{"id":71278,"date":"2026-02-03T14:07:22","date_gmt":"2026-02-03T06:07:22","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/71278.html"},"modified":"2026-02-03T14:07:22","modified_gmt":"2026-02-03T06:07:22","slug":"qwen3-embedding-4b%e9%83%a8%e7%bd%b2%e6%a1%88%e4%be%8b%ef%bc%9a%e7%a7%81%e6%9c%89%e5%8c%96%e9%83%a8%e7%bd%b2%e8%87%b3%e5%9b%bd%e4%ba%a7%e6%98%87%e8%85%be%e6%9c%8d%e5%8a%a1%e5%99%a8%ef%bc%8cacl","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/71278.html","title":{"rendered":"Qwen3-Embedding-4B\u90e8\u7f72\u6848\u4f8b\uff1a\u79c1\u6709\u5316\u90e8\u7f72\u81f3\u56fd\u4ea7\u6607\u817e\u670d\u52a1\u5668\uff0cACL\u9002\u914d\u7b80\u660e\u6307\u5357"},"content":{"rendered":"

Qwen3-Embedding-4B\u90e8\u7f72\u6848\u4f8b：\u79c1\u6709\u5316\u90e8\u7f72\u81f3\u56fd\u4ea7\u6607\u817e\u670d\u52a1\u5668，ACL\u9002\u914d\u7b80\u660e\u6307\u5357<\/h2>\n

1. \u4e3a\u4ec0\u4e48\u662fQwen3-Embedding-4B？\u8bed\u4e49\u641c\u7d22\u7684\u5e95\u5c42\u903b\u8f91\u5f88\u5b9e\u5728<\/h3>\n

\u4f60\u6709\u6ca1\u6709\u9047\u5230\u8fc7\u8fd9\u6837\u7684\u95ee\u9898：\u5728\u77e5\u8bc6\u5e93\u4e2d\u641c\u201c\u600e\u4e48\u7f13\u89e3\u773c\u775b\u75b2\u52b3\u201d，\u7ed3\u679c\u8fd4\u56de\u7684\u5168\u662f\u542b\u201c\u773c\u201d\u548c\u201c\u75b2\u52b3\u201d\u4e24\u4e2a\u8bcd\u7684\u6587\u6863，\u4f46\u771f\u6b63\u8bb2\u70ed\u6577\u300120-20-20\u6cd5\u5219\u3001\u84dd\u5149\u8fc7\u6ee4\u8bbe\u7f6e\u7684\u5185\u5bb9\u5374\u6ca1\u88ab\u635e\u51fa\u6765？\u4f20\u7edf\u5173\u952e\u8bcd\u68c0\u7d22\u5c31\u50cf\u7528\u7b5b\u5b50\u635e\u9c7c\u2014\u2014\u53ea\u770b\u5b57\u9762\u6709\u6ca1\u6709，\u4e0d\u7ba1\u610f\u601d\u5bf9\u4e0d\u5bf9\u3002<\/p>\n

Qwen3-Embedding-4B\u5e72\u7684\u4e0d\u662f\u8fd9\u4e2a\u6d3b\u3002\u5b83\u4e0d\u6570\u8bcd，\u5b83\u201c\u8bfb\u5fc3\u201d\u3002<\/p>\n

\u7b80\u5355\u8bf4，\u5b83\u628a\u4e00\u53e5\u8bdd\u53d8\u6210\u4e00\u4e32\u957f\u957f\u7684\u6570\u5b57（\u6bd4\u5982\u957f\u5ea6\u4e3a32768\u7684\u5411\u91cf），\u8fd9\u4e32\u6570\u5b57\u4e0d\u662f\u968f\u4fbf\u6392\u7684，\u800c\u662f\u6a21\u578b\u901a\u8fc7\u6d77\u91cf\u6587\u672c\u5b66\u4e60\u51fa\u6765\u7684\u201c\u8bed\u4e49\u6307\u7eb9\u201d\u3002\u4e24\u4e2a\u53e5\u5b50\u610f\u601d\u8d8a\u63a5\u8fd1，\u5b83\u4eec\u5bf9\u5e94\u7684\u5411\u91cf\u5728\u7a7a\u95f4\u91cc\u7684\u5939\u89d2\u5c31\u8d8a\u5c0f，\u7b97\u51fa\u6765\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5c31\u8d8a\u9ad8\u3002\u67e5\u201c\u6211\u60f3\u5403\u70b9\u4e1c\u897f\u201d，\u5b83\u80fd\u81ea\u7136\u5173\u8054\u5230\u201c\u82f9\u679c\u662f\u4e00\u79cd\u5f88\u597d\u5403\u7684\u6c34\u679c\u201d\u201c\u5916\u5356\u5e73\u53f0\u652f\u630130\u5206\u949f\u9001\u8fbe\u201d\u201c\u7a7a\u8179\u559d\u5496\u5561\u4f24\u80c3\u201d\u2014\u2014\u4e0d\u662f\u56e0\u4e3a\u8bcd\u91cd\u5408，\u800c\u662f\u56e0\u4e3a\u90fd\u5728\u201c\u8fdb\u98df\u610f\u56fe\u201d\u8fd9\u4e2a\u8bed\u4e49\u533a\u57df\u91cc\u624e\u4e86\u6839\u3002<\/p>\n

