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\"\u8ba9\u9f99\u867e\u770b\u61c2\u5c4f\u5e55\uff01\u8c37\u6b4c\u591a\u6a21\u6001\u65b0\u6210\u679c\uff0c\u6587\u672c\u56fe\u50cf\u89c6\u9891\u97f3\u9891\u8fdb\u540c\u4e00\u7a7a\u95f4\"\/<\/div><\/figure>\n
\"\u8ba9\u9f99\u867e\u770b\u61c2\u5c4f\u5e55\uff01\u8c37\u6b4c\u591a\u6a21\u6001\u65b0\u6210\u679c\uff0c\u6587\u672c\u56fe\u50cf\u89c6\u9891\u97f3\u9891\u8fdb\u540c\u4e00\u7a7a\u95f4\"\/<\/div><\/figure>\n
\"\u8ba9\u9f99\u867e\u770b\u61c2\u5c4f\u5e55\uff01\u8c37\u6b4c\u591a\u6a21\u6001\u65b0\u6210\u679c\uff0c\u6587\u672c\u56fe\u50cf\u89c6\u9891\u97f3\u9891\u8fdb\u540c\u4e00\u7a7a\u95f4\"\/<\/div><\/figure>\n

henry \u53d1\u81ea \u51f9\u975e\u5bfa<\/p>\n

\u539f\u751f\uff0c\u542f\u52a8\uff01<\/p>\n

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Gemini Embedding 2\u3002<\/p>\n

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\u5728\u8bc4\u6d4b\u65b9\u9762\uff0cGemini Embedding 2\u4e0d\u4ec5\u6574\u4f53\u6027\u80fd\u8f83\u4e0a\u4e00\u4ee3\u63d0\u5347\uff0c\u540c\u65f6\u4e5f\u4e3a\u591a\u6a21\u6001\u5d4c\u5165\u4efb\u52a1\u6811\u7acb\u4e86\u65b0\u7684\u6027\u80fd\u57fa\u51c6\u3002<\/p>\n

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\u4e4d\u770b\u4e4b\u4e0b\uff0c\u8fd9\u4f3c\u4e4e\u53ea\u662f\u4e00\u6b21\u5e95\u5c42\u7684\u6570\u636e\u5de5\u7a0b\u5347\u7ea7\u3002<\/p>\n

\u4f46\u5b9e\u9645\u4e0a\uff0c\u5b83\u6b63\u4e3a\u50cf\u9f99\u867e\u8fd9\u6837\u7684AI Agent\u771f\u6b63\u201c\u770b\u61c2\u201d\u4e16\u754c\uff0c\u63d0\u4f9b\u4e86\u5173\u952e\u57fa\u7840\u3002<\/p>\n

\u76ee\u524d\uff0cGemini Embedding 2\u5df2\u7ecf\u901a\u8fc7Gemini API\u548cVertex AI\u5c55\u5f00\u516c\u6d4b\u3002<\/p>\n

\u5d4c\u5165\u6a21\u578b\uff08Embedding Model\uff09\u672c\u8d28\u4e0a\u662f\u628a\u6570\u636e\u8f6c\u5316\u4e3a\u7a20\u5bc6\u5411\u91cf\u8868\u793a\u3002<\/p>\n

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\u4f8b\u5982\uff0c\u5728\u8c37\u6b4c\u6b64\u524d\u7684\u8bba\u6587\u300aGemini Embedding: Generalizable Embeddings from Gemini\u300b\u4e2d\u2014\u2014<\/p>\n

Gemini Embedding\u901a\u8fc7\u5728\u5927\u8bed\u8a00\u6a21\u578b\u53c2\u6570\u4e2d\u5df2\u6709\u7684\u6d77\u91cf\u77e5\u8bc6\u57fa\u7840\u4e0a\u6784\u5efa\u8868\u5f81\uff0c\u5e76\u5c06\u5f97\u6765\u7684\u5d4c\u5165\u7528\u4e8e\uff1a\u8bed\u4e49\u68c0\u7d22\u3001\u6587\u672c\u805a\u7c7b\u3001\u5206\u7c7b\uff0c\u6392\u5e8f\u7b49\u4e0b\u6e38\u4efb\u52a1\u3002<\/p>\n

\u4f46\u8fd9\u53ea\u505c\u7559\u5728\u6587\u5b57\u9636\u6bb5\u3002<\/p>\n

\u6700\u65b0\u7684Gemini Embedding 2\uff0c\u5219\u9996\u6b21\u5f7b\u5e95\u6253\u901a\u4e86\u591a\u6a21\u6001\u6570\u636e\u3002<\/p>\n

