{"id":47688,"date":"2025-07-30T08:47:13","date_gmt":"2025-07-30T00:47:13","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/47688.html"},"modified":"2025-07-30T08:47:13","modified_gmt":"2025-07-30T00:47:13","slug":"%e7%ac%ac%e4%ba%94%e7%ab%a0%ef%bc%9allamaindex-%e6%a0%b8%e5%bf%83%e6%a6%82%e5%bf%b5%e4%b8%8e%e5%85%a5%e9%97%a8","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/47688.html","title":{"rendered":"\u7b2c\u4e94\u7ae0\uff1aLlamaIndex \u6838\u5fc3\u6982\u5ff5\u4e0e\u5165\u95e8"},"content":{"rendered":"

1. \u5185\u5bb9\u8bb2\u89e3<\/h3>\n

1.1 RAG (Retrieval-Augmented Generation) \u7b80\u4ecb<\/h4>\n

\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u867d\u7136\u77e5\u8bc6\u6e0a\u535a\uff0c\u4f46\u5b83\u4eec\u5b58\u5728\u4e24\u4e2a\u4e3b\u8981\u95ee\u9898\uff1a<\/p>\n

  • \u77e5\u8bc6\u622a\u6b62\uff1a\u5b83\u4eec\u7684\u77e5\u8bc6\u505c\u7559\u5728\u67d0\u4e2a\u65f6\u95f4\u70b9\uff0c\u65e0\u6cd5\u83b7\u53d6\u6700\u65b0\u7684\u4fe1\u606f\u3002<\/li>\n
  • \u7f3a\u4e4f\u79c1\u6709\u77e5\u8bc6\uff1a\u5b83\u4eec\u4e0d\u77e5\u9053\u4f60\u516c\u53f8\u7684\u5185\u90e8\u6587\u6863\u3001\u4f60\u7684\u4e2a\u4eba\u7b14\u8bb0\u6216\u7279\u5b9a\u7684\u9879\u76ee\u4ee3\u7801\u3002<\/li>\n

    RAG (Retrieval-Augmented Generation\uff0c\u68c0\u7d22\u589e\u5f3a\u751f\u6210) \u662f\u4e00\u79cd\u5f3a\u5927\u7684\u6280\u672f\uff0c\u65e8\u5728\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\u3002\u5176\u6838\u5fc3\u601d\u60f3\u975e\u5e38\u76f4\u89c2\uff1a<\/p>\n

  • \u68c0\u7d22 (Retrieval)\uff1a\u5f53\u7528\u6237\u63d0\u51fa\u4e00\u4e2a\u95ee\u9898\u65f6\uff0c\u6211\u4eec\u4e0d\u76f4\u63a5\u628a\u95ee\u9898\u6254\u7ed9 LLM\u3002\u800c\u662f\u5148\u7528\u8fd9\u4e2a\u95ee\u9898\u53bb\u4e00\u4e2a\u76f8\u5173\u7684\u77e5\u8bc6\u5e93\uff08\u5982\u516c\u53f8\u6587\u6863\u3001\u4ea7\u54c1\u624b\u518c\u7b49\uff09\u4e2d\u8fdb\u884c\u641c\u7d22\uff0c\u627e\u51fa\u6700\u76f8\u5173\u7684\u4fe1\u606f\u7247\u6bb5\u3002<\/li>\n
  • \u589e\u5f3a (Augmented)\uff1a\u6211\u4eec\u5c06\u539f\u59cb\u95ee\u9898\u548c\u4ece\u77e5\u8bc6\u5e93\u4e2d\u68c0\u7d22\u5230\u7684\u76f8\u5173\u4fe1\u606f\u7247\u6bb5\u4e00\u8d77\u6253\u5305\uff0c\u7ec4\u5408\u6210\u4e00\u4e2a\u65b0\u7684\u3001\u66f4\u4e30\u5bcc\u7684\u63d0\u793a\u3002<\/li>\n
  • \u751f\u6210 (Generation)\uff1a\u6700\u540e\uff0c\u6211\u4eec\u5c06\u8fd9\u4e2a\u589e\u5f3a\u540e\u7684\u63d0\u793a\u53d1\u9001\u7ed9 LLM\uff0c\u8ba9\u5b83\u57fa\u4e8e\u6211\u4eec\u63d0\u4f9b\u7684\u4e0a\u4e0b\u6587\u4fe1\u606f\u6765\u751f\u6210\u6700\u7ec8\u7684\u3001\u66f4\u51c6\u786e\u7684\u56de\u7b54\u3002<\/li>\n

