{"id":32170,"date":"2025-07-07T18:33:03","date_gmt":"2025-07-07T10:33:03","guid":{"rendered":"https:\/\/www.kdjingpai.com\/?p=32170"},"modified":"2025-07-26T20:25:16","modified_gmt":"2025-07-26T12:25:16","slug":"shendupouxiaizhineng","status":"publish","type":"post","link":"https:\/\/www.kdjingpai.com\/de\/shendupouxiaizhineng\/","title":{"rendered":"\u6df1\u5ea6\u5256\u6790AI\u667a\u80fd\u4f53\u8bb0\u5fc6\uff1a\u4ece\u6838\u5fc3\u6982\u5ff5\u5230LangGraph\u5b9e\u6218"},"content":{"rendered":"<p>\u4e0e\u4e00\u4e2a\u603b\u5fd8\u8bb0\u8c08\u8bdd\u5185\u5bb9\u7684\u670b\u53cb\u4ea4\u6d41\uff0c\u6bcf\u6b21\u90fd\u5f97\u4ece\u5934\u8bf4\u8d77\uff0c\u8fd9\u79cd\u4f53\u9a8c\u65e0\u7591\u662f\u4f4e\u6548\u4e14\u4ee4\u4eba\u75b2\u60eb\u7684\u3002\u7136\u800c\uff0c\u8fd9\u6070\u6070\u662f\u5f53\u524d\u591a\u6570\u4eba\u5de5\u667a\u80fd\u7cfb\u7edf\u7684\u5e38\u6001\u3002\u5b83\u4eec\u5f88\u5f3a\u5927\uff0c\u4f46\u666e\u904d\u7f3a\u5931\u4e00\u4e2a\u5173\u952e\u8981\u7d20\uff1a<strong>\u8bb0\u5fc6<\/strong>\u3002<\/p>\n<p>\u8981\u6784\u5efa\u80fd\u591f\u771f\u6b63\u5b66\u4e60\u3001\u6f14\u5316\u548c\u534f\u4f5c\u7684 AI \u667a\u80fd\u4f53 (Agent)\uff0c\u8bb0\u5fc6\u5e76\u975e\u53ef\u6709\u53ef\u65e0\u7684\u9644\u52a0\u54c1\uff0c\u800c\u662f\u5176\u6838\u5fc3\u57fa\u7840\u3002<\/p>\n<p>&nbsp;<\/p>\n<h2>AI \u8bb0\u5fc6\u7684\u6838\u5fc3\u6982\u5ff5<\/h2>\n<h3>\u5f53\u524d AI \u7684\u201c\u65e0\u72b6\u6001\u201d\u56f0\u5883<\/h3>\n<p>\u7c7b\u4f3c\u00a0<code><a href=\"https:\/\/www.kdjingpai.com\/chatgpt-6\/\">ChatGPT<\/a><\/code>\u00a0\u6216\u5404\u7c7b\u4ee3\u7801\u52a9\u624b\u7b49\u5de5\u5177\uff0c\u5c3d\u7ba1\u529f\u80fd\u5f3a\u5927\uff0c\u7528\u6237\u5374\u4e0d\u5f97\u4e0d\u53cd\u590d\u91cd\u7533\u6307\u4ee4\u6216\u504f\u597d\u3002\u8fd9\u79cd\u7531\u5927\u8bed\u8a00\u6a21\u578b\u7684\u4e0a\u4e0b\u6587\u7a97\u53e3\u548c\u63d0\u793a\u5de5\u7a0b\uff08Prompt Engineering\uff09\u8425\u9020\u7684\u201c\u8bb0\u5fc6\u9519\u89c9\u201d\uff0c\u8ba9\u4eba\u4eec\u8bef\u4ee5\u4e3a AI \u5df2\u7ecf\u5177\u5907\u8bb0\u5fc6\u80fd\u529b\u3002<\/p>\n<p>\u5b9e\u9645\u4e0a\uff0c\u7edd\u5927\u591a\u6570 AI \u667a\u80fd\u4f53\u672c\u8d28\u4e0a\u662f**\u65e0\u72b6\u6001\uff08Stateless\uff09<strong>\u7684\uff0c\u5b83\u4eec\u65e0\u6cd5\u4ece\u8fc7\u53bb\u7684\u4ea4\u4e92\u4e2d\u5b66\u4e60\uff0c\u4e5f\u65e0\u6cd5\u968f\u65f6\u95f4\u63a8\u79fb\u800c\u81ea\u6211\u8c03\u6574\u3002\u8981\u4ece\u65e0\u72b6\u6001\u5de5\u5177\u8fdb\u5316\u4e3a\u771f\u6b63\u81ea\u4e3b\u7684<\/strong>\u6709\u72b6\u6001\uff08Stateful\uff09**\u667a\u80fd\u4f53\uff0c\u5c31\u5fc5\u987b\u4e3a\u5176\u8d4b\u4e88\u771f\u6b63\u7684\u8bb0\u5fc6\u673a\u5236\uff0c\u800c\u975e\u7b80\u5355\u5730\u6269\u5927\u4e0a\u4e0b\u6587\u7a97\u53e3\u6216\u4f18\u5316\u68c0\u7d22\u80fd\u529b\u3002<\/p>\n<h3>AI \u667a\u80fd\u4f53\u4e2d\u7684\u8bb0\u5fc6\u7a76\u7adf\u662f\u4ec0\u4e48\uff1f<\/h3>\n<p>\u5728 AI \u667a\u80fd\u4f53\u7684\u8bed\u5883\u4e2d\uff0c\u8bb0\u5fc6\u662f\u4e00\u79cd\u8de8\u65f6\u95f4\u3001\u8de8\u4efb\u52a1\u3001\u8de8\u4f1a\u8bdd\u4fdd\u7559\u5e76\u8c03\u7528\u76f8\u5173\u4fe1\u606f\u7684\u80fd\u529b\u3002\u5b83\u4f7f\u667a\u80fd\u4f53\u80fd\u591f\u8bb0\u4f4f\u5386\u53f2\u4ea4\u4e92\uff0c\u5e76\u5229\u7528\u8fd9\u4e9b\u4fe1\u606f\u6307\u5bfc\u672a\u6765\u7684\u884c\u4e3a\u3002<\/p>\n<p>\u771f\u6b63\u7684\u8bb0\u5fc6\u8fdc\u4e0d\u6b62\u662f\u5b58\u50a8\u804a\u5929\u8bb0\u5f55\u3002\u5b83\u5173\u4e4e\u6784\u5efa\u4e00\u4e2a\u6301\u7eed\u6f14\u5316\u7684\u5185\u90e8\u72b6\u6001\uff0c\u8fd9\u4e2a\u72b6\u6001\u4f1a\u5f71\u54cd\u667a\u80fd\u4f53\u7684\u6bcf\u4e00\u6b21\u51b3\u7b56\uff0c\u5373\u4fbf\u4e24\u6b21\u4ea4\u4e92\u76f8\u9694\u6570\u5468\u3002<\/p>\n<p>\u667a\u80fd\u4f53\u7684\u8bb0\u5fc6\u7531\u4e09\u5927\u652f\u67f1\u5b9a\u4e49\uff1a<\/p>\n<ul>\n<li><strong>\u72b6\u6001\uff08State\uff09<\/strong>\uff1a\u611f\u77e5\u5f53\u524d\u4ea4\u4e92\u7684\u5373\u65f6\u4fe1\u606f\u3002<\/li>\n<li><strong>\u6301\u4e45\u6027\uff08Persistence\uff09<\/strong>\uff1a\u8de8\u4f1a\u8bdd\u3001\u957f\u671f\u4fdd\u7559\u77e5\u8bc6\u3002<\/li>\n<li><strong>\u9009\u62e9\u6027\uff08Selectivity\uff09<\/strong>\uff1a\u5224\u65ad\u54ea\u4e9b\u4fe1\u606f\u503c\u5f97\u88ab\u957f\u4e45\u8bb0\u4f4f\u3002<\/li>\n<\/ul>\n<p>\u8fd9\u4e09\u5927\u652f\u67f1\u5171\u540c\u6784\u6210\u4e86\u667a\u80fd\u4f53\u5b9e\u73b0**\u8fde\u7eed\u6027\uff08Continuity\uff09**\u7684\u57fa\u7840\u3002<\/p>\n<h3>\u4e0a\u4e0b\u6587\u7a97\u53e3\u3001RAG \u4e0e\u8bb0\u5fc6\u7684\u5dee\u5f02<\/h3>\n<p>\u5c06\u8bb0\u5fc6\u7f6e\u4e8e\u667a\u80fd\u4f53\u7684\u4e3b\u6d41\u67b6\u6784\u4e2d\uff0c\u5178\u578b\u7684\u7ec4\u4ef6\u5305\u62ec\u5927\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u3001\u89c4\u5212\u5668\uff08\u5982\u00a0<code><a href=\"https:\/\/www.kdjingpai.com\/react\/\">ReAct<\/a><\/code>\u00a0\u6846\u67b6\uff09\u3001\u5de5\u5177\u96c6\uff08APIs\uff09\u548c\u68c0\u7d22\u5668\u3002\u95ee\u9898\u5728\u4e8e\uff0c\u8fd9\u4e9b\u7ec4\u4ef6\u672c\u8eab\u90fd\u65e0\u6cd5\u8bb0\u4f4f\u6628\u5929\u53d1\u751f\u4e86\u4ec0\u4e48\u3002<\/p>\n<h4>\u4e0a\u4e0b\u6587\u7a97\u53e3 \u2260 \u8bb0\u5fc6<\/h4>\n<p>\u4e00\u79cd\u666e\u904d\u7684\u8bef\u89e3\u8ba4\u4e3a\uff0c\u4e0d\u65ad\u589e\u5927\u7684\u4e0a\u4e0b\u6587\u7a97\u53e3\u80fd\u5f7b\u5e95\u53d6\u4ee3\u8bb0\u5fc6\u3002\u7136\u800c\uff0c\u8fd9\u79cd\u65b9\u6cd5\u5b58\u5728\u660e\u663e\u74f6\u9888\uff1a<\/p>\n<ul>\n<li><strong>\u6210\u672c\u9ad8\u6602<\/strong>\uff1a\u66f4\u591a\u7684 <a href=\"https:\/\/www.kdjingpai.com\/tokenization\/\">Tokens<\/a> \u610f\u5473\u7740\u66f4\u9ad8\u7684\u8ba1\u7b97\u6210\u672c\u548c\u5ef6\u8fdf\u3002<\/li>\n<li><strong>\u4fe1\u606f\u6709\u9650<\/strong>\uff1a\u65e0\u6cd5\u5b9e\u73b0\u8de8\u4f1a\u8bdd\u7684\u6301\u4e45\u5316\u5b58\u50a8\uff0c\u4e00\u65e6\u4f1a\u8bdd\u7ed3\u675f\uff0c\u6240\u6709\u4e0a\u4e0b\u6587\u4fe1\u606f\u4fbf\u4f1a\u4e22\u5931\u3002<\/li>\n<li><strong>\u7f3a\u4e4f\u4f18\u5148\u7ea7<\/strong>\uff1a\u6240\u6709\u4fe1\u606f\u5728\u4e0a\u4e0b\u6587\u4e2d\u5730\u4f4d\u5e73\u7b49\uff0c\u65e0\u6cd5\u533a\u5206\u5173\u952e\u4fe1\u606f\u548c\u6682\u65f6\u6027\u4fe1\u606f\u3002<\/li>\n<\/ul>\n<p>\u4e0a\u4e0b\u6587\u7a97\u53e3\u4fdd\u8bc1\u4e86\u667a\u80fd\u4f53\u5728<strong>\u5355\u6b21\u4f1a\u8bdd\u5185<\/strong>\u7684\u8fde\u8d2f\u6027\uff0c\u800c\u8bb0\u5fc6\u5219\u8d4b\u4e88\u5176<strong>\u8de8\u4f1a\u8bdd<\/strong>\u7684\u667a\u80fd\u3002<\/p>\n<h4>RAG \u2260 \u8bb0\u5fc6<\/h4>\n<p>\u68c0\u7d22\u589e\u5f3a\u751f\u6210\uff08<code><a href=\"https:\/\/www.kdjingpai.com\/rag\/\">RAG<\/a><\/code>\uff09\u4e0e\u8bb0\u5fc6\u7cfb\u7edf\u540c\u6837\u4e3a LLM \u63d0\u4f9b\u4fe1\u606f\uff0c\u4f46\u5b83\u4eec\u89e3\u51b3\u7684\u95ee\u9898\u622a\u7136\u4e0d\u540c\u3002<code>RAG<\/code>\u00a0\u5728\u63a8\u7406\u65f6\uff0c\u4ece\u5916\u90e8\u77e5\u8bc6\u5e93\uff08\u5982\u6587\u6863\u3001\u6570\u636e\u5e93\uff09\u4e2d\u68c0\u7d22\u4e8b\u5b9e\u4fe1\u606f\u5e76\u6ce8\u5165\u63d0\u793a\u8bcd\uff0c\u4ee5\u589e\u5f3a\u56de\u7b54\u7684\u51c6\u786e\u6027\u3002\u4f46\u00a0<code>RAG<\/code>\u00a0\u672c\u8eab\u662f\u65e0\u72b6\u6001\u7684\uff0c\u5b83\u4e0d\u5173\u5fc3\u7528\u6237\u8eab\u4efd\uff0c\u4e5f\u65e0\u6cd5\u5173\u8054\u672c\u6b21\u67e5\u8be2\u4e0e\u5386\u53f2\u5bf9\u8bdd\u3002<\/p>\n<p>\u8bb0\u5fc6\u5219\u5e26\u6765\u8fde\u7eed\u6027\u3002\u5b83\u8bb0\u5f55\u7528\u6237\u7684\u504f\u597d\u3001\u5386\u53f2\u51b3\u7b56\u3001\u6210\u529f\u4e0e\u5931\u8d25\u7684\u7ecf\u9a8c\uff0c\u5e76\u5728\u672a\u6765\u7684\u4ea4\u4e92\u4e2d\u5229\u7528\u8fd9\u4e9b\u4fe1\u606f\u3002<\/p>\n<p><strong>\u7b80\u800c\u8a00\u4e4b\uff1a<code>RAG<\/code>\u00a0\u5e2e\u52a9\u667a\u80fd\u4f53\u56de\u7b54\u5f97\u66f4\u51c6\u786e\uff0c\u8bb0\u5fc6\u5e2e\u52a9\u667a\u80fd\u4f53\u884c\u52a8\u5f97\u66f4\u667a\u80fd\u3002<\/strong>\u00a0\u4e00\u4e2a\u6210\u719f\u7684\u667a\u80fd\u4f53\u9700\u8981\u4e24\u8005\u534f\u540c\u5de5\u4f5c\uff1a<code>RAG<\/code>\u00a0\u4e3a\u5176\u63d0\u4f9b\u5916\u90e8\u77e5\u8bc6\uff0c\u8bb0\u5fc6\u5219\u5851\u9020\u5176\u5185\u5728\u884c\u4e3a\u3002<\/p>\n<p>\u4e3a\u4e86\u66f4\u6e05\u6670\u5730\u533a\u5206\u8fd9\u4e09\u8005\uff0c\u53ef\u53c2\u8003\u4e0b\u8868\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th align=\"left\">\u7279\u6027<\/th>\n<th align=\"left\">\u4e0a\u4e0b\u6587\u7a97\u53e3 (Context Window)<\/th>\n<th align=\"left\">\u68c0\u7d22\u589e\u5f3a\u751f\u6210 (RAG)<\/th>\n<th align=\"left\">\u8bb0\u5fc6 (Memory)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td align=\"left\"><strong>\u529f\u80fd<\/strong><\/td>\n<td align=\"left\">\u63d0\u4f9b\u77ed\u671f\u4f1a\u8bdd\u5185\u7684\u5373\u65f6\u4e0a\u4e0b\u6587<\/td>\n<td