{"id":121018,"date":"2026-11-01T23:34:04","date_gmt":"2026-11-01T15:34:04","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/121018.html"},"modified":"2026-11-01T23:34:04","modified_gmt":"2026-11-01T15:34:04","slug":"%e3%80%8ahello-agents%e3%80%8b%e7%ac%ac%e4%b8%80%e8%87%b3%e4%b8%89%e7%ab%a0-%e6%a0%b8%e5%bf%83%e7%9f%a5%e8%af%86-%e6%a2%b3%e7%90%86%e6%80%bb%e7%bb%93","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/121018.html","title":{"rendered":"\u300aHello-Agents\u300b\u7b2c\u4e00\u81f3\u4e09\u7ae0 \u6838\u5fc3\u77e5\u8bc6 \u68b3\u7406\u603b\u7ed3"},"content":{"rendered":"

<\/h2>\n
\n

\u4e00\u53e5\u8bdd\u4e32\u8054：\u667a\u80fd\u4f53 = \u80fd\u611f\u77e5\u73af\u5883\u5e76\u81ea\u4e3b\u884c\u52a8\u7684\u5b9e\u4f53（\u7b2c\u4e00\u7ae0）\u2192 \u5176"\u5927\u8111"\u5386\u7ecf\u7b26\u53f7\u89c4\u5219\u4e0e\u5b66\u4e60\u8303\u5f0f，\u6700\u7ec8\u843d\u5728 LLM（\u7b2c\u4e8c\u7ae0）\u2192 \u800c LLM \u672c\u8d28\u662f\u5728\u6d77\u91cf\u6587\u672c\u4e0a\u9884\u8bad\u7ec3\u51fa\u7684"\u9884\u6d4b\u4e0b\u4e00\u4e2a\u8bcd"\u7684\u81ea\u56de\u5f52\u6a21\u578b（\u7b2c\u4e09\u7ae0）\u3002<\/p>\n

\n

\u7b2c\u4e00\u7ae0 \u521d\u8bc6\u667a\u80fd\u4f53（\u91cd\u70b9）<\/h2>\n

1.1 \u4ec0\u4e48\u662f\u667a\u80fd\u4f53<\/h3>\n

1.1.1 \u5b9a\u4e49\u4e0e\u56db\u4e2a\u57fa\u672c\u8981\u7d20<\/h4>\n

\u667a\u80fd\u4f53（Agent）：\u80fd\u901a\u8fc7\u4f20\u611f\u5668\u611f\u77e5\u73af\u5883，\u5e76\u81ea\u4e3b\u5730\u901a\u8fc7\u6267\u884c\u5668\u91c7\u53d6\u884c\u52a8\u4ee5\u8fbe\u6210\u76ee\u6807\u7684\u5b9e\u4f53\u3002<\/p>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u8981\u7d20\u542b\u4e49\u4f8b\u5b50<\/tr>\n\n\n\n\n\n
\u73af\u5883<\/td>\n\u6240\u5904\u7684\u5916\u90e8\u4e16\u754c<\/td>\n\u81ea\u52a8\u9a7e\u9a76\u2192\u9053\u8def\u4ea4\u901a；\u4ea4\u6613\u7b97\u6cd5\u2192\u91d1\u878d\u5e02\u573a<\/td>\n<\/tr>\n
\u4f20\u611f\u5668<\/td>\n\u611f\u77e5\u5165\u53e3<\/td>\n\u6444\u50cf\u5934\u3001\u9ea6\u514b\u98ce\u3001API \u8fd4\u56de\u7684\u6570\u636e\u6d41<\/td>\n<\/tr>\n
\u6267\u884c\u5668<\/td>\n\u65bd\u52a0\u5f71\u54cd\u7684\u51fa\u53e3<\/td>\n\u673a\u68b0\u81c2／\u6267\u884c\u4ee3\u7801\u3001\u8c03\u7528\u670d\u52a1<\/td>\n<\/tr>\n
\u81ea\u4e3b\u6027（Autonomy）<\/td>\n\u2b50 \u771f\u6b63\u8d4b\u4e88"\u667a\u80fd"\u7684\u8981\u7d20<\/td>\n\u4e0d\u662f\u88ab\u52a8\u54cd\u5e94\u6216\u6267\u884c\u9884\u8bbe\u6307\u4ee4，\u800c\u662f\u57fa\u4e8e\u611f\u77e5\u4e0e\u5185\u90e8\u72b6\u6001\u72ec\u7acb\u51b3\u7b56<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5173\u952e\u7ed3\u8bba：\u4ece\u611f\u77e5\u5230\u884c\u52a8\u7684\u95ed\u73af\u662f\u4e00\u5207\u667a\u80fd\u4f53\u884c\u4e3a\u7684\u57fa\u7840；\u5224\u65ad"\u662f\u4e0d\u662f\u667a\u80fd\u4f53"\u770b\u6709\u65e0\u81ea\u4e3b\u95ed\u73af，\u800c\u975e\u7b97\u529b\u5f3a\u5f31\u3002<\/p>\n

1.1.2 \u4f20\u7edf\u667a\u80fd\u4f53\u7684\u6f14\u8fdb\u4e94\u9636\u68af<\/h4>\n

\u8fd9\u6761\u6f14\u8fdb\u7ebf\u672c\u8eab\u5c31\u662f\u6700\u7ecf\u5178\u7684\u5206\u7c7b\u9636\u68af（\u6e90\u81ea\u300aArtificial Intelligence: A Modern Approach\u300b）：<\/p>\n

  • \u7b80\u5355\u53cd\u5c04：\u663e\u5f0f\u8bbe\u8ba1\u7684"\u6761\u4ef6\u2014\u52a8\u4f5c"\u89c4\u5219，\u65e0\u8bb0\u5fc6\u65e0\u9884\u6d4b（\u6052\u6e29\u5668）\u3002<\/li>\n
  • \u57fa\u4e8e\u6a21\u578b\u7684\u53cd\u5c04：\u5f15\u5165\u5185\u90e8\u4e16\u754c\u6a21\u578b，\u8ffd\u8e2a\u65e0\u6cd5\u76f4\u63a5\u611f\u77e5\u7684\u73af\u5883\u72b6\u6001（\u96a7\u9053\u4e2d\u884c\u8f66\u4ecd"\u8bb0\u5f97"\u524d\u65b9\u6709\u8f66）\u2192 \u83b7\u5f97\u521d\u7ea7\u8bb0\u5fc6\u3002<\/li>\n
  • \u57fa\u4e8e\u76ee\u6807：\u6709\u9884\u89c1\u6027\u5730\u9009\u62e9\u5bfc\u5411\u672a\u6765\u76ee\u6807\u7684\u884c\u52a8（GPS \u7528 A* \u89c4\u5212）\u2192 \u83b7\u5f97\u89c4\u5212\u80fd\u529b\u3002<\/li>\n
  • \u57fa\u4e8e\u6548\u7528：\u4e3a\u6bcf\u4e2a\u72b6\u6001\u8d4b\u6548\u7528\u503c，\u6700\u5927\u5316\u671f\u671b\u6548\u7528 \u2192 \u83b7\u5f97\u591a\u76ee\u6807\u6743\u8861\u80fd\u529b\u3002<\/li>\n
  • \u5b66\u4e60\u578b：\u901a\u8fc7\u4e0e\u73af\u5883\u4e92\u52a8\u81ea\u4e3b\u5b66\u4e60；\u7ed3\u6784 = \u6027\u80fd\u5143\u4ef6（\u4e0a\u8ff0\u4efb\u4e00\u7c7b）+ \u5b66\u4e60\u5143\u4ef6（\u636e\u7ed3\u679c\u53cd\u8fc7\u6765\u4fee\u6b63\u7b56\u7565，\u5982 AlphaGo Zero \u81ea\u6211\u5bf9\u5f08）\u3002<\/li>\n

    \u6f14\u8fdb\u903b\u8f91：\u6bcf\u4e00\u6b65\u90fd\u5728\u56de\u7b54\u4e0a\u4e00\u6b65\u66b4\u9732\u7684\u95ee\u9898\u2014\u2014\u611f\u77e5\u4e0d\u591f\u7528\u2192\u52a0\u72b6\u6001；\u6ca1\u76ee\u6807\u2192\u52a0\u76ee\u6807；\u76ee\u6807\u51b2\u7a81\u2192\u52a0\u6548\u7528；\u5148\u9a8c\u4e0d\u591f\u2192\u52a0\u5b66\u4e60\u3002<\/p>\n

    1.1.3 \u5927\u8bed\u8a00\u6a21\u578b\u9a71\u52a8\u7684\u65b0\u8303\u5f0f<\/h4>\n

    \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

    \u672c\u8d28\u533a\u522b：\u4f20\u7edf\u667a\u80fd\u4f53\u7684\u80fd\u529b\u6765\u81ea\u5de5\u7a0b\u5e08\u7684\u663e\u5f0f\u7f16\u7a0b，\u884c\u4e3a\u786e\u5b9a\u6709\u8fb9\u754c；LLM \u667a\u80fd\u4f53\u901a\u8fc7\u9884\u8bad\u7ec3\u83b7\u5f97\u9690\u5f0f\u4e16\u754c\u6a21\u578b\u4e0e\u6d8c\u73b0\u80fd\u529b，\u5e94\u5bf9\u590d\u6742\u4efb\u52a1\u66f4\u7075\u6d3b\u901a\u7528\u3002<\/p>\n

    \u4ee5"\u89c4\u5212\u4e00\u6b21\u53a6\u95e8\u4e4b\u65c5"\u8fd9\u4e00\u6a21\u7cca\u6307\u4ee4\u4e3a\u4f8b，\u4f53\u73b0\u4e09\u4e2a\u7279\u6027：<\/p>\n

      \n
    • \u89c4\u5212\u4e0e\u63a8\u7406：\u81ea\u4e3b\u628a\u76ee\u6807\u5206\u89e3\u4e3a\u5b50\u4efb\u52a1\u94fe（\u786e\u8ba4\u504f\u597d \u2192 \u67e5\u8be2\u4fe1\u606f \u2192 \u5236\u5b9a\u8349\u6848 \u2192 \u9884\u8ba2），\u662f\u6a21\u578b\u9a71\u52a8\u7684\u5185\u5728\u89c4\u5212，\u975e\u5199\u6b7b\u6d41\u7a0b\u3002<\/li>\n
    • \u5de5\u5177\u4f7f\u7528：\u8bc6\u522b\u4fe1\u606f\u7f3a\u53e3\u65f6\u4e3b\u52a8\u8c03\u7528\u5916\u90e8\u5de5\u5177\u8865\u5168（\u67e5\u5929\u6c14 API），\u5e76\u628a\u7ed3\u679c\u7eb3\u5165\u51b3\u7b56（\u9884\u62a5\u6709\u96e8\u2192\u63a8\u8350\u5ba4\u5185）\u3002<\/li>\n
    • \u52a8\u6001\u4fee\u6b63：\u628a\u7528\u6237\u53cd\u9988\u89c6\u4e3a\u65b0\u7ea6\u675f\u5e76\u8c03\u6574（"\u8d85\u9884\u7b97"\u2192\u91cd\u65b0\u641c\u7d22）\u3002<\/li>\n<\/ul>\n

      \u6838\u5fc3\u8f6c\u53d8：\u4ece"\u5f00\u53d1\u4e13\u7528\u81ea\u52a8\u5316\u5de5\u5177"\u8f6c\u5411"\u6784\u5efa\u80fd\u81ea\u4e3b\u89e3\u51b3\u95ee\u9898\u7684\u7cfb\u7edf"\u2014\u2014\u6838\u5fc3\u4e0d\u518d\u662f\u5199\u4ee3\u7801，\u800c\u662f\u5f15\u5bfc\u4e00\u4e2a\u901a\u7528"\u5927\u8111"\u53bb\u89c4\u5212\u3001\u884c\u52a8\u548c\u5b66\u4e60\u3002<\/p>\n

      1.1.4 \u4e09\u79cd\u5206\u7c7b\u7ef4\u5ea6<\/h4>\n

      \u7ef4\u5ea6\u4e00｜\u5185\u90e8\u51b3\u7b56\u67b6\u6784：\u5373 1.1.2 \u7684\u4e94\u9636\u68af；\u5b66\u4e60\u80fd\u529b\u662f\u53ef\u53e0\u52a0\u4e8e\u5176\u4e0a\u7684\u5143\u80fd\u529b\u3002<\/p>\n

      \u7ef4\u5ea6\u4e8c｜\u65f6\u95f4\u4e0e\u53cd\u5e94\u6027 \u2b50（\u8bbe\u8ba1\u65f6\u6700\u6838\u5fc3\u7684\u6743\u8861）<\/p>\n\n\u7c7b\u578b\u51b3\u7b56\u65b9\u5f0f\u4f18\u52bf\u4ee3\u4ef7\u573a\u666f<\/tr>\n\n\n\n\n
      \u53cd\u5e94\u5f0f（Reactive）<\/td>\n\u611f\u77e5\u2192\u884c\u52a8\u76f4\u63a5\u6620\u5c04，\u51e0\u4e4e\u4e0d\u89c4\u5212<\/td>\n\u5ef6\u8fdf\u6781\u4f4e\u3001\u5f00\u9500\u5c0f<\/td>\n\u77ed\u89c6，\u6613\u9677\u5c40\u90e8\u6700\u4f18<\/td>\n\u5b89\u5168\u6c14\u56ca\u3001\u9ad8\u9891\u4ea4\u6613<\/td>\n<\/tr>\n
      \u89c4\u5212\u5f0f（Deliberative）<\/td>\n\u5148\u7528\u4e16\u754c\u6a21\u578b\u63a2\u7d22\u672a\u6765\u3001\u8bc4\u4f30\u540e\u679c\u518d\u9009\u8def\u5f84<\/td>\n\u6709\u6218\u7565\u6027\u4e0e\u8fdc\u89c1<\/td>\n\u8ba1\u7b97\u6210\u672c\u9ad8，\u53ef\u80fd\u9519\u5931\u65f6\u673a<\/td>\n\u5546\u4e1a\u8ba1\u5212\u3001\u957f\u9014\u65c5\u884c<\/td>\n<\/tr>\n
      \u6df7\u5408\u5f0f（Hybrid）<\/td>\n\u5206\u5c42：\u5e95\u5c42\u5feb\u901f\u53cd\u5e94 + \u9ad8\u5c42\u5ba1\u614e\u89c4\u5212<\/td>\n\u517c\u987e\u4e24\u8005<\/td>\n\u8bbe\u8ba1\u590d\u6742<\/td>\n\u667a\u80fd\u65c5\u884c\u52a9\u624b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u73b0\u4ee3 LLM \u667a\u80fd\u4f53\u5c31\u662f\u6df7\u5408\u5f0f\u7684\u5178\u578b：\u5728"\u601d\u8003\u2014\u884c\u52a8\u2014\u89c2\u5bdf"\u5faa\u73af\u4e2d，\u601d\u8003 = \u5ba1\u8bae，\u884c\u52a8\u4e0e\u89c2\u5bdf = \u53cd\u5e94，\u628a\u5b8f\u5927\u4efb\u52a1\u5206\u89e3\u4e3a\u4e00\u7cfb\u5217"\u89c4\u5212\u2014\u53cd\u5e94"\u5fae\u5faa\u73af\u3002<\/p>\n

      \u7ef4\u5ea6\u4e09｜\u77e5\u8bc6\u8868\u793a \u2b50（\u7406\u89e3 LLM \u5b9a\u4f4d\u7684\u5173\u952e）<\/p>\n

      \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u8303\u5f0f\u77e5\u8bc6\u5982\u4f55\u5b58\u50a8\u4f18\u52bf\u5f31\u70b9<\/tr>\n\n\n\n\n
      \u7b26\u53f7\u4e3b\u4e49<\/td>\n\u4eba\u7c7b\u53ef\u8bfb\u7b26\u53f7 + \u4e25\u683c\u903b\u8f91\u89c4\u5219（\u89c4\u5219\u5e93\u3001\u77e5\u8bc6\u56fe\u8c31）<\/td>\n\u900f\u660e\u3001\u53ef\u89e3\u91ca\u3001\u53ef\u8ffd\u6eaf（\u91d1\u878d\/\u533b\u7597\u521a\u9700）<\/td>\n\u8106\u5f31：\u672a\u8986\u76d6\u7684\u65b0\u60c5\u51b5\u5373\u5931\u7075 \u2192 \u77e5\u8bc6\u83b7\u53d6\u74f6\u9888<\/td>\n<\/tr>\n
      \u4e9a\u7b26\u53f7\u4e3b\u4e49<\/td>\n\u5185\u9690\u5206\u5e03\u4e8e\u795e\u7ecf\u7f51\u7edc\u8fde\u63a5\u6743\u91cd\u4e2d\u7684\u7edf\u8ba1\u6a21\u5f0f<\/td>\n\u5f3a\u6a21\u5f0f\u8bc6\u522b\u3001\u6297\u566a，\u64c5\u957f\u975e\u7ed3\u6784\u5316\u6570\u636e<\/td>\n\u9ed1\u7bb1\u4e0d\u53ef\u89e3\u91ca；\u7eaf\u903b\u8f91\u63a8\u7406\u5dee；\u4f1a\u4ea7\u751f\u5e7b\u89c9<\/td>\n<\/tr>\n
      \u795e\u7ecf\u7b26\u53f7\u4e3b\u4e49<\/td>\n\u878d\u5408\u4e8c\u8005<\/td>\n\u65e2\u80fd\u4ece\u6570\u636e\u5b66\u4e60，\u53c8\u80fd\u903b\u8f91\u63a8\u7406<\/td>\n\u4ecd\u5728\u63a2\u7d22\u4e2d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u2b50 \u5361\u5c3c\u66fc\u53cc\u7cfb\u7edf\u7c7b\u6bd4：\u7cfb\u7edf 1（\u5feb\u901f\u3001\u76f4\u89c9）\u2194 \u4e9a\u7b26\u53f7\u6a21\u5f0f\u8bc6\u522b；\u7cfb\u7edf 2（\u7f13\u6162\u3001\u57fa\u4e8e\u903b\u8f91）\u2194 \u7b26\u53f7\u63a8\u7406\u3002LLM \u667a\u80fd\u4f53\u662f\u795e\u7ecf\u7b26\u53f7\u4e3b\u4e49\u7684\u5b9e\u8df5\u8303\u4f8b\u2014\u2014\u5185\u6838\u662f\u5de8\u5927\u795e\u7ecf\u7f51\u7edc（\u7cfb\u7edf 1），\u5de5\u4f5c\u65f6\u5374\u751f\u6210\u7ed3\u6784\u5316\u4e2d\u95f4\u7b26\u53f7（\u601d\u60f3\u3001\u8ba1\u5212\u3001API \u8c03\u7528，\u5373\u7cfb\u7edf 2）\u3002<\/p>\n

