rows"></div>
+          </div>
+
+          <div class="weight-section" style="margin-top:16px">
+            <div class="weight-section-title">???? <span class="muted" style="font-size:12px">?? PDF ???????????????????????</span></div>
+            <div id="doc-weight-rows" class="weight-rows"></div>
+            <button class="btn btn-sm" id="add-doc-weight-btn" style="margin-top:8px">? ??????</button>
+          </div>
+        </div>
+
         <div class="panel" id="task-panel" hidden>
           <div class="task-head">
             <h2>评估进度</h2>
@@ -103,12 +125,25 @@
 
       <!-- 报告详情视图 -->
       <section class="view" id="view-report" hidden>
+        <!-- 历史报告切换下拉（顶部，始终可见） -->
+        <div class="report-switcher no-print" id="report-switcher">
+          <label class="report-switcher-label">切换报告</label>
+          <select class="select report-switcher-select" id="report-switcher-select">
+            <option value="">— 加载中… —</option>
+          </select>
+        </div>
+
         <div class="empty" id="report-empty">
           <p>请先从「运行列表」选择一次运行。</p>
         </div>
         <div id="report-content" hidden>
           <!-- 顶部元信息条 -->
           <div class="report-meta" id="report-meta"></div>
+          <div class="report-actions no-print">
+            <button class="btn btn-ghost btn-export-pdf" id="export-pdf-btn" onclick="Report.exportPdf()">
+              📄 导出 PDF
+            </button>
+          </div>
 
           <!-- ① 指标均值卡片 -->
           <div class="section-label">① 指标均值 OVERVIEW</div>
@@ -187,9 +222,11 @@
             </div>
             <div class="form-actions">
               <button class="btn btn-primary" id="save-profile-btn">保存</button>
+              <button class="btn btn-test" id="test-profile-btn">测试连通性</button>
               <button class="btn" id="cancel-profile-btn">取消</button>
               <span class="form-error muted" id="profile-form-error"></span>
             </div>
+            <div class="profile-test-result" id="profile-form-test-result" hidden></div>
           </div>
         </div>
 
@@ -199,13 +236,42 @@
           <p class="muted">点击「新建配置」添加第一个。</p>
         </div>
       </section>
+
+      <!-- 评分记录视图 -->
+      <section class="view" id="view-scorejobs" hidden>
+        <div class="panel">
+          <div class="panel-head">
+            <h2>评分记录</h2>
+            <span class="muted" style="font-size:13px">来自 Dify 异步评分任务（POST /api/score/async）</span>
+          </div>
+          <p class="muted">评分完成后自动生成完整报告（含指标得分与 LLM 优化建议），点击「查看报告」跳转报告详情页。</p>
+        </div>
+        <div id="scorejobs-list"></div>
+        <div class="empty" id="scorejobs-empty" hidden>
+          <p>暂无评分记录。</p>
+          <p class="muted">在 Dify 工作流中调用 <code>POST /api/score/async</code> 后，记录将在此显示。</p>
+        </div>
+      </section>
+
+      <!-- API 文档视图 -->
+      <section class="view" id="view-apidocs" hidden>
+        <iframe
+          id="apidocs-frame"
+          src="/docs"
+          class="apidocs-frame"
+          title="API 文档"
+          allowfullscreen>
+        </iframe>
+      </section>
     </main>
   </div>
 
   <script src="/static/js/api.js"></script>
+  <script src="/static/js/metric_presenter.js"></script>
   <script src="/static/js/report.js"></script>
   <script src="/static/js/profiles.js"></script>
   <script src="/static/js/runner.js"></script>
+  <script src="/static/js/score_jobs.js"></script>
   <script src="/static/js/app.js"></script>
 </body>
 </html>

