{"id":91107,"date":"2026-08-06T22:24:40","date_gmt":"2026-08-06T14:24:40","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/91107.html"},"modified":"2026-08-06T22:24:40","modified_gmt":"2026-08-06T14:24:40","slug":"rag-%e5%90%91%e9%87%8f%e6%a3%80%e7%b4%a2%ef%bc%9a%e4%bb%8e%e6%9f%a5%e5%ad%97%e5%88%b0%e6%9f%a5%e6%84%8f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/91107.html","title":{"rendered":"RAG \u5411\u91cf\u68c0\u7d22\uff1a\u4ece\u201c\u67e5\u5b57\u201c\u5230\u201c\u67e5\u610f\u201c"},"content":{"rendered":"

LLM \u5f88\u806a\u660e，\u4f46\u5b83\u6709\u4e00\u4e2a\u786c\u4f24：\u77e5\u8bc6\u622a\u6b62\u5230\u8bad\u7ec3\u90a3\u5929\u3002\u4f60\u4eca\u5929\u95ee\u5b83"\u516c\u53f8\u4e0a\u5468\u66f4\u65b0\u7684\u62a5\u9500\u6d41\u7a0b"，\u5b83\u53ea\u80fd\u778e\u7f16\u3002RAG（Retrieval-Augmented Generation，\u68c0\u7d22\u589e\u5f3a\u751f\u6210）\u5c31\u662f\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898：\u5148\u68c0\u7d22\u5916\u90e8\u77e5\u8bc6，\u518d\u8ba9 LLM \u57fa\u4e8e\u68c0\u7d22\u7ed3\u679c\u751f\u6210\u7b54\u6848\u3002\u800c RAG \u7684\u68c0\u7d22\u73af\u8282，\u6838\u5fc3\u9760\u7684\u5c31\u662f\u5411\u91cf\u68c0\u7d22\u3002<\/p>\n

\u8fd9\u7bc7\u6587\u7ae0\u4ece\u96f6\u8bb2\u6e05\u695a：\u4ec0\u4e48\u662f\u5411\u91cf\u68c0\u7d22\u3001Embedding \u5e72\u4e86\u4ec0\u4e48\u3001\u76f8\u4f3c\u5ea6\u600e\u4e48\u7b97\u3001\u5927\u89c4\u6a21\u68c0\u7d22\u600e\u4e48\u52a0\u901f，\u4ee5\u53ca\u5b83\u5728 Agent \u91cc\u5230\u5e95\u957f\u4ec0\u4e48\u6837\u3002<\/p>\n

\u4e00\u3001\u4f20\u7edf\u641c\u7d22 vs \u5411\u91cf\u68c0\u7d22：\u4e3a\u4ec0\u4e48"\u540c\u4e49\u8bcd"\u662f\u4e2a\u5927\u9ebb\u70e6<\/h3>\n

\u4f20\u7edf\u641c\u7d22\u5f15\u64ce（\u6bd4\u5982\u6570\u636e\u5e93\u91cc LIKE '%\u5173\u952e\u8bcd%' \u6216\u5012\u6392\u7d22\u5f15\u7684 BM25）\u7684\u6838\u5fc3\u903b\u8f91\u662f\u5b57\u9762\u5339\u914d\u3002\u4f60\u7528"\u8c03\u85aa"\u53bb\u641c，\u6587\u6863\u91cc\u5199\u7684\u662f"\u85aa\u916c\u8c03\u6574\u6d41\u7a0b"\u2014\u2014\u4e00\u4e2a\u8bcd\u5bf9\u4e0d\u4e0a，\u76f4\u63a5\u641c\u4e0d\u5230\u3002<\/p>\n

\u8fd9\u53eb\u8bcd\u6c47\u9e3f\u6c9f（lexical gap）：\u4f60\u8bf4\u7684\u8bdd\u548c\u6587\u6863\u91cc\u5199\u7684\u8bdd\u7528\u7684\u662f\u4e0d\u540c\u8bcd，\u4f46\u610f\u601d\u4e00\u6837\u3002\u4f20\u7edf\u641c\u7d22\u5bf9\u6b64\u6beb\u65e0\u529e\u6cd5\u2014\u2014\u5b83\u6ca1\u6709"\u8bed\u4e49\u7406\u89e3"\u80fd\u529b，\u53ea\u6709"\u5b57\u7b26\u4e32\u5339\u914d"\u80fd\u529b\u3002<\/p>\n

