{"id":23644,"date":"2024-12-23T10:44:19","date_gmt":"2024-12-23T02:44:19","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=23644"},"modified":"2024-12-23T10:44:19","modified_gmt":"2024-12-23T02:44:19","slug":"%e5%a6%82%e4%bd%95%e4%bc%98%e5%8c%96%e5%a4%a7%e5%9e%8b%e8%af%ad%e8%a8%80%e6%a8%a1%e5%9e%8b%ef%bc%88llm%ef%bc%89%e7%9a%84%e5%88%86%e5%9d%97%e7%ad%96%e7%95%a5","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2024\/12\/23\/23644\/","title":{"rendered":"\u5982\u4f55\u4f18\u5316\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u7684\u5206\u5757\u7b56\u7565"},"content":{"rendered":"<p><img data-dominant-color=\"0f4e7c\" data-has-transparency=\"false\" style=\"--dominant-color: #0f4e7c;\" loading=\"lazy\" decoding=\"async\" class=\"not-transparent alignnone size-full wp-image-23646\" src=\"https:\/\/aiforumimage.oss-cn-shanghai.aliyuncs.com\/wp-content\/uploads\/2024\/12\/1a7127e1-e036-4666-900e-2a22037f5f52-300x167-1.png\" width=\"300\" height=\"167\" alt=\"\" srcset=\"https:\/\/aiforumimage.oss-cn-shanghai.aliyuncs.com\/wp-content\/uploads\/2024\/12\/1a7127e1-e036-4666-900e-2a22037f5f52-300x167-1.png 300w, https:\/\/aiforumimage.oss-cn-shanghai.aliyuncs.com\/wp-content\/uploads\/2024\/12\/1a7127e1-e036-4666-900e-2a22037f5f52-300x167-1-150x84.png 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p style=\"font-weight: 400;\">\u672c\u6587\u5c06\u66f4\u6df1\u5165\u5730\u63a2\u8ba8LLM\u4e0d\u540c\u7684\u5206\u5757\u65b9\u6cd5\u53ca\u5176\u7b56\u7565\uff0c\u4ee5\u53ca\u5b83\u4eec\u5728\u4e3a\u73b0\u5b9e\u4e16\u754c\u7684\u5e94\u7528\u7a0b\u5e8f\u4f18\u5316LLM\u4e2d\u7684\u4f5c\u7528\u3002<\/p>\n<p>&nbsp;<\/p>\n<p>\u672c\u6587\u63a2\u8ba8\u4e86LLM\u5206\u5757\u7684\u4e0d\u540c\u65b9\u6cd5\uff0c\u5305\u62ec\u56fa\u5b9a\u5927\u5c0f\u5206\u5757\u3001\u9012\u5f52\u5206\u5757\u3001\u8bed\u4e49\u5206\u5757\u548c\u4ee3\u7406\u5206\u5757\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u5404\u6709\u72ec\u7279\u7684\u4f18\u52bf\u3002<\/p>\n<p>\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u901a\u8fc7\u5176\u751f\u6210\u7c7b\u4f3c\u4eba\u7c7b\u6c34\u5e73\u7684\u6587\u672c\u3001\u89e3\u7b54\u590d\u6742\u95ee\u9898\u7684\u80fd\u529b\u4ee5\u53ca\u5bf9\u5927\u91cf\u4fe1\u606f\u8fdb\u884c\u5206\u6790\u6240\u5c55\u73b0\u51fa\u7684\u60ca\u4eba\u51c6\u786e\u6027\uff0c\u5df2\u7ecf\u6539\u53d8\u4e86\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u9886\u57df\u3002\u4ece\u5ba2\u6237\u670d\u52a1\u5230\u533b\u5b66\u7814\u7a76\uff0cLLM\u5728\u5904\u7406\u5404\u79cd\u67e5\u8be2\u5e76\u751f\u6210\u8be6\u7ec6\u56de\u590d\u7684\u80fd\u529b\u4f7f\u5b83\u4eec\u5728\u8bb8\u591a\u9886\u57df\u90fd\u5177\u6709\u4e0d\u53ef\u4f30\u91cf\u7684\u4ef7\u503c\u3002\u7136\u800c\uff0c\u968f\u7740LLM\u7684\u89c4\u6a21\u6269\u5927\u4ee5\u5904\u7406\u4e0d\u65ad\u589e\u957f\u7684\u6570\u636e\uff0c\u5b83\u4eec\u5728\u7ba1\u7406\u957f\u6587\u6863\u548c\u9ad8\u6548\u68c0\u7d22\u6700\u76f8\u5173\u4fe1\u606f\u65b9\u9762\u9762\u4e34\u7740\u6311\u6218\u3002<\/p>\n<p>\u5c3d\u7ba1LLM\u64c5\u957f\u5904\u7406\u548c\u751f\u6210\u7c7b\u4f3c\u4eba\u7c7b\u7684\u6587\u672c\uff0c\u4f46\u5b83\u4eec\u7684\u201c\u573a\u666f\u7a97\u53e3\u201d\u76f8\u5bf9\u6709\u9650\u3002\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u4e00\u6b21\u53ea\u80fd\u5728\u5185\u5b58\u4e2d\u4fdd\u7559\u6709\u9650\u7684\u4fe1\u606f\uff0c\u8fd9\u4f7f\u5f97\u7ba1\u7406\u975e\u5e38\u957f\u7684\u6587\u6863\u9762\u4e34\u91cd\u91cd\u56f0\u96be\u3002\u6b64\u5916\uff0cLLM\u8fd8\u96be\u4ee5\u4ece\u5927\u578b\u6570\u636e\u96c6\u4e2d\u5feb\u901f\u627e\u5230\u6700\u76f8\u5173\u7684\u4fe1\u606f\u3002\u66f4\u91cd\u8981\u7684\u662f\uff0cLLM\u662f\u5728\u56fa\u5b9a\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u7684\uff0c\u56e0\u6b64\u968f\u7740\u65b0\u4fe1\u606f\u7684\u4e0d\u65ad\u6d8c\u73b0\uff0c\u5b83\u4eec\u53ef\u80fd\u4f1a\u9010\u6e10\u8fc7\u65f6\u3002\u4e3a\u4e86\u4fdd\u6301\u51c6\u786e\u6027\u548c\u5b9e\u7528\u6027\uff0c\u9700\u8981\u5b9a\u671f\u66f4\u65b0\u6570\u636e\u3002<\/p>\n<p>\u68c0\u7d22\u589e\u5f3a\u751f\u6210\uff08RAG\uff09\u89e3\u51b3\u4e86\u8fd9\u4e9b\u6311\u6218\u3002\u5728RAG\u5de5\u4f5c\u6d41\u4e2d\u6709\u8bb8\u591a\u7ec4\u4ef6\uff0c\u4f8b\u5982\u67e5\u8be2\u3001\u5d4c\u5165\u3001\u7d22\u5f15\u7b49\u7b49\u3002\u4ee5\u4e0b\u5bf9LLM\u5206\u5757\u7b56\u7565\u8fdb\u884c\u63a2\u8ba8\u3002<\/p>\n<p>\u901a\u8fc7\u5c06\u6587\u6863\u5206\u6210\u66f4\u5c0f\u7684\u3001\u6709\u610f\u4e49\u7684\u90e8\u5206\uff0c\u5e76\u5c06\u5b83\u4eec\u5d4c\u5165\u5230\u5411\u91cf\u6570\u636e\u5e93\u4e2d\uff0cRAG\u7cfb\u7edf\u53ef\u4ee5\u641c\u7d22\u548c\u68c0\u7d22\u6bcf\u4e2a\u67e5\u8be2\u6700\u76f8\u5173\u7684\u5757\u3002\u8fd9\u79cd\u65b9\u6cd5\u4f7fLLM\u80fd\u591f\u4e13\u6ce8\u4e8e\u7279\u5b9a\u7684\u4fe1\u606f\uff0c\u4ece\u800c\u63d0\u9ad8\u54cd\u5e94\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n<p>\u672c\u6587\u5c06\u66f4\u6df1\u5165\u5730\u63a2\u8ba8LLM\u4e0d\u540c\u7684\u5206\u5757\u65b9\u6cd5\u53ca\u5176\u7b56\u7565\uff0c\u4ee5\u53ca\u5b83\u4eec\u5728\u4e3a\u73b0\u5b9e\u4e16\u754c\u7684\u5e94\u7528\u7a0b\u5e8f\u4f18\u5316LLM\u4e2d\u7684\u4f5c\u7528\u3002<\/p>\n<h3>\u4ec0\u4e48\u662f\u5206\u5757\uff1f<\/h3>\n<p>\u5206\u5757\u662f\u5c06\u5927\u6570\u636e\u6e90\u62c6\u5206\u6210\u66f4\u5c0f\u7684\u3001\u53ef\u7ba1\u7406\u7684\u90e8\u5206\u6216\u201c\u5757\u201d\u3002\u8fd9\u4e9b\u5757\u5b58\u50a8\u5728\u5411\u91cf\u6570\u636e\u5e93\u4e2d\uff0c\u5141\u8bb8\u57fa\u4e8e\u76f8\u4f3c\u6027\u7684\u5feb\u901f\u6709\u6548\u641c\u7d22\u3002\u5f53\u7528\u6237\u63d0\u4ea4\u67e5\u8be2\u65f6\uff0c\u5411\u91cf\u6570\u636e\u5e93\u4f1a\u627e\u5230\u6700\u76f8\u5173\u7684\u5757\uff0c\u5e76\u5c06\u5b83\u4eec\u53d1\u9001\u7ed9LLM\u3002\u8fd9\u6837\uff0c\u8fd9\u4e9b\u6a21\u578b\u53ef\u4ee5\u53ea\u5173\u6ce8\u6700\u76f8\u5173\u7684\u4fe1\u606f\uff0c\u4f7f\u5176\u54cd\u5e94\u66f4\u5feb\u3001\u66f4\u51c6\u786e\u3002<\/p>\n<p>\u5206\u5757\u53ef\u4ee5\u5e2e\u52a9\u8bed\u8a00\u6a21\u578b\u66f4\u987a\u5229\u5730\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\uff0c\u5e76\u901a\u8fc7\u7f29\u5c0f\u9700\u8981\u67e5\u770b\u7684\u6570\u636e\u8303\u56f4\u6765\u63d0\u4f9b\u7cbe\u786e\u7684\u7b54\u6848\u3002<\/p>\n<p>\u5bf9\u4e8e\u9700\u8981\u5feb\u901f\u3001\u7cbe\u786e\u7b54\u6848\u7684\u5e94\u7528\u7a0b\u5e8f\uff08\u4f8b\u5982\u5ba2\u6237\u652f\u6301\u6216\u6cd5\u5f8b\u6587\u6863\u641c\u7d22\uff09\uff0c\u5206\u5757\u662f\u63d0\u9ad8\u6027\u80fd\u548c\u53ef\u9760\u6027\u7684\u57fa\u672c\u7b56\u7565\u3002<\/p>\n<p>\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5728RAG\u4e2d\u4f7f\u7528\u7684\u4e3b\u8981\u5206\u5757\u7b56\u7565\uff1a<\/p>\n<ul data-id=\"u738a58b-2jXA96MV\">\n<li data-id=\"ld70c578-BYVk9IeD\">\u56fa\u5b9a\u5927\u5c0f\u5206\u5757<\/li>\n<li data-id=\"ld70c578-JNMJehTV\">\u9012\u5f52\u5206\u5757<\/li>\n<li data-id=\"ld70c578-l7BFlMkf\">\u8bed\u4e49\u5206\u5757<\/li>\n<li data-id=\"ld70c578-945U7hRA\">\u4ee3\u7406\u5206\u5757<\/li>\n<\/ul>\n<p>\u73b0\u5728\u6df1\u5165\u63a2\u8ba8\u5404\u79cd\u5206\u5757\u7b56\u7565\u7684\u7ec6\u8282\u3002<\/p>\n<h4>1.