{"id":85465,"date":"2026-07-27T16:00:40","date_gmt":"2026-07-27T08:00:40","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/85465.html"},"modified":"2026-07-27T16:00:40","modified_gmt":"2026-07-27T08:00:40","slug":"rag%e6%a3%80%e7%b4%a2%e7%9a%84%e6%98%af%e6%96%87%e6%a1%a3%ef%bc%8c%e4%bd%86%e5%a6%82%e6%9e%9c%e9%97%ae%e9%a2%98%e9%9c%80%e8%a6%81%e8%b7%a8%e8%b6%8a%e5%a4%9a%e4%b8%aa%e6%a6%82%e5%bf%b5%e6%89%8d","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/85465.html","title":{"rendered":"RAG\u68c0\u7d22\u7684\u662f\u6587\u6863\uff0c\u4f46\u5982\u679c\u95ee\u9898\u9700\u8981\u8de8\u8d8a\u591a\u4e2a\u6982\u5ff5\u624d\u80fd\u63a8\u7406\u51fa\u7b54\u6848\uff0c\u7b80\u5355\u7684\u6587\u6863\u68c0\u7d22\u8fd8\u80fd\u4e0d\u80fd\u67b6\u8d77\u8fd9\u5ea7\u6865\u6881\uff1f"},"content":{"rendered":"<p>\u5728RAG&#xff08;\u68c0\u7d22\u589e\u5f3a\u751f\u6210&#xff09;\u5df2\u7ecf\u6210\u4e3a\u89e3\u51b3\u5927\u6a21\u578b\u5e7b\u89c9\u95ee\u9898\u7684\u4e3b\u6d41\u65b9\u6848\u65f6&#xff0c;\u6709\u4e00\u4e2a\u9879\u76ee\u63d0\u51fa\u4e86\u4e00\u4e2a\u76f4\u51fb\u672c\u8d28\u7684\u8d28\u7591&#xff1a;RAG\u68c0\u7d22\u7684\u662f\u6587\u6863&#xff0c;\u4f46\u5982\u679c\u95ee\u9898\u9700\u8981\u8de8\u8d8a\u591a\u4e2a\u6982\u5ff5\u624d\u80fd\u63a8\u7406\u51fa\u7b54\u6848&#xff0c;\u7b80\u5355\u7684\u6587\u6863\u68c0\u7d22\u8fd8\u80fd\u4e0d\u80fd\u67b6\u8d77\u8fd9\u5ea7\u6865\u6881&#xff1f;<\/p>\n<p>\u9879\u76ee\u5730\u5740&#xff1a;https:\/\/github.com\/dfytensor\/GSG-LLM<\/p>\n<hr \/>\n<h3>\u4e00\u3001RAG\u7684\u76f2\u533a&#xff1a;\u5f53\u68c0\u7d22\u65e0\u6cd5&#034;\u67b6\u6865&#034;<\/h3>\n<p>RAG\u7684\u601d\u8def\u5f88\u76f4\u89c2&#xff1a;\u7528\u6237\u63d0\u95ee \u2192 \u68c0\u7d22\u76f8\u5173\u6587\u6863 \u2192 \u628a\u6587\u6863\u585e\u8fdb\u4e0a\u4e0b\u6587 \u2192 \u8ba9LLM\u751f\u6210\u7b54\u6848\u3002<\/p>\n<p>\u8fd9\u4e2a\u6d41\u7a0b\u5728&#034;\u5df4\u9ece\u662f\u54ea\u4e2a\u56fd\u5bb6\u7684\u9996\u90fd&#034;\u8fd9\u7c7b\u5355\u8df3&#xff08;Single-hop&#xff09; \u95ee\u9898\u4e0a\u8868\u73b0\u826f\u597d\u2014\u2014\u68c0\u7d22&#034;\u5df4\u9ece&#034;\u5c31\u80fd\u62ff\u5230\u7b54\u6848\u3002<\/p>\n<p>\u4f46\u73b0\u5b9e\u4e2d\u7684\u95ee\u9898\u5f80\u5f80\u662f\u591a\u8df3&#xff08;Multi-hop&#xff09; \u7684&#xff1a;<\/p>\n<p>\u201c\u90a3\u4f4d\u57282020\u5e74\u83b7\u5f97\u8bfa\u8d1d\u5c14\u6587\u5b66\u5956\u7684\u7f8e\u56fd\u5973\u8bd7\u4eba\u7684\u7b2c\u4e00\u90e8\u8bd7\u96c6\u662f\u4ec0\u4e48&#xff1f;\u201d<\/p>\n<p>\u8fd9\u4e2a\u95ee\u9898\u9700\u8981\u5148\u68c0\u7d22&#034;2020\u5e74\u8bfa\u8d1d\u5c14\u6587\u5b66\u5956\u5f97\u4e3b&#034;\u2192 \u5f97\u5230&#034;\u9732\u6613\u4e1d\u00b7\u683c\u4e3d\u514b&#034;\u2192 \u518d\u68c0\u7d22&#034;\u683c\u4e3d\u514b\u7684\u7b2c\u4e00\u90e8\u8bd7\u96c6&#034;\u2192 \u5f97\u5230\u300a\u5934\u751f\u5b50\u300b\u3002\u8fd9\u662f\u4e24\u4e2a\u68c0\u7d22\u6b65\u9aa4\u7684\u4e32\u8054\u3002<\/p>\n<p>\u4f20\u7edf\u7684RAG\u662f\u4e00\u6b21\u6027\u68c0\u7d22&#xff0c;\u5b83\u68c0\u7d22\u7684\u662f\u6587\u6863&#xff0c;\u800c\u4e0d\u662f\u6982\u5ff5\u4e4b\u95f4\u7684\u51e0\u4f55\u5173\u7cfb\u3002\u5f53\u95ee\u9898\u9700\u8981\u8de8\u8d8a\u591a\u4e2a\u6982\u5ff5\u65f6&#xff0c;\u4e00\u6b21\u6027\u68c0\u7d22\u5f80\u5f80\u6293\u4e0d\u4f4f\u90a3\u6761&#034;\u63a8\u7406\u94fe&#034;\u3002<\/p>\n<p>GSG-LLM \u9879\u76ee\u7ed9\u51fa\u7684\u7b54\u6848\u662f&#xff1a;\u628a\u6587\u672c\u8bed\u4e49\u7a7a\u95f4\u53d8\u6210\u4e00\u4e2a\u51e0\u4f55\u6d41\u5f62&#xff0c;\u7528&#034;\u951a\u70b9&#034;&#xff08;Anchors&#xff09;\u6765\u6784\u5efa\u77e5\u8bc6\u7684\u9aa8\u67b6&#xff0c;\u7136\u540e\u901a\u8fc7&#034;\u7ec6\u5206&#034;&#xff08;Subdivision&#xff09;\u9010\u6b65\u903c\u8fd1\u7b54\u6848\u3002<\/p>\n<p>\u8fd9\u4e0d\u662f\u5728\u5df2\u6709RAG\u4e0a\u4fee\u4fee\u8865\u8865\u2014\u2014\u5b83\u662f\u4ece\u51e0\u4f55\u5b66\u7684\u89d2\u5ea6\u91cd\u65b0\u601d\u8003\u4e86&#034;LLM\u5e94\u8be5\u5982\u4f55\u68c0\u7d22\u548c\u63a8\u7406&#034;\u3002<\/p>\n<hr \/>\n<h3>\u4e8c\u3001GSG\u7684\u7075\u611f\u6765\u6e90&#xff1a;\u4ece\u56fe\u5f62\u5b66\u5230\u5927\u8bed\u8a00\u6a21\u578b<\/h3>\n<p>GSG\u5168\u79f0 Geometric Subdivision Generation&#xff08;\u51e0\u4f55\u7ec6\u5206\u751f\u6210&#xff09; &#xff0c;\u8fd9\u4e2a\u6982\u5ff5\u6700\u65e9\u51fa\u73b0\u5728\u56fe\u50cf\u548c\u7f51\u683c\u5408\u6210\u9886\u57df\u3002<\/p>\n<p>\u5728\u56fe\u5f62\u5b66\u4e2d&#xff0c;GSG\u53d1\u73b0\u4e86\u4e00\u4e2a\u89c4\u5f8b&#xff1a;\u951a\u70b9&#xff08;Anchors&#xff09;\u63d0\u4f9b\u4e86\u4e00\u4e2a\u51e0\u4f55\u9aa8\u67b6\u2014\u2014\u53ea\u9700\u8981\u5c11\u91cf\u7cbe\u5fc3\u653e\u7f6e\u7684\u9876\u70b9&#xff0c;\u5c31\u80fd\u4ee5\u66f4\u5c11\u7684\u53bb\u566a\u6b65\u6570\u548c\u66f4\u9ad8\u7684\u8d28\u91cf\u91cd\u5efa\u51fa\u5b8c\u6574\u7684\u5f62\u72b6&#xff0c;\u8fdc\u4f18\u4e8e\u5bc6\u96c6\u91c7\u6837\u3002<\/p>\n<p>\u8fd9\u4e2a\u9879\u76ee\u95ee\u4e86\u4e00\u4e2a\u8de8\u754c\u7684\u95ee\u9898&#xff1a;\u540c\u6837\u7684\u539f\u7406\u80fd\u4e0d\u80fd\u7528\u6765&#034;\u951a\u5b9a&#034;\u8bed\u8a00\u6a21\u578b&#xff1f;<\/p>\n<table>\n<tr>\u56fe\u5f62\u5b66GSGGSG-LLM<\/tr>\n<tbody>\n<tr>\n<td>\u951a\u70b9 &#061; \u5f62\u72b6\u7684\u9876\u70b9<\/td>\n<td>\u951a\u70b9 &#061; \u4e8b\u5b9e\u7684\u5d4c\u5165\u5411\u91cf&#xff08;Fact Embeddings&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u7ec6\u5206 &#061; \u7ec6\u5316\u7f51\u683c<\/td>\n<td>\u7ec6\u5206 &#061; \u4ece\u7c97\u5230\u7ec6\u7684\u68c0\u7d22 &#043; \u63a8\u7406<\/td>\n<\/tr>\n<tr>\n<td>\u6821\u6b63 &#061; \u53bb\u566a\u6b65\u9aa4<\/td>\n<td>\u6821\u6b63 &#061; \u51f8\u5305&#xff08;Convex-hull&#xff09;logit\u504f\u7f6e<\/td>\n<\/tr>\n<tr>\n<td>20\u6b65 &#061; DDPM 500\u6b65<\/td>\n<td>\u5c11\u91cf\u951a\u70b9token &#061; \u5b8c\u6574\u751f\u6210<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u6838\u5fc3\u6d1e\u5bdf&#xff1a; \u77e5\u8bc6\u7684\u8bed\u4e49\u7a7a\u95f4\u5c31\u50cf\u4e00\u4e2a\u51e0\u4f55\u6d41\u5f62&#xff0c;\u4e0d\u9700\u8981\u68c0\u7d22\u6240\u6709\u76f8\u5173\u6587\u6863&#xff0c;\u53ea\u9700\u8981\u627e\u5230\u8fd9\u4e2a\u7a7a\u95f4\u91cc\u7684&#034;\u951a\u70b9&#034;&#xff08;\u5173\u952e\u6982\u5ff5\u8282\u70b9&#xff09;&#xff0c;\u7136\u540e\u901a\u8fc7&#034;\u7ec6\u5206&#034;\u9010\u6b65\u903c\u8fd1\u76ee\u6807\u533a\u57df\u2014\u2014\u63a8\u7406\u8fc7\u7a0b\u5c31\u53d8\u6210\u4e86\u5728\u8bed\u4e49\u51e0\u4f55\u7a7a\u95f4\u4e2d\u7684\u5bfc\u822a\u3002<\/p>\n<hr \/>\n<h3>\u4e09\u3001\u4e09\u5927\u6838\u5fc3\u521b\u65b0&#xff1a;GSG\u662f\u600e\u4e48\u505a\u5230\u7684&#xff1f;<\/h3>\n<p>exp_gsg_anchor_llm.py \u662f\u8fd9\u4e2a\u9879\u76ee\u7684\u6838\u5fc3\u8d21\u732e\u3002\u4e0e\u7b80\u5355\u7684RAG\u4e0d\u540c&#xff0c;\u5b83\u5f15\u5165\u4e86\u4e09\u9879\u521b\u65b0&#xff1a;<\/p>\n<h4>3.1 \u9884\u5b66\u4e60\u7684\u951a\u70b9\u6d41\u5f62&#xff08;Pre-learned Anchor Manifold&#xff09;<\/h4>\n<p>\u4f20\u7edfRAG\u68c0\u7d22\u7684\u662f\u6587\u6863\u3002GSG\u68c0\u7d22\u7684\u662f\u951a\u70b9\u2014\u2014\u901a\u8fc7K-means Medoids &#043; GSG\u7ec6\u5206&#xff08;\u4e2d\u70b9\u951a\u70b9&#xff09;&#043; \u76f8\u4f3c\u6027\u526a\u679d&#xff0c;\u5728\u77e5\u8bc6\u5e93\u7684\u5d4c\u5165\u7a7a\u95f4\u4e0a\u6784\u5efa\u51fa\u4e00\u7ec4\u951a\u70b9\u3002<\/p>\n<p>\u8fd9\u4e9b\u951a\u70b9\u4e0d\u662f\u539f\u59cb\u6587\u6863&#xff0c;\u800c\u662f\u77e5\u8bc6\u62d3\u6251\u7ed3\u6784\u7684&#034;\u9876\u70b9&#034; \u2014\u2014\u5b83\u4eec\u4ee3\u8868\u4e86\u8bed\u4e49\u7a7a\u95f4\u4e2d\u6700\u6838\u5fc3\u3001\u6700\u5177\u4ee3\u8868\u6027\u7684\u6982\u5ff5\u8282\u70b9\u3002<\/p>\n<h4>3.2 \u51f8\u5305Logit\u504f\u7f6e&#xff08;Convex-hull Logit Bias&#xff09;<\/h4>\n<p>GSG\u4e0d\u662f\u7b80\u5355\u5730\u7528\u68c0\u7d22\u7ed3\u679c\u586b\u5145\u4e0a\u4e0b\u6587\u3002\u5b83\u5728\u751f\u6210\u8fc7\u7a0b\u4e2d&#xff0c;\u5c06token\u7684logits\u67d4\u548c\u5730\u504f\u7f6e\u5230\u951a\u70b9\u8bcd\u6c47\u8868\u65b9\u5411&#xff0c;\u5c06\u751f\u6210\u7ea6\u675f\u5728\u951a\u70b9\u5b9a\u4e49\u7684\u8bed\u4e49\u51f8\u5305\u5185&#xff0c;\u800c\u4e0d\u662f\u786c\u6027\u63a9\u7801\u3002<\/p>\n<p>\u901a\u4fd7\u5730\u8bf4&#xff1a;\u6a21\u578b\u53ef\u4ee5\u81ea\u7531\u53d1\u6325&#xff0c;\u4f46\u4e0d\u80fd\u8dd1\u51fa\u951a\u70b9\u56f4\u6210\u7684&#034;\u8bed\u4e49\u56f4\u680f&#034; \u2014\u2014\u8fd9\u65e2\u4fdd\u8bc1\u4e86\u591a\u6837\u6027&#xff0c;\u53c8\u9632\u6b62\u4e86\u5e7b\u89c9\u3002<\/p>\n<h4>3.3 \u6e10\u8fdb\u5f0f\u7ec6\u5206&#xff08;Progressive Subdivision&#xff09;<\/h4>\n<p>\u8fd9\u662fGSG\u6700\u5de7\u5999\u7684\u8bbe\u8ba1&#xff1a;8 \u2192 4 \u2192 2 \u2192 \u7b54\u6848\u3002<\/p>\n<p>\u6bcf\u4e00\u6b65\u90fd\u7528\u4e0a\u4e00\u6b65\u7684\u8f93\u51fa\u4f5c\u4e3a\u7cbe\u5316\u540e\u7684\u67e5\u8be2&#xff0c;\u68c0\u7d22\u66f4\u7ec6\u7c92\u5ea6\u7684\u951a\u70b9\u3002\u8fd9\u5c31\u50cf\u5728\u5730\u56fe\u4e0a\u5bfc\u822a&#xff1a;\u5148\u5b9a\u4f4d\u5230\u56fd\u5bb6\u2192\u518d\u5b9a\u4f4d\u5230\u57ce\u5e02\u2192\u518d\u5b9a\u4f4d\u5230\u8857\u9053\u2192\u6700\u540e\u627e\u5230\u95e8\u724c\u53f7\u3002\u6bcf\u4e00\u6b65\u90fd\u5728\u7f29\u5c0f\u641c\u7d22\u7a7a\u95f4&#xff0c;\u6bcf\u4e00\u6b65\u90fd\u5728\u903c\u8fd1\u6b63\u786e\u7b54\u6848\u3002<\/p>\n<hr \/>\n<h3>\u56db\u3001\u5b9e\u9a8c\u7ed3\u679c&#xff1a;\u591a\u8df3\u573a\u666f\u4e0b\u7684\u964d\u7ef4\u6253\u51fb<\/h3>\n<p>\u9879\u76ee\u5728Qwen2.5-3B\u4e0a\u8fdb\u884c\u4e86\u4e25\u683c\u7684QA\u57fa\u51c6\u6d4b\u8bd5&#xff0c;\u7ed3\u679c\u4ee4\u4eba\u9707\u64bc&#xff1a;<\/p>\n<table>\n<tr>\u7b56\u7565\u5355\u8df3F12\u8df3F13\u8df3F1Token\u6570<\/tr>\n<tbody>\n<tr>\n<td>\u65e0\u951a\u70b9<\/td>\n<td>0.300<\/td>\n<td>0.188<\/td>\n<td>0.174<\/td>\n<td>~15<\/td>\n<\/tr>\n<tr>\n<td>\u6734\u7d20RAG<\/td>\n<td>0.302<\/td>\n<td>0.181<\/td>\n<td>0.179<\/td>\n<td>~15<\/td>\n<\/tr>\n<tr>\n<td>GSG 