\u5411\u91cf\u68c0\u7d22\u6362\u4e86\u4e00\u4e2a\u601d\u8def：\u4e0d\u627e"\u5b57"，\u627e"\u610f\u601d"\u3002\u5b83\u628a\u67e5\u8be2\u548c\u6587\u6863\u90fd\u8f6c\u6210\u4e00\u4e32\u6570\u5b57（\u5411\u91cf），\u7136\u540e\u5728\u6570\u5b66\u7a7a\u95f4\u91cc\u7b97\u8ddd\u79bb\u3002\u8bed\u4e49\u76f8\u8fd1 \u2192 \u8ddd\u79bb\u8fd1 \u2192 \u547d\u4e2d\u3002\u4f60\u8bf4"\u600e\u4e48\u8c03\u85aa"，\u6587\u6863\u91cc\u6709"\u85aa\u916c\u8c03\u6574\u6d41\u7a0b"，\u5b83\u4eec\u5728\u5411\u91cf\u7a7a\u95f4\u91cc\u7d27\u6328\u7740\u2014\u2014\u54ea\u6015\u5b57\u9762\u4e0a\u5b8c\u5168\u4e0d\u540c\u3002<\/p>\n\n\u5bf9\u6bd4\u7ef4\u5ea6\u4f20\u7edf\u5173\u952e\u8bcd\u641c\u7d22\u5411\u91cf\u68c0\u7d22<\/tr>\n\n\n\n\n\n
\u5339\u914d\u4f9d\u636e<\/td>\n\u5b57\u9762\u547d\u4e2d（TF-IDF\/BM25）<\/td>\n\u8bed\u4e49\u8ddd\u79bb（\u4f59\u5f26\u76f8\u4f3c\u5ea6\u7b49）<\/td>\n<\/tr>\n
\u201c\u8c03\u85aa"\u641c"\u85aa\u916c\u8c03\u6574\u201d<\/td>\n\u641c\u4e0d\u5230<\/td>\n\u80fd\u641c\u5230，\u5411\u91cf\u8ddd\u79bb\u8fd1<\/td>\n<\/tr>\n
\u641c\u4eba\u540d\u3001\u578b\u53f7\u3001\u7f16\u53f7<\/td>\n\u7cbe\u51c6<\/td>\n\u53ef\u80fd\u6f02\u79fb<\/td>\n<\/tr>\n
\u641c"\u5927\u6982\u610f\u601d"<\/td>\n\u65e0\u80fd\u4e3a\u529b<\/td>\n\u5f3a\u9879<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e00\u53e5\u8bdd：\u4f20\u7edf\u641c\u7d22\u50cf\u5728\u627e\u5b57，\u5411\u91cf\u68c0\u7d22\u50cf\u5728\u627e\u610f\u3002\u6210\u719f\u7684\u7cfb\u7edf\u90fd\u4f1a\u505a\u6df7\u5408\u68c0\u7d22（Hybrid Search）\u2014\u2014\u4e24\u8def\u7ed3\u679c\u878d\u5408，\u4e92\u8865\u77ed\u677f\u3002<\/p>\n

\u4e8c\u3001Embedding：\u628a\u6587\u5b57\u53d8\u6210"\u8bed\u4e49\u5750\u6807"<\/h3>\n

\u4e3a\u4ec0\u4e48\u9700\u8981 Embedding<\/h4>\n

\u8ba1\u7b97\u673a\u53ea\u8ba4\u8bc6\u6570\u5b57\u3002\u60f3\u8ba9\u8ba1\u7b97\u673a\u5224\u65ad"\u732b\u5728\u6c99\u53d1\u4e0a\u7761\u89c9"\u548c"\u732b\u54aa\u5728\u6c99\u53d1\u4e0a\u6253\u76f9"\u610f\u601d\u76f8\u8fd1，\u5f97\u5148\u628a\u8fd9\u4e24\u53e5\u8bdd\u90fd\u53d8\u6210\u6570\u5b57\u3002Embedding \u5c31\u662f\u8fd9\u4e2a\u8f6c\u6362\u8fc7\u7a0b\u2014\u2014\u628a\u6587\u672c\u6620\u5c04\u5230\u4e00\u4e2a\u9ad8\u7ef4\u5411\u91cf\u7a7a\u95f4\u91cc\u7684\u4e00\u4e2a\u70b9\u3002<\/p>\n

\u8f93\u5165\u4e00\u6bb5\u6587\u5b57，Embedding \u6a21\u578b\u8f93\u51fa\u4e00\u4e2a\u56fa\u5b9a\u957f\u5ea6\u7684\u6d6e\u70b9\u6570\u6570\u7ec4，\u6bd4\u5982 OpenAI \u7684 text-embedding-3-small \u5410\u51fa 1536 \u4e2a\u6d6e\u70b9\u6570：<\/p>\n

[0.012, -0.453, 0.891, -0.023, …, 0.147] # \u5171 1536 \u7ef4<\/p>\n

\u4f60\u6ca1\u6cd5\u60f3\u8c61 1536 \u7ef4\u7a7a\u95f4\u957f\u4ec0\u4e48\u6837（\u4eba\u7c7b\u5927\u8111\u8d85\u8fc7\u4e09\u7ef4\u5c31\u6293\u778e\u4e86），\u4f46\u6570\u5b66\u4e0a\u5b8c\u5168\u5408\u7406\u3002Embedding \u6a21\u578b\u5728\u8bad\u7ec3\u65f6\u5c31\u662f\u9760\u6d77\u91cf\u8bed\u6599\u5b66\u5230\u4e86\u4e00\u4e2a\u89c4\u5f8b：\u628a\u8bed\u4e49\u76f8\u8fd1\u7684\u6587\u5b57\u653e\u5728\u9ad8\u7ef4\u7a7a\u95f4\u4e2d\u76f8\u90bb\u7684\u4f4d\u7f6e\u3002<\/p>\n

