AI_EMBEDDING - MaxCompute: Central de Documentação do Alibaba Cloud

A função AI_EMBEDDING converte dados de texto ou imagem em um vetor.

Sintaxe

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

Parâmetros

model_name: Obrigatório. STRING que especifica o modelo a ser usado. Para mais informações, consulte Função SQL de IA.
version_name: Obrigatório. STRING que especifica a versão do modelo. Para usar a versão padrão, especifique DEFAULT_VERSION.
input: Obrigatório. Dados a serem convertidos. Os seguintes tipos de dados são suportados:
- STRING: Texto a ser convertido em vetor.
- BINARY: Dados binários do OSS (usados com GET_DATA_FROM_OSS).
model_parameters: Opcional. String que especifica parâmetros do modelo no formato JSON, como max_tokens, temperature e top_p. Exemplo:

'{"max_tokens": 500, "temperature": 0.6, "top_p": 0.95}'.
- max_tokens: Número máximo de tokens gerados em uma única chamada ao modelo. O valor padrão para modelos públicos do MaxCompute é 4096.
- temperature: Valor entre 0 e 1 que controla a aleatoriedade da saída do modelo. Valores mais altos resultam em saídas mais criativas e diversificadas; valores mais baixos tornam a saída mais determinística e conservadora.
- top_p: Valor entre 0 e 1 que limita o intervalo de rótulos candidatos considerados pelo modelo. Um valor maior amplia o intervalo e aumenta a diversidade; um valor menor restringe o intervalo e concentra a saída.

Valor retornado

Retorna um valor VECTOR. Embora a dimensão do vetor seja personalizável, um tipo comum é VECTOR(FLOAT, 1024), que representa um array de números de ponto flutuante com 1024 dimensões. Esse valor corresponde à forma vetorizada dos dados de texto ou imagem de entrada.

Se o valor de entrada for NULL ou uma string vazia, a função retorna NULL.
Caso a entrada não seja do tipo STRING ou BINARY, a função retorna um erro.

Execute o comando SET odps.sql.ai.embedding.dimension = 256; para definir a dimensão do vetor de embedding retornado pela função.

Exemplos

Exemplo 1: Gerar um vetor de texto com 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, 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0.00948181] |
+-----------+

Exemplo 2: Processar dados multimodais com qwen3-vl-embedding

Faça upload das imagens de e-commerce para o OSS. Este exemplo utiliza 50 imagens de pôsteres de produtos do conjunto de dados público Alibaba Cloud Tianchi: Conjunto de Dados de Texto e Imagem para Design de Pôsteres.

Crie uma tabela de objetos

-- 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         |
+------------+

Chame o modelo qwen3-vl-embedding do Model Studio para processar dados binários de imagem

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] |
+------+-----------------+