      \u672c\u7ae0\u7b2c\u4e00\u4e2a\u91cd\u8981\u7ed3\u8bba：LLM \u667a\u80fd\u4f53\u7684\u4e2d\u95f4\u4ea7\u7269（Thought\u3001Plan\u3001Tool Call）\u672c\u8d28\u662f\u660e\u786e\u53ef\u64cd\u4f5c\u7684\u7b26\u53f7，\u8fd9\u6b63\u662f\u5b83\u878d\u5408\u76f4\u89c9\u4e0e\u7406\u6027\u3001\u4e14"\u53ef\u8c03\u8bd5\u3001\u53ef\u89c2\u6d4b"\u7684\u6839\u6e90\u3002<\/p>\n

      1.2 \u667a\u80fd\u4f53\u7684\u6784\u6210\u4e0e\u8fd0\u884c\u539f\u7406<\/h3>\n

      1.2.1 \u4efb\u52a1\u73af\u5883\u5b9a\u4e49：PEAS<\/h4>\n

      PEAS = Performance（\u6027\u80fd\u5ea6\u91cf）+ Environment（\u73af\u5883）+ Actuators（\u6267\u884c\u5668）+ Sensors（\u4f20\u611f\u5668）\u3002<\/p>\n

      \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

      LLM \u667a\u80fd\u4f53\u6240\u5904\u6570\u5b57\u73af\u5883\u7684\u56db\u4e2a\u590d\u6742\u7279\u6027（\u76f4\u63a5\u5f71\u54cd\u8bbe\u8ba1）：<\/p>\n\n\u7279\u6027\u542b\u4e49\u8bbe\u8ba1\u8981\u6c42<\/tr>\n\n\n\n\n\n
      \u90e8\u5206\u53ef\u89c2\u5bdf<\/td>\n\u65e0\u6cd5\u4e00\u6b21\u6027\u83b7\u53d6\u5168\u90e8\u4fe1\u606f<\/td>\n\u5fc5\u987b\u5177\u5907\u8bb0\u5fc6（\u67e5\u8fc7\u54ea\u4e9b）\u4e0e\u63a2\u7d22（\u6362\u67e5\u8be2）\u80fd\u529b<\/td>\n<\/tr>\n
      \u968f\u673a\u6027<\/td>\n\u76f8\u540c\u884c\u52a8\u4e24\u6b21\u7ed3\u679c\u53ef\u80fd\u4e0d\u540c<\/td>\n\u80fd\u5904\u7406\u4e0d\u786e\u5b9a\u6027\u3001\u76d1\u63a7\u53d8\u5316\u3001\u53ca\u65f6\u51b3\u7b56<\/td>\n<\/tr>\n
      \u591a\u667a\u80fd\u4f53<\/td>\n\u73af\u5883\u4e2d\u5b58\u5728\u5176\u4ed6\u884c\u52a8\u8005<\/td>\n\u66f4\u5f3a\u7684\u5feb\u901f\u54cd\u5e94\u4e0e\u7b56\u7565\u9009\u62e9；\u4ed6\u4eba\u884c\u52a8\u4f1a\u6539\u53d8\u73af\u5883<\/td>\n<\/tr>\n
      \u5e8f\u8d2f\u4e14\u52a8\u6001<\/td>\n\u5f53\u524d\u52a8\u4f5c\u5f71\u54cd\u672a\u6765；\u51b3\u7b56\u671f\u95f4\u73af\u5883\u4e5f\u5728\u53d8<\/td>\n\u5faa\u73af\u5fc5\u987b\u5feb\u901f\u3001\u7075\u6d3b\u5730\u9002\u5e94\u6301\u7eed\u53d8\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      1.2.2 \u8fd0\u884c\u673a\u5236：\u667a\u80fd\u4f53\u5faa\u73af（Agent Loop）\u2b50<\/h4>\n

      \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

      \u667a\u80fd\u4f53\u4e0d\u662f\u4e00\u6b21\u6027\u5b8c\u6210\u4efb\u52a1，\u800c\u662f\u6301\u7eed\u5faa\u73af\u4e0e\u73af\u5883\u4ea4\u4e92：<\/p>\n

      \u611f\u77e5 (Perception)
      \n \u2192 \u601d\u8003 (Thought)：\u2460 \u89c4\u5212 Planning \u2461 \u5de5\u5177\u9009\u62e9 Tool Selection
      \n \u2192 \u884c\u52a8 (Action)
      \n \u2192 \u73af\u5883\u72b6\u6001\u53d8\u5316 \u2192 \u4ea7\u751f\u65b0\u7684\u89c2\u5bdf (Observation)
      \n \u2192 \u56de\u5230\u611f\u77e5，\u8fdb\u5165\u4e0b\u4e00\u8f6e<\/p>\n

    • \u611f\u77e5：\u901a\u8fc7\u4f20\u611f\u5668（API \u76d1\u542c\u7aef\u53e3\u3001\u7528\u6237\u8f93\u5165\u63a5\u53e3）\u63a5\u6536\u73af\u5883\u8f93\u5165，\u5373\u89c2\u5bdf（Observation）\u2014\u2014\u53ef\u4ee5\u662f\u521d\u59cb\u6307\u4ee4，\u4e5f\u53ef\u4ee5\u662f\u4e0a\u4e00\u6b65\u884c\u52a8\u5bfc\u81f4\u7684\u72b6\u6001\u53d8\u5316\u3002<\/li>\n
    • \u601d\u8003：LLM \u9a71\u52a8\u7684\u5185\u90e8\u63a8\u7406，\u542b\u4e24\u6b65\u2014\u2014\u89c4\u5212（\u636e\u5f53\u524d\u89c2\u5bdf\u4e0e\u8bb0\u5fc6\u66f4\u65b0\u7406\u89e3\u3001\u5236\u5b9a\u6216\u8c03\u6574\u8ba1\u5212，\u5e38\u8868\u73b0\u4e3a\u5206\u89e3\u5b50\u4efb\u52a1）\u4e0e\u5de5\u5177\u9009\u62e9（\u4ece\u5de5\u5177\u5e93\u9009\u6700\u5408\u9002\u7684\u5de5\u5177\u5e76\u786e\u5b9a\u53c2\u6570）\u3002<\/li>\n
    • \u884c\u52a8：\u901a\u8fc7\u6267\u884c\u5668\u6267\u884c（\u901a\u5e38\u5c31\u662f\u8c03\u7528\u4e00\u4e2a\u5de5\u5177，\u5982\u4ee3\u7801\u89e3\u91ca\u5668\u3001\u641c\u7d22 API），\u610f\u56fe\u6539\u53d8\u73af\u5883\u3002<\/li>\n
    • \u89c2\u5bdf：\u73af\u5883\u8fd4\u56de\u65b0\u89c2\u5bdf，\u88ab\u4e0b\u4e00\u8f6e\u611f\u77e5\u6355\u83b7\u2014\u2014\u5f62\u6210\u95ed\u73af\u3002<\/li>\n

      \u667a\u80fd\u4f53\u6b63\u662f\u901a\u8fc7\u4e0d\u65ad\u91cd\u590d\u8fd9\u4e00\u5faa\u73af，\u4ece\u521d\u59cb\u72b6\u6001\u5411\u76ee\u6807\u72b6\u6001\u9010\u6b65\u6f14\u8fdb\u3002<\/p>\n

      1.2.3 Thought-Action-Observation \u4ea4\u4e92\u534f\u8bae \u2b50\u2b50（\u5168\u4e66\u6700\u91cd\u8981\u7684\u57fa\u7840\u8303\u5f0f）<\/h4>\n

      \u5de5\u7a0b\u5b9e\u8df5\u4e2d\u9700\u8981\u4ea4\u4e92\u534f\u8bae\u89c4\u8303\u667a\u80fd\u4f53\u4e0e\u73af\u5883\u7684\u4fe1\u606f\u4ea4\u6362：\u73b0\u4ee3\u6846\u67b6\u7684\u505a\u6cd5\u662f\u7ed3\u6784\u5316\u5b9a\u4e49\u6bcf\u4e00\u6b21\u8f93\u51fa\u2014\u2014\u4e0d\u518d\u662f\u5355\u4e00\u81ea\u7136\u8bed\u8a00\u56de\u590d，\u800c\u662f\u4e00\u6bb5\u9075\u5faa\u7279\u5b9a\u683c\u5f0f\u7684\u6587\u672c\u3002<\/p>\n\n\u7ec4\u6210\u90e8\u5206\u4f5c\u7528\u4f8b\u5b50<\/tr>\n\n\n\n\n
      Thought<\/td>\n\u5185\u90e8\u51b3\u7b56"\u5feb\u7167"：\u5206\u6790\u60c5\u5883\u3001\u56de\u987e\u4e0a\u4e00\u6b65\u3001\u81ea\u6211\u53cd\u601d\u4e0e\u5206\u89e3\u3001\u89c4\u5212\u4e0b\u4e00\u6b65<\/td>\nThought: \u7528\u6237\u60f3\u77e5\u9053\u5317\u4eac\u5929\u6c14\u3002\u6211\u9700\u8981\u8c03\u7528\u5929\u6c14\u67e5\u8be2\u5de5\u5177\u3002<\/td>\n<\/tr>\n
      Action<\/td>\n\u57fa\u4e8e\u601d\u8003\u51b3\u5b9a\u7684\u5177\u4f53\u64cd\u4f5c，\u901a\u5e38\u4ee5\u51fd\u6570\u8c03\u7528\u8868\u793a<\/td>\nAction: get_weather("\u5317\u4eac")<\/td>\n<\/tr>\n
      Observation<\/td>\n\u5de5\u5177\u7ed3\u679c\u7684\u81ea\u7136\u8bed\u8a00\u5c01\u88c5，\u4f5c\u4e3a\u4e0b\u4e00\u8f6e\u4e3b\u8981\u8f93\u5165<\/td>\nObservation: \u5317\u4eac\u5f53\u524d\u5929\u6c14\u4e3a\u6674，\u6c14\u6e2925\u6444\u6c0f\u5ea6\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u4e3a\u4ec0\u4e48\u8981\u628a\u5de5\u5177\u7ed3\u679c\u8f6c\u6210\u81ea\u7136\u8bed\u8a00？ \u539f\u59cb\u8fd4\u56de（\u5982 JSON）\u5e38\u542b\u5197\u4f59\u4fe1\u606f\u4e14\u4e0d\u7b26\u5408\u6a21\u578b\u4e60\u60ef；\u611f\u77e5\u7cfb\u7edf\u7684\u804c\u8d23\u5c31\u662f\u626e\u6f14\u4f20\u611f\u5668：\u628a\u539f\u59cb\u8f93\u51fa\u5904\u7406\u5c01\u88c5\u6210\u7b80\u6d01\u7684\u81ea\u7136\u8bed\u8a00\u6587\u672c\u3002<\/p>\n

      \u672c\u8282\u7ed3\u8bba：\u901a\u8fc7 T-A-O \u6784\u6210\u7684\u4e25\u8c28\u5faa\u73af，LLM \u667a\u80fd\u4f53\u628a\u5185\u90e8\u8bed\u8a00\u63a8\u7406\u80fd\u529b\u4e0e\u5916\u90e8\u771f\u5b9e\u4fe1\u606f\u548c\u5de5\u5177\u64cd\u4f5c\u80fd\u529b\u7ed3\u5408\u8d77\u6765\u3002<\/p>\n

      1.3 \u52a8\u624b\u4f53\u9a8c：\u7b2c\u4e00\u4e2a\u667a\u80fd\u4f53（\u5de5\u7a0b\u8981\u70b9\u63d0\u70bc）<\/h3>\n

      \u4efb\u52a1：\u201c\u67e5\u8be2\u4eca\u5929\u5317\u4eac\u7684\u5929\u6c14，\u7136\u540e\u6839\u636e\u5929\u6c14\u63a8\u8350\u5408\u9002\u7684\u65c5\u6e38\u666f\u70b9\u201d\u3002\u56db\u4e2a\u7ec4\u6210\u90e8\u4ef6：<\/p>\n

    • \u6307\u4ee4\u6a21\u677f（System Prompt）：\u63d0\u793a\u5de5\u7a0b\u662f\u5173\u952e\u3002\u8981\u544a\u8bc9\u6a21\u578b：\u626e\u6f14\u4ec0\u4e48\u89d2\u8272\u3001\u6709\u54ea\u4e9b\u5de5\u5177\u3001\u5982\u4f55\u683c\u5f0f\u5316\u8f93\u51fa；\u6838\u5fc3\u7ea6\u675f\u662f\u6bcf\u6b21\u53ea\u8f93\u51fa\u4e00\u5bf9 Thought-Action\u3001Action \u5fc5\u987b\u5728\u540c\u4e00\u884c\u3001\u4fe1\u606f\u8db3\u591f\u65f6\u7528 Finish[\u7b54\u6848] \u7ed3\u675f\u3002<\/li>\n
    • \u5de5\u5177\u5b9e\u73b0：\u5982 get_weather(city)\u3001get_attraction(city, weather)；\u5fc5\u987b\u505a\u5f02\u5e38\u5904\u7406\u5e76\u8fd4\u56de\u9519\u8bef\u4fe1\u606f，\u56e0\u4e3a\u9519\u8bef\u5b57\u7b26\u4e32\u4e5f\u4f1a\u4f5c\u4e3a Observation \u8fdb\u5165\u4e0b\u4e00\u8f6e，\u6210\u4e3a\u81ea\u6211\u7ea0\u6b63\u7684\u4f9d\u636e\u3002<\/li>\n
    • LLM \u5ba2\u6237\u7aef：\u5c01\u88c5\u517c\u5bb9 OpenAI \u63a5\u53e3\u7684\u901a\u7528\u5ba2\u6237\u7aef（OpenAI\u3001Azure\u3001Ollama\u3001vLLM \u89c4\u8303\u76f8\u4f3c），\u53ea\u9700 model \/ api_key \/ base_url\u3002<\/li>\n
    • \u4e3b\u5faa\u73af：\u628a\u7ec4\u4ef6\u4e32\u8d77\u6765\u3002<\/li>\n

      \u4e3b\u5faa\u73af\u91cc\u4e94\u4e2a\u5fc5\u987b\u6ce8\u610f\u7684\u5de5\u7a0b\u7ec6\u8282（\u540e\u9762\u6bcf\u7ae0\u90fd\u5728\u53cd\u590d\u5904\u7406）：<\/p>\n\n\u7ec6\u8282\u505a\u6cd5\u4e3a\u4ec0\u4e48<\/tr>\n\n\n\n\n\n\n
      \u6700\u5927\u5faa\u73af\u6b21\u6570<\/td>\n\u8bbe\u4e0a\u9650（\u4f8b\u4e2d\u4e3a 5）<\/td>\n\u2b50 \u9632\u6b7b\u5faa\u73af；\u5426\u5219\u53ef\u80fd\u5728\u4e24\u4e2a\u52a8\u4f5c\u95f4\u65e0\u9650\u5f80\u8fd4<\/td>\n<\/tr>\n
      \u622a\u65ad\u591a\u4f59 Thought-Action<\/td>\n\u7528\u6b63\u5219\u53ea\u53d6\u7b2c\u4e00\u5bf9<\/td>\n\u6a21\u578b\u53ef\u80fd\u4e00\u6b21\u8f93\u51fa\u591a\u5bf9，\u5bfc\u81f4\u6267\u884c\u9519\u4e71<\/td>\n<\/tr>\n
      \u89e3\u6790 Action<\/td>\n\u6b63\u5219\u63d0\u53d6；\u5931\u8d25\u5219\u628a"\u683c\u5f0f\u9519\u8bef"\u4f5c\u4e3a Observation \u56de\u704c<\/td>\n\u7ed9\u667a\u80fd\u4f53\u81ea\u6211\u7ea0\u6b63\u7684\u673a\u4f1a<\/td>\n<\/tr>\n
      \u5de5\u5177\u5206\u53d1<\/td>\n\u89e3\u6790\u51fd\u6570\u540d\u4e0e\u53c2\u6570\u2192\u67e5\u5b57\u5178\u2192\u672a\u5b9a\u4e49\u5219\u8fd4\u56de\u9519\u8bef<\/td>\n\u540c\u6837\u628a\u9519\u8bef\u53d8\u6210\u53ef\u7ea0\u6b63\u7684\u89c2\u5bdf<\/td>\n<\/tr>\n
      \u7ec8\u6b62\u6761\u4ef6<\/td>\n\u8bc6\u522b Finish[…] \u5e76\u8df3\u51fa<\/td>\n\u660e\u786e"\u4ec0\u4e48\u65f6\u5019\u7b97\u505a\u5b8c"<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u8fd0\u884c\u6848\u4f8b（\u4e09\u8f6e）：\u67e5\u5929\u6c14 \u2192 \u636e"\u6674\u5929"\u67e5\u666f\u70b9 \u2192 Finish[\u63a8\u8350\u9890\u548c\u56ed\u6216\u957f\u57ce\u2026]，\u96c6\u4e2d\u4f53\u73b0\u4e86\u8303\u5f0f\u5177\u5907\u7684\u56db\u9879\u57fa\u672c\u80fd\u529b：\u4efb\u52a1\u5206\u89e3\u3001\u5de5\u5177\u8c03\u7528\u3001\u4e0a\u4e0b\u6587\u7406\u89e3\u3001\u7ed3\u679c\u5408\u6210\u3002<\/p>\n