\u7ecf\u5178\u7c7b\u6bd4：\u5411\u91cf\u7b97\u672f<\/h4>\n

Embedding \u7684\u5999\u5904\u8fd8\u4e0d\u6b62\u4e8e\u6b64\u3002\u7ecf\u5178\u5b9e\u9a8c："\u56fd\u738b" – "\u7537\u4eba" + "\u5973\u4eba" \u2248 "\u5973\u738b"\u3002\u5411\u91cf\u53ef\u4ee5\u505a\u52a0\u51cf\u6cd5，\u800c\u4e14\u7ed3\u679c\u8fd8\u7b26\u5408\u4eba\u7c7b\u76f4\u89c9\u2014\u2014\u8fd9\u8bf4\u660e\u5411\u91cf\u786e\u5b9e\u7f16\u7801\u4e86\u8bed\u4e49\u4fe1\u606f，\u4e0d\u53ea\u662f\u968f\u673a\u6570\u5b57\u3002<\/p>\n

\u4e3b\u6d41 Embedding \u6a21\u578b\u4e00\u89c8<\/h4>\n\n\u6a21\u578b\u7ef4\u5ea6\u7279\u70b9<\/tr>\n\n\n\n\n\n\n
OpenAI text-embedding-3-small<\/td>\n1536<\/td>\n\u901a\u7528\u6027\u5f3a，\u6027\u4ef7\u6bd4\u9ad8，\u4e2d\u6587\u652f\u6301\u4e0d\u9519<\/td>\n<\/tr>\n
OpenAI text-embedding-3-large<\/td>\n3072<\/td>\n\u7cbe\u5ea6\u66f4\u9ad8，\u7ef4\u5ea6\u66f4\u5927，\u9002\u5408\u9ad8\u8981\u6c42\u573a\u666f<\/td>\n<\/tr>\n
BGE (BAAI)<\/td>\n768\/1024<\/td>\n\u5f00\u6e90，\u4e2d\u6587\u4f18\u5316，\u81ea\u90e8\u7f72\u514d API \u8d39\u7528<\/td>\n<\/tr>\n
Jina Embeddings v3<\/td>\n1024<\/td>\n\u591a\u8bed\u8a00，\u652f\u6301 8192 token \u957f\u6587\u672c\u8f93\u5165<\/td>\n<\/tr>\n
Cohere Embed v3<\/td>\n1024<\/td>\n\u652f\u6301\u591a\u79cd\u8f93\u5165\u7c7b\u578b（\u641c\u7d22\u6587\u6863\/\u641c\u7d22\u67e5\u8be2\u7b49）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u9009\u6a21\u578b\u7684\u6838\u5fc3\u539f\u5219：\u5165\u5e93\u548c\u68c0\u7d22\u5fc5\u987b\u7528\u540c\u4e00\u4e2a Embedding \u6a21\u578b\u3002\u4e0d\u540c\u6a21\u578b\u4ea7\u51fa\u7684\u5411\u91cf\u7a7a\u95f4\u4e0d\u4e00\u81f4，\u6362\u6a21\u578b = \u63a8\u5012\u91cd\u6765\u3002<\/p>\n

\u4e09\u3001\u76f8\u4f3c\u5ea6：\u600e\u4e48\u5224\u65ad\u4e24\u4e2a\u5411\u91cf"\u9760\u5f97\u8fd1"<\/h3>\n

\u5411\u91cf\u68c0\u7d22\u6700\u540e\u4e00\u6b65，\u5c31\u662f\u62ff\u7528\u6237 query \u7684\u5411\u91cf，\u53bb\u5411\u91cf\u5e93\u91cc\u627e\u8ddd\u79bb\u6700\u8fd1\u7684 K \u4e2a\u6587\u6863\u5411\u91cf\u3002\u8fd9\u91cc"\u8ddd\u79bb"\u6709\u4e09\u79cd\u5e38\u7528\u5ea6\u91cf：<\/p>\n

\u4f59\u5f26\u76f8\u4f3c\u5ea6（Cosine Similarity）<\/h4>\n

\u6700\u901a\u7528\u3002\u8ba1\u7b97\u4e24\u4e2a\u5411\u91cf\u5939\u89d2\u7684\u4f59\u5f26\u503c，\u8303\u56f4 [-1, 1]\u3002\u53ea\u770b\u65b9\u5411\u4e0d\u770b\u957f\u5ea6，\u6240\u4ee5\u957f\u7684\u6587\u6863\u548c\u77ed\u7684 query \u4e5f\u80fd\u516c\u5e73\u6bd4\u8f83\u3002<\/p>\n

\u4f59\u5f26\u76f8\u4f3c\u5ea6 = (A \u00b7 B) \/ (||A|| \u00d7 ||B||)<\/p>\n

\u6b27\u6c0f\u8ddd\u79bb（Euclidean Distance）<\/h4>\n

\u4e24\u70b9\u4e4b\u95f4\u76f4\u7ebf\u8ddd\u79bb\u3002\u5f53\u5411\u91cf\u5df2\u7ecf L2 \u5f52\u4e00\u5316\u540e，\u6b27\u6c0f\u8ddd\u79bb\u548c\u4f59\u5f26\u76f8\u4f3c\u5ea6\u7b49\u4ef7\u3002<\/p>\n

\u70b9\u79ef（Dot Product）<\/h4>\n

\u4e24\u4e2a\u5411\u91cf\u9010\u5143\u7d20\u76f8\u4e58\u518d\u6c42\u548c\u3002\u5411\u91cf\u5f52\u4e00\u5316\u540e = \u4f59\u5f26\u76f8\u4f3c\u5ea6\u3002\u9002\u5408\u9700\u8981\u52a0\u6743\u6392\u5e8f\u7684\u573a\u666f\u3002<\/p>\n

