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AI_EMBEDDING

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The AI_EMBEDDING function converts text or image data into a vector.

Syntax

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

Parameters

  • model_name: Required. A STRING specifying the model to use. For more information, see SQL AI Function.

  • version_name: Required. A STRING specifying the model's version. To use the default version, you can specify DEFAULT_VERSION.

  • input: Required. The data to convert. The following data types are supported:

    • STRING: The text to convert into a vector.

    • BINARY: OSS binary data (used with GET_DATA_FROM_OSS).

  • model_parameters: Optional. STRING. Specifies model parameters such as max_tokens, temperature, and top_p. The format is a JSON string:

    '{"max_tokens": 500, "temperature": 0.6, "top_p": 0.95}'.

    • max_tokens: The maximum number of tokens to generate in a single model call. For MaxCompute public models, the default value is 4,096.

    • temperature: A value between 0 and 1 that controls the randomness of the output. A higher value results in more creative and diverse output, while a lower value produces more deterministic and conservative output.

    • top_p: A value between 0 and 1 that limits the range of candidate labels the model can choose from. A higher value allows for a broader range and more diversity, while a lower value narrows the range and produces more focused results.

Return value

Returns a VECTOR value. While the vector's dimension is customizable, a common type is VECTOR(FLOAT, 1024), which is a 1024-dimensional array of floating-point numbers. This value represents the vectorized form of the input text or image data.

  • If the input value is NULL or an empty string, the function returns NULL.

  • If the input is not of type STRING or BINARY, the function returns an error.

You can execute the SET odps.sql.ai.embedding.dimension = 256; command to specify the dimension of the embedding vector returned by the function.

Examples

Example 1: Generate a text vector with text-embedding-v4

SET odps.namespace.schema=true;
SELECT AI_EMBEDDING(
 bigdata_public_modelset.default.`text-embedding-v4`,
  DEFAULT_VERSION,
  'MaxCompute (formerly ODPS) is a fast, fully managed, multi-tenant big data processing platform that can process data at the TB to PB scale. MaxCompute provides multiple computing models, including SQL, MapReduce, Graph, and machine learning. It uses a serverless architecture and can automatically perform elastic scaling of computing resources based on workload, without requiring users to manage infrastructure. The platform is deeply integrated with Alibaba Cloud services such as DataWorks (data development), PAI (machine learning platform), and Quick BI (data visualization).'
) 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, -0.007168902, -0.01496893, -0.004187738, 0.01130095, 0.02055258, 0.03097738, -0.003224335, 0.02494822, 0.004351089, -0.06153895, 0.02528978, 0.02555708, -0.003640139, -0.02083474, 0.05078746, -0.01997343, -0.02840831, 0.06159835, 0.01911212, 0.03474931, -0.03237329, 0.03760054, -0.05283678, -0.010603, 0.01029114, -0.002789969, 0.01591934, 0.01969128, -0.02564618, 0.02747275, -0.05292588, -0.01424128, -0.05304468, 0.05491579, 2.656782e-05, 0.01872602, -0.01654305, -0.0224831, 0.02797765, 0.02552738, 0.008724454, -0.04665912, -0.02560163, -0.01779046, 0.01923092, -0.0143378, 0.001187083, 0.03195749, -0.006263044, -0.03911525, -0.02065654, -0.03495722, 0.0221267, -0.02667084, 0.03742234, 0.0449365, 0.02499278, -0.03281879, 0.03602643, 0.01022432, 0.0001189171, -0.03403651, -0.04668882, 0.02637384, 0.1023471, -0.0009587618, -0.01271914, -0.02384931, 0.01513229, 0.005776702, -0.01030599, -0.01297159, -0.0223049, 0.01217711, 0.01447146, -0.001735609, -0.009392709, 0.003890735, -0.02141389, 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0.00948181] |
+-----------+

Example 2: Process multimodal data with qwen3-vl-embedding

  • Upload the e-commerce images to OSS. This example uses 50 product poster images from the Alibaba Cloud Tianchi public dataset: Poster Design Text and Image Dataset.

  • Create an object table

    -- Enable the three-layer model
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         |
+------------+

  • Call the Model Studio model qwen3-vl-embedding to process binary image data

    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;

-- Returned result
+------+-----------------+
| 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] |
+------+-----------------+
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