webapp/static/js/api.js

@@ -65,4 +65,21 @@ const API = {
       });
   },
   applyProfiles(body) { return API.post("/api/llm-profiles/apply", body); },
+
+  // 异步评分记录 API
+  scoreJobsAsync(body) { return API.post("/api/score/async", body); },
+  getScoreJob(jobId) { return API.get(`/api/score/jobs/${encodeURIComponent(jobId)}`); },
+  listScoreJobs() { return API.get("/api/score/jobs"); },
+
+  // 测试已保存 profile 的连通性
+  testProfile(id) {
+    return fetch(`/api/llm-profiles/${encodeURIComponent(id)}/test`, { method: "POST" })
+      .then(async r => {
+        if (!r.ok) { const d = await API._extractError(r); throw new Error(d); }
+        return r.json();
+      });
+  },
+
+  // 测试表单中填写的内联参数（保存前即可测试）
+  probeConnectivity(body) { return API.post("/api/llm-profiles/probe", body); },
 };

webapp/static/js/app.js

@@ -5,8 +5,8 @@
 const App = {
   currentRunId: null,
   activeView: null,
-  views: ["runs", "new", "report", "profiles"],
+  views: ["runs", "new", "report", "profiles", "scorejobs", "apidocs"],
-  titles: { runs: "运行列表", new: "新建评估", report: "报告详情", profiles: "LLM 配置" },
+  titles: { runs: "运行列表", new: "新建评估", report: "报告详情", profiles: "LLM 配置", scorejobs: "评分记录", apidocs: "API 文档" },
 
   // 初始化：绑定导航、从 URL/sessionStorage 恢复上次位置、启动健康检查。
   init() {
@@ -68,10 +68,11 @@ const App = {
     sessionStorage.setItem("rag_view", view);
     if (App.currentRunId) sessionStorage.setItem("rag_run_id", App.currentRunId);
 
-    if (view === "runs")     App.loadRuns();
+    if (view === "runs")       App.loadRuns();
-    if (view === "new")      Runner.loadScenarios();
+    if (view === "new")        Runner.loadScenarios();
-    if (view === "report")   Report.render(App.currentRunId);
+    if (view === "report")     Report.render(App.currentRunId);
-    if (view === "profiles") Profiles.load();
+    if (view === "profiles")   Profiles.load();
+    if (view === "scorejobs")  ScoreJobs.load();
   },
 
   // ----------------------------------------------------------------
@@ -146,7 +147,7 @@ const App = {
     const chips = (run.metrics || [])
       .map((m) => {
         const val = run.metric_means ? run.metric_means[m] : null;
-        const cls = App.scoreClass(val);
+        const cls = App.scoreClass(m, val);
         const text = val === null || val === undefined ? "n/a" : val.toFixed(2);
         return `<span class="metric-chip" title="${App.escape(m)}">${App.escape(App.shortMetric(m))} <b class="${cls}">${text}</b></span>`;
       })
@@ -173,11 +174,8 @@ const App = {
     if (btn) btn.disabled = false;
   },
 
-  scoreClass(value) {
+  scoreClass(metricName, value) {
-    if (value === null || value === undefined) return "na";
+    return MetricPresenter.scoreClass(metricName, value);
-    if (value >= 0.8) return "good";
-    if (value >= 0.65) return "warn";
-    return "bad";
   },
 
   shortMetric(name) {

webapp/static/js/metric_presenter.js

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

webapp/static/js/profiles.js

@@ -8,6 +8,7 @@ const Profiles = {
     document.getElementById("add-profile-btn").addEventListener("click", () => Profiles.showForm());
     document.getElementById("save-profile-btn").addEventListener("click", () => Profiles.save());
     document.getElementById("cancel-profile-btn").addEventListener("click", () => Profiles.hideForm());
+    document.getElementById("test-profile-btn").addEventListener("click", () => Profiles.testForm());
   },
 
