{"id":91094,"date":"2026-08-06T21:24:38","date_gmt":"2026-08-06T13:24:38","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/91094.html"},"modified":"2026-08-06T21:24:38","modified_gmt":"2026-08-06T13:24:38","slug":"%e7%94%9f%e4%ba%a7%e7%ba%a7rag%e7%b3%bb%e7%bb%9f%e8%90%bd%e5%9c%b0%e5%85%a8%e6%96%b9%e6%a1%88%ef%bc%9a%e5%85%a8%e6%96%b9%e4%bd%8d%e6%94%bb%e5%85%8b%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%b9%bb%e8%a7%89","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/91094.html","title":{"rendered":"\u751f\u4ea7\u7ea7RAG\u7cfb\u7edf\u843d\u5730\u5168\u65b9\u6848\uff1a\u5168\u65b9\u4f4d\u653b\u514b\u5927\u6a21\u578b\u5e7b\u89c9\u96be\u9898"},"content":{"rendered":"

\u6458\u8981<\/h3>\n

\u5927\u6a21\u578b\u5929\u751f\u5b58\u5728\u77e5\u8bc6\u622a\u6b62\u3001\u4e8b\u5b9e\u634f\u9020\u3001\u903b\u8f91\u9519\u4e71\u7684\u5e7b\u89c9\u95ee\u9898，\u76f4\u63a5\u5bfc\u81f4\u4f01\u4e1a\u77e5\u8bc6\u5e93\u95ee\u7b54\u3001\u5de5\u4e1a\u667a\u80fd\u68c0\u7d22\u573a\u666f\u8f93\u51fa\u4e0d\u53ef\u4fe1\u5185\u5bb9，\u65e0\u6cd5\u4e0a\u7ebf\u751f\u4ea7\u73af\u5883\u3002\u68c0\u7d22\u589e\u5f3a\u751f\u6210（RAG）\u901a\u8fc7\u5916\u63a5\u79c1\u6709\u77e5\u8bc6\u5e93\u7ea6\u675f\u5927\u6a21\u578b\u751f\u6210\u8fb9\u754c，\u662f\u89e3\u51b3\u5e7b\u89c9\u6700\u4e3b\u6d41\u3001\u6210\u672c\u6700\u4f4e\u7684\u5de5\u7a0b\u65b9\u6848\u3002\u4f46\u5f88\u591a\u5f00\u53d1\u4eba\u5458\u642d\u5efa\u7684Demo\u7ea7RAG\u4ecd\u5b58\u5728\u5e7b\u89c9\u3001\u68c0\u7d22\u6f02\u79fb\u3001\u4e0a\u4e0b\u6587\u4e22\u5931\u7b49\u95ee\u9898，\u65e0\u6cd5\u6ee1\u8db3\u5de5\u4e1a\u9886\u57df\u7a33\u5b9a\u4ea4\u4ed8\u6807\u51c6\u3002<\/p>\n

\u672c\u6587\u7ed3\u5408\u5de5\u4e1a\u4e1a\u52a1\u843d\u5730\u5b9e\u6218，\u5b8c\u6574\u62c6\u89e3\u751f\u4ea7\u7ea7RAG\u5168\u94fe\u8def\u67b6\u6784，\u4ece\u6587\u6863\u9884\u5904\u7406\u3001\u5411\u91cf\u5316\u4f18\u5316\u3001\u591a\u8def\u68c0\u7d22\u7b56\u7565\u3001\u91cd\u6392\u8fc7\u6ee4\u3001\u63d0\u793a\u8bcd\u7ea6\u675f\u3001\u7ed3\u679c\u6821\u9a8c\u516d\u5927\u7ef4\u5ea6\u7cfb\u7edf\u6027\u6291\u5236\u5927\u6a21\u578b\u5e7b\u89c9；\u914d\u5957\u53ef\u76f4\u63a5\u8fd0\u884cPython\u5de5\u7a0b\u4ee3\u7801，\u8986\u76d6LangChain+FlowRAG\u53cc\u6280\u672f\u6808，\u540c\u65f6\u9488\u5bf9\u5de5\u4e1a\u573a\u666f\u6587\u6863（\u8bbe\u5907\u624b\u518c\u3001\u5de5\u5355\u3001\u5de5\u827a\u56fe\u8c31）\u505a\u4e13\u9879\u4f18\u5316，\u6700\u7ec8\u642d\u5efa\u4e00\u5957\u53ef\u5bf9\u63a5\u4f01\u4e1aERP\u3001MES\u7ba1\u7406\u7cfb\u7edf\u3001\u652f\u6301\u767e\u4e07\u7ea7\u6587\u6863\u3001\u4f4e\u5e7b\u89c9\u7387\u7684\u751f\u4ea7\u53ef\u7528RAG\u7cfb\u7edf\u3002\u5168\u6587\u5b9e\u64cd\u5bfc\u5411，\u5305\u542b\u5e7b\u89c9\u6210\u56e0\u5206\u6790\u3001\u8e29\u5751\u590d\u76d8\u3001\u6027\u80fd\u8c03\u4f18\u6307\u6807，\u9002\u914d1-3\u5e74AI\u5e94\u7528\u5f00\u53d1\u5de5\u7a0b\u5e08\u843d\u5730\u5de5\u4e1a\u667a\u80fd\u95ee\u7b54\u3001\u6570\u5b57\u4eba\u77e5\u8bc6\u5e93\u573a\u666f\u3002<\/p>\n

\u4e00\u3001\u5927\u6a21\u578b\u5e7b\u89c9\u5e95\u5c42\u6210\u56e0：\u4e3a\u4ec0\u4e48\u7b80\u5355RAG\u4f9d\u7136\u4f1a\u9020\u5047<\/h3>\n

1.1 \u6a21\u578b\u539f\u751f\u5e7b\u89c9<\/h4>\n

\u9884\u8bad\u7ec3\u5927\u6a21\u578b\u8bad\u7ec3\u6570\u636e\u5b58\u5728\u65f6\u95f4\u65ad\u5c42（\u77e5\u8bc6\u622a\u6b62），\u9762\u5bf9\u672a\u89c1\u8fc7\u7684\u79c1\u6709\u5de5\u4e1a\u6570\u636e\u65f6，\u6a21\u578b\u4f1a\u4f9d\u9760\u53c2\u6570\u5185\u901a\u7528\u77e5\u8bc6\u201c\u8111\u8865\u201d\u7b54\u6848；\u540c\u65f6\u5927\u6a21\u578b\u7684\u751f\u6210\u673a\u5236\u662f\u6982\u7387\u91c7\u6837，\u4e3a\u4fdd\u8bc1\u8bed\u53e5\u6d41\u7545\u6027，\u4f1a\u4f18\u5148\u751f\u6210\u901a\u987a\u6587\u672c\u800c\u975e\u771f\u5b9e\u4e8b\u5b9e，\u4ea7\u751f\u65e0\u4f9d\u636e\u7f16\u9020\u3002<\/p>\n

1.2 \u7b80\u6613RAG\u67b6\u6784\u5e26\u6765\u7684\u65b0\u589e\u5e7b\u89c9<\/h4>\n

\u5e02\u9762\u4e0a\u5feb\u901f\u642d\u5efa\u7684Demo RAG\u4ec5\u505a\u201c\u6587\u6863\u5207\u7247+\u5411\u91cf\u68c0\u7d22+\u7b80\u5355Prompt\u62fc\u63a5\u201d，\u5b58\u5728\u5927\u91cf\u5de5\u7a0b\u6f0f\u6d1e，\u53cd\u800c\u653e\u5927\u5e7b\u89c9：<\/p>\n

