{"id":33566,"date":"2023-06-24T06:23:27","date_gmt":"2023-01-14T08:41:34","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%b0%9d%e8%af%95%e4%bd%bf%e7%94%a8redis%e7%bc%93%e5%ad%98%e7%94%a8%e6%88%b7%e7%9a%84%e9%97%ae%e9%a2%98%ef%bc%8c%e5%b9%b6%e8%80%83%e8%99%91%e5%87%8f%e5%b0%91%e4%bd%bf%e7%94%a8openapi%e7%9a%84token\/"},"modified":"2024-04-30T10:27:20","modified_gmt":"2024-04-30T02:27:20","slug":"%e5%b0%9d%e8%af%95%e4%bd%bf%e7%94%a8redis%e7%bc%93%e5%ad%98%e7%94%a8%e6%88%b7%e7%9a%84%e9%97%ae%e9%a2%98%ef%bc%8c%e5%b9%b6%e8%80%83%e8%99%91%e5%87%8f%e5%b0%91%e4%bd%bf%e7%94%a8openapi%e7%9a%84token","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%b0%9d%e8%af%95%e4%bd%bf%e7%94%a8redis%e7%bc%93%e5%ad%98%e7%94%a8%e6%88%b7%e7%9a%84%e9%97%ae%e9%a2%98%ef%bc%8c%e5%b9%b6%e8%80%83%e8%99%91%e5%87%8f%e5%b0%91%e4%bd%bf%e7%94%a8openapi%e7%9a%84token\/","title":{"rendered":"\u5c1d\u8bd5\u4f7f\u7528Redis\u7f13\u5b58\u7528\u6237\u7684\u95ee\u9898\uff0c\u5e76\u8003\u8651\u51cf\u5c11\u4f7f\u7528openapi\u7684token"},"content":{"rendered":"

\u665a\u4e0a\u597d\uff01\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u4ecb\u7ecd\u4f7f\u7528Python\u548cRedis\u5c06\u7528\u6237\u7684\u95ee\u9898\u7f13\u5b58\u5230Redis\u4e2d\uff0c\u5e76\u5bf9\u7f13\u5b58\u7684\u67e5\u8be2\u8fdb\u884c\u76f8\u4f3c\u5ea6\u641c\u7d22\uff0c\u4ee5\u5b9e\u73b0\u51cf\u5c11LLM\u4ee4\u724c\u4f7f\u7528\u91cf\u548c\u63d0\u9ad8\u56de\u7b54\u901f\u5ea6\u7684\u65b9\u6cd5\u3002\u8fd9\u662f\u5173\u4e8e\u4ece\u95ee\u9898\u5230\u95ee\u9898\u7684\u76f8\u4f3c\u5ea6\u641c\u7d22\u7684\u5b9e\u73b0\uff01<\/p>\n

\u80cc\u666f<\/h2>\n

\u5728\u4f7f\u7528OpenAI\u7684API\u65f6\uff0c\u6709\u65f6\u4f1a\u8fbe\u5230\u4f7f\u7528\u9650\u5236\uff08$120\uff09\uff0c\u5bfc\u81f4\u4f7f\u7528\u53d7\u9650\u3002\u540c\u65f6\uff0c\u6211\u4e5f\u8ba4\u4e3a\u6bcf\u6b21\u90fd\u4f7f\u7528OPENAPI\u6765\u56de\u7b54\u76f8\u4f3c\u7684\u95ee\u9898\u662f\u6d6a\u8d39\u7684\uff0c\u6240\u4ee5\u6211\u5728LangChain\u4e0a\u8003\u8651\u5b9e\u73b0\u7f13\u5b58\u3002\u7136\u800c\uff0c\u73b0\u6709\u7684\u5b9e\u73b0\u5e76\u4e0d\u8db3\u591f\u7406\u60f3\uff0c\u56e0\u6b64\u6211\u51b3\u5b9a\u81ea\u5df1\u4f7f\u7528Redis\u6765\u5b9e\u73b0\u4e00\u4e2a\u95ee\u9898\u76f8\u4f3c\u6027\u641c\u7d22\u7cfb\u7edf\u3002<\/p>\n