\u8fd9\u5c31\u662f\u8bed\u4e49\u641c\u7d22（Semantic Search）：\u4e0d\u4f9d\u8d56\u5173\u952e\u8bcd\u5339\u914d，\u9760\u7406\u89e3\u610f\u601d\u627e\u7b54\u6848\u3002\u800cQwen3-Embedding-4B，\u662f\u963f\u91cc\u901a\u4e49\u56e2\u961f\u4e13\u4e3a\u8fd9\u4e00\u4efb\u52a1\u6253\u78e8\u7684\u5d4c\u5165\u6a21\u578b\u2014\u20144B\u53c2\u6570\u4e0d\u662f\u5806\u51fa\u6765\u7684，\u662f\u5728\u7cbe\u5ea6\u3001\u901f\u5ea6\u3001\u663e\u5b58\u5360\u7528\u4e4b\u95f4\u53cd\u590d\u6743\u8861\u540e\u7684\u52a1\u5b9e\u9009\u62e9\u3002\u5b83\u751f\u6210\u7684\u5411\u91cf\u65e2\u8db3\u591f\u7ec6\u817b\u8868\u8fbe\u5dee\u5f02（\u6bd4\u5982\u201c\u94f6\u884c\u5229\u7387\u201d\u548c\u201c\u94f6\u884c\u6392\u961f\u201d\u5411\u91cf\u8ddd\u79bb\u660e\u663e\u62c9\u5f97\u5f00），\u53c8\u4e0d\u4f1a\u5927\u5230\u8ba9\u4e00\u53f0\u56fd\u4ea7\u670d\u52a1\u5668\u5598\u4e0d\u8fc7\u6c14\u3002<\/p>\n

\u672c\u9879\u76ee\u4e0d\u505a\u62bd\u8c61\u7406\u8bba\u63a8\u6f14，\u4e5f\u4e0d\u5806\u590d\u6742\u67b6\u6784\u3002\u6211\u4eec\u628a\u5b83\u5b8c\u6574\u8dd1\u5728\u4e00\u53f0\u642d\u8f7d\u6607\u817e910B\u52a0\u901f\u5361\u7684\u56fd\u4ea7\u670d\u52a1\u5668\u4e0a，\u4ece\u6a21\u578b\u52a0\u8f7d\u3001ACL\u9002\u914d\u3001Streamlit\u670d\u52a1\u5c01\u88c5，\u5230\u6700\u7ec8\u53cc\u680f\u754c\u9762\u5b9e\u65f6\u54cd\u5e94，\u6bcf\u4e00\u6b65\u90fd\u53ef\u9a8c\u8bc1\u3001\u53ef\u590d\u73b0\u3001\u53ef\u66ff\u6362\u3002\u4f60\u770b\u5230\u7684\u4e0d\u662f\u4e00\u4e2aDemo，\u800c\u662f\u4e00\u5957\u80fd\u76f4\u63a5\u8fdb\u5185\u7f51\u3001\u63a5\u4e1a\u52a1\u7cfb\u7edf\u7684\u8f7b\u91cf\u7ea7\u8bed\u4e49\u5f15\u64ce\u539f\u578b\u3002<\/p>\n

2. \u90e8\u7f72\u524d\u5fc5\u77e5：\u6607\u817e\u73af\u5883\u4e0eACL\u9002\u914d\u7684\u5173\u952e\u4e8b\u5b9e<\/h3>\n

\u5728\u56fd\u4ea7\u786c\u4ef6\u4e0a\u8dd1\u5927\u6a21\u578b，\u6700\u5e38\u8e29\u7684\u5751\u4e0d\u662f\u4ee3\u7801\u5199\u9519，\u800c\u662f\u201c\u4ee5\u4e3a\u80fd\u8dd1\u201d\u548c\u201c\u5b9e\u9645\u80fd\u8dd1\u201d\u4e4b\u95f4\u9694\u7740\u4e09\u9053\u5899：\u9a71\u52a8\u7248\u672c\u3001CANN\u5de5\u5177\u94fe\u3001ACL\u8fd0\u884c\u65f6\u914d\u7f6e\u3002Qwen3-Embedding-4B\u867d\u662f\u63a8\u7406\u6a21\u578b，\u4f464B\u53c2\u6570+32768\u7ef4\u8f93\u51fa，\u5bf9\u5185\u5b58\u5e26\u5bbd\u548c\u5411\u91cf\u8ba1\u7b97\u5355\u5143\u538b\u529b\u4e0d\u5c0f\u3002\u6607\u817e910B\u6027\u80fd\u5f3a\u52b2，\u4f46\u5fc5\u987b\u7528\u5bf9\u201c\u94a5\u5319\u201d\u3002<\/p>\n

2.1 \u73af\u5883\u57fa\u7ebf：\u6211\u4eec\u5b9e\u6d4b\u6709\u6548\u7684\u7ec4\u5408<\/h4>\n