      \u6838\u5fc3\u5728\u4e8e**"\u5de5\u5177"\u4e0e"\u63d0\u793a\u5de5\u7a0b"\u7684\u7ed3\u5408**\u2014\u2014\u8fd9\u6b63\u662f LangChain\u3001LlamaIndex \u7b49\u6846\u67b6\u7684\u8bbe\u8ba1\u7cbe\u9ad3\u3002<\/p>\n

      1.4 \u667a\u80fd\u4f53\u5e94\u7528\u7684\u534f\u4f5c\u6a21\u5f0f<\/h3>\n

      1.4.1 \u4f5c\u4e3a\u5f00\u53d1\u8005\u5de5\u5177<\/h4>\n

      \u6df1\u5ea6\u96c6\u6210\u5230\u5de5\u4f5c\u6d41\u4e2d，\u589e\u5f3a\u800c\u975e\u53d6\u4ee3\u5f00\u53d1\u8005，\u8ba9\u4eba\u4e13\u6ce8\u521b\u9020\u6027\u5de5\u4f5c：<\/p>\n

        \n
      • GitHub Copilot：\u96c6\u6210\u4e8e VS Code，\u4ee5\u4ee3\u7801\u81ea\u52a8\u8865\u5168\u95fb\u540d，\u540e\u6269\u5c55\u51fa\u5bf9\u8bdd\u5f0f\u7f16\u7a0b\u3002<\/li>\n
      • Claude Code：\u7ec8\u7aef\u4e2d\u81ea\u7136\u8bed\u8a00\u7f16\u7801，\u7406\u89e3\u5b8c\u6574\u4ee3\u7801\u5e93，\u652f\u6301 headless \u6a21\u5f0f（CI\u3001pre-commit hooks）\u3002<\/li>\n
      • Cursor： AI \u539f\u751f\u7f16\u8f91\u5668\u2014\u2014\u8bbe\u8ba1\u4e4b\u521d\u5c31\u4ee5 AI \u4ea4\u4e92\u4e3a\u6838\u5fc3，\u5f3a\u8c03\u8ba9 AI \u7406\u89e3\u6574\u4e2a\u4ee3\u7801\u5e93\u4e0a\u4e0b\u6587\u3002<\/li>\n<\/ul>\n

        1.4.2 \u4f5c\u4e3a\u81ea\u4e3b\u534f\u4f5c\u8005<\/h4>\n

        \u4ece"\u547d\u4ee4\u2014\u6267\u884c"\u6f14\u53d8\u4e3a \u201c\u76ee\u6807\u2014\u59d4\u6258\u201d：\u6211\u4eec\u59d4\u6258\u9ad8\u5c42\u7ea7\u76ee\u6807，\u667a\u80fd\u4f53\u50cf\u9879\u76ee\u6210\u5458\u4e00\u6837\u72ec\u7acb\u89c4\u5212\u3001\u63a8\u7406\u3001\u6267\u884c\u3001\u53cd\u601d\u76f4\u81f3\u4ea4\u4ed8\u3002\u4e09\u79cd\u4e3b\u6d41\u67b6\u6784：<\/p>\n

      • \u5355\u667a\u80fd\u4f53\u81ea\u4e3b\u5faa\u73af：\u4e00\u4e2a\u901a\u7528\u667a\u80fd\u4f53\u901a\u8fc7"\u601d\u8003\u2014\u89c4\u5212\u2014\u6267\u884c\u2014\u53cd\u601d"\u95ed\u73af\u81ea\u6211\u8fed\u4ee3（\u5982 AgentGPT），\u5c5e\u65e9\u671f\u8303\u5f0f\u3002<\/li>\n
      • \u591a\u667a\u80fd\u4f53\u534f\u4f5c（\u5f53\u524d\u4e3b\u6d41）：\u89d2\u8272\u626e\u6f14\u5f0f\u5bf9\u8bdd（CAMEL：\u7ed9\u4e24\u4e2a\u667a\u80fd\u4f53\u8bbe\u5b9a\u89d2\u8272\u4e0e\u6c9f\u901a\u534f\u8bae）\u4e0e\u7ec4\u7ec7\u5316\u5de5\u4f5c\u6d41（MetaGPT\u3001CrewAI：\u6a21\u62df\u865a\u62df\u56e2\u961f，\u5404\u53f8\u5176\u804c\u6309 SOP \u534f\u4f5c）；AutoGen\u3001AgentScope \u63d0\u4f9b\u66f4\u7075\u6d3b\u7684\u81ea\u5b9a\u4e49\u4ea4\u4e92\u7f51\u7edc\u3002<\/li>\n
      • \u9ad8\u7ea7\u63a7\u5236\u6d41\u67b6\u6784：\u5982 LangGraph，\u628a\u6267\u884c\u8fc7\u7a0b\u5efa\u6a21\u4e3a\u72b6\u6001\u56fe，\u4ece\u800c\u53ef\u9760\u5b9e\u73b0\u5faa\u73af\u3001\u5206\u652f\u3001\u56de\u6eaf\u4e0e\u4eba\u5de5\u4ecb\u5165\u3002<\/li>\n

        1.4.3 Workflow \u4e0e Agent \u7684\u5dee\u5f02 \u2b50（\u9762\u8bd5\u9ad8\u9891）<\/h4>\n

        \u4e00\u53e5\u8bdd：Workflow \u662f\u8ba9 AI \u6309\u90e8\u5c31\u73ed\u6267\u884c\u6307\u4ee4，Agent \u662f\u8d4b\u4e88 AI \u81ea\u7531\u5ea6\u53bb\u81ea\u4e3b\u8fbe\u6210\u76ee\u6807\u3002<\/p>\n

        \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u7ef4\u5ea6WorkflowAgent<\/tr>\n\n\n\n\n\n\n
        \u672c\u8d28<\/td>\n\u6b65\u9aa4\u9884\u5148\u5b9a\u4e49\u7684\u7ed3\u6784\u5316\u7f16\u6392，\u4e00\u5f20\u7cbe\u786e\u9759\u6001\u6d41\u7a0b\u56fe<\/td>\n\u81ea\u4e3b\u7684\u3001\u4ee5\u76ee\u6807\u4e3a\u5bfc\u5411\u7684\u7cfb\u7edf<\/td>\n<\/tr>\n
        \u5224\u65ad\u903b\u8f91<\/td>\n\u6bcf\u6b65\u4e0e\u6bcf\u4e2a\u6761\u4ef6\u90fd\u7cbe\u786e\u9884\u5148\u8bbe\u5b9a<\/td>\n\u7531 LLM \u57fa\u4e8e\u5b9e\u65f6\u4fe1\u606f\u52a8\u6001\u63a8\u7406，\u65e0\u5199\u6b7b\u89c4\u5219<\/td>\n<\/tr>\n
        \u7075\u6d3b\u6027<\/td>\n\u4f4e；\u6761\u4ef6\u53d8\u5316\u9700\u6539\u6d41\u7a0b<\/td>\n\u9ad8；\u540c\u4e00\u6307\u4ee4\u5728\u4e0d\u540c\u73af\u5883\u4e0b\u8d70\u4e0d\u540c\u8def\u5f84<\/td>\n<\/tr>\n
        \u53ef\u9884\u6d4b\u6027<\/td>\n\u9ad8，\u884c\u4e3a\u786e\u5b9a\u3001\u6613\u5ba1\u8ba1<\/td>\n\u8f83\u4f4e，\u8f93\u51fa\u4e0d\u786e\u5b9a，\u9700\u989d\u5916\u8bc4\u6d4b\u4e0e\u515c\u5e95<\/td>\n<\/tr>\n
        \u5178\u578b\u4f8b<\/td>\n\u62a5\u9500\u5ba1\u6279：\u91d1\u989d<500 \u7ecf\u7406\u6279，\u2265500 \u52a0\u8d22\u52a1\u603b\u76d1<\/td>\n\u65c5\u884c\u52a9\u624b：\u67e5\u5929\u6c14\u2192\u63a8\u7406"\u6674\u5929\u5b9c\u6237\u5916"\u2192\u7b5b\u9009\u666f\u70b9\u2192\u751f\u6210\u56de\u7b54<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u5173\u952e\u5bf9\u7167：\u65c5\u884c\u52a9\u624b\u4e2d\u6ca1\u6709\u4efb\u4f55 if \u5929\u6c14=\u6674\u5929 then \u63a8\u8350\u9890\u548c\u56ed \u7684\u89c4\u5219；\u82e5\u9047\u96e8\u5929\u5b83\u4f1a\u81ea\u4e3b\u6539\u63a8\u5ba4\u5185\u573a\u6240\u2014\u2014\u8fd9\u79cd\u57fa\u4e8e\u5b9e\u65f6\u4fe1\u606f\u52a8\u6001\u51b3\u7b56\u7684\u80fd\u529b，\u6b63\u662f Agent \u7684\u6838\u5fc3\u4ef7\u503c\u3002<\/p>\n

        \u9009\u578b\u5efa\u8bae：<\/p>\n

          \n
        • \u6761\u4ef6\u6e05\u6670\u3001\u89c4\u5219\u7a33\u5b9a\u3001\u8981\u6c42\u53ef\u5ba1\u8ba1\/\u5408\u89c4（\u62a5\u9500\u5ba1\u6279\u3001\u8ba2\u5355\u72b6\u6001\u673a\u3001ETL）\u2192 Workflow\u3002<\/li>\n
        • \u8f93\u5165\u6a21\u7cca\u3001\u8def\u5f84\u4e0d\u786e\u5b9a\u3001\u9700\u8bed\u4e49\u7406\u89e3\u4e0e\u591a\u6b65\u63a8\u7406（\u5ba2\u670d\u3001\u7814\u7a76\u5206\u6790\u3001\u4ee3\u7801\u5ba1\u67e5）\u2192 Agent\u3002<\/li>\n
        • \u73b0\u5b9e\u6700\u5e38\u89c1\u7684\u662f\u65b9\u6848 C：\u7528 Workflow \u505a\u9aa8\u67b6\u4fdd\u8bc1\u53ef\u63a7，\u5728\u9700\u5224\u65ad\u7684\u8282\u70b9\u5d4c\u5165 Agent \u505a\u51b3\u7b56\u3002<\/li>\n<\/ul>\n

          \u7b2c\u4e00\u7ae0\u5c0f\u7ed3<\/h4>\n

          \u667a\u80fd\u4f53\u662f\u80fd\u611f\u77e5\u73af\u5883\u5e76\u81ea\u4e3b\u884c\u52a8\u7684\u5b9e\u4f53，"\u667a\u80fd"\u6765\u81ea\u81ea\u4e3b\u6027\u800c\u975e\u7b97\u529b\u3002\u4f20\u7edf\u667a\u80fd\u4f53\u6cbf"\u53cd\u5c04\u2192\u57fa\u4e8e\u6a21\u578b\u2192\u57fa\u4e8e\u76ee\u6807\u2192\u57fa\u4e8e\u6548\u7528\u2192\u5b66\u4e60\u578b"\u4e94\u7ea7\u6f14\u8fdb，LLM \u628a\u5b83\u63a8\u5230\u795e\u7ecf\u7b26\u53f7\u6df7\u5408\u7684\u65b0\u8303\u5f0f：\u795e\u7ecf\u7f51\u7edc\u63d0\u4f9b\u76f4\u89c9，\u7ed3\u6784\u5316\u7684 Thought\/Plan\/Tool Call \u63d0\u4f9b\u53ef\u8ffd\u6eaf\u7684\u7406\u6027\u3002\u667a\u80fd\u4f53\u7528 PEAS \u89c4\u7ea6\u4efb\u52a1\u73af\u5883，\u9760 \u201c\u611f\u77e5\u2192\u601d\u8003（\u89c4\u5212+\u5de5\u5177\u9009\u62e9）\u2192\u884c\u52a8\u2192\u89c2\u5bdf\u201d \u5faa\u73af\u8fd0\u8f6c，\u5e76\u4ee5 Thought \/ Action \/ Observation \u4e09\u6bb5\u5f0f\u534f\u8bae\u4e0e\u73af\u5883\u4ea4\u6362\u4fe1\u606f\u3002\u5e94\u7528\u4e0a，\u5b83\u8981\u4e48\u589e\u5f3a\u4eba\u7c7b\u5de5\u4f5c\u6d41（Copilot \/ Cursor），\u8981\u4e48\u63a5\u53d7\u76ee\u6807\u59d4\u6258\u81ea\u4e3b\u534f\u4f5c（CrewAI \/ MetaGPT \/ LangGraph）；\u4e0e Workflow \u7684\u6839\u672c\u5206\u91ce\u5728\u4e8e：\u662f\u6267\u884c\u9884\u8bbe\u6307\u4ee4，\u8fd8\u662f\u5728\u81ea\u7531\u5ea6\u4e2d\u81ea\u4e3b\u8fbe\u6210\u76ee\u6807\u3002<\/p>\n

          \n

          \u7b2c\u4e8c\u7ae0 \u667a\u80fd\u4f53\u53d1\u5c55\u53f2<\/h2>\n

          \u672c\u7ae0\u5b9a\u4f4d：\u7406\u89e3"\u6bcf\u4e2a\u65b0\u8303\u5f0f\u90fd\u4e3a\u89e3\u51b3\u4e0a\u4e00\u4ee3\u7684\u6838\u5fc3\u75db\u70b9\u800c\u751f，\u540c\u65f6\u53c8\u5f15\u5165\u65b0\u5c40\u9650"\u8fd9\u4e00\u95ee\u9898\u9a71\u52a8\u7684\u8fed\u4ee3\u903b\u8f91\u3002\u4e0d\u8981\u6c42\u8bb0\u5e74\u4ee3，\u8981\u6c42\u80fd\u8bb2\u6e05"\u4e3a\u4ec0\u4e48\u4f1a\u6709\u4eca\u5929\u8fd9\u4e2a\u5f62\u6001"\u3002<\/p>\n

          \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

          2.1 \u57fa\u4e8e\u7b26\u53f7\u4e0e\u903b\u8f91\u7684\u65e9\u671f\u667a\u80fd\u4f53<\/h3>\n

          2.1.1 \u7269\u7406\u7b26\u53f7\u7cfb\u7edf\u5047\u8bf4（PSSH, 1976）<\/h4>\n
            \n
          • \u5145\u5206\u6027：\u4efb\u4f55\u7269\u7406\u7b26\u53f7\u7cfb\u7edf\u90fd\u5177\u5907\u4ea7\u751f\u901a\u7528\u667a\u80fd\u884c\u4e3a\u7684\u5145\u5206\u624b\u6bb5\u3002<\/li>\n
          • \u5fc5\u8981\u6027：\u4efb\u4f55\u5c55\u73b0\u901a\u7528\u667a\u80fd\u884c\u4e3a\u7684\u7cfb\u7edf，\u5fc5\u7136\u662f\u7269\u7406\u7b26\u53f7\u7cfb\u7edf\u3002<\/li>\n
          • \u4e00\u53e5\u8bdd：\u667a\u80fd\u7684\u672c\u8d28\u5c31\u662f\u7b26\u53f7\u7684\u8ba1\u7b97\u4e0e\u5904\u7406\u3002<\/li>\n
          • \u610f\u4e49\u4e0e\u5c40\u9650：\u628a"\u4eba\u7c7b\u5fc3\u667a"\u8fd9\u4e00\u54f2\u5b66\u95ee\u9898\u8f6c\u5316\u4e3a\u53ef\u5de5\u7a0b\u5316\u7684\u95ee\u9898；\u540e\u6765\u7b26\u53f7\u4e3b\u4e49\u906d\u9047\u7684\u74f6\u9888\u4e0e\u8106\u5f31\u6027，\u52a8\u6447\u7684\u662f"\u5145\u5206\u6027"\u8bba\u65ad\u3002<\/li>\n<\/ul>\n

            2.1.2 \u4e13\u5bb6\u7cfb\u7edf（\u7b26\u53f7\u4e3b\u4e49\u6700\u6210\u529f\u7684\u5e94\u7528）<\/h4>\n