\u6587\u672c\u3001\u56fe\u7247\u3001\u89c6\u9891\u3001\u97f3\u9891\u548c\u6587\u6863\uff0c\u90fd\u88ab\u538b\u7f29\u5230\u540c\u4e00\u4e2a\u5411\u91cf\u7a7a\u95f4\u4e4b\u4e2d\u3002<\/p>\n

\u800c\u8fd9\uff0c\u5c31\u610f\u5473\u7740\u6a21\u578b\u5b9e\u73b0\u4e86\u201c\u8de8\u6a21\u6001\u8bed\u4e49\u5bf9\u9f50\u201d\uff0c\u80fd\u591f\u8ba9\u732b\u8fd9\u4e2a\u300c\u6587\u5b57\u6982\u5ff5\u300d\u4e0e\u732b\u7684\u7167\u7247\u8fd9\u4e2a\u300c\u89c6\u89c9\u6982\u5ff5\u300d\uff0c\u5728\u7edf\u4e00\u7684\u5d4c\u5165\u7a7a\u95f4\u4e2d\u7684\u6570\u5b66\u5411\u91cf\u7684\u8ddd\u79bb\u6781\u5ea6\u63a5\u8fd1\u3002<\/p>\n

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\u4eceRAG\u68c0\u7d22\u3001\u8bed\u4e49\u641c\u7d22\u3001\u60c5\u611f\u5206\u6790\uff0c\u5230\u6570\u636e\u805a\u7c7b\u7b49\u5e94\u7528\u573a\u666f\uff0c\u90fd\u80fd\u76f4\u63a5\u53d7\u76ca\u3002<\/p>\n

\u66f4\u91cd\u8981\u7684\u662f\uff0c\u8fd9\u7c7b\u80fd\u529b\u5bf9AI Agent\u610f\u4e49\u5de8\u5927\u3002<\/p>\n

\u8fc7\u53bb\u7684Agent\u5728\u64cd\u4f5c\u7535\u8111\u65f6\uff0c\u5f80\u5f80\u53ea\u80fd\u4f9d\u8d56\u6587\u5b57\u4fe1\u606f\u3002<\/p>\n

\u4f8b\u5982\u8bc6\u522b\u6309\u94ae\u4e0a\u7684\u201c\u8bbe\u7f6e\u201d\u201c\u786e\u8ba4\u201d\u7b49\u6807\u7b7e\u3002\u4f46\u771f\u5b9e\u4e16\u754c\u7684UI\u754c\u9762\uff0c\u5927\u91cf\u4fe1\u606f\u5176\u5b9e\u6765\u81ea\u89c6\u89c9\u7ed3\u6784\uff1a<\/p>\n

\u56fe\u6807\u3001\u5e03\u5c40\u3001\u989c\u8272\u3001\u63a7\u4ef6\u4f4d\u7f6e\uff0c\u6b63\u662f\u4f20\u7edf\u6587\u672c\u5d4c\u5165\u6a21\u578b\u96be\u4ee5\u5904\u7406\u7684\u90e8\u5206\u3002<\/p>\n

\u800c\u6709\u4e86\u591a\u6a21\u6001\u5d4c\u5165\u4e4b\u540e\uff0c\u60c5\u51b5\u5c31\u4e0d\u540c\u4e86\u3002<\/p>\n

\u5bf9\u4e8e\u50cfOpenClaw\uff08\u9f99\u867e\uff09\u8fd9\u6837\u9700\u8981\u64cd\u4f5c\u7535\u8111\uff0c\u8bc6\u522b\u5c4f\u5e55\u7684Agent\u6765\u8bf4\uff0c\u5b83\u4e0d\u518d\u53ea\u662f\u8bc6\u522b\u6587\u5b57\u3002<\/p>\n

\u5b83\u53ef\u4ee5\u76f4\u63a5\u7406\u89e3\uff1a\u54ea\u4e2a\u50cf\u7d20\u533a\u57df\u662f\u8bbe\u7f6e\u56fe\u6807\u3001\u54ea\u4e2a\u6309\u94ae\u4e0e\u5f53\u524d\u4efb\u52a1\u6700\u76f8\u5173\uff0c\u5c4f\u5e55\u622a\u56fe\u4e0e\u6587\u672c\u6307\u4ee4\u4e4b\u95f4\u7684\u5173\u7cfb<\/p>\n