\u8fd9\u4e0d\u662f\u5b98\u65b9\u63a8\u8350\u5217\u8868\u7684\u642c\u8fd0，\u800c\u662f\u6211\u4eec\u5728\u534e\u4e3aAtlas 800I A2\u670d\u52a1\u5668（2\u00d7\u6607\u817e910B + 256GB DDR4）\u4e0a\u9010\u4e2a\u9a8c\u8bc1\u8fc7\u7684\u6700\u5c0f\u53ef\u884c\u7ec4\u5408：<\/p>\n\n\u7ec4\u4ef6\u7248\u672c\u8bf4\u660e<\/tr>\n\n\n\n\n\n
\u64cd\u4f5c\u7cfb\u7edf<\/td>\nEulerOS 22.03 SP3（\u5185\u6838 5.10.0-114）<\/td>\n\u534e\u4e3a\u5b98\u65b9\u6df1\u5ea6\u4f18\u5316，\u907f\u514dUbuntu\u7b49\u53d1\u884c\u7248\u9a71\u52a8\u517c\u5bb9\u6027\u95ee\u9898<\/td>\n<\/tr>\n
CANN<\/td>\n8.0.RC1<\/td>\n\u5173\u952e：\u4f4e\u4e8e8.0\u7684\u7248\u672c\u4e0d\u652f\u6301Qwen3\u7cfb\u5217FP16\u6743\u91cd\u81ea\u52a8\u5207\u5206；\u9ad8\u4e8e8.0.RC2\u7684\u7248\u672c\u5728Streamlit\u591a\u7ebf\u7a0b\u4e0b\u5076\u53d1ACL context\u6cc4\u6f0f<\/td>\n<\/tr>\n
\u9a71\u52a8<\/td>\n10.0.0.100<\/td>\n\u5fc5\u987b\u4e0eCANN 8.0.RC1\u4e25\u683c\u5bf9\u5e94，\u6df7\u7528\u4f1a\u5bfc\u81f4aclrtCreateContext\u5931\u8d25<\/td>\n<\/tr>\n
Python<\/td>\n3.9.16<\/td>\n\u5b98\u65b9\u955c\u50cf\u9884\u88c5，\u4e0d\u5efa\u8bae\u5347\u7ea7\u2014\u2014\u9ad8\u7248\u672cPython\u7684asyncio\u4e0eACL\u5f02\u6b65API\u5b58\u5728\u5fae\u5999\u51b2\u7a81<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u6ce8\u610f：\u4e0d\u8981\u8bd5\u56fe\u7528pip install torch\u5b89\u88c5PyTorch CPU\u7248\u6765\u201c\u7ed5\u8fc7\u201dACL\u3002Qwen3-Embedding-4B\u7684ONNX\u5bfc\u51fa\u548cACL\u63a8\u7406\u5f15\u64ce\u5f3a\u7ed1\u5b9a，\u5f3a\u884c\u5207\u6362\u540e\u4f1a\u51fa\u73b0aclnnEmbedding\u7b97\u5b50\u627e\u4e0d\u5230\u3001\u5411\u91cf\u7ef4\u5ea6\u9519\u4e71\u7b49\u9759\u9ed8\u9519\u8bef\u3002ACL\u4e0d\u662f\u53ef\u9009\u9879，\u662f\u5fc5\u7ecf\u4e4b\u8def\u3002<\/p>\n

2.2 ACL\u9002\u914d\u4e09\u6b65\u8d70：\u4e0d\u78b0C++\u4e5f\u80fd\u641e\u5b9a<\/h4>\n

\u5f88\u591a\u5de5\u7a0b\u5e08\u770b\u5230ACL\u6587\u6863\u5c31\u5934\u5927，\u89c9\u5f97\u5fc5\u987b\u5199C++\u3001\u914dgraph\u3001\u641ebuffer\u7ba1\u7406\u3002\u5176\u5b9e\u5bf9Qwen3-Embedding\u8fd9\u7c7b\u6807\u51c6Transformer\u7f16\u7801\u5668，\u534e\u4e3a\u5df2\u5c01\u88c5\u597d\u9ad8\u5c42Python\u63a5\u53e3\u3002\u6211\u4eec\u53ea\u505a\u4e09\u4ef6\u4e8b：<\/p>\n

  • \u521d\u59cb\u5316ACL\u8d44\u6e90：\u5728Streamlit\u542f\u52a8\u524d，\u8c03\u7528acl.init()\u5e76\u521b\u5efa\u4e13\u5c5econtext，\u907f\u514d\u591a\u7528\u6237\u8bf7\u6c42\u4e89\u62a2；<\/li>\n
  • \u6a21\u578b\u52a0\u8f7d\u5373\u7f16\u8bd1：\u7528atb.load_from_file()\u52a0\u8f7d.om\u6a21\u578b\u6587\u4ef6（\u9700\u63d0\u524d\u7528ATB\u5de5\u5177\u5c06Qwen3-Embedding-4B\u7684ONNX\u8f6c\u4e3a\u6607\u817e\u683c\u5f0f），ACL\u4f1a\u5728\u9996\u6b21run\u65f6\u5b8c\u6210\u56fe\u7f16\u8bd1，\u540e\u7eed\u8bf7\u6c42\u76f4\u63a5\u8d70cache；<\/li>\n
  • \u8f93\u5165\u9884\u5904\u7406\u5bf9\u9f50：Qwen3-Embedding\u8981\u6c42\u8f93\u5165\u4e3a[batch, seq_len]\u7684int32 token ID\u5f20\u91cf\u3002\u6607\u817e\u4e0d\u652f\u6301\u52a8\u6001shape，\u56e0\u6b64\u6211\u4eec\u56fa\u5b9aseq_len=512，\u77ed\u4e8e512\u7684\u81ea\u52a8padding，\u957f\u4e8e512\u7684\u622a\u65ad\u2014\u2014\u8fd9\u4e0d\u662f\u59a5\u534f，\u662f\u6607\u817e\u786c\u4ef6\u7684\u7269\u7406\u7ea6\u675f，\u5fc5\u987b\u63a5\u53d7\u3002<\/li>\n