\u73af\u5883\u7684\u51c6\u5907<\/h2>\n

Python\u7248\u672c\u662f3.9.18\uff0c
\nRedis\u7248\u672c\u662f4.6.0\uff0c
\nDocker\u7248\u672c\u662f24.0.6\uff08\u6784\u5efa\u7248\u672c\u4e3aed223bc\uff09\uff0c
\ndocker-compose\u7248\u672c\u662f1.29.2\uff08\u6784\u5efa\u7248\u672c\u4e3a5becea4c\uff09\uff0c
\nredis-om\u7248\u672c\u662f0.2.1\u3002<\/p>\n

\u203b\u5173\u4e8e\u4eceDocker\u6784\u5efa\u7684\u5185\u5bb9\u5c06\u5728\u53e6\u4e00\u7bc7\u6587\u7ae0\u4e2d\u53d1\u5e03\u3002<\/p>\n

\u5efa\u7b51\u9879\u76ee<\/h2>\n
    \n
  1. \n
      Redis\u914d\u7f6e\u548c\u8fde\u63a5<\/ol>\n<\/li>\n<\/ol>\n

       <\/p>\n

        \n
      1. \n
          \u521b\u5efa\u7d22\u5f15<\/ol>\n<\/li>\n<\/ol>\n

           <\/p>\n

            \n
          1. \n
              \u6dfb\u52a0\u65b0\u7684\u95ee\u9898<\/ol>\n<\/li>\n<\/ol>\n

               <\/p>\n

                \u641c\u7d22\u76f8\u4f3c\u95ee\u9898<\/ol>\n

                \u5b9e\u73b0\u90e8\u5206\u7684\u8be6\u7ec6\u89e3\u91ca<\/h3>\n

                1. Redis\u7684\u914d\u7f6e\u548c\u8fde\u63a5<\/h4>\n

                \u9996\u5148\uff0c\u60a8\u9700\u8981\u8fde\u63a5\u5230Redis\u670d\u52a1\u5668\u3002\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff0c\u5373\u53ef\u8fde\u63a5\u5230Redis\u3002<\/p>\n