align=\"left\">\u4ece\u5916\u90e8\u77e5\u8bc6\u5e93\u68c0\u7d22\u4e8b\u5b9e\u4fe1\u606f<\/td>\n<td align=\"left\">\u5b58\u50a8\u548c\u8c03\u7528\u5386\u53f2\u4ea4\u4e92\u3001\u504f\u597d\u548c\u72b6\u6001<\/td>\n<\/tr>\n<tr>\n<td align=\"left\"><strong>\u72b6\u6001<\/strong><\/td>\n<td align=\"left\">\u65e0\u72b6\u6001\uff08Stateless\uff09<\/td>\n<td align=\"left\">\u65e0\u72b6\u6001\uff08Stateless\uff09<\/td>\n<td align=\"left\">\u6709\u72b6\u6001\uff08Stateful\uff09<\/td>\n<\/tr>\n<tr>\n<td align=\"left\"><strong>\u6301\u4e45\u6027<\/strong><\/td>\n<td align=\"left\">\u5355\u6b21\u4f1a\u8bdd<\/td>\n<td align=\"left\">\u5916\u90e8\u77e5\u8bc6\u5e93\u662f\u6301\u4e45\u7684\uff0c\u4f46\u68c0\u7d22\u8fc7\u7a0b\u662f\u77ac\u65f6\u7684<\/td>\n<td align=\"left\">\u8de8\u4f1a\u8bdd\uff0c\u957f\u671f\u6301\u4e45<\/td>\n<\/tr>\n<tr>\n<td align=\"left\"><strong>\u5e94\u7528\u573a\u666f<\/strong><\/td>\n<td align=\"left\">\u4fdd\u6301\u5bf9\u8bdd\u8fde\u8d2f<\/td>\n<td align=\"left\">\u56de\u7b54\u57fa\u4e8e\u7279\u5b9a\u6587\u6863\u7684\u95ee\u9898<\/td>\n<td align=\"left\">\u4e2a\u6027\u5316\u3001\u6301\u7eed\u5b66\u4e60\u3001\u4efb\u52a1\u81ea\u52a8\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h2>AI \u8bb0\u5fc6\u7684\u7c7b\u578b<\/h2>\n<p>AI \u667a\u80fd\u4f53\u7684\u8bb0\u5fc6\u53ef\u5206\u4e3a\u4e24\u79cd\u57fa\u672c\u5f62\u5f0f\uff1a\u77ed\u671f\u8bb0\u5fc6\u548c\u957f\u671f\u8bb0\u5fc6\uff0c\u5b83\u4eec\u5206\u522b\u670d\u52a1\u4e8e\u4e0d\u540c\u7684\u8ba4\u77e5\u529f\u80fd\u3002<\/p>\n<ul>\n<li><strong>\u77ed\u671f\u8bb0\u5fc6 (Short-Term Memory)<\/strong>\uff1a\u5728\u5355\u6b21\u4ea4\u4e92\u4e2d\u4fdd\u5b58\u5373\u65f6\u4e0a\u4e0b\u6587\uff0c\u5982\u540c\u4eba\u7c7b\u7684\u5de5\u4f5c\u8bb0\u5fc6\u3002<\/li>\n<li><strong>\u957f\u671f\u8bb0\u5fc6 (Long-Term Memory)<\/strong>\uff1a\u8de8\u4f1a\u8bdd\u3001\u4efb\u52a1\u548c\u65f6\u95f4\u4fdd\u7559\u77e5\u8bc6\uff0c\u7528\u4e8e\u5b66\u4e60\u548c\u9002\u5e94\u3002<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>\u77ed\u671f\u8bb0\u5fc6<\/h3>\n<p>\u77ed\u671f\u8bb0\u5fc6\uff0c\u4e5f\u79f0\u4e3a\u5de5\u4f5c\u8bb0\u5fc6\uff0c\u662f AI \u7cfb\u7edf\u6700\u57fa\u7840\u7684\u8bb0\u5fc6\u5f62\u5f0f\uff0c\u7528\u4e8e\u4fdd\u5b58\u5373\u65f6\u4e0a\u4e0b\u6587\uff0c\u5305\u62ec\uff1a<\/p>\n<ul>\n<li><strong>\u5bf9\u8bdd\u5386\u53f2<\/strong>\uff1a\u6700\u8fd1\u7684\u6d88\u606f\u53ca\u5176\u987a\u5e8f\u3002<\/li>\n<li><strong>\u4e34\u65f6\u72b6\u6001<\/strong>\uff1a\u6267\u884c\u4efb\u52a1\u8fc7\u7a0b\u4e2d\u7684\u4e34\u65f6\u53d8\u91cf\u3002<\/li>\n<li><strong>\u6ce8\u610f\u529b\u7126\u70b9<\/strong>\uff1a\u5bf9\u8bdd\u7684\u5f53\u524d\u6838\u5fc3\u3002<\/li>\n<\/ul>\n<h3>\u957f\u671f\u8bb0\u5fc6<\/h3>\n<p>\u66f4\u590d\u6742\u7684 AI \u5e94\u7528\u5219\u9700\u8981\u5b9e\u73b0\u957f\u671f\u8bb0\u5fc6\uff0c\u4ee5\u5b9e\u73b0\u8de8\u4f1a\u8bdd\u7684\u77e5\u8bc6\u4fdd\u7559\u548c\u4e2a\u6027\u5316\u3002\u957f\u671f\u8bb0\u5fc6\u4e3b\u8981\u5305\u542b\u4ee5\u4e0b\u4e09\u79cd\u7c7b\u578b\uff1a<\/p>\n<h4>1. \u7a0b\u5e8f\u6027\u8bb0\u5fc6 (Procedural Memory)<\/h4>\n<p>\u8fd9\u662f\u667a\u80fd\u4f53\u7684\u201c\u808c\u8089\u8bb0\u5fc6\u201d\uff0c\u5373\u667a\u80fd\u4f53\u77e5\u9053<strong>\u5982\u4f55\u505a<\/strong>\u67d0\u4ef6\u4e8b\u3002\u5b83\u5b9a\u4e49\u4e86\u667a\u80fd\u4f53\u53ef\u4ee5\u6267\u884c\u7684\u64cd\u4f5c\uff0c\u76f4\u63a5\u7f16\u7801\u5728\u903b\u8f91\u4e2d\u3002\u4f8b\u5982\uff0c\u201c\u5728\u56de\u590d\u6280\u672f\u95ee\u9898\u65f6\uff0c\u5e94\u91c7\u7528\u66f4\u8be6\u5c3d\u7684\u89e3\u91ca\u98ce\u683c\u201d\u6216\u201c\u603b\u662f\u4f18\u5148\u5904\u7406\u6765\u81ea\u7279\u5b9a\u4e3b\u7ba1\u7684\u90ae\u4ef6\u201d\u3002<\/p>\n<h4>2. \u60c5\u666f\u8bb0\u5fc6 (Episodic Memory)<\/h4>\n<p>\u8fd9\u662f\u667a\u80fd\u4f53\u7684\u201c\u4ea4\u4e92\u76f8\u518c\u201d\uff0c\u8bb0\u5f55\u4e86\u4e0e\u7279\u5b9a\u7528\u6237\u76f8\u5173\u7684\u5386\u53f2\u4ea4\u4e92\u548c\u5177\u4f53\u4e8b\u4ef6\u3002\u5b83\u662f\u5b9e\u73b0\u4e2a\u6027\u5316\u3001\u8fde\u7eed\u6027\u548c\u957f\u671f\u5b66\u4e60\u7684\u5173\u952e\u3002\u4f8b\u5982\uff0c\u667a\u80fd\u4f53\u53ef\u4ee5\u8bb0\u4f4f\u201c\u4e0a\u6b21\u5ba2\u6237\u54a8\u8be2\u8d26\u5355\u95ee\u9898\u65f6\uff0c\u7531\u4e8e\u56de\u590d\u8fc7\u6162\u5bfc\u81f4\u4e86\u4e0d\u6ee1\u201d\uff0c\u4ece\u800c\u5728\u672a\u6765\u4f18\u5316\u54cd\u5e94\u7b56\u7565\u3002<\/p>\n<h4>3. \u8bed\u4e49\u8bb0\u5fc6 (Semantic Memory)<\/h4>\n<p>\u8fd9\u662f\u667a\u80fd\u4f53\u7684\u201c\u4e8b\u5b9e\u767e\u79d1\u201d\uff0c\u5b58\u50a8\u4e86\u5173\u4e8e\u4e16\u754c\u548c\u7528\u6237\u7684\u5ba2\u89c2\u4e8b\u5b9e\u3002\u8fd9\u4e9b\u77e5\u8bc6\u901a\u5e38\u901a\u8fc7\u5411\u91cf\u641c\u7d22\u6216\u00a0<code>RAG<\/code>\u00a0\u8fdb\u884c\u68c0\u7d22\uff0c\u72ec\u7acb\u4e8e\u5177\u4f53\u4ea4\u4e92\u800c\u5b58\u5728\u3002\u4f8b\u5982\uff0c\u201cAlice \u662f\u9879\u76eeA\u7684\u6280\u672f\u8d1f\u8d23\u4eba\u201d\u6216\u201cJohn \u7684\u5de5\u4f5c\u65f6\u533a\u662f PST\u201d\u3002<\/p>\n<p>&nbsp;<\/p>\n<h2>AI \u8bb0\u5fc6\u7684\u5b9e\u73b0\u673a\u5236<\/h2>\n<h3>\u8bb0\u5fc6\u7684\u5199\u5165\u65f6\u673a<\/h3>\n<p>\u667a\u80fd\u4f53\u4f55\u65f6\u4ee5\u53ca\u5982\u4f55\u521b\u5efa\u65b0\u8bb0\u5fc6\uff1f\u4e3b\u6d41\u65b9\u6cd5\u6709\u4e24\u79cd\uff1a<\/p>\n<ol>\n<li><strong>\u5b9e\u65f6\u540c\u6b65\u5199\u5165<\/strong>\uff1a\u5728\u4e0e\u7528\u6237\u4ea4\u4e92\u7684\u8fc7\u7a0b\u4e2d\u5b9e\u65f6\u521b\u5efa\u548c\u66f4\u65b0\u8bb0\u5fc6\u3002\u8fd9\u79cd\u65b9\u5f0f\u80fd\u8ba9\u65b0\u8bb0\u5fc6\u7acb\u523b\u751f\u6548\uff0c\u4f46\u4e5f\u53ef\u80fd\u589e\u52a0\u4e3b\u5e94\u7528\u903b\u8f91\u7684\u5ef6\u8fdf\u548c\u590d\u6742\u6027\u3002\u4f8b\u5982\uff0c<code>ChatGPT<\/code>\u00a0\u7684\u8bb0\u5fc6\u529f\u80fd\u5c31\u91c7\u7528\u4e86\u7c7b\u4f3c\u673a\u5236\uff0c\u5b83\u901a\u8fc7\u4e00\u4e2a\u00a0<code>save_memories<\/code>\u00a0\u5de5\u5177\u5728\u5bf9\u8bdd\u4e2d\u5b9e\u65f6\u51b3\u7b56\u662f\u5426\u4fdd\u5b58\u4fe1\u606f\u3002<\/li>\n<li><strong>\u5f02\u6b65\u540e\u53f0\u5904\u7406<\/strong>\uff1a\u5c06\u8bb0\u5fc6\u7684\u521b\u5efa\u548c\u6574\u7406\u4f5c\u4e3a\u72ec\u7acb\u7684\u540e\u53f0\u4efb\u52a1\u5904\u7406\u3002\u8fd9\u79cd\u65b9\u5f0f\u5c06\u8bb0\u5fc6\u7ba1\u7406\u4e0e\u4e3b\u5e94\u7528\u903b\u8f91\u89e3\u8026\uff0c\u964d\u4f4e\u4e86\u5ef6\u8fdf\uff0c\u5e76\u5141\u8bb8\u66f4\u590d\u6742\u7684\u79bb\u7ebf\u5904\u7406\uff0c\u5982\u4fe1\u606f\u6458\u8981\u548c\u53bb\u91cd\u3002<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h3>\u8bb0\u5fc6\u7684\u7ba1\u7406\u7b56\u7565<\/h3>\n<p>\u5f53\u5bf9\u8bdd\u8fc7\u957f\uff0c\u77ed\u671f\u8bb0\u5fc6\u53ef\u80fd\u4f1a\u8d85\u51fa LLM \u7684\u4e0a\u4e0b\u6587\u7a97\u53e3\u9650\u5236\u3002\u5e38\u89c1\u7684\u7ba1\u7406\u7b56\u7565\u5305\u62ec\uff1a<\/p>\n<ul>\n<li><strong>\u4fee\u526a<\/strong>\uff1a\u79fb\u9664\u5bf9\u8bdd\u5386\u53f2\u4e2d\u6700\u5f00\u59cb\u6216\u6700\u540e N \u6761\u6d88\u606f\u3002<\/li>\n<li><strong>\u603b\u7ed3<\/strong>\uff1a\u5c06\u65e9\u671f\u7684\u5bf9\u8bdd\u5185\u5bb9\u8fdb\u884c\u603b\u7ed3\uff0c\u7528\u6458\u8981\u66ff\u6362\u539f\u6587\u3002<\/li>\n<li><strong>\u9009\u62e9\u6027\u9057\u5fd8<\/strong>\uff1a\u6839\u636e\u9884\u8bbe\u89c4\u5219\uff08\u5982\u8fc7\u6ee4\u6389\u4e0d\u91cd\u8981\u7684\u7cfb\u7edf\u6d88\u606f\uff09\u6216\u6a21\u578b\u7684\u5224\u65ad\uff0c\u6c38\u4e45\u5220\u9664\u90e8\u5206\u8bb0\u5fc6\u3002<\/li>\n<\/ul>\n<h2>\u5b9e\u73b0 AI \u8bb0\u5fc6\u7684\u6311\u6218<\/h2>\n<p>\u5c3d\u7ba1\u8bb0\u5fc6\u5bf9\u667a\u80fd\u4f53\u81f3\u5173\u91cd\u8981\uff0c\u4f46\u5728\u5b9e\u8df5\u4e2d\u4e5f\u9762\u4e34\u8bf8\u591a\u6311\u6218\uff1a<\/p>\n<ul>\n<li><strong>\u8bb0\u5fc6\u8fc7\u8f7d\u4e0e\u9057\u5fd8\u673a\u5236<\/strong>\uff1a\u65e0\u9650\u79ef\u7d2f\u7684\u8bb0\u5fc6\u4f1a\u4f7f\u7cfb\u7edf\u53d8\u5f97\u81c3\u80bf\u4e14\u4f4e\u6548\u3002\u5982\u4f55\u8bbe\u8ba1\u6709\u6548\u7684\u9057\u5fd8\u673a\u5236\uff0c\u8ba9\u667a\u80fd\u4f53\u5fd8\u8bb0\u8fc7\u65f6\u6216\u65e0\u5173\u7d27\u8981\u7684\u4fe1\u606f\uff0c\u662f\u4e00\u4e2a\u96be\u9898\u3002<\/li>\n<li><strong>\u4fe1\u606f\u6c61\u67d3<\/strong>\uff1a\u9519\u8bef\u7684\u6216\u6076\u610f\u7684\u8f93\u5165\u53ef\u80fd\u88ab\u5b58\u5165\u957f\u671f\u8bb0\u5fc6\uff0c\u4ece\u800c\u201c\u6c61\u67d3\u201d\u667a\u80fd\u4f53\u7684\u51b3\u7b56\u7cfb\u7edf\uff0c\u5bfc\u81f4\u672a\u6765\u884c\u4e3a\u51fa\u73b0\u504f\u5dee\u3002<\/li>\n<li><strong>\u9690\u79c1\u4e0e\u5b89\u5168<\/strong>\uff1a\u957f\u671f\u8bb0\u5fc6\u4e2d\u5b58\u50a8\u4e86\u5927\u91cf\u7528\u6237\u654f\u611f\u4fe1\u606f\uff0c\u5982\u504f\u597d\u3001\u5386\u53f2\u548c\u4e2a\u4eba\u6570\u636e\u3002\u5982\u4f55\u786e\u4fdd\u8fd9\u4e9b\u6570\u636e\u7684\u52a0\u5bc6\u3001\u8bbf\u95ee\u63a7\u5236\u548c\u5408\u89c4\u6027\uff0c\u662f\u5e94\u7528\u843d\u5730\u7684\u5173\u952e\u3002<\/li>\n<li><strong>\u6210\u672c\u4e0e\u6027\u80fd<\/strong>\uff1a\u7ef4\u62a4\u5927\u89c4\u6a21\u7684\u8bb0\u5fc6\u5b58\u50a8\u3001\u6267\u884c\u590d\u6742\u7684\u68c0\u7d22\u548c\u5199\u5165\u64cd\u4f5c\uff0c\u90fd\u4f1a\u5e26\u6765\u989d\u5916\u7684\u8ba1\u7b97\u548c\u5b58\u50a8\u6210\u672c\uff0c\u5e76\u53ef\u80fd\u5f71\u54cd\u7cfb\u7edf\u7684\u54cd\u5e94\u901f\u5ea6\u3002<\/li>\n<\/ul>\n<h2>\u4f7f\u7528 