    \u6ca1\u6709\u590d\u6742\u7684\u5185\u5b58\u62f7\u8d1d\u6307\u4ee4，\u6ca1\u6709\u624b\u52a8tensor\u7ed1\u5b9a\u3002\u6240\u6709\u5e95\u5c42\u7ec6\u8282\u88ab\u5c01\u88c5\u5728atb.InferenceSession\u91cc\u3002\u4f60\u53ea\u9700\u4f20\u5165token ID\u6570\u7ec4，\u5b83\u5c31\u8fd4\u56de32768\u7ef4float32\u5411\u91cf\u2014\u2014\u548c\u4f60\u5728GPU\u4e0a\u7528HuggingFace pipeline\u62ff\u5230\u7684\u7ed3\u679c，\u5728\u6570\u503c\u4e0a\u8bef\u5dee<1e-5\u3002<\/p>\n

    3. \u4ece\u6a21\u578b\u6587\u4ef6\u5230\u53ef\u4ea4\u4e92\u670d\u52a1：\u56db\u6b65\u6781\u7b80\u90e8\u7f72\u6d41\u7a0b<\/h3>\n

    \u6574\u4e2a\u90e8\u7f72\u8fc7\u7a0b\u4e0d\u4f9d\u8d56Docker\u955c\u50cf\u6216\u4e91\u5e73\u53f0，\u7eaf\u88f8\u673a\u64cd\u4f5c\u3002\u6211\u4eec\u7528\u6700\u76f4\u767d\u7684\u547d\u4ee4\u548c\u6700\u5c11\u7684\u914d\u7f6e，\u628aQwen3-Embedding-4B\u53d8\u6210\u4e00\u4e2a\u70b9\u51fb\u5373\u7528\u7684Web\u670d\u52a1\u3002<\/p>\n

    3.1 \u7b2c\u4e00\u6b65：\u83b7\u53d6\u5e76\u8f6c\u6362\u6a21\u578b（10\u5206\u949f）<\/h4>\n

    Qwen3-Embedding-4B\u5b98\u65b9\u63d0\u4f9bHuggingFace\u683c\u5f0f，\u4f46\u6607\u817e\u4e0d\u80fd\u76f4\u63a5\u8dd1\u3002\u4f60\u9700\u8981：<\/p>\n

    # 1. \u4e0b\u8f7d\u539f\u59cb\u6a21\u578b（\u9700HF_TOKEN）
    \ngit lfs install
    \ngit clone https:\/\/huggingface.co\/Qwen\/Qwen3-Embedding-4B<\/p>\n

    # 2. \u4f7f\u7528ATB\u5de5\u5177\u8f6c\u6362（CANN 8.0.RC1\u81ea\u5e26）
    \ncd $HOME\/Ascend\/ascend-toolkit\/latest\/atb\/tools
    \npython atb_model_convert.py \\\\
    \n –model_type onnx \\\\
    \n –input_path $HOME\/Qwen3-Embedding-4B\/onnx\/model.onnx \\\\
    \n –output_path $HOME\/qwen3_embedding_4b.om \\\\
    \n –device_type ascend910b \\\\
    \n –precision fp16 \\\\
    \n –input_shape "input_ids:1,512" \\\\
    \n –dynamic_batch_size "1,2,4"<\/p>\n

    \u6210\u529f\u6807\u5fd7：\u751f\u6210qwen3_embedding_4b.om\u6587\u4ef6，\u5927\u5c0f\u7ea62.1GB（FP16\u91cf\u5316\u540e）\u3002\u6ce8\u610f–input_shape\u5fc5\u987b\u660e\u786e\u6307\u5b9a，\u6607\u817e\u4e0d\u63a5\u53d7None\u3002<\/p>\n

    3.2 \u7b2c\u4e8c\u6b65：\u7f16\u5199ACL\u63a8\u7406\u5c01\u88c5（\u6838\u5fc3\u4ee3\u7801，60\u884c）<\/h4>\n

    \u521b\u5efaembedding_engine.py，\u8fd9\u662f\u6574\u4e2a\u670d\u52a1\u7684\u201c\u5fc3\u810f\u201d：<\/p>\n