\u56fa\u5b9a\u5927\u5c0f\u5206\u5757<\/h4>\n<p>\u56fa\u5b9a\u5927\u5c0f\u5206\u5757\u6d89\u53ca\u5c06\u6570\u636e\u5206\u6210\u5927\u5c0f\u76f8\u540c\u7684\u90e8\u5206\uff0c\u4ece\u800c\u66f4\u5bb9\u6613\u5904\u7406\u5927\u578b\u6587\u6863\u3002<\/p>\n<p>\u6709\u65f6\uff0c\u5f00\u53d1\u4eba\u5458\u4f1a\u5728\u5404\u4e2a\u5757\u4e4b\u95f4\u6dfb\u52a0\u5c11\u8bb8\u91cd\u53e0\u90e8\u5206\uff0c\u4e5f\u5c31\u662f\u8ba9\u4e00\u4e2a\u6bb5\u843d\u7684\u5c0f\u90e8\u5206\u5185\u5bb9\u5728\u4e0b\u4e00\u4e2a\u6bb5\u843d\u7684\u5f00\u5934\u91cd\u590d\u51fa\u73b0\u3002\u8fd9\u79cd\u91cd\u53e0\u7684\u65b9\u6cd5\u6709\u52a9\u4e8e\u6a21\u578b\u5728\u6bcf\u4e2a\u5757\u7684\u8fb9\u754c\u4e0a\u4fdd\u7559\u573a\u666f\uff0c\u786e\u4fdd\u5173\u952e\u4fe1\u606f\u4e0d\u4f1a\u5728\u8fb9\u7f18\u4e22\u5931\u3002\u8fd9\u79cd\u7b56\u7565\u5bf9\u4e8e\u9700\u8981\u8fde\u7eed\u4fe1\u606f\u6d41\u7684\u4efb\u52a1\u7279\u522b\u6709\u7528\uff0c\u56e0\u4e3a\u5b83\u4f7f\u6a21\u578b\u80fd\u591f\u66f4\u51c6\u786e\u5730\u89e3\u91ca\u6587\u672c\uff0c\u5e76\u7406\u89e3\u6bb5\u843d\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u4ece\u800c\u4ea7\u751f\u66f4\u8fde\u8d2f\u548c\u573a\u666f\u611f\u77e5\u7684\u54cd\u5e94\u3002<\/p>\n<p>\u4e0a\u56fe\u662f\u56fa\u5b9a\u5927\u5c0f\u5206\u5757\u7684\u5b8c\u7f8e\u793a\u4f8b\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5757\u90fd\u7531\u4e00\u79cd\u72ec\u7279\u7684\u989c\u8272\u8868\u793a\u3002\u7eff\u8272\u90e8\u5206\u8868\u793a\u5757\u4e4b\u95f4\u7684\u91cd\u53e0\u90e8\u5206\uff0c\u786e\u4fdd\u6a21\u578b\u5728\u5904\u7406\u4e0b\u4e00\u4e2a\u5206\u5757\u65f6\u80fd\u591f\u8bbf\u95ee\u524d\u4e00\u4e2a\u5206\u5757\u7684\u76f8\u5173\u573a\u666f\u4fe1\u606f\u3002<\/p>\n<p>\u8fd9\u79cd\u91cd\u53e0\u7b56\u7565\u63d0\u9ad8\u4e86\u6a21\u578b\u5904\u7406\u548c\u7406\u89e3\u5168\u6587\u7684\u80fd\u529b\uff0c\u4ece\u800c\u5728\u6458\u8981\u6216\u7ffb\u8bd1\u7b49\u4efb\u52a1\u4e2d\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd\uff0c\u5728\u8fd9\u4e9b\u4efb\u52a1\u4e2d\uff0c\u7ef4\u62a4\u8de8\u5757\u8fb9\u754c\u7684\u4fe1\u606f\u6d41\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<h4>\u4ee3\u7801\u793a\u4f8b<\/h4>\n<p>\u73b0\u5728\u4f7f\u7528\u4e00\u4e2a\u4ee3\u7801\u793a\u4f8b\u91cd\u65b0\u521b\u5efa\u8fd9\u4e2a\u793a\u4f8b\u3002\u5c06\u4f7f\u7528<a href=\"https:\/\/www.ibm.com\/topics\/langchain\" target=\"_blank\" rel=\"noopener\">LangChain<\/a>\u6765\u5b9e\u73b0\u56fa\u5b9a\u5927\u5c0f\u5206\u5757\u3002<\/p>\n<p>\u590d\u5236<\/p>\n<p>Python 1 from langchain.text_splitter import RecursiveCharacterTextSplitter 2 3 # Function to split text with fixed-size chunks and overlap 4 def split_text_with_overlap(text, chunk_size, overlap_size): 5 # Create a text splitter with overlap 6 text_splitter = RecursiveCharacterTextSplitter( 7 chunk_size=chunk_size, 8 chunk_overlap=overlap_size 9 ) 10 11 # Split the text 12 chunks = text_splitter.split_text(text) 13 14 return chunks 15 16 # Example text 17 text = &#8220;&#8221;&#8221;Artificial Intelligence (AI) simulates human intelligence in machines for tasks like visual perception, speech recognition, and language translation. It has evolved from rule-based systems to data-driven models, enhancing performance through machine learning and deep learning.&#8221;&#8221;&#8221; 18 19 # Define chunk size and overlap size 20 chunk_size = 80 # 80 characters per chunk 21 overlap_size = 10 # 10 characters overlap between chunks 22 23 # Get the chunks with overlap 24 chunks = split_text_with_overlap(text, chunk_size, overlap_size) 25 26 # Print the chunks and overlaps 27 for i in range(len(chunks)): 28 print(f&#8221;Chunk {i+1}:&#8221;) 29 print(chunks[i]) # Print the chunk itself 30 31 # If there&#8217;s a next chunk, print the overlap between current and next chunk 32 if i &lt; len(chunks) &#8211; 1: 33 overlap = chunks[i][-overlap_size:] # Get the overlap part 34 print(f&#8221;Overlap with Chunk {i+2}:&#8221;) 35 print(overlap) 36 37 print(&#8220;\\n&#8221; + &#8220;=&#8221;*50 + &#8220;\\n&#8221;) \u6267\u884c\u4e0a\u8ff0\u4ee3\u7801\u540e\uff0c\u5b83\u5c06\u751f\u6210\u4ee5\u4e0b\u8f93\u51fa\uff1a HTML 1 Chunk 1: 2 Artificial Intelligence (AI) simulates human intelligence in machines for tasks 3 Overlap with Chunk 2: 4 for tasks 5 6 ================================================== 7 8 Chunk 2: 9 for tasks like visual perception, speech recognition, and language translation. 10 Overlap with Chunk 3: 11 anslation. 12 13 ================================================== 14 15 Chunk 3: 16 It has evolved from rule-based systems to data-driven models, enhancing 17 Overlap with Chunk 4: 18 enhancing 19 20 ================================================== 21 22 Chunk 4: 23 enhancing performance through machine learning and deep learning.<\/p>\n<ul>\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<li>39.<\/li>\n<li>40.<\/li>\n<li>41.<\/li>\n<li>42.<\/li>\n<li>43.<\/li>\n<li>44.<\/li>\n<li>45.<\/li>\n<li>46.<\/li>\n<li>47.<\/li>\n<li>48.<\/li>\n<li>49.<\/li>\n<li>50.<\/li>\n<li>51.<\/li>\n<li>52.<\/li>\n<li>53.<\/li>\n<li>54.<\/li>\n<li>55.<\/li>\n<li>56.<\/li>\n<li>57.<\/li>\n<li>58.<\/li>\n<li>59.<\/li>\n<li>60.<\/li>\n<li>61.<\/li>\n<li>62.<\/li>\n<li>63.<\/li>\n<\/ul>\n<h4>2.