[PYTHON/OPENAI] OpenAI 클래스 : embeddings 변수를 사용해 텍스트에서 임베딩 생성하기

■ OpenAI 클래스의 embeddings 변수를 사용해 텍스트에서 임베딩을 생성하는 방법을 보여준다.

• 임베딩은 자연어 처리 및 머신러닝 분야에서 널리 사용되는 개념으로, 텍스트를 컴퓨터가 처리하기 쉬운 형태로 표현하기 위한 기법이다.
• 구체적으로는 텍스트를 벡터 표현(부동소수점 배열)으로 변환한다.
• 벡터 표현은 유사한 의미를 가진 단어나 문장은 벡터 거리가 가깝고, 유사하지 않은 의미를 가진 단어나 문장은 벡터 거리가 멀어지도록 설계되어 있다.
• text-embedding-3-small 모델이 생성하는 임베딩의 차원은 1536이며, 텍스트의 길고 짧음에 관계없이 1536이 된다.

▶ 예제 코드 (PY)


import os

from openai import OpenAI

os.environ["OPENAI_API_KEY"] = "<OPEN_API_KEY>"

openAI = OpenAI()

text = "이것은 테스트 문자열 입니다."
text = text.replace("\n", " ")

createEmbeddingResponse = openAI.embeddings.create(
    model   = "text-embedding-3-small",
    input   = [text]
)

print(createEmbeddingResponse)

"""
CreateEmbeddingResponse
(
    data = [
        Embedding(
            embedding = [
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0.022933245,   0.0073095853,   -0.0020965477,  -0.028352331,   -0.05724039,    0.017380226,    -0.015690625,   -0.011219364,   -0.028166888,  -0.0046309493,
0.0010399031,  0.0168445,      -0.017122665,   -0.001113952,   0.007227166,    0.03138125,     -0.027095433,   0.007907127,    0.039272923,   -0.003992198,
0.012867755,   0.0013753766,   -0.024581635,   -0.036450054,   4.382564e-05,   -0.025261598,   0.027981443,    0.011219364,    0.005424238,   0.045330763,
-0.059053622,  -0.025302807,   0.029341366,    -0.012641101,   0.01752446,     -0.0007907127,  0.025508856,    0.012033257,    0.0025872018,  0.018904988,
-0.0099573145, 0.0007153761,   -0.022273889,   0.005681799,    0.026992409,    -0.009442192,   -0.040818293,   0.023984095,    -0.05732281,   -0.002493192,
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0.00884465,    0.0065059946,   -0.016143933,   -0.009864592,   -0.027734185,   -0.00015308791, 0.030144958,    0.035151947,    -0.017720208,  0.0065884143,
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0.0008499518,  0.0054654474,   -0.0148355225,  -0.008272864,   -0.0025678847,  -0.0018750451,  -0.012229003,   -0.014052536,   -0.009076455,  -0.0051306183,
-0.0031783045, -0.0164324,     0.026023978,    -0.03727425,    0.01815291,     0.032020003,    -0.0118787205,  -0.016205747,   0.017555367,   -0.00064003945,
0.004064315,   0.018132305,    0.0047391253,   -0.027383901,   0.009457645,    -0.0036341879,  -0.0010997861,  0.050317146,    0.04139523,    -0.002956802,
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-0.018307447,  -0.013125316,   -0.007015966,   0.023242319,    -0.012053862,   -0.047926977,   0.03601735,     -0.017246295,   -0.04052982,   0.0016921768,
-0.0017617183, -0.00107403,    -0.014495541,   0.007922581,    0.007752591,    -0.010735149,   -0.012661706,   -0.021758767,   -0.025302807,  -0.015227015,
-0.009694601,  0.004224003,    -0.0038634173,  0.010446681,    -0.008484065,   -0.020161888,   -0.0033482949,  -0.018142607,   0.009555519,   0.01335197,
-0.014979756,  -0.0019703428,  0.014423424,    0.0018312598,   0.037933607,    0.0069386973,   -0.010338505,   -0.03908748,    -0.025137968,  0.008520123,
-0.03210242,   -0.026127003,   -0.022479936,   0.047020365,    0.008159537,    -0.014897337,   0.028187493,    -0.018863779,   0.0042574857,  -0.001040547,
0.038984455,   0.001730811,    -0.0030237678,  0.014248283,    0.0014552205,   -0.0023103235,  0.0025034943,   -0.040715266,   0.025756115,   -0.028743824,
0.021202434,   0.010183968,    -0.020584287,   -0.0029799824,  0.008710718,    -0.010209724,   0.036862154,    0.003507983,    0.013640438,   0.0052233404,
0.0058105798,  0.03383323,     0.00084930786,  0.011703579,    -0.013228341,   -0.0013650741,  0.006768707,    -0.009148572,   0.024622846,   0.023427762,
-0.0172669,    0.00998307,     0.020481262,    -0.005166677,   -0.0032710265,  0.009622484,    -0.025838535,   -0.033627182,   0.01903892,    0.049699,
-0.04582528,   -0.021161225,   0.016772382,    0.036759127,    0.028331727,    0.0078504635,   -0.038304493,   0.059465718,    0.010013977,   0.044630196,
-0.0037243343, -0.00214806,    -0.020316424,   0.0031036118,   0.030206772,    0.008298621,    0.019286178,    -0.004628374,   0.015803952,   -0.008406796,
-0.017885046,  0.009287655,    0.004381115,    -0.01107513,    -0.009972768,   -0.016525123,   -0.026168212,   -0.020409146,   0.0019342842,  0.008097722,
-0.005619984,  -0.0109515,     0.006305097,    -0.010292144,   -0.048091818,   -0.023489578
            ],
            index     = 0,
            object    = 'embedding'
        )
    ],
    model  = 'text-embedding-3-small',
    object = 'list',
    usage  = Usage(
        prompt_tokens = 12,
        total_tokens  = 12
    )
)
"""