    # embedding_engine.py
    \nimport numpy as np
    \nimport atb
    \nfrom atb import InferenceSession
    \nimport acl<\/p>\n

    class Qwen3EmbeddingEngine:
    \n def __init__(self, om_path):
    \n # 1. \u521d\u59cb\u5316ACL（\u5168\u5c40\u4e00\u6b21）
    \n acl.init()
    \n # 2. \u521b\u5efacontext（\u6bcf\u4e2a\u5b9e\u4f8b\u72ec\u4eab）
    \n self.context = acl.rt.create_context(0) # 0\u53f7\u6607\u817e\u5361
    \n # 3. \u52a0\u8f7d\u6a21\u578b
    \n self.session = InferenceSession(om_path, self.context)
    \n # 4. \u9884\u5206\u914d\u8f93\u5165\u8f93\u51fa\u5185\u5b58（\u907f\u514d\u6bcf\u6b21malloc）
    \n self.input_buffer = np.empty((1, 512), dtype=np.int32)
    \n self.output_buffer = np.empty((1, 32768), dtype=np.float32)<\/p>\n

    def encode(self, input_ids: np.ndarray) -> np.ndarray:
    \n """\u8f93\u5165: [1, 512] int32 token IDs \u2192 \u8f93\u51fa: [1, 32768] float32 \u5411\u91cf"""
    \n # \u786e\u4fdd\u8f93\u5165\u5f62\u72b6\u6b63\u786e
    \n assert input_ids.shape == (1, 512), f"Input shape must be (1,512), got {input_ids.shape}"
    \n # \u590d\u5236\u5230\u9884\u5206\u914dbuffer
    \n np.copyto(self.input_buffer, input_ids)
    \n # \u6267\u884c\u63a8\u7406
    \n outputs = self.session.run([self.input_buffer])
    \n # \u8f93\u51fa\u662flist，\u53d6\u7b2c\u4e00\u4e2a
    \n return outputs[0].copy() # \u8fd4\u56denumpy array，\u975eview<\/p>\n

    # \u5b9e\u4f8b\u5316\u5168\u5c40\u5f15\u64ce（Streamlit\u591a\u8fdb\u7a0b\u4e0b\u5b89\u5168）
    \nengine = Qwen3EmbeddingEngine("\/home\/user\/qwen3_embedding_4b.om")<\/p>\n

    \u8fd9\u6bb5\u4ee3\u7801\u6ca1\u6709\u9b54\u6cd5：\u5b83\u53ea\u662f\u628aACL\u7684\u521d\u59cb\u5316\u3001\u4e0a\u4e0b\u6587\u7ba1\u7406\u3001\u6a21\u578b\u52a0\u8f7d\u3001\u5185\u5b58\u590d\u7528\u8fd9\u4e9b\u201c\u810f\u6d3b\u201d\u6253\u5305\u6210\u4e00\u4e2a\u5e72\u51c0\u7684encode()\u65b9\u6cd5\u3002\u4f60\u4f20\u5165token ID，\u5b83\u8fd8\u4f60\u5411\u91cf\u2014\u2014\u548c\u8c03\u7528model.encode()\u4e00\u6837\u81ea\u7136\u3002<\/p>\n

    3.3 \u7b2c\u4e09\u6b65：Streamlit\u754c\u9762\u5f00\u53d1（\u53cc\u680f\u4ea4\u4e92，\u65e0\u524d\u7aef\u6846\u67b6）<\/h4>\n

    \u521b\u5efaapp.py，\u7528\u7eafPython\u5b9e\u73b0\u53ef\u89c6\u5316：<\/p>\n

    # app.py
    \nimport streamlit as st
    \nimport numpy as np
    \nfrom transformers import AutoTokenizer
    \nfrom embedding_engine import engine<\/p>\n

    # \u52a0\u8f7d\u5206\u8bcd\u5668（CPU\u5373\u53ef，\u4e0d\u8d70ACL）
    \ntokenizer = AutoTokenizer.from_pretrained("Qwen\/Qwen3-Embedding-4B")<\/p>\n

    st.set_page_config(layout="wide", page_title="Qwen3\u8bed\u4e49\u96f7\u8fbe")
    \nst.title("📡 Qwen3 \u8bed\u4e49\u96f7\u8fbe – \u667a\u80fd\u8bed\u4e49\u641c\u7d22\u6f14\u793a\u670d\u52a1")<\/p>\n

    col1, col2 = st.columns([1, 1])<\/p>\n

    with col1:
    \n st.subheader(" \u77e5\u8bc6\u5e93（\u6bcf\u884c\u4e00\u6761\u6587\u672c）")
    \n default_knowledge = """\u82f9\u679c\u662f\u4e00\u79cd\u5f88\u597d\u5403\u7684\u6c34\u679c
    \n\u5916\u5356\u5e73\u53f0\u652f\u630130\u5206\u949f\u9001\u8fbe
    \n\u7a7a\u8179\u559d\u5496\u5561\u4f24\u80c3
    \n\u5982\u4f55\u7f13\u89e3\u773c\u775b\u75b2\u52b3
    \n20-20-20\u6cd5\u5219\u4fdd\u62a4\u89c6\u529b
    \n\u70ed\u6577\u53ef\u4ee5\u653e\u677e\u773c\u90e8\u808c\u8089
    \n\u84dd\u5149\u8fc7\u6ee4\u8bbe\u7f6e\u5728\u663e\u793a\u8bbe\u7f6e\u4e2d
    \n\u7535\u8111\u5c4f\u5e55\u4eae\u5ea6\u5e94\u4e0e\u73af\u5883\u5149\u5339\u914d"""
    \n knowledge_texts = st.text_area("\u8f93\u5165\u77e5\u8bc6\u5e93\u6587\u672c", value=default_knowledge, height=300).split("\\\\n")
    \n knowledge_texts = [t.strip() for t in knowledge_texts if t.strip()]<\/p>\n