   // 加载并渲染 Profile 列表
@@ -39,6 +40,7 @@ const Profiles = {
       <div class="profile-card-head">
         <div class="profile-card-name">${App.escape(p.name)}</div>
         <div class="profile-card-actions">
+          <button class="btn btn-sm btn-test" data-action="test">测试</button>
           <button class="btn btn-sm" data-action="edit">编辑</button>
           <button class="btn btn-sm btn-danger" data-action="delete">删除</button>
         </div>
@@ -46,12 +48,72 @@ const Profiles = {
       <div class="profile-card-field"><span class="field-label">模型</span> <code>${App.escape(p.model)}</code></div>
       <div class="profile-card-field"><span class="field-label">Base URL</span> <code>${App.escape(p.base_url)}</code></div>
       <div class="profile-card-field"><span class="field-label">超时</span> ${p.timeout_seconds}s</div>
+      <div class="profile-test-result" data-result hidden></div>
     `;
+    card.querySelector("[data-action=test]").addEventListener("click", () => Profiles.testCard(p, card));
     card.querySelector("[data-action=edit]").addEventListener("click", () => Profiles.showForm(p));
     card.querySelector("[data-action=delete]").addEventListener("click", () => Profiles.remove(p.profile_id, p.name));
     return card;
   },
 
+  // 测试已保存的 profile（卡片上的测试按钮）
+  async testCard(p, card) {
+    const btn = card.querySelector("[data-action=test]");
+    const resultEl = card.querySelector("[data-result]");
+    btn.disabled = true;
+    btn.textContent = "测试中…";
+    resultEl.hidden = true;
+    resultEl.className = "profile-test-result";
+    try {
+      const res = await API.testProfile(p.profile_id);
+      Profiles._showTestResult(resultEl, res);
+    } catch (err) {
+      Profiles._showTestResult(resultEl, { ok: false, message: err.message });
+    } finally {
+      btn.disabled = false;
+      btn.textContent = "测试";
+    }
+  },
+
+  // 测试表单中当前填写的参数（保存前即可测试）
+  async testForm() {
+    const body = {
+      model: document.getElementById("pf-model").value.trim(),
+      base_url: document.getElementById("pf-base-url").value.trim(),
+      api_key: document.getElementById("pf-api-key").value.trim(),
+      timeout_seconds: parseInt(document.getElementById("pf-timeout").value, 10) || 30,
+    };
+    const errEl = document.getElementById("profile-form-error");
+    if (!body.model || !body.base_url || !body.api_key) {
+      errEl.textContent = "请先填写模型名称、Base URL 和 API Key";
+      return;
+    }
+    errEl.textContent = "";
+    const testBtn = document.getElementById("test-profile-btn");
+    const resultEl = document.getElementById("profile-form-test-result");
+    testBtn.disabled = true;
+    testBtn.textContent = "测试中…";
+    resultEl.hidden = true;
+    resultEl.className = "profile-test-result";
+    try {
+      const res = await API.probeConnectivity(body);
+      Profiles._showTestResult(resultEl, res);
+    } catch (err) {
+      Profiles._showTestResult(resultEl, { ok: false, message: err.message });
+    } finally {
+      testBtn.disabled = false;
+      testBtn.textContent = "测试连通性";
+    }
+  },
+
+  // 渲染测试结果到指定元素
+  _showTestResult(el, res) {
+    el.hidden = false;
+    el.classList.add(res.ok ? "ok" : "fail");
+    const latency = res.latency_ms != null ? ` (${res.latency_ms}ms)` : "";
+    el.textContent = res.ok ? `✓ 连接成功${latency}` : `✗ ${res.message}`;
+  },
+
   // 显示新建或编辑表单
   showForm(profile = null) {
     const panel = document.getElementById("profile-form-panel");
@@ -65,6 +127,9 @@ const Profiles = {
     document.getElementById("pf-api-key").value = profile ? profile.api_key : "";
     document.getElementById("pf-timeout").value = profile ? profile.timeout_seconds : 30;
     document.getElementById("profile-form-error").textContent = "";
+    const resultEl = document.getElementById("profile-form-test-result");
+    resultEl.hidden = true;
+    resultEl.className = "profile-test-result";
     panel.scrollIntoView({ behavior: "smooth", block: "start" });
   },
 

webapp/static/js/report.js

@@ -4,11 +4,16 @@ const Report = {
   distChart: null,
   currentDetail: null,
   activeGrouping: null,
+  _switcherLoaded: false,
 
