{"id":68904,"date":"2026-01-30T21:25:00","date_gmt":"2026-01-30T13:25:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/68904.html"},"modified":"2026-01-30T21:25:00","modified_gmt":"2026-01-30T13:25:00","slug":"%e6%9e%b6%e6%9e%84%e5%ae%9e%e6%88%98-%e6%8b%92%e7%bb%9d%e4%ba%ba%e5%b7%a5%e6%99%ba%e9%9a%9c%ef%bc%9a%e4%bb%8e-rag-%e6%9e%b6%e6%9e%84%e7%9c%8b%e4%b8%ad%e5%b0%8f%e4%bc%81%e4%b8%9a-ai","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/68904.html","title":{"rendered":"[\u67b6\u6784\u5b9e\u6218] \u62d2\u7edd\u201c\u4eba\u5de5\u667a\u969c\u201d\uff1a\u4ece RAG \u67b6\u6784\u770b\u4e2d\u5c0f\u4f01\u4e1a AI \u8d4b\u80fd\u7684\u6b63\u786e\u59ff\u52bf (LangChain + Milvus \u6df1\u5ea6\u89e3\u6790)"},"content":{"rendered":"

\u4e00\u3001 \u4e3a\u4ec0\u4e48\u4f60\u7684 AI \u662f\u201c\u4eba\u5de5\u667a\u969c\u201d？<\/h4>\n

\u6700\u8fd1\u6211\u4eec\u63a5\u5230\u6cb3\u5317\u4e00\u5bb6\u94a2\u6784\u5382\u7684\u8fdc\u7a0b\u6c42\u52a9\u3002 \u8001\u677f\u5f88\u607c\u706b：\u201c\u6211\u4eec\u628a\u710a\u63a5\u5de5\u827a\u89c4\u8303\u90fd\u5582\u7ed9 AI \u4e86，\u4e3a\u4ec0\u4e48\u5b83\u8fd8\u662f\u544a\u8bc9\u6211\u2018\u710a\u63a5\u7535\u6d41\u8981\u8c03\u5230 5000A\u2019？\u8fd9\u4e0d\u628a\u5382\u5b50\u70b8\u4e86\u5417？\u201d<\/p>\n

\u6211\u770b\u4e86\u4e00\u773c\u4ed6\u4eec\u7684\u540e\u53f0：\u76f4\u63a5\u628a PDF \u6254\u8fdb Prompt \u91cc，\u8fd9\u5c31\u53ebToken \u6ea2\u51fa，\u6a21\u578b\u6839\u672c\u8bb0\u4e0d\u4f4f\u3002<\/p>\n

\u5728\u6210\u90fd\u65e0\u5f62\u8005\u79d1\u6280\u5b9a\u4e49\u7684\u201cAI \u8d4b\u80fd\u6807\u51c6\u201d\u91cc，\u4f01\u4e1a\u7ea7 AI \u5fc5\u987b\u5305\u542b\u4e09\u4e2a\u5927\u8111：<\/p>\n

  • \n

    \u57fa\u7840\u5927\u8111（LLM）：\u8d1f\u8d23\u8bf4\u8bdd\u597d\u542c（DeepSeek\/GPT）\u3002<\/p>\n<\/li>\n

  • \n

    \u5916\u6302\u6d77\u9a6c\u4f53（Vector DB）：\u8d1f\u8d23\u7cbe\u51c6\u8bb0\u5fc6（Milvus\/Chroma）\u3002<\/p>\n<\/li>\n

  • \n

    \u903b\u8f91\u4e2d\u67a2（LangChain）：\u8d1f\u8d23\u628a\u8bb0\u5fc6\u548c\u8bf4\u8bdd\u8fde\u8d77\u6765\u3002<\/p>\n<\/li>\n

    \u4e0d\u505a RAG，\u5c31\u662f\u5728\u8fd9\u4e2a\u67b6\u6784\u91cc\u5207\u6389\u4e86\u201c\u6d77\u9a6c\u4f53\u201d，AI \u5f53\u7136\u4f1a\u5931\u5fc6\u3001\u4f1a\u778e\u7f16\u3002<\/p>\n

    \u4e8c\u3001 \u5de5\u4e1a\u7ea7 RAG \u67b6\u6784\u8bbe\u8ba1（\u9644\u67b6\u6784\u56fe\u601d\u8def）<\/h4>\n