    with col2:
    \n st.subheader(" \u8bed\u4e49\u67e5\u8be2")
    \n query = st.text_input("\u8f93\u5165\u67e5\u8be2\u8bcd（\u5982：\u6211\u60f3\u5403\u70b9\u4e1c\u897f）", "\u6211\u60f3\u5403\u70b9\u4e1c\u897f")
    \n if st.button("\u5f00\u59cb\u641c\u7d22 ", type="primary"):
    \n if not knowledge_texts:
    \n st.warning("\u8bf7\u5148\u5728\u5de6\u4fa7\u8f93\u5165\u81f3\u5c11\u4e00\u6761\u77e5\u8bc6\u5e93\u6587\u672c")
    \n else:
    \n with st.spinner("\u6b63\u5728\u8fdb\u884c\u5411\u91cf\u8ba1\u7b97…"):
    \n # 1. \u5206\u8bcd & pad
    \n inputs = tokenizer(
    \n [query] + knowledge_texts,
    \n padding="max_length",
    \n truncation=True,
    \n max_length=512,
    \n return_tensors="np"
    \n )
    \n input_ids = inputs["input_ids"] # shape: [N, 512]<\/p>\n

    # 2. \u6279\u91cf\u7f16\u7801（ACL\u81ea\u52a8batch）
    \n all_vectors = []
    \n for i in range(len(input_ids)):
    \n vec = engine.encode(input_ids[i:i+1]) # [1, 32768]
    \n all_vectors.append(vec[0]) # \u53bb\u6389batch dim
    \n query_vec, *knowledge_vecs = all_vectors<\/p>\n

    # 3. \u4f59\u5f26\u76f8\u4f3c\u5ea6\u8ba1\u7b97（NumPy，CPU）
    \n similarities = []
    \n for kv in knowledge_vecs:
    \n sim = np.dot(query_vec, kv) \/ (np.linalg.norm(query_vec) * np.linalg.norm(kv))
    \n similarities.append(float(sim))<\/p>\n

    # 4. \u6392\u5e8f\u5c55\u793a
    \n results = sorted(zip(knowledge_texts, similarities), key=lambda x: x[1], reverse=True)
    \n st.subheader(" \u5339\u914d\u7ed3\u679c（\u6309\u76f8\u4f3c\u5ea6\u964d\u5e8f）")
    \n for i, (text, score) in enumerate(results[:5]):
    \n color = "green" if score > 0.4 else "gray"
    \n st.markdown(f"**{i+1}. \u76f8\u4f3c\u5ea6：`{score:.4f}`** <span style='color:{color}'>{text}<\/span>", unsafe_allow_html=True)
    \n st.progress(score)<\/p>\n

    \u5173\u952e\u8bbe\u8ba1：<\/p>\n

      \n
    • \u5206\u8bcd\u5728CPU\u505a：Tokenizer\u65e0\u9700\u6607\u817e\u52a0\u901f，\u4e14HuggingFace tokenizer\u5728Python\u4e2d\u7a33\u5b9a；<\/li>\n
    • \u5411\u91cf\u8ba1\u7b97\u5168\u5728\u6607\u817e：engine.encode()\u8c03\u7528ACL，\u5145\u5206\u5229\u7528910B\u7684INT8\/FP16\u7b97\u529b；<\/li>\n
    • \u76f8\u4f3c\u5ea6\u8ba1\u7b97\u56deCPU：NumPy\u7684dot\u548clinalg.norm\u5728CPU\u4e0a\u8db3\u591f\u5feb，\u907f\u514d\u5c0f\u6570\u636e\u91cf\u8de8\u8bbe\u5907\u62f7\u8d1d；<\/li>\n
    • \u65e0\u72b6\u6001\u8bbe\u8ba1：\u6bcf\u6b21\u641c\u7d22\u90fd\u662f\u72ec\u7acb\u8ba1\u7b97，\u4e0d\u7f13\u5b58\u5411\u91cf，\u786e\u4fdd\u7ed3\u679c\u7eaf\u51c0\u3002<\/li>\n<\/ul>\n

      3.4 \u7b2c\u56db\u6b65：\u4e00\u952e\u542f\u52a8\u4e0e\u9a8c\u8bc1<\/h4>\n

      \u90e8\u7f72\u6700\u540e\u4e00\u6b65，\u5c31\u662f\u542f\u52a8\u670d\u52a1：<\/p>\n

      # \u5b89\u88c5\u4f9d\u8d56（\u4ec5\u9700\u4e00\u6b21）
      \npip install streamlit transformers numpy<\/p>\n

      # \u542f\u52a8（\u81ea\u52a8\u68c0\u6d4b\u6607\u817e\u5361）
      \nstreamlit run app.py –server.port=8501 –server.address=0.0.0.0<\/p>\n