2\u6b65<\/td>\n<td>0.313<\/td>\n<td>0.175<\/td>\n<td>0.199<\/td>\n<td>~15<\/td>\n<\/tr>\n<tr>\n<td>GSG 3\u6b65<\/td>\n<td>0.378<\/td>\n<td>0.219<\/td>\n<td>0.237<\/td>\n<td>~12<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u51e0\u4e2a\u5173\u952e\u53d1\u73b0&#xff1a;<\/p>\n<li>\n<p>\u8df3\u6570\u8d8a\u591a&#xff0c;\u4f18\u52bf\u8d8a\u5927&#xff1a;\u57283\u8df3\u95ee\u9898\u4e0a&#xff0c;GSG 3\u6b65\u7684F1&#xff08;0.237&#xff09;\u6bd4\u65e0\u951a\u70b9&#xff08;0.174&#xff09;\u63d0\u5347\u4e8636%&#xff0c;\u6bd4\u6734\u7d20RAG&#xff08;0.179&#xff09;\u63d0\u5347\u4e8632%\u3002<\/p>\n<\/li>\n<li>\n<p>\u7528\u66f4\u5c11\u7684token&#xff0c;\u6253\u66f4\u9ad8\u7684\u5206&#xff1a;GSG 3\u6b65\u53ea\u7528~12\u4e2atoken&#xff0c;\u6bd4\u57fa\u7ebf\u5c11\u4e8620%\u3002\u951a\u70b9\u538b\u7f29\u4e86\u641c\u7d22\u7a7a\u95f4&#xff0c;\u8ba9\u6a21\u578b\u7528\u66f4\u5c11\u7684\u8ba1\u7b97\u62ff\u5230\u66f4\u597d\u7684\u7ed3\u679c\u3002<\/p>\n<\/li>\n<li>\n<p>\u4f18\u52bf\u968f\u8df3\u6570\u589e\u957f&#xff1a;\u8fd9\u6b63\u662fGSG\u7684\u7406\u8bba\u9884\u6d4b\u2014\u2014\u951a\u70b9\u538b\u7f29\u4e86\u641c\u7d22\u7a7a\u95f4&#xff0c;\u8df3\u6570\u8d8a\u591a&#xff0c;\u538b\u7f29\u6548\u679c\u8d8a\u660e\u663e\u3002<\/p>\n<\/li>\n<p>\u4e00\u53e5\u8bdd\u603b\u7ed3&#xff1a;\u5728\u9700\u8981\u591a\u6b65\u63a8\u7406\u7684\u573a\u666f\u4e0b&#xff0c;GSG\u7528\u66f4\u5c11\u7684token&#xff0c;\u62ff\u5230\u4e86\u8fdc\u8d85RAG\u7684\u51c6\u786e\u7387\u3002<\/p>\n<hr \/>\n<h3>\u4e94\u3001\u66f4\u6fc0\u8fdb\u7684\u65b9\u5411&#xff1a;\u5f7b\u5e95\u544a\u522b\u81ea\u56de\u5f52<\/h3>\n<p>GSG-LLM\u8fd8\u4e0d\u6b62\u4e8e&#034;\u66f4\u597d\u7684RAG&#034;\u3002\u9879\u76ee\u4e2d\u8fd8\u6709\u4e00\u4e2a\u66f4 ambitious \u7684\u63a2\u7d22\u2014\u2014\u5b8c\u5168\u8df3\u8fc7\u9010token\u7684\u81ea\u56de\u5f52\u751f\u6210&#xff1a;<\/p>\n<p>\u7528\u6237\u63d0\u793a \u2192 VAE\u7f16\u7801 \u2192 \u68c0\u7d22\u951a\u70b9 \u2192 \u63d2\u503c \u2192 Langevin\u6821\u6b63 \u2192 VAE\u89e3\u7801 \u2192 \u6587\u672c<\/p>\n<p>\u8fd9\u4e2a\u975e\u81ea\u56de\u5f52\u7ba1\u7ebf\u7684\u4e09\u6b65\u8d70&#xff1a;<\/p>\n<ul>\n<li>Step 1&#xff08;exp_vae_step1.py&#xff09;&#xff1a;\u8bad\u7ec3\u4e00\u4e2aTransformer-VAE&#xff0c;\u5c06\u6587\u672c \u21cc \u6f5c\u5728\u7a7a\u95f4z&#xff08;256\u7ef4&#xff09;<\/li>\n<li>Step 