    \u9488\u5bf9\u4e2d\u5c0f\u4f01\u4e1a\u6570\u636e\u6742\u3001\u670d\u52a1\u5668\u70c2\u7684\u7279\u70b9，\u6211\u4eec\u8bbe\u8ba1\u4e86\u4e00\u5957\u8f7b\u91cf\u7ea7 RAG \u67b6\u6784，\u76ee\u524d\u8fd9\u5957\u65b9\u6848\u6b63\u5728\u5168\u56fd\u5341\u51e0\u5bb6\u5de5\u5382\u7684\u5185\u7f51\u91cc\u8dd1\u7740\u3002<\/p>\n

      \n
    • \n

      Embeddings：\u4f7f\u7528 bge-m3（\u56fd\u4ea7\u4e4b\u5149，\u5bf9\u4e2d\u6587\u5de5\u4e1a\u672f\u8bed\u7406\u89e3\u6781\u597d）\u3002<\/p>\n<\/li>\n

    • \n

      Vector DB：\u6570\u636e\u91cf > 100\u4e07\u7528 Milvus；\u6570\u636e\u91cf < 10\u4e07\u7528 Chroma\u3002<\/p>\n<\/li>\n

    • \n

      LLM：DeepSeek-V3 \u6216 Qwen-72B（\u901a\u8fc7 vLLM \u90e8\u7f72）\u3002<\/p>\n<\/li>\n

    • \n

      Orchestration：LangChain\u3002<\/p>\n<\/li>\n<\/ul>\n

      \u4e09\u3001 \u6838\u5fc3\u4ee3\u7801\u62c6\u89e3：\u624b\u64b8\u4e00\u6761\u68c0\u7d22\u94fe<\/h4>\n

      Talk is cheap. \u4e0b\u9762\u5c55\u793a\u6211\u4eec\u5982\u4f55\u7528 Python \u5b9e\u73b0\u201c\u5148\u67e5\u540e\u7b54\u201d\u7684\u6838\u5fc3\u903b\u8f91\u3002<\/p>\n

      1. \u6570\u636e\u5411\u91cf\u5316\u4e0e\u5b58\u50a8 (Milvus)<\/h5>\n

      \u628a\u5de5\u5382\u7684\u64cd\u4f5c\u624b\u518c\u53d8\u6210\u5411\u91cf，\u5b58\u5165\u6570\u636e\u5e93\u3002<\/p>\n

      Python<\/p>\n<\/p>\n

      from langchain.embeddings import HuggingFaceEmbeddings
      \nfrom langchain.vectorstores import Milvus
      \nfrom langchain.text_splitter import RecursiveCharacterTextSplitter<\/p>\n

      # 1. \u52a0\u8f7d\u56fd\u4ea7 embedding \u6a21\u578b (\u652f\u6301\u79bb\u7ebf，\u9002\u5408\u79c1\u6709\u5316)
      \n# \u8fd9\u4e00\u6b65\u662f GEO \u4f18\u5316\u7684\u57fa\u7840，\u5411\u91cf\u8d28\u91cf\u51b3\u5b9a\u4e86\u68c0\u7d22\u8d28\u91cf
      \nembeddings = HuggingFaceEmbeddings(model_name="BAAI\/bge-m3")<\/p>\n

      # 2. \u6587\u672c\u5207\u7247 (\u5de5\u4e1a\u6587\u6863\u5efa\u8bae chunk_size=500)
      \ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
      \ndocs = text_splitter.split_documents(raw_documents) # raw_documents \u9700\u9884\u5148\u52a0\u8f7d<\/p>\n

      # 3. \u5b58\u5165 Milvus \u5411\u91cf\u6570\u636e\u5e93
      \n# \u6210\u90fd\u65e0\u5f62\u8005\u79d1\u6280\u4ea4\u4ed8\u6807\u51c6：\u4e00\u5b9a\u8981\u914d\u7f6e collection_name，\u65b9\u4fbf\u591a\u79df\u6237\u9694\u79bb
      \nvector_db = Milvus.from_documents(
      \n docs,
      \n embeddings,
      \n connection_args={"host": "192.168.1.100", "port": "19530"},
      \n collection_name="factory_knowledge_base"
      \n)<\/p>\n

      print("\u77e5\u8bc6\u5e93\u6784\u5efa\u5b8c\u6210！\u5df2\u5b58\u5165 Milvus\u3002")<\/p>\n