   // 加载并渲染指定运行的完整报告。
   async render(runId) {
     const empty = document.getElementById("report-empty");
     const content = document.getElementById("report-content");
+
+    // 加载历史报告下拉（仅首次）
+    Report._loadSwitcher(runId);
+
     if (!runId) {
       empty.hidden = false;
       content.hidden = true;
@@ -28,6 +33,10 @@ const Report = {
       Report.renderLowest(detail.report);
       Report.renderAdvice(detail.summary, detail.report);
       content.style.opacity = "1";
+
+      // 同步下拉选中项
+      const sel = document.getElementById("report-switcher-select");
+      if (sel) sel.value = runId;
     } catch (err) {
       empty.hidden = false;
       content.hidden = true;
@@ -35,6 +44,55 @@ const Report = {
     }
   },
 
+  // 加载并填充历史报告下拉选择框
+  async _loadSwitcher(currentRunId) {
+    const sel = document.getElementById("report-switcher-select");
+    if (!sel) return;
+
+    // 已加载过就只更新选中值，不重复请求
+    if (Report._switcherLoaded) {
+      if (currentRunId) sel.value = currentRunId;
+      return;
+    }
+
+    try {
+      const data = await API.runs();
+      const runs = data.runs || [];
+      sel.innerHTML = "";
+      if (runs.length === 0) {
+        sel.innerHTML = '<option value="">（无历史运行）</option>';
+        return;
+      }
+      runs.forEach((run) => {
+        const opt = document.createElement("option");
+        opt.value = run.run_id;
+        const timeStr = App.shortTime(run.finished_at);
+        const meanText = run.metric_means
+          ? Object.entries(run.metric_means)
+              .filter(([, v]) => v !== null && v !== undefined)
+              .slice(0, 2)
+              .map(([k, v]) => `${App.shortMetric(k)}=${v.toFixed(2)}`)
+              .join(" ")
+          : "";
+        opt.textContent = `${run.scenario_name || run.run_id}  ${timeStr}${meanText ? "  [" + meanText + "]" : ""}`;
+        sel.appendChild(opt);
+      });
+      Report._switcherLoaded = true;
+      if (currentRunId) sel.value = currentRunId;
+    } catch (_e) {
+      sel.innerHTML = '<option value="">（加载失败）</option>';
+    }
+
+    // 绑定切换事件（只绑一次）
+    sel.addEventListener("change", () => {
+      const rid = sel.value;
+      if (!rid) return;
+      App.currentRunId = rid;
+      App.enableReportNav();
+      Report.render(rid);
+    });
+  },
+
   // 顶部元信息条。
   renderMeta(summary) {
     const el = document.getElementById("report-meta");
@@ -59,16 +117,30 @@ const Report = {
     const metrics = report.metrics && report.metrics.length ? report.metrics : summary.metrics;
     metrics.forEach((metric) => {
       const value = report.metric_means ? report.metric_means[metric] : null;
-      const cls = App.scoreClass(value);
+      const cls = App.scoreClass(metric, value);
       const text = value === null || value === undefined ? "n/a" : value.toFixed(2);
+      const description = MetricPresenter.describeMetric(metric);
       const card = document.createElement("div");
       card.className = "metric-card";
       card.innerHTML = `
         <div class="metric-value ${cls}">${text}</div>
         <div class="metric-name">${App.escape(metric)}</div>
+        <div class="metric-desc">${App.escape(description)}</div>
       `;
       wrap.appendChild(card);
     });
+
+    // 综合加权得分卡片（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）。
@@ -98,17 +170,13 @@ const Report = {
     const bins = distributions[metric] || [];
     const labels = bins.map((b) => b.label);
     const counts = bins.map((b) => b.count);
-    const colors = bins.map((b) => Report._binColor(b.lower));
+    const colors = bins.map((b) => Report._binColor(metric, b.lower));
     Report._drawDistChart(labels, counts, colors);
   },
 