\u5b9e\u9645\u5de5\u7a0b\u91cc 90% \u7684\u573a\u666f\u7528\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5c31\u5bf9\u4e86\u3002\u63a8\u7406\u5f00\u9500\u5c0f，\u8bed\u4e49\u8868\u8fbe\u529b\u8db3\u591f\u3002<\/p>\n

\u56db\u3001ANN \u52a0\u901f：\u767e\u4e07\u5411\u91cf\u600e\u4e48\u6beb\u79d2\u7ea7\u641c\u5b8c<\/h3>\n

\u4e3a\u4ec0\u4e48\u8981\u8fd1\u4f3c<\/h4>\n

\u5982\u679c\u66b4\u529b\u641c\u7d22（kNN）\u2014\u2014\u62ff query \u5411\u91cf\u548c\u5e93\u91cc\u6bcf\u4e2a\u5411\u91cf\u90fd\u7b97\u4e00\u904d\u8ddd\u79bb\u518d\u6392\u5e8f\u2014\u2014\u590d\u6742\u5ea6\u662f O(N)\u3002N = \u5341\u4e07\u7684\u65f6\u5019\u8fd8\u884c，N = \u767e\u4e07\u3001\u5343\u4e07\u5c31\u5d29\u4e86：\u6bcf\u6b21\u67e5\u8be2\u8981\u8dd1\u51e0\u767e\u4e07\u6b21\u6d6e\u70b9\u8fd0\u7b97，\u5ef6\u8fdf\u79d2\u7ea7\u8d77\u6b65\u3002<\/p>\n

\u6240\u4ee5\u5b9e\u9645\u7528\u7684\u4e0d\u662f\u7cbe\u786e\u641c\u7d22，\u800c\u662f ANN（Approximate Nearest Neighbor，\u8fd1\u4f3c\u6700\u8fd1\u90bb）\u2014\u2014\u727a\u7272\u4e00\u4e01\u70b9\u7cbe\u5ea6（\u6bd4\u5982\u4ece"100% \u53ec\u56de"\u964d\u5230 \u201c99.5% \u53ec\u56de\u201d），\u6362\u56de\u51e0\u5341\u4e0a\u767e\u500d\u7684\u52a0\u901f\u3002<\/p>\n

\u4e24\u5927\u4e3b\u6d41\u7b97\u6cd5<\/h4>\n

IVF（Inverted File，\u5012\u6392\u6587\u4ef6）：\u5148\u628a\u6240\u6709\u5411\u91cf\u7528 K-Means \u805a\u6210 N \u4e2a\u6876（\u7c07）\u3002\u67e5\u8be2\u65f6\u53ea\u641c\u79bb query \u6700\u8fd1\u7684\u51e0\u4e2a\u6876，\u6876\u5185\u505a\u66b4\u529b\u641c\u7d22\u3002\u6838\u5fc3\u601d\u60f3\u662f"\u5148\u7c97\u7b5b\u518d\u7ec6\u67e5"\u3002<\/p>\n

\u5173\u952e\u53c2\u6570：<\/p>\n

    \n
  • nlist：\u5206\u591a\u5c11\u4e2a\u6876\u3002\u8d8a\u5927\u8d8a\u7ec6，\u4f46\u5efa\u7d22\u5f15\u8d8a\u6162\u3002<\/li>\n
  • nprobe：\u67e5\u51e0\u4e2a\u6876\u3002\u8d8a\u5927\u53ec\u56de\u8d8a\u9ad8，\u4f46\u8d8a\u6162\u3002<\/li>\n<\/ul>\n

    HNSW（Hierarchical Navigable Small World，\u5206\u5c42\u53ef\u5bfc\u822a\u5c0f\u4e16\u754c\u56fe）：\u76ee\u524d\u7cbe\u5ea6\u548c\u901f\u5ea6\u7684\u6807\u6746\u3002\u6784\u5efa\u4e00\u4e2a\u5206\u5c42\u56fe\u7ed3\u6784\u2014\u2014\u4e0a\u5c42\u7a00\u758f\u7528\u4e8e"\u5bfc\u822a\u5230\u5927\u81f4\u533a\u57df"，\u4e0b\u5c42\u7a20\u5bc6\u7528\u4e8e"\u7cbe\u786e\u5b9a\u4f4d"\u3002\u641c\u7d22\u65f6\u4ece\u9876\u5c42\u9010\u5c42\u4e0b\u964d，\u7c7b\u4f3c\u5728\u591a\u5c42\u505c\u8f66\u573a\u91cc\u4ece\u9876\u5c42\u4fef\u77b0\u9010\u6b65\u627e\u5230\u8f66\u4f4d\u3002<\/p>\n