                import<\/span> redis<\/span>\r\n\r\n# Redis\u63a5\u7d9a\u8a2d\u5b9a\r\n<\/span>r<\/span> =<\/span> redis<\/span>.<\/span>Redis<\/span>(<\/span>host<\/span>=<\/span>\"<\/span>pj_config_ai_redis_1<\/span>\"<\/span>,<\/span> port<\/span>=<\/span>6379<\/span>)<\/span>\r\n<\/code><\/pre>\n
                2. \u521b\u5efa\u7d22\u5f15<\/h4>\n
                \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f1a\u5728Redis\u4e2d\u521b\u5efa\u7d22\u5f15\u3002\u8fd9\u4e2a\u7d22\u5f15\u7684\u4f5c\u7528\u662f\u7528\u4e8e\u4fdd\u5b58\u95ee\u9898\u7684\u5d4c\u5165\u5411\u91cf\u3002<\/p>\n
                from<\/span> redis.commands.search.field<\/span> import<\/span> TagField<\/span>,<\/span> VectorField<\/span>\r\nfrom<\/span> redis.commands.search.indexDefinition<\/span> import<\/span> IndexDefinition<\/span>,<\/span> IndexType<\/span>\r\n\r\n# \u5b9a\u6570\u306e\u8a2d\u5b9a\r\n<\/span>INDEX_NAME<\/span> =<\/span> \"<\/span>index<\/span>\"<\/span>                              # Vector Index Name\r\n<\/span>VECTOR_DIMENSIONS<\/span> =<\/span> 1536<\/span>                          # Vector Dimensions\r\n<\/span>\r\ndef<\/span> create_index<\/span>(<\/span>vector_dimensions<\/span>:<\/span> int<\/span>):<\/span>\r\n    try<\/span>:<\/span>\r\n        # check to see if index exists\r\n<\/span>        r<\/span>.<\/span>ft<\/span>(<\/span>INDEX_NAME<\/span>).<\/span>info<\/span>()<\/span>\r\n        print<\/span>(<\/span>\"<\/span>Index already exists!<\/span>\"<\/span>)<\/span>\r\n    except<\/span>:<\/span>\r\n        # schema\r\n<\/span>        schema<\/span> =<\/span> (<\/span>\r\n            TagField<\/span>(<\/span>\"<\/span>tag<\/span>\"<\/span>),<\/span>                       # Tag Field Name\r\n<\/span>            VectorField<\/span>(<\/span>\"<\/span>vector<\/span>\"<\/span>,<\/span>                  # Vector Field Name\r\n<\/span>                \"<\/span>FLAT<\/span>\"<\/span>,<\/span> {<\/span>                          # Vector Index Type: FLAT or HNSW\r\n<\/span>                    \"<\/span>TYPE<\/span>\"<\/span>:<\/span> \"<\/span>FLOAT32<\/span>\"<\/span>,<\/span>             # FLOAT32 or FLOAT64\r\n<\/span>                    \"<\/span>DIM<\/span>\"<\/span>:<\/span> vector_dimensions<\/span>,<\/span>      # Number of Vector Dimensions\r\n<\/span>                    \"<\/span>DISTANCE_METRIC<\/span>\"<\/span>:<\/span> \"<\/span>COSINE<\/span>\"<\/span>,<\/span>   # Vector Search Distance Metric\r\n<\/span>                }<\/span>\r\n            ),<\/span>\r\n        )<\/span>\r\n\r\n        # index Definition\r\n<\/span>        definition<\/span> =<\/span> IndexDefinition<\/span>(<\/span>prefix<\/span>=<\/span>[<\/span>DOC_PREFIX<\/span>],<\/span> index_type<\/span>=<\/span>IndexType<\/span>.<\/span>HASH<\/span>)<\/span>\r\n\r\n        # create Index\r\n<\/span>        r<\/span>.<\/span>ft<\/span>(<\/span>INDEX_NAME<\/span>).<\/span>create_index<\/span>(<\/span>fields<\/span>=<\/span>schema<\/span>,<\/span> definition<\/span>=<\/span>definition<\/span>)<\/span>\r\n<\/code><\/pre>\n