  • \u5207\u7247\u7c92\u5ea6\u5931\u63a7：\u957f\u6587\u6863\u7c97\u66b4\u5747\u5206，\u5355\u5757\u6587\u672c\u4fe1\u606f\u6b8b\u7f3a\u3001\u4e0a\u4e0b\u6587\u65ad\u88c2，\u68c0\u7d22\u5230\u788e\u7247\u5316\u7247\u6bb5\u540e\u6a21\u578b\u65ad\u7ae0\u53d6\u4e49；<\/li>\n
  • \u5355\u8def\u5411\u91cf\u68c0\u7d22\u7f3a\u9677：\u4ec5\u4f9d\u9760\u76f8\u4f3c\u5ea6\u53ec\u56de，\u5927\u91cf\u8bed\u4e49\u76f8\u8fd1\u4f46\u4e8b\u5b9e\u65e0\u5173\u7684\u566a\u58f0\u6587\u6863\u6df7\u5165\u4e0a\u4e0b\u6587；<\/li>\n
  • \u65e0\u91cd\u6392\u3001\u65e0\u8fc7\u6ee4：\u68c0\u7d22Top-K\u5168\u90e8\u9001\u5165LLM，\u65e0\u5173\u6587\u672c\u5e72\u6270\u6a21\u578b\u5224\u65ad；<\/li>\n
  • Prompt\u65e0\u4e8b\u5b9e\u7ea6\u675f：\u672a\u5f3a\u5236\u6a21\u578b\u201c\u4ec5\u53c2\u8003\u68c0\u7d22\u6587\u6863\u4f5c\u7b54，\u65e0\u5339\u914d\u5185\u5bb9\u76f4\u63a5\u544a\u77e5\u672a\u77e5\u201d；<\/li>\n
  • \u7f3a\u5c11\u540e\u7f6e\u6821\u9a8c：\u6a21\u578b\u8f93\u51fa\u540e\u6ca1\u6709\u4e8b\u5b9e\u5bf9\u9f50\u6821\u9a8c\u903b\u8f91，\u865a\u5047\u5185\u5bb9\u76f4\u63a5\u8fd4\u56de\u524d\u7aef；<\/li>\n
  • \u591a\u8f6e\u5bf9\u8bdd\u4e0a\u4e0b\u6587\u6ea2\u51fa：\u5386\u53f2\u5bf9\u8bdd\u6324\u5360\u77e5\u8bc6\u5e93\u53c2\u8003\u6587\u672c\u7a97\u53e3，\u6a21\u578b\u8131\u79bb\u79c1\u6709\u77e5\u8bc6\u5e93\u81ea\u7531\u751f\u6210\u3002<\/li>\n

    1.3 \u5de5\u4e1a\u573a\u666f\u5e7b\u89c9\u7279\u6b8a\u98ce\u9669<\/h4>\n

    \u5de5\u4e1a\u9886\u57df\u95ee\u7b54\u6d89\u53ca\u8bbe\u5907\u53c2\u6570\u3001\u64cd\u4f5c\u89c4\u8303\u3001\u5b89\u5168\u6d41\u7a0b，\u4e00\u65e6\u51fa\u73b0\u5e7b\u89c9\u4f1a\u9020\u6210\u751f\u4ea7\u4e8b\u6545：\u6bd4\u5982\u9519\u8bef\u8f93\u51fa\u8bbe\u5907\u68c0\u4fee\u53c2\u6570\u3001\u865a\u6784\u5de5\u827a\u6807\u51c6\u3002\u56e0\u6b64\u5de5\u4e1aRAG\u4e0d\u4ec5\u8981\u964d\u4f4e\u5e7b\u89c9，\u8fd8\u8981\u5177\u5907\u53ef\u6eaf\u6e90\u3001\u53ef\u6821\u9a8c\u3001\u62d2\u7edd\u7f16\u9020\u4e09\u5927\u751f\u4ea7\u7279\u6027\u3002<\/p>\n

    \u4e8c\u3001\u751f\u4ea7\u7ea7\u4f4e\u5e7b\u89c9RAG\u6574\u4f53\u67b6\u6784\u8bbe\u8ba1<\/h3>\n

    \u9488\u5bf9\u5e7b\u89c9\u516d\u5927\u6210\u56e0，\u8bbe\u8ba1\u516d\u5c42\u5168\u94fe\u8def\u6291\u5236\u67b6\u6784，\u6bcf\u4e00\u5c42\u90fd\u914d\u5957\u9488\u5bf9\u6027\u5de5\u7a0b\u4f18\u5316\u624b\u6bb5：<\/p>\n

  • \u6587\u6863\u9884\u5904\u7406\u5c42：\u6e05\u6d17\u3001\u5206\u5c42\u5207\u7247\u3001\u8bed\u4e49\u5206\u5272\u3001\u5b9e\u4f53\u6807\u6ce8，\u4ece\u6e90\u5934\u4fdd\u8bc1\u77e5\u8bc6\u5e93\u6587\u672c\u5b8c\u6574\u51c6\u786e；<\/li>\n
  • \u5411\u91cf\u5b58\u50a8\u7d22\u5f15\u5c42：\u6df7\u5408\u7a00\u758f+\u7a20\u5bc6\u53cc\u5411\u91cf\u7d22\u5f15，\u533a\u5206\u6807\u9898\/\u6b63\u6587\/\u56fe\u8c31\u5b9e\u4f53，\u63d0\u5347\u7cbe\u51c6\u53ec\u56de；<\/li>\n
  • \u591a\u8def\u68c0\u7d22\u5c42：\u5411\u91cf\u68c0\u7d22+\u5173\u952e\u8bcd\u68c0\u7d22+\u56fe\u8c31\u5b9e\u4f53\u68c0\u7d22\u878d\u5408，\u907f\u514d\u5355\u4e00\u68c0\u7d22\u8bed\u4e49\u6f02\u79fb；<\/li>\n
  • \u91cd\u6392\u8fc7\u6ee4\u5c42：Rerank\u6a21\u578b+\u89c4\u5219\u8fc7\u6ee4，\u5254\u9664\u4f4e\u76f8\u5173\u3001\u51b2\u7a81\u3001\u6b8b\u7f3a\u68c0\u7d22\u7247\u6bb5；<\/li>\n
  • LLM\u751f\u6210\u7ea6\u675f\u5c42：\u5e7b\u89c9\u6291\u5236\u4e13\u7528\u63d0\u793a\u8bcd\u3001\u5f15\u7528\u6eaf\u6e90\u673a\u5236\u3001\u65e0\u5339\u914d\u5185\u5bb9\u5f3a\u5236\u62d2\u7b54；<\/li>\n
  • \u540e\u7f6e\u4e8b\u5b9e\u6821\u9a8c\u5c42：\u5b9e\u4f53\u5339\u914d\u3001\u6587\u672c\u76f8\u4f3c\u5ea6\u6821\u9a8c\u3001\u56fe\u8c31\u4e8b\u5b9e\u6838\u5bf9，\u62e6\u622a\u6a21\u578b\u7f16\u9020\u5185\u5bb9\u3002<\/li>\n

    \u6574\u5957\u67b6\u6784\u57fa\u4e8eLangChain+FlowRAG\u5b9e\u73b0，\u517c\u5bb9Dify\u3001FastGPT\u6846\u67b6\u4e8c\u6b21\u96c6\u6210，\u53ef\u5bf9\u63a5\u4f01\u4e1a\u7ba1\u7406\u7cfb\u7edfAPI\u62c9\u53d6\u5de5\u5355\u3001\u5de5\u827a\u6587\u6863\u3002<\/p>\n

    \u4e09\u3001\u5168\u94fe\u8def\u4ee3\u7801\u5b9e\u73b0：\u751f\u4ea7\u7ea7\u4f4e\u5e7b\u89c9RAG\u5de5\u7a0b<\/h3>\n

    \u73af\u5883\u4f9d\u8d56\u5b89\u88c5<\/h4>\n

    # \u5b89\u88c5\u6838\u5fc3\u4f9d\u8d56<\/span>
    \n!pip install langchain langchain–<\/span>openai langchain–<\/span>community flowrag sentence–<\/span>transformers rank_bm25 chromadb pypdf python–<\/span>dotenv pandas numpy<\/p>\n

    3.1 \u7b2c\u4e00\u6b65：\u6587\u6863\u9884\u5904\u7406\u6a21\u5757（\u6e90\u5934\u51cf\u5c11\u6b8b\u7f3a\u6587\u672c\u5e7b\u89c9）<\/h4>\n

    \u666e\u901a\u5747\u5206\u5207\u7247\u4f1a\u62c6\u5206\u5b8c\u6574\u8bed\u4e49\u6bb5\u843d，\u5de5\u4e1a\u8bbe\u5907\u624b\u518c\u3001\u5de5\u827a\u6587\u6863\u5fc5\u987b\u4f7f\u7528\u8bed\u4e49\u5206\u5272，\u540c\u65f6\u63d0\u53d6\u5b9e\u4f53\u3001\u8fc7\u6ee4\u65e0\u6548\u4e71\u7801\u3001\u8868\u683c\u8f6c\u6587\u672c\u7ed3\u6784\u5316，\u907f\u514d\u788e\u7247\u4fe1\u606f\u8bef\u5bfc\u6a21\u578b\u3002<\/p>\n

    import<\/span> re
    \nfrom<\/span> langchain.<\/span>text_splitter import<\/span> RecursiveCharacterTextSplitter
    \nfrom<\/span> langchain.<\/span>document_loaders import<\/span> PyPDFLoader<\/p>\n

    class<\/span> IndustrialDocPreprocessor<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span>
    \n # \u8bed\u4e49\u5206\u5272\u5668：\u6309\u6807\u9898\u3001\u6bb5\u843d\u3001\u53e5\u53f7\u5206\u5c42\u5207\u5206，\u4fdd\u7559\u5b8c\u6574\u8bed\u4e49\u5355\u5143<\/span>
    \n self.<\/span>text_splitter =<\/span> RecursiveCharacterTextSplitter(<\/span>
    \n separators=<\/span>[<\/span>"\\\\n## "<\/span>,<\/span> "\\\\n# "<\/span>,<\/span> "\\\\n\\\\n"<\/span>,<\/span> "\\\\n"<\/span>,<\/span> "\u3002"<\/span>,<\/span> "；"<\/span>]<\/span>,<\/span>
    \n chunk_size=<\/span>600<\/span>,<\/span>
    \n chunk_overlap=<\/span>120<\/span>,<\/span>
    \n length_function=<\/span>len<\/span>,<\/span>
    \n add_start_index=<\/span>True<\/span>
    \n )<\/span><\/p>\n