    \u901a\u8fc7 RAG\uff0cLLM \u5c31\u597d\u50cf\u6709\u4e86\u4e00\u4e2a\u53ef\u4ee5\u968f\u65f6\u67e5\u9605\u7684\u201c\u5f00\u5377\u8d44\u6599\u201d\uff0c\u4ece\u800c\u80fd\u591f\u56de\u7b54\u7279\u5b9a\u9886\u57df\u7684\u95ee\u9898\uff0c\u5e76\u5927\u5927\u51cf\u5c11\u201c\u5e7b\u89c9\u201d\uff08\u80e1\u8bf4\u516b\u9053\uff09\u7684\u4ea7\u751f\u3002<\/p>\n

    1.2 LlamaIndex \u7684\u6838\u5fc3\u6d41\u7a0b\uff1aLoad, Index, Query<\/h4>\n

    LlamaIndex (\u524d\u8eab\u4e3a GPT Index) \u662f\u4e00\u4e2a\u4e13\u95e8\u4e3a\u6784\u5efa\u548c\u4f18\u5316 RAG \u5e94\u7528\u800c\u8bbe\u8ba1\u7684\u6846\u67b6\u3002\u5b83\u5c06\u590d\u6742\u7684 RAG \u6d41\u7a0b\u7b80\u5316\u4e3a\u4e09\u4e2a\u6838\u5fc3\u6b65\u9aa4\uff1a<\/p>\n

      \n
    • \n

      Load (\u52a0\u8f7d\u6570\u636e):<\/p>\n

        \n
      • \u8fd9\u662f RAG \u7684\u7b2c\u4e00\u6b65\uff1a\u5c06\u4f60\u7684\u6570\u636e\u6e90\uff08PDF\u3001Markdown\u3001Notion\u3001\u6570\u636e\u5e93\u7b49\uff09\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\u3002<\/li>\n
      • LlamaIndex \u63d0\u4f9b\u4e86\u5927\u91cf\u7684 Reader (\u4e5f\u79f0\u4e3a Loader)\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u4ece\u5404\u79cd\u6570\u636e\u6e90\u8bfb\u53d6\u6570\u636e\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7edf\u4e00\u7684 Document \u5bf9\u8c61\u3002<\/li>\n<\/ul>\n<\/li>\n
      • \n