                3. \u6dfb\u52a0\u65b0\u95ee\u9898<\/h4>\n
                \u63a5\u6536\u7528\u6237\u7684\u65b0\u95ee\u9898\uff0c\u5e76\u5c06\u5176\u4fdd\u5b58\u5230Redis\u4e2d\u4f5c\u4e3a\u5d4c\u5165\u5411\u91cf\u3002<\/p>\n
                import<\/span> numpy<\/span> as<\/span> np<\/span>\r\nimport<\/span> openai<\/span>\r\nimport<\/span> hashlib<\/span>\r\n\r\nDOC_PREFIX<\/span> =<\/span> \"<\/span>doc:<\/span>\"<\/span>                               # RediSearch Key Prefix for the Index\r\n<\/span>SIMILARITY_THRESHOLD<\/span> =<\/span> 0.09<\/span>                       # \u985e\u4f3c\u5ea6\u306e\u95be\u5024\r\n<\/span>\r\ndef<\/span> create_document_key<\/span>(<\/span>question<\/span>):<\/span>\r\n    # \u8cea\u554f\u309220\u6587\u5b57\u306b\u5236\u9650\r\n<\/span>    truncated_question<\/span> =<\/span> question<\/span>[:<\/span>20<\/span>]<\/span>\r\n\r\n    # \u30e6\u30fc\u30b6\u30fc\u306e\u8cea\u554f\u3092\u30cf\u30c3\u30b7\u30e5\u5316\r\n<\/span>    question_hash<\/span> =<\/span> hashlib<\/span>.<\/span>sha256<\/span>(<\/span>question<\/span>.<\/span>encode<\/span>(<\/span>'<\/span>utf-8<\/span>'<\/span>,<\/span> errors<\/span>=<\/span>'<\/span>replace<\/span>'<\/span>)).<\/span>hexdigest<\/span>()<\/span>  # \u6700\u521d\u306e10\u6587\u5b57\u306e\u30cf\u30c3\u30b7\u30e5\u3092\u4f7f\u7528\r\n<\/span>\r\n    # \u30e6\u30cb\u30fc\u30af\u306a\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u30ad\u30fc\u3092\u4f5c\u6210\r\n<\/span>    document_key<\/span> =<\/span> f<\/span>\"<\/span>{<\/span>truncated_question<\/span>}<\/span>_<\/span>{<\/span>question_hash<\/span>}<\/span>\"<\/span>\r\n\r\n    return<\/span> document_key<\/span>\r\n\r\ndef<\/span> add_new_question<\/span>(<\/span>new_question<\/span>,<\/span> new_answer<\/span>):<\/span>\r\n    # \u65b0\u3057\u3044\u8cea\u554f\u306e\u30d9\u30af\u30c8\u30eb\u3092\u4f5c\u6210\r\n<\/span>    response<\/span> =<\/span> openai<\/span>.<\/span>Embedding<\/span>.<\/span>create<\/span>(<\/span>input<\/span>=<\/span>[<\/span>new_question<\/span>],<\/span> engine<\/span>=<\/span>\"<\/span>text-embedding-ada-002<\/span>\"<\/span>)<\/span>\r\n    new_question_embedding<\/span> =<\/span> np<\/span>.<\/span>array<\/span>([<\/span>r<\/span>[<\/span>\"<\/span>embedding<\/span>\"<\/span>]<\/span> for<\/span> r<\/span> in<\/span> response<\/span>[<\/span>\"<\/span>data<\/span>\"<\/span>]],<\/span> dtype<\/span>=<\/span>np<\/span>.<\/span>float32<\/span>)[<\/span>0<\/span>]<\/span>\r\n\r\n    # \u65e2\u5b58\u306e\u8cea\u554f\u3068\u985e\u4f3c\u5ea6\u3092\u8a08\u7b97\r\n<\/span>    query<\/span> =<\/span> (<\/span>\r\n        Query<\/span>(<\/span>\"<\/span>(@tag:{ openai })=>[KNN 1 @vector $vec as score]<\/span>\"<\/span>)<\/span>\r\n        .<\/span>sort_by<\/span>(<\/span>\"<\/span>score<\/span>\"<\/span>)<\/span>\r\n        .<\/span>return_fields<\/span>(<\/span>\"<\/span>score<\/span>\"<\/span>)<\/span>\r\n        .<\/span>paging<\/span>(<\/span>0<\/span>,<\/span> 1<\/span>)<\/span>\r\n        .