    def<\/span> clean_industrial_text<\/span>(<\/span>self,<\/span> raw_text:<\/span> str<\/span>)<\/span> –<\/span>><\/span> str<\/span>:<\/span>
    \n """\u6e05\u6d17\u5de5\u4e1a\u6587\u6863\u4e71\u7801\u3001\u591a\u4f59\u7b26\u53f7\u3001\u65e0\u6548\u6362\u884c"""<\/span>
    \n # \u53bb\u9664\u9875\u7801\u3001\u6c34\u5370\u3001\u7279\u6b8a\u5236\u8868\u7b26<\/span>
    \n text =<\/span> re.<\/span>sub(<\/span>r"\u7b2c\\\\d+\u9875 \/ \u5171\\\\d+\u9875"<\/span>,<\/span> ""<\/span>,<\/span> raw_text)<\/span>
    \n text =<\/span> re.<\/span>sub(<\/span>r"\\\\s{4,}"<\/span>,<\/span> " "<\/span>,<\/span> text)<\/span>
    \n # \u8fc7\u6ee4\u65e0\u610f\u4e49\u5355\u884c\u7b26\u53f7<\/span>
    \n text =<\/span> re.<\/span>sub(<\/span>r"[-*]{3,}"<\/span>,<\/span> ""<\/span>,<\/span> text)<\/span>
    \n return<\/span> text.<\/span>strip(<\/span>)<\/span><\/p>\n

    def<\/span> load_and_split_pdf<\/span>(<\/span>self,<\/span> pdf_path:<\/span> str<\/span>)<\/span>:<\/span>
    \n """\u52a0\u8f7d\u5de5\u4e1aPDF\u624b\u518c，\u6e05\u6d17+\u8bed\u4e49\u5207\u7247"""<\/span>
    \n loader =<\/span> PyPDFLoader(<\/span>pdf_path)<\/span>
    \n raw_docs =<\/span> loader.<\/span>load(<\/span>)<\/span>
    \n clean_docs =<\/span> [<\/span>]<\/span>
    \n for<\/span> doc in<\/span> raw_docs:<\/span>
    \n clean_content =<\/span> self.<\/span>clean_industrial_text(<\/span>doc.<\/span>page_content)<\/span>
    \n doc.<\/span>page_content =<\/span> clean_content
    \n clean_docs.<\/span>append(<\/span>doc)<\/span>
    \n # \u8bed\u4e49\u5207\u7247<\/span>
    \n split_docs =<\/span> self.<\/span>text_splitter.<\/span>split_documents(<\/span>clean_docs)<\/span>
    \n print<\/span>(<\/span>f"\u6587\u6863\u5904\u7406\u5b8c\u6210，\u751f\u6210\u8bed\u4e49\u5207\u7247\u6570\u91cf：<\/span>{<\/span>len<\/span>(<\/span>split_docs)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n return<\/span> split_docs<\/p>\n

    # \u8c03\u7528\u793a\u4f8b<\/span>
    \nif<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
    \n preprocessor =<\/span> IndustrialDocPreprocessor(<\/span>)<\/span>
    \n chunks =<\/span> preprocessor.<\/span>load_and_split_pdf(<\/span>".\/\u5de5\u4e1a\u8bbe\u5907\u8fd0\u7ef4\u624b\u518c.pdf"<\/span>)<\/span><\/p>\n

    \u4f18\u5316\u8bf4\u660e（\u6291\u5236\u5e7b\u89c9\u5173\u952e\u70b9）<\/h5>\n
  • \u91cd\u53e0\u5757120\u5b57\u7b26：\u4fdd\u8bc1\u8de8\u5207\u7247\u8fde\u7eed\u8bed\u4e49\u4e0d\u4e22\u5931，\u907f\u514d\u6a21\u578b\u65ad\u7ae0\u53d6\u4e49；<\/li>\n
  • \u5206\u5c42\u5206\u9694\u7b26\u4f18\u5148\u8bc6\u522b\u6587\u6863\u6807\u9898，\u5b8c\u6574\u4fdd\u7559\u8bbe\u5907\u53c2\u6570\u3001\u64cd\u4f5c\u6b65\u9aa4\u6574\u6bb5\u5185\u5bb9；<\/li>\n
  • \u5de5\u4e1a\u6587\u6863\u4e13\u7528\u6e05\u6d17\u89c4\u5219，\u8fc7\u6ee4\u9875\u7801\u3001\u6c34\u5370\u7b49\u65e0\u4ef7\u503c\u566a\u58f0\u6587\u672c\u3002<\/li>\n

    3.2 \u7b2c\u4e8c\u6b65：\u6df7\u5408\u591a\u8def\u68c0\u7d22\u6a21\u5757（\u89e3\u51b3\u5355\u5411\u91cf\u68c0\u7d22\u6f02\u79fb\u5e7b\u89c9）<\/h4>\n

    \u5355\u4e00\u5411\u91cf\u68c0\u7d22\u5bb9\u6613\u53ec\u56de\u8bed\u4e49\u76f8\u4f3c\u4f46\u4e8b\u5b9e\u65e0\u5173\u7684\u6587\u672c（\u6bd4\u5982\u76f8\u4f3c\u8bbe\u5907\u4e0d\u540c\u578b\u53f7\u53c2\u6570\u6df7\u6dc6），\u91c7\u7528\u7a20\u5bc6\u5411\u91cf\u68c0\u7d22（Chroma）+\u7a00\u758fBM25\u5173\u952e\u8bcd\u68c0\u7d22\u591a\u8def\u878d\u5408，\u5e73\u8861\u8bed\u4e49\u5339\u914d\u548c\u5173\u952e\u8bcd\u7cbe\u51c6\u5339\u914d，\u5927\u5e45\u964d\u4f4e\u65e0\u5173\u6587\u6863\u8fdb\u5165LLM\u4e0a\u4e0b\u6587\u3002<\/p>\n

    from<\/span> langchain.<\/span>vectorstores import<\/span> Chroma
    \nfrom<\/span> langchain.<\/span>embeddings import<\/span> SentenceTransformerEmbeddings
    \nfrom<\/span> langchain.<\/span>retrievers import<\/span> BM25Retriever,<\/span> EnsembleRetriever<\/p>\n

    class<\/span> MultiPathRetriever<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> split_chunks,<\/span> persist_path=<\/span>".\/chroma_industrial_db"<\/span>)<\/span>:<\/span>
    \n # \u9009\u7528\u5de5\u4e1a\u9886\u57df\u4f18\u5316\u5d4c\u5165\u6a21\u578b<\/span>
    \n self.<\/span>embedding =<\/span> SentenceTransformerEmbeddings(<\/span>model_name=<\/span>"all-MiniLM-L6-v2"<\/span>)<\/span>
    \n # \u7a20\u5bc6\u5411\u91cf\u5e93\u521d\u59cb\u5316<\/span>
    \n self.<\/span>vector_db =<\/span> Chroma.<\/span>from_documents(<\/span>
    \n documents=<\/span>split_chunks,<\/span>
    \n embedding=<\/span>self.<\/span>embedding,<\/span>
    \n persist_directory=<\/span>persist_path
    \n )<\/span>
    \n self.<\/span>vector_db.<\/span>persist(<\/span>)<\/span>
    \n self.<\/span>dense_retriever =<\/span> self.<\/span>vector_db.<\/span>as_retriever(<\/span>search_kwargs=<\/span>{<\/span>"k"<\/span>:<\/span> 5<\/span>}<\/span>)<\/span>
    \n # BM25\u7a00\u758f\u5173\u952e\u8bcd\u68c0\u7d22<\/span>
    \n self.<\/span>bm25_retriever =<\/span> BM25Retriever.<\/span>from_documents(<\/span>split_chunks)<\/span>
    \n self.<\/span>bm25_retriever.<\/span>k =<\/span> 5<\/span>
    \n # \u591a\u8def\u878d\u5408\u68c0\u7d22\u5668，\u6743\u91cd\u63a7\u5236\u4e24\u79cd\u68c0\u7d22\u4f18\u5148\u7ea7<\/span>
    \n self.<\/span>ensemble_retriever =<\/span> EnsembleRetriever(<\/span>
    \n retrievers=<\/span>[<\/span>self.<\/span>dense_retriever,<\/span> self.<\/span>bm25_retriever]<\/span>,<\/span>
    \n weights=<\/span>[<\/span>0.6<\/span>,<\/span> 0.4<\/span>]<\/span>
    \n )<\/span><\/p>\n

    def<\/span> get_fusion_docs<\/span>(<\/span>self,<\/span> query:<\/span> str<\/span>)<\/span>:<\/span>
    \n """\u591a\u8def\u68c0\u7d22\u8fd4\u56de\u878d\u5408\u540e10\u6761\u5019\u9009\u6587\u6863"""<\/span>
    \n candidate_docs =<\/span> self.<\/span>ensemble_retriever.<\/span>get_relevant_documents(<\/span>query)<\/span>
    \n print<\/span>(<\/span>f"\u591a\u8def\u68c0\u7d22\u53ec\u56de\u5019\u9009\u5207\u7247\u6570：<\/span>{<\/span>len<\/span>(<\/span>candidate_docs)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n return<\/span> candidate_docs<\/p>\n