\u9012\u5f52\u5206\u5757<\/h4>\n<p>\u9012\u5f52\u5206\u5757\u662f\u4e00\u79cd\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u5c06\u6587\u672c\u53cd\u590d\u62c6\u5206\u4e3a\u66f4\u5c0f\u7684\u5b50\u5757\uff0c\u4ece\u800c\u7cfb\u7edf\u5730\u5c06\u5e9e\u5927\u7684\u6587\u672c\u5185\u5bb9\u62c6\u5206\u4e3a\u66f4\u6613\u4e8e\u7ba1\u7406\u7684\u90e8\u5206\u3002\u8fd9\u79cd\u65b9\u6cd5\u5728\u5904\u7406\u590d\u6742\u6216\u5177\u6709\u5c42\u6b21\u7ed3\u6784\u7684\u6587\u6863\u65f6\u7279\u522b\u6709\u6548\uff0c\u80fd\u591f\u786e\u4fdd\u6bcf\u4e2a\u62c6\u5206\u540e\u7684\u90e8\u5206\u90fd\u4fdd\u6301\u4e00\u81f4\u6027\u4e14\u573a\u666f\u5b8c\u6574\u3002\u8be5\u8fc7\u7a0b\u4f1a\u6301\u7eed\u8fdb\u884c\uff0c\u76f4\u81f3\u6587\u672c\u88ab\u62c6\u5206\u6210\u9002\u5408\u6a21\u578b\u8fdb\u884c\u6709\u6548\u5904\u7406\u7684\u5927\u5c0f\u3002<\/p>\n<p>\u4ee5\u9700\u8981\u7531\u5177\u6709\u6709\u9650\u573a\u666f\u7a97\u53e3\u7684\u8bed\u8a00\u6a21\u578b\u5904\u7406\u7684\u4e00\u4e2a\u5197\u957f\u6587\u6863\u4e3a\u4f8b\uff0c\u9012\u5f52\u5206\u5757\u65b9\u6cd5\u4f1a\u9996\u5148\u5c06\u8be5\u6587\u6863\u62c6\u5206\u4e3a\u51e0\u4e2a\u4e3b\u8981\u90e8\u5206\u3002\u82e5\u8fd9\u4e9b\u90e8\u5206\u4ecd\u7136\u8fc7\u4e8e\u5e9e\u5927\uff0c\u8be5\u65b9\u6cd5\u4f1a\u8fdb\u4e00\u6b65\u5c06\u5176\u7ec6\u5206\u4e3a\u66f4\u5c0f\u7684\u5b50\u90e8\u5206\uff0c\u5e76\u6301\u7eed\u8fd9\u4e00\u8fc7\u7a0b\uff0c\u76f4\u81f3\u6bcf\u4e2a\u5757\u90fd\u7b26\u5408\u6a21\u578b\u7684\u5904\u7406\u80fd\u529b\u3002\u8fd9\u79cd\u5c42\u6b21\u5206\u660e\u7684\u62c6\u5206\u65b9\u5f0f\u4e0d\u4ec5\u4fdd\u7559\u4e86\u539f\u59cb\u6587\u6863\u7684\u903b\u8f91\u6d41\u7a0b\u548c\u573a\u666f\uff0c\u800c\u4e14\u4f7fLLM\u80fd\u591f\u66f4\u6709\u6548\u5730\u5904\u7406\u957f\u6587\u672c\u3002<\/p>\n<p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9012\u5f52\u5206\u5757\u53ef\u4ee5\u6839\u636e\u6587\u6863\u7684\u7ed3\u6784\u548c\u4efb\u52a1\u7684\u7279\u5b9a\u9700\u6c42\u91c7\u7528\u591a\u79cd\u7b56\u7565\u6765\u5b9e\u73b0\uff0c\u6839\u636e\u6807\u9898\u3001\u6bb5\u843d\u6216\u53e5\u5b50\u8fdb\u884c\u62c6\u5206\u3002<\/p>\n<p>\u5728\u4e0a\u56fe\u4e2d\uff0c\u6587\u672c\u901a\u8fc7\u9012\u5f52\u5206\u5757\u88ab\u62c6\u5206\u4e3a\u56db\u4e2a\u4e0d\u540c\u989c\u8272\u7684\u5757\uff0c\u6bcf\u4e2a\u5757\u90fd\u4ee3\u8868\u4e86\u4e00\u4e2a\u66f4\u5c0f\u3001\u66f4\u6613\u7ba1\u7406\u7684\u90e8\u5206\uff0c\u5e76\u4e14\u6bcf\u4e2a\u5757\u5305\u542b\u6700\u591a80\u4e2a\u5355\u8bcd\u3002\u8fd9\u4e9b\u5757\u4e4b\u95f4\u6ca1\u6709\u91cd\u53e0\u3002\u989c\u8272\u7f16\u7801\u6709\u52a9\u4e8e\u5c55\u793a\u5185\u5bb9\u662f\u5982\u4f55\u88ab\u5206\u5272\u6210\u903b\u8f91\u90e8\u5206\uff0c\u4f7f\u6a21\u578b\u66f4\u5bb9\u6613\u5904\u7406\u548c\u7406\u89e3\u957f\u6587\u672c\uff0c\u907f\u514d\u4e86\u91cd\u8981\u573a\u666f\u7684\u4e22\u5931\u3002<\/p>\n<h4>\u4ee3\u7801\u793a\u4f8b<\/h4>\n<p>\u73b0\u5728\u7f16\u5199\u4e00\u4e2a\u793a\u4f8b\uff0c\u6f14\u793a\u5982\u4f55\u5b9e\u73b0\u9012\u5f52\u5206\u5757\u3002<\/p>\n<p>\u590d\u5236<\/p>\n<p>Python 1 from langchain.text_splitter import RecursiveCharacterTextSplitter 2 3 # Function to split text into chunks using recursive chunking 4 def split_text_recursive(text, chunk_size=80): 5 # Initialize the RecursiveCharacterTextSplitter 6 text_splitter = RecursiveCharacterTextSplitter( 7 chunk_size=chunk_size, # Maximum size of each chunk (80 words) 8 chunk_overlap=0 # No overlap between chunks 9 ) 10 11 # Split the text into chunks 12 chunks = text_splitter.split_text(text) 13 14 return chunks 15 16 # Example text 17 text = &#8220;&#8221;&#8221;Artificial Intelligence (AI) simulates human intelligence in machines for tasks like visual perception, speech recognition, and language translation. It has evolved from rule-based systems to data-driven models, enhancing performance through machine learning and deep learning.&#8221;&#8221;&#8221; 18 19 # Split the text using recursive chunking 20 chunks = split_text_recursive(text, chunk_size=80) 21 22 # Print the resulting chunks 23 for i, chunk in enumerate(chunks): 24 print(f&#8221;Chunk {i+1}:&#8221;) 25 print(chunk) 26 print(&#8220;=&#8221;*50)<\/p>\n<ul>\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<\/ul>\n<p>\u4e0a\u8ff0\u4ee3\u7801\u5c06\u751f\u6210\u4ee5\u4e0b\u8f93\u51fa\uff1a<\/p>\n<p>\u590d\u5236<\/p>\n<p>HTML 1 Chunk 1: 2 Artificial Intelligence (AI) simulates human intelligence in machines for tasks 3 ================================================== 4 Chunk 2: 5 like visual perception, speech recognition, and language translation. It has 6 ================================================== 7 Chunk 3: 8 evolved from rule-based systems to data-driven models, enhancing performance 9 ================================================== 10 Chunk 4: 11 through machine learning and deep learning.<\/p>\n<ul>\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<\/ul>\n<p>\u5728\u7406\u89e3\u4e86\u8fd9\u4e24\u79cd\u57fa\u4e8e\u957f\u5ea6\u7684\u5206\u5757\u7b56\u7565\u4e4b\u540e\uff0c\u662f\u7406\u89e3\u4e00\u79cd\u66f4\u5173\u6ce8\u6587\u672c\u542b\u4e49\/\u573a\u666f\u7684\u5206\u5757\u7b56\u7565\u7684\u65f6\u5019\u4e86\u3002<\/p>\n<h4>3.\u8bed\u4e49\u5206\u5757<\/h4>\n<p>\u8bed\u4e49\u5206\u5757\u662f\u6307\u6839\u636e\u5185\u5bb9\u7684\u542b\u4e49\u6216\u573a\u666f\u5c06\u6587\u672c\u62c6\u5206\u6210\u5757\u3002\u8fd9\u79cd\u65b9\u6cd5\u901a\u5e38\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6216\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u6280\u672f\uff0c\u4f8b\u5982\u53e5\u5b50\u5d4c\u5165\uff0c\u6765\u8bc6\u522b\u6587\u672c\u4e2d\u5177\u6709\u76f8\u4f3c\u542b\u4e49\u6216\u8bed\u4e49\u7ed3\u6784\u7684\u90e8\u5206\u3002<\/p>\n<p>\u5728\u4e0a\u56fe\u4e2d\uff0c\u6bcf\u4e2a\u5757\u90fd\u91c7\u7528\u4e0d\u540c\u7684\u989c\u8272\u8868\u793a\u2014\u2014\u84dd\u8272\u4ee3\u8868\u4eba\u5de5\u667a\u80fd\uff0c\u9ec4\u8272\u4ee3\u8868\u63d0\u793a\u5de5\u7a0b\u3002\u8fd9\u4e9b\u5757\u662f\u5206\u9694\u5f00\u7684\uff0c\u56e0\u4e3a\u5b83\u4eec\u6db5\u76d6\u4e86\u4e0d\u540c\u7684\u60f3\u6cd5\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u786e\u4fdd\u6a21\u578b\u5bf9\u6bcf\u4e2a\u4e3b\u9898\u90fd\u80fd\u6709\u6e05\u6670\u4e14\u51c6\u786e\u7684\u7406\u89e3\uff0c\u907f\u514d\u4e86\u4e0d\u540c\u4e3b\u9898\u95f4\u7684\u6df7\u6dc6\u4e0e\u5e72\u6270\u3002<\/p>\n<h4>\u4ee3\u7801\u793a\u4f8b<\/h4>\n<p>\u73b0\u5728\u7f16\u5199\u4e00\u4e2a\u5b9e\u73b0\u8bed\u4e49\u5206\u5757\u7684\u793a\u4f8b\u3002<\/p>\n<p>\u590d\u5236<\/p>\n<p>Python 1 import os 2 from langchain_experimental.text_splitter import SemanticChunker 3 from langchain_openai.embeddings import OpenAIEmbeddings 4 5 # Set the OpenAI API key as an environment variable (Replace with your actual API key) 6 os.environ[&#8220;OPENAI_API_KEY&#8221;] = &#8220;replace with your actual OpenAI API key&#8221; 7 8 # Function to split text into semantic chunks 9 def split_text_semantically(text, breakpoint_type=&#8221;percentile&#8221;): 10 # Initialize the SemanticChunker with OpenAI embeddings 11 text_splitter = SemanticChunker(OpenAIEmbeddings(), breakpoint_threshold_type=breakpoint_type) 12 13 # Create documents (chunks) 14 docs = text_splitter.create_documents([text]) 15 16 # Return the list of chunks 17 return [doc.page_content for doc in docs] 18 19 def main(): 20 # Example content (State of the Union address or your own text) 21 document_content = &#8220;&#8221;&#8221; 22 Artificial Intelligence (AI) simulates human intelligence in machines for tasks like visual perception, speech recognition, and language translation. It has evolved from rule-based systems to data-driven models, enhancing performance through machine learning and deep learning. 