{"id":64,"date":"2026-06-29T10:20:55","date_gmt":"2026-06-29T02:20:55","guid":{"rendered":"https:\/\/genetop.top\/index.php\/2026\/06\/29\/graphrag-2026-05-04-semantic-chunk-why-embedding\/"},"modified":"2026-06-29T10:20:55","modified_gmt":"2026-06-29T02:20:55","slug":"graphrag-2026-05-04-semantic-chunk-why-embedding","status":"publish","type":"post","link":"https:\/\/genetop.top\/index.php\/2026\/06\/29\/graphrag-2026-05-04-semantic-chunk-why-embedding\/","title":{"rendered":"Semantic Chunk \u4e3a\u4ec0\u4e48\u9700\u8981 Embedding API"},"content":{"rendered":"
\n

\u56fa\u5b9a\u957f\u5ea6\u5206\u5757\u4e0d\u9700\u8981\u4efb\u4f55\u5916\u90e8\u670d\u52a1\uff0c\u8bed\u4e49\u5206\u5757\u5374\u5fc5\u987b\u8c03\u7528 Embedding API\u2014\u2014\u8fd9\u80cc\u540e\u7684\u539f\u56e0\u662f\u4ec0\u4e48\uff1f<\/p>\n<\/blockquote>\n

\u5148\u8bf4\u7ed3\u8bba<\/h2>\n

Semantic chunking \u7684\u6838\u5fc3\u601d\u60f3\u662f\u5728\u8bed\u4e49\u8fb9\u754c\u5904\u5207\u5206\u6587\u672c<\/strong>\u3002\u5224\u65ad”\u4e24\u6bb5\u6587\u5b57\u662f\u5426\u5c5e\u4e8e\u540c\u4e00\u4e2a\u8bdd\u9898”\u9700\u8981\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u5411\u91cf\u540e\u8ba1\u7b97\u76f8\u4f3c\u5ea6\u2014\u2014\u8fd9\u5c31\u662f Embedding API \u4e0d\u53ef\u6216\u7f3a\u7684\u539f\u56e0\u3002<\/p>\n

\u4f20\u7edf\u5206\u5757 vs \u8bed\u4e49\u5206\u5757<\/h2>\n\n\n\n\n\n\n\n
\u7ef4\u5ea6<\/th>\n\u56fa\u5b9a\u957f\u5ea6 \/ \u9012\u5f52\u5206\u5757<\/th>\n\u8bed\u4e49\u5206\u5757<\/th>\n<\/tr>\n<\/thead>\n
\u5207\u5206\u4f9d\u636e<\/td>\n\u5b57\u7b26\u6570\u3001token \u6570\u3001\u5206\u9694\u7b26<\/td>\n\u76f8\u90bb\u53e5\u5b50\u7684\u8bed\u4e49\u76f8\u4f3c\u5ea6<\/strong><\/td>\n<\/tr>\n
\u662f\u5426\u9700\u8981 Embedding<\/td>\n\u274c \u4e0d\u9700\u8981<\/td>\n\u2705 \u5fc5\u987b<\/td>\n<\/tr>\n
\u5207\u5206\u8d28\u91cf<\/td>\n\u53ef\u80fd\u5728\u8bdd\u9898\u4e2d\u95f4\u65ad\u5f00<\/td>\n\u5728\u8bdd\u9898\u8f6c\u6362\u5904\u5207\u5206\uff0c\u4fdd\u6301\u8bed\u4e49\u5b8c\u6574<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u56fa\u5b9a\u957f\u5ea6\u5206\u5757\u5c31\u50cf\u7528\u5c3a\u5b50\u91cf\u7eb8\u2014\u2014\u4e0d\u7ba1\u5185\u5bb9\u5199\u4e86\u4ec0\u4e48\uff0c\u5230\u4e86 500 \u5b57\u5c31\u526a\u4e00\u5200\u3002\u8bed\u4e49\u5206\u5757\u5219\u50cf\u4e00\u4e2a\u8bfb\u8005\uff0c\u8bfb\u5b8c\u4e00\u6bb5\u540e\u5224\u65ad”\u4e0b\u4e00\u6bb5\u662f\u4e0d\u662f\u5728\u8bf4\u540c\u4e00\u4ef6\u4e8b”\uff0c\u5982\u679c\u4e0d\u662f\uff0c\u5c31\u5728\u8fd9\u91cc\u5207\u5f00\u3002<\/p>\n

\u4e24\u79cd\u4e3b\u6d41\u8bed\u4e49\u5206\u5757\u7b56\u7565<\/h2>\n

\u7b56\u7565\u4e00\uff1a\u76f8\u90bb\u76f8\u4f3c\u5ea6\u6cd5\uff08Kamradt \u65b9\u6cd5\uff09<\/h3>\n

\u6838\u5fc3\u601d\u8def\uff1a\u8ba1\u7b97\u76f8\u90bb\u53e5\u5b50\u4e4b\u95f4\u7684\u8bed\u4e49\u8ddd\u79bb\uff0c\u5728\u8ddd\u79bb\u7a81\u53d8\u5904\u5207\u5206\u3002<\/p>\n

\u6d41\u7a0b\uff1a\n1. \u5c06\u6587\u672c\u5207\u6210\u5c0f\u53e5\u5b50\n2. \u4e3a\u6bcf\u4e2a\u53e5\u5b50\u62fc\u63a5\u524d\u540e buffer_size \u4e2a\u53e5\u5b50\u4f5c\u4e3a\u4e0a\u4e0b\u6587\n3. \u8c03\u7528 Embedding API \u83b7\u53d6\u6bcf\u4e2a\u7ec4\u5408\u53e5\u5b50\u7684\u5411\u91cf\n4. \u8ba1\u7b97\u76f8\u90bb\u53e5\u5b50\u5411\u91cf\u7684\u4f59\u5f26\u8ddd\u79bb\n5. \u901a\u8fc7\u4e8c\u5206\u641c\u7d22\u627e\u5230\u9608\u503c\uff0c\u5728\u8ddd\u79bb\u8d85\u8fc7\u9608\u503c\u7684\u4f4d\u7f6e\u5207\u5206\n<\/code><\/pre>\n
\u4f2a\u4ee3\u7801\uff1a<\/p>\n