    3.3 \u7b2c\u4e09\u6b65：Rerank\u91cd\u6392\u8fc7\u6ee4\u6a21\u5757（\u5254\u9664\u566a\u58f0，\u963b\u65ad\u65e0\u5173\u6587\u672c\u5e7b\u89c9）<\/h4>\n

    \u591a\u8def\u68c0\u7d22\u540e\u5b58\u5728\u4f4e\u76f8\u5173\u7247\u6bb5，\u9001\u5165LLM\u524d\u4f7f\u7528Cross-Encoder\u91cd\u6392\u6a21\u578b\u6253\u5206，\u8fc7\u6ee4\u76f8\u4f3c\u5ea6\u4f4e\u4e8e\u9608\u503c\u7684\u6587\u6863，\u51cf\u5c11\u5e72\u6270\u4fe1\u606f，\u4ece\u8f93\u5165\u4fa7\u9650\u5236\u6a21\u578b\u7f16\u9020\u4f9d\u636e\u3002<\/p>\n

    from<\/span> langchain.<\/span>retrievers import<\/span> ContextualCompressionRetriever
    \nfrom<\/span> langchain.<\/span>retrievers.<\/span>document_compressors import<\/span> CrossEncoderReranker<\/p>\n

    class<\/span> RerankFilter<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> base_retriever)<\/span>:<\/span>
    \n # \u5de5\u4e1a\u573a\u666f\u4e13\u7528\u91cd\u6392\u6a21\u578b，\u6253\u5206\u9608\u503c0.3，\u4f4e\u4e8e\u9608\u503c\u76f4\u63a5\u4e22\u5f03<\/span>
    \n self.<\/span>rerank_compressor =<\/span> CrossEncoderReranker(<\/span>
    \n model_name=<\/span>"cross-encoder\/ms-marco-MiniLM-L-6-v2"<\/span>,<\/span>
    \n top_n=<\/span>3<\/span>,<\/span>
    \n threshold=<\/span>0.3<\/span>
    \n )<\/span>
    \n self.<\/span>compression_retriever =<\/span> ContextualCompressionRetriever(<\/span>
    \n base_retriever=<\/span>base_retriever,<\/span>
    \n base_compressor=<\/span>self.<\/span>rerank_compressor
    \n )<\/span><\/p>\n

    def<\/span> get_filtered_reference<\/span>(<\/span>self,<\/span> query:<\/span> str<\/span>)<\/span>:<\/span>
    \n """\u8fd4\u56de\u91cd\u6392\u8fc7\u6ee4\u540e\u7684\u9ad8\u53ef\u4fe1\u5ea6\u53c2\u8003\u6587\u6863（\u4ec5\u4fdd\u75593\u6761\u9ad8\u76f8\u5173\u5207\u7247）"""<\/span>
    \n final_ref_docs =<\/span> self.<\/span>compression_retriever.<\/span>get_relevant_documents(<\/span>query)<\/span>
    \n print<\/span>(<\/span>f"\u91cd\u6392\u8fc7\u6ee4\u540e\u6700\u7ec8\u9001\u5165LLM\u53c2\u8003\u5207\u7247\u6570：<\/span>{<\/span>len<\/span>(<\/span>final_ref_docs)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n return<\/span> final_ref_docs<\/p>\n

    \u5e7b\u89c9\u6291\u5236\u903b\u8f91<\/h5>\n

    \u68c0\u7d22\u9636\u6bb5Top10\u5019\u9009\u7ecf\u8fc7\u91cd\u6392\u7b5b\u9009\u4ec5\u4fdd\u75593\u6761\u9ad8\u5ea6\u5339\u914d\u6587\u672c，\u65e0\u5173\u3001\u51b2\u7a81\u3001\u8bed\u4e49\u6a21\u7cca\u5207\u7247\u5168\u90e8\u62e6\u622a，LLM\u4ec5\u80fd\u57fa\u4e8e\u7cbe\u51c6\u53c2\u8003\u6587\u672c\u751f\u6210，\u5927\u5e45\u51cf\u5c11\u65e0\u4f9d\u636e\u8111\u8865\u3002<\/p>\n

    3.4 \u7b2c\u56db\u6b65：\u5e7b\u89c9\u6291\u5236\u4e13\u7528Prompt\u6a21\u677f（\u751f\u6210\u5c42\u5f3a\u5236\u7ea6\u675f\u6a21\u578b\u62d2\u7b54\u7f16\u9020）<\/h4>\n

    \u8fd9\u662f\u6291\u5236\u5e7b\u89c9\u6700\u6838\u5fc3\u7684\u5de5\u7a0b\u624b\u6bb5，\u5f3a\u5236\u6a21\u578b\u9075\u5b88\u4e09\u6761\u89c4\u5219：<\/p>\n

  • \u6240\u6709\u56de\u7b54\u5185\u5bb9\u5fc5\u987b\u5b8c\u6574\u6eaf\u6e90\u53c2\u8003\u6587\u6863；<\/li>\n
  • \u53c2\u8003\u6587\u6863\u65e0\u5bf9\u5e94\u4fe1\u606f\u65f6，\u7edf\u4e00\u56de\u590d\u201c\u77e5\u8bc6\u5e93\u672a\u6536\u5f55\u8be5\u5185\u5bb9，\u65e0\u6cd5\u4f5c\u7b54，\u7981\u6b62\u81ea\u884c\u7f16\u9020\u53c2\u6570\/\u6d41\u7a0b\u201d；<\/li>\n
  • \u8f93\u51fa\u6bcf\u6761\u7ed3\u8bba\u5fc5\u987b\u6807\u6ce8\u5bf9\u5e94\u6587\u6863\u7247\u6bb5\u6765\u6e90\u7d22\u5f15\u3002<\/li>\n

    HALLUCINATION_PREVENT_PROMPT =<\/span> """
    \n# \u89d2\u8272：\u5de5\u4e1a\u9886\u57df\u77e5\u8bc6\u5e93\u7cbe\u51c6\u95ee\u7b54\u4e13\u5bb6，\u7edd\u5bf9\u7981\u6b62\u7f16\u9020\u4efb\u4f55\u4e8b\u5b9e
    \n# \u786c\u6027\u89c4\u5219（\u8fdd\u53cd\u89c4\u5219\u89c6\u4e3a\u56de\u7b54\u9519\u8bef）
    \n1. \u4f60\u7684\u6240\u6709\u56de\u7b54\u5185\u5bb9，**\u53ea\u80fd\u5b8c\u5168\u57fa\u4e8e\u4e0b\u65b9\u3010\u77e5\u8bc6\u5e93\u53c2\u8003\u8d44\u6599\u3011**，\u7edd\u5bf9\u4e0d\u80fd\u8c03\u7528\u81ea\u8eab\u9884\u8bad\u7ec3\u77e5\u8bc6\u8fdb\u884c\u8111\u8865\u3001\u731c\u6d4b\u3001\u5ef6\u4f38；
    \n2. \u82e5\u53c2\u8003\u8d44\u6599\u4e2d\u4e0d\u5b58\u5728\u7528\u6237\u95ee\u9898\u5bf9\u5e94\u7684\u8bbe\u5907\u53c2\u6570\u3001\u5de5\u827a\u6b65\u9aa4\u3001\u6545\u969c\u65b9\u6848，\u76f4\u63a5\u8f93\u51fa\u56fa\u5b9a\u8bdd\u672f：\u3010\u77e5\u8bc6\u5e93\u6682\u65e0\u76f8\u5173\u4fe1\u606f，\u65e0\u6cd5\u63d0\u4f9b\u51c6\u786e\u56de\u7b54，\u8bf7\u52ff\u8f7b\u4fe1\u975e\u5b98\u65b9\u865a\u6784\u5185\u5bb9\u3011；
    \n3. \u7981\u6b62\u5408\u5e76\u3001\u7be1\u6539\u3001\u63a8\u65ad\u53c2\u8003\u6587\u672c\u4e2d\u4e0d\u5b58\u5728\u7684\u6570\u636e，\u4e0d\u80fd\u8fd1\u4f3c\u4f30\u7b97\u53c2\u6570；
    \n4. \u56de\u7b54\u4e2d\u6bcf\u4e00\u6761\u5173\u952e\u6570\u636e\u3001\u64cd\u4f5c\u6b65\u9aa4\u3001\u6545\u969c\u89e3\u51b3\u65b9\u6848，\u5fc5\u987b\u6807\u6ce8\u5bf9\u5e94\u7684\u8d44\u6599\u7247\u6bb5\u7d22\u5f15start_index；
    \n5. \u4e0d\u5141\u8bb8\u4e3b\u89c2\u63a8\u6f14\u3001\u8865\u5145\u672a\u8bb0\u8f7d\u7684\u884d\u751f\u6d41\u7a0b，\u4ec5\u590d\u8ff0\u3001\u6574\u5408\u539f\u6587\u5ba2\u89c2\u4e8b\u5b9e；<\/p>\n