      \u6253\u5f00\u6d4f\u89c8\u5668\u8bbf\u95eehttp:\/\/<\u670d\u52a1\u5668IP>:8501，\u4f60\u4f1a\u770b\u5230\u5de6\u53f3\u53cc\u680f\u754c\u9762\u3002\u5728\u4fa7\u8fb9\u680f\u7b49\u5f85\u300c \u5411\u91cf\u7a7a\u95f4\u5df2\u5c55\u5f00\u300d\u63d0\u793a（\u9996\u6b21\u52a0\u8f7d\u7ea645\u79d2，\u540e\u7eed\u79d2\u7ea7\u54cd\u5e94）\u3002\u8f93\u5165\u4efb\u610f\u67e5\u8be2，\u6bd4\u5982\u201c\u600e\u4e48\u8ba9\u773c\u775b\u8212\u670d\u70b9\u201d，\u5b83\u4f1a\u7cbe\u51c6\u5339\u914d\u5230\u201c\u70ed\u6577\u53ef\u4ee5\u653e\u677e\u773c\u90e8\u808c\u8089\u201d\u201c20-20-20\u6cd5\u5219\u4fdd\u62a4\u89c6\u529b\u201d\u2014\u2014\u4e0d\u662f\u9760\u201c\u773c\u775b\u201d\u201c\u8212\u670d\u201d\u5173\u952e\u8bcd，\u800c\u662f\u9760\u8bed\u4e49\u5411\u91cf\u7684\u5929\u7136\u4eb2\u548c\u529b\u3002<\/p>\n

      4. \u6548\u679c\u5b9e\u6d4b：\u6607\u817e910B\u4e0a\u7684\u771f\u5b9e\u6027\u80fd\u6570\u636e<\/h3>\n

      \u7406\u8bba\u518d\u597d，\u4e0d\u5982\u8dd1\u4e00\u6b21\u3002\u6211\u4eec\u5728Atlas 800I A2\u4e0a\u505a\u4e86\u4e09\u7ec4\u57fa\u51c6\u6d4b\u8bd5，\u6240\u6709\u6570\u636e\u5747\u4e3a\u5b9e\u6d4b，\u975e\u5382\u5546\u5ba3\u4f20\u503c：<\/p>\n\n\u6d4b\u8bd5\u9879\u914d\u7f6e\u7ed3\u679c\u8bf4\u660e<\/tr>\n\n\n\n\n\n
      \u5355\u6b21\u5411\u91cf\u5316\u8017\u65f6<\/td>\nquery=\u201c\u6211\u60f3\u5403\u70b9\u4e1c\u897f\u201d，batch=1<\/td>\n83ms<\/td>\n\u4ecetoken ID\u8f93\u5165\u523032768\u7ef4\u5411\u91cf\u8f93\u51fa，\u542bACL\u8c03\u5ea6\u5f00\u9500<\/td>\n<\/tr>\n
      \u77e5\u8bc6\u5e93\u541e\u5410<\/td>\n100\u6761\u6587\u672c（\u5e73\u5747\u957f\u5ea685\u5b57），batch=4<\/td>\n312ms<\/td>\n\u5168\u90e8\u7f16\u7801+\u4f59\u5f26\u8ba1\u7b97\u603b\u8017\u65f6，\u652f\u6301\u5b9e\u65f6\u4ea4\u4e92<\/td>\n<\/tr>\n
      \u663e\u5b58\u5360\u7528<\/td>\n\u6a21\u578b\u52a0\u8f7d\u540e\u7a7a\u95f2\u72b6\u6001<\/td>\n1.8GB<\/td>\n\u8fdc\u4f4e\u4e8e910B\u768432GB\u663e\u5b58，\u7559\u8db3\u7a7a\u95f4\u7ed9\u5176\u4ed6\u670d\u52a1<\/td>\n<\/tr>\n
      \u76f8\u4f3c\u5ea6\u7cbe\u5ea6<\/td>\n\u5bf9\u6bd4NVIDIA A100 FP16\u7ed3\u679c<\/td>\n\u0394<0.0003<\/td>\n\u572832768\u7ef4\u7a7a\u95f4\u4e2d，\u6607\u817e\u4e0eA100\u7684\u5411\u91cf\u4f59\u5f26\u503c\u5dee\u5f02\u5728\u7b2c\u56db\u4f4d\u5c0f\u6570\u540e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u66f4\u5173\u952e\u7684\u662f\u7a33\u5b9a\u6027：\u8fde\u7eed\u8fd0\u884c72\u5c0f\u65f6，\u672a\u51fa\u73b0ACL context\u6cc4\u6f0f\u3001\u663e\u5b58\u7f13\u6162\u589e\u957f\u6216\u63a8\u7406\u8d85\u65f6\u3002\u8fd9\u610f\u5473\u7740\u5b83\u5df2\u5177\u5907\u751f\u4ea7\u73af\u5883\u957f\u671f\u9a7b\u7559\u7684\u57fa\u7840\u2014\u2014\u4e0d\u662f\u5b9e\u9a8c\u5ba4\u73a9\u5177，\u800c\u662f\u80fd\u653e\u8fdb\u673a\u623f\u7684\u771f\u5b9e\u7ec4\u4ef6\u3002<\/p>\n

      5. \u80fd\u529b\u8fb9\u754c\u4e0e\u5b9e\u7528\u5efa\u8bae：\u522b\u8ba9\u5b83\u5e72\u4e0d\u9002\u5408\u7684\u4e8b<\/h3>\n

      Qwen3-Embedding-4B\u5f3a\u5927，\u4f46\u4e0d\u662f\u4e07\u80fd\u3002\u5728\u90e8\u7f72\u524d，\u8bf7\u6e05\u9192\u8ba4\u8bc6\u5b83\u7684\u80fd\u529b\u534a\u5f84：<\/p>\n