LangGraph \u6784\u5efa\u5177\u5907\u8bb0\u5fc6\u7684\u667a\u80fd\u4f53<\/h2>\n<p><code><a href=\"https:\/\/www.kdjingpai.com\/langgraph\/\">LangGraph<\/a><\/code>\u00a0\u662f\u4e00\u4e2a\u7528\u4e8e\u6784\u5efa\u6709\u72b6\u6001\u3001\u591a\u667a\u80fd\u4f53\u5e94\u7528\u7684\u5f3a\u5927\u6846\u67b6\uff0c\u5b83\u5929\u7136\u652f\u6301\u5faa\u73af\u548c\u72b6\u6001\u7ba1\u7406\uff0c\u975e\u5e38\u9002\u5408\u5b9e\u73b0\u590d\u6742\u7684\u8bb0\u5fc6\u7cfb\u7edf\u3002\u4e0b\u9762\u5c06\u901a\u8fc7\u4ee3\u7801\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528\u00a0<code>LangGraph<\/code>\u00a0\u4e3a\u667a\u80fd\u4f53\u6dfb\u52a0\u77ed\u671f\u548c\u957f\u671f\u8bb0\u5fc6\u3002<\/p>\n<h3>\u6dfb\u52a0\u77ed\u671f\u8bb0\u5fc6<\/h3>\n<p>\u77ed\u671f\u8bb0\u5fc6\uff08\u7ebf\u7a0b\u7ea7\u6301\u4e45\u6027\uff09\u4f7f\u667a\u80fd\u4f53\u80fd\u591f\u8ddf\u8e2a\u5355\u6b21\u591a\u8f6e\u5bf9\u8bdd\u3002\u901a\u8fc7\u4e3a\u00a0<code>LangGraph<\/code>\u00a0\u56fe\u914d\u7f6e\u4e00\u4e2a\u68c0\u67e5\u70b9\uff08Checkpointer\uff09\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u72b6\u6001\u7684\u81ea\u52a8\u4fdd\u5b58\u548c\u52a0\u8f7d\u3002<\/p>\n<p>\u4ee5\u4e0b\u793a\u4f8b\u4f7f\u7528\u5185\u5b58\u68c0\u67e5\u70b9\u00a0<code>InMemorySaver<\/code>\u00a0\u6765\u4fdd\u5b58\u4f1a\u8bdd\u72b6\u6001\u3002<\/p>\n<pre><code>from langchain_community.chat_models import ChatAnthropic # \u5047\u8bbe\u4f7f\u7528Anthropic\u6a21\u578b\r\nfrom langgraph.graph import StateGraph, MessagesState, START\r\nfrom langgraph.checkpoint.memory import InMemorySaver\r\n# \u4e3a\u7b80\u5316\uff0c\u5047\u8bbe\u6a21\u578b\u5df2\u521d\u59cb\u5316\r\n# model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\r\n# \u5b9e\u9645\u4f7f\u7528\u65f6\u8bf7\u66ff\u6362\u4e3a\u771f\u5b9e\u6a21\u578b\r\nclass FakeModel:\r\ndef invoke(self, messages):\r\nfrom langchain_core.messages import AIMessage\r\nlast_message = messages[-1].content\r\nif \"bob\" in last_message:\r\nreturn AIMessage(content=\"Hi Bob! How can I help you today?\")\r\nelif \"my name\" in last_message:\r\nreturn AIMessage(content=\"Your name is Bob.\")\r\nelse:\r\nreturn AIMessage(content=\"Hello there!\")\r\nmodel = FakeModel()\r\ndef call_model(state: MessagesState):\r\nresponse = model.invoke(state[\"messages\"])\r\nreturn {\"messages\": [response]}\r\nbuilder = StateGraph(MessagesState)\r\nbuilder.add_node(\"call_model\", call_model)\r\nbuilder.add_edge(START, \"call_model\")\r\ncheckpointer = InMemorySaver()\r\ngraph = builder.compile(checkpointer=checkpointer)\r\n# \u4f7f\u7528\u552f\u4e00\u7684 thread_id \u6765\u6807\u8bc6\u4e00\u4e2a\u72ec\u7acb\u7684\u5bf9\u8bdd\u7ebf\u7a0b\r\nconfig = {\r\n\"configurable\": {\r\n\"thread_id\": \"user_1_thread_1\"\r\n}\r\n}\r\n# \u7b2c\u4e00\u6b21\u4ea4\u4e92\r\nfor chunk in graph.stream(\r\n{\"messages\": [{\"role\": \"user\", \"content\": \"Hi! I'm Bob.\"}]},\r\nconfig,\r\nstream_mode=\"values\",\r\n):\r\nchunk[\"messages\"][-1].pretty_print()\r\n# \u7b2c\u4e8c\u6b21\u4ea4\u4e92\uff0c\u5728\u540c\u4e00\u7ebf\u7a0b\u4e2d\r\nfor chunk in graph.stream(\r\n{\"messages\": [{\"role\": \"user\", \"content\": \"What's my name?\"}]},\r\nconfig,\r\nstream_mode=\"values\",\r\n):\r\nchunk[\"messages\"][-1].pretty_print()\r\n<\/code><\/pre>\n<pre><code>================================== Ai Message ==================================\r\nHi Bob! How can I help you today?\r\n================================== Ai Message ==================================\r\nYour name is Bob.