   // 低分箱偏红、高分箱偏绿，直观暴露长尾。
-  _binColor(lower) {
+  _binColor(metric, lower) {
-    if (lower >= 0.8) return "#16a34a";
+    return MetricPresenter.binColor(metric, lower);
-    if (lower >= 0.6) return "#84cc16";
-    if (lower >= 0.4) return "#eab308";
-    if (lower >= 0.2) return "#f97316";
-    return "#dc2626";
   },
 
   // 实际绘制 Chart.js 柱状图。
@@ -177,7 +245,7 @@ const Report = {
       body += `<tr><td>${App.escape(stat.key)}</td><td>${stat.count}</td>`;
       metrics.forEach((m) => {
         const v = stat.means ? stat.means[m] : null;
-        const cls = App.scoreClass(v);
+        const cls = App.scoreClass(m, v);
         const text = v === null || v === undefined ? "—" : v.toFixed(2);
         body += `<td class="${cls}">${text}</td>`;
       });
@@ -201,7 +269,7 @@ const Report = {
       const scoreBadges = metrics
         .map((m) => {
           const v = sample.metrics ? sample.metrics[m] : null;
-          const cls = App.scoreClass(v);
+          const cls = App.scoreClass(m, v);
           const text = v === null || v === undefined ? "—" : v.toFixed(2);
           return `<span class="score-badge ${cls}" title="${App.escape(m)}">${text}</span>`;
         })
@@ -286,4 +354,22 @@ const Report = {
 
     body.innerHTML = `<div class="advice-md">${html}</div>`;
   },
+
+  // 导出 PDF：展开所有低分样本 → 打印 → 还原折叠状态
+  exportPdf() {
+    // 1. 记录当前各 detail 展开状态，并全部展开
+    const details = document.querySelectorAll("#lowest-table .lowest-detail");
+    const wasHidden = Array.from(details).map((el) => el.hidden);
+    details.forEach((el) => { el.hidden = false; });
+
+    // 2. 打印完成后还原折叠状态
+    const restore = () => {
+      details.forEach((el, i) => { el.hidden = wasHidden[i]; });
+      window.removeEventListener("afterprint", restore);
+    };
+    window.addEventListener("afterprint", restore);
+
+    // 3. 触发打印（浏览器弹出打印对话框，用户选"另存为 PDF"）
+    window.print();
+  },
 };

webapp/static/js/runner.js

@@ -1,11 +1,11 @@
-// runner.js — 新建评估视图：列出场景、LLM角色配置、触发评估、轮询任务状态与日志。
+// runner.js — 新建评估视图：列出场景、LLM角色配置、权重配置、触发评估、轮询任务状态。
 
 const Runner = {
   selectedScenario: null,
+  selectedScenarioInfo: null,
   pollTimer: null,
   lastRunId: null,
 
-  // 绑定运行按钮。
   init() {
     document.getElementById("run-btn").addEventListener("click", () => Runner.trigger());
     document.getElementById("view-report-btn").addEventListener("click", () => {
@@ -14,9 +14,9 @@ const Runner = {
         App.navigate("report", Runner.lastRunId);
       }
     });
+    document.getElementById("add-doc-weight-btn").addEventListener("click", () => Runner._addDocWeightRow());
   },
 
-  // 加载并渲染可触发的场景列表。
   async loadScenarios() {
     const list = document.getElementById("scenario-list");
     list.innerHTML = '<p class="muted">加载中…</p>';
@@ -32,17 +32,14 @@ const Runner = {
     } catch (err) {
       list.innerHTML = `<p class="muted">加载失败：${App.escape(err.message)}</p>`;
     }
-    // 同时加载 profiles 供角色选择
     Runner._populateProfileSelects();
   },
 
-  // 填充三个角色下拉框
   async _populateProfileSelects() {
     const cached = Profiles.getAll();
     const profiles = cached.length > 0
       ? cached
       : (await API.profiles().catch(() => ({ profiles: [] }))).profiles;
-
     ["role-judge", "role-answer", "role-dataset"].forEach(id => {
       const sel = document.getElementById(id);
       sel.innerHTML = '<option value="">— 使用场景原始配置 —</option>';
@@ -55,17 +52,14 @@ const Runner = {
     });
   },
 