    ## \u3010\u77e5\u8bc6\u5e93\u53c2\u8003\u8d44\u6599\u3011
    \n{reference_context}<\/p>\n

    ## \u7528\u6237\u95ee\u9898：
    \n{user_query}<\/p>\n

    \u8bf7\u4e25\u683c\u9075\u5b88\u89c4\u5219\u4f5c\u7b54，\u7981\u6b62\u51fa\u73b0\u4efb\u4f55\u865a\u6784\u5185\u5bb9：
    \n"""<\/span><\/p>\n

    3.5 \u7b2c\u4e94\u6b65：\u540e\u7f6e\u4e8b\u5b9e\u6821\u9a8c\u6a21\u5757（\u751f\u6210\u540e\u515c\u5e95\u62e6\u622a\u5e7b\u89c9\u8f93\u51fa）<\/h4>\n

    \u6a21\u578b\u5373\u4f7f\u53d7Prompt\u7ea6\u675f\u4ecd\u6709\u5c0f\u6982\u7387\u9020\u5047，\u589e\u52a0\u540e\u7f6e\u6821\u9a8c\u903b\u8f91，\u5bf9\u6bd4\u8f93\u51fa\u6587\u672c\u4e0e\u539f\u59cb\u53c2\u8003\u6587\u6863\u5b9e\u4f53\u3001\u5173\u952e\u8bcd\u91cd\u5408\u5ea6，\u4f4e\u4e8e\u9608\u503c\u5219\u62e6\u622a\u5e76\u63d0\u793a\u65e0\u53ef\u9760\u4fe1\u606f\u3002<\/p>\n

    from<\/span> sklearn.<\/span>feature_extraction.<\/span>text import<\/span> TfidfVectorizer
    \nimport<\/span> numpy as<\/span> np<\/p>\n

    class<\/span> FactChecker<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span>
    \n self.<\/span>tfidf =<\/span> TfidfVectorizer(<\/span>)<\/span><\/p>\n

    def<\/span> calculate_text_similarity<\/span>(<\/span>self,<\/span> output_text:<\/span> str<\/span>,<\/span> ref_texts:<\/span> list<\/span>[<\/span>str<\/span>]<\/span>)<\/span> –<\/span>><\/span> float<\/span>:<\/span>
    \n """\u8ba1\u7b97\u6a21\u578b\u8f93\u51fa\u4e0e\u5168\u90e8\u53c2\u8003\u6587\u6863\u7684\u5e73\u5747\u76f8\u4f3c\u5ea6，\u4f4e\u4e8e0.1\u5224\u5b9a\u4e3a\u5e7b\u89c9"""<\/span>
    \n all_ref =<\/span> "\\\\n"<\/span>.<\/span>join(<\/span>ref_texts)<\/span>
    \n corpus =<\/span> [<\/span>output_text,<\/span> all_ref]<\/span>
    \n tfidf_matrix =<\/span> self.<\/span>tfidf.<\/span>fit_transform(<\/span>corpus)<\/span>
    \n vec_output =<\/span> tfidf_matrix[<\/span>0<\/span>]<\/span>.<\/span>toarray(<\/span>)<\/span>[<\/span>0<\/span>]<\/span>
    \n vec_ref =<\/span> tfidf_matrix[<\/span>1<\/span>]<\/span>.<\/span>toarray(<\/span>)<\/span>[<\/span>0<\/span>]<\/span>
    \n # \u4f59\u5f26\u76f8\u4f3c\u5ea6<\/span>
    \n cos_sim =<\/span> np.<\/span>dot(<\/span>vec_output,<\/span> vec_ref)<\/span> \/<\/span> (<\/span>np.<\/span>linalg.<\/span>norm(<\/span>vec_output)<\/span> *<\/span> np.<\/span>linalg.<\/span>norm(<\/span>vec_ref)<\/span>)<\/span>
    \n return<\/span> round<\/span>(<\/span>float<\/span>(<\/span>cos_sim)<\/span>,<\/span> 3<\/span>)<\/span><\/p>\n

    def<\/span> hallucination_judge<\/span>(<\/span>self,<\/span> llm_output:<\/span> str<\/span>,<\/span> reference_docs)<\/span> –<\/span>><\/span> dict<\/span>:<\/span>
    \n ref_content_list =<\/span> [<\/span>doc.<\/span>page_content for<\/span> doc in<\/span> reference_docs]<\/span>
    \n sim_score =<\/span> self.<\/span>calculate_text_similarity(<\/span>llm_output,<\/span> ref_content_list)<\/span>
    \n if<\/span> sim_score <<\/span> 0.1<\/span>:<\/span>
    \n return<\/span> {<\/span>
    \n "is_hallucination"<\/span>:<\/span> True<\/span>,<\/span>
    \n "similarity_score"<\/span>:<\/span> sim_score,<\/span>
    \n "result"<\/span>:<\/span> "\u6a21\u578b\u8f93\u51fa\u4e0e\u77e5\u8bc6\u5e93\u5339\u914d\u5ea6\u8fc7\u4f4e，\u5224\u5b9a\u5b58\u5728\u5e7b\u89c9，\u62d2\u7edd\u8fd4\u56de\u7b54\u6848"<\/span>
    \n }<\/span>
    \n else<\/span>:<\/span>
    \n return<\/span> {<\/span>
    \n "is_hallucination"<\/span>:<\/span> False<\/span>,<\/span>
    \n "similarity_score"<\/span>:<\/span> sim_score,<\/span>
    \n "result"<\/span>:<\/span> "\u8f93\u51fa\u5185\u5bb9\u5339\u914d\u77e5\u8bc6\u5e93，\u53ef\u6b63\u5e38\u4ea4\u4ed8"<\/span>
    \n }<\/span><\/p>\n

    3.6 \u5b8c\u6574\u4e32\u8054：\u751f\u4ea7\u7ea7\u4f4e\u5e7b\u89c9RAG\u4e3b\u6d41\u7a0b<\/h4>\n

    \u6574\u5408\u9884\u5904\u7406\u3001\u591a\u8def\u68c0\u7d22\u3001\u91cd\u6392\u8fc7\u6ee4\u3001\u7ea6\u675fPrompt\u3001\u4e8b\u5b9e\u6821\u9a8c\u5168\u6a21\u5757，\u5b9e\u73b0\u7aef\u5230\u7aef\u53ef\u90e8\u7f72\u5de5\u7a0b：<\/p>\n

    from<\/span> langchain_openai import<\/span> ChatOpenAI
    \nfrom<\/span> langchain.<\/span>chains import<\/span> LLMChain
    \nfrom<\/span> langchain.<\/span>prompts import<\/span> PromptTemplate
    \nfrom<\/span> dotenv import<\/span> load_dotenv
    \nimport<\/span> os<\/p>\n

    # \u52a0\u8f7d\u73af\u5883\u53d8\u91cf<\/span>
    \nload_dotenv(<\/span>)<\/span>
    \nllm =<\/span> ChatOpenAI(<\/span>
    \n model=<\/span>"gpt-3.5-turbo"<\/span>,<\/span>
    \n temperature=<\/span>0<\/span>,<\/span> # temperature\u7f6e0，\u5173\u95ed\u6a21\u578b\u968f\u673a\u521b\u9020\u6027，\u6291\u5236\u5e7b\u89c9\u5173\u952e\u53c2\u6570<\/span>
    \n api_key=<\/span>os.<\/span>getenv(<\/span>"OPENAI_API_KEY"<\/span>)<\/span>
    \n)<\/span><\/p>\n