        Index (\u6784\u5efa\u7d22\u5f15):<\/p>\n

          \n
        • \u52a0\u8f7d\u6570\u636e\u540e\uff0c\u6211\u4eec\u9700\u8981\u5c06\u5176\u5904\u7406\u6210\u4e00\u79cd\u6613\u4e8e\u68c0\u7d22\u7684\u7ed3\u6784\uff0c\u8fd9\u4e2a\u8fc7\u7a0b\u5c31\u662f\u201c\u6784\u5efa\u7d22\u5f15\u201d\u3002<\/li>\n
        • \u6700\u5e38\u89c1\u7684\u7d22\u5f15\u7c7b\u578b\u662f VectorStoreIndex (\u5411\u91cf\u5b58\u50a8\u7d22\u5f15)\u3002\u5b83\u7684\u5de5\u4f5c\u539f\u7406\u662f\uff1a\n
        • \u5c06\u52a0\u8f7d\u7684 Document \u5206\u5272\u6210\u66f4\u5c0f\u7684\u6587\u672c\u5757\uff08Node\uff09\u3002<\/li>\n
        • \u4f7f\u7528\u4e00\u4e2a\u201c\u5d4c\u5165\u6a21\u578b\u201d\uff08Embedding Model\uff0c\u5982 OpenAI \u7684 text-embedding-ada-002\uff09\u5c06\u6bcf\u4e2a\u6587\u672c\u5757\u8f6c\u6362\u6210\u4e00\u4e2a\u6570\u5b66\u5411\u91cf\uff08\u4e00\u957f\u4e32\u6570\u5b57\uff09\u3002\u8fd9\u4e2a\u5411\u91cf\u53ef\u4ee5\u88ab\u8ba4\u4e3a\u662f\u8be5\u6587\u672c\u5757\u5728\u8bed\u4e49\u7a7a\u95f4\u4e2d\u7684\u201c\u5750\u6807\u201d\u3002<\/li>\n
        • \u5c06\u8fd9\u4e9b\u6587\u672c\u5757\u53ca\u5176\u5bf9\u5e94\u7684\u5411\u91cf\u5b58\u50a8\u8d77\u6765\uff08\u53ef\u4ee5\u5b58\u5728\u5185\u5b58\u4e2d\uff0c\u4e5f\u53ef\u4ee5\u5b58\u5728\u4e13\u95e8\u7684\u5411\u91cf\u6570\u636e\u5e93\u91cc\uff09\u3002<\/li>\n<\/li>\n<\/ul>\n<\/li>\n
        • \n

          Query (\u67e5\u8be2):<\/p>\n

            \n
          • \u5f53\u7528\u6237\u63d0\u51fa\u95ee\u9898\u65f6\uff0c\u67e5\u8be2\u9636\u6bb5\u5f00\u59cb\uff1a\n
          • LlamaIndex \u9996\u5148\u4f7f\u7528\u76f8\u540c\u7684\u5d4c\u5165\u6a21\u578b\u5c06\u7528\u6237\u7684\u95ee\u9898\u4e5f\u8f6c\u6362\u6210\u4e00\u4e2a\u5411\u91cf\u3002<\/li>\n
          • \u7136\u540e\uff0c\u5b83\u5728\u7d22\u5f15\u4e2d\u8ba1\u7b97\u7528\u6237\u95ee\u9898\u5411\u91cf\u4e0e\u6240\u6709\u6587\u672c\u5757\u5411\u91cf\u4e4b\u95f4\u7684\u201c\u76f8\u4f3c\u5ea6\u201d\uff08\u901a\u5e38\u662f\u4f59\u5f26\u76f8\u4f3c\u5ea6\uff09\u3002<\/li>\n
          • \u5b83\u627e\u51fa\u4e0e\u95ee\u9898\u6700\u76f8\u4f3c\u7684 Top-K \u4e2a\u6587\u672c\u5757\u3002<\/li>\n
          • \u6700\u540e\uff0c\u5b83\u5c06\u8fd9\u4e9b\u6700\u76f8\u5173\u7684\u6587\u672c\u5757\u548c\u539f\u59cb\u95ee\u9898\u4e00\u8d77\u63d0\u4ea4\u7ed9 LLM\uff0c\u751f\u6210\u6700\u7ec8\u7b54\u6848\u3002<\/li>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

            1.3 \u4f7f\u7528 SimpleDirectoryReader \u52a0\u8f7d\u672c\u5730\u6587\u6863<\/h4>\n

