AI_EMBEDDING - MaxCompute - Alibaba Cloud Documentation Center

Fungsi AI_EMBEDDING mengonversi teks atau gambar menjadi vektor.

Sintaks

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

Parameter

model_name: Wajib. STRING yang menentukan model yang akan digunakan. Untuk informasi selengkapnya, lihat SQL AI Function.
version_name: Wajib. STRING yang menentukan versi model. Untuk menggunakan versi default, tentukan DEFAULT_VERSION.
input: Wajib. Data yang akan dikonversi. Tipe data berikut didukung:
- STRING: Teks yang akan dikonversi menjadi vektor.
- BINARY: Data biner OSS (digunakan bersama GET_DATA_FROM_OSS).
model_parameters: Opsional. String dalam format JSON yang menentukan parameter model, seperti max_tokens, temperature, dan top_p. Contohnya:
'{"max_tokens": 500, "temperature": 0.6, "top_p": 0.95}'.
- max_tokens: Jumlah maksimum token yang dihasilkan dalam satu panggilan model. Nilai default untuk model publik MaxCompute adalah 4.096.
- temperature: Nilai antara 0 dan 1 yang mengatur tingkat keacakan output model. Nilai lebih tinggi menghasilkan output yang lebih kreatif dan beragam, sedangkan nilai lebih rendah menghasilkan output yang lebih deterministik dan konservatif.
- top_p: Nilai antara 0 dan 1 yang membatasi rentang label kandidat yang dipertimbangkan oleh model. Nilai lebih tinggi menghasilkan rentang yang lebih luas dan variasi yang lebih besar, sedangkan nilai lebih rendah menghasilkan rentang yang lebih sempit dan output yang lebih fokus.

Nilai kembalian

Mengembalikan nilai VECTOR. Meskipun dimensi vektor dapat dikustomisasi, tipe umumnya adalah VECTOR(FLOAT, 1024), yaitu array bilangan titik mengambang berdimensi 1.024. Nilai ini merepresentasikan bentuk vektor dari data teks atau gambar input.

Jika input bernilai NULL atau string kosong, fungsi mengembalikan NULL.
Jika input bukan bertipe STRING atau BINARY, fungsi mengembalikan error.

Anda dapat menjalankan perintah SET odps.sql.ai.embedding.dimension = 256; untuk menentukan dimensi vektor embedding yang dikembalikan oleh fungsi.

Contoh

Contoh 1: Hasilkan vektor teks dengan 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] |
+-----------+

Contoh 2: Proses data multimodal dengan qwen3-vl-embedding

Unggah gambar e-commerce ke OSS. Contoh ini menggunakan 50 gambar poster produk dari dataset publik Alibaba Cloud Tianchi: Poster Design Text and Image Dataset.

Buat tabel objek

-- Aktifkan model tiga lapis
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         |
+------------+

Panggil model Model Studio qwen3-vl-embedding untuk memproses data gambar biner

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;

-- Hasil yang dikembalikan
+------+-----------------+
| 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] |
+------+-----------------+