docs(guidelines): 更新智能体行动格式规范

- 修改Action格式说明，明确可选操作类型包括function_name调用和Finish指令
- 更新Finish操作的使用说明，强调在获得最终答案时的输出要求
- 统一各文档中的Action格式描述，确保表述一致
- 完善智能体交互流程的指导说明

code/chapter1/FirstAgentTest.ipynb

@@ -2,10 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "938b2e36-f95c-4b0f-8770-335c6bb5bc0e",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T15:56:53.621032679Z",
+     "start_time": "2026-01-14T15:56:53.608893241Z"
+    }
+   },
    "source": [
     "import requests\n",
     "import os\n",
@@ -36,29 +39,27 @@
     "# 行动格式:\n",
     "你的回答必须严格遵循以下格式。首先是你的思考过程，然后是你要执行的具体行动，每次回复只输出一对Thought-Action：\n",
     "Thought: [这里是你的思考过程和下一步计划]\n",
-    "Action: [这里是你要调用的工具，格式为 function_name(arg_name=\"arg_value\")]\n",
+    "Action: 你决定采取的行动，必须是以下格式之一:\n",
+    "- `function_name(arg_name=\"arg_value\")`:调用一个可用工具。\n",
+    "- `Finish[最终答案]`:当你认为已经获得最终答案时。\n",
+    "- 当你收集到足够的信息，能够回答用户的最终问题时，你必须在Action:字段后使用 Finish[最终答案] 来输出最终答案。\n",
     "\n",
-    "# 任务完成:\n",
-    "当你收集到足够的信息，能够回答用户的最终问题时，你必须在`Action:`字段后使用 `Finish[最终答案]` 来输出最终答案。\n",
     "\n",
     "请开始吧！\n",
     "\"\"\""
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 8
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
    "id": "66d9d404-3c95-42f2-8975-436769b3cb87",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ 工具函数定义完成!\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T15:56:54.595070634Z",
+     "start_time": "2026-01-14T15:56:54.532054736Z"
     }
-   ],
+   },
    "source": [
     "def get_weather(city: str) -> str:\n",
     "    \"\"\"\n",
@@ -132,22 +133,27 @@
     "    \"get_attraction\": get_attraction,\n",
     "}\n",
     "print(\"✅ 工具函数定义完成!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e953fee4-9e3c-4e34-bf48-4ea002c3bb92",
-   "metadata": {},
+   ],
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ 智能助手类定义完成!\n"
+      "✅ 工具函数定义完成!\n"
      ]
     }
    ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "code",
+   "id": "e953fee4-9e3c-4e34-bf48-4ea002c3bb92",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T15:56:55.726641969Z",
+     "start_time": "2026-01-14T15:56:55.671146801Z"
+    }
+   },
    "source": [
     "class OpenAICompatibleClient:\n",
     "    \"\"\"\n",
@@ -205,23 +211,29 @@
     "        \"\"\"添加观察结果到历史\"\"\"\n",
     "        self.prompt_history.append(f\"Observation: {observation}\")\n",
     "print(\"✅ 智能助手类定义完成!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ab5d3142-c119-46ad-a7a1-ec1aa2e79435",
-   "metadata": {},
+   ],
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ 显示函数定义完成!\n"
+      "✅ 智能助手类定义完成!\n"
      ]
     }
    ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "code",
+   "id": "ab5d3142-c119-46ad-a7a1-ec1aa2e79435",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T15:56:56.670907293Z",
+     "start_time": "2026-01-14T15:56:56.608751066Z"
+    }
+   },
    "source": [
+    "\n",
     "def display_conversation(history):\n",
     "    \"\"\"美观地显示对话历史\"\"\"\n",
     "    print(\"\\n\" + \"=\"*60)\n",
@@ -264,14 +276,27 @@