23 24 Prompt Engineering involves designing input prompts to guide AI models in producing accurate and relevant responses, improving tasks such as text generation and summarization. 25 &#8220;&#8221;&#8221; 26 27 # Split text using the chosen threshold type (percentile) 28 threshold_type = &#8220;percentile&#8221; 29 print(f&#8221;\\nChunks using {threshold_type} threshold:&#8221;) 30 chunks = split_text_semantically(document_content, breakpoint_type=threshold_type) 31 32 # Print each chunk&#8217;s content 33 for idx, chunk in enumerate(chunks): 34 print(f&#8221;Chunk {idx + 1}:\\n{chunk}\\n&#8221;) 35 36 if __name__ == &#8220;__main__&#8221;: 37 main()<\/p>\n<ul>\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<\/ul>\n<p>\u4e0a\u8ff0\u4ee3\u7801\u5c06\u751f\u6210\u4ee5\u4e0b\u8f93\u51fa\uff1a<\/p>\n<p>\u590d\u5236<\/p>\n<p>HTML 1 Chunks using percentile threshold: 2 Chunk 1: 3 Artificial Intelligence (AI) simulates human intelligence in machines for tasks like visual perception, speech recognition, and language translation. It has evolved from rule-based systems to data-driven models, enhancing performance through machine learning and deep learning. 4 5 Chunk 2: 6 Prompt Engineering involves designing input prompts to guide AI models in producing accurate and relevant responses, improving tasks such as text generation and summarization.<\/p>\n<ul>\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<\/ul>\n<h4>4.\u4ee3\u7406\u5206\u5757<\/h4>\n<p>\u5728\u8fd9\u4e9b\u7b56\u7565\u4e2d\uff0c\u4ee3\u7406\u5206\u5757\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u7b56\u7565\u3002\u8fd9\u4e2a\u7b56\u7565\u5229\u7528\u50cfGPT\u8fd9\u6837\u7684LLM\u4f5c\u4e3a\u5206\u5757\u8fc7\u7a0b\u4e2d\u7684\u4ee3\u7406\u3002LLM\u4e0d\u518d\u4f9d\u8d56\u4e8e\u4eba\u5de5\u8bbe\u5b9a\u7684\u89c4\u5219\u6765\u786e\u5b9a\u5185\u5bb9\u7684\u62c6\u5206\u65b9\u5f0f\uff0c\u800c\u662f\u51ed\u501f\u5176\u5f3a\u5927\u7684\u7406\u89e3\u80fd\u529b\uff0c\u4e3b\u52a8\u5730\u5bf9\u8f93\u5165\u4fe1\u606f\u8fdb\u884c\u7ec4\u7ec7\u6216\u5212\u5206\u3002LLM\u4f1a\u4f9d\u636e\u4efb\u52a1\u7684\u5177\u4f53\u573a\u666f\uff0c\u81ea\u4e3b\u51b3\u5b9a\u5982\u4f55\u5c06\u5185\u5bb9\u62c6\u5206\u6210\u6613\u4e8e\u7ba1\u7406\u7684\u90e8\u5206\uff0c\u4ece\u800c\u627e\u5230\u6700\u4f73\u7684\u62c6\u5206\u65b9\u6848\u3002<\/p>\n<p>\u4e0a\u56fe\u663e\u793a\u4e86\u4e00\u4e2a\u5206\u5757\u4ee3\u7406\u5c06\u4e00\u4e2a\u5e9e\u5927\u7684\u6587\u672c\u62c6\u5206\u6210\u66f4\u5c0f\u7684\u3001\u6709\u610f\u4e49\u7684\u90e8\u5206\u3002\u8fd9\u4e2a\u4ee3\u7406\u662f\u7531\u4eba\u5de5\u667a\u80fd\u9a71\u52a8\u7684\uff0c\u8fd9\u6709\u52a9\u4e8e\u5b83\u66f4\u597d\u5730\u7406\u89e3\u6587\u672c\uff0c\u5e76\u5c06\u5176\u5206\u6210\u6709\u610f\u4e49\u7684\u5757\u3002\u8fd9\u88ab\u79f0\u4e3a\u201c\u4ee3\u7406\u5206\u5757\u201d\uff0c\u4e0e\u7b80\u5355\u5730\u5c06\u6587\u672c\u62c6\u5206\u4e3a\u76f8\u7b49\u7684\u90e8\u5206\u76f8\u6bd4\uff0c\u8fd9\u662f\u4e00\u79cd\u66f4\u667a\u80fd\u7684\u5904\u7406\u6587\u672c\u7684\u65b9\u5f0f\u3002<\/p>\n<p>\u63a5\u4e0b\u6765\u63a2\u8ba8\u5982\u4f55\u5728\u4ee3\u7801\u793a\u4f8b\u4e2d\u5b9e\u73b0\u3002<\/p>\n<p>\u590d\u5236<\/p>\n<p>Python 1 from langchain.chat_models import ChatOpenAI 2 from langchain.prompts import PromptTemplate 3 from langchain.chains import LLMChain 4 from langchain.agents import initialize_agent, Tool, AgentType 5 6 # Initialize OpenAI chat model (replace with your API key) 7 llm = ChatOpenAI(model=&#8221;gpt-3.5-turbo&#8221;, api_key=&#8221;replace with your actual OpenAI API key&#8221;) 8 9 # Step 1: Define Chunking and Summarization Prompt Template 10 chunk_prompt_template = &#8220;&#8221;&#8221; 11 You are given a large piece of text. Your job is to break it into smaller parts (chunks) if necessary and summarize each chunk. 12 Once all parts are summarized, combine them into a final summary. 13 If the text is already small enough to process at once, provide a full summary in one step. 14 Please summarize the following text:\\n{input} 15 &#8220;&#8221;&#8221; 16 chunk_prompt = PromptTemplate(input_variables=[&#8220;input&#8221;], template=chunk_prompt_template) 17 18 # Step 2: Define Chunk Processing Tool 19 def chunk_processing_tool(query): 20 &#8220;&#8221;&#8221;Processes text chunks and generates summaries using the defined prompt.&#8221;&#8221;&#8221; 21 chunk_chain = LLMChain(llm=llm, prompt=chunk_prompt) 22 print(f&#8221;Processing chunk:\\n{query}\\n&#8221;) # Show the chunk being processed 23 return chunk_chain.run(input=query) 24 25 # Step 3: Define External Tool (Optional, can be used to fetch extra information if needed) 26 def external_tool(query): 27 &#8220;&#8221;&#8221;Simulates an external tool that could fetch additional information.&#8221;&#8221;&#8221; 28 return f&#8221;External response based on the query: {query}&#8221; 29 30 # Step 4: Initialize the agent with tools 31 tools = [ 32 Tool( 33 name=&#8221;Chunk Processing&#8221;, 34 func=chunk_processing_tool, 35 description=&#8221;Processes text chunks and generates summaries.&#8221; 36 ), 37 Tool( 38 name=&#8221;External Query&#8221;, 39 func=external_tool, 40 description=&#8221;Fetches additional data to enhance chunk processing.&#8221; 41 ) 42 ] 43 44 # Initialize the agent with defined tools and zero-shot capabilities 45 agent = initialize_agent( 46 tools=tools, 47 agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, 48 llm=llm, 49 verbose=True 50 ) 51 52 # Step 5: Agentic Chunk Processing Function 53 def agent_process_chunks(text): 54 &#8220;&#8221;&#8221;Uses the agent to process text chunks and generate a final output.&#8221;&#8221;&#8221; 55 # Step 1: Chunking the text into smaller, manageable sections 56 def chunk_text(text, chunk_size=500): 57 &#8220;&#8221;&#8221;Splits large text into smaller chunks.&#8221;&#8221;&#8221; 58 return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)] 59 60 chunks = chunk_text(text) 61 62 # Step 2: Process each chunk with the agent 63 chunk_results = [] 64 for idx, chunk in enumerate(chunks): 65 print(f&#8221;Processing chunk {idx + 1}\/{len(chunks)}&#8230;&#8221;) 66 response = agent.invoke({&#8220;input&#8221;: chunk}) # Process chunk using the agent 67 chunk_results.append(response[&#8216;output&#8217;]) # Collect the chunk result 68 69 # Step 3: Combine the chunk results into a final output 70 final_output = &#8220;\\n&#8221;.join(chunk_results) 71 return final_output 72 73 # Step 6: Running the agent on an example large text input 74 if __name__ == &#8220;__main__&#8221;: 75 # Example large text content 76 text_to_process = &#8220;&#8221;&#8221; 77 Artificial intelligence (AI) is transforming industries by enabling machines to perform tasks that 78 previously required human intelligence. From healthcare to finance, AI is driving innovation and improving 79 efficiency. For instance, in healthcare, AI algorithms assist doctors in diagnosing diseases, interpreting 80 medical images, and predicting patient outcomes. Meanwhile, in finance, AI helps detect fraud, manage 81 investments, and automate customer service. 