# Step 1: \u62fc\u63a5\u4e0a\u4e0b\u6587\u7a97\u53e3\nfor i, sentence in enumerate(sentences):\n    combined = ""\n    for j in range(max(0, i - buffer_size), i):\n        combined += sentences[j] + " "\n    combined += sentence\n    for j in range(i + 1, min(n, i + 1 + buffer_size)):\n        combined += " " + sentences[j]\n    combined_texts.append(combined)\n\n# Step 2: \u83b7\u53d6\u6240\u6709\u7ec4\u5408\u53e5\u5b50\u7684 embedding\uff08\u4e00\u6b21\u6279\u91cf\u8c03\u7528\uff09\nembeddings = embedding_client.embed_texts(combined_texts)\nembedding_matrix = np.array(embeddings)\n\n# Step 3: \u53ea\u8ba1\u7b97\u76f8\u90bb\u53e5\u5b50\u95f4\u7684\u4f59\u5f26\u8ddd\u79bb\ndistances = []\nfor i in range(len(sentences) - 1):\n    similarity = dot(embedding_matrix[i], embedding_matrix[i + 1])\n    distances.append(1 - similarity)  # \u8ddd\u79bb\u8d8a\u5927 = \u8bdd\u9898\u5dee\u5f02\u8d8a\u5927\n\n# Step 4: \u4e8c\u5206\u641c\u7d22\u627e\u9608\u503c\uff0c\u4f7f\u5207\u5206\u6570\u91cf\u63a5\u8fd1 total_size \/ avg_chunk_size\nthreshold = binary_search_threshold(distances, target_cuts)\n\n# Step 5: \u5728\u8ddd\u79bb\u8d85\u8fc7\u9608\u503c\u7684\u4f4d\u7f6e\u5207\u5206\nbreakpoints = [i for i, d in enumerate(distances) if d > threshold]\n<\/code><\/pre>\n
\u76f4\u89c9\u7406\u89e3\uff1a\u60f3\u8c61\u4f60\u5728\u8bfb\u4e00\u7bc7\u6587\u7ae0\uff0c\u6bcf\u8bfb\u5b8c\u4e00\u53e5\u5c31\u95ee\u81ea\u5df1”\u8fd9\u53e5\u548c\u4e0b\u4e00\u53e5\u662f\u4e0d\u662f\u5728\u8bf4\u540c\u4e00\u4ef6\u4e8b\uff1f”\u3002\u5982\u679c\u7a81\u7136\u89c9\u5f97\u8bdd\u9898\u8df3\u4e86\uff0c\u5c31\u5728\u8fd9\u91cc\u5207\u4e00\u5200\u3002<\/p>\n
\u5173\u952e\u7279\u5f81\uff1a\u53ea\u770b\u76f8\u90bb\u5173\u7cfb\u3002<\/strong> \u5b83\u53ea\u8ba1\u7b97 sentence[i] \u548c sentence[i+1] \u4e4b\u95f4\u7684\u8ddd\u79bb\uff0c\u662f\u4e00\u79cd\u5c40\u90e8\u8d2a\u5fc3<\/strong>\u7b56\u7565\u3002<\/p>\n
\u7b56\u7565\u4e8c\uff1a\u805a\u7c7b\u6700\u4f18\u5206\u5272\u6cd5\uff08\u52a8\u6001\u89c4\u5212\u65b9\u6cd5\uff09<\/h3>\n
\u6838\u5fc3\u601d\u8def\uff1a\u6784\u5efa\u6240\u6709\u53e5\u5b50\u5bf9\u4e4b\u95f4\u7684\u76f8\u4f3c\u5ea6\u77e9\u9635\uff0c\u7528\u52a8\u6001\u89c4\u5212\u627e\u5230\u4f7f\u7c07\u5185\u76f8\u4f3c\u5ea6\u603b\u548c\u6700\u5927\u7684\u6700\u4f18\u5206\u5272\u3002<\/p>\n
\u6d41\u7a0b\uff1a\n1. \u5c06\u6587\u672c\u5207\u6210\u5c0f\u53e5\u5b50\n2. \u8c03\u7528 Embedding API \u83b7\u53d6\u6240\u6709\u53e5\u5b50\u7684\u5411\u91cf\n3. \u6784\u5efa N\u00d7N \u76f8\u4f3c\u5ea6\u77e9\u9635\n4. \u5bf9\u77e9\u9635\u505a\u5747\u503c\u5f52\u4e00\u5316\uff08\u9632\u6b62\u9000\u5316\u4e3a\u5355\u4e00\u5927\u7c07\uff09\n5. \u7528\u52a8\u6001\u89c4\u5212\u627e\u5230\u6700\u4f18\u5206\u5272\u65b9\u6848\n<\/code><\/pre>\n
\u4f2a\u4ee3\u7801\uff1a<\/p>\n
# Step 1: \u83b7\u53d6\u6240\u6709\u53e5\u5b50\u7684 embedding\uff08\u6ce8\u610f\uff1a\u6ca1\u6709 buffer \u62fc\u63a5\uff09\nembeddings = embedding_client.embed_texts(sentences)\nembedding_matrix = np.array(embeddings)\n\n# Step 2: \u6784\u5efa N\u00d7N \u76f8\u4f3c\u5ea6\u77e9\u9635\nsimilarity_matrix = dot(embedding_matrix, embedding_matrix.T)\n\n# Step 3: \u5747\u503c\u5f52\u4e00\u5316\uff0c\u9632\u6b62 DP \u9000\u5316\u4e3a"\u5168\u90e8\u653e\u4e00\u4e2a\u7c07"\nmean_sim = mean(upper_triangle(similarity_matrix))\nsimilarity_matrix -= mean_sim\nfill_diagonal(similarity_matrix, 0)\n\n# Step 4: \u52a8\u6001\u89c4\u5212\u5bfb\u627e\u6700\u4f18\u5207\u5206\n# dp[i] = \u524d i+1 \u4e2a\u53e5\u5b50\u7684\u6700\u5927\u7c07\u5185\u76f8\u4f3c\u5ea6\u603b\u548c\nfor i in range(n):\n    for size in range(1, i + 2):\n        start = i - size + 1\n        if cluster_size(start, i) > max_chunk_size and size > 1:\n            break\n        reward = sum(similarity_matrix[start:i+1, start:i+1])\n        if start > 0:\n            reward += dp[start - 1]\n        dp[i] = max(dp[i], reward)\n\n# Step 5: \u56de\u6eaf\u5f97\u5230\u6700\u4f18\u5206\u5272\nclusters = backtrack(segmentation)\n<\/code><\/pre>\n
\u5173\u952e\u7279\u5f81\uff1a\u5168\u5c40\u6700\u4f18\u3002<\/strong> \u5b83\u8003\u8651\u6240\u6709\u53e5\u5b50\u5bf9\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u901a\u8fc7 DP \u627e\u5230\u6574\u4f53\u6700\u4f18\u7684\u5206\u5272\u65b9\u6848\u3002<\/p>\n