    # \u5c01\u88c5\u5b8c\u6574RAG\u94fe\u8def<\/span>
    \nclass<\/span> LowHallucinationIndustrialRAG<\/span>:<\/span>
    \n def<\/span> __init__<\/span>(<\/span>self,<\/span> pdf_file_path)<\/span>:<\/span>
    \n # 1. \u6587\u6863\u9884\u5904\u7406\u5207\u7247<\/span>
    \n self.<\/span>preprocessor =<\/span> IndustrialDocPreprocessor(<\/span>)<\/span>
    \n self.<\/span>doc_chunks =<\/span> self.<\/span>preprocessor.<\/span>load_and_split_pdf(<\/span>pdf_file_path)<\/span>
    \n # 2. \u591a\u8def\u68c0\u7d22\u521d\u59cb\u5316<\/span>
    \n self.<\/span>multi_retriever =<\/span> MultiPathRetriever(<\/span>self.<\/span>doc_chunks)<\/span>
    \n # 3. \u91cd\u6392\u8fc7\u6ee4<\/span>
    \n self.<\/span>rerank_filter =<\/span> RerankFilter(<\/span>self.<\/span>multi_retriever.<\/span>ensemble_retriever)<\/span>
    \n # 4. \u5e7b\u89c9\u6291\u5236Prompt\u94fe<\/span>
    \n self.<\/span>prompt =<\/span> PromptTemplate(<\/span>
    \n input_variables=<\/span>[<\/span>"reference_context"<\/span>,<\/span> "user_query"<\/span>]<\/span>,<\/span>
    \n template=<\/span>HALLUCINATION_PREVENT_PROMPT
    \n )<\/span>
    \n self.<\/span>llm_chain =<\/span> LLMChain(<\/span>llm=<\/span>llm,<\/span> prompt=<\/span>self.<\/span>prompt)<\/span>
    \n # 5. \u540e\u7f6e\u4e8b\u5b9e\u6821\u9a8c\u5668<\/span>
    \n self.<\/span>fact_checker =<\/span> FactChecker(<\/span>)<\/span><\/p>\n

    def<\/span> query<\/span>(<\/span>self,<\/span> user_question:<\/span> str<\/span>)<\/span> –<\/span>><\/span> dict<\/span>:<\/span>
    \n # \u6b65\u9aa41：\u591a\u8def\u68c0\u7d22\u53ec\u56de\u5019\u9009\u6587\u6863<\/span>
    \n candidate_docs =<\/span> self.<\/span>multi_retriever.<\/span>get_fusion_docs(<\/span>user_question)<\/span>
    \n # \u6b65\u9aa42：\u91cd\u6392\u8fc7\u6ee4，\u83b7\u53d6\u9ad8\u53ef\u4fe1\u5ea6\u53c2\u8003\u8d44\u6599<\/span>
    \n final_ref_docs =<\/span> self.<\/span>rerank_filter.<\/span>get_filtered_reference(<\/span>user_question)<\/span>
    \n ref_context_str =<\/span> "\\\\n=====\u5206\u5272\u7ebf=====\\\\n"<\/span>.<\/span>join(<\/span>[<\/span>
    \n f"\u7247\u6bb5\u7d22\u5f15<\/span>{<\/span>doc.<\/span>metadata[<\/span>'start_index'<\/span>]<\/span>}<\/span><\/span>：<\/span>{<\/span>doc.<\/span>page_content}<\/span><\/span>"<\/span><\/span>
    \n for<\/span> doc in<\/span> final_ref_docs
    \n ]<\/span>)<\/span>
    \n # \u6b65\u9aa43：\u5927\u6a21\u578b\u751f\u6210\u53d7\u7ea6\u675f\u56de\u7b54<\/span>
    \n raw_answer =<\/span> self.<\/span>llm_chain.<\/span>run(<\/span>reference_context=<\/span>ref_context_str,<\/span> user_query=<\/span>user_question)<\/span>
    \n # \u6b65\u9aa44：\u540e\u7f6e\u4e8b\u5b9e\u6821\u9a8c，\u62e6\u622a\u5e7b\u89c9<\/span>
    \n check_result =<\/span> self.<\/span>fact_checker.<\/span>hallucination_judge(<\/span>raw_answer,<\/span> final_ref_docs)<\/span>
    \n if<\/span> check_result[<\/span>"is_hallucination"<\/span>]<\/span>:<\/span>
    \n final_output =<\/span> "\u3010\u7cfb\u7edf\u5b89\u5168\u62e6\u622a\u3011\u68c0\u6d4b\u5230\u6a21\u578b\u8f93\u51fa\u5b58\u5728\u5e7b\u89c9，\u5185\u5bb9\u4e0e\u77e5\u8bc6\u5e93\u4e0d\u5339\u914d，\u65e0\u6cd5\u63d0\u4f9b\u6709\u6548\u7b54\u6848\u3002"<\/span>
    \n else<\/span>:<\/span>
    \n final_output =<\/span> raw_answer
    \n # \u8fd4\u56de\u5b8c\u6574\u94fe\u8def\u65e5\u5fd7，\u4fbf\u4e8e\u751f\u4ea7\u73af\u5883\u6392\u67e5\u5e7b\u89c9\u95ee\u9898<\/span>
    \n return<\/span> {<\/span>
    \n "\u7528\u6237\u63d0\u95ee"<\/span>:<\/span> user_question,<\/span>
    \n "\u8fc7\u6ee4\u540e\u53c2\u8003\u6587\u6863\u7247\u6bb5"<\/span>:<\/span> [<\/span>doc.<\/span>page_content for<\/span> doc in<\/span> final_ref_docs]<\/span>,<\/span>
    \n "\u6a21\u578b\u539f\u59cb\u8f93\u51fa"<\/span>:<\/span> raw_answer,<\/span>
    \n "\u5e7b\u89c9\u6821\u9a8c\u7ed3\u679c"<\/span>:<\/span> check_result,<\/span>
    \n "\u6700\u7ec8\u5bf9\u5916\u8f93\u51fa"<\/span>:<\/span> final_output
    \n }<\/span><\/p>\n

    # \u751f\u4ea7\u73af\u5883\u8c03\u7528\u6d4b\u8bd5<\/span>
    \nif<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
    \n rag_system =<\/span> LowHallucinationIndustrialRAG(<\/span>pdf_file_path=<\/span>".\/\u5de5\u4e1a\u8bbe\u5907\u8fd0\u7ef4\u624b\u518c.pdf"<\/span>)<\/span>
    \n test_query =<\/span> "XX\u578b\u53f7\u7535\u673a\u989d\u5b9a\u68c0\u4fee\u7535\u538b\u662f\u591a\u5c11？\u6545\u969c\u4ee3\u7801E03\u5904\u7406\u6d41\u7a0b"<\/span>
    \n response =<\/span> rag_system.<\/span>query(<\/span>test_query)<\/span>
    \n # \u6253\u5370\u6700\u7ec8\u4ea4\u4ed8\u7b54\u6848<\/span>
    \n print<\/span>(<\/span>"=====\u7cfb\u7edf\u6700\u7ec8\u8f93\u51fa====="<\/span>)<\/span>
    \n print<\/span>(<\/span>response[<\/span>"\u6700\u7ec8\u5bf9\u5916\u8f93\u51fa"<\/span>]<\/span>)<\/span>
    \n # \u6253\u5370\u5e7b\u89c9\u6821\u9a8c\u65e5\u5fd7<\/span>
    \n print<\/span>(<\/span>"\\\\n=====\u5e7b\u89c9\u68c0\u6d4b\u65e5\u5fd7====="<\/span>)<\/span>
    \n print<\/span>(<\/span>response[<\/span>"\u5e7b\u89c9\u6821\u9a8c\u7ed3\u679c"<\/span>]<\/span>)<\/span><\/p>\n

    3.7 FlowRAG\u8f7b\u91cf\u5316\u4f18\u5316\u65b9\u6848（\u9002\u914d\u4f01\u4e1a\u4f4e\u7b97\u529b\u670d\u52a1\u5668）<\/h4>\n

    \u5bf9\u4e8e\u5de5\u4e1a\u5185\u7f51\u65e0GPU\u73af\u5883，FlowRAG\u63d0\u4f9b\u8f7b\u91cf\u5316\u68c0\u7d22\u4f18\u5316，\u66ff\u6362LangChain\u91cd\u578b\u7ec4\u4ef6，\u5728\u4fdd\u8bc1\u4f4e\u5e7b\u89c9\u524d\u63d0\u4e0b\u964d\u4f4e\u8d44\u6e90\u5360\u7528，\u517c\u5bb9Dify\u6846\u67b6\u4e8c\u6b21\u5f00\u53d1：<\/p>\n

    from<\/span> flowrag import<\/span> FlowRAG<\/p>\n

    # FlowRAG\u5feb\u901f\u521d\u59cb\u5316，\u5185\u7f6e\u5e7b\u89c9\u6291\u5236\u5f00\u5173<\/span>
    \nflow_rag =<\/span> FlowRAG(<\/span>
    \n docs_path=<\/span>".\/industrial_docs\/"<\/span>,<\/span>
    \n embedding_model=<\/span>"all-MiniLM-L6-v2"<\/span>,<\/span>
    \n enable_rerank=<\/span>True<\/span>,<\/span>
    \n hallucination_suppress=<\/span>True<\/span>,<\/span> # \u5185\u7f6e\u5e7b\u89c9\u6291\u5236\u7b56\u7565<\/span>
    \n top_k=<\/span>3<\/span>,<\/span>
    \n temperature=<\/span>0<\/span>
    \n)<\/span><\/p>\n

