curl --request POST \
--url https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent \
--header 'Authorization: Bearer sk-xxxxxxxxxx' \
--header 'Content-Type: application/json' \
--data '{
"content": {
"parts": [{"text": "This is a test text"}]
}
}'
import requests
url = "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent"
headers = {
"Authorization": "Bearer sk-xxxxxxxxxx",
"Content-Type": "application/json"
}
payload = {
"content": {
"parts": [{"text": "This is a test text"}]
}
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
const response = await fetch('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent', {
method: 'POST',
headers: {
'Authorization': 'Bearer sk-xxxxxxxxxx',
'Content-Type': 'application/json'
},
body: JSON.stringify({
content: {
parts: [{ text: 'This is a test text' }]
}
})
});
const data = await response.json();
console.log(data);
<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent', [
'headers' => [
'Authorization' => 'Bearer sk-xxxxxxxxxx',
'Content-Type' => 'application/json',
],
'json' => [
'content' => [
'parts' => [['text' => 'This is a test text']]
]
]
]);
echo $response->getBody();
package main
import (
"bytes"
"encoding/json"
"net/http"
)
func main() {
payload := map[string]interface{}{
"content": map[string]interface{}{
"parts": []map[string]string{
{"text": "This is a test text"},
},
},
}
body, _ := json.Marshal(payload)
req, _ := http.NewRequest("POST", "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent", bytes.NewBuffer(body))
req.Header.Set("Authorization", "Bearer sk-xxxxxxxxxx")
req.Header.Set("Content-Type", "application/json")
http.DefaultClient.Do(req)
}
import java.net.http.*;
import java.net.URI;
HttpClient client = HttpClient.newHttpClient();
String json = """
{
"content": {
"parts": [{"text": "This is a test text"}]
}
}
""";
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent"))
.header("Authorization", "Bearer sk-xxxxxxxxxx")
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(json))
.build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
require 'net/http'
require 'json'
uri = URI('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent')
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true
request = Net::HTTP::Post.new(uri)
request['Authorization'] = 'Bearer sk-xxxxxxxxxx'
request['Content-Type'] = 'application/json'
request.body = {
content: {
parts: [{ text: 'This is a test text' }]
}
}.to_json
response = http.request(request)
puts response.body
{
"embedding": {
"values": [0.0023064255, -0.009327292, 0.015797347]
},
"metadata": {
"usage": {
"prompt_tokens": 6,
"total_tokens": 6
}
}
}
埋め込みシリーズ
Geminiのテキスト埋め込み(embedContent)
Geminiのネイティブインターフェースを使用して、テキストをベクトル埋め込みに変換
POST
/
v1
/
models
/
{model}
:embedContent
curl --request POST \
--url https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent \
--header 'Authorization: Bearer sk-xxxxxxxxxx' \
--header 'Content-Type: application/json' \
--data '{
"content": {
"parts": [{"text": "This is a test text"}]
}
}'
import requests
url = "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent"
headers = {
"Authorization": "Bearer sk-xxxxxxxxxx",
"Content-Type": "application/json"
}
payload = {
"content": {
"parts": [{"text": "This is a test text"}]
}
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
const response = await fetch('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent', {
method: 'POST',
headers: {
'Authorization': 'Bearer sk-xxxxxxxxxx',
'Content-Type': 'application/json'
},
body: JSON.stringify({
content: {
parts: [{ text: 'This is a test text' }]
}
})
});
const data = await response.json();
console.log(data);
<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent', [
'headers' => [
'Authorization' => 'Bearer sk-xxxxxxxxxx',
'Content-Type' => 'application/json',
],
'json' => [
'content' => [
'parts' => [['text' => 'This is a test text']]
]
]
]);
echo $response->getBody();
package main
import (
"bytes"
"encoding/json"
"net/http"
)
func main() {
payload := map[string]interface{}{
"content": map[string]interface{}{
"parts": []map[string]string{
{"text": "This is a test text"},
},
},
}
body, _ := json.Marshal(payload)
req, _ := http.NewRequest("POST", "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent", bytes.NewBuffer(body))
req.Header.Set("Authorization", "Bearer sk-xxxxxxxxxx")
req.Header.Set("Content-Type", "application/json")
http.DefaultClient.Do(req)
}
import java.net.http.*;
import java.net.URI;
HttpClient client = HttpClient.newHttpClient();
String json = """
{
"content": {
"parts": [{"text": "This is a test text"}]
}
}
""";
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent"))
.header("Authorization", "Bearer sk-xxxxxxxxxx")
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(json))
.build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
require 'net/http'
require 'json'
uri = URI('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent')
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true
request = Net::HTTP::Post.new(uri)
request['Authorization'] = 'Bearer sk-xxxxxxxxxx'
request['Content-Type'] = 'application/json'
request.body = {
content: {
parts: [{ text: 'This is a test text' }]
}
}.to_json
response = http.request(request)
puts response.body
{
"embedding": {
"values": [0.0023064255, -0.009327292, 0.015797347]
},
"metadata": {
"usage": {
"prompt_tokens": 6,
"total_tokens": 6
}
}
}
はじめに
Geminiのネイティブインターフェースを使用して、テキストを埋め込みベクトルに変換します。モデルはURLパス(例:gemini-embedding-001)で指定します。Googleの埋め込みモデルが必要な場合や、Gemini APIに合わせる必要がある場合に適しています。
テキスト埋め込みのOpenAI形式を補完するドキュメントです。このドキュメントではGeminiのネイティブパスを扱います。同じ機能はPOST /v1/embeddings経由でも利用できます。
認証
Bearerトークン。例:Bearer sk-xxxxxxxxxx
パスパラメーター
string
必須
埋め込みモデル名。例:
gemini-embedding-001。リクエストパラメーター
object
必須
埋め込み対象のコンテンツです。各項目が
{ "text": "text content" }であるparts配列を含める必要があります。integer
出力ベクトルの次元数(
gemini-embedding-001、text-embedding-004など、一部のモデルのみ対応)。string
タスクの種類。例:
RETRIEVAL_DOCUMENT、RETRIEVAL_QUERY(任意)。コード例
- cURL
- cURL(次元数指定あり)
- Python
curl -X POST "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-xxxxxxxxxx" \
-d '{
"content": {
"parts": [
{ "text": "Text content to embed" }
]
}
}'
curl -X POST "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-xxxxxxxxxx" \
-d '{
"content": {
"parts": [
{ "text": "Text content to embed" }
]
},
"outputDimensionality": 768
}'
import requests
url = "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer sk-xxxxxxxxxx"
}
payload = {
"content": {
"parts": [
{ "text": "Text content to embed" }
]
}
}
response = requests.post(url, json=payload, headers=headers)
data = response.json()
embedding = data["embedding"]["values"]
print(f"Vector dimensions: {len(embedding)}")
レスポンス例
{
"embedding": {
"values": [0.0023064255, -0.009327292, 0.015797347, ...]