\u4e24\u79cd\u7b56\u7565\u7684\u6df1\u5ea6\u5bf9\u6bd4<\/h2>\n
\u7b97\u6cd5\u672c\u8d28\u5dee\u5f02<\/h3>\n\n\n\n\n\n\n\n\n\n
\u7ef4\u5ea6<\/th>\nKamradt\uff08\u76f8\u90bb\u76f8\u4f3c\u5ea6\uff09<\/th>\nCluster\uff08\u52a8\u6001\u89c4\u5212\uff09<\/th>\n<\/tr>\n<\/thead>\n
\u89c6\u91ce<\/td>\n\u5c40\u90e8<\/strong>\u2014\u2014\u53ea\u770b\u76f8\u90bb\u53e5\u5b50<\/td>\n\u5168\u5c40<\/strong>\u2014\u2014\u770b\u6240\u6709\u53e5\u5b50\u5bf9<\/td>\n<\/tr>\n
\u51b3\u7b56\u65b9\u5f0f<\/td>\n\u8d2a\u5fc3\uff1a\u8ddd\u79bb\u8d85\u9608\u503c\u5c31\u5207<\/td>\n\u6700\u4f18\u5316\uff1a\u6700\u5927\u5316\u7c07\u5185\u603b\u76f8\u4f3c\u5ea6<\/td>\n<\/tr>\n
\u9608\u503c\u786e\u5b9a<\/td>\n\u4e8c\u5206\u641c\u7d22\u76ee\u6807\u5207\u5206\u6570<\/td>\n\u65e0\u9700\u9608\u503c\uff0cDP \u81ea\u52a8\u51b3\u5b9a<\/td>\n<\/tr>\n
\u4e0a\u4e0b\u6587\u589e\u5f3a<\/td>\n\u2705 \u6709 buffer_size \u62fc\u63a5<\/td>\n\u274c \u76f4\u63a5\u7528\u539f\u59cb\u53e5\u5b50<\/td>\n<\/tr>\n
\u5927\u5c0f\u7ea6\u675f<\/td>\navg_chunk_size + max_chunk_size \u53cc\u91cd\u7ea6\u675f<\/td>\nmax_chunk_size \u786c\u7ea6\u675f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u6838\u5fc3\u533a\u522b\u7528\u4e00\u53e5\u8bdd\u6982\u62ec\uff1a<\/strong><\/p>\n
    \n
  • Kamradt \u95ee\u7684\u662f\uff1a”\u8fd9\u4e24\u4e2a\u76f8\u90bb\u53e5\u5b50\u4e4b\u95f4\u662f\u5426\u5b58\u5728\u8bdd\u9898\u8df3\u8f6c\uff1f”<\/li>\n
  • Cluster \u95ee\u7684\u662f\uff1a”\u54ea\u79cd\u5206\u7ec4\u65b9\u5f0f\u80fd\u8ba9\u6bcf\u7ec4\u5185\u90e8\u7684\u53e5\u5b50\u6700\u76f8\u4f3c\uff1f”<\/li>\n<\/ul>\n
    \u4e00\u4e2a\u76f4\u89c2\u7684\u4f8b\u5b50<\/h3>\n
    \u5047\u8bbe\u6709 6 \u4e2a\u53e5\u5b50\uff0c\u8bdd\u9898\u5206\u5e03\u5982\u4e0b\uff1a<\/p>\n
    \u53e5\u5b501: \u8ba8\u8bba\u82f9\u679c\u516c\u53f8\u7684\u8d22\u62a5\n\u53e5\u5b502: \u8ba8\u8bba\u82f9\u679c\u516c\u53f8\u7684\u65b0\u4ea7\u54c1\n\u53e5\u5b503: \u8ba8\u8bba\u5929\u6c14\u9884\u62a5\n\u53e5\u5b504: \u8ba8\u8bba\u660e\u5929\u7684\u6c14\u6e29\n\u53e5\u5b505: \u8ba8\u8bba\u82f9\u679c\u516c\u53f8\u7684\u80a1\u4ef7\n\u53e5\u5b506: \u8ba8\u8bba\u82f9\u679c\u516c\u53f8\u7684\u7ade\u4e89\u5bf9\u624b\n<\/code><\/pre>\n
    Kamradt \u7684\u5207\u6cd5\uff1a<\/strong> \u9010\u5bf9\u6bd4\u8f83\u76f8\u90bb\u8ddd\u79bb
    \n– \u53e5\u5b502\u21923\uff1a\u8bdd\u9898\u8df3\u8f6c\uff08\u82f9\u679c\u2192\u5929\u6c14\uff09\uff0c\u5207\uff01
    \n– \u53e5\u5b504\u21925\uff1a\u8bdd\u9898\u8df3\u8f6c\uff08\u5929\u6c14\u2192\u82f9\u679c\uff09\uff0c\u5207\uff01
    \n– \u7ed3\u679c\uff1a[1,2] [3,4] [5,6]<\/p>\n
    Cluster \u7684\u5207\u6cd5\uff1a<\/strong> \u5168\u5c40\u76f8\u4f3c\u5ea6\u77e9\u9635\u663e\u793a 1,2,5,6 \u5f7c\u6b64\u9ad8\u5ea6\u76f8\u4f3c
    \n– \u4f46\u7531\u4e8e DP \u8981\u6c42\u8fde\u7eed\u5206\u5272\uff08\u4e0d\u80fd\u8df3\u7740\u5206\u7ec4\uff09\uff0c\u5b83\u4ecd\u7136\u53ea\u80fd\u5207\u8fde\u7eed\u7247\u6bb5
    \n– \u7ed3\u679c\u53ef\u80fd\u4e5f\u662f [1,2] [3,4] [5,6]\uff0c\u4f46\u51b3\u7b56\u4f9d\u636e\u4e0d\u540c<\/p>\n
    \u5173\u952e\u5dee\u5f02\u51fa\u73b0\u5728\u8fb9\u754c\u6a21\u7cca\u7684\u60c5\u51b5\uff1a<\/strong><\/p>\n