     "    \n",
     "    return tool_name, kwargs\n",
     "print(\"✅ 显示函数定义完成!\")"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ 显示函数定义完成!\n"
+     ]
+    }
+   ],
+   "execution_count": 11
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "cc543309-fe16-44a9-9735-bce828b9c7ad",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T15:56:57.497421849Z",
+     "start_time": "2026-01-14T15:56:57.463244826Z"
+    }
+   },
    "source": [
     "def run_assistant(user_input, max_iterations=5, display=True):\n",
     "    \"\"\"\n",
@@ -350,13 +375,41 @@
     "        print(f\"⏰ 达到最大循环次数: {timeout_answer}\")\n",
     "    \n",
     "    return timeout_answer, assistant.prompt_history"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 12
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "id": "3f6e44eb-ff3d-4060-b4c2-ea3e139bf307",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T15:57:56.198306041Z",
+     "start_time": "2026-01-14T15:56:58.506429375Z"
+    }
+   },
+   "source": [
+    "# 测试示例\n",
+    "def test_basic_example():\n",
+    "    \"\"\"测试北京天气+景点推荐的示例\"\"\"\n",
+    "    print(\"🚀 开始测试北京天气+景点推荐示例\")\n",
+    "    user_input = \"你好，请帮我查询一下今天北京的天气，然后根据天气推荐一个合适的旅游景点。\"\n",
+    "    \n",
+    "    final_answer, history = run_assistant(user_input, display=True)\n",
+    "    \n",
+    "    print(\"\\n\" + \"=\"*60)\n",
+    "    print(\"📊 测试完成!\")\n",
+    "    print(\"=\"*60)\n",
+    "    print(f\"最终答案: {final_answer}\")\n",
+    "    \n",
+    "    # 显示完整对话历史\n",
+    "    display_conversation(history)\n",
+    "    \n",
+    "    return final_answer, history\n",
+    "\n",
+    "# 运行测试示例\n",
+    "final_answer, history = test_basic_example()"
+   ],
    "outputs": [
     {
      "name": "stdout",
@@ -370,38 +423,37 @@
       "正在调用大语言模型...\n",
       "大语言模型响应成功。\n",
       "🤖 模型输出:\n",
-      "Thought: 用户希望查询今天北京的天气情况，然后基于该天气推荐一个合适的旅游景点。我的第一步是获取北京的实时天气信息，以便了解当前的天气状况。执行查询天气的操作。\n",
-      "\n",
+      "Thought: 我将先查询今天北京的实时天气，然后根据天气选择合适的景点并给出推荐。\n",
       "Action: get_weather(city=\"北京\")\n",
       "🛠️  调用工具: get_weather({'city': '北京'})\n",
-      "📊 观察结果: 北京当前天气：Clear，气温9摄氏度\n",
+      "📊 观察结果: 北京当前天气：Clear，气温-1摄氏度\n",
       "==================================================\n",
       "\n",
       "🔄 循环 2/5\n",
       "正在调用大语言模型...\n",
       "大语言模型响应成功。\n",
       "🤖 模型输出:\n",
-      "Thought: 我已经获得了北京当前的天气信息：晴天，气温9摄氏度。接下来的步骤是根据这个天气条件推荐一个合适的旅游景点。晴天通常适合户外活动，这将影响我的选择。接下来，我将调用工具获取推荐的旅游景点。 \n",
+      "Thought: 现在北京天气为晴朗且气温-1°C，适合观光但需保暖；接下来根据晴天条件从景点中筛选一个合适的推荐。\n",
       "\n",
       "Action: get_attraction(city=\"北京\", weather=\"Clear\")\n",
       "🛠️  调用工具: get_attraction({'city': '北京', 'weather': 'Clear'})\n",
-      "📊 观察结果: 在晴天，北京的长城（如八达岭）和故宫是最值得去的景点。晴天下，这些地方的景色最美，游览最舒适。\n",
+      "📊 观察结果: In clear weather, Beijing's top attractions include the Forbidden City, the Great Wall at Mutianyu, and the Summer Palace. Plan for at least three days to explore major sites.\n",
       "==================================================\n",