2&#xff08;exp_vae_step2.py&#xff09;&#xff1a;\u6784\u5efa1000\u951a\u70b9\u7684\u6d41\u5f62 &#043; \u901a\u8fc7\u53bb\u566a\u5206\u6570\u5339\u914d&#xff08;DSM&#xff09;\u8bad\u7ec3\u6f5c\u5728\u5206\u6570\u7f51\u7edc<\/li>\n<li>Step 3&#xff08;exp_vae_step3.py&#xff09;&#xff1a;\u5728\u540c\u4e00\u4e2aVAE\u4e0a\u5bf9\u6bd4GSG\u89e3\u7801 vs \u81ea\u56de\u5f52\u89e3\u7801\u2014\u2014\u6bd4\u8f83\u65b0\u9896\u6027\u3001\u591a\u6837\u6027\u3001BLEU\u3001\u71b5<\/li>\n<\/ul>\n<p>\u8fd9\u610f\u5473\u7740\u4ec0\u4e48&#xff1f;\u610f\u5473\u7740\u751f\u6210\u4e0d\u518d\u662f\u4e00\u4e2a\u5b57\u4e00\u4e2a\u5b57\u5f80\u5916\u8e66&#xff0c;\u800c\u662f\u5728\u8bed\u4e49\u7684\u6f5c\u5728\u7a7a\u95f4\u91cc\u76f4\u63a5&#034;\u8df3\u8dc3&#034;\u5230\u7b54\u6848\u533a\u57df&#xff0c;\u7136\u540e\u4e00\u6b21\u6027\u89e3\u7801\u51fa\u5b8c\u6574\u6587\u672c\u3002<\/p>\n<p>\u8fd9\u662f\u5bf9&#034;\u5927\u6a21\u578b\u5fc5\u987b\u81ea\u56de\u5f52&#034;\u8fd9\u4e2a\u57fa\u672c\u5047\u8bbe\u7684\u6311\u6218\u3002<\/p>\n<hr \/>\n<h3>\u516d\u3001\u5feb\u901f\u4e0a\u624b<\/h3>\n<h4>\u73af\u5883\u8981\u6c42<\/h4>\n<ul>\n<li>Python \u2265 3.10<\/li>\n<li>CUDA 12.4 GPU&#xff08;\u63a8\u8350&#xff09;<\/li>\n<li>PyTorch \u2265 2.6, transformers \u2265 4.40<\/li>\n<\/ul>\n<h4>\u4e00\u952e\u5b89\u88c5<\/h4>\n<p>uv <span class=\"token function\">sync<\/span>  <span class=\"token comment\"># \u81ea\u52a8\u5b89\u88c5 torch cu124, transformers, sklearn \u7b49\u4f9d\u8d56<\/span><\/p>\n<h4>\u8fd0\u884c\u57fa\u7ebf\u5b9e\u9a8c<\/h4>\n<p>python exp_anchor_llm.py  <span class=\"token comment\"># \u6253\u5370\u7ed3\u679c\u8868\u683c\u5e76\u4fdd\u5b58 JSON<\/span><\/p>\n<p>\u7ed3\u679c\u4f1a\u81ea\u52a8\u5199\u5165 experiments\/*.json\u3002<\/p>\n<h4>\u5df2\u6d4b\u8bd5\u7684\u57fa\u5ea7\u6a21\u578b<\/h4>\n<ul>\n<li>Qwen2.5-0.5B-Instruct<\/li>\n<li>Qwen2.5-3B-Instruct<\/li>\n<li>Nanbeige 4.2-3B&#xff08;\u6ca1\u9519&#xff0c;\u5c31\u662f\u4e0a\u4e00\u7bc7\u6587\u7ae0\u91cc\u7684\u90a3\u4e2a3B\u6a21\u578b&#xff09;<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e03\u3001\u5199\u5728\u6700\u540e&#xff1a;\u5f53\u5927\u6a21\u578b\u5f00\u59cb&#034;\u51e0\u4f55\u5730\u601d\u8003&#034;<\/h3>\n<p>RAG\u5f88\u5f3a\u5927&#xff0c;\u4f46\u5b83\u672c\u8d28\u4e0a\u662f\u5728\u505a\u6587\u672c\u5339\u914d\u2014\u2014\u627e\u76f8\u4f3c\u7684\u6587\u6863&#xff0c;\u585e\u8fdb\u4e0a\u4e0b\u6587