    \u8003\u8651\u4e00\u7bc7\u4ece”\u7535\u52a8\u8f66\u6280\u672f”\u6e10\u53d8\u5230”\u80fd\u6e90\u653f\u7b56”\u7684\u6587\u7ae0\uff1a<\/p>\n
    \u53e5\u5b501: \u7279\u65af\u62c9\u53d1\u5e03\u4e86\u65b0\u4e00\u4ee3\u7535\u6c60\u6280\u672f\n\u53e5\u5b502: \u65b0\u7535\u6c60\u7684\u80fd\u91cf\u5bc6\u5ea6\u63d0\u5347\u4e86 50%\n\u53e5\u5b503: \u66f4\u9ad8\u7684\u80fd\u91cf\u5bc6\u5ea6\u610f\u5473\u7740\u66f4\u957f\u7684\u7eed\u822a\u91cc\u7a0b\n\u53e5\u5b504: \u7eed\u822a\u7126\u8651\u4e00\u76f4\u662f\u6d88\u8d39\u8005\u8d2d\u4e70\u7535\u52a8\u8f66\u7684\u969c\u788d\n\u53e5\u5b505: \u653f\u5e9c\u4e3a\u7f13\u89e3\u8fd9\u4e00\u95ee\u9898\u63a8\u51fa\u4e86\u5145\u7535\u6869\u8865\u8d34\u653f\u7b56\n\u53e5\u5b506: \u8865\u8d34\u653f\u7b56\u540c\u65f6\u8986\u76d6\u4e86\u5bb6\u7528\u548c\u5546\u7528\u5145\u7535\u8bbe\u65bd\n\u53e5\u5b507: \u5546\u7528\u5145\u7535\u8bbe\u65bd\u7684\u7535\u4ef7\u91c7\u7528\u5cf0\u8c37\u5206\u65f6\u5b9a\u4ef7\n\u53e5\u5b508: \u5206\u65f6\u7535\u4ef7\u673a\u5236\u662f\u7535\u529b\u5e02\u573a\u5316\u6539\u9769\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\n<\/code><\/pre>\n
    Kamradt \u770b\u5230\u7684\uff08\u76f8\u90bb\u8ddd\u79bb\uff09\uff1a<\/strong><\/p>\n
    1\u21922: 0.08  (\u90fd\u5728\u8bf4\u7535\u6c60)\n2\u21923: 0.10  (\u7535\u6c60\u2192\u7eed\u822a\uff0c\u5f88\u8fd1)\n3\u21924: 0.12  (\u7eed\u822a\u2192\u7eed\u822a\u7126\u8651\uff0c\u5f88\u8fd1)\n4\u21925: 0.15  (\u6d88\u8d39\u8005\u2192\u653f\u5e9c\u653f\u7b56\uff0c\u7a0d\u8fdc\u4f46\u4e0d\u7a81\u51fa)\n5\u21926: 0.09  (\u90fd\u5728\u8bf4\u8865\u8d34)\n6\u21927: 0.13  (\u8865\u8d34\u2192\u7535\u4ef7\uff0c\u6709\u70b9\u8fdc)\n7\u21928: 0.11  (\u90fd\u5728\u8bf4\u7535\u4ef7)\n<\/code><\/pre>\n
    \u6ca1\u6709\u4efb\u4f55\u4e00\u4e2a\u8ddd\u79bb\u660e\u663e”\u8df3\u8d77\u6765”\u2014\u2014\u8bdd\u9898\u662f\u4e00\u6b65\u6b65\u6ed1\u8fc7\u53bb\u7684\u3002Kamradt \u7684\u4e8c\u5206\u641c\u7d22\u5f88\u96be\u627e\u5230\u4e00\u4e2a\u5408\u7406\u7684\u9608\u503c\uff0c\u53ef\u80fd\u5207\u51fa [1-4][5-8] \u6216 [1-3][4-6][7-8] \u8fd9\u6837\u4e0d\u592a\u7406\u60f3\u7684\u7ed3\u679c\u3002<\/p>\n
    Cluster \u770b\u5230\u7684\uff08\u5168\u5c40\u76f8\u4f3c\u5ea6\u77e9\u9635\u6458\u8981\uff09\uff1a<\/strong><\/p>\n
            \u53e51   \u53e52   \u53e53   \u53e54   \u53e55   \u53e56   \u53e57   \u53e58\n\u53e51     --   0.9   0.7   0.4   0.2   0.1   0.1   0.05\n\u53e52          --    0.8   0.5   0.2   0.15  0.1   0.05\n\u53e53                --    0.6   0.3   0.2   0.15  0.1\n\u53e54                      --    0.5   0.4   0.3   0.2\n\u53e55                            --    0.8   0.6   0.4\n\u53e56                                  --    0.7   0.5\n\u53e57                                        --    0.8\n\u53e58                                              --\n<\/code><\/pre>\n
    \u5168\u5c40\u89c6\u89d2\u6e05\u6670\u5730\u663e\u793a\uff1a\u53e5\u5b50 1-3 \u5f7c\u6b64\u9ad8\u5ea6\u76f8\u4f3c\uff08\u7535\u6c60\/\u7eed\u822a\u6280\u672f\uff09\uff0c\u53e5\u5b50 5-8 \u5f7c\u6b64\u9ad8\u5ea6\u76f8\u4f3c\uff08\u653f\u7b56\/\u7535\u4ef7\uff09\uff0c\u53e5\u5b50 4 \u662f\u8fc7\u6e21\u53e5\u3002DP \u4f18\u5316\u4f1a\u53d1\u73b0 [1-3][4-8] \u6216 [1-4][5-8] \u7684\u7c07\u5185\u603b\u76f8\u4f3c\u5ea6\u6700\u5927\uff0c\u4ece\u800c\u505a\u51fa\u66f4\u5408\u7406\u7684\u5207\u5206\u3002<\/p>\n