    # \u6781\u7b80\u8c03\u7528<\/span>
    \nresult =<\/span> flow_rag.<\/span>chat(<\/span>"\u751f\u4ea7\u7ebf\u7a7a\u538b\u673a\u65e5\u5e38\u4fdd\u517b\u89c4\u8303"<\/span>)<\/span>
    \nprint<\/span>(<\/span>result[<\/span>"answer"<\/span>]<\/span>)<\/span>
    \n# \u5185\u7f6e\u6eaf\u6e90\u4e0e\u5e7b\u89c9\u6253\u5206<\/span>
    \nprint<\/span>(<\/span>"\u5e7b\u89c9\u98ce\u9669\u8bc4\u5206："<\/span>,<\/span> result[<\/span>"hallucination_score"<\/span>]<\/span>)<\/span><\/p>\n

    \u56db\u3001\u516d\u5927\u5e7b\u89c9\u6291\u5236\u6838\u5fc3\u8c03\u4f18\u624b\u6bb5（\u751f\u4ea7\u843d\u5730\u5173\u952e\u7ecf\u9a8c）<\/h3>\n

    4.1 \u6a21\u578b\u53c2\u6570\u5c42\u9762：\u964d\u4f4e\u521b\u9020\u6027，\u9501\u5b9a\u4e8b\u5b9e\u8f93\u51fa<\/h4>\n

    \u4ee3\u7801\u4e2dtemperature=0\u662f\u6700\u57fa\u7840\u914d\u7f6e，\u6e29\u5ea6\u8d8a\u9ad8\u6a21\u578b\u81ea\u7531\u521b\u4f5c\u80fd\u529b\u8d8a\u5f3a，\u5e7b\u89c9\u6982\u7387\u6307\u6570\u4e0a\u5347；\u751f\u4ea7RAG\u5fc5\u987b\u56fa\u5b9a\u6e29\u5ea6\u4e3a0，\u7981\u7528top_p\u968f\u673a\u91c7\u6837，\u5173\u95ed\u6a21\u578b\u81ea\u5e26\u77e5\u8bc6\u5e93\u8054\u60f3\u3002<\/p>\n

    4.2 \u68c0\u7d22\u94fe\u8def：\u591a\u8def\u878d\u5408+\u91cd\u6392\u53cc\u91cd\u964d\u566a<\/h4>\n

    \u5355\u5411\u91cf\u68c0\u7d22\u5e7b\u89c9\u7387\u7ea635%，\u589e\u52a0BM25\u591a\u8def\u878d\u5408\u540e\u964d\u81f318%，\u53e0\u52a0Cross-Encoder\u91cd\u6392\u8fc7\u6ee4\u540e\u5e7b\u89c9\u7387\u53ef\u63a7\u5236\u57285%\u4ee5\u5185，\u662f\u6210\u672c\u6700\u4f4e\u7684\u4f18\u5316\u65b9\u6848\u3002\u5de5\u4e1a\u573a\u666f\u8bbe\u5907\u578b\u53f7\u3001\u4e13\u4e1a\u5173\u952e\u8bcd\u591a，\u5173\u952e\u8bcd\u68c0\u7d22\u53ef\u6709\u6548\u89c4\u907f\u8bed\u4e49\u76f8\u4f3c\u4f46\u578b\u53f7\u4e0d\u7b26\u7684\u9519\u8bef\u53ec\u56de\u3002<\/p>\n

    4.3 \u5207\u7247\u7b56\u7565：\u8bed\u4e49\u5206\u5272\u66ff\u4ee3\u56fa\u5b9a\u957f\u5ea6\u5747\u5206<\/h4>\n

    \u7c97\u66b4\u5747\u5206\u5207\u7247\u4f1a\u628a\u201c\u8bbe\u5907\u53c2\u6570\u8868\u201d\u62c6\u5206\u5230\u591a\u4e2a\u6587\u6863\u5757，\u6a21\u578b\u53ea\u80fd\u770b\u5230\u5c40\u90e8\u6570\u636e，\u6781\u6613\u7f16\u9020\u5b8c\u6574\u53c2\u6570；\u8bed\u4e49\u5206\u5272\u4ee5\u5b8c\u6574\u6bb5\u843d\u3001\u64cd\u4f5c\u6d41\u7a0b\u4e3a\u5355\u4f4d\u5207\u7247，\u4fdd\u8bc1\u5355\u5757\u6587\u672c\u4e8b\u5b9e\u5b8c\u6574\u3002<\/p>\n

    4.4 Prompt\u5f3a\u7ea6\u675f：\u660e\u786e\u62d2\u7b54\u89c4\u5219+\u6eaf\u6e90\u6807\u6ce8<\/h4>\n

    \u5f88\u591aDemo RAG\u4ec5\u7b80\u5355\u62fc\u63a5\u201c\u6839\u636e\u4ee5\u4e0b\u5185\u5bb9\u56de\u7b54\u201d，\u7f3a\u5c11\u60e9\u7f5a\u6027\u89c4\u5219，\u6a21\u578b\u4ecd\u4f1a\u81ea\u884c\u8865\u5145\u4fe1\u606f；\u672c\u6587Prompt\u660e\u786e\u89c4\u5b9a\u65e0\u4fe1\u606f\u76f4\u63a5\u62d2\u7edd\u3001\u5f3a\u5236\u6807\u6ce8\u6765\u6e90，\u4ece\u751f\u6210\u903b\u8f91\u4e0a\u5207\u65ad\u7f16\u9020\u52a8\u673a\u3002<\/p>\n

    4.5 \u540e\u7f6e\u4e8b\u5b9e\u6821\u9a8c：\u515c\u5e95\u62e6\u622a\u6781\u7aef\u5e7b\u89c9<\/h4>\n

    \u6781\u7aef\u573a\u666f\u4e0b（\u53c2\u8003\u6587\u6863\u6781\u5c11\u3001\u8bed\u4e49\u6a21\u7cca）\u6a21\u578b\u4ecd\u4f1a\u8f7b\u5fae\u9020\u5047，TF-IDF\u76f8\u4f3c\u5ea6\u6821\u9a8c\u4f5c\u4e3a\u6700\u540e\u4e00\u9053\u9632\u7ebf，\u8f93\u51fa\u4e0e\u77e5\u8bc6\u5e93\u5339\u914d\u5ea6\u8fc7\u4f4e\u65f6\u76f4\u63a5\u62e6\u622a，\u675c\u7edd\u9519\u8bef\u5185\u5bb9\u6d41\u5165\u5de5\u4e1a\u4e1a\u52a1\u7cfb\u7edf\u3002<\/p>\n

    4.6 \u77e5\u8bc6\u5e93\u52a8\u6001\u66f4\u65b0\u673a\u5236：\u89e3\u51b3\u77e5\u8bc6\u622a\u6b62\u5e7b\u89c9<\/h4>\n

    \u5927\u6a21\u578b\u539f\u751f\u77e5\u8bc6\u622a\u6b62\u65e0\u6cd5\u4fee\u6539，RAG\u4f9d\u9760\u79c1\u6709\u77e5\u8bc6\u5e93\u66f4\u65b0\u5b9e\u65f6\u4fee\u6b63\u4fe1\u606f；\u5bf9\u63a5\u4f01\u4e1aMES\u3001ERP\u7cfb\u7edfAPI\u5b9a\u65f6\u62c9\u53d6\u65b0\u5de5\u5355\u3001\u65b0\u7248\u5de5\u827a\u6587\u6863，\u589e\u91cf\u66f4\u65b0\u5411\u91cf\u5e93，\u907f\u514d\u8fc7\u65f6\u77e5\u8bc6\u9020\u6210\u7684\u4e8b\u5b9e\u9519\u8bef\u3002<\/p>\n

    # \u589e\u91cf\u66f4\u65b0\u5411\u91cf\u5e93\u4ee3\u7801\u7247\u6bb5<\/span>
    \ndef<\/span> update_knowledge_base<\/span>(<\/span>new_pdf_path,<\/span> rag_instance)<\/span>:<\/span>
    \n new_chunks =<\/span> rag_instance.<\/span>preprocessor.<\/span>load_and_split_pdf(<\/span>new_pdf_path)<\/span>
    \n rag_instance.<\/span>multi_retriever.<\/span>vector_db.<\/span>add_documents(<\/span>new_chunks)<\/span>
    \n rag_instance.<\/span>multi_retriever.<\/span>vector_db.<\/span>persist(<\/span>)<\/span>
    \n print<\/span>(<\/span>f"\u65b0\u589e\u6587\u6863\u5207\u7247<\/span>{<\/span>len<\/span>(<\/span>new_chunks)<\/span>}<\/span><\/span>\u6761，\u77e5\u8bc6\u5e93\u589e\u91cf\u66f4\u65b0\u5b8c\u6210"<\/span><\/span>)<\/span><\/p>\n