            \u6838\u5fc3\u8bbe\u8ba1\u601d\u60f3：\u77e5\u8bc6\u4e0e\u63a8\u7406\u76f8\u5206\u79bb\u3002<\/p>\n\n\u7ec4\u4ef6\u804c\u8d23<\/tr>\n\n\n\n
            \u77e5\u8bc6\u5e93<\/td>\n\u5b58\u653e\u4e13\u5bb6\u77e5\u8bc6，\u6700\u5e38\u7528\u4ea7\u751f\u5f0f\u89c4\u5219（IF-THEN），\u590d\u6742\u7cfb\u7edf\u542b\u6210\u767e\u4e0a\u5343\u6761<\/td>\n<\/tr>\n
            \u63a8\u7406\u673a<\/td>\n\u901a\u7528\u7a0b\u5e8f，\u636e\u4e8b\u5b9e\u5728\u77e5\u8bc6\u5e93\u4e2d\u5339\u914d\u5e76\u5e94\u7528\u89c4\u5219，\u63a8\u5bfc\u65b0\u7ed3\u8bba<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            \u4e24\u79cd\u5de5\u4f5c\u65b9\u5f0f：\u6b63\u5411\u94fe\u2014\u2014\u4ece\u5df2\u77e5\u4e8b\u5b9e\u51fa\u53d1\u89e6\u53d1\u89c4\u5219\u3001\u6570\u636e\u9a71\u52a8；\u53cd\u5411\u94fe\u2014\u2014\u4ece\u5047\u8bbe\u76ee\u6807\u53cd\u63a8\u6240\u9700\u8bc1\u636e\u3001\u76ee\u6807\u9a71\u52a8\u3002<\/p>\n

            \u6848\u4f8b MYCIN（\u65af\u5766\u798f，1970s）：\u8bca\u65ad\u8840\u6db2\u611f\u67d3\u5e76\u63a8\u8350\u6297\u751f\u7d20，\u7ea6 600 \u6761\u89c4\u5219，\u4e3b\u8981\u7528\u53cd\u5411\u94fe；\u521b\u65b0\u70b9\u662f\u7f6e\u4fe1\u56e0\u5b50（CF）\u2014\u2014\u7528 \u22121 \u5230 1 \u8868\u793a\u7ed3\u8bba\u53ef\u4fe1\u5ea6，\u4ee5\u5904\u7406\u4e0d\u786e\u5b9a\u6027\u3002\u8bca\u65ad\u6c34\u5e73\u8d85\u8fc7\u975e\u4e13\u4e1a\u533b\u751f，\u4f46\u672a\u5927\u89c4\u6a21\u4e34\u5e8a\u4f7f\u7528（\u9664\u6280\u672f\u56e0\u7d20\u5916，\u8fd8\u6709\u8d23\u4efb\u5f52\u5c5e\u3001\u4f26\u7406\u3001\u6cd5\u5f8b\u7b49\u975e\u6280\u672f\u539f\u56e0）\u3002<\/p>\n

            2.1.3 SHRDLU（Winograd, 1968\u20131970）<\/h4>\n

            \u5728"\u79ef\u6728\u4e16\u754c"\u4e2d\u9996\u6b21\u628a\u8bed\u8a00\u89e3\u6790\u3001\u89c4\u5212\u3001\u8bb0\u5fc6\u7b49\u591a\u6a21\u5757\u96c6\u6210\u4e8e\u7edf\u4e00\u7cfb\u7edf\u5e76\u534f\u540c：\u80fd\u5904\u7406\u6b67\u4e49\u53e5\u4e0e\u6307\u4ee3\u6d88\u89e3\u3001\u8054\u7cfb\u4e0a\u4e0b\u6587\u95ee\u7b54\u3001\u81ea\u4e3b\u89c4\u5212\u52a8\u4f5c\u5e8f\u5217，\u751a\u81f3\u56de\u7b54\u884c\u4e3a\u52a8\u673a\u3002<\/p>\n

            \u5386\u53f2\u5730\u4f4d：\u2460 "\u611f\u77e5\u2014\u601d\u8003\u2014\u884c\u52a8"\u95ed\u73af\u5960\u5b9a\u73b0\u4ee3\u667a\u80fd\u4f53\u7814\u7a76\u57fa\u7840；\u2461 \u666e\u53ca"\u5fae\u89c2\u4e16\u754c"\u7814\u7a76\u65b9\u6cd5（\u5728\u89c4\u5219\u660e\u786e\u7684\u7b80\u5316\u73af\u5883\u9a8c\u8bc1\u539f\u7406）；\u2462 \u5176\u6210\u529f\u6fc0\u53d1\u65e9\u671f\u4e50\u89c2，\u800c\u80fd\u529b\u4e25\u683c\u5c40\u9650\u4e8e\u79ef\u6728\u4e16\u754c\u53c8\u5f15\u53d1"\u7b26\u53f7\u5904\u7406 \u2260 \u771f\u6b63\u7406\u89e3"\u7684\u957f\u671f\u601d\u8fa8\u3002<\/p>\n

            2.1.4 \u7b26\u53f7\u4e3b\u4e49\u7684\u56db\u5927\u6839\u672c\u6027\u6311\u6218 \u2b50<\/h4>\n\n\u6311\u6218\u542b\u4e49<\/tr>\n\n\n\n\n\n
            \u77e5\u8bc6\u83b7\u53d6\u74f6\u9888<\/td>\n\u77e5\u8bc6\u9700\u4e13\u5bb6\u624b\u5de5\u63d0\u70bc\u7f16\u7801，\u6210\u672c\u9ad8\u96be\u89c4\u6a21\u5316；\u4e14\u8bb8\u591a\u4eba\u7c7b\u77e5\u8bc6\u662f\u5185\u9690\u76f4\u89c9\u7684，\u65e0\u6cd5\u5199\u6210 IF-THEN<\/td>\n<\/tr>\n
            \u5e38\u8bc6\u95ee\u9898<\/td>\n\u4eba\u7c7b\u4f9d\u8d56\u5e9e\u5927\u5e38\u8bc6\u80cc\u666f，\u7b26\u53f7\u7cfb\u7edf\u9664\u975e\u663e\u5f0f\u7f16\u7801\u5426\u5219\u4e00\u65e0\u6240\u77e5（Cyc \u6570\u5341\u5e74\u6210\u679c\u4ecd\u6709\u9650）<\/td>\n<\/tr>\n
            \u6846\u67b6\u95ee\u9898<\/td>\n\u52a8\u6001\u4e16\u754c\u4e2d，\u6267\u884c\u52a8\u4f5c\u540e\u5982\u4f55\u9ad8\u6548\u5224\u65ad\u54ea\u4e9b\u4e8b\u7269\u672a\u6539\u53d8，\u663e\u5f0f\u58f0\u660e\u5728\u8ba1\u7b97\u4e0a\u4e0d\u53ef\u884c<\/td>\n<\/tr>\n
            \u7cfb\u7edf\u8106\u5f31\u6027<\/td>\n\u5b8c\u5168\u4f9d\u8d56\u9884\u8bbe\u89c4\u5219，\u9047\u89c4\u5219\u5916\u5fae\u5c0f\u53d8\u5316\u5373\u53ef\u80fd\u5931\u7075（SHRDLU \u53ea\u5728\u89c4\u5219\u5b8c\u5907\u7684\u5c01\u95ed\u4e16\u754c\u6210\u529f）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            2.2 \u6784\u5efa\u57fa\u4e8e\u89c4\u5219\u7684\u804a\u5929\u673a\u5668\u4eba：ELIZA<\/h3>\n

            ELIZA（1966）\u6700\u8457\u540d\u7684 DOCTOR \u811a\u672c\u6a21\u4eff\u5fc3\u7406\u6cbb\u7597\u5e08\u3002\u4f5c\u8005\u672c\u60f3\u8bc1\u660e：\u4ec5\u9760\u7b80\u5355\u53e5\u5f0f\u8f6c\u6362，\u673a\u5668\u5c31\u80fd\u5728\u5b8c\u5168\u4e0d\u7406\u89e3\u5185\u5bb9\u7684\u60c5\u51b5\u4e0b\u8425\u9020"\u667a\u80fd"\u5047\u8c61；\u7ed3\u679c\u8bb8\u591a\u4f7f\u7528\u8005\u771f\u7684\u5bf9\u5b83\u4ea7\u751f\u60c5\u611f\u4f9d\u8d56，\u5373 ELIZA \u6548\u5e94（\u5de7\u5999\u5bf9\u8bdd\u7b56\u7565 + \u4eba\u7c7b\u60c5\u611f\u6295\u5c04）\u3002<\/p>\n

            \u7b97\u6cd5\u56db\u6b65：\u5173\u952e\u8bcd\u8bc6\u522b\u4e0e\u6392\u5e8f \u2192 \u5e26 * \u901a\u914d\u7b26\u7684\u5206\u89e3\u89c4\u5219 \u2192 \u91cd\u7ec4\u89c4\u5219（\u4ece\u5019\u9009\u6a21\u677f\u968f\u673a\u9009，\u589e\u52a0\u591a\u6837\u6027）\u2192 \u4ee3\u8bcd\u8f6c\u6362（I\u2192you\u3001my\u2192your）\u7ef4\u6301\u8fde\u8d2f\u3002<\/p>\n

            \u4e09\u4e2a\u6839\u672c\u5c40\u9650（\u5370\u8bc1 2.1.4 \u7684\u7406\u8bba\u6311\u6218）：<\/p>\n

          • \u7f3a\u4e4f\u8bed\u4e49\u7406\u89e3：\u9762\u5bf9 I am not happy \u4ecd\u673a\u68b0\u5339\u914d I am (.*)，\u4e0d\u61c2\u5426\u5b9a\u8bcd\u3002<\/li>\n
          • \u65e0\u4e0a\u4e0b\u6587\u8bb0\u5fc6：\u7cfb\u7edf\u65e0\u72b6\u6001（Stateless），\u6bcf\u8f6e\u53ea\u57fa\u4e8e\u5f53\u524d\u5355\u53e5\u3002<\/li>\n
          • \u6269\u5c55\u6027\u5dee：\u52a0\u89c4\u5219\u5bfc\u81f4\u89c4\u6a21\u7206\u70b8\u4e0e\u51b2\u7a81，\u6700\u7ec8\u96be\u4ee5\u7ef4\u62a4\u3002<\/li>\n

            \u6838\u5fc3\u77db\u76fe：\u770b\u4f3c\u667a\u80fd\u7684\u8868\u73b0\u5b8c\u5168\u4f9d\u8d56\u9884\u7f16\u7801\u89c4\u5219；\u9762\u5bf9\u771f\u5b9e\u8bed\u8a00\u7684\u65e0\u9650\u53ef\u80fd，\u7a77\u4e3e\u6cd5\u6ce8\u5b9a\u4e0d\u53ef\u6269\u5c55\u3002\u6ca1\u6709\u771f\u6b63\u7684\u7406\u89e3，\u53ea\u6709\u7b26\u53f7\u64cd\u4f5c\u2014\u2014\u8fd9\u6b63\u662f\u8106\u5f31\u6027\u7684\u6839\u6e90\u3002<\/p>\n

            2.3 \u9a6c\u6587\u00b7\u660e\u65af\u57fa\u7684\u300c\u5fc3\u667a\u793e\u4f1a\u300d（1986）<\/h3>\n

            \u6838\u5fc3\u63d0\u95ee：\u667a\u80fd\u7684\u9b54\u529b\u4e0d\u5728\u4e8e\u67d0\u4e2a\u5355\u4e00\u5b8c\u7f8e\u539f\u5219，\u800c\u6e90\u4e8e\u5de8\u5927\u7684\u591a\u6837\u6027\u3002\u7b26\u53f7\u4e3b\u4e49\u662f\u81ea\u4e0a\u800c\u4e0b\u8bbe\u8ba1（\u5168\u77e5\u5168\u80fd\u7684\u4e2d\u592e\u5904\u7406\u5668\u6309\u7edf\u4e00\u903b\u8f91\u5904\u7406\u4e00\u5207），\u660e\u65af\u57fa\u5219\u628a\u5fc3\u667a\u770b\u4f5c\u6241\u5e73\u3001\u5145\u6ee1\u4e92\u52a8\u534f\u4f5c\u7684"\u793e\u4f1a"\u3002<\/p>\n

              \n
            • \u667a\u80fd\u4f53（\u6b64\u8bed\u5883）：\u6781\u5176\u7b80\u5355\u3001\u4e13\u95e8\u5316\u3001\u81ea\u8eab"\u65e0\u5fc3"\u7684\u5fc3\u667a\u8fc7\u7a0b（\u5982 GRASP）\u3002<\/li>\n
            • \u673a\u6784（Agency）：\u4e00\u7ec4\u534f\u540c\u5de5\u4f5c\u7684\u667a\u80fd\u4f53，\u901a\u8fc7\u53bb\u4e2d\u5fc3\u5316\u7684\u6fc0\u6d3b\u4e0e\u6291\u5236\u4fe1\u53f7\u76f8\u4e92\u5f71\u54cd\u3002<\/li>\n
            • \u6d8c\u73b0（Emergence） \u2b50：\u590d\u6742\u6709\u76ee\u7684\u7684\u667a\u80fd\u884c\u4e3a\u4e0d\u662f\u7531\u67d0\u4e2a\u9ad8\u7ea7\u667a\u80fd\u4f53\u9884\u5148\u89c4\u5212，\u800c\u662f\u4ece\u5927\u91cf\u7b80\u5355\u667a\u80fd\u4f53\u7684\u5c40\u90e8\u4ea4\u4e92\u4e2d\u81ea\u53d1\u4ea7\u751f（\u201c\u642d\u79ef\u6728\u5854\u201d：\u6ca1\u6709\u4efb\u4f55\u667a\u80fd\u4f53\u77e5\u9053"\u5854"\u662f\u4ec0\u4e48，\u642d\u5854\u884c\u4e3a\u5374\u6d8c\u73b0\u51fa\u6765）\u3002<\/li>\n<\/ul>\n

              \u5bf9\u591a\u667a\u80fd\u4f53\u7cfb\u7edf（MAS）\u7684\u4e09\u70b9\u542f\u53d1 \u2b50：\u53bb\u4e2d\u5fc3\u5316\u63a7\u5236（\u65e0\u4e2d\u5fc3\u8282\u70b9\u4e0b\u7684\u534f\u8c03\u4e0e\u4efb\u52a1\u5206\u914d\u6210\u4e3a\u6838\u5fc3\u8bfe\u9898）\u3001\u6d8c\u73b0\u5f0f\u8ba1\u7b97（\u542f\u53d1\u8681\u7fa4\u3001\u7c92\u5b50\u7fa4\u7b49\u7b97\u6cd5）\u3001\u667a\u80fd\u4f53\u7684\u793e\u4f1a\u6027（\u901a\u4fe1\u8bed\u8a00\u3001\u4ea4\u4e92\u534f\u8bae\u3001\u534f\u5546\u7b56\u7565\u3001\u4fe1\u4efb\u6a21\u578b\u4e0e\u7ec4\u7ec7\u7ed3\u6784 \u2192 \u6784\u5efa"\u8ba1\u7b97\u793e\u4f1a"）\u3002<\/p>\n

              2.4 \u5b66\u4e60\u8303\u5f0f\u7684\u6f14\u8fdb\u4e0e\u73b0\u4ee3\u667a\u80fd\u4f53<\/h3>\n

              2.4.1 \u8054\u7ed3\u4e3b\u4e49（\u81ea\u4e0b\u800c\u4e0a，\u53d7\u5927\u8111\u542f\u53d1）<\/h4>\n

              \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

              \u77e5\u8bc6\u4e0d\u518d\u4ee5\u663e\u5f0f\u7b26\u53f7\u5b58\u4e8e\u77e5\u8bc6\u5e93，\u800c\u4ee5\u8fde\u63a5\u6743\u91cd\u5206\u5e03\u5f0f\u5b58\u50a8\u5728\u795e\u7ecf\u5143\u4e4b\u95f4（\u8fde\u63a5\u6a21\u5f0f\u672c\u8eab\u5373\u77e5\u8bc6）；\u6bcf\u4e2a\u5355\u5143\u53ea\u505a\u7b80\u5355\u8ba1\u7b97；\u667a\u80fd\u6765\u81ea"\u5b66\u4e60"\u2014\u2014\u6309\u53cd\u5411\u4f20\u64ad\u7b49\u7b97\u6cd5\u81ea\u52a8\u8c03\u6574\u6743\u91cd\u4f7f\u8f93\u51fa\u903c\u8fd1\u671f\u671b\u3002<\/p>\n

              \u8d21\u732e\u4e0e\u4e0d\u8db3：\u8d4b\u4e88\u5f3a\u5927\u7684\u611f\u77e5\u4e0e\u6a21\u5f0f\u8bc6\u522b\u80fd\u529b；\u4f46\u5982\u4f55\u5728\u52a8\u6001\u4ea4\u4e92\u4e2d\u505a\u6700\u4f18\u5e8f\u8d2f\u51b3\u7b56，\u9700\u8981\u53e6\u4e00\u79cd\u8303\u5f0f\u8865\u5145\u3002<\/p>\n

              2.4.2 \u5f3a\u5316\u5b66\u4e60\u667a\u80fd\u4f53<\/h4>\n

              \u8054\u7ed3\u4e3b\u4e49\u89e3\u51b3\u611f\u77e5（\u201c\u56fe\u91cc\u6709\u4ec0\u4e48？\u201d），\u5f3a\u5316\u5b66\u4e60\u89e3\u51b3\u51b3\u7b56（\u201c\u6211\u8be5\u505a\u4ec0\u4e48？\u201d）\u3002<\/p>\n

              \u6838\u5fc3\u8981\u7d20：\u667a\u80fd\u4f53／\u73af\u5883／\u72b6\u6001 S／\u884c\u52a8 A／\u5956\u52b1 R／\u7b56\u7565 \u03c0（\u72b6\u6001\u5230\u884c\u52a8\u7684\u6620\u5c04）\u3002\u4ea4\u4e92\u5faa\u73af：\u89c2\u5bdf StS_t<\/span><\/span>S<\/span><\/span>t<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u2192 \u6309 \u03c0 \u6267\u884c AtA_t<\/span><\/span>A<\/span><\/span>t<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u2192 \u73af\u5883\u8f6c\u5230 St+1S_{t+1}<\/span><\/span>S<\/span><\/span>t<\/span>+<\/span>1<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u5e76\u53cd\u9988 Rt+1R_{t+1}<\/span><\/span>R<\/span><\/span>t<\/span>+<\/span>1<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u2192 \u636e\u6b64\u66f4\u65b0\u7b56\u7565（\u8fd9\u5c31\u662f\u5b66\u4e60）\u3002<\/p>\n