-  // 构造单个场景条目。
   renderScenarioItem(sc) {
     const item = document.createElement("div");
     const invalid = !!sc.error;
     item.className = "scenario-item" + (invalid ? " invalid" : "");
-
     const modeTag = sc.mode
       ? `<span class="tag mode-${App.escape(sc.mode)}">${App.escape(sc.mode)}</span>`
       : "";
     const metricCount = (sc.metrics || []).length;
-
     item.innerHTML = `
       <div>
         <div class="scenario-name">${App.escape(sc.scenario_name || sc.path)}</div>
@@ -77,27 +71,94 @@ const Runner = {
         <span class="tag">${metricCount} 指标</span>
       </div>
     `;
-
     if (!invalid) {
       item.addEventListener("click", () => {
         document.querySelectorAll(".scenario-item").forEach((el) => el.classList.remove("selected"));
         item.classList.add("selected");
         Runner.selectedScenario = sc.path;
+        Runner.selectedScenarioInfo = sc;
         document.getElementById("selected-scenario").textContent = sc.path;
         document.getElementById("run-btn").disabled = false;
-        // 显示 LLM 角色面板
         document.getElementById("llm-assignment-panel").hidden = false;
+        Runner._renderWeightPanel(sc);
+        document.getElementById("weight-config-panel").hidden = false;
       });
     }
     return item;
   },
 
-  // 触发评估：先 apply profiles（若选了），再触发任务。
+  // 根据选中场景渲染指标权重行（动态生成，按场景 metrics 列表）
+  _renderWeightPanel(sc) {
+    const metricRows = document.getElementById("metric-weight-rows");
+    metricRows.innerHTML = "";
+    const metrics = sc.metrics || [];
+    const existingWeights = sc.metric_weights || {};
+    metrics.forEach(metric => {
+      const row = document.createElement("div");
+      row.className = "weight-row";
+      const currentVal = existingWeights[metric] != null ? existingWeights[metric] : 1.0;
+      row.innerHTML = `
+        <span class="weight-row-label">${App.escape(metric)}</span>
+        <input class="weight-row-input" type="number" min="0" step="0.1"
+               data-metric="${App.escape(metric)}" value="${currentVal}" />
+      `;
+      metricRows.appendChild(row);
+    });
+
+    // 填充已有文档权重
+    const docRows = document.getElementById("doc-weight-rows");
+    docRows.innerHTML = "";
+    const existingDocWeights = sc.doc_weights || {};
+    Object.entries(existingDocWeights).forEach(([docName, w]) => {
+      Runner._addDocWeightRow(docName, w);
+    });
+  },
+
+  // 添加一行文档权重输入
+  _addDocWeightRow(docName, weight) {
+    const name = docName !== undefined ? docName : "";
+    const w = weight !== undefined ? weight : 1.0;
+    const container = document.getElementById("doc-weight-rows");
+    const row = document.createElement("div");
+    row.className = "weight-row";
+    row.innerHTML = `
+      <input class="doc-weight-name" type="text" placeholder="PDF 文件名（如 322_双源CT.pdf）" value="${App.escape(String(name))}" />
+      <input class="weight-row-input" type="number" min="0" step="0.1" value="${w}" />
+      <button class="weight-row-remove" title="删除">✕</button>
+    `;
+    row.querySelector(".weight-row-remove").addEventListener("click", () => row.remove());
+    container.appendChild(row);
+  },
+
+  // 收集权重面板当前值；全等权时返回 null（不发送）
+  _collectWeights() {
+    const metricWeights = {};
+    document.querySelectorAll("#metric-weight-rows .weight-row-input").forEach(input => {
+      const metric = input.dataset.metric;
+      const val = parseFloat(input.value);
+      if (metric && !isNaN(val)) metricWeights[metric] = val;
+    });
+
+    const docWeights = {};
+    document.querySelectorAll("#doc-weight-rows .weight-row").forEach(row => {
+      const nameInput = row.querySelector(".doc-weight-name");
+      const valInput = row.querySelector(".weight-row-input");
+      if (!nameInput || !valInput) return;
+      const name = nameInput.value.trim();
+      const val = parseFloat(valInput.value);
+      if (name && !isNaN(val)) docWeights[name] = val;
+    });
+
+    const allMetricDefault = Object.values(metricWeights).every(v => Math.abs(v - 1.0) < 1e-9);
+    const noDocWeights = Object.keys(docWeights).length === 0;
+    if (allMetricDefault && noDocWeights) return { metricWeights: null, docWeights: null };
+    return { metricWeights, docWeights };
+  },
+
   async trigger() {
     if (!Runner.selectedScenario) return;
     const runBtn = document.getElementById("run-btn");
     runBtn.disabled = true;
-
     const panel = document.getElementById("task-panel");
     const logBox = document.getElementById("task-log");
     const statusBadge = document.getElementById("task-status");
@@ -106,12 +167,8 @@ const Runner = {
     reportBtn.hidden = true;
     logBox.textContent = "";
     Runner._setStatus(statusBadge, "queued");
-
     try {
-      // Step 1: apply LLM profiles to YAML if any selected
       await Runner._applyProfilesIfNeeded(logBox);
-
-      // Step 2: trigger evaluation
       const resp = await API.triggerEvaluation(Runner.selectedScenario);
       Runner.poll(resp.task_id);
     } catch (err) {
@@ -121,20 +178,22 @@ const Runner = {
     }
   },
 
