curl --request POST \
--url 'https://api.elkapi.com/v1/embeddings' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '{
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}'
import requests
url = "https://api.elkapi.com/v1/embeddings"
headers = {
"Authorization": "Bearer <token>"
}
headers["Content-Type"] = "application/json"
payload = {
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}
response = requests.request("POST", url, headers=headers, json=payload)
print(response.json())
const url = "https://api.elkapi.com/v1/embeddings";
const headers = {
"Authorization": "Bearer <token>"
};
headers["Content-Type"] = "application/json";
const payload = {
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
};
const response = await fetch(url, {
method: "POST",
headers,
body: JSON.stringify(payload)
});
console.log(await response.json());
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 1,
"embedding": [
1
]
}
],
"model": "text-embedding-3-large",
"usage": {
"prompt_tokens": 1,
"total_tokens": 1
}
}
네이티브 OpenAI 형식
Embedding 생성
- 텍스트를 벡터 임베딩으로 변환합니다
POST
/
v1
/
embeddings
curl --request POST \
--url 'https://api.elkapi.com/v1/embeddings' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '{
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}'
import requests
url = "https://api.elkapi.com/v1/embeddings"
headers = {
"Authorization": "Bearer <token>"
}
headers["Content-Type"] = "application/json"
payload = {
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}
response = requests.request("POST", url, headers=headers, json=payload)
print(response.json())
const url = "https://api.elkapi.com/v1/embeddings";
const headers = {
"Authorization": "Bearer <token>"
};
headers["Content-Type"] = "application/json";
const payload = {
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
};
const response = await fetch(url, {
method: "POST",
headers,
body: JSON.stringify(payload)
});
console.log(await response.json());
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 1,
"embedding": [
1
]
}
],
"model": "text-embedding-3-large",
"usage": {
"prompt_tokens": 1,
"total_tokens": 1
}
}
curl --request POST \
--url 'https://api.elkapi.com/v1/embeddings' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '{
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}'
import requests
url = "https://api.elkapi.com/v1/embeddings"
headers = {
"Authorization": "Bearer <token>"
}
headers["Content-Type"] = "application/json"
payload = {
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}
response = requests.request("POST", url, headers=headers, json=payload)
print(response.json())
const url = "https://api.elkapi.com/v1/embeddings";
const headers = {
"Authorization": "Bearer <token>"
};
headers["Content-Type"] = "application/json";
const payload = {
"model": "text-embedding-3-large",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
};
const response = await fetch(url, {
method: "POST",
headers,
body: JSON.stringify(payload)
});
console.log(await response.json());
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 1,
"embedding": [
1
]
}
],
"model": "text-embedding-3-large",
"usage": {
"prompt_tokens": 1,
"total_tokens": 1
}
}
Authorizations
string
필수
모든 엔드포인트는 Bearer Token 인증이 필요합니다.요청 헤더에 추가하세요:
Authorization: Bearer YOUR_API_KEY
Body
string
기본값:"text-embedding-3-large"
필수
예시:
text-embedding-3-largestring or array<string>
필수
임베딩할 텍스트
string
기본값:"float"
허용 값:
float, base64integer
출력 벡터 차원
Response
string
예시:
liststring
기본값:"text-embedding-3-large"
예시:
text-embedding-3-large⌘I