\u6362\u53e5\u8bdd\u8bf4\uff0cGemini Embedding 2\u63d0\u4f9b\u4e86\u4e00\u6761\u7edf\u4e00\u7684\u611f\u5b98\u603b\u7ebf\u3002\u89c6\u89c9\u3001\u542c\u89c9\u4e0e\u6587\u672c\u4fe1\u606f\uff0c\u90fd\u80fd\u5728\u540c\u4e00\u4e2a\u8bed\u4e49\u7a7a\u95f4\u4e2d\u8fdb\u884c\u5173\u8054\u3002<\/p>\n

\u8fd9\u4e5f\u4e3a\u672a\u6765Agent\u771f\u6b63\u7406\u89e3\u5c4f\u5e55\u3001\u7406\u89e3\u73af\u5883\u5e76\u4ee3\u66ff\u4eba\u7c7b\u64cd\u4f5c\u7535\u8111\uff0c\u5960\u5b9a\u4e86\u6700\u91cd\u8981\u7684\u8bed\u4e49\u57fa\u7840\u3002<\/p>\n

\u5728\u6280\u672f\u5c42\u9762\uff0cGemini Embedding 2\u7ee7\u7eed\u91c7\u7528Matryoshka Representation Learning\uff08MRL\uff09\u3002<\/p>\n

\u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u5d4c\u5165\u5411\u91cf\u5728\u4fdd\u6301\u8bed\u4e49\u4fe1\u606f\u7684\u540c\u65f6\u8fdb\u884c\u52a8\u6001\u7ef4\u5ea6\u7f29\u51cf\u3002<\/p>\n

\uff08\u6ce8\uff1aMRL\u5f3a\u5236\u6a21\u578b\u628a\u6700\u6838\u5fc3\u3001\u6700\u5173\u952e\u7684\u7279\u5f81\u538b\u7f29\u5728\u5411\u91cf\u7684\u524d\u51e0\u5341\u7ef4\u91cc\uff0c\u6b21\u8981\u7684\u7279\u5f81\u653e\u5728\u540e\u9762\uff0c\u8fd9\u8ba9\u5f00\u53d1\u8005\u53ef\u4ee5\u6839\u636e\u9884\u7b97\u548c\u7b97\u529b\uff0c\u81ea\u7531\u51b3\u5b9a\u4fe1\u606f\u5bc6\u5ea6\u7684\u5206\u5e03\u7ba1\u7406\uff09<\/p>\n

Gemini Embedding 2\u7684\u9ed8\u8ba4\u8f93\u51fa\u7ef4\u5ea6\u4e3a3072\u7ef4\uff0c\u4f46\u5f00\u53d1\u8005\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u7f29\u51cf\uff0c\u4f8b\u5982\uff1a3072\u7ef4\u30011536\u7ef4\u3001768\u7ef4\uff0c\u4ece\u800c\u5728\u6027\u80fd\u4e0e\u5b58\u50a8\u6210\u672c\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002<\/p>\n

\u9664\u4e86\u652f\u6301API\u8c03\u7528\u5916\uff0cGemini Embedding 2\u4e5f\u652f\u6301\u901a\u8fc7LangChain\u3001LlamaIndex\u3001Haystack\u3001Weaviate\u3001QDrant\u3001ChromaDB\u548cVector Search\u7b49\u5de5\u5177\u8c03\u7528\u3002<\/p>\n

\u901a\u8fc7\u4e3a\u4e0d\u540c\u7c7b\u578b\u7684\u6570\u636e\u8d4b\u4e88\u7edf\u4e00\u7684\u8bed\u4e49\u8868\u793a\uff0cGemini Embedding 2\u6b63\u5728\u4e3a\u4e0b\u4e00\u4ee3AI\u5e94\u7528\uff1a\u591a\u6a21\u6001Agent\uff0c\u4e43\u81f3\u5177\u8eab\u667a\u80fd\u673a\u5668\u4eba\u63d0\u4f9b\u5173\u952e\u57fa\u7840\u8bbe\u65bd\u3002<\/p>\n

\u53c2\u8003\u94fe\u63a5<\/p>\n

[1]https:\/\/blog.google\/innovation-and-ai\/models-and-research\/gemini-models\/gemini-embedding-2\/<\/p>\n

[2]https:\/\/arxiv.org\/pdf\/2503.07891<\/p>\n

\n

\u7248\u6743\u58f0\u660e\uff1a<\/strong><\/p>\n

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henry \u53d1\u81ea \u51f9\u975e\u5bfa<\/p>\n

\u539f\u751f\uff0c\u542f\u52a8\uff01<\/p>\n

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Gemini Embedding 2\u3002<\/p>\n

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\u5728\u8f93\u5165\u80fd\u529b\u4e0a\uff0cGemini Embedding 2\u652f\u6301\u591a\u79cd\u6570\u636e\u7c7b\u578b\uff1a<\/p>\n

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