82 83 However, the widespread adoption of AI also raises ethical concerns. Issues like privacy invasion, 84 algorithmic bias, and the potential loss of jobs due to automation are significant challenges. Experts 85 argue that it&#8217;s essential to develop AI responsibly to ensure that it benefits society as a whole. 86 Proper regulations, transparency, and accountability can help address these issues, ensuring that AI 87 technologies are used for the greater good. 88 89 Beyond individual industries, AI is also impacting the global economy. Nations are investing heavily 90 in AI research and development to maintain a competitive edge. This technological race could redefine 91 global power dynamics, with countries that excel in AI leading the way in economic and military strength. 92 Despite the potential for AI to contribute positively to society, its development and application require 93 careful consideration of ethical, legal, and societal implications. 94 &#8220;&#8221;&#8221; 95 96 # Process the text and print the final result 97 final_result = agent_process_chunks(text_to_process) 98 print(&#8220;\\nFinal Output:\\n&#8221;, final_result)<\/p>\n<ul>\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<li>39.<\/li>\n<li>40.<\/li>\n<li>41.<\/li>\n<li>42.<\/li>\n<li>43.<\/li>\n<li>44.<\/li>\n<li>45.<\/li>\n<li>46.<\/li>\n<li>47.<\/li>\n<li>48.<\/li>\n<li>49.<\/li>\n<li>50.<\/li>\n<li>51.<\/li>\n<li>52.<\/li>\n<li>53.<\/li>\n<li>54.<\/li>\n<li>55.<\/li>\n<li>56.<\/li>\n<li>57.<\/li>\n<li>58.<\/li>\n<li>59.<\/li>\n<li>60.<\/li>\n<li>61.<\/li>\n<li>62.<\/li>\n<li>63.<\/li>\n<li>64.<\/li>\n<li>65.<\/li>\n<li>66.<\/li>\n<li>67.<\/li>\n<li>68.<\/li>\n<li>69.<\/li>\n<li>70.<\/li>\n<li>71.<\/li>\n<li>72.<\/li>\n<li>73.<\/li>\n<li>74.<\/li>\n<li>75.<\/li>\n<li>76.<\/li>\n<li>77.<\/li>\n<li>78.<\/li>\n<li>79.<\/li>\n<li>80.<\/li>\n<li>81.<\/li>\n<li>82.<\/li>\n<li>83.<\/li>\n<li>84.<\/li>\n<li>85.<\/li>\n<li>86.<\/li>\n<li>87.<\/li>\n<li>88.<\/li>\n<li>89.<\/li>\n<li>90.<\/li>\n<li>91.<\/li>\n<li>92.<\/li>\n<li>93.<\/li>\n<li>94.<\/li>\n<li>95.<\/li>\n<li>96.<\/li>\n<li>97.<\/li>\n<li>98.<\/li>\n<li>99.<\/li>\n<\/ul>\n<p>\u4e0a\u8ff0\u4ee3\u7801\u5c06\u751f\u6210\u4ee5\u4e0b\u8f93\u51fa\uff1a<\/p>\n<p>\u590d\u5236<\/p>\n<p>HTML 1 Processing chunk 1\/3&#8230; 2 3 4 &gt; Entering new AgentExecutor chain&#8230; 5 I should use Chunk Processing to extract the key information from the text provided. 6 Action: Chunk Processing 7 Action Input: Artificial intelligence (AI) is transforming industries by enabling machines to perform tasks that previously required human intelligence. From healthcare to finance, AI is driving innovation and improving efficiency. For instance, in healthcare, AI algorithms assist doctors in diagnosing diseases, interpreting medical images, and predicting patient outcomes. Meanwhile, in finance, AI helps detect fraud, manage investments, and automate customer service.Processing chunk: 8 Artificial intelligence (AI) is transforming industries by enabling machines to perform tasks that previously required human intelligence. From healthcare to finance, AI is driving innovation and improving efficiency. For instance, in healthcare, AI algorithms assist doctors in diagnosing diseases, interpreting medical images, and predicting patient outcomes. Meanwhile, in finance, AI helps detect fraud, manage investments, and automate customer service. 9 10 Observation: Artificial intelligence (AI) is revolutionizing various industries by allowing machines to complete tasks that once needed human intelligence. In healthcare, AI algorithms aid doctors in diagnosing illnesses, analyzing medical images, and forecasting patient results. In finance, AI is used to identify fraud, oversee investments, and streamline customer service. AI is playing a vital role in enhancing efficiency and driving innovation across different sectors. 11 Thought:I need more specific information about the impact of AI in different industries. 12 Action: External Query 13 Action Input: Impact of artificial intelligence in healthcare 14 Observation: External response based on the query: Impact of artificial intelligence in healthcare 15 Thought:I should now look for information on the impact of AI in finance. 16 Action: External Query 17 Action Input: Impact of artificial intelligence in finance 18 Observation: External response based on the query: Impact of artificial intelligence in finance 19 Thought:I now have a better understanding of how AI is impacting healthcare and finance. 20 Final Answer: Artificial intelligence is revolutionizing industries like healthcare and finance by enhancing efficiency, driving innovation, and enabling machines to perform tasks that previously required human intelligence. In healthcare, AI aids in diagnosing diseases, interpreting medical images, and predicting patient outcomes, while in finance, it helps detect fraud, manage investments, and automate customer service. 21 22 &gt; Finished chain. 23 Processing chunk 2\/3&#8230; 24 25 &gt; Entering new AgentExecutor chain&#8230; 26 This question is discussing ethical concerns related to the widespread adoption of AI and the need to develop AI responsibly. 27 Action: Chunk Processing 28 Action Input: The text providedProcessing chunk: 29 The text provided 30 31 Observation: I&#8217;m sorry, but you haven&#8217;t provided any text to be summarized. Could you please provide the text so I can assist you with summarizing it? 32 Thought:I need to provide the text for chunk processing to summarize. 33 Action: External Query 34 Action Input: Retrieve the text related to the ethical concerns of AI adoption and responsible development 35 Observation: External response based on the query: Retrieve the text related to the ethical concerns of AI adoption and responsible development 36 Thought:Now that I have the text related to ethical concerns of AI adoption and responsible development, I can move forward with chunk processing. 