    \u5173\u952e\u53c2\u6570：<\/p>\n

      \n
    • M：\u6bcf\u4e2a\u8282\u70b9\u7684\u6700\u5927\u8fde\u63a5\u6570（16~64）\u3002\u8d8a\u5927\u8d8a\u7cbe\u786e，\u4f46\u5185\u5b58\u4e5f\u8d8a\u5927\u3002<\/li>\n
    • efSearch：\u641c\u7d22\u65f6\u8003\u5bdf\u7684\u5019\u9009\u6570（50~200）\u3002\u8d8a\u5927\u53ec\u56de\u8d8a\u9ad8，\u4f46\u8d8a\u6162\u3002<\/li>\n<\/ul>\n\n\u5bf9\u6bd4\u7ef4\u5ea6IVFHNSW<\/tr>\n\n\n\n\n\n\n
      \u53ec\u56de\u7cbe\u5ea6<\/td>\n\u9ad8，\u4f46\u4f9d\u8d56 nprobe \u8c03\u53c2<\/td>\n\u6781\u9ad8，\u901a\u5e38\u4f18\u4e8e IVF<\/td>\n<\/tr>\n
      \u68c0\u7d22\u901f\u5ea6<\/td>\n\u5feb<\/td>\n\u6781\u5feb，\u63a5\u8fd1 O(log N)<\/td>\n<\/tr>\n
      \u6784\u5efa\u901f\u5ea6<\/td>\n\u5feb（\u53ea\u805a\u7c7b）<\/td>\n\u6162（\u9010\u70b9\u5efa\u56fe）<\/td>\n<\/tr>\n
      \u5185\u5b58\u5360\u7528<\/td>\n\u9ad8（\u5b58\u5168\u90e8\u539f\u59cb\u5411\u91cf）<\/td>\n\u9ad8（\u5b58\u56fe\u7ed3\u6784 + \u5411\u91cf）<\/td>\n<\/tr>\n
      \u9002\u5408\u573a\u666f<\/td>\n\u5e73\u8861\u901f\u5ea6\u4e0e\u7cbe\u5ea6\u7684\u901a\u7528\u573a\u666f<\/td>\n\u9ad8\u8981\u6c42\u573a\u666f（RAG\u3001\u63a8\u8350\u7cfb\u7edf）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u4ee3\u8868\u5411\u91cf\u6570\u636e\u5e93<\/h4>\n\n\u6570\u636e\u5e93\u6838\u5fc3\u7b97\u6cd5\u7279\u70b9<\/tr>\n\n\n\n\n\n\n
      FAISS (Meta)<\/td>\nIVF, HNSW, PQ<\/td>\n\u5e93\u800c\u975e\u6570\u636e\u5e93，\u6781\u9ad8\u6027\u80fd，GPU \u52a0\u901f<\/td>\n<\/tr>\n
      Milvus<\/td>\nIVF, HNSW<\/td>\n\u5206\u5e03\u5f0f\u67b6\u6784，\u652f\u6301\u5341\u4ebf\u7ea7<\/td>\n<\/tr>\n
      Qdrant<\/td>\nHNSW<\/td>\n\u8fc7\u6ee4\u529f\u80fd\u4e30\u5bcc，Rust \u5b9e\u73b0\u6027\u80fd\u597d<\/td>\n<\/tr>\n
      Chroma<\/td>\nHNSW<\/td>\n\u8f7b\u91cf\u6613\u7528，\u9002\u5408\u539f\u578b\u548c\u4e2d\u5c0f\u89c4\u6a21<\/td>\n<\/tr>\n
      pgvector<\/td>\nIVF, HNSW<\/td>\nPostgreSQL \u6269\u5c55，\u548c\u4e1a\u52a1\u5e93\u540c\u5b58<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u4e94\u3001\u5206\u5757\u7b56\u7565：\u5207\u591a\u5927\u624d\u5408\u9002<\/h3>\n

      Embedding \u6a21\u578b\u518d\u597d，\u5206\u5757\u6ca1\u641e\u5bf9，\u68c0\u7d22\u7167\u6837\u70c2\u3002\u8fd9\u662f\u5de5\u7a0b\u4e2d\u6700\u5bb9\u6613\u88ab\u4f4e\u4f30\u7684\u73af\u8282\u3002<\/p>\n

      \u6838\u5fc3\u77db\u76fe<\/h4>\n
        \n
      • \u5207\u592a\u5927（\u6bd4\u5982 2048 token）：\u4e00\u6bb5\u91cc\u585e\u4e86\u4e09\u4e2a\u4e0d\u540c\u8bdd\u9898，Embedding \u5411\u91cf\u53d8\u6210\u4e00\u4e2a"\u6742\u7cc5\u8bed\u4e49\u70b9"，\u641c\u4ec0\u4e48\u90fd\u80fd\u547d\u4e2d\u4e00\u70b9，\u4f46\u7cbe\u51c6\u5ea6\u6781\u5dee\u3002<\/li>\n
      • \u5207\u592a\u5c0f（\u6bd4\u5982 128 token）：\u4e00\u53e5\u5b8c\u6574\u7684\u8bdd\u88ab\u8170\u65a9，\u4e0a\u4e0b\u6587\u4e22\u5931，LLM \u62ff\u5230\u788e\u7247\u7b54\u4e0d\u51fa\u5b8c\u6574\u7b54\u6848\u3002<\/li>\n<\/ul>\n

        \u5de5\u7a0b\u4e0a\u7684\u7ecf\u9a8c\u503c<\/h4>\n
          \n
        • \u901a\u7528\u573a\u666f：512 token \u4e3a\u4e00\u5757，\u76f8\u90bb\u5757\u91cd\u53e0 10%~15%（overlap）\u3002\u8fd9\u6837\u5173\u952e\u53e5\u843d\u5728\u8fb9\u754c\u5904\u4e5f\u4e0d\u88ab\u5207\u5f00\u3002<\/li>\n
        • \u6280\u672f\u6587\u6863\/\u4ee3\u7801：\u6309\u81ea\u7136\u8fb9\u754c\u5207\u2014\u2014\u51fd\u6570\u7ea7\u3001\u6bb5\u843d\u7ea7\u3001\u7ae0\u8282\u7ea7\u3002\u4e0d\u8981\u673a\u68b0\u6309\u5b57\u7b26\u6570\u5207\u3002<\/li>\n
        • QA \u573a\u666f：\u6bcf\u4e2a Q&A \u5bf9\u72ec\u7acb\u6210\u5757\u3002<\/li>\n<\/ul>\n