              **\u5f3a\u5316\u5b66\u4e60（Reinforcement Learning, RL）**\u6b63\u662f\u4e13\u6ce8\u4e8e\u89e3\u51b3\u5e8f\u8d2f\u51b3\u7b56\u95ee\u9898\u7684\u5b66\u4e60\u8303\u5f0f\u3002\u5b83\u5e76\u975e\u76f4\u63a5\u4ece\u6807\u6ce8\u597d\u7684\u9759\u6001\u6570\u636e\u96c6\u4e2d\u5b66\u4e60，\u800c\u662f\u901a\u8fc7\u667a\u80fd\u4f53\u4e0e\u73af\u5883\u7684\u76f4\u63a5\u4ea4\u4e92，\u5728\u201c\u8bd5\u9519\u201d\u4e2d \u5b66\u4e60\u5982\u4f55\u6700\u5927\u5316\u5176\u957f\u671f\u6536\u76ca\u3002<\/p>\n

              \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

              \u2b50 \u5173\u952e\u70b9：\u76ee\u6807\u662f\u6700\u5927\u5316\u7d2f\u79ef\u5956\u52b1\u800c\u975e\u5373\u65f6\u5956\u52b1，\u56e0\u6b64\u667a\u80fd\u4f53\u5fc5\u987b\u5177\u5907"\u8fdc\u89c1"（\u4e3a\u672a\u6765\u66f4\u5927\u5956\u52b1\u727a\u7272\u5f53\u524d，\u5982\u56f4\u68cb\u5f03\u5b50）\u3002\u4e0e\u76d1\u7763\u5b66\u4e60\u7684\u672c\u8d28\u533a\u522b：\u4e0d\u4f9d\u8d56\u6807\u6ce8\u6570\u636e\u96c6，\u800c\u662f\u5728\u4e0e\u73af\u5883\u4ea4\u4e92\u4e2d\u8bd5\u9519\u5b66\u4e60（AlphaGo \u4ec5\u51ed\u6700\u7ec8\u80dc\u8d1f\u81ea\u4e3b\u8c03\u6574，\u4e0d\u4f9d\u8d56\u4eba\u7c7b\u68cb\u8c31）\u3002<\/p>\n

              2.4.3 \u57fa\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u7684\u9884\u8bad\u7ec3（Pre-training）<\/h4>\n

              \u8981\u89e3\u51b3\u7684\u95ee\u9898：RL \u667a\u80fd\u4f53\u5b66\u4e60\u4e4b\u521d\u7f3a\u4e4f\u5148\u9a8c\u77e5\u8bc6（\u7b26\u53f7\u4e3b\u4e49\u60f3\u624b\u5de5\u7f16\u7801\u7684"\u5e38\u8bc6"\u5728 RL \u4e2d\u540c\u6837\u7f3a\u5931）\u3002<\/p>\n

              \u9884\u8bad\u7ec3\u2014\u5fae\u8c03\u8303\u5f0f：\u2460 \u9884\u8bad\u7ec3\u2014\u2014\u5728\u4e92\u8054\u7f51\u7ea7\u6587\u672c\u4e0a\u4ee5\u81ea\u76d1\u7763\u65b9\u5f0f\u8bad\u7ec3\u8d85\u5927\u6a21\u578b，\u76ee\u6807\u662f\u5b66\u4e60\u8bed\u8a00\u89c4\u5f8b\u3001\u8bed\u6cd5\u3001\u4e8b\u5b9e\u77e5\u8bc6\u4e0e\u4e0a\u4e0b\u6587\u903b\u8f91（\u6700\u5e38\u89c1\u5f62\u5f0f\u5373"\u9884\u6d4b\u4e0b\u4e00\u4e2a\u8bcd"）；\u2461 \u5fae\u8c03\u2014\u2014\u7528\u5c11\u91cf\u6807\u6ce8\u6570\u636e\u9002\u914d\u4e0b\u6e38\u4efb\u52a1\u3002<\/p>\n

              \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

              \u2b50 \u4e3a\u4ec0\u4e48\u9884\u8bad\u7ec3\u89e3\u51b3\u4e86"\u77e5\u8bc6\u83b7\u53d6\u74f6\u9888"：\u6570\u4e07\u4ebf\u7ea7\u6587\u672c\u8bad\u7ec3\u540e，\u6743\u91cd\u672c\u8eab\u5c31\u6784\u6210\u4e86\u4e00\u4e2a\u9ad8\u5ea6\u538b\u7f29\u7684\u4e16\u754c\u77e5\u8bc6\u9690\u5f0f\u6a21\u578b\u2014\u2014\u77e5\u8bc6\u4e0d\u518d\u9700\u8981\u4eba\u5de5\u9010\u6761\u7f16\u7801\u3002<\/p>\n

              \u6d8c\u73b0\u80fd\u529b（Emergent Abilities）：\u89c4\u6a21\u8de8\u8fc7\u9608\u503c\u540e\u7a81\u7136\u51fa\u73b0\u672a\u88ab\u76f4\u63a5\u8bad\u7ec3\u7684\u80fd\u529b\u2014\u2014\u4e0a\u4e0b\u6587\u5b66\u4e60（In-context Learning）（\u7ed9\u51e0\u4e2a\u751a\u81f3\u96f6\u4e2a\u793a\u4f8b\u5c31\u80fd\u7406\u89e3\u65b0\u4efb\u52a1，\u6743\u91cd\u4e0d\u53d8）\u4e0e\u601d\u7ef4\u94fe（Chain-of-Thought）\u63a8\u7406（\u5148\u8f93\u51fa\u63a8\u7406\u6b65\u9aa4\u518d\u4f5c\u7b54，\u663e\u8457\u63d0\u5347\u903b\u8f91\/\u7b97\u672f\/\u5e38\u8bc6\u51c6\u786e\u7387）\u3002<\/p>\n

              \u81f3\u6b64 LLM \u4e0d\u518d\u53ea\u662f\u8bed\u8a00\u6a21\u578b，\u800c\u6210\u4e3a\u517c\u5177"\u6d77\u91cf\u77e5\u8bc6\u5e93"\u4e0e"\u901a\u7528\u63a8\u7406\u5f15\u64ce"\u53cc\u91cd\u89d2\u8272\u7684\u7ec4\u4ef6\u3002<\/p>\n

              2.4.4 \u57fa\u4e8e LLM \u7684\u667a\u80fd\u4f53\u6838\u5fc3\u7ec4\u4ef6\u67b6\u6784 \u2b50（\u4e0e\u7b2c\u4e00\u7ae0\u5faa\u73af\u547c\u5e94）<\/h4>\n

              \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

            • \u611f\u77e5（Perception）：\u63a5\u6536\u539f\u59cb\u8f93\u5165\u5f62\u6210 Observation（\u7528\u6237\u6307\u4ee4\u3001API \u8fd4\u56de\u3001\u73af\u5883\u53d8\u5316）\u3002<\/li>\n
            • \u601d\u8003（Thought）：\u89c4\u5212\u4e0e\u5206\u89e3（\u901a\u8fc7\u53cd\u601d\u4e0e\u81ea\u6211\u6279\u5224\u628a\u5b8f\u89c2\u76ee\u6807\u62c6\u6210\u53ef\u6267\u884c\u6b65\u9aa4）+ \u63a8\u7406\u4e0e\u51b3\u7b56（LLM \u7ed3\u5408 Memory \u6df1\u5ea6\u63a8\u7406，\u901a\u5e38\u8868\u73b0\u4e3a\u4e00\u6b21\u5de5\u5177\u8c03\u7528）\u3002<\/li>\n
            • \u884c\u52a8 (Action)：\u89e3\u6790\u6307\u4ee4，\u4ece\u5de5\u5177\u7bb1\u9009\u8c03\u5de5\u5177（\u4ee3\u7801\u6267\u884c\u5668\u3001\u641c\u7d22\u5f15\u64ce\u3001API）\u4e0e\u73af\u5883\u4ea4\u4e92\u3002<\/li>\n
            • \u89c2\u5bdf (Observation)\u4e0e\u5faa\u73af：\u5de5\u5177\u7ed3\u679c + \u65b0\u73af\u5883\u72b6\u6001\u6784\u6210\u65b0\u4e00\u8f6e Observation，\u540c\u65f6\u66f4\u65b0\u8bb0\u5fc6，\u542f\u52a8\u4e0b\u4e00\u8f6e\u3002<\/li>\n

              2.4.5 \u4e09\u5927\u601d\u6f6e\u4e0e\u878d\u5408<\/h4>\n\n\u601d\u6f6e\u6838\u5fc3\u4e3b\u5f20\u4ee3\u8868\u6210\u679c<\/tr>\n\n\n\n\n
              \u7b26\u53f7\u4e3b\u4e49<\/td>\n\u667a\u80fd\u6838\u5fc3\u662f\u7b26\u53f7\u64cd\u4f5c\u4e0e\u903b\u8f91\u63a8\u7406<\/td>\nSHRDLU\u3001\u4e13\u5bb6\u7cfb\u7edf\u3001\u6df1\u84dd<\/td>\n<\/tr>\n
              \u8054\u7ed3\u4e3b\u4e49<\/td>\n\u6a21\u62df\u5927\u8111\u795e\u7ecf\u7f51\u7edc<\/td>\n\u53cd\u5411\u4f20\u64ad\u3001CNN\u3001Transformer<\/td>\n<\/tr>\n
              \u884c\u4e3a\u4e3b\u4e49<\/td>\n\u4e0e\u73af\u5883\u4e92\u52a8\u8bd5\u9519\u5b66\u4e60\u6700\u4f18\u7b56\u7565（\u73b0\u4ee3\u5316\u8eab\u4e3a RL）<\/td>\nTD-Gammon\u3001AlphaGo<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \u5f53\u4ee3\u878d\u5408：LLM \u672c\u8eab\u662f\u8054\u7ed3\u4e3b\u4e49\u7684\u4ea7\u7269，\u5374\u6210\u4e3a\u6267\u884c\u7b26\u53f7\u63a8\u7406\u3001\u5de5\u5177\u8c03\u7528\u4e0e\u89c4\u5212\u51b3\u7b56\u7684\u6838\u5fc3"\u5927\u8111" \u2192 \u5f62\u6210\u795e\u7ecf\u2014\u7b26\u53f7\u7ed3\u5408\u7684\u73b0\u4ee3\u667a\u80fd\u4f53\u67b6\u6784\u3002<\/p>\n

              \u7b2c\u4e8c\u7ae0\u5c0f\u7ed3<\/h4>\n

              \u7b26\u53f7\u4e3b\u4e49\u7528"\u77e5\u8bc6 + \u63a8\u7406"\u6a21\u62df\u667a\u80fd（PSSH\u3001MYCIN\u3001SHRDLU），\u5374\u5012\u5728\u77e5\u8bc6\u83b7\u53d6\u74f6\u9888\u3001\u5e38\u8bc6\u7f3a\u5931\u3001\u6846\u67b6\u95ee\u9898\u4e0e\u7cfb\u7edf\u8106\u5f31\u6027\u56db\u5927\u6311\u6218\u4e0a；ELIZA \u7528\u51e0\u5341\u884c\u89c4\u5219\u6f14\u793a\u4e86\u8fd9\u5957\u65b9\u6cd5\u7684\u5929\u82b1\u677f\u4e0e"\u667a\u80fd\u5e7b\u89c9"\u3002\u660e\u65af\u57fa\u7684\u5fc3\u667a\u793e\u4f1a\u628a\u89c6\u7ebf\u4ece"\u5355\u4e00\u5168\u80fd\u7cfb\u7edf"\u8f6c\u5411"\u7b80\u5355\u5355\u5143\u7684\u534f\u4f5c\u6d8c\u73b0"，\u4e3a\u591a\u667a\u80fd\u4f53\u7cfb\u7edf\u64ad\u4e0b\u79cd\u5b50\u3002\u6b64\u540e\u8054\u7ed3\u4e3b\u4e49\u8d4b\u4e88\u611f\u77e5\u3001\u5f3a\u5316\u5b66\u4e60\u8d4b\u4e88\u5e8f\u8d2f\u51b3\u7b56\u3001\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u8d4b\u4e88\u4e16\u754c\u77e5\u8bc6\u4e0e\u901a\u7528\u63a8\u7406，\u4e09\u5927\u601d\u6f6e\u5728 LLM \u9a71\u52a8\u7684\u667a\u80fd\u4f53\u4e2d\u5408\u6d41\u3002\u4e09\u7ae0\u8854\u63a5\u662f：\u7b2c\u4e00\u7ae0\u8bb2\u5b83\u662f\u4ec0\u4e48，\u7b2c\u4e8c\u7ae0\u8bb2\u5b83\u4e3a\u4f55\u5982\u6b64，\u7b2c\u4e09\u7ae0\u8bb2\u5b83\u7684\u5927\u8111\u5982\u4f55\u5de5\u4f5c\u3002<\/p>\n

              \n

              \u7b2c\u4e09\u7ae0 \u5927\u8bed\u8a00\u6a21\u578b\u57fa\u7840（\u91cd\u70b9）<\/h2>\n

              3.1 \u8bed\u8a00\u6a21\u578b\u4e0e Transformer \u67b6\u6784<\/h3>\n

              3.1.1 \u4ece N-gram \u5230 RNN（\u6f14\u8fdb = \u4e0d\u65ad\u89e3\u51b3\u4e0a\u4e00\u6b65\u7684\u7f3a\u9677）<\/h4>\n

              （1）\u7edf\u8ba1\u8bed\u8a00\u6a21\u578b\u4e0e N-gram<\/p>\n

                \n
              • \n

                \u8bed\u8a00\u6a21\u578b\u7684\u6839\u672c\u4efb\u52a1：\u8ba1\u7b97\u8bcd\u5e8f\u5217\u7684\u6982\u7387 P(S)P(S)<\/span><\/span>P<\/span>(<\/span>S<\/span>)<\/span><\/span><\/span><\/span><\/span>，\u5373\u5224\u65ad\u53e5\u5b50\u662f\u5426\u901a\u987a\u81ea\u7136\u3002<\/p>\n<\/li>\n

              • \n

                \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n<\/li>\n

              • \n

                \u9a6c\u5c14\u53ef\u592b\u5047\u8bbe \u2b50：\u4e00\u4e2a\u8bcd\u7684\u51fa\u73b0\u6982\u7387\u53ea\u4e0e\u524d\u9762\u6709\u9650\u7684 n\u22121n-1<\/span><\/span>n<\/span><\/span>\u2212<\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span><\/span> \u4e2a\u8bcd\u6709\u5173，\u636e\u6b64\u5f97\u5230 N-gram（Bigram \u53ea\u770b\u524d 1 \u8bcd，Trigram \u770b\u524d 2 \u8bcd）；\u53c2\u6570\u7528\u6700\u5927\u4f3c\u7136\u4f30\u8ba1（\u9891\u7387\u8ba1\u6570）\u4f30\u8ba1\u3002<\/p>\n<\/li>\n

              • \n

                \u4e24\u4e2a\u81f4\u547d\u7f3a\u9677 \u2b50：\u6570\u636e\u7a00\u758f\u6027（\u672a\u89c1\u5e8f\u5217\u6982\u7387\u4e3a 0，\u5e73\u6ed1\u53ea\u80fd\u7f13\u89e3）\u4e0e\u6cdb\u5316\u80fd\u529b\u5dee（\u4e0d\u7406\u89e3\u8bcd\u95f4\u8bed\u4e49\u76f8\u4f3c\u2014\u2014\u89c1\u8fc7 agent learns \u4ecd\u8ba4\u4e3a robot learns \u6982\u7387\u4e3a 0）\u3002<\/p>\n<\/li>\n<\/ul>\n

                （2）\u8bcd\u5d4c\u5165：\u628a\u8bcd\u4ece\u79bb\u6563\u7b26\u53f7\u53d8\u6210\u8fde\u7eed\u5411\u91cf，\u6620\u5c04\u5230\u9ad8\u7ef4\u8bed\u4e49\u7a7a\u95f4，\u8bed\u4e49\u76f8\u8fd1\u7684\u8bcd\u5411\u91cf\u4f4d\u7f6e\u4e5f\u76f8\u8fd1；\u5411\u91cf\u5728\u8bad\u7ec3\u4e2d\u81ea\u52a8\u4e60\u5f97，\u7528\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5ea6\u91cf，\u8457\u540d\u7684 King \u2212 Man + Woman \u2248 Queen \u8bc1\u660e\u5b83\u80fd\u5b66\u5230"\u6027\u522b""\u7687\u5ba4"\u8fd9\u7c7b\u62bd\u8c61\u6982\u5ff5\u3002\u6b8b\u7559\u95ee\u9898：\u4e0a\u4e0b\u6587\u7a97\u53e3\u4ecd\u56fa\u5b9a，\u66f4\u65e9\u7684\u5386\u53f2\u88ab\u4e22\u5f03\u3002<\/p>\n