\u3002<\/p>\n<p>GSG\u5728\u505a\u7684\u662f\u53e6\u4e00\u4ef6\u4e8b&#xff1a;\u628a\u77e5\u8bc6\u7684\u8bed\u4e49\u7a7a\u95f4\u5f53\u6210\u4e00\u4e2a\u51e0\u4f55\u6d41\u5f62&#xff0c;\u7528\u951a\u70b9\u52fe\u52d2\u51fa\u5b83\u7684\u9aa8\u67b6&#xff0c;\u7136\u540e\u7528&#034;\u7ec6\u5206&#034;\u8fd9\u4e2a\u51e0\u4f55\u64cd\u4f5c\u6765\u5b8c\u6210\u63a8\u7406\u3002<\/p>\n<p>\u8fd9\u4e2a\u89c6\u89d2\u7684\u8f6c\u6362&#xff0c;\u5e26\u6765\u7684\u4e0d\u4ec5\u662f\u6027\u80fd\u7684\u63d0\u5347\u2014\u2014\u5b83\u8ba9\u6211\u4eec\u91cd\u65b0\u601d\u8003\u4e86&#034;\u68c0\u7d22&#034;\u548c&#034;\u63a8\u7406&#034;\u4e4b\u95f4\u7684\u5173\u7cfb&#xff1a;<\/p>\n<ul>\n<li>RAG&#xff1a;\u68c0\u7d22\u6587\u6863 \u2192 \u8ba9LLM\u81ea\u5df1\u63a8\u7406<\/li>\n<li>GSG&#xff1a;\u68c0\u7d22\u6982\u5ff5&#xff08;\u951a\u70b9&#xff09;\u2192 \u5728\u8bed\u4e49\u7a7a\u95f4\u4e2d\u5bfc\u822a \u2192 \u9010\u6b65\u903c\u8fd1\u7b54\u6848<\/li>\n<\/ul>\n<p>\u5728\u591a\u8df3\u63a8\u7406\u573a\u666f\u4e0b&#xff0c;\u540e\u8005\u663e\u7136\u66f4\u7b26\u5408\u4eba\u7c7b\u8ba4\u77e5\u7684\u65b9\u5f0f\u2014\u2014\u6211\u4eec\u4e0d\u4f1a\u4e00\u6b21\u6027\u7ffb\u904d\u6240\u6709\u8d44\u6599&#xff0c;\u800c\u662f\u5148\u5b9a\u4f4d\u5927\u81f4\u65b9\u5411&#xff0c;\u518d\u9010\u6b65\u7f29\u5c0f\u8303\u56f4&#xff0c;\u6700\u540e\u7cbe\u51c6\u9501\u5b9a\u7b54\u6848\u3002<\/p>\n<p>GSG-LLM\u628a\u8fd9\u4e2a\u8fc7\u7a0b\u6570\u5b66\u5316\u4e86\u3001\u5de5\u7a0b\u5316\u4e86\u3001\u5f00\u6e90\u4e86\u3002<\/p>\n<hr \/>\n<p>\u9879\u76ee\u5730\u5740&#xff1a;https:\/\/github.com\/dfytensor\/GSG-LLM<\/p>\n<p>\u5982\u679c\u4f60&#xff1a;<\/p>\n<ul>\n<li>\u88abRAG\u5728\u591a\u8df3\u95ee\u9898\u4e0a\u7684\u8868\u73b0\u56f0\u6270\u8fc7<\/li>\n<li>\u60f3\u63a2\u7d22&#034;\u975e\u81ea\u56de\u5f52\u751f\u6210&#034;\u8fd9\u4e2a\u524d\u6cbf\u65b9\u5411<\/li>\n<li>\u5bf9\u5927\u6a21\u578b\u4e0e\u51e0\u4f55\u5b66\u7684\u4ea4\u53c9\u611f\u5174\u8da3<\/li>\n<\/ul>\n<p>\u90a3\u8fd9\u4e2a\u9879\u76ee\u503c\u5f97\u4f60\u82b1\u4e00\u4e2a\u665a\u4e0a\u8dd1\u4e00\u8dd1\u5b9e\u9a8c\u3002<\/p>\n<p>\u4ece\u56fe\u5f62\u5b66\u5230\u5927\u8bed\u8a00\u6a21\u578b&#xff0c;\u4ece\u7f51\u683c\u7ec6\u5206\u5230\u8bed\u4e49\u5bfc\u822a\u2014\u2014\u6709\u65f6\u5019&#xff0c;\u6700\u60ca\u8273\u7684\u7a81\u7834&#xff0c;\u6070\u6070\u6765\u81ea\u6700\u610f\u60f3\u4e0d\u5230\u7684\u8de8\u754c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