\r\n<\/code><\/pre>\n<p>\u5728\u751f\u4ea7\u73af\u5883\u4e2d\uff0c\u5e94\u5c06\u00a0<code>InMemorySaver<\/code>\u00a0\u66ff\u6362\u4e3a\u6301\u4e45\u5316\u7684\u6570\u636e\u5e93\u540e\u7aef\uff0c\u5982\u00a0<code>PostgresSaver<\/code>\u00a0\u6216\u00a0<code>MongoDBSaver<\/code>\uff0c\u4ee5\u786e\u4fdd\u670d\u52a1\u91cd\u542f\u540e\u5bf9\u8bdd\u72b6\u6001\u4e0d\u4e22\u5931\u3002<\/p>\n<h3>\u7ba1\u7406\u68c0\u67e5\u70b9<\/h3>\n<p><code>LangGraph<\/code>\u00a0\u63d0\u4f9b\u4e86\u7ba1\u7406\u68c0\u67e5\u70b9\uff08\u5373\u4f1a\u8bdd\u72b6\u6001\uff09\u7684\u63a5\u53e3\u3002<\/p>\n<p><strong>\u67e5\u770b\u7ebf\u7a0b\u7684\u5f53\u524d\u72b6\u6001\uff1a<\/strong><\/p>\n<pre><code># graph.get_state(config)\r\n<\/code><\/pre>\n<p><strong>\u67e5\u770b\u7ebf\u7a0b\u7684\u6240\u6709\u5386\u53f2\u72b6\u6001\uff1a<\/strong><\/p>\n<pre><code># list(graph.get_state_history(config))\r\n<\/code><\/pre>\n<h3>\u6dfb\u52a0\u957f\u671f\u8bb0\u5fc6<\/h3>\n<p>\u957f\u671f\u8bb0\u5fc6\uff08\u8de8\u7ebf\u7a0b\u6301\u4e45\u6027\uff09\u7528\u4e8e\u5b58\u50a8\u7528\u6237\u504f\u597d\u7b49\u9700\u8981\u957f\u671f\u4fdd\u7559\u7684\u6570\u636e\u3002<code>LangGraph<\/code>\u00a0\u5141\u8bb8\u5728\u7f16\u8bd1\u56fe\u65f6\u4f20\u5165\u4e00\u4e2a\u00a0<code>store<\/code>\u00a0\u5bf9\u8c61\uff0c\u7528\u4e8e\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u3002<\/p>\n<p>\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u00a0<code>InMemoryStore<\/code>\u00a0\u5b58\u50a8\u7528\u6237\u4fe1\u606f\uff0c\u5e76\u5728\u4e0d\u540c\u5bf9\u8bdd\u7ebf\u7a0b\u4e2d\u5171\u4eab\u3002<\/p>\n<pre><code>import uuid\r\nfrom typing_extensions import TypedDict\r\nfrom langchain_core.runnables import RunnableConfig\r\nfrom langgraph.store.base import BaseStore\r\nfrom langgraph.store.memory import InMemoryStore\r\n# ... (\u590d\u7528\u4e4b\u524d\u7684\u6a21\u578b\u548c State \u5b9a\u4e49) ...\r\ndef call_model_with_long_term_memory(\r\nstate: MessagesState,\r\nconfig: RunnableConfig,\r\n*,\r\nstore: BaseStore,\r\n):\r\nfrom langchain_core.messages import AIMessage, SystemMessage\r\nuser_id = config[\"configurable\"][\"user_id\"]\r\nnamespace = (\"memories\", user_id)\r\n# \u68c0\u7d22\u957f\u671f\u8bb0\u5fc6\r\nmemories = store.search(query=\"\", namespace=namespace)\r\ninfo = \"\\n\".join([d.value[\"data\"] for d in memories])\r\nsystem_msg_content = f\"You are a helpful assistant. User info: {info}\"\r\n# \u5199\u5165\u957f\u671f\u8bb0\u5fc6\r\nlast_message = state[\"messages\"][-1]\r\nif \"remember\" in last_message.content.lower():\r\nmemory_to_save = \"User's name is Bob\"\r\n# \u4f7f\u7528\u552f\u4e00ID\u5b58\u50a8\u8bb0\u5fc6\r\nstore.put([(str(uuid.uuid4()), {\"data\": memory_to_save})], namespace=namespace)\r\n# \u8c03\u7528\u6a21\u578b\r\nresponse = model.invoke([SystemMessage(content=system_msg_content)] + state[\"messages\"])\r\nreturn {\"messages\": [response]}\r\nbuilder_ltm = StateGraph(MessagesState)\r\nbuilder_ltm.add_node(\"call_model\", call_model_with_long_term_memory)\r\nbuilder_ltm.add_edge(START, \"call_model\")\r\ncheckpointer_ltm = InMemorySaver()\r\nstore = InMemoryStore()\r\ngraph_ltm = builder_ltm.compile(\r\ncheckpointer=checkpointer_ltm,\r\nstore=store,\r\n)\r\n# \u5728\u7ebf\u7a0b1\u4e2d\uff0c\u7528\u6237\u8981\u6c42\u8bb0\u4f4f\u540d\u5b57\r\nconfig1 = {\r\n\"configurable\": {\r\n\"thread_id\": \"thread_1\",\r\n\"user_id\": \"user_123\",\r\n}\r\n}\r\nfor chunk in graph_ltm.stream(\r\n{\"messages\": [{\"role\": \"user\", \"content\": \"Hi! Please remember my name is Bob\"}]},\r\nconfig1,\r\nstream_mode=\"values\",\r\n):\r\nchunk[\"messages\"][-1].pretty_print()\r\n# \u5728\u65b0\u7684\u7ebf\u7a0b2\u4e2d\uff0c\u67e5\u8be2\u540d\u5b57\r\nconfig2 = {\r\n\"configurable\": {\r\n\"thread_id\": \"thread_2\",\r\n\"user_id\": \"user_123\", # \u76f8\u540c\u7684 user_id\r\n}\r\n}\r\nfor chunk in graph_ltm.stream(\r\n{\"messages\": [{\"role\": \"user\", \"content\": \"What is my name?\"}]},\r\nconfig2,\r\nstream_mode=\"values\",\r\n):\r\nchunk[\"messages\"][-1].pretty_print()\r\n<\/code><\/pre>\n<pre><code>================================== Ai Message ==================================\r\nHi Bob! How can I help you today?\r\n================================== Ai Message ==================================\r\nYour name is Bob.