          # chunking_config.py \u2014 \u5178\u578b\u5206\u5757\u914d\u7f6e<\/span>
          \nfrom<\/span> langchain.<\/span>text_splitter import<\/span> RecursiveCharacterTextSplitter<\/p>\n

          splitter =<\/span> RecursiveCharacterTextSplitter(<\/span>
          \n chunk_size=<\/span>500<\/span>,<\/span> # \u6bcf\u5757 500 \u5b57\u7b26<\/span>
          \n chunk_overlap=<\/span>50<\/span>,<\/span> # \u76f8\u90bb\u5757\u91cd\u53e0 50 \u5b57\u7b26（10%）<\/span>
          \n separators=<\/span>[<\/span>"\\\\n\\\\n"<\/span>,<\/span> "\\\\n"<\/span>,<\/span> "\u3002"<\/span>,<\/span> "."<\/span>,<\/span> " "<\/span>]<\/span> # \u4f18\u5148\u6309\u81ea\u7136\u8fb9\u754c\u5207<\/span>
          \n)<\/span>
          \nchunks =<\/span> splitter.<\/span>create_documents(<\/span>[<\/span>document]<\/span>)<\/span><\/p>\n

          \u5206\u5757\u540c\u65f6\u8981\u5e26\u4e0a\u5143\u6570\u636e：\u6765\u6e90\u6587\u6863\u540d\u3001\u7ae0\u8282\u6807\u9898\u3001\u65f6\u95f4\u6233\u3001\u7248\u672c\u53f7\u3002\u68c0\u7d22\u65f6\u53ef\u4ee5\u7528\u5143\u6570\u636e\u505a\u8fc7\u6ee4（\u201c\u53ea\u641c 2024 \u5e74\u7684\u6587\u6863\u201d），\u7cbe\u5ea6\u5927\u5e45\u63d0\u5347\u3002<\/p>\n

          \u516d\u3001Rerank：\u5c11\u82b1\u94b1\u3001\u591a\u529e\u4e8b<\/h3>\n

          \u5411\u91cf\u68c0\u7d22\u62c9\u56de top-20 \u4e2a\u5019\u9009\u5757，\u76f4\u63a5\u5168\u585e\u7ed9 LLM？token \u6d6a\u8d39\u4e0d\u8bf4，\u540e\u9762\u5341\u51e0\u6761\u53ef\u80fd\u662f\u566a\u97f3\u3002<\/p>\n

          \u4e24\u9636\u6bb5\u68c0\u7d22\u662f\u6027\u4ef7\u6bd4\u6700\u4f18\u89e3：<\/p>\n

        • \u7c97\u7b5b（\u5411\u91cf\u68c0\u7d22）：\u4ece\u5168\u5e93\u62c9 top-20 ~ top-50<\/li>\n
        • \u7cbe\u6392（Rerank \u6a21\u578b）：\u7528\u4e00\u4e2a\u4e13\u95e8\u7684\u91cd\u6392\u5e8f\u6a21\u578b\u5bf9\u5019\u9009\u96c6\u6253\u5206，\u53d6 top-3 ~ top-5 \u5582 LLM<\/li>\n

          Rerank \u6a21\u578b\u6bd4 Embedding \u6a21\u578b\u66f4"\u8d35"\u4f46\u66f4\u51c6\u2014\u2014\u5b83\u4f1a\u628a query \u548c\u5019\u9009\u6587\u6863\u9010\u5bf9\u7cbe\u7ec6\u6bd4\u5bf9，\u800c\u4e0d\u662f\u53ea\u7b97\u4e00\u6b21\u5411\u91cf\u8ddd\u79bb\u3002Cohere Rerank\u3001BGE-Reranker \u662f\u5e38\u7528\u9009\u62e9\u3002<\/p>\n

          # retrieval.py \u2014 \u4e24\u9636\u6bb5\u68c0\u7d22\u4f2a\u4ee3\u7801<\/span>
          \ndef<\/span> retrieve_and_rerank<\/span>(<\/span>query:<\/span> str<\/span>,<\/span> top_k:<\/span> int<\/span> =<\/span> 20<\/span>,<\/span> final_k:<\/span> int<\/span> =<\/span> 3<\/span>)<\/span> –<\/span>><\/span> list<\/span>[<\/span>str<\/span>]<\/span>:<\/span>
          \n # \u9636\u6bb51：\u5411\u91cf\u7c97\u7b5b<\/span>
          \n query_vec =<\/span> embed(<\/span>query)<\/span>
          \n candidates =<\/span> vector_db.<\/span>search(<\/span>query_vec,<\/span> top_k=<\/span>top_k)<\/span><\/p>\n

