AI_EMBEDDING - 云原生大数据计算服务 MaxCompute

AI_EMBEDDING函数用于将文本/图像数据转换为向量。

命令格式

VECTOR AI_EMBEDDING(
 STRING <model_name>,
  STRING <version_name>,
  STRING <input>
  [, STRING <model_parameters>] 
)

参数说明

model_name：必填。STRING 类型。待使用的模型名，请参见SQL AI Function。
version_name：必填。STRING 类型。待使用的模型版本名。如果调用默认版本，支持直接输入 DEFAULT_VERSION。
input：必填。输入数据支持：
- STRING类型。需要转换为向量的文本数据。
- BINARY类型：OSS 二进制数据（配合GET_DATA_FROM_OSS）
model_parameters：选填。STRING 类型。支持指定模型调用相关参数 max_tokens、temperature、top_p，格式为
'{"max_tokens": 500, "temperature": 0.6, "top_p": 0.95}' 。参数说明如下：
- max_tokens：设置模型单次调用输出 token 的最大数量，对于 MaxCompute 公共模型，默认值为 4096。
- temperature：介于 0 到 1 之间的值，用于控制模型输出的随机性强度。值越大，输出越具创意和多样性；值越低，输出越确定和保守。
- top_p：介于 0 到 1 之间的值，用于限定模型选择的备选标签范围。值越大，备选范围越广，多样性越高；值越小，范围越窄，结果越集中。

返回值说明

返回 VECTOR(FLOAT, 1024) 类型，由 [-0.1, +0.1] 范围内的浮点数值组成的 1024 维数组，表示输入文本/图像数据的向量化形式。返回规则如下：

input输入值为NULL或空字符串时，返回NULL。
input非 STRING 或BINARY类型时，返回报错。

支持通过执行SET odps.sql.ai.embedding.dimension = 256; 命令，指定函数返回的Embedding 向量的维度。

使用示例

示例1：调用公共模型 text-embedding-v4生成文本向量

SET odps.namespace.schema=true;
SELECT AI_EMBEDDING(
 bigdata_public_modelset.default.`text-embedding-v4`,
  DEFAULT_VERSION,
  'MaxCompute（原ODPS）是一种快速、完全托管的多租户大数据处理平台，可处理TB至PB级数据。MaxCompute提供多种计算模型，包括SQL、MapReduce、Graph和机器学习。它采用Serverless架构，可根据负载自动弹性伸缩计算资源，无需用户管理基础设施。该平台与DataWorks（数据开发）、PAI（机器学习平台）、Quick BI（数据可视化）等阿里云服务深度集成。'
) AS EMBEDDING
;
+-----------+
| embedding |
+-----------+
| [-0.03828365, 0.07733949, -0.01174646, -0.01618665, 0.007562431, -0.01743406, 0.05218337, 0.07882451, -0.01651335, 0.07264686, 0.05351988, -0.009682287, -0.01188753, -0.01921607, -0.05271798, -0.01270429, -0.03154168, -0.06290517, -0.022097, -0.01565204, 0.05016375, -0.002522666, 0.03071008, 0.001777375, 0.002530091, 0.03813514, 0.04357029, -0.02144359, 0.0551831, 0.03082888, 0.03537302, -0.01799836, 0.03269999, 0.0327891, 0.03985776, 0.03964986, 0.0334425, -0.0163797, 0.02626989, 0.01869632, 0.01484271, 0.0167658, -0.005145571, 0.02503733, -0.007491893, -0.05663841, -0.04054087, 0.02220095, -0.01562234, -0.01479073, 0.03953106, -0.01606784, 0.0003021074, 0.03988746, 0.007306266, 0.02586893, 0.02337411, 0.0008269668, 0.02083474, 0.06427138, 0.03786784, 0.02187425, -0.05856893, 0.04309509, 0.01315722, 0.04113487, 0.01396655, -0.02695299, -0.04538201, -0.06142015, 0.05494549, 0.001147173, 0.03504632, 0.01970613, 0.01518426, 0.0444613, 0.01433038, -0.006218493, -0.02196335, -0.06825121, 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0.00948181] |
+-----------+

示例2：调用模型qwen3-vl-embedding处理多模态数据

将需要处理的电商数据上传图片至OSS，本文示例为50张商品海报图片数据。数据来源于阿里云天池公共数据集：海报设计文本图像数据集。

创建Object Table

-- 开启三层模型
SET odps.namespace.schema=true;

CREATE OBJECT TABLE IF NOT EXISTS image_demo
WITH SERDEPROPERTIES ('odps.properties.rolearn'='acs:ram::11**370:role/aliyunodpsdefaultrole')
LOCATION 'oss://oss-cn-hangzhou-internal.aliyuncs.com/qijuan-bucket/photo/poster_11/';

ALTER TABLE image_demo REFRESH METADATA;

SELECT COUNT(*) AS ROW_COUNT FROM image_demo; 
+------------+
| row_count  |
+------------+
| 50         |
+------------+

调用百炼模型qwen3-vl-embedding，处理二进制图像数据

SET odps.sql.ai.embedding.dimension = 256;
SET odps.namespace.schema=true;