-  // 如果用户选了 profile，就先 apply 写回 YAML
   async _applyProfilesIfNeeded(logBox) {
     const judgeId = document.getElementById("role-judge").value;
     const answerId = document.getElementById("role-answer").value;
     const datasetId = document.getElementById("role-dataset").value;
+    const { metricWeights, docWeights } = Runner._collectWeights();
 
-    if (!judgeId && !answerId && !datasetId) return; // 全空，跳过
+    if (!judgeId && !answerId && !datasetId && !metricWeights && !docWeights) return;
 
-    logBox.textContent = "正在将 LLM 配置写入场景文件…\n";
+    logBox.textContent = "正在将 LLM 配置和权重写入场景文件…\n";
     const body = {
       scenario_path: Runner.selectedScenario,
       judge_profile_id: judgeId || null,
       answer_profile_id: answerId || null,
       dataset_profile_id: datasetId || null,
+      metric_weights: metricWeights,
+      doc_weights: docWeights,
     };
     const result = await API.applyProfiles(body);
     const fields = (result.patched_fields || []).join(", ");
@@ -143,13 +202,11 @@ const Runner = {
       : "（未找到可更新的字段，继续运行）\n";
   },
 
-  // 周期性轮询任务状态，刷新日志与徽标。
   poll(taskId) {
     const logBox = document.getElementById("task-log");
     const statusBadge = document.getElementById("task-status");
     const reportBtn = document.getElementById("view-report-btn");
     const runBtn = document.getElementById("run-btn");
-
     if (Runner.pollTimer) clearInterval(Runner.pollTimer);
     Runner.pollTimer = setInterval(async () => {
       try {
@@ -157,7 +214,6 @@ const Runner = {
         logBox.textContent = (status.logs || []).join("\n");
         logBox.scrollTop = logBox.scrollHeight;
         Runner._setStatus(statusBadge, status.status);
-
         if (status.status === "completed" || status.status === "failed") {
           clearInterval(Runner.pollTimer);
           runBtn.disabled = false;
@@ -175,7 +231,6 @@ const Runner = {
     }, 1200);
   },
 
-  // 更新状态徽标的文本与配色类。
   _setStatus(badge, status) {
     badge.textContent = status;
     badge.className = "badge " + status;

webapp/static/js/score_jobs.js

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

webmain.py

@@ -5,13 +5,73 @@ and the same runs/ artifacts. Example:
 
     python webmain.py
     python webmain.py --host 0.0.0.0 --port 8800
+    python webmain.py --host 0.0.0.0 --port 8800 --log-level debug
 """
 
 from __future__ import annotations
 
 import argparse
+import logging
+import logging.config
+from datetime import datetime
+from pathlib import Path
 