            SimpleDirectoryReader \u662f LlamaIndex \u4e2d\u6700\u5e38\u7528\u7684\u52a0\u8f7d\u5668\u4e4b\u4e00\u3002\u5b83\u975e\u5e38\u65b9\u4fbf\uff0c\u53ef\u4ee5\u9012\u5f52\u5730\u52a0\u8f7d\u4e00\u4e2a\u6587\u4ef6\u5939\u5185\u6240\u6709\u652f\u6301\u7684\u6587\u4ef6\u7c7b\u578b\uff08\u5982 .txt, .md, .pdf \u7b49\uff09\u3002\u4f60\u53ea\u9700\u8981\u544a\u8bc9\u5b83\u6587\u4ef6\u5939\u7684\u8def\u5f84\uff0c\u5b83\u5c31\u4f1a\u81ea\u52a8\u5904\u7406\u5269\u4e0b\u7684\u4e8b\u60c5\u3002<\/p>\n

            1.4 \u521b\u5efa\u5411\u91cf\u7d22\u5f15\uff08VectorStoreIndex\uff09<\/h4>\n

            VectorStoreIndex \u662f\u6784\u5efa RAG \u5e94\u7528\u7684\u8d77\u70b9\u3002\u901a\u8fc7\u8c03\u7528 VectorStoreIndex.from_documents()\uff0cLlamaIndex \u4f1a\u5728\u540e\u53f0\u81ea\u52a8\u5b8c\u6210\u6587\u672c\u5206\u5272\u3001\u5411\u91cf\u5d4c\u5165\u548c\u7d22\u5f15\u6784\u5efa\u7684\u5168\u8fc7\u7a0b\uff0c\u6781\u5927\u5730\u7b80\u5316\u4e86\u5f00\u53d1\u6d41\u7a0b\u3002<\/p>\n

            1.5 \u4ece\u7d22\u5f15\u4e2d\u67e5\u8be2\u4fe1\u606f<\/h4>\n

            \u4e00\u65e6\u7d22\u5f15\u6784\u5efa\u5b8c\u6210\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u901a\u8fc7\u5176 as_query_engine() \u65b9\u6cd5\u521b\u5efa\u4e00\u4e2a\u67e5\u8be2\u5f15\u64ce\u3002\u8fd9\u4e2a\u67e5\u8be2\u5f15\u64ce\u5c01\u88c5\u4e86\u4ece\u63d0\u95ee\u5230\u751f\u6210\u7b54\u6848\u7684\u6574\u4e2a RAG \u6d41\u7a0b\u3002\u6211\u4eec\u53ea\u9700\u8981\u8c03\u7528\u5176 query() \u65b9\u6cd5\u5e76\u4f20\u5165\u95ee\u9898\u5b57\u7b26\u4e32\uff0c\u5c31\u53ef\u4ee5\u5f97\u5230\u6700\u7ec8\u7684\u7b54\u6848\u3002<\/p>\n

            \n

            2. \u4ee3\u7801\u793a\u4f8b<\/h3>\n

            \u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u65b0\u7684 FastAPI \u7aef\u70b9\uff0c\u5141\u8bb8\u7528\u6237\u4e0a\u4f20\u4e00\u4e2a\u6587\u672c\u6587\u4ef6\uff0c\u7136\u540e\u57fa\u4e8e\u8be5\u6587\u4ef6\u7684\u5185\u5bb9\u8fdb\u884c\u95ee\u7b54\u3002<\/p>\n

            2.1 \u51c6\u5907\u5de5\u4f5c\uff1a\u521b\u5efa\u6570\u636e\u76ee\u5f55<\/h4>\n

            \u5728\u9879\u76ee\u6839\u76ee\u5f55\u4e0b\u521b\u5efa\u4e00\u4e2a\u7528\u4e8e\u5b58\u653e\u4e0a\u4f20\u6587\u4ef6\u7684\u76ee\u5f55\uff1a<\/p>\n