          # \u9636\u6bb52：Rerank \u7cbe\u6392<\/span>
          \n scores =<\/span> reranker.<\/span>compute_scores(<\/span>query,<\/span> [<\/span>c.<\/span>text for<\/span> c in<\/span> candidates]<\/span>)<\/span>
          \n ranked =<\/span> sorted<\/span>(<\/span>zip<\/span>(<\/span>candidates,<\/span> scores)<\/span>,<\/span> key=<\/span>lambda<\/span> x:<\/span> x[<\/span>1<\/span>]<\/span>,<\/span> reverse=<\/span>True<\/span>)<\/span><\/p>\n

          return<\/span> [<\/span>c.<\/span>text for<\/span> c,<\/span> _ in<\/span> ranked[<\/span>:<\/span>final_k]<\/span>]<\/span><\/p>\n

          \u4e03\u3001\u5728 Agent \u4e2d\u957f\u4ec0\u4e48\u6837<\/h3>\n

          Agent \u7528 RAG \u4e0d\u9700\u8981\u628a\u6bcf\u4e2a\u7ec6\u8282\u90fd\u81ea\u5df1\u624b\u5199，\u6210\u719f\u7684\u6846\u67b6\u5df2\u7ecf\u5c01\u88c5\u597d\u4e86\u3002\u4ee5\u4f60\u6b63\u5728\u5b66\u7684 LangChain \u4e3a\u4f8b：<\/p>\n

          # agent_rag_pipeline.py \u2014 Agent \u4e2d RAG \u7684\u6700\u5c0f\u95ed\u73af<\/span>
          \nfrom<\/span> langchain.<\/span>document_loaders import<\/span> TextLoader
          \nfrom<\/span> langchain.<\/span>text_splitter import<\/span> RecursiveCharacterTextSplitter
          \nfrom<\/span> langchain.<\/span>embeddings import<\/span> OpenAIEmbeddings
          \nfrom<\/span> langchain.<\/span>vectorstores import<\/span> Chroma<\/p>\n

          # 1. \u52a0\u8f7d\u6587\u6863 + \u5206\u5757<\/span>
          \nloader =<\/span> TextLoader(<\/span>"company_policy.txt"<\/span>)<\/span>
          \ndocs =<\/span> loader.<\/span>load(<\/span>)<\/span>
          \nchunks =<\/span> RecursiveCharacterTextSplitter(<\/span>
          \n chunk_size=<\/span>500<\/span>,<\/span> chunk_overlap=<\/span>50<\/span>
          \n)<\/span>.<\/span>split_documents(<\/span>docs)<\/span><\/p>\n

          # 2. \u5411\u91cf\u5316 + \u5b58\u5165\u5411\u91cf\u5e93<\/span>
          \nembeddings =<\/span> OpenAIEmbeddings(<\/span>model=<\/span>"text-embedding-3-small"<\/span>)<\/span>
          \nvectorstore =<\/span> Chroma.<\/span>from_documents(<\/span>chunks,<\/span> embeddings)<\/span><\/p>\n

          # 3. \u4f5c\u4e3a\u68c0\u7d22\u5668\u63a5\u5165 Agent<\/span>
          \nretriever =<\/span> vectorstore.<\/span>as_retriever(<\/span>search_kwargs=<\/span>{<\/span>"k"<\/span>:<\/span> 5<\/span>}<\/span>)<\/span><\/p>\n

          # 4. Agent \u8c03\u7528\u65f6，retriever \u81ea\u52a8\u641c\u76f8\u5173\u6587\u6863\u6ce8\u5165\u4e0a\u4e0b\u6587<\/span><\/p>\n

          \u5b8c\u6574\u94fe\u8def：\u7528\u6237\u95ee \u2192 Agent \u51b3\u7b56 \u2192 \u8c03 retriever \u641c\u5411\u91cf\u5e93 \u2192 \u62ff top-k \u6587\u6863 \u2192 \u62fc prompt \u2192 LLM \u751f\u6210 \u2192 \u8fd4\u56de\u7b54\u6848\u3002<\/p>\n

          \u5173\u952e\u8bbe\u8ba1\u70b9：<\/p>\n

            \n
          • retriever \u5bf9 Agent \u6765\u8bf4\u662f\u4e00\u4e2a\u5de5\u5177（Tool），\u548c\u5176\u4ed6\u5de5\u5177（API \u8c03\u7528\u3001\u6570\u636e\u5e93\u67e5\u8be2\u3001\u4ee3\u7801\u6267\u884c）\u5e76\u5217\u3002Agent \u81ea\u5df1\u51b3\u5b9a\u4ec0\u4e48\u65f6\u5019\u8be5\u7528\u68c0\u7d22\u3001\u4ec0\u4e48\u65f6\u5019\u4e0d\u8be5\u3002<\/li>\n
          • \u68c0\u7d22\u5230\u7684\u5185\u5bb9\u6ce8\u5165\u5230 LLM \u7684 system prompt \u6216\u4e0a\u4e0b\u6587\u91cc，\u800c\u4e0d\u662f\u66ff\u6362\u7528\u6237\u539f\u59cb\u95ee\u9898\u3002<\/li>\n<\/ul>\n