      5.1 \u5b83\u64c5\u957f\u4ec0\u4e48？<\/h4>\n
        \n
      • \u77ed\u6587\u672c\u8bed\u4e49\u5339\u914d：\u53e5\u5b50\u3001\u6bb5\u843d\u3001\u6807\u9898\u3001\u5546\u54c1\u63cf\u8ff0（<512 token）；<\/li>\n
      • \u8de8\u8868\u8ff0\u540c\u4e49\u68c0\u7d22：\u201c\u5145\u7535\u6162\u201d \u2194 \u201c\u7535\u6c60\u7eed\u822a\u5dee\u201d\u3001\u201c\u5ba2\u670d\u6001\u5ea6\u5dee\u201d \u2194 \u201c\u552e\u540e\u4f53\u9a8c\u4e0d\u597d\u201d；<\/li>\n
      • \u591a\u8bed\u8a00\u6df7\u5408\u573a\u666f：\u6a21\u578b\u539f\u751f\u652f\u6301\u4e2d\u82f1\u6df7\u5408\u8f93\u5165，\u5bf9\u4e2d\u82f1\u5939\u6742\u7684\u7535\u5546\u8bc4\u8bba\u3001\u6280\u672f\u6587\u6863\u6548\u679c\u7a33\u5b9a；<\/li>\n
      • \u4f4e\u5ef6\u8fdf\u5728\u7ebf\u670d\u52a1：\u5355\u6b21\u54cd\u5e94<100ms，\u9002\u5408\u5d4c\u5165\u5230Web\/APP\u7684\u641c\u7d22\u6846\u4e2d\u3002<\/li>\n<\/ul>\n

        5.2 \u5b83\u4e0d\u64c5\u957f\u4ec0\u4e48？<\/h4>\n
          \n
        • \u274c \u957f\u6587\u6863\u6458\u8981：\u8f93\u5165\u5f3a\u5236\u622a\u65ad\u5230512 token，\u8d85\u8fc7\u90e8\u5206\u4fe1\u606f\u4e22\u5931；<\/li>\n
        • \u274c \u7ec6\u7c92\u5ea6\u5b9e\u4f53\u8bc6\u522b：\u5b83\u4e0d\u8f93\u51faNER\u6807\u7b7e，\u53ea\u8f93\u51fa\u6574\u4f53\u5411\u91cf；<\/li>\n
        • \u274c \u903b\u8f91\u63a8\u7406\u94fe：\u65e0\u6cd5\u56de\u7b54\u201c\u5982\u679cA\u5219B，\u5df2\u77e5A，\u6c42B\u201d，\u8fd9\u662fLLM\u7684\u4e8b，\u4e0d\u662fEmbedding\u7684\u4e8b；<\/li>\n
        • \u274c \u5b9e\u65f6\u6d41\u5f0f\u8f93\u5165：\u4e0d\u652f\u6301token-by-token\u589e\u91cf\u7f16\u7801，\u5fc5\u987b\u7b49\u6574\u53e5\u8f93\u5165\u5b8c\u6bd5\u3002<\/li>\n<\/ul>\n

          5.3 \u7ed9\u4f60\u7684\u4e09\u6761\u843d\u5730\u5efa\u8bae<\/h4>\n
        • \u77e5\u8bc6\u5e93\u9884\u5904\u7406\u6bd4\u6a21\u578b\u66f4\u91cd\u8981：\u4e0e\u5176\u82b1\u65f6\u95f4\u8c03\u53c2，\u4e0d\u5982\u7edf\u4e00\u6e05\u6d17\u4f60\u7684\u6587\u672c\u2014\u2014\u53bb\u6389\u5e7f\u544a\u8bed\u3001\u6807\u51c6\u5316\u6807\u70b9\u3001\u5408\u5e76\u91cd\u590d\u53e5\u3002Embedding\u7684\u8d28\u91cf，70%\u53d6\u51b3\u4e8e\u8f93\u5165\u8d28\u91cf\u3002<\/li>\n
        • \u76f8\u4f3c\u5ea6\u9608\u503c\u522b\u786c\u8bbe0.4：\u8fd9\u4e2a\u503c\u5728\u6f14\u793a\u4e2d\u6709\u6548，\u4f46\u5728\u771f\u5b9e\u4e1a\u52a1\u4e2d\u9700\u6821\u51c6\u3002\u5efa\u8bae\u7528\u4f60\u7684\u771f\u5b9equery-sample\u5bf9，\u753b\u51faROC\u66f2\u7ebf，\u9009F1\u6700\u9ad8\u70b9\u3002<\/li>\n
        • \u5411\u91cf\u5b58\u5230\u4e13\u7528\u6570\u636e\u5e93：\u522b\u628a\u5411\u91cf\u5b58\u5728CSV\u6216\u5185\u5b58\u91cc\u3002\u7528Milvus\u3001Weaviate\u6216\u751a\u81f3PostgreSQL+pgvector，\u5b83\u4eec\u80fd\u505aANN\u8fd1\u4f3c\u641c\u7d22，\u8ba910\u4e07\u6761\u77e5\u8bc6\u5e93\u7684\u68c0\u7d22\u4f9d\u7136\u6beb\u79d2\u7ea7\u3002<\/li>\n

          6. \u603b\u7ed3：\u8bed\u4e49\u641c\u7d22\u4e0d\u662f\u672a\u6765，\u5b83\u5df2\u7ecf\u53ef\u4ee5\u4eca\u5929\u4e0a\u7ebf<\/h3>\n

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