fix: address reviewed documentation issues

docs/chapter11/第十一章 Agentic-RL.md

@@ -187,7 +187,7 @@ rl_tool = RLTrainingTool()
 
 # 1. 快速测试:SFT训练(10个样本，1个epoch)
 sft_result_str = rl_tool.run({
-    "action": "train"，
+    "action": "train",
     "algorithm": "sft",
     "model_name": "Qwen/Qwen3-0.6B",
     "output_dir": "./models/quick_test_sft",

docs/chapter3/Chapter3-Fundamentals-of-Large-Language-Models.md

@@ -1003,7 +1003,7 @@ This chapter's LLM foundations mainly help everyone better understand large mode
 
 [10] Hoffmann, J., Borgeaud, E., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, R., ... & Sifre, L. (2022). Training Compute-Optimal Large Language Models. arXiv preprint arXiv:2203.07678.
 
-[11] Ji, Z., Lee, N., Fries, R., Yu, T., & Su, D. (2023). Survey of Hallucination in Large Language Models.
+[11] Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., ... & Liu, T. (2023). A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. *arXiv preprint arXiv:2311.05232*.
 
 [12] Bender, E. M., Gebru, T., McMillan-Major, A., & Mitchell, M. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? .
 

docs/chapter3/第三章 大语言模型基础.md

@@ -1013,7 +1013,7 @@ print(response)
 
 [10] Hoffmann, J., Borgeaud, E., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, R., ... & Sifre, L. (2022). Training Compute-Optimal Large Language Models. arXiv preprint arXiv:2203.07678.
 
-[11] Ji, Z., Lee, N., Fries, R., Yu, T., & Su, D. (2023). Survey of Hallucination in Large Language Models.
+[11] Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., ... & Liu, T. (2023). A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. *arXiv preprint arXiv:2311.05232*.
 
 [12] Bender, E. M., Gebru, T., McMillan-Major, A., & Mitchell, M. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? .
 

docs/chapter5/Chapter5-Building-Agents-with-Low-Code-Platforms.md

@@ -1103,7 +1103,7 @@ You are a 24/7 on-call, professional and efficient AI email assistant. Your task
 For the `Simple Vector Store` tool, we need to perform key configurations to ensure it can correctly "read" the knowledge we stored earlier:
 
 - **Operation Mode**: `Retrieve Documents (As Tool for AI Agent)` (read mode as a tool).
-- **Memory Key**: Must fill in the **exact same** Key as in the first part, i.e., `my_private_knowledge`.
+- **Memory Key**: Must fill in the **exact same** Key as in the first part, i.e., `my-dailytime`.
 - **Embeddings**: Must use the **exact same** `Embeddings Google Gemini` model as in the first part.
 
 Only when the `Memory Key` and `Embeddings` model are completely consistent can the Agent use the correct "key" and "language" to access the knowledge base, as shown in Figure 5.62.

docs/chapter5/第五章 基于低代码平台的智能体搭建.md

@@ -1107,7 +1107,7 @@ return [
 对于 `Simple Vector Store` 工具，我们需要进行关键配置，以确保它能正确“读取”我们之前存入的知识：
 
 - <strong>Operation Mode</strong>: `Retrieve Documents (As Tool for AI Agent)` (作为工具的读取模式)。
-- <strong>Memory Key</strong>: 必须填写与第一部分<strong>完全相同</strong>的 Key，即 `my_private_knowledge`。
+- <strong>Memory Key</strong>: 必须填写与第一部分<strong>完全相同</strong>的 Key，即 `my-dailytime`。
 - <strong>Embeddings</strong>: 必须使用与第一部分<strong>完全相同</strong>的 `Embeddings Google Gemini` 模型。
 
 只有 `Memory Key` 和 `Embeddings` 模型完全一致，Agent 才能用正确的“钥匙”和“语言”来访问知识库,如图5.62所示。

docs/index.html

@@ -222,9 +222,11 @@
             alias: {
                 // 英文路径映射
                 '/en/README.md': '/README_EN.md',
+                '/en/README_EN.md': '/README_EN.md',
+                '/en/Preface.md': '/Preface.md',
                 '/en/_sidebar.md': '/_sidebar_en.md',
                 '/en/.*/_sidebar.md': '/_sidebar_en.md',
-                '/en/chapter(\\d+)/Chapter(.*)': '/chapter$1/Chapter$2',
+                '/en/(.*)': '/$1',
 
                 // 默认中文侧边栏
                 '/_sidebar.md': '/_sidebar.md',