5728RAG&#xff08;\u68c0\u7d22\u589e\u5f3a\u751f\u6210&#xff09;\u5df2\u7ecf\u6210\u4e3a\u89e3\u51b3\u5927\u6a21\u578b\u5e7b\u89c9\u95ee\u9898\u7684\u4e3b\u6d41\u65b9\u6848\u65f6&#xff0c;\u6709\u4e00\u4e2a\u9879\u76ee\u63d0\u51fa\u4e86\u4e00\u4e2a\u76f4\u51fb\u672c\u8d28\u7684\u8d28\u7591&#xff1a;RAG\u68c0\u7d22\u7684\u662f\u6587\u6863&#xff0c;\u4f46\u5982\u679c\u95ee\u9898\u9700\u8981\u8de8\u8d8a\u591a\u4e2a\u6982\u5ff5\u624d\u80fd\u63a8\u7406\u51fa\u7b54\u6848&#xff0c;\u7b80\u5355\u7684\u6587\u6863\u68c0\u7d22\u8fd8\u80fd\u4e0d\u80fd\u67b6\u8d77\u8fd9\u5ea7\u6865\u6881&#xff1f; \u9879\u76ee\u5730\u5740&#xff1a;https:\/\/github.com\/dfytensor\/GSG-LLM \u4e00\u3001RAG\u7684\u76f2\u533a&#xff1a;\u5f53\u68c0\u7d22\u65e0\u6cd5\\&#8221;\u67b6\u6865\\&#8221;<br \/>\nRAG\u7684\u601d\u8def\u5f88\u76f4\u89c2&amp;#xff1<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50],"topic":[],"class_list":["post-85465","post","type-post","status-publish","format-standard","hentry","category-server","tag-50"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>RAG\u68c0\u7d22\u7684\u662f\u6587\u6863\uff0c\u4f46\u5982\u679c\u95ee\u9898\u9700\u8981\u8de8\u8d8a\u591a\u4e2a\u6982\u5ff5\u624d\u80fd\u63a8\u7406\u51fa\u7b54\u6848\uff0c\u7b80\u5355\u7684\u6587\u6863\u68c0\u7d22\u8fd8\u80fd\u4e0d\u80fd\u67b6\u8d77\u8fd9\u5ea7\u6865\u6881\uff1f - 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content=\"\u5728RAG&#xff08;\u68c0\u7d22\u589e\u5f3a\u751f\u6210&#xff09;\u5df2\u7ecf\u6210\u4e3a\u89e3\u51b3\u5927\u6a21\u578b\u5e7b\u89c9\u95ee\u9898\u7684\u4e3b\u6d41\u65b9\u6848\u65f6&#xff0c;\u6709\u4e00\u4e2a\u9879\u76ee\u63d0\u51fa\u4e86\u4e00\u4e2a\u76f4\u51fb\u672c\u8d28\u7684\u8d28\u7591&#xff1a;RAG\u68c0\u7d22\u7684\u662f\u6587\u6863&#xff0c;\u4f46\u5982\u679c\u95ee\u9898\u9700\u8981\u8de8\u8d8a\u591a\u4e2a\u6982\u5ff5\u624d\u80fd\u63a8\u7406\u51fa\u7b54\u6848&#xff0c;\u7b80\u5355\u7684\u6587\u6863\u68c0\u7d22\u8fd8\u80fd\u4e0d\u80fd\u67b6\u8d77\u8fd9\u5ea7\u6865\u6881&#xff1f; \u9879\u76ee\u5730\u5740&#xff1a;https:\/\/github.com\/dfytensor\/GSG-LLM \u4e00\u3001RAG\u7684\u76f2\u533a&#xff1a;\u5f53\u68c0\u7d22\u65e0\u6cd5&quot;\u67b6\u6865&quot; RAG\u7684\u601d\u8def\u5f88\u76f4\u89c2&amp;#xff1\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/85465.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" 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