    \u672c\u8d28\u533a\u522b\uff1a<\/strong> Kamradt \u53ea\u770b”\u76f8\u90bb\u4e24\u53e5\u4e4b\u95f4\u7684\u843d\u5dee”\uff0c\u6e10\u53d8\u8fc7\u6e21\u4e2d\u6bcf\u4e00\u6b65\u843d\u5dee\u90fd\u5f88\u5c0f\uff0c\u5c31\u50cf\u6e29\u6c34\u716e\u9752\u86d9\uff1bCluster \u770b”\u6574\u7ec4\u53e5\u5b50\u4e4b\u95f4\u7684\u6574\u4f53\u76f8\u4f3c\u5ea6”\uff0c\u5373\u4f7f\u8fc7\u6e21\u5e73\u6ed1\uff0c\u5b83\u4e5f\u80fd\u53d1\u73b0\u53e5\u5b50 1 \u548c\u53e5\u5b50 8 \u4e4b\u95f4\u5176\u5b9e\u5df2\u7ecf\u6beb\u65e0\u5173\u7cfb\u4e86\u3002<\/p>\n
    Embedding \u5f00\u9500\u5bf9\u6bd4<\/h3>\n
    \u8fd9\u662f\u4e24\u79cd\u7b56\u7565\u6700\u91cd\u8981\u7684\u5b9e\u9645\u5dee\u5f02\u4e4b\u4e00\uff1a<\/p>\n\n\n\n\n\n\n\n\n\n\n
    \u7ef4\u5ea6<\/th>\nKamradt<\/th>\nCluster<\/th>\n<\/tr>\n<\/thead>\n
    Embedding \u8f93\u5165<\/td>\ncombined_sentence\uff08\u542b buffer \u4e0a\u4e0b\u6587\uff09<\/td>\n\u539f\u59cb\u53e5\u5b50\uff08\u65e0 buffer\uff09<\/td>\n<\/tr>\n
    Embedding \u8c03\u7528\u6b21\u6570<\/td>\nN \u4e2a\u6587\u672c\uff0c1 \u6b21\u6279\u91cf\u8c03\u7528<\/td>\nN \u4e2a\u6587\u672c\uff0c1 \u6b21\u6279\u91cf\u8c03\u7528<\/td>\n<\/tr>\n
    \u6bcf\u4e2a\u6587\u672c\u7684\u5e73\u5747\u957f\u5ea6<\/td>\n\u8f83\u957f\uff08~7 \u53e5\uff0cbuffer_size=3\uff09<\/td>\n\u8f83\u77ed\uff081 \u53e5\uff09<\/td>\n<\/tr>\n
    \u603b token \u6d88\u8017<\/td>\n\u8f83\u9ad8<\/strong>\uff08buffer \u5bfc\u81f4\u8f93\u5165\u81a8\u80c0\uff09<\/td>\n\u8f83\u4f4e<\/strong>\uff08\u65e0\u5197\u4f59\uff09<\/td>\n<\/tr>\n
    \u540e\u7eed\u8ba1\u7b97\u5f00\u9500<\/td>\nO(N)\u2014\u2014\u53ea\u7b97\u76f8\u90bb\u8ddd\u79bb<\/td>\nO(N\u00b2)<\/strong>\u2014\u2014\u6784\u5efa\u5b8c\u6574\u76f8\u4f3c\u5ea6\u77e9\u9635<\/td>\n<\/tr>\n
    DP \u8ba1\u7b97\u5f00\u9500<\/td>\n\u65e0<\/td>\nO(N \u00d7 max_cluster_size)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \u5177\u4f53\u6570\u5b57\u5bf9\u6bd4\uff08\u5047\u8bbe 1000 \u4e2a\u53e5\u5b50\uff0c\u5e73\u5747\u6bcf\u53e5 30 tokens\uff09<\/h4>\n
    Kamradt\uff1a<\/strong>
    \n– Embedding \u8f93\u5165\uff1a1000 \u4e2a combined_sentence\uff0c\u6bcf\u4e2a\u7ea6 7\u00d730 = 210 tokens
    \n– \u603b token \u6d88\u8017\uff1a1000 \u00d7 210 = 210,000 tokens<\/strong>
    \n– \u8ddd\u79bb\u8ba1\u7b97\uff1a999 \u6b21\u70b9\u79ef \u2192 \u53ef\u5ffd\u7565
    \n– \u5185\u5b58\uff1a1000 \u00d7 embedding_dim \u7684\u77e9\u9635<\/p>\n
    Cluster\uff1a<\/strong>
    \n– Embedding \u8f93\u5165\uff1a1000 \u4e2a\u539f\u59cb\u53e5\u5b50\uff0c\u6bcf\u4e2a\u7ea6 30 tokens
    \n– \u603b token \u6d88\u8017\uff1a1000 \u00d7 30 = 30,000 tokens<\/strong>
    \n– \u76f8\u4f3c\u5ea6\u77e9\u9635\uff1a1000 \u00d7 1000 = 100 \u4e07\u4e2a\u6d6e\u70b9\u6570<\/strong>\uff08\u7ea6 8MB\uff09
    \n– DP \u8ba1\u7b97\uff1aO(1000 \u00d7 max_cluster_size) \u6b21\u5faa\u73af<\/p>\n
    \u7ed3\u8bba\uff1a<\/strong>
    \n– Embedding API \u8d39\u7528<\/strong>\uff1aKamradt \u6d88\u8017\u7ea6 7 \u500d token\uff08\u56e0\u4e3a buffer \u62fc\u63a5\uff09\uff0cAPI \u6210\u672c\u66f4\u9ad8
    \n– \u8ba1\u7b97\u8d44\u6e90<\/strong>\uff1aCluster \u7684 O(N\u00b2) \u77e9\u9635\u548c DP \u5728\u672c\u5730 CPU\/\u5185\u5b58\u4e0a\u5f00\u9500\u66f4\u5927
    \n– \u7f51\u7edc\u5ef6\u8fdf<\/strong>\uff1a\u4e24\u8005\u76f8\u540c\uff08\u90fd\u662f 1 \u6b21\u6279\u91cf\u8c03\u7528\uff0c\u6216\u6309 batch_size \u5206\u591a\u6b21\uff09<\/p>\n
    \u5927\u89c4\u6a21\u573a\u666f\uff0810 \u4e07\u53e5\u5b50\uff09<\/h4>\n\n\n\n\n\n\n\n\n
    \u6307\u6807<\/th>\nKamradt<\/th>\nCluster<\/th>\n<\/tr>\n<\/thead>\n
    Embedding token \u603b\u91cf<\/td>\n~2100 \u4e07 tokens<\/td>\n~300 \u4e07 tokens<\/td>\n<\/tr>\n
    API \u8c03\u7528\u6b21\u6570\uff08batch_size=500\uff09<\/td>\n200 \u6b21<\/td>\n200 \u6b21<\/td>\n<\/tr>\n