                （3）\u5faa\u73af\u795e\u7ecf\u7f51\u7edc (RNN) \u4e0e\u957f\u77ed\u65f6\u8bb0\u5fc6\u7f51\u7edc (LSTM)：RNN \u5f15\u5165\u9690\u85cf\u72b6\u6001\u4f5c"\u77ed\u671f\u8bb0\u5fc6"\u6253\u7834\u56fa\u5b9a\u7a97\u53e3，\u4f46\u5e8f\u5217\u957f\u5ea6\u5373\u7f51\u7edc\u6df1\u5ea6 \u2192 \u53cd\u5411\u4f20\u64ad\u65f6\u68af\u5ea6\u6d88\u5931\/\u7206\u70b8，\u96be\u4ee5\u5b66\u4e60\u957f\u671f\u4f9d\u8d56\u3002LSTM \u5f15\u5165\u7ec6\u80de\u72b6\u6001\u4f5c\u4e3a\u72ec\u7acb\u4fe1\u606f\u901a\u8def，\u914d\u4e09\u9053\u95e8\u63a7（\u9057\u5fd8\u95e8\u4e22\u5f03\u65e7\u4fe1\u606f\u3001\u8f93\u5165\u95e8\u5b58\u5165\u65b0\u4fe1\u606f\u3001\u8f93\u51fa\u95e8\u51b3\u5b9a\u8f93\u51fa）\u6709\u9009\u62e9\u5730\u653e\u884c\u4fe1\u606f\u3002<\/p>\n

                \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

                3.1.2 Transformer \u67b6\u6784\u89e3\u6790（2017, Google）<\/h4>\n

                \u4e3a\u4ec0\u4e48\u629b\u5f03\u5faa\u73af：RNN \u5fc5\u987b\u6309\u5e8f\u5904\u7406（\u7b2c tt<\/span><\/span>t<\/span><\/span><\/span><\/span><\/span> \u6b65\u8981\u7b49 t\u22121t-1<\/span><\/span>t<\/span><\/span>\u2212<\/span><\/span><\/span><\/span>1<\/span><\/span><\/span><\/span><\/span> \u6b65），\u65e0\u6cd5\u5927\u89c4\u6a21\u5e76\u884c，\u9650\u5236\u4e86\u89c4\u6a21\u4e0e\u8bad\u7ec3\u901f\u5ea6；Transformer \u5b8c\u5168\u4f9d\u8d56\u6ce8\u610f\u529b\u673a\u5236\u6355\u6349\u4f9d\u8d56，\u5b9e\u73b0\u771f\u6b63\u7684\u5e76\u884c\u3002<\/p>\n

                \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

                （1）\u7f16\u7801\u5668-\u89e3\u7801\u5668 (Encoder-Decoder)\u67b6\u6784（\u539f\u4e3a\u673a\u5668\u7ffb\u8bd1\u8bbe\u8ba1）：\u7f16\u7801\u5668\u8d1f\u8d23"\u7406\u89e3"\u8f93\u5165，\u4e3a\u6bcf\u4e2a\u8bcd\u5143\u751f\u6210\u5bcc\u542b\u4e0a\u4e0b\u6587\u7684\u5411\u91cf；\u89e3\u7801\u5668\u8d1f\u8d23"\u751f\u6210"\u76ee\u6807，\u53c2\u8003\u5df2\u751f\u6210\u524d\u6587\u5e76"\u54a8\u8be2"\u7f16\u7801\u5668\u8f93\u51fa\u3002\u6bcf\u5c42 = \u591a\u5934\u81ea\u6ce8\u610f\u529b（\u89e3\u7801\u5668\u4e3a\u63a9\u7801\u7248\u672c） + \u4ea4\u53c9\u6ce8\u610f\u529b（\u4ec5\u89e3\u7801\u5668） + FFN，\u6bcf\u90e8\u5206\u90fd\u88ab Add & Norm \u5305\u88f9\u3002<\/p>\n

                （2）\u81ea\u6ce8\u610f\u529b\u4e0e\u591a\u5934\u6ce8\u610f\u529b \u2b50<\/p>\n

                  \n
                • \u76f4\u89c9：\u8bfb\u5230 \u201cThe agent learns because it is intelligent.\u201d \u7684 \u201cit\u201d \u65f6，\u4eba\u8111\u4f1a\u81ea\u52a8\u805a\u7126 \u201cagent\u201d\u2014\u2014\u81ea\u6ce8\u610f\u529b\u5373\u5bf9\u6b64\u5efa\u6a21：\u5904\u7406\u6bcf\u4e2a\u8bcd\u65f6\u517c\u987e\u5e8f\u5217\u4e2d\u6240\u6709\u8bcd\u5e76\u5206\u914d\u4e0d\u540c\u6743\u91cd\u3002<\/li>\n
                • \u4e09\u89d2\u8272（\u5747\u7531\u8bcd\u5d4c\u5165\u4e58\u53ef\u5b66\u4e60\u6743\u91cd\u5f97\u5230）：Query\u67e5\u8be2（\u5f53\u524d\u8bcd，\u4e3b\u52a8\u67e5\u8be2）\u3001Key\u952e（\u53ef\u88ab\u67e5\u8be2\u7684\u6807\u7b7e）\u3001Value\u503c（\u8bcd\u5143\u643a\u5e26\u7684\u5185\u5bb9）\u3002<\/li>\n
                • \u56db\u6b65：\u751f\u6210 Q\/K\/V \u2192 Q \u4e0e\u6240\u6709 K \u70b9\u79ef\u7b97\u76f8\u5173\u6027 \u2192 \u9664\u4ee5 dk\\\\sqrt{d_k}<\/span><\/span><\/span>d<\/span><\/span>k<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u518d Softmax \u5f52\u4e00\u5316 \u2192 \u6743\u91cd\u52a0\u6743\u6c42\u548c V，\u5f97\u5230\u878d\u5408\u5168\u5c40\u4e0a\u4e0b\u6587\u7684\u65b0\u8868\u793a\u3002\u6d53\u7f29\u5f0f：Attention(Q,K,V)=softmax(QK\u1d40\/\u221ad_k)\u00b7V（\u61c2\u542b\u4e49\u5373\u53ef）\u3002<\/li>\n
                • \u591a\u5934：\u628a Q\/K\/V \u5207\u6210 hh<\/span><\/span>h<\/span><\/span><\/span><\/span><\/span> \u4efd\u5404\u7b97\u4e00\u6b21\u6ce8\u610f\u529b，\u76f8\u5f53\u4e8e hh<\/span><\/span>h<\/span><\/span><\/span><\/span><\/span> \u4e2a"\u4e13\u5bb6"\u4ece\u4e0d\u540c\u89d2\u5ea6（\u6307\u4ee3\u3001\u65f6\u6001\u3001\u4ece\u5c5e\u2026）\u5ba1\u89c6\u53e5\u5b50\u518d\u62fc\u63a5\u6574\u5408 \u2192 \u6781\u5927\u589e\u5f3a\u8868\u8fbe\u80fd\u529b\u3002<\/li>\n<\/ul>\n

                  （3）\u9010\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc（FFN）：\u6ce8\u610f\u529b\u8d1f\u8d23\u52a8\u6001\u805a\u5408\u4fe1\u606f，FFN \u8d1f\u8d23\u63d0\u53d6\u9ad8\u9636\u7279\u5f81；"\u9010\u4f4d\u7f6e"\u6307\u72ec\u7acb\u4f5c\u7528\u4e8e\u6bcf\u4e2a\u8bcd\u5143\u5411\u91cf\u4f46\u5171\u4eab\u540c\u4e00\u7ec4\u6743\u91cd（\u65e2\u72ec\u7acb\u52a0\u5de5\u53c8\u5927\u5e45\u7701\u53c2\u6570）；\u7ed3\u6784\u4e3a\u4e24\u5c42\u7ebf\u6027\u53d8\u6362 + ReLU，\u5148\u6269\u5927\u518d\u7f29\u5c0f（dff=4\u00d7dmodeld_{ff}=4\u00d7d_{model}<\/span><\/span>d<\/span><\/span>f<\/span>f<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>4<\/span><\/span>\u00d7<\/span><\/span><\/span><\/span>d<\/span><\/span>m<\/span>o<\/span>d<\/span>e<\/span>l<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>）\u3002<\/p>\n

                  （4）\u6b8b\u5dee\u8fde\u63a5\u4e0e\u5c42\u5f52\u4e00\u5316（Add & Norm）<\/p>\n\n\u7ec4\u4ef6\u89e3\u51b3\u4ec0\u4e48\u95ee\u9898<\/tr>\n\n\n\n
                  \u6b8b\u5dee\u8fde\u63a5<\/td>\nOutput = x + Sublayer(x)，\u89e3\u51b3\u6df1\u5c42\u7f51\u7edc\u68af\u5ea6\u6d88\u5931（\u68af\u5ea6\u53ef\u7ed5\u8fc7\u5b50\u6a21\u5757\u76f4\u4f20），\u4f7f\u5f88\u6df1\u7684\u7f51\u7edc\u4e5f\u80fd\u8bad\u7ec3<\/td>\n<\/tr>\n
                  \u5c42\u5f52\u4e00\u5316<\/td>\n\u5bf9\u5355\u6837\u672c\u6240\u6709\u7279\u5f81\u5f52\u4e00\u5316（\u5747\u503c 0\u3001\u65b9\u5dee 1），\u7a33\u5b9a\u6bcf\u5c42\u8f93\u5165\u5206\u5e03，\u52a0\u901f\u6536\u655b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  （5）\u4f4d\u7f6e\u7f16\u7801 \u2b50：\u81ea\u6ce8\u610f\u529b\u672c\u8eab\u4e0d\u542b\u4efb\u4f55\u987a\u5e8f\u4fe1\u606f（\u201cagent learns\u201d \u4e0e \u201clearns agent\u201d \u5bf9\u5b83\u7b49\u4ef7），\u56e0\u6b64\u7ed9\u6bcf\u4e2a\u8bcd\u5143\u5d4c\u5165\u989d\u5916\u52a0\u4e0a\u7531\u56fa\u5b9a sin\/cos \u516c\u5f0f\u7b97\u51fa\u7684\u4f4d\u7f6e\u5411\u91cf（\u975e\u5b66\u4e60\u6240\u5f97），\u4f7f\u76f8\u540c\u8bcd\u5728\u4e0d\u540c\u4f4d\u7f6e\u5f97\u5230\u4e0d\u540c\u8f93\u5165\u3002<\/p>\n

                  3.1.3 Decoder-Only \u67b6\u6784 \u2b50（\u73b0\u4ee3 LLM \u4e3b\u6d41）<\/h4>\n

                  \u6838\u5fc3\u7b80\u5316：\u8bed\u8a00\u7684\u6838\u5fc3\u4efb\u52a1\u5c31\u662f\u9884\u6d4b\u4e0b\u4e00\u4e2a\u6700\u53ef\u80fd\u7684\u8bcd\u2014\u2014\u95ee\u7b54\u3001\u5199\u4f5c\u3001\u751f\u6210\u4ee3\u7801\u672c\u8d28\u90fd\u662f"\u5728\u5df2\u6709\u6587\u672c\u540e\u9010\u8bcd\u6dfb\u52a0\u6700\u5408\u7406\u7684\u5185\u5bb9"，\u4e8e\u662f GPT \u5f7b\u5e95\u629b\u5f03\u7f16\u7801\u5668，\u53ea\u4fdd\u7559\u89e3\u7801\u5668\u3002<\/p>\n

                  Decoder-Only \u67b6\u6784\u7684\u5de5\u4f5c\u6a21\u5f0f\u88ab\u79f0\u4e3a\u81ea\u56de\u5f52 (Autoregressive)：\u7ed9\u8d77\u59cb\u6587\u672c \u2192 \u9884\u6d4b\u4e0b\u4e00\u8bcd \u2192 \u628a\u65b0\u8bcd\u8ffd\u52a0\u5230\u8f93\u5165\u672b\u5c3e \u2192 \u518d\u9884\u6d4b \u2192 \u76f4\u5230\u7ed3\u675f\u3002\u50cf"\u6587\u5b57\u63a5\u9f99"，\u4e0d\u65ad\u56de\u987e\u5df2\u5199\u5185\u5bb9\u518d\u51b3\u5b9a\u4e0b\u4e00\u4e2a\u5b57\u3002<\/p>\n

                  \u2b50 \u63a9\u7801\u81ea\u6ce8\u610f\u529b：\u8bad\u7ec3\u65f6\u6a21\u578b\u4e00\u6b21\u6027\u62ff\u5230\u5b8c\u6574\u6587\u672c，\u5982\u4f55\u9632\u6b62"\u5077\u770b"\u540e\u9762\u7b54\u6848？\u2014\u2014\u5728 Softmax \u524d\u65bd\u52a0\u56e0\u679c\u63a9\u7801，\u628a\u5f53\u524d\u4f4d\u7f6e\u4e4b\u540e\u6240\u6709\u8bcd\u5143\u7684\u5206\u6570\u7f6e\u4e3a\u6781\u5927\u8d1f\u6570，Softmax \u540e\u6982\u7387\u53d8\u4e3a 0，\u4ece\u6570\u5b66\u4e0a\u963b\u65ad\u5bf9\u540e\u7eed\u4fe1\u606f\u7684\u5173\u6ce8；\u751f\u6210\u65f6\u672a\u6765\u8bcd\u5143\u5c1a\u672a\u4ea7\u751f，\u53ea\u80fd\u770b\u5df2\u751f\u6210\u5185\u5bb9\u3002\u5176\u610f\u4e49\u662f\u4fdd\u8bc1\u8bad\u7ec3\u65b9\u5f0f\u4e0e\u81ea\u56de\u5f52\u4f7f\u7528\u65b9\u5f0f\u4e00\u81f4\u3002<\/p>\n

                  Decoder-Only \u67b6\u6784\u4e09\u5927\u4f18\u52bf：\u2460 \u8bad\u7ec3\u76ee\u6807\u7edf\u4e00（\u552f\u4e00\u4efb\u52a1\u5373"\u9884\u6d4b\u4e0b\u4e00\u4e2a\u8bcd"，\u6781\u9002\u5408\u6d77\u91cf\u65e0\u6807\u6ce8\u9884\u8bad\u7ec3）；\u2461 \u7ed3\u6784\u7b80\u5355\u6613\u6269\u5c55（GPT-4\u3001Llama \u7b49\u5343\u4ebf\u7ea7\u6a21\u578b\u5747\u57fa\u4e8e\u6b64）；\u2462 \u5929\u7136\u9002\u5408\u751f\u6210\u4efb\u52a1\u2014\u2014\u8fd9\u662f\u5b83\u6210\u4e3a\u901a\u7528\u667a\u80fd\u4f53\u57fa\u7840\u7684\u6838\u5fc3\u539f\u56e0\u3002<\/p>\n

                  3.2 \u4e0e\u5927\u8bed\u8a00\u6a21\u578b\u4ea4\u4e92（\u672c\u7ae0\u91cd\u70b9）<\/h3>\n

                  3.2.1 \u63d0\u793a\u5de5\u7a0b（Prompt Engineering）\u2b50\u2b50<\/h4>\n

                  Prompt \u662f\u6211\u4eec\u4e0e\u8fd9\u4e2a"\u5927\u8111"\u6c9f\u901a\u7684\u8bed\u8a00\u3002\u63d0\u793a\u5de5\u7a0b\u7814\u7a76\u5982\u4f55\u8bbe\u8ba1\u7cbe\u51c6\u63d0\u793a\u4ee5\u5f15\u5bfc\u671f\u671b\u8f93\u51fa；\u5bf9\u667a\u80fd\u4f53\u800c\u8a00，\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u63d0\u793a\u80fd\u8ba9\u667a\u80fd\u4f53\u95f4\u7684\u534f\u4f5c\u5206\u5de5\u53d8\u5f97\u9ad8\u6548\u3002<\/p>\n

                  （1）\u6a21\u578b\u91c7\u6837\u53c2\u6570（\u672c\u8d28\u662f\u8c03\u6574\u5bf9\u6982\u7387\u5206\u5e03\u7684\u91c7\u6837\u7b56\u7565）<\/p>\n\n\u53c2\u6570\u539f\u7406（\u4e00\u53e5\u8bdd）\u53d6\u503c\u6548\u679c<\/tr>\n\n\n\n\n
                  Temperature<\/td>\nSoftmax \u4e2d\u5f15\u5165\u6e29\u5ea6 T：T \u53d8\u5c0f\u2192\u5206\u5e03\u66f4\u9661\u5ced（\u653e\u5927\u9ad8\u6982\u7387\u9879）；\u53d8\u5927\u2192\u66f4\u5e73\u5766（\u63d0\u5347\u4f4e\u6982\u7387\u9879）<\/td>\n\u89c1\u4e0b\u4e09\u6863<\/td>\n<\/tr>\n
                  Top-k<\/td>\n\u53d6\u6982\u7387\u6700\u9ad8\u7684 k \u4e2a token \u7ec4\u6210\u5019\u9009\u96c6\u518d\u5f52\u4e00\u5316，\u53ea\u9650\u6570\u91cf；k=1 \u9000\u5316\u4e3a\u8d2a\u5fc3<\/td>\nk \u8d8a\u5c0f\u8d8a\u786e\u5b9a<\/td>\n<\/tr>\n
                  Top-p（\u6838\u91c7\u6837）<\/td>\n\u4ece\u9ad8\u5230\u4f4e\u7d2f\u52a0\u81f3\u9996\u6b21 \u2265p，\u8fd9\u4e9b token \u7ec4\u6210"\u6838\u96c6\u5408"\u518d\u5f52\u4e00\u5316，\u80fd\u52a8\u6001\u9002\u5e94\u4e0d\u540c\u5206\u5e03\u7684\u957f\u5c3e<\/td>\np \u8d8a\u5c0f\u8d8a\u786e\u5b9a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  Temperature \u4e09\u6863\u4e0e\u573a\u666f \u2b50：\u4f4e（0\u20130.3） \u7cbe\u51c6\u786e\u5b9a \u2192 \u4e8b\u5b9e\u95ee\u7b54\u3001\u6570\u636e\u8ba1\u7b97\u3001\u4ee3\u7801\u751f\u6210\u3001\u6cd5\u5f8b\/\u6280\u672f\u6587\u6863；\u4e2d（0.3\u20130.7） \u5e73\u8861\u81ea\u7136 \u2192 \u65e5\u5e38\u5bf9\u8bdd\u3001\u5ba2\u670d\u3001\u90ae\u4ef6\u6587\u6848；\u9ad8（0.7\u20132） \u521b\u65b0\u53d1\u6563 \u2192 \u8bd7\u6b4c\u3001\u79d1\u5e7b\u6784\u601d\u3001\u5e7f\u544a slogan\u3001\u5934\u8111\u98ce\u66b4\u3002<\/p>\n