       "\n",
       "🔄 循环 3/5\n",
       "正在调用大语言模型...\n",
       "大语言模型响应成功。\n",
       "🤖 模型输出:\n",
-      "Thought: 我已经获得了关于北京当前天气的信息，并找到了两个适合在晴天游览的旅游景点。长城和故宫都是著名的地标性景点，特别在晴天时更是吸引游客。接下来，我准备输出最终的推荐答案。\n",
+      "Thought: 我将基于当前天气信息，选择一个在 Beijing 今天适合的景点并给出简要理由和出行提示。\n",
       "\n",
-      "Action: finish(answer=\"今天北京的天气晴，气温9摄氏度。推荐您去长城（如八达岭）或故宫游览，这两个景点在这样的天气下非常适合。\")\n",
+      "Action: Finish[今天北京的天气是晴（Clear），气温约-1°C。基于当前天气，推荐的景点是颐和园（Summer Palace）。原因：晴朗但寒冷的日子，颐和园的广阔园林和昆明湖景观非常适合观光，且园区内有多处室外赏景点与室内亭榭可提供避寒休憩点。穿着方面请穿厚外套、带帽子和手套，鞋子建议防滑防水。出行建议：尽量乘坐地铁等公共交通前往，出发前可查看开放时间和路线信息。若更倾向室内参观，故宫等室内空间也同样值得一看。]\n",
       "🎉 任务完成!\n",
-      "📋 最终答案: 今天北京的天气晴，气温9摄氏度。推荐您去长城（如八达岭）或故宫游览，这两个景点在这样的天气下非常适合。\n",
+      "📋 最终答案: 今天北京的天气是晴（Clear），气温约-1°C。基于当前天气，推荐的景点是颐和园（Summer Palace）。原因：晴朗但寒冷的日子，颐和园的广阔园林和昆明湖景观非常适合观光，且园区内有多处室外赏景点与室内亭榭可提供避寒休憩点。穿着方面请穿厚外套、带帽子和手套，鞋子建议防滑防水。出行建议：尽量乘坐地铁等公共交通前往，出发前可查看开放时间和路线信息。若更倾向室内参观，故宫等室内空间也同样值得一看。\n",
       "\n",
       "============================================================\n",
       "📊 测试完成!\n",
       "============================================================\n",
-      "最终答案: 今天北京的天气晴，气温9摄氏度。推荐您去长城（如八达岭）或故宫游览，这两个景点在这样的天气下非常适合。\n",
+      "最终答案: 今天北京的天气是晴（Clear），气温约-1°C。基于当前天气，推荐的景点是颐和园（Summer Palace）。原因：晴朗但寒冷的日子，颐和园的广阔园林和昆明湖景观非常适合观光，且园区内有多处室外赏景点与室内亭榭可提供避寒休憩点。穿着方面请穿厚外套、带帽子和手套，鞋子建议防滑防水。出行建议：尽量乘坐地铁等公共交通前往，出发前可查看开放时间和路线信息。若更倾向室内参观，故宫等室内空间也同样值得一看。\n",
       "\n",
       "============================================================\n",
       "📝 对话历史\n",
@@ -409,53 +461,34 @@
       "\n",
       "👤 用户 [1]:  你好，请帮我查询一下今天北京的天气，然后根据天气推荐一个合适的旅游景点。\n",
       "\n",
-      "🤔 思考 [2]: 用户希望查询今天北京的天气情况，然后基于该天气推荐一个合适的旅游景点。我的第一步是获取北京的实时天气信息，以便了解当前的天气状况。执行查询天气的操作。\n",
-      "\n",
+      "🤔 思考 [2]: 我将先查询今天北京的实时天气，然后根据天气选择合适的景点并给出推荐。\n",
       "Action: get_weather(city=\"北京\")\n",
-      "📊 观察 [3]: 北京当前天气：Clear，气温9摄氏度\n",
+      "📊 观察 [3]: 北京当前天气：Clear，气温-1摄氏度\n",
       "\n",
-      "🤔 思考 [4]: 我已经获得了北京当前的天气信息：晴天，气温9摄氏度。接下来的步骤是根据这个天气条件推荐一个合适的旅游景点。晴天通常适合户外活动，这将影响我的选择。接下来，我将调用工具获取推荐的旅游景点。 \n",
+      "🤔 思考 [4]: 现在北京天气为晴朗且气温-1°C，适合观光但需保暖；接下来根据晴天条件从景点中筛选一个合适的推荐。\n",
       "\n",
       "Action: get_attraction(city=\"北京\", weather=\"Clear\")\n",
-      "📊 观察 [5]: 在晴天，北京的长城（如八达岭）和故宫是最值得去的景点。晴天下，这些地方的景色最美，游览最舒适。\n",
+      "📊 观察 [5]: In clear weather, Beijing's top attractions include the Forbidden City, the Great Wall at Mutianyu, and the Summer Palace. Plan for at least three days to explore major sites.\n",
       "\n",
-      "🤔 思考 [6]: 我已经获得了关于北京当前天气的信息，并找到了两个适合在晴天游览的旅游景点。长城和故宫都是著名的地标性景点，特别在晴天时更是吸引游客。接下来，我准备输出最终的推荐答案。\n",
+      "🤔 思考 [6]: 我将基于当前天气信息，选择一个在 Beijing 今天适合的景点并给出简要理由和出行提示。\n",
       "\n",
-      "Action: finish(answer=\"今天北京的天气晴，气温9摄氏度。推荐您去长城（如八达岭）或故宫游览，这两个景点在这样的天气下非常适合。\")\n",
+      "Action: Finish[今天北京的天气是晴（Clear），气温约-1°C。基于当前天气，推荐的景点是颐和园（Summer Palace）。原因：晴朗但寒冷的日子，颐和园的广阔园林和昆明湖景观非常适合观光，且园区内有多处室外赏景点与室内亭榭可提供避寒休憩点。穿着方面请穿厚外套、带帽子和手套，鞋子建议防滑防水。出行建议：尽量乘坐地铁等公共交通前往，出发前可查看开放时间和路线信息。若更倾向室内参观，故宫等室内空间也同样值得一看。]\n",
       "============================================================\n",