    \u76f8\u4f3c\u5ea6\u8ba1\u7b97<\/td>\n99,999 \u6b21\u70b9\u79ef<\/td>\n100 \u4ebf\u6b21\u70b9\u79ef\uff08N\u00b2\u77e9\u9635\uff09<\/td>\n<\/tr>\n
    \u5185\u5b58\u5360\u7528<\/td>\n~400MB\uff08embedding \u77e9\u9635\uff09<\/td>\n~40GB\uff08N\u00b2\u76f8\u4f3c\u5ea6\u77e9\u9635\uff09\u26a0\ufe0f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    10 \u4e07\u53e5\u5b50\u65f6 Cluster \u7b56\u7565\u7684 N\u00b2 \u77e9\u9635\u4f1a\u7206\u5185\u5b58<\/strong>\uff0c\u8fd9\u662f\u5b83\u7684\u786c\u4f24\u3002\u5b9e\u9645\u4f7f\u7528\u4e2d\uff0cCluster \u7b56\u7565\u66f4\u9002\u5408\u4e2d\u7b49\u957f\u5ea6\u6587\u6863\uff08\u51e0\u767e\u5230\u51e0\u5343\u53e5\u5b50\uff09\uff0c\u800c Kamradt \u53ef\u4ee5\u5904\u7406\u4efb\u610f\u957f\u5ea6\u3002<\/p>\n
    \u5207\u5206\u8d28\u91cf\u5bf9\u6bd4<\/h3>\n\n\n\n\n\n\n\n\n\n
    \u573a\u666f<\/th>\nKamradt \u8868\u73b0<\/th>\nCluster \u8868\u73b0<\/th>\n<\/tr>\n<\/thead>\n
    \u8bdd\u9898\u8fb9\u754c\u6e05\u6670<\/td>\n\u2705 \u4f18\u79c0\uff0c\u8ddd\u79bb\u7a81\u53d8\u660e\u663e<\/td>\n\u2705 \u4f18\u79c0<\/td>\n<\/tr>\n
    \u8bdd\u9898\u6e10\u53d8\u8fc7\u6e21<\/td>\n\u26a0\ufe0f \u53ef\u80fd\u627e\u4e0d\u5230\u5207\u70b9<\/td>\n\u2705 \u5168\u5c40\u4f18\u5316\u4ecd\u80fd\u627e\u5230\u6700\u4f73\u5206\u5272<\/td>\n<\/tr>\n
    \u77ed\u6587\u6863\uff08<50 \u53e5\uff09<\/td>\n\u2705 \u5feb\u901f<\/td>\n\u2705 \u8d28\u91cf\u66f4\u9ad8<\/td>\n<\/tr>\n
    \u957f\u6587\u6863\uff08>1 \u4e07\u53e5\uff09<\/td>\n\u2705 \u7ebf\u6027\u6269\u5c55<\/td>\n\u274c \u5185\u5b58\u7206\u70b8<\/td>\n<\/tr>\n
    \u53e5\u5b50\u5f88\u77ed<\/td>\n\u26a0\ufe0f \u9700\u8981 buffer \u8865\u5145\u4e0a\u4e0b\u6587<\/td>\n\u26a0\ufe0f \u77ed\u53e5 embedding \u8d28\u91cf\u5dee<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \u5982\u4f55\u9009\u62e9\uff1f<\/h3>\n\n\n\n\n\n\n\n\n\n
    \u4f60\u7684\u573a\u666f<\/th>\n\u63a8\u8350\u7b56\u7565<\/th>\n\u539f\u56e0<\/th>\n<\/tr>\n<\/thead>\n
    \u6587\u6863\u957f\u5ea6\u4e0d\u786e\u5b9a\uff0c\u9700\u8981\u901a\u7528\u65b9\u6848<\/td>\nKamradt<\/td>\n\u7ebf\u6027\u590d\u6742\u5ea6\uff0c\u4e0d\u4f1a\u7206\u5185\u5b58<\/td>\n<\/tr>\n
    \u6587\u6863\u8f83\u77ed\uff08<2000 \u53e5\uff09\uff0c\u8ffd\u6c42\u6700\u4f18\u5207\u5206<\/td>\nCluster<\/td>\n\u5168\u5c40\u6700\u4f18\uff0c\u8d28\u91cf\u66f4\u9ad8<\/td>\n<\/tr>\n
    Embedding API \u6309 token \u8ba1\u8d39<\/td>\nCluster<\/td>\n\u65e0 buffer \u81a8\u80c0\uff0ctoken \u6d88\u8017\u4f4e 7 \u500d<\/td>\n<\/tr>\n
    \u672c\u5730\u8ba1\u7b97\u8d44\u6e90\u6709\u9650<\/td>\nKamradt<\/td>\nO(N) \u8ba1\u7b97\uff0c\u5185\u5b58\u53cb\u597d<\/td>\n<\/tr>\n
    \u8bdd\u9898\u8fb9\u754c\u6a21\u7cca\uff0c\u9700\u8981\u7cbe\u786e\u5207\u5206<\/td>\nCluster<\/td>\nDP \u5168\u5c40\u4f18\u5316\u66f4\u9c81\u68d2<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \u4e3a\u4ec0\u4e48\u4e0d\u80fd\u7528\u5176\u4ed6\u65b9\u6cd5\u66ff\u4ee3 Embedding\uff1f<\/h2>\n\n\n\n\n\n\n\n
    \u66ff\u4ee3\u65b9\u6848<\/th>\n\u95ee\u9898<\/th>\n<\/tr>\n<\/thead>\n
    \u5173\u952e\u8bcd\u91cd\u53e0 \/ TF-IDF<\/td>\n\u65e0\u6cd5\u6355\u6349\u540c\u4e49\u8bcd\u548c\u4e0a\u4e0b\u6587\u8bed\u4e49\uff08”\u6c7d\u8f66”\u548c”\u8f66\u8f86”\u4f1a\u88ab\u8ba4\u4e3a\u4e0d\u76f8\u5173\uff09<\/td>\n<\/tr>\n
    \u89c4\u5219\u5206\u9694\u7b26\uff08\u6bb5\u843d\u3001\u53e5\u53f7\uff09<\/td>\n\u540c\u4e00\u6bb5\u843d\u53ef\u80fd\u5305\u542b\u591a\u4e2a\u8bdd\u9898\uff0c\u4e0d\u540c\u6bb5\u843d\u53ef\u80fd\u8ba8\u8bba\u540c\u4e00\u8bdd\u9898<\/td>\n<\/tr>\n