37 Action: Chunk Processing 38 Action Input: The retrieved textProcessing chunk: 39 The retrieved text 40 41 Observation: I&#8217;m sorry, but it seems like you have not provided any text for me to summarize. Could you please provide the text you would like me to summarize? Thank you! 42 Thought:I need to ensure that the text related to ethical concerns of AI adoption and responsible development is provided for chunk processing to generate a summary. 43 Action: External Query 44 Action Input: Retrieve the text related to the ethical concerns of AI adoption and responsible development 45 Observation: External response based on the query: Retrieve the text related to the ethical concerns of AI adoption and responsible development 46 Thought:Now that I have the text related to ethical concerns of AI adoption and responsible development, I can proceed with chunk processing to generate a summary. 47 Action: Chunk Processing 48 Action Input: The retrieved textProcessing chunk: 49 The retrieved text 50 51 Observation: I&#8217;m sorry, but you haven&#8217;t provided any text to be summarized. Can you please provide the text so I can help you with the summarization? 52 Thought:I need to make sure that the text related to ethical concerns of AI adoption and responsible development is entered for chunk processing to summarize. 53 Action: Chunk Processing 54 Action Input: Text related to ethical concerns of AI adoption and responsible developmentProcessing chunk: 55 Text related to ethical concerns of AI adoption and responsible development 56 57 Observation: The text discusses the ethical concerns surrounding the adoption of artificial intelligence (AI) and the importance of responsible development. It highlights issues such as bias in AI algorithms, privacy violations, and the potential for autonomous AI systems to make harmful decisions. The text emphasizes the need for transparency, accountability, and ethical guidelines to ensure that AI technologies are developed and deployed in a responsible manner. 58 Thought:The text provides information on ethical concerns related to AI adoption and responsible development, emphasizing the need for regulation, transparency, and accountability. 59 Final Answer: The text discusses the ethical concerns surrounding the adoption of artificial intelligence (AI) and the importance of responsible development. 60 61 &gt; Finished chain. 62 Processing chunk 3\/3&#8230; 63 64 &gt; Entering new AgentExecutor chain&#8230; 65 This question seems to be about the impact of AI on the global economy and the potential implications. 66 Action: Chunk Processing 67 Action Input: The text providedProcessing chunk: 68 The text provided 69 70 Observation: I&#8217;m sorry, but you did not provide any text for me to summarize. Please provide the text that you would like me to summarize. 71 Thought:I need to provide the text for Chunk Processing to summarize. 72 Action: External Query 73 Action Input: Fetch the text about the impact of AI on the global economy and its implications. 74 Observation: External response based on the query: Fetch the text about the impact of AI on the global economy and its implications. 75 Thought:Now that I have the text about the impact of AI on the global economy and its implications, I can proceed with Chunk Processing. 76 Action: Chunk Processing 77 Action Input: The text about the impact of AI on the global economy and its implications.Processing chunk: 78 The text about the impact of AI on the global economy and its implications. 79 80 Observation: The text discusses the significant impact that artificial intelligence (AI) is having on the global economy. It highlights how AI is revolutionizing industries by increasing productivity, reducing costs, and creating new job opportunities. However, there are concerns about job displacement and the need for retraining workers to adapt to the changing landscape. Overall, AI is reshaping the economy and prompting a shift in the way businesses operate. 81 Thought:Based on the summary generated by Chunk Processing, the impact of AI on the global economy seems to be significant, with both positive and negative implications. 82 Final Answer: The impact of AI on the global economy is significant, revolutionizing industries, increasing productivity, reducing costs, creating new job opportunities, but also raising concerns about job displacement and the need for worker retraining. 83 84 &gt; Finished chain. 85 86 Final Output: 87 Artificial intelligence is revolutionizing industries like healthcare and finance by enhancing efficiency, driving innovation, and enabling machines to perform tasks that previously required human intelligence. In healthcare, AI aids in diagnosing diseases, interpreting medical images, and predicting patient outcomes, while in finance, it helps detect fraud, manage investments, and automate customer service. 88 The text discusses the ethical concerns surrounding the adoption of artificial intelligence (AI) and the importance of responsible development. 89 The impact of AI on the global economy is significant, revolutionizing industries, increasing productivity, reducing costs, creating new job opportunities, but also raising concerns about job displacement and the need for worker retraining.<\/p>\n<ul>\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<li>39.<\/li>\n<li>40.<\/li>\n<li>41.<\/li>\n<li>42.<\/li>\n<li>43.<\/li>\n<li>44.<\/li>\n<li>45.<\/li>\n<li>46.<\/li>\n<li>47.<\/li>\n<li>48.<\/li>\n<li>49.<\/li>\n<li>50.<\/li>\n<li>51.<\/li>\n<li>52.<\/li>\n<li>53.<\/li>\n<li>54.<\/li>\n<li>55.<\/li>\n<li>56.<\/li>\n<li>57.<\/li>\n<li>58.<\/li>\n<li>59.<\/li>\n<li>60.<\/li>\n<li>61.<\/li>\n<li>62.<\/li>\n<li>63.<\/li>\n<li>64.<\/li>\n<li>65.<\/li>\n<li>66.<\/li>\n<li>67.<\/li>\n<li>68.<\/li>\n<li>69.<\/li>\n<li>70.<\/li>\n<li>71.<\/li>\n<li>72.<\/li>\n<li>73.<\/li>\n<li>74.<\/li>\n<li>75.<\/li>\n<li>76.<\/li>\n<li>77.<\/li>\n<li>78.<\/li>\n<li>79.<\/li>\n<li>80.<\/li>\n<li>81.<\/li>\n<li>82.<\/li>\n<li>83.<\/li>\n<li>84.<\/li>\n<li>85.<\/li>\n<li>86.<\/li>\n<li>87.<\/li>\n<li>88.<\/li>\n<li>89.<\/li>\n<li>90.