<\/span>dialect<\/span>(<\/span>2<\/span>)<\/span>\r\n    )<\/span>\r\n\r\n    query_params<\/span> =<\/span> {<\/span>\"<\/span>vec<\/span>\"<\/span>:<\/span> new_question_embedding<\/span>.<\/span>tobytes<\/span>()}<\/span>\r\n    result<\/span> =<\/span> r<\/span>.<\/span>ft<\/span>(<\/span>INDEX_NAME<\/span>).<\/span>search<\/span>(<\/span>query<\/span>,<\/span> query_params<\/span>).<\/span>docs<\/span>\r\n\r\n    print<\/span>(<\/span>f<\/span>\"<\/span>add_new_qu: <\/span>{<\/span>result<\/span>}<\/span>\"<\/span>)<\/span>\r\n\r\n    # \u985e\u4f3c\u5ea6\u30b9\u30b3\u30a2\u304c\u95be\u5024\u4ee5\u4e0a\u3067\u3042\u308b\u304b\u30c1\u30a7\u30c3\u30af\r\n<\/span>    if<\/span> result<\/span> and<\/span> float<\/span>(<\/span>result<\/span>[<\/span>0<\/span>].<\/span>score<\/span>)<\/span> <=<\/span> SIMILARITY_THRESHOLD<\/span>:<\/span>\r\n        print<\/span>(<\/span>\"<\/span>Similar question already exists. Not adding to Redis.<\/span>\"<\/span>)<\/span>\r\n    else<\/span>:<\/span>\r\n        # \u985e\u4f3c\u5ea6\u30b9\u30b3\u30a2\u304c\u95be\u5024\u4ee5\u4e0b\u306e\u5834\u5408\u3001\u65b0\u3057\u3044\u8cea\u554f\u3092Redis\u306b\u8ffd\u52a0\r\n<\/span>        doc_key<\/span> =<\/span> create_document_key<\/span>(<\/span>new_question<\/span>)<\/span>\r\n        r<\/span>.<\/span>hset<\/span>(<\/span>f<\/span>\"<\/span>doc:<\/span>{<\/span>doc_key<\/span>}<\/span>\"<\/span>,<\/span> mapping<\/span>=<\/span>{<\/span>\r\n            \"<\/span>vector<\/span>\"<\/span>:<\/span> new_question_embedding<\/span>.<\/span>tobytes<\/span>(),<\/span>\r\n            \"<\/span>answer<\/span>\"<\/span>:<\/span> new_answer<\/span>,<\/span>\r\n            \"<\/span>tag<\/span>\"<\/span>:<\/span> \"<\/span>openai<\/span>\"<\/span>\r\n        })<\/span>\r\n        print<\/span>(<\/span>f<\/span>\"<\/span>Added new question: <\/span>{<\/span>new_question<\/span>}<\/span>\"<\/span>)<\/span>\r\n<\/code><\/pre>\n
                4. \u641c\u7d22\u7c7b\u4f3c\u95ee\u9898\u3002<\/h4>\n
                \u5bf9\u4e8e\u7528\u6237\u63d0\u51fa\u7684\u65b0\u95ee\u9898\uff0c\u6211\u4eec\u4f1a\u641c\u7d22\u7c7b\u4f3c\u7684\u95ee\u9898\u3002<\/p>\n
                from<\/span> redis.commands.search.query<\/span> import<\/span> Query<\/span>\r\n\r\ndef<\/span> search_similar_questions<\/span>(<\/span>query_question<\/span>):<\/span>\r\n    # \u30af\u30a8\u30ea\u306e\u30d9\u30af\u30c8\u30eb\u3092\u4f5c\u6210\r\n<\/span>    response<\/span> =<\/span> openai<\/span>.<\/span>Embedding<\/span>.<\/span>create<\/span>(<\/span>input<\/span>=<\/span>[<\/span>query_question<\/span>],<\/span> engine<\/span>=<\/span>\"<\/span>text-embedding-ada-002<\/span>\"<\/span>)<\/span>\r\n    query_embedding<\/span> =<\/span> np<\/span>.<\/span>array<\/span>([<\/span>r<\/span>[<\/span>\"<\/span>embedding<\/span>\"<\/span>]<\/span> for<\/span> r<\/span> in<\/span> response<\/span>[<\/span>\"<\/span>data<\/span>\"<\/span>]],<\/span> dtype<\/span>=<\/span>np<\/span>.<\/span>float32<\/span>)[<\/span>0<\/span>]<\/span>\r\n\r\n    # \u985e\u4f3c\u3059\u308b\u8cea\u554f\u3092\u691c\u7d22\r\n<\/span>    query<\/span> =<\/span> (<\/span>\r\n        Query<\/span>(<\/span>\"<\/span>(@tag:{ openai })=>[KNN 3 @vector $vec as score]<\/span>\"<\/span>)<\/span>\r\n        .