    LLM \u76f4\u63a5\u5224\u65ad<\/td>\n\u6210\u672c\u8fc7\u9ad8\uff0c\u5ef6\u8fdf\u5927\uff0c\u4e0d\u9002\u5408\u6279\u91cf\u5904\u7406\u6570\u4e07\u53e5\u5b50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Embedding \u5c06\u6587\u672c\u6620\u5c04\u5230\u9ad8\u7ef4\u8bed\u4e49\u7a7a\u95f4\uff0c\u8bed\u4e49\u76f8\u8fd1\u7684\u6587\u672c\u5411\u91cf\u8ddd\u79bb\u5c0f\uff0c\u8bed\u4e49\u4e0d\u540c\u7684\u6587\u672c\u5411\u91cf\u8ddd\u79bb\u5927\u3002\u8fd9\u662f\u76ee\u524d\u5728\u6210\u672c\u3001\u901f\u5ea6\u3001\u8d28\u91cf<\/strong>\u4e4b\u95f4\u6700\u4f18\u7684\u8bed\u4e49\u76f8\u4f3c\u5ea6\u5ea6\u91cf\u65b9\u5f0f\u3002<\/p>\n
    buffer_size\uff1a\u4e0a\u4e0b\u6587\u7a97\u53e3\u7684\u4f5c\u7528<\/h2>\n
    \u8bed\u4e49\u5206\u5757\u4e2d\u6709\u4e00\u4e2a\u5173\u952e\u53c2\u6570 buffer_size<\/code>\uff08\u9ed8\u8ba4\u503c 3\uff09\uff0c\u5b83\u51b3\u5b9a\u4e86\u4e3a\u6bcf\u4e2a\u53e5\u5b50\u751f\u6210 embedding \u65f6\u62fc\u63a5\u591a\u5c11\u4e0a\u4e0b\u6587\u3002<\/p>\n
    # \u62fc\u63a5\u903b\u8f91\u793a\u610f\nfor each sentence[i]:\n    combined = sentence[i-3] + sentence[i-2] + sentence[i-1]  # \u524d 3 \u53e5\n              + sentence[i]                                     # \u5f53\u524d\u53e5\n              + sentence[i+1] + sentence[i+2] + sentence[i+3]  # \u540e 3 \u53e5\n<\/code><\/pre>\n
    \u5173\u952e\u70b9\uff1abuffer_size \u4e0d\u5f71\u54cd Embedding \u8c03\u7528\u6b21\u6570\uff0c\u53ea\u5f71\u54cd\u6bcf\u6b21\u8f93\u5165\u7684\u6587\u672c\u957f\u5ea6\u3002<\/strong><\/p>\n
    \u4ee5 10 \u4e2a\u53e5\u5b50\u4e3a\u4f8b\uff0c\u65e0\u8bba buffer_size \u662f 1 \u8fd8\u662f 10\uff0c\u90fd\u662f\u5bf9 10 \u4e2a combined_sentence \u505a embedding\u3002\u533a\u522b\u5728\u4e8e\u6bcf\u4e2a\u6587\u672c\u5305\u542b\u7684\u4e0a\u4e0b\u6587\u591a\u5c11\uff1a<\/p>\n\n\n\n\n\n\n\n
    buffer_size<\/th>\n\u6bcf\u4e2a\u6587\u672c\u5e73\u5747\u5305\u542b\u53e5\u5b50\u6570<\/th>\n\u6548\u679c<\/th>\n<\/tr>\n<\/thead>\n
    1<\/td>\n~3 \u53e5<\/td>\n\u4e0a\u4e0b\u6587\u5c11\uff0c\u53ef\u80fd\u8bef\u5224<\/td>\n<\/tr>\n
    3\uff08\u9ed8\u8ba4\uff09<\/td>\n~7 \u53e5<\/td>\n\u5e73\u8861\u70b9<\/td>\n<\/tr>\n
    10<\/td>\n~21 \u53e5<\/td>\n\u4e0a\u4e0b\u6587\u4e30\u5bcc\uff0c\u4f46\u53ef\u80fd\u8d85\u51fa\u6a21\u578b token \u9650\u5236<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \u6ce8\u610f\uff1aEmbedding \u6a21\u578b\u6709\u8f93\u5165\u957f\u5ea6\u4e0a\u9650\uff08\u5982 BGE-M3 \u6700\u5927 8192 tokens\uff09\u3002buffer_size \u592a\u5927\u4f1a\u5bfc\u81f4\u6587\u672c\u88ab\u622a\u65ad\uff0c\u53cd\u800c\u4e22\u5931\u5f53\u524d\u53e5\u5b50\u7684\u4fe1\u606f\u3002<\/p>\n
    \u5927\u89c4\u6a21\u573a\u666f\u4e0b\u7684\u6027\u80fd\u8003\u91cf<\/h2>\n
    \u5047\u8bbe\u4e00\u7bc7\u957f\u6587\u6863\u88ab\u5207\u6210 10 \u4e07\u4e2a\u53e5\u5b50\uff1a<\/p>\n
      \n
    • \u9700\u8981 embed \u7684\u6587\u672c\u6570 = 100,000 \u4e2a<\/strong><\/li>\n
    • \u5982\u679c batch_size \u914d\u7f6e\u4e3a 500\uff0c\u5b9e\u9645 API \u8c03\u7528\u6b21\u6570 = 100,000 \u00f7 500 = 200 \u6b21 HTTP \u8bf7\u6c42<\/strong><\/li>\n<\/ul>\n
      \u6027\u80fd\u74f6\u9888\u5728 API \u8c03\u7528\u6b21\u6570\uff08\u7531\u53e5\u5b50\u603b\u6570\u548c batch_size \u51b3\u5b9a\uff09\uff0c\u4e0e buffer_size \u65e0\u5173\u3002<\/p>\n
      \u964d\u7ea7\u7b56\u7565\uff1aEmbedding \u4e0d\u53ef\u7528\u65f6\u600e\u4e48\u529e\uff1f<\/h2>\n
      \u597d\u7684\u7cfb\u7edf\u8bbe\u8ba1\u5e94\u8be5\u8003\u8651 Embedding \u670d\u52a1\u4e0d\u53ef\u7528\u7684\u60c5\u51b5\u3002\u5e38\u89c1\u505a\u6cd5\u662f\uff1a\u5f53 Embedding \u8c03\u7528\u5931\u8d25\u65f6\uff0c\u81ea\u52a8\u964d\u7ea7\u4e3a\u9012\u5f52\u5206\u5757\u7b56\u7565\uff08\u7eaf\u89c4\u5219\u5206\u5757\uff0c\u4e0d\u9700\u8981 Embedding\uff09\u3002<\/p>\n