       "\n"
      ]
     }
    ],
-   "source": [
-    "# 测试示例\n",
-    "def test_basic_example():\n",
-    "    \"\"\"测试北京天气+景点推荐的示例\"\"\"\n",
-    "    print(\"🚀 开始测试北京天气+景点推荐示例\")\n",
-    "    user_input = \"你好，请帮我查询一下今天北京的天气，然后根据天气推荐一个合适的旅游景点。\"\n",
-    "    \n",
-    "    final_answer, history = run_assistant(user_input, display=True)\n",
-    "    \n",
-    "    print(\"\\n\" + \"=\"*60)\n",
-    "    print(\"📊 测试完成!\")\n",
-    "    print(\"=\"*60)\n",
-    "    print(f\"最终答案: {final_answer}\")\n",
-    "    \n",
-    "    # 显示完整对话历史\n",
-    "    display_conversation(history)\n",
-    "    \n",
-    "    return final_answer, history\n",
-    "\n",
-    "# 运行测试示例\n",
-    "final_answer, history = test_basic_example()"
-   ]
+   "execution_count": 13
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "id": "68c735c1-eb3e-40e7-8b70-2be941798187",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T15:58:30.343440202Z",
+     "start_time": "2026-01-14T15:58:30.317379757Z"
+    }
+   },
    "source": [
     "def interactive_travel_assistant():\n",
     "    \"\"\"\n",
@@ -501,13 +534,41 @@
     "    print(f\"🚀 快速测试: {user_input}\")\n",
     "    final_answer, _ = run_assistant(user_input, display=True)\n",
     "    return final_answer"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 14
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "id": "bc58c911-8502-4cf5-aa3f-a9f57a94b679",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T16:02:32.575540116Z",
+     "start_time": "2026-01-14T16:00:39.668510589Z"
+    }
+   },
+   "source": [
+    "# 主启动入口\n",
+    "if __name__ == \"__main__\":\n",
+    "    # 可以选择直接运行测试示例\n",
+    "    print(\"选择运行模式:\")\n",
+    "    print(\"1. 运行测试示例 (北京)\")\n",
+    "    print(\"2. 交互模式\")\n",
+    "    print(\"3. 快速测试其他城市\")\n",
+    "    \n",
+    "    choice = input(\"请输入选择 (1/2/3): \").strip()\n",
+    "    \n",
+    "    if choice == \"1\":\n",
+    "        test_basic_example()\n",
+    "    elif choice == \"2\":\n",
+    "        interactive_travel_assistant()\n",
+    "    elif choice == \"3\":\n",
+    "        city = input(\"请输入要测试的城市: \").strip() or \"上海\"\n",
+    "        quick_test(city)\n",
+    "    else:\n",
+    "        print(\"无效选择，运行测试示例...\")\n",
+    "        test_basic_example()"
+   ],
    "outputs": [
     {
      "name": "stdout",
@@ -516,78 +577,53 @@
       "选择运行模式:\n",
       "1. 运行测试示例 (北京)\n",
       "2. 交互模式\n",
-      "3. 快速测试其他城市\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "请输入选择 (1/2/3):  3\n",
-      "请输入要测试的城市:  伦敦\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🚀 快速测试: 请帮我查询伦敦的天气，并推荐适合的旅游景点\n",
-      "👤 用户输入: 请帮我查询伦敦的天气，并推荐适合的旅游景点\n",
+      "3. 快速测试其他城市\n",
+      "🚀 快速测试: 请帮我查询广州的天气，并推荐适合的旅游景点\n",
+      "👤 用户输入: 请帮我查询广州的天气，并推荐适合的旅游景点\n",
       "==================================================\n",
       "\n",
       "🔄 循环 1/5\n",
       "正在调用大语言模型...\n",
       "大语言模型响应成功。\n",
       "🤖 模型输出:\n",