<\/li>\n<\/ul>\n<h3>\u5206\u5757\u7b56\u7565\u7684\u6bd4\u8f83<\/h3>\n<p>\u4e3a\u4e86\u66f4\u5bb9\u6613\u7406\u89e3\u4e0d\u540c\u7684\u5206\u5757\u65b9\u6cd5\uff0c\u4e0b\u8868\u6bd4\u8f83\u4e86\u56fa\u5b9a\u5927\u5c0f\u5206\u5757\u3001\u9012\u5f52\u5206\u5757\u3001\u8bed\u4e49\u5206\u5757\u548c\u4ee3\u7406\u5206\u5757\u7684\u5de5\u4f5c\u539f\u7406\u3001\u4f55\u65f6\u4f7f\u7528\u5b83\u4eec\u4ee5\u53ca\u5b83\u4eec\u7684\u5c40\u9650\u6027\u3002<\/p>\n<table data-transient-attributes=\"class\" data-width=\"653px\">\n<tbody data-id=\"t6d5e859-Xdi4clb9\">\n<tr data-id=\"t31e458f-ZUaIWjS4\">\n<td data-id=\"t8c0e6ab-Z2VThCf9\" data-transient-attributes=\"table-cell-selection\"><strong>\u5206\u5757\u7c7b\u578b<\/strong><\/td>\n<td data-id=\"t8c0e6ab-bFETC45a\" data-transient-attributes=\"table-cell-selection\"><strong>\u63cf\u8ff0<\/strong><\/td>\n<td data-id=\"t8c0e6ab-ic9ohbMR\" data-transient-attributes=\"table-cell-selection\"><strong>\u65b9\u6cd5<\/strong><\/td>\n<td data-id=\"t8c0e6ab-FGSMTL6f\" data-transient-attributes=\"table-cell-selection\"><strong>\u9002\u7528\u573a\u666f<\/strong><\/td>\n<td data-id=\"t1b895c3-DRJbaS7S\" data-transient-attributes=\"table-cell-selection\"><strong>\u5c40\u9650\u6027<\/strong><\/td>\n<\/tr>\n<tr data-id=\"t1ecfec6-4amWCNYf\">\n<td data-id=\"t04d953d-aeOZYZgW\" data-transient-attributes=\"table-cell-selection\">\u56fa\u5b9a\u5927\u5c0f<\/p>\n<p>\u5206\u5757<\/td>\n<td data-id=\"t04d953d-eYM13SHX\" data-transient-attributes=\"table-cell-selection\">\u5c06\u6587\u672c\u5206\u6210\u5927\u5c0f\u76f8\u7b49\u7684\u5757\uff0c\u800c\u4e0d\u8003\u8651\u5185\u5bb9\u3002<\/td>\n<td data-id=\"t04d953d-i3hYEn8l\" data-transient-attributes=\"table-cell-selection\">\u57fa\u4e8e\u56fa\u5b9a\u7684\u5355\u8bcd\u6216\u5b57\u7b26\u9650\u5236\u521b\u5efa\u7684\u5757\u3002<\/td>\n<td data-id=\"t04d953d-D0eIhhfZ\" data-transient-attributes=\"table-cell-selection\">\u7b80\u5355\u3001\u7ed3\u6784\u5316\u7684\u6587\u672c\uff0c\u573a\u666f\u8fde\u7eed\u6027\u5e76\u4e0d\u91cd\u8981\u3002<\/td>\n<td data-id=\"t0b4e833-f29fiSkg\" data-transient-attributes=\"table-cell-selection\">\u53ef\u80fd\u4f1a\u4e22\u5931\u573a\u666f\u6216\u62c6\u5206\u53e5\u5b50\/\u60f3\u6cd5\u3002<\/td>\n<\/tr>\n<tr data-id=\"t31e458f-3M6c0LIK\">\n<td data-id=\"t8c0e6ab-mYQb2kX9\" data-transient-attributes=\"table-cell-selection\">\u9012\u5f52\u5206\u5757<\/td>\n<td data-id=\"t8c0e6ab-6JR3YCFi\" data-transient-attributes=\"table-cell-selection\">\u4e0d\u65ad\u5730\u5c06\u6587\u672c\u5206\u6210\u66f4\u5c0f\u7684\u5757\uff0c\u76f4\u5230\u8fbe\u5230\u53ef\u7ba1\u7406\u7684\u5927\u5c0f\u3002<\/td>\n<td data-id=\"t8c0e6ab-MpJgbOsK\" data-transient-attributes=\"table-cell-selection\">\u5206\u5c42\u62c6\u5206\uff0c\u5982\u679c\u592a\u5927\uff0c\u5c06\u90e8\u5206\u8fdb\u4e00\u6b65\u62c6\u5206\u3002<\/td>\n<td data-id=\"t8c0e6ab-EpHkI49P\" data-transient-attributes=\"table-cell-selection\">\u5197\u957f\u3001\u590d\u6742\u6216\u5206\u5c42\u7684\u6587\u6863\uff08\u4f8b\u5982\u6280\u672f\u624b\u518c\uff09\u3002<\/td>\n<td data-id=\"t1b895c3-K3SmlLPU\" data-transient-attributes=\"table-cell-selection\">\u5982\u679c\u90e8\u5206\u8fc7\u4e8e\u5bbd\u6cdb\uff0c\u4ecd\u53ef\u80fd\u4f1a\u4e22\u5931\u573a\u666f\u3002<\/td>\n<\/tr>\n<tr data-id=\"t1ecfec6-N5TXCE32\">\n<td data-id=\"t04d953d-FEALT3c3\" data-transient-attributes=\"table-cell-selection\">\u8bed\u4e49\u5206\u5757<\/td>\n<td data-id=\"t04d953d-imMEPeD7\" data-transient-attributes=\"table-cell-selection\">\u6839\u636e\u610f\u4e49\u6216\u76f8\u5173\u4e3b\u9898\u5c06\u6587\u672c\u5206\u6210\u5757\u3002<\/td>\n<td data-id=\"t04d953d-28CPqlGq\" data-transient-attributes=\"table-cell-selection\">\u4f7f\u7528\u53e5\u5b50\u5d4c\u5165\u7b49NLP\u6280\u672f\u5bf9\u76f8\u5173\u5185\u5bb9\u8fdb\u884c\u62c6\u5206\u3002<\/td>\n<td data-id=\"t04d953d-JhgX0hlZ\" data-transient-attributes=\"table-cell-selection\">\u573a\u666f\u654f\u611f\u7684\u4efb\u52a1\uff0c\u8fde\u8d2f\u6027\u548c\u4e3b\u9898\u8fde\u7eed\u6027\u81f3\u5173\u91cd\u8981\u3002<\/td>\n<td data-id=\"t0b4e833-O2lK9PWS\" data-transient-attributes=\"table-cell-selection\">\u9700\u8981NLP\u6280\u672f\uff1b\u5b9e\u65bd\u8d77\u6765\u66f4\u590d\u6742\u3002<\/td>\n<\/tr>\n<tr data-id=\"t31e458f-IbUZ10bc\">\n<td data-id=\"t1c8f800-H4B9gEZh\" data-transient-attributes=\"table-cell-selection\">\u4ee3\u7406\u5206\u5757<\/td>\n<td data-id=\"t1c8f800-PUZed65i\" data-transient-attributes=\"table-cell-selection\">\u5229\u7528\u4eba\u5de5\u667a\u80fd\u6a21\u578b\uff08\u5982GPT\uff09\u5c06\u5185\u5bb9\u81ea\u4e3b\u62c6\u5206\u4e3a\u6709\u610f\u4e49\u7684\u90e8\u5206\u3002<\/td>\n<td data-id=\"t1c8f800-5JFUmUTb\" data-transient-attributes=\"table-cell-selection\">\u57fa\u4e8e\u6a21\u578b\u7684\u7406\u89e3\u548c\u7279\u5b9a\u4efb\u52a1\u7684\u573a\u666f\u91c7\u7528\u4eba\u5de5\u667a\u80fd\u9a71\u52a8\u7684\u62c6\u5206\u3002<\/td>\n<td data-id=\"t1c8f800-d8cNKY8C\" data-transient-attributes=\"table-cell-selection\">\u5728\u5185\u5bb9\u7ed3\u6784\u591a\u53d8\u7684\u590d\u6742\u4efb\u52a1\u4e2d\uff0c\u4eba\u5de5\u667a\u80fd\u53ef\u4ee5\u4f18\u5316\u5206\u5757\u3002<\/td>\n<td data-id=\"t16089ba-7WWBRmH7\" data-transient-attributes=\"table-cell-selection\">\u53ef\u80fd\u5177\u6709\u4e0d\u53ef\u9884\u6d4b\u6027\uff0c\u5e76\u9700\u8981\u8fdb\u884c\u8c03\u6574\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>\u7ed3\u8bba<\/h3>\n<p>\u5206\u5757\u7b56\u7565\u4e0e\u68c0\u7d22\u589e\u5f3a\u751f\u6210\uff08RAG\uff09\u5bf9\u4e8e\u63d0\u5347LLM\u6027\u80fd\u81f3\u5173\u91cd\u8981\u3002\u5206\u5757\u7b56\u7565\u6709\u52a9\u4e8e\u5c06\u590d\u6742\u6570\u636e\u7b80\u5316\u4e3a\u66f4\u5c0f\u3001\u66f4\u6613\u7ba1\u7406\u7684\u90e8\u5206\uff0c\u4ece\u800c\u4fc3\u8fdb\u66f4\u9ad8\u6548\u7684\u5904\u7406\uff1b\u800cRAG\u901a\u8fc7\u5728\u751f\u6210\u5de5\u4f5c\u6d41\u4e2d\u878d\u5165\u5b9e\u65f6\u6570\u636e\u68c0\u7d22\u6765\u6539\u8fdbLLM\u3002\u603b\u7684\u6765\u8bf4\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u901a\u8fc7\u5c06\u6709\u7ec4\u7ec7\u7684\u6570\u636e\u4e0e\u751f\u52a8\u3001\u5b9e\u65f6\u7684\u4fe1\u606f\u76f8\u7ed3\u5408\uff0c\u4f7fLLM\u80fd\u591f\u63d0\u4f9b\u66f4\u7cbe\u786e\u3001\u66f4\u8d34\u5408\u573a\u666f\u7684\u56de\u590d\u3002<\/p>\n<p>\u6587\u7ae0\u6765\u81ea\uff1a51CTO<\/p>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_23644\" class=\"pvc_stats total_only  \" data-element-id=\"23644\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" version=\"1.0\" viewBox=\"0 0 502 315\" preserveAspectRatio=\"xMidYMid meet\"><g transform=\"translate(0,332) scale(0.1,-0.1)\" fill=\"\" stroke=\"none\"><path d=\"M2394 3279 l-29 -30 -3 -207 c-2 -182 0 -211 15 -242 39 -76 157 -76 196 0 15 31 17 60 15 243 l-3 209 -33 29 c-26 23 -41 29 -80 29 -41 0 -53 -5 -78 -31z\"\/><path d=\"M3085 3251 c-45 -19 -58 -50 -96 -229 -47 -217 -49 -260 -13 -295 52 -53 146 -42 177 20 16 31 87 366 87 410 0 70 -86 122 -155 94z\"\/><path d=\"M1751 3234 c-13 -9 -29 -31 -37 -50 -12 -29 -10 -49 21 -204 19 -94 39 -189 45 -210 14 -50 54 -80 110 -80 34 0 48 6 76 34 21 21 34 44 34 59 0 14 -18 113 -40 219 -37 178 -43 195 -70 221 -36 32 -101 37 -139 11z\"\/><path d=\"M1163 3073 c-36 -7 -73 -59 -73 -102 0 -56 133 -378 171 -413 34 -32 83 -37 129 -13 70 36 67 87 -16 290 -86 209 -89 214 -129 231 -35 14 -42 15 -82 7z\"\/><path d=\"M3689 3066 c-15 -9 -33 -30 -42 -48 -48 -103 -147 -355 -147 -375 0 -98 131 -148 192 -74 13 15 57 108 97 206 80 196 84 226 37 273 -30 30 -99 39 -137 18z\"\/><path d=\"M583 2784 c-38 -19 -67 -74 -58 -113 9 -42 211 -354 242 -373 16 -10 45 -18 66 -18 51 0 107 52 107 100 0 39 -1 41 -124 234 -80 126 -108 162 -133 173 -41 17 -61 16 -100 -3z\"\/><path d=\"M4250 2784 c-14 -9 -74 -91 -133 -183 -95 -150 -107 -173 -107 -213 0 -55 33 -94 87 -104 67 -13 90 8 211 198 130 202 137 225 78 284 -27 27 -42 34 -72 34 -22 0 -50 -8 -64 -16z\"\/><path d=\"M2275 2693 c-553 -48 -1095 -270 -1585 -649 -135 -104 -459 -423 -483 -476 -23 -49 -22 -139 2 -186 73 -142 361 -457 571 -626 285 -228 642 -407 990 -497 242 -63 336 -73 660 -74 310 0 370 5 595 52 535 111 1045 392 1455 803 122 121 250 273 275 326 19 41 19 137 0 174 -41 79 -309 363 -465 492 -447 370 -946 591 -1479 653 -113 14 -422 18 -536 8z m395 -428 c171 -34 330 -124 456 -258 112 -119 167 -219 211 -378 27 -96 24 -300 -5 -401 -72 -255 -236 -447 -474 -557 -132 -62 -201 -76 -368 -76 -167 0 -236 14 -368 76 -213 98 -373 271 -451 485 -162 444 86 934 547 1084 153 49 292 57 452 25z m909 -232 c222 -123 408 -262 593 -441 76 -74 138 -139 138 -144 0 -16 -233 -242 -330 -319 -155 -123 -309 -223 -461 -299 l-81 -41 32 46 c18 26 49 83 70 128 143 306 141 649 -6 957 -25 52 -61 116 -79 142 l-34 47 45 -20 c26 -10 76 -36 113 -56z m-2057 25 c-40 -58 -105 -190 -130 -263 -110 -324 -59 -707 132 -981 25 -35 42 -64 37 -64 -19 0 -241 119 -326 174 -188 122 -406 314 -532 468 l-58 71 108 103 c185 178 428 349 672 473 66 33 121 60 123 61 2 0 -10 -19 -26 -42z\"\/><path d=\"M2375 1950 c-198 -44 -350 -190 -395 -379 -18 -76 -8 -221 19 -290 114 -284 457 -406 731 -260 98 52 188 154 231 260 27 69 37 214 19 290 -38 163 -166 304 -326 360 -67 23 -215 33 -279 19z\"\/><\/g><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/aif.amtbbs.org\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=0 \/><\/p>\n<div class=\"pvc_clear\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u672c\u6587\u5c06\u66f4\u6df1\u5165\u5730\u63a2\u8ba8LLM\u4e0d\u540c\u7684\u5206\u5757\u65b9\u6cd5\u53ca\u5176\u7b56\u7565\uff0c\u4ee5\u53ca\u5b83\u4eec\u5728\u4e3a\u73b0\u5b9e\u4e16\u754c\u7684\u5e94\u7528\u7a0b\u5e8f\u4f18\u5316LLM\u4e2d\u7684\u4f5c\u7528\u3002 &#038;nbsp [&hellip;]<\/p>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_23644\" class=\"pvc_stats total_only  \" data-element-id=\"23644\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" version=\"1.0\" viewBox=\"0 0 502 315\" preserveAspectRatio=\"xMidYMid meet\"><g transform=\"translate(0,332) scale(0.1,-0.1)\" fill=\"\" stroke=\"none\"><path d=\"M2394 3279 l-29 -30 -3 -207 c-2 -182 0 -211 15 -242 39 -76 157 -76 196 0 15 31 17 60 15 243 l-3 209 -33 29 c-26 23 -41 29 -80 29 -41 0 -53 -5 -78 -31z\"\/><path d=\"M3085 3251 c-45 -19 -58 -50 -96 -229 -47 -217 -49 -260 -13 -295 52 -53 146 -42 177 20 16 31 87 366 87 410 0 70 -86 122 -155 94z\"\/><path d=\"M1751 3234 c-13 -9 -29 -31 -37 -50 -12 -29 -10 -49 21 -204 19 -94 39 -189 45 -210 14 -50 54 -80 110 -80 34 0 48 6 76 34 21 21 34 44 34 59 0 14 -18 113 -40 219 -37 178 -43 195 -70 221 -36 32 -101 37 -139 11z\"\/><path d=\"M1163 3073 c-36 -7 -73 -59 -73 -102 0 -56 133 -378 171 -413 34 -32 83 -37 129 -13 70 36 67 87 -16 290 -86 209 -89 214 -129 231 -35 14 -42 15 -82 7z\"\/><path d=\"M3689 3066 c-15 -9 -33 -30 -42 -48 -48 -103 -147 -355 -147 -375 0 -98 131 -148 192 -74 13 15 57 108 97 206 80 196 84 226 37 273 -30 30 -99 39 -137 18z\"\/><path d=\"M583 2784 c-38 -19 -67 -74 -58 -113 9 -42 211 -354 242 -373 16 -10 45 -18 66 -18 51 0 107 52 107 100 0 39 -1 41 -124 234 -80 126 -108 162 -133 173 -41 17 -61 16 -100 -3z\"\/><path d=\"M4250 2784 c-14 -9 -74 -91 -133 -183 -95 -150 -107 -173 -107 -213 0 -55 33 -94 87 -104 67 -13 90 8 211 198 130 202 137 225 78 284 -27 27 -42 34 -72 34 -22 0 -50 -8 -64 -16z\"\/><path d=\"M2275 2693 c-553 -48 -1095 -270 -1585 -649 -135 -104 -459 -423 -483 -476 -23 -49 -22 -139 2 -186 73 -142 361 -457 571 -626 285 -228 642 -407 990 -497 242 -63 336 -73 660 -74 310 0 370 5 595 52 535 111 1045 392 1455 803 122 121 250 273 275 326 19 41 19 137 0 174 -41 79 -309 363 -465 492 -447 370 -946 591 -1479 653 -113 14 -422 18 -536 8z m395 -428 c171 -34 330 -124 456 -258 112 -119 167 -219 211 -378 27 -96 24 -300 -5 -401 -72 -255 -236 -447 -474 -557 -132 -62 -201 -76 -368 -76 -167 0 -236 14 -368 76 -213 98 -373 271 -451 485 -162 444 86 934 547 1084 153 49 292 57 452 25z m909 -232 c222 -123 408 -262 593 -441 76 -74 138 -139 138 -144 0 -16 -233 -242 -330 -319 -155 -123 -309 -223 -461 -299 l-81 -41 32 46 c18 26 49 83 70 128 143 306 141 649 -6 957 -25 52 -61 116 -79 142 l-34 47 45 -20 c26 -10 76 -36 113 -56z m-2057 25 c-40 -58 -105 -190 -130 -263 -110 -324 -59 -707 132 -981 25 -35 42 -64 37 -64 -19 0 -241 119 -326 174 -188 122 -406 314 -532 468 l-58 71 108 103 c185 178 428 349 672 473 66 33 121 60 123 61 2 0 -10 -19 -26 -42z\"\/><path d=\"M2375 1950 c-198 -44 -350 -190 -395 -379 -18 -76 -8 -221 19 -290 114 -284 457 -406 731 -260 98 52 188 154 231 260 27 69 37 214 19 290 -38 163 -166 304 -326 360 -67 23 -215 33 -279 19z\"\/><\/g><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/aif.amtbbs.org\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=0 \/><\/p>\n<div class=\"pvc_clear\"><\/div>\n","protected":false},"author":56,"featured_media":23646,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[23,20,80],"tags":[986,1188,1052],"class_list":["post-23644","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-23","category-20","category-80","tag-llm","tag-rag","tag-1052"],"_links":{"self":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/23644","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/users\/56"}],"replies":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/comments?post=23644"}],"version-history":[{"count":1,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/23644\/revisions"}],"predecessor-version":[{"id":23647,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/23644\/revisions\/23647"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media\/23646"}],"wp:attachment":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media?parent=23644"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/categories?post=23644"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/tags?post=23644"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}