},
"metadata": {
"usage": {
"prompt_tokens": 6,
"total_tokens": 6
}
}
}
バッチインターフェース(batchEmbedContents)
バッチ埋め込みにはPOST /v1/models/{model}:batchEmbedContentsを使用します。リクエストボディはrequests配列で、各項目は単一リクエストと同じ構造(content.partsを含む)です。各項目にmodelフィールドを含めないでください。
curl -X POST "https://api.mixroute.ai/v1/models/gemini-embedding-001:batchEmbedContents" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-xxxxxxxxxx" \
-d '{
"requests": [
{ "content": { "parts": [{ "text": "First text" }] } },
{ "content": { "parts": [{ "text": "Second text" }] } }
]
}'
対応モデル
| モデル | 説明 |
|---|---|
| gemini-embedding-001 | 汎用埋め込みモデル。outputDimensionalityに対応 |
| text-embedding-004 | 高精度の埋め込みモデル |
注意事項
content.partsは必須です。少なくとも1つのtextが空でない必要があります- モデルはURLパスで指定します。リクエストボディに
modelフィールドを含めないでください - 使用状況の情報は、レスポンスの
metadata.usageに含まれます(prompt_tokens、total_tokens)。
アプリケーションですでにOpenAI SDKを使用している場合は、コードの変更を最小限に抑えるため、
/v1/embeddings互換インターフェースの使用を検討してください。curl --request POST \
--url https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent \
--header 'Authorization: Bearer sk-xxxxxxxxxx' \
--header 'Content-Type: application/json' \
--data '{
"content": {
"parts": [{"text": "This is a test text"}]
}
}'
import requests
url = "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent"
headers = {
"Authorization": "Bearer sk-xxxxxxxxxx",
"Content-Type": "application/json"
}
payload = {
"content": {
"parts": [{"text": "This is a test text"}]
}
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
const response = await fetch('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent', {
method: 'POST',
headers: {
'Authorization': 'Bearer sk-xxxxxxxxxx',
'Content-Type': 'application/json'
},
body: JSON.stringify({
content: {
parts: [{ text: 'This is a test text' }]
}
})
});
const data = await response.json();
console.log(data);
<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent', [
'headers' => [
'Authorization' => 'Bearer sk-xxxxxxxxxx',
'Content-Type' => 'application/json',
],
'json' => [
'content' => [
'parts' => [['text' => 'This is a test text']]
]
]
]);
echo $response->getBody();
package main
import (
"bytes"
"encoding/json"
"net/http"
)
func main() {
payload := map[string]interface{}{
"content": map[string]interface{}{
"parts": []map[string]string{
{"text": "This is a test text"},
},
},
}
body, _ := json.Marshal(payload)
req, _ := http.NewRequest("POST", "https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent", bytes.NewBuffer(body))
req.Header.Set("Authorization", "Bearer sk-xxxxxxxxxx")
req.Header.Set("Content-Type", "application/json")
http.DefaultClient.Do(req)
}
import java.net.http.*;
import java.net.URI;
HttpClient client = HttpClient.newHttpClient();
String json = """
{
"content": {
"parts": [{"text": "This is a test text"}]
}
}
""";
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent"))
.header("Authorization", "Bearer sk-xxxxxxxxxx")
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(json))
.build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
require 'net/http'
require 'json'
uri = URI('https://api.mixroute.ai/v1/models/gemini-embedding-001:embedContent')
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true
request = Net::HTTP::Post.new(uri)
request['Authorization'] = 'Bearer sk-xxxxxxxxxx'
request['Content-Type'] = 'application/json'
request.body = {
content: {
parts: [{ text: 'This is a test text' }]
}
}.to_json
response = http.request(request)
puts response.body
{
"embedding": {
"values": [0.0023064255, -0.009327292, 0.015797347]
},
"metadata": {
"usage": {
"prompt_tokens": 6,
"total_tokens": 6
}
}
}