-      "Thought: 我需要先查询伦敦的实时天气，这样才能根据天气情况推荐合适的旅游景点。接下来，我将调用天气查询工具获取伦敦的天气信息。 \n",
-      "Action: get_weather(city=\"伦敦\")\n",
-      "🛠️  调用工具: get_weather({'city': '伦敦'})\n",
-      "📊 观察结果: 伦敦当前天气：Partly cloudy，气温16摄氏度\n",
+      "Thought: Plan to fetch Guangzhou的天气信息作为第一步，然后根据天气来筛选推荐的景点。  \n",
+      "Action: get_weather(city=\"广州\")\n",
+      "🛠️  调用工具: get_weather({'city': '广州'})\n",
+      "📊 观察结果: 广州当前天气：Clear，气温15摄氏度\n",
       "==================================================\n",
       "\n",
       "🔄 循环 2/5\n",
       "正在调用大语言模型...\n",
       "大语言模型响应成功。\n",
       "🤖 模型输出:\n",
-      "Thought: 现在我已经查询到了伦敦的实时天气情况：部分多云，气温为16摄氏度。接下来，我将根据这个天气情况推荐适合在伦敦游玩的旅游景点。 \n",
-      "Action: get_attraction(city=\"伦敦\", weather=\"Partly cloudy\")\n",
-      "🛠️  调用工具: get_attraction({'city': '伦敦', 'weather': 'Partly cloudy'})\n",
-      "📊 观察结果: 在伦敦多云天气下，伦敦眼是最值得去的景点。它提供了伟大的城市景观。此外，博物馆和画廊也适合参观。\n",
+      "Thought: Guangzhou当前天气为Clear，适合户外活动。我将基于晴天的条件获取推荐的景点列表。  \n",
+      "Action: get_attraction(city=\"广州\", weather=\"Clear\")\n",
+      "🛠️  调用工具: get_attraction({'city': '广州', 'weather': 'Clear'})\n",
+      "📊 观察结果: 在广州天气晴朗时，最值得去的旅游景点是越秀公园和广州塔。这些地方风景优美，适合观光和休闲。\n",
       "==================================================\n",
       "\n",
       "🔄 循环 3/5\n",
       "正在调用大语言模型...\n",
       "大语言模型响应成功。\n",
       "🤖 模型输出:\n",
-      "Thought: 我已经获取了伦敦的天气情况和适合的旅游景点推荐。当前天气为部分多云，推荐旅游景点为伦敦眼，同时博物馆和画廊也非常适合在这种天气下参观。最终我将整合这些信息并给出用户的建议。 \n",
-      "Action: finish(answer=\"伦敦当前天气为部分多云，气温16摄氏度。根据天气情况，推荐您去伦敦眼欣赏城市景观，此外博物馆和画廊也是很好的选择。\")\n",
+      "Thought: 计划基于广州晴天条件提供景点推荐，先查询符合晴天的景点。\n",
+      "Action: get_attraction(city=\"广州\", weather=\"Clear\")\n",
+      "🛠️  调用工具: get_attraction({'city': '广州', 'weather': 'Clear'})\n",
+      "📊 观察结果: 在广州天气晴朗时，最值得去的旅游景点是越秀公园和广州塔。这些地方风景优美，适合观光和休闲。\n",
+      "==================================================\n",
+      "\n",
+      "🔄 循环 4/5\n",
+      "正在调用大语言模型...\n",
+      "大语言模型响应成功。\n",
+      "🤖 模型输出:\n",
+      "Thought: 我不能提供内部推理过程，但会直接给出结果。  \n",
+      "Action: Finish[广州天气：晴朗，气温15°C。推荐景点：越秀公园、广州塔。]\n",
       "🎉 任务完成!\n",
-      "📋 最终答案: 伦敦当前天气为部分多云，气温16摄氏度。根据天气情况，推荐您去伦敦眼欣赏城市景观，此外博物馆和画廊也是很好的选择。\n"
+      "📋 最终答案: 广州天气：晴朗，气温15°C。推荐景点：越秀公园、广州塔。\n"
      ]
     }
    ],
-   "source": [
-    "# 主启动入口\n",
-    "if __name__ == \"__main__\":\n",
-    "    # 可以选择直接运行测试示例\n",
-    "    print(\"选择运行模式:\")\n",
-    "    print(\"1. 运行测试示例 (北京)\")\n",
-    "    print(\"2. 交互模式\")\n",
-    "    print(\"3. 快速测试其他城市\")\n",
-    "    \n",
-    "    choice = input(\"请输入选择 (1/2/3): \").strip()\n",
-    "    \n",
-    "    if choice == \"1\":\n",
-    "        test_basic_example()\n",
-    "    elif choice == \"2\":\n",
-    "        interactive_travel_assistant()\n",
-    "    elif choice == \"3\":\n",
-    "        city = input(\"请输入要测试的城市: \").strip() or \"上海\"\n",
-    "        quick_test(city)\n",
-    "    else:\n",
-    "        print(\"无效选择，运行测试示例...\")\n",
-    "        test_basic_example()"
-   ]
+   "execution_count": 17
   },
   {
    "cell_type": "code",