      \u8fd9\u610f\u5473\u7740\u8bed\u4e49\u5206\u5757\u662f\u4e00\u79cd\u589e\u5f3a<\/strong>\u800c\u975e\u4f9d\u8d56<\/strong>\u2014\u2014\u6ca1\u6709 Embedding \u670d\u52a1\u65f6\u7cfb\u7edf\u4ecd\u7136\u53ef\u4ee5\u5de5\u4f5c\uff0c\u53ea\u662f\u5207\u5206\u8d28\u91cf\u4f1a\u4e0b\u964d\u3002<\/p>\n
      \u603b\u7ed3<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n
      \u95ee\u9898<\/th>\n\u7b54\u6848<\/th>\n<\/tr>\n<\/thead>\n
      \u4e3a\u4ec0\u4e48\u9700\u8981 Embedding\uff1f<\/td>\n\u5224\u65ad\u8bed\u4e49\u76f8\u4f3c\u5ea6\u9700\u8981\u5411\u91cf\u8868\u793a<\/td>\n<\/tr>\n
      \u80fd\u5426\u7528\u89c4\u5219\u66ff\u4ee3\uff1f<\/td>\n\u4e0d\u80fd\uff0c\u89c4\u5219\u65e0\u6cd5\u6355\u6349\u8bed\u4e49<\/td>\n<\/tr>\n
      \u80fd\u5426\u7528 LLM \u66ff\u4ee3\uff1f<\/td>\n\u7406\u8bba\u4e0a\u53ef\u4ee5\uff0c\u4f46\u6210\u672c\u548c\u5ef6\u8fdf\u4e0d\u53ef\u63a5\u53d7<\/td>\n<\/tr>\n
      Kamradt vs Cluster \u6838\u5fc3\u533a\u522b\uff1f<\/td>\n\u5c40\u90e8\u76f8\u90bb\u6bd4\u8f83 vs \u5168\u5c40\u6700\u4f18\u5206\u5272<\/td>\n<\/tr>\n
      \u54ea\u4e2a Embedding \u5f00\u9500\u66f4\u5927\uff1f<\/td>\nKamradt token \u6d88\u8017\u9ad8\uff08buffer \u81a8\u80c0\uff09\uff0cCluster \u8ba1\u7b97\u5f00\u9500\u9ad8\uff08N\u00b2\u77e9\u9635\uff09<\/td>\n<\/tr>\n
      \u5927\u89c4\u6a21\u6587\u6863\u9009\u54ea\u4e2a\uff1f<\/td>\nKamradt\u2014\u2014\u7ebf\u6027\u590d\u6742\u5ea6\uff0c\u4e0d\u4f1a\u7206\u5185\u5b58<\/td>\n<\/tr>\n
      \u8ffd\u6c42\u6700\u4f18\u5207\u5206\u9009\u54ea\u4e2a\uff1f<\/td>\nCluster\u2014\u2014\u5168\u5c40 DP \u4f18\u5316\uff0c\u4f46\u9650\u4e2d\u7b49\u957f\u5ea6\u6587\u6863<\/td>\n<\/tr>\n
      \u670d\u52a1\u4e0d\u53ef\u7528\u600e\u4e48\u529e\uff1f<\/td>\n\u4e24\u8005\u90fd\u964d\u7ea7\u4e3a\u89c4\u5219\u5206\u5757<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Embedding API \u662f\u8bed\u4e49\u5206\u5757\u7684”\u773c\u775b”\u2014\u2014\u6ca1\u6709\u5b83\uff0c\u5206\u5757\u7b97\u6cd5\u5c31\u662f\u4e00\u4e2a\u76f2\u4eba\u5728\u5207\u86cb\u7cd5\u3002\u4e24\u79cd\u7b56\u7565\u7528\u4e0d\u540c\u7684\u65b9\u5f0f”\u770b”\u6587\u672c\uff1aKamradt \u50cf\u9010\u884c\u626b\u63cf\u7684\u9605\u8bfb\u5668\uff0cCluster \u50cf\u4fef\u77b0\u5168\u6587\u7684\u7f16\u8f91\u3002\u9009\u62e9\u54ea\u79cd\uff0c\u53d6\u51b3\u4e8e\u4f60\u7684\u6587\u6863\u89c4\u6a21\u548c\u5bf9\u5207\u5206\u8d28\u91cf\u7684\u8981\u6c42\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
      \u56fa\u5b9a\u957f\u5ea6\u5206\u5757\u4e0d\u9700\u8981\u4efb\u4f55\u5916\u90e8\u670d\u52a1\uff0c\u8bed\u4e49\u5206\u5757\u5374\u5fc5\u987b\u8c03\u7528 Embedding API\u2014\u2014\u8fd9\u80cc\u540e\u7684\u539f\u56e0\u662f\u4ec0\u4e48\uff1f \u5148\u8bf4\u7ed3 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[66],"tags":[81,61,84,83,85,82,80],"class_list":["post-64","post","type-post","status-publish","format-standard","hentry","category-graphrag","tag-embedding","tag-rag","tag-84","tag-83","tag-85","tag-82","tag-80"],"_links":{"self":[{"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/posts\/64","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/comments?post=64"}],"version-history":[{"count":0,"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/posts\/64\/revisions"}],"wp:attachment":[{"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/media?parent=64"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/categories?post=64"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/genetop.top\/index.php\/wp-json\/wp\/v2\/tags?post=64"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}