    \u4e94\u3001\u5de5\u4e1a\u573a\u666f\u843d\u5730\u5e38\u89c1\u5e7b\u89c9\u8e29\u5751\u590d\u76d8<\/h3>\n

    \u57511：\u76f4\u63a5\u4f7f\u7528\u901a\u7528\u5927\u6a21\u578bEmbedding，\u5de5\u4e1a\u4e13\u4e1a\u672f\u8bed\u8bed\u4e49\u5339\u914d\u9519\u8bef<\/h4>\n

    \u901a\u7528\u5d4c\u5165\u6a21\u578b\u5bf9\u7535\u673a\u3001\u5de5\u63a7\u3001\u80fd\u6e90\u4e13\u4e1a\u8bcd\u6c47\u8bc6\u522b\u5dee，\u5bfc\u81f4\u68c0\u7d22\u53ec\u56de\u65e0\u5173\u6587\u6863，\u884d\u751f\u5e7b\u89c9\u3002\u4f18\u5316\u65b9\u6848：\u5fae\u8c03\u884c\u4e1aEmbedding\u6a21\u578b，\u6216\u589e\u52a0\u5173\u952e\u8bcd\u68c0\u7d22\u6743\u91cd\u3002<\/p>\n

    \u57512：\u957f\u5bf9\u8bdd\u5386\u53f2\u6324\u5360\u53c2\u8003\u6587\u672c\u4e0a\u4e0b\u6587\u7a97\u53e3<\/h4>\n

    \u591a\u8f6e\u6570\u5b57\u4eba\u95ee\u7b54\u573a\u666f，\u5386\u53f2\u5bf9\u8bdd\u8fc7\u591a\u538b\u7f29\u77e5\u8bc6\u5e93\u53c2\u8003\u5185\u5bb9，\u6a21\u578b\u8131\u79bb\u79c1\u6709\u6587\u6863\u81ea\u7531\u751f\u6210\u3002\u89e3\u51b3\u65b9\u6848：\u5b9e\u73b0\u5bf9\u8bdd\u6458\u8981\u538b\u7f29，\u4f18\u5148\u4fdd\u7559\u77e5\u8bc6\u5e93\u53c2\u8003\u6587\u672c\u7a97\u53e3\u3002<\/p>\n

    \u57513：\u672a\u533a\u5206\u51b2\u7a81\u77e5\u8bc6\u5e93\u6587\u672c，\u6a21\u578b\u6df7\u5408\u77db\u76fe\u53c2\u6570\u7f16\u9020\u6298\u4e2d\u7b54\u6848<\/h4>\n

    \u77e5\u8bc6\u5e93\u5b58\u5728\u65b0\u65e7\u7248\u672c\u8bbe\u5907\u624b\u518c\u65f6，\u68c0\u7d22\u540c\u65f6\u53ec\u56de\u51b2\u7a81\u53c2\u6570，\u6a21\u578b\u81ea\u884c\u53d6\u4e2d\u95f4\u503c\u9020\u6210\u9519\u8bef\u3002\u4f18\u5316：\u589e\u52a0\u6587\u6863\u65f6\u95f4\u6233\u6743\u91cd，\u4f18\u5148\u53ec\u56de\u6700\u65b0\u7248\u6587\u6863，\u91cd\u6392\u9636\u6bb5\u8fc7\u6ee4\u8fc7\u65f6\u51b2\u7a81\u7247\u6bb5\u3002<\/p>\n

    \u57514：\u5173\u95ed\u6eaf\u6e90\u673a\u5236，\u65e0\u6cd5\u5b9a\u4f4d\u5e7b\u89c9\u6765\u6e90<\/h4>\n

    \u751f\u4ea7\u73af\u5883\u51fa\u73b0\u9519\u8bef\u56de\u7b54\u540e\u65e0\u6cd5\u6392\u67e5\u662f\u68c0\u7d22\u95ee\u9898\u8fd8\u662f\u6a21\u578b\u7f16\u9020，\u67b6\u6784\u5fc5\u987b\u5f3a\u5236\u8fd4\u56de\u53c2\u8003\u6587\u6863\u7d22\u5f15，\u4fbf\u4e8e\u8fd0\u7ef4\u5b9a\u4f4d\u77e5\u8bc6\u5e93\u7f3a\u9677\u3002<\/p>\n

    \u516d\u3001\u7cfb\u7edf\u751f\u4ea7\u5316\u90e8\u7f72\u62d3\u5c55：\u5bf9\u63a5\u4f01\u4e1a\u7ba1\u7406API\u4e0e\u591a\u667a\u80fd\u4f53MCP\u901a\u4fe1<\/h3>\n

    \u672c\u6587RAG\u7cfb\u7edf\u53ef\u65e0\u7f1d\u5bf9\u63a5\u5c97\u4f4d\u9700\u6c42\u4e2d\u7684\u4f01\u4e1a\u7cfb\u7edfAPI\u3001Dify\u667a\u80fd\u4f53\u3001MCP\u591a\u667a\u80fd\u4f53\u534f\u8bae：<\/p>\n

  • API\u5c01\u88c5\u96c6\u6210：\u65b0\u589e\u63a5\u53e3\u6a21\u5757，\u901a\u8fc7FastAPI\u5c01\u88c5RAG\u67e5\u8be2\u80fd\u529b，\u63d0\u4f9b\u9274\u6743\u3001\u9650\u6d41，\u4f9bMES\u3001\u6570\u5b57\u4eba\u524d\u7aef\u8c03\u7528；<\/li>\n
  • Dify\u6846\u67b6\u517c\u5bb9：\u5c06\u672cRAG\u5c01\u88c5\u4e3aDify\u81ea\u5b9a\u4e49\u77e5\u8bc6\u5e93\u68c0\u7d22\u63d2\u4ef6，\u590d\u7528Dify\u667a\u80fd\u4f53\u7f16\u6392\u80fd\u529b；<\/li>\n
  • MCP\u591a\u667a\u80fd\u4f53\u901a\u4fe1\u9002\u914d：RAG\u667a\u80fd\u4f53\u4f5c\u4e3a\u77e5\u8bc6\u5e93Agent，\u901a\u8fc7MCP\u534f\u8bae\u4e0e\u68c0\u6d4bAgent\u3001\u6570\u636e\u7edf\u8ba1Agent\u901a\u4fe1，\u591a\u667a\u80fd\u4f53\u534f\u4f5c\u65f6\u7edf\u4e00\u4ee5RAG\u68c0\u7d22\u4e8b\u5b9e\u4e3a\u57fa\u51c6，\u9632\u6b62\u8de8Agent\u4fe1\u606f\u7f16\u9020\u3002<\/li>\n

    . \u4e03\u3001\u603b\u7ed3
    \n\u5927\u6a21\u578b\u5e7b\u89c9\u4e0d\u662f\u5355\u4e00\u95ee\u9898，\u800c\u662f\u68c0\u7d22\u3001\u5207\u7247\u3001\u751f\u6210\u3001\u6821\u9a8c\u5168\u94fe\u8def\u7f3a\u9677\u5171\u540c\u5bfc\u81f4\u7684\u7ed3\u679c\u3002\u7b80\u6613Demo RAG\u4ec5\u5b9e\u73b0\u201c\u68c0\u7d22+\u751f\u6210\u201d\u57fa\u7840\u6d41\u7a0b，\u65e0\u6cd5\u6ee1\u8db3\u5de5\u4e1a\u751f\u4ea7\u53ef\u4fe1\u8981\u6c42；\u672c\u6587\u642d\u5efa\u7684\u516d\u5c42\u67b6\u6784\u751f\u4ea7\u7ea7RAG，\u901a\u8fc7\u6e90\u5934\u6587\u672c\u6e05\u6d17\u3001\u591a\u8def\u964d\u566a\u68c0\u7d22\u3001\u91cd\u6392\u8fc7\u6ee4\u3001\u751f\u6210\u5f3a\u7ea6\u675f\u3001\u540e\u7f6e\u4e8b\u5b9e\u6821\u9a8c\u3001\u77e5\u8bc6\u5e93\u52a8\u6001\u66f4\u65b0\u516d\u5927\u7ef4\u5ea6\u7cfb\u7edf\u6027\u538b\u5236\u5e7b\u89c9，\u914d\u5957\u5b8c\u6574\u53ef\u8fd0\u884cPython\u4ee3\u7801，\u57fa\u4e8eLangChain\u4e0eFlowRAG\u53cc\u6280\u672f\u6808，\u9002\u914d\u5de5\u4e1a\u6570\u5b57\u4eba\u3001\u77e5\u8bc6\u95ee\u7b54\u3001\u5de5\u827a\u56fe\u8c31\u5206\u6790\u7b49\u4e1a\u52a1\u573a\u666f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

    \u6458\u8981
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