            \u516b\u3001\u5e38\u89c1\u5751<\/h3>\n\n\u73b0\u8c61\u771f\u76f8<\/tr>\n\n\n\n\n\n\n\n
            \u5411\u91cf\u68c0\u7d22\u7ed3\u679c\u5b8c\u5168\u4e0d\u5bf9<\/td>\n\u5165\u5e93\u548c\u68c0\u7d22\u7528\u4e86\u4e0d\u540c\u7684 Embedding \u6a21\u578b，\u5411\u91cf\u7a7a\u95f4\u4e0d\u7edf\u4e00<\/td>\n<\/tr>\n
            \u6362\u4e86\u4e2a\u76f8\u4f3c\u95ee\u6cd5\u5c31\u641c\u4e0d\u5230\u4e86<\/td>\nEmbedding \u6a21\u578b\u5bf9\u9886\u57df\u672f\u8bed\u6ca1\u8bad\u7ec3\u8fc7，\u8003\u8651\u5fae\u8c03\u6216\u6362\u9886\u57df\u6a21\u578b<\/td>\n<\/tr>\n
            \u660e\u660e\u6587\u6863\u6709\u7b54\u6848，\u68c0\u7d22\u5c31\u662f\u547d\u4e2d\u4e0d\u4e86<\/td>\n\u5206\u5757\u592a\u5927，\u8bed\u4e49\u88ab\u7a00\u91ca；\u6216\u5206\u5757\u592a\u5c0f，\u5173\u952e\u4e0a\u4e0b\u6587\u88ab\u5207\u65ad<\/td>\n<\/tr>\n
            \u5411\u91cf\u68c0\u7d22\u5feb\u4f46\u5185\u5b58\u7206\u70b8<\/td>\n\u6ca1\u505a\u5411\u91cf\u538b\u7f29（PQ），1536 \u7ef4 \u00d7 \u767e\u4e07\u6761 = \u51e0\u4e2a GB \u8d77<\/td>\n<\/tr>\n
            Rerank \u52a0\u4e86\u53cd\u800c\u53d8\u6162<\/td>\nRerank \u6a21\u578b\u548c Embedding \u6a21\u578b\u6709"\u6392\u5e8f\u504f\u597d\u51b2\u7a81"\u2014\u2014\u7c97\u7b5b\u9009\u51fa\u7684\u5019\u9009\u96c6\u672c\u8eab\u4e0d\u884c，\u7cbe\u6392\u6551\u4e0d\u56de\u6765<\/td>\n<\/tr>\n
            \u6df7\u5408\u68c0\u7d22\u5206\u6570\u65e0\u6cd5\u5bf9\u9f50<\/td>\n\u5411\u91cf\u4f59\u5f26\u503c（-1~1）\u548c BM25 \u5f97\u5206\u91cf\u7eb2\u4e0d\u540c，\u76f4\u63a5\u52a0\u6743\u65e0\u6548；\u7528 RRF（\u5012\u6570\u6392\u540d\u878d\u5408）\u4ee3\u66ff<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            \u4e5d\u3001\u603b\u7ed3<\/h3>\n

            \u5411\u91cf\u68c0\u7d22\u7528\u4e00\u53e5\u8bdd\u6982\u62ec：\u628a\u6587\u672c\u6620\u5c04\u5230\u9ad8\u7ef4\u5411\u91cf\u7684\u8bed\u4e49\u7a7a\u95f4，\u9760\u8ddd\u79bb\u5ea6\u91cf"\u610f\u601d\u6709\u591a\u8fd1"，\u7528 ANN \u7b97\u6cd5\u5728\u5927\u89c4\u6a21\u6570\u636e\u4e2d\u6beb\u79d2\u7ea7\u627e\u51fa\u6700\u76f8\u5173\u7684\u6587\u6863\u3002\u5b83\u5728 RAG \u94fe\u8def\u91cc\u626e\u6f14"\u5916\u90e8\u5927\u8111\u7684\u8bb0\u5fc6\u7d22\u5f15"\u2014\u2014\u4e0d\u662f\u53d6\u4ee3 LLM，\u800c\u662f\u8ba9 LLM \u6709\u636e\u53ef\u4f9d\u3002<\/p>\n

            \u5de5\u7a0b\u4e0a\u7684\u4f18\u5148\u7ea7：\u5206\u5757\u7b56\u7565 > Embedding \u6a21\u578b\u9009\u62e9 > \u5411\u91cf\u6570\u636e\u5e93\u9009\u578b\u3002\u5f88\u591a\u9879\u76ee\u5728\u5411\u91cf\u6570\u636e\u5e93\u4e0a\u7ea0\u7ed3\u534a\u5929，\u7ed3\u679c\u5206\u5757\u4e00\u584c\u7cca\u6d82\u2014\u2014\u65b9\u5411\u8dd1\u504f\u4e86\u3002<\/p>\n

            \u6700\u540e\u8bb0\u4f4f\u4e00\u4e2a\u6838\u5fc3\u5224\u65ad：\u5411\u91cf\u68c0\u7d22\u64c5\u957f"\u8bed\u4e49\u6a21\u7cca\u5339\u914d"，\u4e0d\u64c5\u957f"\u7cbe\u786e\u6761\u4ef6\u67e5\u627e"\u3002\u4eba\u540d\u3001\u578b\u53f7\u3001\u7f16\u53f7\u3001\u65e5\u671f\u8303\u56f4\u3001\u6cd5\u6761\u7f16\u53f7\u2014\u2014\u8fd9\u4e9b\u8001\u8001\u5b9e\u5b9e\u7528\u8fc7\u6ee4\u6761\u4ef6\u6216\u5173\u952e\u8bcd\u641c\u7d22，\u522b\u786c\u5f80\u5411\u91cf\u4e0a\u5957\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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