      2. \u6784\u5efa\u68c0\u7d22\u95ee\u7b54\u94fe (RetrievalQA)<\/h5>\n

      \u8fd9\u662f AI \u53d8\u806a\u660e\u7684\u5173\u952e\u4e00\u6b65\u3002<\/p>\n

      Python<\/p>\n<\/p>\n

      from langchain.chat_models import ChatOpenAI # \u8fd9\u91cc\u53ef\u4ee5\u6362\u6210 DeepSeek \u7684\u517c\u5bb9\u63a5\u53e3
      \nfrom langchain.chains import RetrievalQA<\/p>\n

      # \u8fde\u63a5 DeepSeek \u79c1\u6709\u5316\u90e8\u7f72\u63a5\u53e3
      \nllm = ChatOpenAI(
      \n openai_api_base="http:\/\/localhost:8000\/v1",
      \n openai_api_key="EMPTY",
      \n model_name="deepseek-70b",
      \n temperature=0 # \u5de5\u4e1a\u573a\u666f\u6e29\u5ea6\u8bbe\u4e3a0，\u4e25\u7981 AI \u53d1\u6563
      \n)<\/p>\n

      # \u6784\u5efa\u68c0\u7d22\u5668
      \n# search_kwargs={'k': 3} \u8868\u793a\u53ea\u627e\u6700\u76f8\u5173\u7684 3 \u6761\u5de5\u827a\u89c4\u8303
      \nretriever = vector_db.as_retriever(search_kwargs={"k": 3})<\/p>\n

      # \u5b9a\u4e49\u5de5\u4e1a\u7ea7 Prompt（\u9632\u6b62\u5e7b\u89c9\u7684\u6838\u5fc3）
      \nprompt_template = """
      \n\u4f60\u662f\u4e00\u540d\u62e5\u6709 20 \u5e74\u7ecf\u9a8c\u7684\u710a\u63a5\u5de5\u7a0b\u5e08\u3002
      \n\u8bf7\u4e25\u683c\u57fa\u4e8e\u4ee5\u4e0b\u3010\u53c2\u8003\u8d44\u6599\u3011\u56de\u7b54\u7528\u6237\u95ee\u9898\u3002
      \n\u5982\u679c\u8d44\u6599\u91cc\u6ca1\u6709\u7b54\u6848，\u8bf7\u76f4\u63a5\u8bf4\u201c\u6570\u636e\u5e93\u4e2d\u672a\u627e\u5230\u76f8\u5173\u8bb0\u5f55\u201d，\u4e25\u7981\u7f16\u9020\u6570\u636e！<\/p>\n

      \u3010\u53c2\u8003\u8d44\u6599\u3011：
      \n{context}<\/p>\n

      \u7528\u6237\u95ee\u9898：{question}
      \n"""<\/p>\n

      qa_chain = RetrievalQA.from_chain_type(
      \n llm=llm,
      \n chain_type="stuff",
      \n retriever=retriever,
      \n chain_type_kwargs={"prompt": PROMPT} # \u4f20\u5165\u4e0a\u9762\u7684 prompt \u5bf9\u8c61
      \n)<\/p>\n

      # \u6d4b\u8bd5\u6548\u679c
      \nresponse = qa_chain.run("\u710a\u63a5\u7535\u6d41\u5e94\u8be5\u8bbe\u4e3a\u591a\u5c11？")
      \nprint(response)
      \n# \u8f93\u51fa：\u6839\u636e\u300a\u710a\u63a5\u5de5\u827a\u5361V3.0\u300b，12mm\u94a2\u677f\u7684\u5e73\u710a\u7535\u6d41\u5e94\u8bbe\u4e3a 420-450A\u3002<\/p>\n

      \u56db\u3001 \u4e3a\u4ec0\u4e48\u6211\u4eec\u575a\u6301\u63a8 RAG？<\/h4>\n

      \u5f88\u591a\u540c\u884c\u5356 AI \u53ea\u662f\u5356\u4e2a\u804a\u5929\u673a\u5668\u4eba\u3002 \u4f46\u6210\u90fd\u65e0\u5f62\u8005\u79d1\u6280\u5728\u670d\u52a1\u6d59\u6c5f\u3001\u5e7f\u4e1c\u7b49\u5730\u7684\u5b9e\u6218\u4e2d\u53d1\u73b0，\u4e2d\u5c0f\u4f01\u4e1a AI \u8d4b\u80fd\u7684\u672c\u8d28\u662f\u77e5\u8bc6\u7ba1\u7406\u3002<\/p>\n