            mkdir<\/span> data<\/p>\n

            2.2 \u66f4\u65b0 Pydantic \u6a21\u578b<\/h4>\n

            app\/api\/v1\/models\/ai.py (\u65b0\u589e\u4ee5\u4e0b\u5185\u5bb9)<\/p>\n

            # … existing code …<\/span>
            \nfrom<\/span> fastapi import<\/span> UploadFile,<\/span> File<\/p>\n

            class<\/span> RAGQueryRequest<\/span>(<\/span>BaseModel)<\/span>:<\/span>
            \n query:<\/span> str<\/span> =<\/span> Field(<\/span>.<\/span>.<\/span>.<\/span>,<\/span> description=<\/span>\\”The query to ask the RAG system\\”<\/span>)<\/span>
            \n index_id:<\/span> str<\/span> =<\/span> Field(<\/span>.<\/span>.<\/span>.<\/span>,<\/span> description=<\/span>\\”The ID of the index to query (e.g., filename)\\”<\/span>)<\/span><\/p>\n

            class<\/span> RAGQueryResponse<\/span>(<\/span>BaseModel)<\/span>:<\/span>
            \n response:<\/span> str<\/span>
            \n source_nodes:<\/span> List[<\/span>Dict]<\/span> # \u5305\u542b\u6765\u6e90\u8282\u70b9\u7684\u4fe1\u606f<\/span><\/p>\n

            2.3 \u66f4\u65b0 AI \u670d\u52a1\u5c42<\/h4>\n

            app\/services\/ai.py (\u65b0\u589e\u65b9\u6cd5\u548c\u72b6\u6001\u7ba1\u7406\u5230 AIService)<\/p>\n

            # … existing code …<\/span>
            \nfrom<\/span> llama_index.<\/span>core import<\/span> SimpleDirectoryReader,<\/span> VectorStoreIndex,<\/span> Settings
            \nfrom<\/span> llama_index.<\/span>llms.<\/span>openai import<\/span> OpenAI
            \nfrom<\/span> llama_index.<\/span>embeddings.<\/span>openai import<\/span> OpenAIEmbedding
            \nimport<\/span> os<\/p>\n

            DATA_DIR =<\/span> \\”data\\”<\/span><\/p>\n

            class<\/span> AIService<\/span>:<\/span>
            \n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span>
            \n # … existing __init__ code …<\/span>
            \n # LlamaIndex \u5168\u5c40\u8bbe\u7f6e<\/span>
            \n Settings.<\/span>llm =<\/span> OpenAI(<\/span>model=<\/span>\\”gpt-3.5-turbo\\”<\/span>,<\/span> api_key=<\/span>settings.<\/span>OPENAI_API_KEY,<\/span> base_url=<\/span>settings.<\/span>OPENAI_API_BASE)<\/span>
            \n Settings.<\/span>embed_model =<\/span> OpenAIEmbedding(<\/span>api_key=<\/span>settings.<\/span>OPENAI_API_KEY,<\/span> base_url=<\/span>settings.<\/span>OPENAI_API_BASE)<\/span><\/p>\n

            # \u7528\u4e8e\u7f13\u5b58\u7d22\u5f15\u7684\u5b57\u5178<\/span>
            \n self.<\/span>indices =<\/span> {<\/p>\n

            <\/span>}<\/span>
            \n os.<\/span>makedirs(<\/span>DATA_DIR,<\/span> exist_ok=<\/span>True<\/span>)<\/span><\/p>\n

            # … existing methods …<\/span><\/p>\n

            async<\/span> def<\/span> create_index_from_file<\/span>(<\/span>self,<\/span> file<\/span>:<\/span> UploadFile)<\/span> –<\/span>><\/span> str<\/span>:<\/span>
            \n # \u5c06\u4e0a\u4f20\u7684\u6587\u4ef6\u4fdd\u5b58\u5230 data \u76ee\u5f55<\/span>
            \n file_path =<\/span> os.<\/span>path.<\/span>join(<\/span>DATA_DIR,<\/span> file<\/span>.<\/span>filename)<\/span>
            \n with<\/span> open<\/span>(<\/span>file_path,<\/span> \\”wb\\”<\/span>)<\/span> as<\/span> buffer<\/span>:<\/span>
            \n buffer<\/span>.<\/span>write(<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

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