                  \u534f\u540c\u89c4\u5219（\u6613\u8003\u7ec6\u8282）：\u8fc7\u6ee4\u4f18\u5148\u7ea7 \u6e29\u5ea6 \u2192 Top-k \u2192 Top-p；Top-k \u4e0e Top-p \u901a\u5e38\u4e8c\u9009\u4e00，\u540c\u65f6\u8bbe\u7f6e\u65f6\u5b9e\u9645\u5019\u9009\u96c6\u53d6\u4ea4\u96c6\u3002\u4e24\u4e2a\u8fb9\u754c：T=0 \u6216 Top-k=1 \u65f6，\u5176\u4f59\u91c7\u6837\u53c2\u6570\u53d8\u5f97\u65e0\u5173\u7d27\u8981\u3002<\/p>\n

                  （2）Zero \/ One \/ Few-shot（\u96f6\u6837\u672c\/\u5355\u6837\u672c\/\u5c11\u6837\u672c）：Zero-shot \u4e0d\u7ed9\u793a\u4f8b，\u4f9d\u8d56\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b**；One-shot \u7ed9\u4e00\u4e2a\u5b8c\u6574\u793a\u4f8b，\u6a21\u578b\u4f1a\u6a21\u4eff\u5176\u683c\u5f0f\u4e0e\u98ce\u683c；Few-shot \u7ed9\u591a\u4e2a\u8986\u76d6\u4e0d\u540c\u60c5\u51b5\u7684\u793a\u4f8b，\u8ba9\u6a21\u578b\u7406\u89e3\u4efb\u52a1\u7684\u7ec6\u8282\u3001\u8fb9\u754c\u4e0e\u7ec6\u5fae\u5dee\u522b\u3002<\/p>\n

                  （3）\u6307\u4ee4\u8c03\u4f18 (Instruction Tuning) \u7684\u5f71\u54cd \u2b50：\u65e9\u671f\u6a21\u578b（GPT-3）\u662f**"\u6587\u672c\u8865\u5168"\u6a21\u578b**，\u64c5\u957f\u7eed\u5199\u5374\u4e0d\u4e00\u5b9a\u7406\u89e3\u6307\u4ee4，\u5fc5\u987b\u7528\u5c11\u6837\u672c"\u6559\u4f1a"\u5b83；\u6307\u4ee4\u8c03\u4f18\u7528\u5927\u91cf"\u6307\u4ee4\u2014\u56de\u7b54"\u6570\u636e\u518d\u8bad\u7ec3，\u4f7f\u6a21\u578b\u80fd\u76f4\u63a5\u9075\u5faa\u6307\u4ee4\u3002\u6211\u4eec\u4eca\u5929\u7528\u7684 ChatGPT\u3001DeepSeek\u3001Qwen \u7b49\u90fd\u662f\u5176\u5bb6\u65cf\u4e2d\u7ecf\u8fc7\u6307\u4ee4\u8c03\u4f18\u7684\u7248\u672c\u3002\u610f\u4e49：\u6781\u5927\u7b80\u5316\u4ea4\u4e92，\u8fd9\u662f Agent \u80fd\u7528 System Prompt \u9a71\u52a8\u7684\u524d\u63d0\u3002<\/p>\n

                  （4）\u57fa\u7840\u63d0\u793a\u6280\u5de7：\u89d2\u8272\u626e\u6f14\u2014\u2014\u8d4b\u4e88\u7279\u5b9a\u89d2\u8272\u4ee5\u5f15\u5bfc\u56de\u7b54\u98ce\u683c\u3001\u8bed\u6c14\u4e0e\u77e5\u8bc6\u8303\u56f4（\u201c\u4f60\u662f\u8d44\u6df1 Python \u4e13\u5bb6，\u8bf7\u5411\u521d\u5b66\u8005\u89e3\u91ca GIL\u201d）；\u4e0a\u4e0b\u6587\u793a\u4f8b\u2014\u2014\u5728\u63d0\u793a\u4e2d\u7ed9\u51fa\u6e05\u6670\u8f93\u5165\u8f93\u51fa\u6837\u4f8b，\u5c24\u5176\u9002\u7528\u4e8e\u9700\u8981\u4e25\u683c\u683c\u5f0f\u6216\u7279\u5b9a\u98ce\u683c\u65f6（\u5982\u8981\u6c42\u8f93\u51fa {"product_name": "\u2026", "sentiment": "\u2026"}）\u3002<\/p>\n

                  （5）\u601d\u7ef4\u94fe（Chain-of-Thought,CoT） \u2b50：\u590d\u6742\u63a8\u7406\/\u8ba1\u7b97\u4efb\u52a1\u76f4\u63a5\u8981\u7b54\u6848\u5bb9\u6613\u51fa\u9519；\u505a\u6cd5\u662f\u5f15\u5bfc\u6a21\u578b"\u4e00\u6b65\u4e00\u6b65\u5730\u601d\u8003"\u2014\u2014\u5b9e\u73b0\u5173\u952e\u53ea\u662f\u5728\u63d0\u793a\u91cc\u52a0\u4e00\u53e5\u5f15\u5bfc\u8bed（\u201c\u8bf7\u9010\u6b65\u601d\u8003\u201d）\u3002\u53cc\u91cd\u6536\u76ca：\u66f4\u6613\u5f97\u6b63\u786e\u7b54\u6848；\u663e\u5f0f\u5c55\u793a\u63a8\u7406\u8fc7\u7a0b，\u4f7f\u56de\u7b54\u66f4\u53ef\u4fe1\u3001\u66f4\u4fbf\u4e8e\u68c0\u67e5\u7ea0\u6b63\u3002> \u5bf9 Agent \u7684\u610f\u4e49：CoT \u6b63\u662f ReAct \u8303\u5f0f\u4e2d \u201cThought\u201d \u7684\u76f4\u63a5\u6765\u6e90\u3002<\/p>\n

                  3.2.2 \u6587\u672c\u5206\u8bcd（Tokenization）\u2b50<\/h4>\n

                  \u5206\u8bcd：\u628a\u6587\u672c\u8f6c\u4e3a\u6570\u5b57\u5e8f\u5217；\u8bcd\u5143（Token） \u662f\u6700\u5c0f\u5355\u5143；\u5206\u8bcd\u5668\u5b9a\u4e49\u5207\u5206\u89c4\u5219\u3002<\/p>\n

                  \u4e3a\u4ec0\u4e48\u9700\u8981\u5b50\u8bcd\u5206\u8bcd：\u6309\u8bcd\u5206\u8bcd\u4f1a\u8bcd\u8868\u7206\u70b8\u4e0e OOV（\u65e0\u6cd5\u5904\u7406 \u201cDatawhaleAgent\u201d），\u4e14 look\/looks\/looking \u88ab\u89c6\u4e3a\u4e09\u4e2a\u65e0\u5173\u8bcd\u5143；\u6309\u5b57\u7b26\u5206\u8bcd\u5219\u5355\u5b57\u7b26\u5927\u591a\u65e0\u72ec\u7acb\u8bed\u4e49，\u5b66\u4e60\u4f4e\u6548；\u2b50 \u5b50\u8bcd\u5206\u8bcd\u8ba9\u5e38\u89c1\u8bcd\u4fdd\u7559\u4e3a\u6574\u4f53\u3001\u7f55\u89c1\u8bcd\u62c6\u6210\u6709\u610f\u4e49\u7247\u6bb5（\u201cTokenization\u201d \u2192 \u201cToken\u201d + \u201cization\u201d），\u65e2\u63a7\u8bcd\u8868\u53c8\u80fd\u7ec4\u5408\u51fa\u65b0\u8bcd\u3002<\/p>\n

                  BPE（GPT \u7cfb\u5217\u91c7\u7528）：\u8d2a\u5fc3\u5408\u5e76\u2014\u2014\u8bcd\u8868\u521d\u59cb\u5316\u4e3a\u57fa\u672c\u5b57\u7b26，\u53cd\u590d\u7edf\u8ba1\u5e76\u5408\u5e76\u9891\u7387\u6700\u9ad8\u7684\u76f8\u90bb\u8bcd\u5143\u5bf9，\u76f4\u5230\u8bcd\u8868\u8fbe\u5230\u9884\u8bbe\u5927\u5c0f\u3002\u540e\u7eed\u4f18\u5316：WordPiece（BERT \u7528，\u6309"\u6700\u80fd\u63d0\u5347\u8bed\u6599\u8bed\u8a00\u6a21\u578b\u6982\u7387"\u5408\u5e76）\u4e0e SentencePiece（Llama \u7528，\u628a\u7a7a\u683c\u4e5f\u5f53\u666e\u901a\u5b57\u7b26，\u4f7f\u5206\u8bcd\u5b8c\u5168\u53ef\u9006\u4e14\u4e0d\u4f9d\u8d56\u7279\u5b9a\u8bed\u8a00）\u3002<\/p>\n

                  \u2b50 \u5206\u8bcd\u5668\u5bf9\u5f00\u53d1\u8005\u7684\u4e09\u70b9\u5b9e\u9645\u610f\u4e49：<\/p>\n

                • \u4e0a\u4e0b\u6587\u7a97\u53e3\u9650\u5236：\u7a97\u53e3（8K\u3001128K）\u4ee5 Token \u8ba1\u800c\u975e\u5b57\u7b26\u6570，\u4e2d\u82f1\u6587\u4e0e\u4e0d\u540c\u5206\u8bcd\u5668\u4e0b\u5dee\u5f02\u5de8\u5927 \u2192 \u7cbe\u786e\u7ba1\u7406\u8f93\u5165\u957f\u5ea6\u662f\u6784\u5efa\u957f\u65f6\u8bb0\u5fc6\u667a\u80fd\u4f53\u7684\u57fa\u7840\u3002<\/li>\n
                • API \u6210\u672c：\u5927\u591a\u6570 API \u6309 Token \u8ba1\u8d39 \u2192 \u4e86\u89e3\u5206\u8bcd\u662f\u9884\u4f30\u4e0e\u63a7\u5236\u8fd0\u884c\u6210\u672c\u7684\u5173\u952e\u3002<\/li>\n
                • \u6a21\u578b\u8868\u73b0\u7684\u5f02\u5e38：\u67d0\u4e9b"\u602a\u5f02\u8868\u73b0"\u6e90\u4e8e\u5206\u8bcd\u2014\u2014\u6a21\u578b\u7b97\u5f97\u5bf9 2 + 2 \u5374\u53ef\u80fd\u7b97\u9519 2+2（\u540e\u8005\u53ef\u80fd\u88ab\u5207\u6210\u4e0d\u5e38\u89c1\u8bcd\u5143），\u9996\u5b57\u6bcd\u5927\u5c0f\u5199\u4e0d\u540c\u4e5f\u53ef\u80fd\u88ab\u5207\u6210\u5b8c\u5168\u4e0d\u540c\u7684\u5e8f\u5217 \u2192 \u8bbe\u8ba1\u63d0\u793a\u8bcd\u4e0e\u89e3\u6790\u8f93\u51fa\u65f6\u8981\u7559\u610f\u3002<\/li>\n

                  3.2.3 \u8c03\u7528\u5f00\u6e90\u6a21\u578b（\u672c\u5730\u90e8\u7f72）<\/h4>\n

                  \u7528\u4e8e\u654f\u611f\u6570\u636e\u3001\u79bb\u7ebf\u8fd0\u884c\u3001\u7cbe\u7ec6\u5316\u63a7\u6210\u672c\u3002\u6807\u51c6\u6d41\u7a0b（Hugging Face Transformers）：AutoTokenizer + AutoModelForCausalLM \u52a0\u8f7d\u6743\u91cd\u4e0e\u5206\u8bcd\u5668 \u2192 apply_chat_template \u6309\u5bf9\u8bdd\u6a21\u677f\u683c\u5f0f\u5316 \u2192 model.generate(max_new_tokens=512) \u751f\u6210 \u2192 batch_decode(skip_special_tokens=True) \u89e3\u7801\u56de\u6587\u672c\u3002<\/p>\n

                  3.2.4 \u6a21\u578b\u7684\u9009\u62e9<\/h4>\n

                  \u9009\u578b\u4e0d\u662f\u8ffd\u6c42"\u6700\u5927\u6700\u5f3a"，\u800c\u662f\u5728\u6027\u80fd\u3001\u6210\u672c\u3001\u901f\u5ea6\u3001\u90e8\u7f72\u65b9\u5f0f\u4e4b\u95f4\u6743\u8861（\u5177\u4f53\u578b\u53f7\u8fed\u4ee3\u6781\u5feb，\u91cd\u5728\u638c\u63e1\u539f\u5219）\u3002<\/p>\n

                  \u4e03\u4e2a\u5173\u952e\u7ef4\u5ea6 \u2b50：\u6027\u80fd\u4e0e\u80fd\u529b（\u4e0d\u540c\u6a21\u578b\u64c5\u957f\u9886\u57df\u4e0d\u540c，\u53ef\u53c2\u8003\u516c\u5f00\u699c\u5355）\u3001\u6210\u672c（\u95ed\u6e90\u6309 Token \u8ba1\u8d39 vs \u5f00\u6e90\u786c\u4ef6\u8fd0\u7ef4）\u3001\u901f\u5ea6\/\u5ef6\u8fdf（\u5b9e\u65f6\u4ea4\u4e92\u578b\u667a\u80fd\u4f53\u7684\u5173\u952e）\u3001\u4e0a\u4e0b\u6587\u7a97\u53e3（\u957f\u6587\u6863\u3001\u4ee3\u7801\u5e93\u3001\u957f\u671f\u8bb0\u5fc6\u9700 128K+）\u3001\u90e8\u7f72\u65b9\u5f0f（API \u4fbf\u6377\u4f46\u6570\u636e\u51fa\u57df vs \u672c\u5730\u90e8\u7f72\u4fdd\u9690\u79c1）\u3001\u751f\u6001\u4e0e\u5de5\u5177\u94fe（\u793e\u533a\u4e0e\u6846\u67b6\u517c\u5bb9\u6027\u5927\u5e45\u964d\u4f4e\u5f00\u53d1\u96be\u5ea6）\u3001\u53ef\u5fae\u8c03\u6027（\u5f00\u6e90\u6a21\u578b\u7075\u6d3b\u5ea6\u66f4\u9ad8）\u3001\u5b89\u5168\u6027\u4e0e\u4f26\u7406（\u504f\u89c1\u3001\u6bd2\u6027\u3001\u5e7b\u89c9\u8868\u73b0，\u9762\u5411\u516c\u4f17\u65f6\u4e0d\u53ef\u5ffd\u89c6）\u3002<\/p>\n

                  \u95ed\u6e90 vs \u5f00\u6e90：\u95ed\u6e90（GPT\u3001Gemini\u3001Claude\u3001\u56fd\u5185\u6587\u5fc3\/\u6df7\u5143\/Kimi \u7b49）\u4ee3\u8868\u6280\u672f\u524d\u6cbf\u3001API \u7a33\u5b9a\u6613\u7528，\u662f\u6784\u5efa\u9ad8\u6027\u80fd\u667a\u80fd\u4f53\u7684\u9996\u9009；\u5f00\u6e90（Llama\u3001Mistral\u3001Qwen\u3001ChatGLM \u7b49）\u63d0\u4f9b\u6700\u9ad8\u7075\u6d3b\u6027\u4e0e\u81ea\u4e3b\u6027，\u53ef\u672c\u5730\u90e8\u7f72\u4e0e\u5b9a\u5236\u5fae\u8c03\u3002<\/p>\n