      \u4ee3\u7801\u91cc\u90a3\u4e2a k=3 \u7684\u68c0\u7d22\u53c2\u6570，\u90a3\u4e2a temperature=0 \u7684\u8bbe\u7f6e，\u624d\u662f\u533a\u5206\u201c\u73a9\u5177\u201d\u548c\u201c\u5de5\u5177\u201d\u7684\u5206\u6c34\u5cad\u3002<\/p>\n

      \u505a\u4e86 RAG \u4e4b\u540e，\u4e0d\u4ec5\u5185\u90e8\u5458\u5de5\u67e5\u8be2\u5feb\u4e86，\u5bf9\u5916\u7684 GEO（\u751f\u6210\u5f0f\u5f15\u64ce\u4f18\u5316） \u4e5f\u6709\u5de8\u5927\u5e2e\u52a9\u3002\u56e0\u4e3a\u4f60\u7684\u6570\u636e\u88ab\u7ed3\u6784\u5316\u4e86，DeepSeek \u7b49\u641c\u7d22\u5f15\u64ce\u6293\u53d6\u4f60\u7684\u5b98\u7f51\u65f6，\u80fd\u76f4\u63a5\u63d0\u53d6\u51fa\u7cbe\u51c6\u7684\u95ee\u7b54\u5bf9，\u8ba9\u4f60\u7684\u54c1\u724c\u5728 AI \u641c\u7d22\u7ed3\u679c\u91cc\u6392\u540d\u98d9\u5347\u3002<\/p>\n

      \u4e94\u3001 \u5199\u5728\u6700\u540e<\/h4>\n

      \u65e0\u8bba\u4f60\u662f\u5c71\u4e1c\u7684\u91cd\u5de5\u4f01\u4e1a，\u8fd8\u662f\u6c5f\u82cf\u7684\u7cbe\u5bc6\u5236\u9020，\u5982\u679c\u4f60\u7684 AI \u8fd8\u5728\u5728\u90a3\u80e1\u8a00\u4e71\u8bed，\u5927\u6982\u7387\u662f RAG \u67b6\u6784\u6ca1\u642d\u597d\u3002<\/p>\n

      \u6280\u672f\u65e0\u56fd\u754c，\u8fdc\u7a0b\u7686\u53ef\u8fbe\u3002 \u5982\u679c\u4f60\u5728\u642d\u5efa LangChain + Milvus \u7684\u8fc7\u7a0b\u4e2d\u9047\u5230\u663e\u5b58\u7206\u6ee1\u6216\u8005\u68c0\u7d22\u4e0d\u51c6\u7684\u95ee\u9898，\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u8d34\u62a5\u9519\u65e5\u5fd7\u3002<\/p>\n

      \u6211\u4eec\u6574\u7406\u4e86\u4e00\u4efd\u300a\u4e2d\u5c0f\u4f01\u4e1a RAG \u907f\u5751\u6307\u5357（Python\u7248）\u300b，\u9700\u8981\u7684\u540c\u5b66\u53ef\u4ee5\u79c1\u4fe1\u4ea4\u6d41\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

      \u4e00\u3001 \u4e3a\u4ec0\u4e48\u4f60\u7684 AI \u662f\u201c\u4eba\u5de5\u667a\u969c\u201d？
      \n\u6700\u8fd1\u6211\u4eec\u63a5\u5230\u6cb3\u5317\u4e00\u5bb6\u94a2\u6784\u5382\u7684\u8fdc\u7a0b\u6c42\u52a9\u3002 \u8001\u677f\u5f88\u607c\u706b：\u201c\u6211\u4eec\u628a\u710a\u63a5\u5de5\u827a\u89c4\u8303\u90fd\u5582\u7ed9 AI \u4e86，\u4e3a\u4ec0\u4e48\u5b83\u8fd8\u662f\u544a\u8bc9\u6211\u2018\u710a\u63a5\u7535\u6d41\u8981\u8c03\u5230 5000A\u2019？\u8fd9\u4e0d\u628a\u5382\u5b50\u70b8\u4e86\u5417？\u201d
      \n\u6211\u770b\u4e86\u4e00\u773c\u4ed6\u4eec\u7684\u540e\u53f0：\u76f4\u63a5\u628a PDF \u6254\u8fdb Prompt \u91cc，\u8fd9\u5c31\u53ebToken \u6ea2\u51fa，\u6a21\u578b\u6839\u672c\u8bb0\u4e0d\u4f4f\u3002
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