SELECT
  key,
  ai_embedding(
        bigdata_public_modelset.default.`qwen3-vl-embedding`,
        DEFAULT_VERSION,
        image_binary
  ) as image_embedding 
  from (
    select GET_DATA_FROM_OSS(  
      'muze_project.default.image_demo', key
    ) as image_binary, key as key
    from muze_project.default.image_demo
) Limit 1;

-- 返回结果
+------+-----------------+
| key  | image_embedding |
+------+-----------------+
| O1CN012qinii2KVuHBiDhZ5_!!2672389563-0-alimamacc.jpg | [0.0003754136, 0.04734562, -0.04436165, 0.06617778, -0.00628291, -0.06838813, -0.07895358, 0.07997034, -0.01888741, -0.07811365, 0.1178557, 0.1373951, 0.005310358, 0.09601746, 0.03823899, -0.0988467, -0.1755015, -0.07515179, -0.1755899, 0.02340757, -0.03963152, -0.09610587, 0.005056168, 0.05570073, 0.05760163, -0.01075334, -0.01340575, -0.1062735, -0.01304104, -0.1213038, -0.04668252, 0.02276657, -0.1294379, -0.001079616, -0.002848584, -0.08386055, 0.03812848, 0.008205912, -0.03355306, 0.01960577, -0.02195979, 0.0117922, -0.06144764, 0.02446854, 0.05508184, -0.1587029, 0.03523292, -0.09380711, -0.08200386, -0.0663104, 0.03678017, 0.009393973, -0.01564925, 0.05693853, -0.06082874, 0.04265969, 0.1003497, -0.01674337, 0.02451274, -0.09292297, 0.02119722, 0.0809871, -0.1238678, -0.05459556, -0.01836798, -0.01358258, 0.03109958, -0.02060043, 0.07161523, 0.02241291, -0.1177673, -0.09822781, 0.04774349, -0.1650687, 0.1407549, -0.04484793, 0.01221216, 0.03609496, -0.01081412, -0.009322137, -0.08275538, 0.02396016, -0.02937551, -0.007669903, -0.100615, 0.05132425, -0.07453289, -0.02566212, -0.03357517, -0.009742103, 0.02105355, 0.05331356, -0.08744131, 0.132886, -0.02884502, 0.02798299, 0.06308329, -0.01047704, 0.04195238, -0.01219006, 0.06361377, -0.008869016, -0.05145687, -0.02924289, 0.04186396, -0.007001273, -0.06728295, 0.01486458, -0.06648722, 7.08865e-05, -0.04659411, 0.09265773, 0.04955597, -0.004141637, 0.06078453, -0.03140903, 0.1207733, 0.008542989, -0.0165997, -0.1011455, 0.03165216, 0.01514087, -0.05897205, -0.08920959, 0.01524034, 0.02709885, 0.000991893, 0.08085448, 0.1293495, 0.04995383, -0.1000845, 0.0226229, 0.089298, -0.03273523, 0.1019412, 0.1101637, -0.001219835, -0.02398226, -0.02862399, 0.06104977, 0.02192664, -0.001776566, 0.02095409, -0.05702694, -0.03558658, 0.01377046, -0.02265605, -0.04960018, 0.06476316, 0.008498782, -0.01961683, -0.03311099, 0.04584259, 0.02165034, 0.03832741, 0.03996307, -0.04369855, 0.006459738, -0.06330433, -0.006459738, -0.04540052, 0.1090143, -0.01732912, -0.08571724, 0.04250497, 0.0491139, -0.08814862, -0.002001745, -0.02840295, -0.1573767, 0.03989676, -0.09031476, 0.008780601, 0.06224336, 0.04937914, -0.02780616, 0.01169273, 0.05052852, -0.009935508, 0.03412775, 0.04049355, -0.00637685, -0.09495649, 0.06259701, 0.004580943, 0.001950631, -0.04513528, 0.07731792, -0.03598444, 0.06940489, 0.06706192, 0.08430262, 0.03494558, -0.1180325, -0.03536554, -0.05278308, -0.01206849, -0.1465902, 0.05733639, 0.03737696, 0.03067961, 0.04062617, -0.11706, 0.05530287, 0.01146065, 0.08443524, 0.01742858, 0.01351627, -0.01708598, 0.02714306, -0.01518508, -0.1252825, 0.03974203, -0.1362458, -0.05375563, -0.04248286, 0.01930738, 0.05340197, -0.04137769, 0.06485157, -0.01140539, -0.04265969, 0.05870681, -0.02906606, -0.07899779, 0.0255295, 0.1053893, -0.01311841, 0.06644302, -0.01416832, 0.0023927, -0.05397666, -0.0784673, 0.007852256, 0.04299124, -0.0002819919, 0.1355385, 0.07020061, -0.005769005, 0.06250861, 0.03958731, -0.004611336, -0.01288632, 0.1067155, 0.03472454, -0.03967572, -0.02225819, 0.02643575, -0.01790381, 0.02621471, -0.03056909, -0.08253434, 0.05070535, -0.02793878, -0.003243684, -0.003149744] |
+------+-----------------+