<\/span>sort_by<\/span>(<\/span>\"<\/span>score<\/span>\"<\/span>)<\/span>\r\n        .<\/span>return_fields<\/span>(<\/span>\"<\/span>answer<\/span>\"<\/span>,<\/span> \"<\/span>score<\/span>\"<\/span>)<\/span>\r\n        .<\/span>paging<\/span>(<\/span>0<\/span>,<\/span> 5<\/span>)<\/span>\r\n        .<\/span>dialect<\/span>(<\/span>2<\/span>)<\/span>\r\n    )<\/span>\r\n\r\n    query_params<\/span> =<\/span> {<\/span>\"<\/span>vec<\/span>\"<\/span>:<\/span> query_embedding<\/span>.<\/span>t<\/span>\r\n\r\nobytes<\/span>()}<\/span>\r\n    result<\/span> =<\/span> r<\/span>.<\/span>ft<\/span>(<\/span>INDEX_NAME<\/span>).<\/span>search<\/span>(<\/span>query<\/span>,<\/span> query_params<\/span>).<\/span>docs<\/span>\r\n\r\n    if<\/span> result<\/span>:<\/span>\r\n        for<\/span> doc<\/span> in<\/span> result<\/span>:<\/span>\r\n            print<\/span>(<\/span>f<\/span>\"<\/span>Answer: <\/span>{<\/span>doc<\/span>.<\/span>answer<\/span>}<\/span>, Score: <\/span>{<\/span>doc<\/span>.<\/span>score<\/span>}<\/span>\"<\/span>)<\/span>\r\n    else<\/span>:<\/span>\r\n        print<\/span>(<\/span>\"<\/span>No similar questions found.<\/span>\"<\/span>)<\/span> # score\u3067result\u3092\u7d5e\u3063\u305f\u5834\u5408\u6709\u52b9\r\n<\/span><\/code><\/pre>\n
                \u52a8\u4f5c\u9a8c\u8bc1<\/h3>\n
                \u8fd0\u884c\u4e0a\u8ff0\u4ee3\u7801\u540e\uff0c\u5c06\u641c\u7d22\u4e0e\u7528\u6237\u65b0\u63d0\u95ee\u7c7b\u4f3c\u7684\u95ee\u9898\uff0c\u5e76\u663e\u793a\u76f8\u5e94\u7684\u7b54\u6848\u548c\u76f8\u4f3c\u5ea6\u5206\u6570\u3002<\/p>\n
                \u4f7f\u7528\u4ee5\u4e0b\u5185\u5bb9\u8bbf\u95eeRedis\u3002<\/p>\n
                pero0125<\/span>@hpdesktop<\/span>:<\/span>~\/<\/span>pj_config_ai<\/span>$<\/span> docker<\/span> exec<\/span> -<\/span>it<\/span> pj_config_ai_redis_1<\/span> redis<\/span>-<\/span>cli<\/span> --<\/span>raw<\/span>\r\n<\/code><\/pre>\n
                \u6211\u4f1a\u63d0\u524d\u51c6\u5907\u597d\u6570\u636e\u96c6\u3002<\/p>\n
                127.0<\/span>.<\/span>0.1<\/span>:<\/span>6379<\/span>><\/span> keys<\/span> *<\/span>\r\ndoc<\/span>:<\/span>\u671d\u306e\u6328\u62f6\u306f<\/span>\uff1f<\/span>_1addbec461763ef8b4049aa8ef82e82d312d462fcf996eb0a187776e3bf12fc3<\/span>\r\ndoc<\/span>:<\/span>\u8fd1\u6240\u306e\u72ac\u306f\u3069\u3046\u3067\u3059\u304b<\/span>\uff1f<\/span>_a4329822fc61481fd433ca0e9951210bda5be739156d84d06988b3b00a462e2f<\/span>\r\ndoc<\/span>:<\/span>\u65e5\u672c\u306e\u6328\u62f6\u3092\u6559\u3048\u3066_60a934ccb7b415fd23a82edbff4635ff1c2d235d4fea9c08733106e2295cb0db<\/span>\r\ndoc<\/span>:<\/span>\u3042\u306a\u305f\u306e\u732b\u306f\u3069\u3046\u3067\u3059\u304b<\/span>\uff1f<\/span>_0e1747975d38d91d50e2519667756e6e89482fefca0bb565d6783f5dae84483d<\/span>\r\ndoc<\/span>:<\/span>\u732b\u306b\u3064\u3044\u3066\u6559\u3048\u3066_56c04baa15a49eaecf31ac51fd2bda114767a194cf78e65f81129bf4522866ff<\/span>\r\n<\/code><\/pre>\n