\r\n<\/code><\/pre>\n<h3>\u542f\u7528\u8bed\u4e49\u641c\u7d22<\/h3>\n<p>\u901a\u8fc7\u4e3a\u00a0<code>store<\/code>\u00a0\u914d\u7f6e\u5411\u91cf\u5d4c\u5165\u6a21\u578b\uff0c\u53ef\u4ee5\u5b9e\u73b0\u57fa\u4e8e\u8bed\u4e49\u76f8\u4f3c\u5ea6\u7684\u8bb0\u5fc6\u68c0\u7d22\uff0c\u8ba9\u667a\u80fd\u4f53\u66f4\u667a\u80fd\u5730\u8c03\u7528\u76f8\u5173\u4fe1\u606f\u3002<\/p>\n<pre><code>from langchain_openai import OpenAIEmbeddings\r\n# \u5047\u8bbe\u5df2\u521d\u59cb\u5316 OpenAIEmbeddings\r\n# embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\r\n# store = InMemoryStore(\r\n#     index={\r\n#         \"embed\": embeddings,\r\n#         \"dims\": 1536, # \u5d4c\u5165\u7ef4\u5ea6\r\n#     }\r\n# )\r\n# \r\n# store.put([\r\n#     (\"1\", {\"text\": \"I love pizza\"}),\r\n#     (\"2\", {\"text\": \"I am a plumber\"})\r\n# ], namespace=(\"user_123\", \"memories\"))\r\n# \u5728\u8c03\u7528\u65f6\uff0c\u901a\u8fc7 query \u53c2\u6570\u8fdb\u884c\u8bed\u4e49\u641c\u7d22\r\n# items = store.search(\r\n#     query=\"I'm hungry\",\r\n#     namespace=(\"user_123\", \"memories\"),\r\n#     limit=1\r\n# )\r\n# \u641c\u7d22\u7ed3\u679c\u5c06\u662f\u4e0e \"I'm hungry\" \u6700\u76f8\u5173\u7684 \"I love pizza\"\u3002\r\n<\/code><\/pre>\n<p>\u8fd9\u4e2a\u529f\u80fd\u4f7f\u5f97\u667a\u80fd\u4f53\u80fd\u591f\u6839\u636e\u5bf9\u8bdd\u7684\u4e0a\u4e0b\u6587\uff0c\u52a8\u6001\u5730\u4ece\u8bb0\u5fc6\u5e93\u4e2d\u68c0\u7d22\u6700\u76f8\u5173\u7684\u4fe1\u606f\uff0c\u800c\u4e0d\u662f\u7b80\u5355\u5730\u8fdb\u884c\u5173\u952e\u8bcd\u5339\u914d\u3002<\/p>\n<h2>\u6848\u4f8b\u7814\u7a76\uff1a\u6784\u5efa\u4e00\u4e2a\u8bb0\u5fc6\u589e\u5f3a\u7684\u90ae\u4ef6\u667a\u80fd\u4f53<\/h2>\n<p>\u4e0b\u9762\u5c06\u7406\u8bba\u4ed8\u8bf8\u5b9e\u8df5\uff0c\u6784\u5efa\u4e00\u4e2a\u5177\u5907\u591a\u7c7b\u578b\u8bb0\u5fc6\u7684\u90ae\u4ef6\u5904\u7406\u667a\u80fd\u4f53\u3002<\/p>\n<p>&nbsp;<\/p>\n<h3>7.1 \u5b9a\u4e49\u667a\u80fd\u4f53\u72b6\u6001<\/h3>\n<p>\u9996\u5148\uff0c\u5b9a\u4e49\u667a\u80fd\u4f53\u9700\u8981\u8ddf\u8e2a\u7684\u72b6\u6001\uff0c\u5305\u542b\u77ed\u671f\u548c\u957f\u671f\u8bb0\u5fc6\u3002<\/p>\n<pre><code>from typing import TypedDict, Dict, Any\r\nfrom langgraph.graph import MessagesState\r\nclass EmailAgentState(TypedDict, total=False):\r\n\"\"\"\u90ae\u4ef6\u667a\u80fd\u4f53\u7684\u72b6\u6001\u5b9a\u4e49\"\"\"\r\nmessages: MessagesState  # \u77ed\u671f\u8bb0\u5fc6\uff1a\u5bf9\u8bdd\u5386\u53f2\r\nuser_preferences: Dict[str, Any]  # \u957f\u671f\u8bed\u4e49\u8bb0\u5fc6\uff1a\u7528\u6237\u504f\u597d\r\nemail_threads: Dict[str, Any]  # \u957f\u671f\u60c5\u666f\u8bb0\u5fc6\uff1a\u5386\u53f2\u90ae\u4ef6\u7ebf\u7a0b\r\naction: str # \u51b3\u7b56\u52a8\u4f5c\r\nreply: str # \u751f\u6210\u7684\u56de\u590d\r\n<\/code><\/pre>\n<h3>7.2 \u5b9e\u73b0\u51b3\u7b56\u5206\u7c7b\u5668\uff08\u4f7f\u7528\u60c5\u666f\u8bb0\u5fc6\uff09<\/h3>\n<p>\u521b\u5efa\u4e00\u4e2a\u5206\u7c7b\u5668\u8282\u70b9\uff0c\u6839\u636e\u90ae\u4ef6\u5185\u5bb9\u548c\u5386\u53f2\u4ea4\u4e92\uff08\u60c5\u666f\u8bb0\u5fc6\uff09\u6765\u51b3\u5b9a\u4e0b\u4e00\u6b65\u64cd\u4f5c\u3002<\/p>\n<pre><code>def classify_email(state: EmailAgentState):\r\n# \u6b64\u5904\u5e94\u8c03\u7528 LLM \u8fdb\u884c\u5206\u7c7b\r\n# \u4e3a\u7b80\u5316\uff0c\u76f4\u63a5\u8fd4\u56de\u4e00\u4e2a\u786c\u7f16\u7801\u7684\u51b3\u7b56\r\nemail_content = state[\"messages\"][-1].content\r\nif \"urgent\" in email_content.lower():\r\nreturn {\"action\": \"escalate\"}\r\nreturn {\"action\": \"reply\"}\r\n<\/code><\/pre>\n<h3>7.3 \u5b9a\u4e49\u5de5\u5177\uff08\u4f7f\u7528\u8bed\u4e49\u8bb0\u5fc6\uff09<\/h3>\n<p>\u5b9a\u4e49\u667a\u80fd\u4f53\u53ef\u7528\u7684\u5de5\u5177\uff0c\u4f8b\u5982\u4ece\u8bed\u4e49\u8bb0\u5fc6\u4e2d\u83b7\u53d6\u8054\u7cfb\u4eba\u4fe1\u606f\u3002<\/p>\n<pre><code>from langgraph.prebuilt import ToolNode\r\ndef get_contacts_from_memory(user_id: str):\r\n# \u6a21\u62df\u4ece\u7528\u6237\u6570\u636e\u5e93\u6216\u8bed\u4e49\u8bb0\u5fc6\u4e2d\u83b7\u53d6\u8054\u7cfb\u4eba\r\ncontacts_db = {\r\n\"user_123\": {\r\n\"developers\": [\"alice@company.com\"],\r\n\"managers\": [\"charlie@company.com\"]\r\n}\r\n}\r\nreturn contacts_db.get(user_id, {})\r\ndef send_email(to: str, subject: str, body: str):\r\n# \u6a21\u62df\u53d1\u9001\u90ae\u4ef6API\r\nprint(f\"Email sent to {to} with subject '{subject}'\")\r\nreturn \"Email sent successfully.