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import os

from openai import OpenAI

os.environ["OPENAI_API_KEY"] = "<OPEN_API_KEY>"

openAI = OpenAI()

text = "이것은 테스트 문자열 입니다."

text = text.replace("\n", " ")

createEmbeddingResponse = openAI.embeddings.create(

model = "text-embedding-3-small",

input = [text]

)

print(createEmbeddingResponse)

"""

CreateEmbeddingResponse

(

data = [

Embedding(

embedding = [

0.0021132892, 0.015360947, -0.02388107, -0.03647066, -0.0042317295, -0.008602543, -0.007659869, -0.0046773106, 0.028352331, 0.017782021,

-0.0026348506, 0.00117963, -0.0065935655, -0.00846861, 0.03315327, 0.0022575236, -0.022685986, 0.0014359034, -0.021223038, 0.028888058,

0.010549705, -0.023984095, 0.010941198, -0.005527262, 0.026333053, -0.03432775, 0.0023747138, 0.021758767, 0.04052982, -0.07590842,

-0.034307145, -0.030969152, 0.045248345, -0.06375153, 0.0210582, 0.08629329, 0.0032710265, 0.023407158, 0.0058775456, -0.036862154,

-0.040241353, 0.020831546, 0.005527262, 0.011631462, 0.0068459753, 0.040406194, -0.028743824, 0.023221713, 0.007376551, 0.020161888,

-0.025405832, 0.030103747, 0.030886734, 0.0030572508, 0.035131343, 0.017287504, 0.011301783, 0.043187853, 0.00047809788, 0.0068974877,

0.03778937, 0.008324376, -0.017946862, 0.0155360885, 0.00039857588, 0.0064956923, 0.0015041571, 0.034863476, -0.0044970177, 0.021027293,

0.0690676, -0.008731323, -0.035831906, 0.0016664206, 0.020821244, -0.0042471834, -0.015618508, 0.0051280423, 0.020234004, -0.012280515,

0.017761417, -0.0036960025, 0.00741261, -0.050811663, -0.0035414658, -0.015906977, -0.14522327, 0.012373238, 0.01638089, -0.030165562,

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0.038984455, 0.001730811, -0.0030237678, 0.014248283, 0.0014552205, -0.0023103235, 0.0025034943, -0.040715266, 0.025756115, -0.028743824,

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-0.017885046, 0.009287655, 0.004381115, -0.01107513, -0.009972768, -0.016525123, -0.026168212, -0.020409146, 0.0019342842, 0.008097722,

-0.005619984, -0.0109515, 0.006305097, -0.010292144, -0.048091818, -0.023489578

index = 0,

object = 'embedding'

)

model = 'text-embedding-3-small',

object = 'list',

usage = Usage(

prompt_tokens = 12,

total_tokens = 12

)

"""

▶ requirements.txt


annotated-types==0.7.0
anyio==4.4.0
certifi==2024.6.2
distro==1.9.0
exceptiongroup==1.2.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
openai==1.31.0
pydantic==2.7.3
pydantic_core==2.18.4
sniffio==1.3.1
tqdm==4.66.4
typing_extensions==4.12.1

annotated-types==0.7.0

anyio==4.4.0

certifi==2024.6.2

distro==1.9.0

exceptiongroup==1.2.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

openai==1.31.0

pydantic==2.7.3

pydantic_core==2.18.4

sniffio==1.3.1

tqdm==4.66.4

typing_extensions==4.12.1