                \u6267\u884c\u4e0a\u8ff0\u4ee3\u7801\u4ee5\u751f\u6210\u95ee\u9898\u3002<\/p>\n
                root<\/span>@a20c6bbbdee0<\/span>:<\/span>\/<\/span>app<\/span>\/<\/span>call_langchain<\/span># python3 redisearch.py \r\n<\/span>Index<\/span> already<\/span> exists<\/span>!<\/span>\r\n\u65b0\u3057\u3044\u8cea\u554f\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044<\/span>:<\/span> \u732b\u306e\u8abf\u5b50\u306f\u3069\u3046<\/span>\uff1f<\/span>\r\n\u305d\u306e\u8cea\u554f\u306e\u56de\u7b54\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044<\/span>:<\/span> \u5143\u6c17\u3060\u3088<\/span>\r\nadd_new_qu<\/span>:<\/span> [<\/span>Document<\/span> {<\/span>'<\/span>id<\/span>'<\/span>:<\/span> '<\/span>doc:\u3042\u306a\u305f\u306e\u732b\u306f\u3069\u3046\u3067\u3059\u304b\uff1f_0e1747975d38d91d50e2519667756e6e89482fefca0bb565d6783f5dae84483d<\/span>'<\/span>,<\/span> '<\/span>payload<\/span>'<\/span>:<\/span> None<\/span>,<\/span> '<\/span>score<\/span>'<\/span>:<\/span> '<\/span>0.0652241706848<\/span>'<\/span>}]<\/span>\r\nSimilar<\/span> question<\/span> already<\/span> exists<\/span>.<\/span> Not<\/span> adding<\/span> to<\/span> Redis<\/span>.<\/span>\r\n\u8cea\u554f\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044<\/span>:<\/span>\r\n<\/code><\/pre>\n
                \u7c7b\u4f3c\u7684\u95ee\u9898\u5df2\u7ecf\u5b58\u5728\u3002\u4e0d\u6dfb\u52a0\u5230Redis\u3002\u7531\u6b64\u53ef\u89c1\uff0c\u7531\u4e8e\u5f97\u5206\u4e3a0.09\u6216\u4ee5\u4e0b\uff0c\u65b0\u7684\u7f13\u5b58\u6570\u636e\u6ca1\u6709\u88ab\u6ce8\u518c\u3002\u5f97\u5206\u8d8a\u63a5\u8fd10\uff0c\u610f\u4e49\u8d8a\u76f8\u4f3c\u3002<\/p>\n
                \u8cea\u554f\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044<\/span>:<\/span> \u30da\u30c3\u30c8\u306e\u8abf\u5b50\u306f\u3069\u3046<\/span>\uff1f<\/span>\r\nAnswer<\/span>:<\/span> My<\/span> cats<\/span> escaped<\/span> and<\/span> got<\/span> out<\/span> before<\/span> I<\/span> could<\/span> close<\/span> the<\/span> door<\/span>.,<\/span> Score<\/span>:<\/span> 0.092055439949<\/span>\r\nAnswer<\/span>:<\/span> The<\/span> dog<\/span> next<\/span> door<\/span> barks<\/span> really<\/span> loudly<\/span>.,<\/span> Score<\/span>:<\/span> 0.0969498157501<\/span>\r\nAnswer<\/span>:<\/span> \u306d\u3053<\/span>,<\/span> Score<\/span>:<\/span> 0.134126365185<\/span>\r\nroot<\/span>@a20c6bbbdee0<\/span>:<\/span>\/<\/span>app<\/span>\/<\/span>call_langchain<\/span># \r\n<\/span><\/code><\/pre>\n
                \u95ee\u95ee\u5ba0\u7269\u7684\u72b6\u6001\u5982\u4f55\uff1f\u5b83\u4f1a\u4ece\u7f13\u5b58\u6570\u636e\u4e2d\u6309\u5206\u6570\u987a\u5e8f\u4e3a\u60a8\u6311\u9009\u7b54\u6848\u3002<\/p>\n
                \u901a\u8fc7\u4f7f\u7528LLM\u6765\u751f\u6210\u56de\u7b54\uff0c\u5c06\u5176\u4e0e\u7528\u6237\u7684\u95ee\u9898\u7ed3\u5408\u8d77\u6765\u5e76\u8fdb\u884c\u7f13\u5b58\uff0c\u8fd9\u6837\u5c31\u53ef\u4ee5\u4ece\u7f13\u5b58\u4e2d\u83b7\u53d6\u7c7b\u4f3c\u7684\u95ee\u9898\u7684\u7b54\u6848\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"
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