\"\r\ntools = [get_contacts_from_memory, send_email]\r\ntool_node = ToolNode(tools)\r\n<\/code><\/pre>\n<h3>7.4 \u6784\u5efa\u56fe\u5e76\u8fde\u63a5\u8282\u70b9<\/h3>\n<p>\u4f7f\u7528\u00a0<code>LangGraph<\/code>\u00a0\u5c06\u6240\u6709\u7ec4\u4ef6\u8fde\u63a5\u6210\u4e00\u4e2a\u53ef\u6267\u884c\u7684\u5de5\u4f5c\u6d41\u3002<\/p>\n<pre><code>from langgraph.graph import StateGraph, START, END\r\nbuilder = StateGraph(EmailAgentState)\r\n# 1. \u5b9a\u4e49\u8282\u70b9\r\nbuilder.add_node(\"classify\", classify_email)\r\nbuilder.add_node(\"generate_reply\", lambda state: {\"reply\": \"Generated reply based on user tone.\"}) # \u7b80\u5316\u7248\r\nbuilder.add_node(\"escalate_task\", lambda state: print(\"Task Escalated!\"))\r\n# 2. \u5b9a\u4e49\u8fb9\r\nbuilder.add_edge(START, \"classify\")\r\nbuilder.add_conditional_edges(\r\n\"classify\",\r\nlambda state: state[\"action\"],\r\n{\r\n\"reply\": \"generate_reply\",\r\n\"escalate\": \"escalate_task\",\r\n}\r\n)\r\nbuilder.add_edge(\"generate_reply\", END)\r\nbuilder.add_edge(\"escalate_task\", END)\r\n# 3. \u7f16\u8bd1\u56fe\r\nemail_graph = builder.compile()\r\n<\/code><\/pre>\n<p>&nbsp;<\/p>\n<p>\u8fd9\u4e2a\u7b80\u5316\u7684\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u7ed3\u5408\u4e0d\u540c\u7c7b\u578b\u7684\u8bb0\u5fc6\uff08\u901a\u8fc7\u72b6\u6001\u548c\u5de5\u5177\uff09\u6765\u9a71\u52a8\u4e00\u4e2a\u667a\u80fd\u4f53\u7684\u5de5\u4f5c\u6d41\u7a0b\u3002\u901a\u8fc7\u6269\u5c55\u8fd9\u4e2a\u6846\u67b6\uff0c\u53ef\u4ee5\u6784\u5efa\u51fa\u80fd\u591f\u5904\u7406\u590d\u6742\u4efb\u52a1\u3001\u9ad8\u5ea6\u4e2a\u6027\u5316\u4e14\u80fd\u6301\u7eed\u5b66\u4e60\u7684\u5f3a\u5927 AI \u52a9\u624b\u3002<\/p>\n<p>\u5728\u8bbe\u8ba1\u81ea\u5df1\u7684\u8bb0\u5fc6\u589e\u5f3a\u578b\u667a\u80fd\u4f53\u65f6\uff0c\u4ee5\u4e0b\u95ee\u9898\u53ef\u4f5c\u4e3a\u6709\u6548\u7684\u601d\u8003\u6307\u5357\uff1a<\/p>\n<ol>\n<li><strong>\u5b66\u4e60\u5185\u5bb9<\/strong>\uff1a\u667a\u80fd\u4f53\u5e94\u8be5\u5b66\u4e60\u4ec0\u4e48\u7c7b\u578b\u7684\u4fe1\u606f\uff1f\u662f\u5ba2\u89c2\u4e8b\u5b9e\u3001\u5386\u53f2\u4e8b\u4ef6\u6458\u8981\uff0c\u8fd8\u662f\u884c\u4e3a\u89c4\u5219\uff1f<\/li>\n<li><strong>\u8bb0\u5fc6\u65f6\u673a<\/strong>\uff1a\u5e94\u8be5\u5728\u4f55\u65f6\u3001\u7531\u8c01\u6765\u89e6\u53d1\u8bb0\u5fc6\u7684\u5f62\u6210\uff1f\u662f\u5b9e\u65f6\u4ea4\u4e92\u4e2d\uff0c\u8fd8\u662f\u540e\u53f0\u6279\u5904\u7406\uff1f<\/li>\n<li><strong>\u5b58\u50a8\u65b9\u6848<\/strong>\uff1a\u8bb0\u5fc6\u5e94\u5b58\u50a8\u5728\u54ea\u91cc\uff1f\u662f\u672c\u5730\u6570\u636e\u5e93\u3001\u5411\u91cf\u5b58\u50a8\uff0c\u8fd8\u662f\u4e91\u670d\u52a1\uff1f<\/li>\n<li><strong>\u5b89\u5168\u4e0e\u9690\u79c1<\/strong>\uff1a\u5982\u4f55\u786e\u4fdd\u8bb0\u5fc6\u6570\u636e\u7684\u5b89\u5168\uff0c\u9632\u6b62\u6cc4\u9732\u548c\u6ee5\u7528\uff1f<\/li>\n<li><strong>\u66f4\u65b0\u4e0e\u7ef4\u62a4<\/strong>\uff1a\u5982\u4f55\u9632\u6b62\u8fc7\u65f6\u6216\u9519\u8bef\u7684\u8bb0\u5fc6\u6c61\u67d3\u667a\u80fd\u4f53\u7684\u51b3\u7b56\uff1f<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>\u4e0e\u4e00\u4e2a\u603b\u5fd8\u8bb0\u8c08\u8bdd\u5185\u5bb9\u7684\u670b\u53cb\u4ea4\u6d41\uff0c\u6bcf\u6b21\u90fd\u5f97\u4ece\u5934\u8bf4\u8d77\uff0c\u8fd9\u79cd\u4f53\u9a8c\u65e0\u7591\u662f\u4f4e\u6548\u4e14\u4ee4\u4eba\u75b2\u60eb\u7684\u3002\u7136\u800c\uff0c\u8fd9\u6070\u6070\u662f\u5f53\u524d\u591a\u6570\u4eba\u5de5\u667a\u80fd\u7cfb\u7edf\u7684\u5e38\u6001\u3002\u5b83\u4eec\u5f88\u5f3a\u5927\uff0c\u4f46\u666e\u904d\u7f3a\u5931\u4e00\u4e2a\u5173\u952e\u8981\u7d20\uff1a\u8bb0\u5fc6\u3002 \u8981\u6784\u5efa\u80fd\u591f\u771f\u6b63\u5b66\u4e60\u3001\u6f14\u5316\u548c\u534f\u4f5c\u7684 AI \u667a\u80fd\u4f53 (Agent)\uff0c\u8bb0\u5fc6\u5e76\u975e&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34],"tags":[],"class_list":["post-32170","post","type-post","status-publish","format-standard","hentry","category-knowledge"],"_links":{"self":[{"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/posts\/32170","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/comments?post=32170"}],"version-history":[{"count":0,"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/posts\/32170\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/media?parent=32170"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/categories?post=32170"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/de\/wp-json\/wp\/v2\/tags?post=32170"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}