GPT - 向量
curl --request POST \
--url https://api.tikway.ai/v1/embeddings \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "openai/text-embedding-3-large",
"input": "Hello, world!"
}
'import requests
url = "https://api.tikway.ai/v1/embeddings"
payload = {
"model": "openai/text-embedding-3-large",
"input": "Hello, world!"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: 'openai/text-embedding-3-large', input: 'Hello, world!'})
};
fetch('https://api.tikway.ai/v1/embeddings', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.tikway.ai/v1/embeddings",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'model' => 'openai/text-embedding-3-large',
'input' => 'Hello, world!'
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.tikway.ai/v1/embeddings"
payload := strings.NewReader("{\n \"model\": \"openai/text-embedding-3-large\",\n \"input\": \"Hello, world!\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.tikway.ai/v1/embeddings")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"openai/text-embedding-3-large\",\n \"input\": \"Hello, world!\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.tikway.ai/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"openai/text-embedding-3-large\",\n \"input\": \"Hello, world!\"\n}"
response = http.request(request)
puts response.read_body{
"data": [
{
"embedding": [
-0.005828857421875,
-0.0241241455078125,
-0.02203369140625
],
"index": 0,
"object": "embedding"
}
],
"model": "openai/text-embedding-3-large",
"object": "list",
"usage": {
"prompt_tokens": 4,
"total_tokens": 4
}
}向量嵌入
GPT - 向量
POST
https://api.tikway.ai
/
v1
/
embeddings
GPT - 向量
curl --request POST \
--url https://api.tikway.ai/v1/embeddings \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "openai/text-embedding-3-large",
"input": "Hello, world!"
}
'import requests
url = "https://api.tikway.ai/v1/embeddings"
payload = {
"model": "openai/text-embedding-3-large",
"input": "Hello, world!"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: 'openai/text-embedding-3-large', input: 'Hello, world!'})
};
fetch('https://api.tikway.ai/v1/embeddings', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.tikway.ai/v1/embeddings",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'model' => 'openai/text-embedding-3-large',
'input' => 'Hello, world!'
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.tikway.ai/v1/embeddings"
payload := strings.NewReader("{\n \"model\": \"openai/text-embedding-3-large\",\n \"input\": \"Hello, world!\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.tikway.ai/v1/embeddings")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"openai/text-embedding-3-large\",\n \"input\": \"Hello, world!\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.tikway.ai/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"openai/text-embedding-3-large\",\n \"input\": \"Hello, world!\"\n}"
response = http.request(request)
puts response.read_body{
"data": [
{
"embedding": [
-0.005828857421875,
-0.0241241455078125,
-0.02203369140625
],
"index": 0,
"object": "embedding"
}
],
"model": "openai/text-embedding-3-large",
"object": "list",
"usage": {
"prompt_tokens": 4,
"total_tokens": 4
}
}授权
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
请求体
application/json
将文本或 token ID 转换为向量表示。
向量模型 ID。
Minimum string length:
1示例:
"openai/text-embedding-3-small"
"openai/text-embedding-3-large"
"bailian/text-embedding-v4"
"google/gemini-embedding-2-preview"
需要生成向量的输入。支持单个字符串、字符串数组、token ID 数组或 token ID 二维数组。Gemini embedding 模型当前只支持单个字符串。
Minimum string length:
1示例:
"OfoxAI 是一个 LLM Gateway"
向量编码格式。float 返回浮点数数组;base64 返回 Base64 编码的 float32 数据。
可用选项:
float, base64 输出向量维度,仅部分模型支持。Gemini embedding 模型在当前网关中限制为 128 至 3072。
必填范围:
x >= 1终端用户标识,用于上游滥用监控。
响应
200 - application/json
OpenAI Embeddings 兼容响应。
固定为 list。
Allowed value:
"list"实际使用的模型 ID。