curl --request POST \
--url 'https://api.elkapi.com/v1/engines/gemini-3.1-pro-preview/embeddings' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '{
"model": "gemini-3.1-pro-preview",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}'
import requests
url = "https://api.elkapi.com/v1/engines/gemini-3.1-pro-preview/embeddings"
headers = {
"Authorization": "Bearer <token>"
}
headers["Content-Type"] = "application/json"
payload = {
"model": "gemini-3.1-pro-preview",
"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/engines/gemini-3.1-pro-preview/embeddings";
const headers = {
"Authorization": "Bearer <token>"
};
headers["Content-Type"] = "application/json";
const payload = {
"model": "gemini-3.1-pro-preview",
"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": "gemini-3.1-pro-preview",
"usage": {
"prompt_tokens": 1,
"total_tokens": 1
}
}
네이티브 Gemini 형식
Gemini Embedding
- 지정한 엔진/모델로 임베딩을 생성합니다
POST
/
v1
/
engines
/
{model}
/
embeddings
curl --request POST \
--url 'https://api.elkapi.com/v1/engines/gemini-3.1-pro-preview/embeddings' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '{
"model": "gemini-3.1-pro-preview",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}'
import requests
url = "https://api.elkapi.com/v1/engines/gemini-3.1-pro-preview/embeddings"
headers = {
"Authorization": "Bearer <token>"
}
headers["Content-Type"] = "application/json"
payload = {
"model": "gemini-3.1-pro-preview",
"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/engines/gemini-3.1-pro-preview/embeddings";
const headers = {
"Authorization": "Bearer <token>"
};
headers["Content-Type"] = "application/json";
const payload = {
"model": "gemini-3.1-pro-preview",
"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": "gemini-3.1-pro-preview",
"usage": {
"prompt_tokens": 1,
"total_tokens": 1
}
}
curl --request POST \
--url 'https://api.elkapi.com/v1/engines/gemini-3.1-pro-preview/embeddings' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '{
"model": "gemini-3.1-pro-preview",
"input": "오늘 날씨가 정말 좋아 산책하기에 좋습니다."
}'
import requests
url = "https://api.elkapi.com/v1/engines/gemini-3.1-pro-preview/embeddings"
headers = {
"Authorization": "Bearer <token>"
}
headers["Content-Type"] = "application/json"
payload = {
"model": "gemini-3.1-pro-preview",
"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/engines/gemini-3.1-pro-preview/embeddings";
const headers = {
"Authorization": "Bearer <token>"
};
headers["Content-Type"] = "application/json";
const payload = {
"model": "gemini-3.1-pro-preview",
"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": "gemini-3.1-pro-preview",
"usage": {
"prompt_tokens": 1,
"total_tokens": 1
}
}
Authorizations
string
필수
모든 엔드포인트는 Bearer Token 인증이 필요합니다.요청 헤더에 추가하세요:
Authorization: Bearer YOUR_API_KEY
Path Parameters
string
기본값:"gemini-3.1-pro-preview"
필수
모델/엔진 ID예시:
gemini-3.1-pro-previewBody
string
기본값:"gemini-3.1-pro-preview"
필수
예시:
gemini-3.1-pro-previewstring or array<string>
필수
임베딩할 텍스트
string
기본값:"float"
허용 값:
float, base64integer
출력 벡터 차원
Response
string
예시:
liststring
기본값:"gemini-3.1-pro-preview"
예시:
gemini-3.1-pro-preview⌘I