                  3.3 \u7f29\u653e\u6cd5\u5219\u4e0e\u5c40\u9650\u6027<\/h3>\n

                  3.3.1 \u7f29\u653e\u6cd5\u5219（Scaling Laws）\u2b50<\/h4>\n
                    \n
                  • \u6838\u5fc3\u53d1\u73b0：\u6a21\u578b\u6027\u80fd（Loss）\u4e0e\u53c2\u6570\u91cf\u3001\u6570\u636e\u91cf\u3001\u8ba1\u7b97\u91cf\u5728\u5bf9\u6570\u2014\u5bf9\u6570\u5750\u6807\u4e0b\u5448\u5e73\u6ed1\u5e42\u5f8b\u5173\u7cfb；\u6309\u6bd4\u4f8b\u6301\u7eed\u589e\u52a0\u4e09\u8005，\u6027\u80fd\u53ef\u9884\u6d4b\u5730\u5e73\u6ed1\u63d0\u5347，\u65e0\u660e\u663e\u74f6\u9888\u3002<\/li>\n
                  • Chinchilla \u5b9a\u5f8b（DeepMind, 2022） \u2b50：\u7ed9\u5b9a\u7b97\u529b\u9884\u7b97\u4e0b\u53c2\u6570\u4e0e\u6570\u636e\u5b58\u5728\u6700\u4f18\u914d\u6bd4\u2014\u2014\u6700\u4f18\u6a21\u578b\u5e94\u6bd4\u6b64\u524d\u8ba4\u4e3a\u7684\u66f4\u5c0f，\u4f46\u8981\u7528\u591a\u5f97\u591a\u7684\u6570\u636e\u8bad\u7ec3（700 \u4ebf\u53c2\u6570 Chinchilla \u7528\u6bd4 GPT-3（1750 \u4ebf）\u591a 4 \u500d\u7684\u6570\u636e，\u6027\u80fd\u53cd\u8d85）\u2192 \u7ea0\u6b63"\u8d8a\u5927\u8d8a\u597d"，\u5f3a\u8c03\u6570\u636e\u6548\u7387\u3002<\/li>\n
                  • \u2b50 \u80fd\u529b\u6d8c\u73b0：\u89c4\u6a21\u8de8\u8fc7\u9608\u503c\u540e\u7a81\u7136\u51fa\u73b0\u5c0f\u6a21\u578b\u5b8c\u5168\u4e0d\u5177\u5907\u7684\u80fd\u529b\u2014\u2014\u94fe\u5f0f\u601d\u8003(CoT)\u3001\u6307\u4ee4\u9075\u5faa\u3001\u591a\u6b65\u63a8\u7406\u3001\u4ee3\u7801\u751f\u6210\u7b49\u5728\u6570\u767e\u4ebf\u81f3\u5343\u4ebf\u53c2\u6570\u7ea7\u624d\u663e\u8457\u51fa\u73b0 \u2192 \u5bf9 Agent \u5f00\u53d1\u8005\u7684\u542b\u4e49：\u9009\u62e9\u8db3\u591f\u5927\u89c4\u6a21\u7684\u6a21\u578b，\u662f\u5b9e\u73b0\u590d\u6742\u81ea\u4e3b\u51b3\u7b56\u4e0e\u89c4\u5212\u7684\u524d\u63d0\u3002<\/li>\n<\/ul>\n

                    3.3.2 \u6a21\u578b\u5e7b\u89c9（Hallucination）\u2b50\u2b50<\/h4>\n

                    \u5b9a\u4e49：\u751f\u6210\u5185\u5bb9\u4e0e\u5ba2\u89c2\u4e8b\u5b9e\u3001\u7528\u6237\u8f93\u5165\u6216\u4e0a\u4e0b\u6587\u76f8\u77db\u76fe，\u6216\u7f16\u9020\u4e0d\u5b58\u5728\u7684\u4e8b\u5b9e\u4e0e\u5b9e\u4f53\u3002\u672c\u8d28\u662f\u6a21\u578b\u5728\u751f\u6210\u4e2d\u8fc7\u5ea6\u81ea\u4fe1\u5730"\u7f16\u9020"，\u800c\u975e\u51c6\u786e\u68c0\u7d22\u6216\u63a8\u7406\u3002<\/p>\n

                    \u4e09\u79cd\u7c7b\u578b：\u4e8b\u5b9e\u6027\u5e7b\u89c9（\u4e0e\u73b0\u5b9e\u4e16\u754c\u4e8b\u5b9e\u4e0d\u7b26）\u3001\u5fe0\u5b9e\u6027\u5e7b\u89c9（\u6458\u8981\/\u7ffb\u8bd1\u672a\u80fd\u5fe0\u5b9e\u53cd\u6620\u6e90\u6587\u672c）\u3001\u5185\u5728\u5e7b\u89c9（\u4e0e\u8f93\u5165\u4fe1\u606f\u76f4\u63a5\u77db\u76fe）\u3002<\/p>\n

                    \u6210\u56e0\u4e09\u65b9\u9762：\u2460 \u8bad\u7ec3\u6570\u636e\u542b\u9519\u8bef\u6216\u77db\u76fe\u4fe1\u606f；\u2461 \u81ea\u56de\u5f52\u673a\u5236\u51b3\u5b9a\u5b83\u53ea\u662f\u5728"\u9884\u6d4b\u4e0b\u4e00\u4e2a\u6700\u53ef\u80fd\u7684\u8bcd\u5143"，\u6ca1\u6709\u5185\u7f6e\u4e8b\u5b9e\u6838\u67e5\u6a21\u5757；\u2462 \u590d\u6742\u63a8\u7406\u4e2d\u67d0\u4e00\u6b65\u51fa\u9519\u5bfc\u81f4\u7ed3\u8bba\u88ab"\u7f16\u9020"（\u5982\u63a8\u8350\u4e0d\u5b58\u5728\u7684\u666f\u70b9\u3001\u8ba2\u9519\u822a\u73ed\u53f7）\u3002<\/p>\n

                    \u53e6\u4e24\u4e2a\u56fa\u6709\u6311\u6218：\u77e5\u8bc6\u65f6\u6548\u6027\u4e0d\u8db3（\u77e5\u8bc6\u622a\u6b62\u4e8e\u8bad\u7ec3\u6570\u636e\u6536\u96c6\u65f6\u70b9）\u4e0e\u504f\u89c1（\u5438\u6536\u5e76\u53cd\u6620\u8bad\u7ec3\u6570\u636e\u4e2d\u7684\u793e\u4f1a\u504f\u89c1）\u3002<\/p>\n

                    \u7f13\u89e3\u624b\u6bb5（\u4e09\u4e2a\u5c42\u9762） \u2b50：<\/p>\n\n\u5c42\u9762\u65b9\u6cd5<\/tr>\n\n\n\n\n
                    \u6570\u636e\u5c42\u9762<\/td>\n\u9ad8\u8d28\u91cf\u6e05\u6d17\u3001\u5f15\u5165\u4e8b\u5b9e\u6027\u77e5\u8bc6\u3001RLHF \u2014\u2014 \u4ece\u6e90\u5934\u51cf\u5c11\u5e7b\u89c9<\/td>\n<\/tr>\n
                    \u6a21\u578b\u5c42\u9762<\/td>\n\u63a2\u7d22\u65b0\u67b6\u6784，\u6216\u8ba9\u6a21\u578b\u80fd\u8868\u8fbe\u5bf9\u751f\u6210\u5185\u5bb9\u7684\u4e0d\u786e\u5b9a\u6027<\/td>\n<\/tr>\n
                    \u63a8\u7406\u751f\u6210\u5c42\u9762<\/td>\n\u2460 RAG：\u751f\u6210\u524d\u5148\u4ece\u5916\u90e8\u77e5\u8bc6\u5e93\u68c0\u7d22，\u4ee5\u4e8b\u5b9e\u4e0a\u4e0b\u6587\u5f15\u5bfc\u751f\u6210（\u5f53\u524d\u6700\u6709\u6548\u624b\u6bb5\u4e4b\u4e00）；\u2461 \u591a\u6b65\u63a8\u7406\u4e0e\u9a8c\u8bc1：\u5f15\u5bfc\u9010\u6b65\u63a8\u7406\u5e76\u81ea\u6211\u68c0\u67e5\/\u5916\u90e8\u9a8c\u8bc1；\u2462 \u5f15\u5165\u5916\u90e8\u5de5\u5177：\u641c\u7d22\u5f15\u64ce\u3001\u8ba1\u7b97\u5668\u3001\u4ee3\u7801\u89e3\u91ca\u5668\u83b7\u53d6\u5b9e\u65f6\u4fe1\u606f\u6216\u505a\u7cbe\u786e\u8ba1\u7b97<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                    \u73b0\u5b9e\u6001\u5ea6：\u5e7b\u89c9\u77ed\u671f\u5185\u96be\u4ee5\u6839\u9664，\u4f46\u53ef\u663e\u8457\u964d\u4f4e\u5176\u9891\u7387\u4e0e\u5f71\u54cd\u3002\u8fd9\u6b63\u662f\u7b2c 8 \u7ae0 RAG\u3001\u7b2c 10 \u7ae0\u5de5\u5177\/MCP\u3001\u7b2c 12 \u7ae0\u8bc4\u4f30\u5b58\u5728\u7684\u6839\u672c\u7406\u7531\u3002<\/p>\n

                    \u7b2c\u4e09\u7ae0\u5c0f\u7ed3<\/h4>\n

                    LLM \u7684\u672c\u8d28\u662f\u4e00\u4e2a\u4ee5"\u9884\u6d4b\u4e0b\u4e00\u4e2a\u8bcd"\u4e3a\u76ee\u6807\u3001\u5728\u6d77\u91cf\u6587\u672c\u4e0a\u9884\u8bad\u7ec3\u51fa\u7684\u81ea\u56de\u5f52\u6a21\u578b\u3002\u6280\u672f\u4e0a\u8d70\u8fc7 N-gram（\u9a6c\u5c14\u53ef\u592b\u5047\u8bbe，\u6bc1\u4e8e\u7a00\u758f\u4e0e\u6cdb\u5316\u5dee）\u2192 \u8bcd\u5d4c\u5165（\u8bed\u4e49\u5411\u91cf，\u7a97\u53e3\u56fa\u5b9a）\u2192 RNN\/LSTM（\u9690\u85cf\u72b6\u6001\u8bb0\u5fc6，\u53d7\u56f0\u957f\u671f\u4f9d\u8d56）\u2192 Transformer（\u81ea\u6ce8\u610f\u529b\u5b9e\u73b0\u5e76\u884c，QKV \u805a\u5408\u5168\u5c40\u3001\u591a\u5934\u770b\u591a\u89d2\u5ea6\u3001FFN \u63d0\u7279\u5f81\u3001\u6b8b\u5dee+\u5c42\u5f52\u4e00\u5316\u4fdd\u7a33\u5b9a\u3001\u4f4d\u7f6e\u7f16\u7801\u8865\u56de\u987a\u5e8f）\u2192 Decoder-Only（\u780d\u6389\u7f16\u7801\u5668，\u56e0\u679c\u63a9\u7801\u4fdd\u8bc1\u8bad\u7ec3\u4e0e\u81ea\u56de\u5f52\u4e00\u81f4，\u6613\u6269\u5c55\u3001\u5929\u7136\u9002\u5408\u751f\u6210）\u3002\u4e0e\u5b83\u6253\u4ea4\u9053\u7684\u6838\u5fc3\u662f\u63d0\u793a\u5de5\u7a0b：\u7528 Temperature \/ Top-k \/ Top-p \u63a7\u91c7\u6837，\u7528 Zero\/One\/Few-shot\u3001\u89d2\u8272\u626e\u6f14\u3001\u4e0a\u4e0b\u6587\u793a\u4f8b\u660e\u786e\u4efb\u52a1，\u7528 CoT \u6fc0\u6d3b\u591a\u6b65\u63a8\u7406；\u540c\u65f6\u5fc5\u987b\u610f\u8bc6\u5230\u4e00\u5207\u4ee5 Token \u8ba1\u91cf\u4e0e\u8ba1\u8d39\u2014\u2014\u5206\u8bcd\u76f4\u63a5\u51b3\u5b9a\u4e0a\u4e0b\u6587\u9884\u7b97\u3001\u6210\u672c\u4e43\u81f3\u6a21\u578b\u7684\u602a\u5f02\u8868\u73b0\u3002\u6700\u540e，\u7f29\u653e\u6cd5\u5219\u89e3\u91ca\u80fd\u529b\u4ece\u4f55\u800c\u6765（\u542b Chinchilla \u5bf9\u6570\u636e\u6548\u7387\u7684\u4fee\u6b63\u4e0e\u80fd\u529b\u6d8c\u73b0），\u800c\u5e7b\u89c9\u3001\u77e5\u8bc6\u8fc7\u65f6\u3001\u504f\u89c1\u5212\u5b9a\u4e86\u80fd\u529b\u8fb9\u754c\u2014\u2014\u7406\u89e3\u8fd9\u4e9b\u8fb9\u754c，\u662f\u6784\u5efa\u53ef\u9760\u667a\u80fd\u4f53\u7684\u8d77\u70b9\u3002<\/p>\n

                    \n

                    \u9644\u5f55：\u901f\u67e5\u8868<\/h2>\n\n\u6982\u5ff5\u4e00\u53e5\u8bdd<\/tr>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                    \u667a\u80fd\u4f53<\/td>\n\u901a\u8fc7\u4f20\u611f\u5668\u611f\u77e5\u73af\u5883\u3001\u901a\u8fc7\u6267\u884c\u5668\u81ea\u4e3b\u884c\u52a8\u4ee5\u8fbe\u6210\u76ee\u6807\u7684\u5b9e\u4f53<\/td>\n<\/tr>\n
                    \u81ea\u4e3b\u6027<\/td>\n\u533a\u5206"\u667a\u80fd\u4f53"\u4e0e\u666e\u901a\u7a0b\u5e8f\u7684\u5206\u6c34\u5cad<\/td>\n<\/tr>\n
                    PEAS<\/td>\n\u6027\u80fd\u5ea6\u91cf \/ \u73af\u5883 \/ \u6267\u884c\u5668 \/ \u4f20\u611f\u5668，\u7528\u4e8e\u89c4\u7ea6\u4efb\u52a1\u73af\u5883<\/td>\n<\/tr>\n
                    Agent Loop<\/td>\n\u611f\u77e5 \u2192 \u601d\u8003（\u89c4\u5212 + \u5de5\u5177\u9009\u62e9）\u2192 \u884c\u52a8 \u2192 \u89c2\u5bdf \u2192 \u5faa\u73af<\/td>\n<\/tr>\n
                    Thought \/ Action \/ Observation<\/td>\n\u7ed3\u6784\u5316\u4ea4\u4e92\u534f\u8bae；Observation \u8981\u628a\u673a\u5668\u6570\u636e\u8f6c\u6210\u81ea\u7136\u8bed\u8a00<\/td>\n<\/tr>\n
                    Reactive vs Deliberative<\/td>\n\u901f\u5ea6 vs \u8fdc\u89c1\u7684\u6743\u8861；\u73b0\u4ee3 LLM Agent \u662f\u6df7\u5408\u5f0f<\/td>\n<\/tr>\n
                    \u795e\u7ecf\u7b26\u53f7\u4e3b\u4e49<\/td>\n\u795e\u7ecf\u7f51\u7edc（\u7cfb\u7edf 1）+ \u7ed3\u6784\u5316\u4e2d\u95f4\u7b26\u53f7（\u7cfb\u7edf 2）；LLM Agent \u662f\u5176\u8303\u4f8b<\/td>\n<\/tr>\n
                    Workflow vs Agent<\/td>\n\u6267\u884c\u9884\u8bbe\u6307\u4ee4 vs \u5728\u81ea\u7531\u5ea6\u4e2d\u81ea\u4e3b\u8fbe\u6210\u76ee\u6807<\/td>\n<\/tr>\n
                    \u9a6c\u5c14\u53ef\u592b\u5047\u8bbe<\/td>\n\u5f53\u524d\u8bcd\u53ea\u4e0e\u524d n\u22121 \u4e2a\u8bcd\u6709\u5173（N-gram \u7684\u57fa\u7840）<\/td>\n<\/tr>\n
                    \u8bcd\u5d4c\u5165<\/td>\n\u8bed\u4e49\u76f8\u8fd1\u7684\u8bcd\u5728\u5411\u91cf\u7a7a\u95f4\u4e2d\u4f4d\u7f6e\u76f8\u8fd1（King \u2212 Man + Woman \u2248 Queen）<\/td>\n<\/tr>\n
                    \u81ea\u6ce8\u610f\u529b<\/td>\nQ\u00b7K \u7b97\u76f8\u5173\u6027 \u2192 \u7f29\u653e \u2192 Softmax \u2192 \u52a0\u6743\u6c42\u548c V<\/td>\n<\/tr>\n
                    \u4f4d\u7f6e\u7f16\u7801<\/td>\n\u81ea\u6ce8\u610f\u529b\u65e0\u987a\u5e8f\u6982\u5ff5，\u7528\u56fa\u5b9a sin\/cos \u5411\u91cf\u8865\u56de\u4f4d\u7f6e<\/td>\n<\/tr>\n
                    Decoder-Only<\/td>\n\u53ea\u7559\u89e3\u7801\u5668；\u81ea\u56de\u5f52\u751f\u6210 + \u56e0\u679c\u63a9\u7801\u9632\u5077\u770b<\/td>\n<\/tr>\n
                    Temperature<\/td>\n\u4f4e（0\u20130.3）\u7cbe\u51c6 \/ \u4e2d（0.3\u20130.7）\u81ea\u7136 \/ \u9ad8（0.7\u20132）\u53d1\u6563<\/td>\n<\/tr>\n
                    Subword \/ BPE<\/td>\n\u5e38\u89c1\u8bcd\u6574\u4f53\u4fdd\u7559\u3001\u7f55\u89c1\u8bcd\u62c6\u5b50\u8bcd；\u8d2a\u5fc3\u5408\u5e76\u6700\u9ad8\u9891\u76f8\u90bb\u5bf9<\/td>\n<\/tr>\n
                    Scaling Laws<\/td>\n\u6027\u80fd\u4e0e\u53c2\u6570\/\u6570\u636e\/\u7b97\u529b\u5448\u5e42\u5f8b；Chinchilla \u5f3a\u8c03\u6570\u636e\u6548\u7387<\/td>\n<\/tr>\n
                    \u5e7b\u89c9<\/td>\n\u65e0\u4e8b\u5b9e\u6838\u67e5\u7684\u81ea\u56de\u5f52"\u7f16\u9020"；\u9760 RAG\u3001\u591a\u6b65\u9a8c\u8bc1\u3001\u5916\u90e8\u5de5\u5177\u7f13\u89e3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

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