feat(course): Add course assembly script and module structure for advisor training
- Introduced `build_all.py` script to automate the assembly of course modules into a single `index.html` file. - Created `index.html` for the main course overview, featuring a structured layout and navigation for various modules. - Developed `_base.html` and `_footer.html` templates for the advisor module, ensuring consistent styling and structure. - Added `build.sh` script for individual module assembly, enhancing modularity and ease of updates. - Implemented multiple module HTML files detailing specific training scenarios and functionalities for advisors, including interactive elements and quizzes. This update significantly enhances the course delivery framework, providing a comprehensive and interactive learning experience for advisors.
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<section class="module" id="module-3">
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<div class="module-inner">
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<p class="eyebrow animate-in">模块 3 · 铁律与流式</p>
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<h1 class="module-title animate-in">两条铁律 +<br>prepare_customer_stream</h1>
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<p class="module-lead animate-in">
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客户线能跑通，靠文件头写死的两条
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<span class="term" data-definition="铁律 = 写进代码注释、测试会卡住的硬规则；违反会导致合规或数据错乱。">铁律</span>。
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流式 SSE 看起来是「边生成边推」，实现上是<strong>先跑完整张 LangGraph，再切块推送</strong>。
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</p>
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<div class="screen animate-in">
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<h2>铁律（指挥 AI 改代码时别碰）</h2>
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<div class="pattern-cards">
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<div class="pattern-card">
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<h3>铁律 1 · 数值不经过 LLM 编造</h3>
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<p>持仓、流水、风评等查询结果 100% 来自 <code>core_ro_tool</code> 的 <code>fact_text</code>。LLM 在 <code>interpret</code> / <code>generate</code> 里只做解读与组织语言，不能自己算数。</p>
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</div>
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<div class="pattern-card">
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<h3>铁律 2 · customer_id 只来自 JWT</h3>
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<p><code>chat.py</code> 在进图之前已从令牌解析客户号并做归属校验。图内 <code>state["customer_id"]</code> 不信用户消息里的「帮我查 CUST-xxx」。</p>
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</div>
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</div>
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<div class="callout callout-warning">
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<strong>和 advisor 线的差异：</strong> 理财师走 <code>agent_service</code> 的 tool→llm→guard；客户走独立 14 节点图。别混用 <code>run_tool</code> 关键词意图那套来改客户 RAG 分支。
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</div>
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</div>
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<div class="screen animate-in">
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<h2>流式真相：图跑完再切块</h2>
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<p>方案 C 下 <code>prepare_customer_stream</code> 先调用 <code>run_customer_chat</code> 跑完整图，再用 <code>_chunk_reply_text</code> 切成 16 份左右推 SSE——Tool/RAG 仍是同步完成，不是 token 级流式。</p>
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<div class="translation-block">
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<div class="translation-code">
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<span class="translation-label">CODE · prepare_customer_stream</span>
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<pre><code><span class="code-line"><span class="code-keyword">def</span> prepare_customer_stream(</span>
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<span class="code-line"> ctx, message, session_id, customer_id, end_session=<span class="code-keyword">False</span></span>
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<span class="code-line">) -> dict[str, Any]:</span>
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<span class="code-line"> reply, has_disclaimer, intent, transfer = run_customer_chat(</span>
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<span class="code-line"> ctx, message, session_id, customer_id, end_session</span>
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<span class="code-line"> )</span>
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<span class="code-line"> <span class="code-keyword">return</span> {</span>
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<span class="code-line"> <span class="code-string">"reply"</span>: reply,</span>
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<span class="code-line"> <span class="code-string">"has_disclaimer"</span>: has_disclaimer,</span>
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<span class="code-line"> <span class="code-string">"intent"</span>: intent,</span>
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<span class="code-line"> <span class="code-string">"transfer_to_human"</span>: transfer,</span>
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<span class="code-line"> <span class="code-string">"chunks"</span>: _chunk_reply_text(reply),</span>
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<span class="code-line"> }</span></code></pre>
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</div>
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<div class="translation-english">
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<span class="translation-label">白话</span>
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<div class="translation-lines">
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<p class="tl">入参里的 customer_id 是 chat.py 验完 JWT 后塞进来的，函数内部不再解析用户文本。</p>
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<p class="tl">先拿到完整 reply（含 intent、是否转人工、是否附免责），再切片给 SSE 层逐块写。</p>
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<p class="tl">改「真流式」要先动 LangGraph 执行模型，不是只改前端 ChatPanel。</p>
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</div>
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</div>
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</div>
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</div>
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<div class="screen animate-in">
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<h2>相关文件（改流式 / 铁律时打开这些）</h2>
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<div class="file-tree animate-in">
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<div class="tree-item tree-folder">app/service/</div>
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<div class="tree-item tree-indent">customer_service.py — 14 节点图 + prepare_customer_stream</div>
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<div class="tree-item tree-indent">visitor_service.py — 游客 9 节点（无 Tool 查持仓）</div>
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<div class="tree-item tree-indent">rag_service.py — fin_faq / fin_product / fin_policy</div>
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<div class="tree-item tree-folder">app/api/</div>
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<div class="tree-item tree-indent">chat.py — customer 分支分流 + SSE 写帧</div>
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<div class="tree-item tree-indent">auth_adapter.py — host_auth_for_customer_service</div>
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<div class="tree-item tree-folder">app/tool/</div>
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<div class="tree-item tree-indent">core_ro_tool.py — query_holdings / query_trades 等</div>
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</div>
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<div class="quiz-container" id="quiz-customer-m3">
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<div class="quiz-question-block"
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data-correct="option-c"
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data-explanation-right="对。prepare_customer_stream 先 run_customer_chat 整图 invoke，再 _chunk_reply_text 切块；不是 LLM token 级流。"
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data-explanation-wrong="客户 SSE 是「整图跑完再切块」，Tool/RAG 在推流前已同步完成。">
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<h3 class="quiz-question">客户 SSE 流式回复，LangGraph 什么时候跑完？</h3>
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<div class="quiz-options">
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<button class="quiz-option" data-value="option-a" onclick="selectOption(this)">
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<div class="quiz-option-radio"></div><span>每推一块就跑一个节点</span>
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</button>
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<button class="quiz-option" data-value="option-b" onclick="selectOption(this)">
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<div class="quiz-option-radio"></div><span>只跑 interpret，前面跳过</span>
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</button>
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<button class="quiz-option" data-value="option-c" onclick="selectOption(this)">
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<div class="quiz-option-radio"></div><span>推流前整图 invoke 完成</span>
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</button>
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</div>
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<div class="quiz-feedback"></div>
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</div>
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<button class="quiz-check-btn" onclick="checkQuiz('quiz-customer-m3')">检查答案</button>
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<button class="quiz-reset-btn" onclick="resetQuiz('quiz-customer-m3')">重做</button>
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</div>
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</div>
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</div>
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</section>
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