AI_EMBEDDING - MaxCompute - Centre de documentation Alibaba Cloud

La fonction AI_EMBEDDING convertit des données textuelles ou visuelles en vecteur.

Syntaxe

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

Paramètres

model_name : Obligatoire. Chaîne (STRING) indiquant le modèle à utiliser. Pour plus d'informations, consultez la rubrique SQL AI Function.
version_name : Obligatoire. Chaîne (STRING) spécifiant la version du modèle. Pour utiliser la version par défaut, définissez DEFAULT_VERSION.
input : Obligatoire. Données à convertir. Les types de données suivants sont pris en charge :
- STRING : Texte à convertir en vecteur.
- BINARY : Données binaires issues d'OSS (utilisées avec la fonction GET_DATA_FROM_OSS).
model_parameters : Facultatif. Type STRING. Spécifie les paramètres du modèle, tels que max_tokens, temperature et top_p. Le format attendu est une chaîne JSON :

'{"max_tokens": 500, "temperature": 0.6, "top_p": 0.95}'.
- max_tokens : Nombre maximal de jetons à générer lors d'un seul appel au modèle. Pour les modèles publics MaxCompute, la valeur par défaut est 4 096.
- temperature : Valeur comprise entre 0 et 1 qui contrôle l'aléatoire de la sortie. Une valeur élevée produit des résultats plus créatifs et diversifiés, tandis qu'une valeur faible génère des sorties plus déterministes et conservatrices.
- top_p : Valeur comprise entre 0 et 1 qui limite la plage des étiquettes candidates que le modèle peut sélectionner. Une valeur plus élevée autorise une gamme plus large et une plus grande diversité, alors qu'une valeur plus basse restreint la sélection pour obtenir des résultats plus ciblés.

Valeur de retour

Renvoie une valeur de type VECTOR. Bien que la dimension du vecteur soit configurable, le type courant est VECTOR(FLOAT, 1024), correspondant à un tableau de nombres à virgule flottante de 1 024 dimensions. Cette valeur représente la forme vectorisée des données textuelles ou visuelles en entrée.

Si la valeur d'entrée est NULL ou une chaîne vide, la fonction renvoie NULL.
Si l'entrée n'est pas de type STRING ou BINARY, la fonction renvoie une erreur.

Exécutez la commande SET odps.sql.ai.embedding.dimension = 256; pour définir la dimension du vecteur d'embedding renvoyé par la fonction.

Exemples

Exemple 1 : Générer un vecteur de texte avec 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 |
+-----------+
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0.00948181] |
+-----------+

Exemple 2 : Traiter des données multimodales avec qwen3-vl-embedding

Téléchargez les images e-commerce sur OSS. Cet exemple utilise 50 affiches produits issues du jeu de données public Alibaba Cloud Tianchi : Poster Design Text and Image Dataset.

Créez une table objet.

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

Appelez le modèle Model Studio qwen3-vl-embedding pour traiter les données binaires d'image.

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