-import uvicorn
+
+REPO_ROOT = Path(__file__).resolve().parent
+
+
+def _build_log_config(log_file: Path, level: str) -> dict:
+    """Build a uvicorn-compatible logging config dict.
+
+    Writes to both stderr (console) and a rotating daily log file.
+    All webapp.* and rag_eval.* loggers inherit from root so every
+    logger.info() call in the API routes is captured.
+    """
+    level_upper = level.upper()
+    return {
+        "version": 1,
+        "disable_existing_loggers": False,
+        "formatters": {
+            "detailed": {
+                "format": "%(asctime)s  %(levelname)-8s  %(name)s  %(message)s",
+                "datefmt": "%Y-%m-%d %H:%M:%S",
+            },
+            "console": {
+                "format": "%(asctime)s  %(levelname)-8s  %(name)-30s  %(message)s",
+                "datefmt": "%H:%M:%S",
+            },
+        },
+        "handlers": {
+            "console": {
+                "class": "logging.StreamHandler",
+                "stream": "ext://sys.stderr",
+                "formatter": "console",
+                "level": level_upper,
+            },
+            "file": {
+                "class": "logging.handlers.RotatingFileHandler",
+                "filename": str(log_file),
+                "maxBytes": 50 * 1024 * 1024,   # 50 MB per file
+                "backupCount": 7,                 # keep 7 rotated files
+                "encoding": "utf-8",
+                "formatter": "detailed",
+                "level": "DEBUG",                 # file always captures everything
+            },
+        },
+        "loggers": {
+            # Our application loggers — detailed level
+            "webapp": {"handlers": ["console", "file"], "level": level_upper, "propagate": False},
+            "rag_eval": {"handlers": ["console", "file"], "level": level_upper, "propagate": False},
+            # uvicorn access log — captured to file, shown on console
+            "uvicorn.access": {"handlers": ["console", "file"], "level": "INFO", "propagate": False},
+            "uvicorn.error": {"handlers": ["console", "file"], "level": "INFO", "propagate": False},
+            "uvicorn": {"handlers": ["console", "file"], "level": "INFO", "propagate": False},
+        },
+        "root": {
+            "handlers": ["console", "file"],
+            "level": "WARNING",   # suppress noisy third-party libs at WARNING
+        },
+    }
 
 
 def parse_args() -> argparse.Namespace:
@@ -24,17 +84,52 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Enable auto-reload for local development.",
     )
+    parser.add_argument(
+        "--log-level",
+        default="info",
+        choices=["debug", "info", "warning", "error"],
+        help="Console log level (default: info). File always captures DEBUG.",
+    )
+    parser.add_argument(
+        "--log-file",
+        default=None,
+        help="Log file path (default: logs/server_YYYY-MM-DD.log).",
+    )
     return parser.parse_args()
 
 
 def main() -> None:
-    """Start uvicorn with the configured application."""
+    """Start uvicorn with the configured application and logging."""
+    import uvicorn
+
     args = parse_args()
+
+    # Resolve log file path
+    logs_dir = REPO_ROOT / "logs"
+    logs_dir.mkdir(parents=True, exist_ok=True)
+    if args.log_file:
+        log_file = Path(args.log_file)
+    else:
+        date_str = datetime.now().strftime("%Y-%m-%d")
+        log_file = logs_dir / f"server_{date_str}.log"
+
+    log_config = _build_log_config(log_file, args.log_level)
+
+    # Apply config before uvicorn starts so our loggers are ready immediately
+    logging.config.dictConfig(log_config)
+
+    logger = logging.getLogger("webapp.server")
+    logger.info(
+        "Starting RAGAS Console  host=%s  port=%d  log_level=%s  log_file=%s",
+        args.host, args.port, args.log_level, log_file,
+    )
+
     uvicorn.run(
         "webapp.server:app",
         host=args.host,
         port=args.port,
         reload=args.reload,
+        log_config=log_config,   # hand our config to uvicorn so it uses same handlers
     )
 
 

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