code/chapter1/FirstAgentTest.py

@@ -8,10 +8,10 @@ AGENT_SYSTEM_PROMPT = """
 # 行动格式:
 你的回答必须严格遵循以下格式。首先是你的思考过程，然后是你要执行的具体行动，每次回复只输出一对Thought-Action：
 Thought: [这里是你的思考过程和下一步计划]
-Action: [这里是你要调用的工具，格式为 function_name(arg_name="arg_value")]
-
-# 任务完成:
-当你收集到足够的信息，能够回答用户的最终问题时，你必须在`Action:`字段后使用 `Finish[最终答案]` 来输出最终答案。
+Action: 你决定采取的行动，必须是以下格式之一:
+- `function_name(arg_name="arg_value")`:调用一个可用工具。
+- `Finish[最终答案]`:当你认为已经获得最终答案时。
+- 当你收集到足够的信息，能够回答用户的最终问题时，你必须在Action:字段后使用 Finish[最终答案] 来输出最终答案。
 
 请开始吧！
 """

docs/chapter1/Chapter1-Introduction-to-Agents.md

@@ -240,10 +240,10 @@ You are an intelligent travel assistant. Your task is to analyze user requests a
 # Action Format:
 Your response must strictly follow the following format. First is your thinking process, then the specific action you want to execute. Each response should output only one Thought-Action pair:
 Thought: [Here is your thinking process and next step plan]
-Action: [Here is the tool you want to call, in the format function_name(arg_name="arg_value")]
-
-# Task Completion:
-When you have collected enough information to answer the user's final question, you must use `Finish[final answer]` after the Action: field to output the final answer.
+Action: The action you decide to take, which must be in one of the following formats:
+- function_name(arg_name="arg_value"): Call an available tool.
+- Finish[final answer]: When you believe you have obtained the final answer.
+- When you have collected enough information to answer the user's final question, you must use `Finish[final answer]` after the Action: field to output the final answer.
 
 Let's begin!
 """

docs/chapter1/第一章 初识智能体.md

@@ -244,10 +244,10 @@ AGENT_SYSTEM_PROMPT = """
 # 行动格式:
 你的回答必须严格遵循以下格式。首先是你的思考过程，然后是你要执行的具体行动，每次回复只输出一对Thought-Action：
 Thought: [这里是你的思考过程和下一步计划]
-Action: [这里是你要调用的工具，格式为 function_name(arg_name="arg_value")]
-
-# 任务完成:
-当你收集到足够的信息，能够回答用户的最终问题时，你必须在`Action:`字段后使用 `Finish[最终答案]` 来输出最终答案。
+Action: 你决定采取的行动，必须是以下格式之一:
+- `function_name(arg_name="arg_value")`:调用一个可用工具。
+- `Finish[最终答案]`:当你认为已经获得最终答案时。
+- 当你收集到足够的信息，能够回答用户的最终问题时，你必须在Action:字段后使用 Finish[最终答案] 来输出最终答案。
 
 请开始吧！
 """