原生 OpenAI 格式
curl --request POST \
--url https://api.gravitex.ai/v1/embeddings \
--header 'Authorization: <authorization>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "<string>",
"input": {},
"encoding_format": "<string>",
"dimensions": 123
}
'import requests
url = "https://api.gravitex.ai/v1/embeddings"
payload = {
"model": "<string>",
"input": {},
"encoding_format": "<string>",
"dimensions": 123
}
headers = {
"Authorization": "<authorization>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: '<authorization>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: '<string>', input: {}, encoding_format: '<string>', dimensions: 123})
};
fetch('https://api.gravitex.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.gravitex.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' => '<string>',
'input' => [
],
'encoding_format' => '<string>',
'dimensions' => 123
]),
CURLOPT_HTTPHEADER => [
"Authorization: <authorization>",
"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.gravitex.ai/v1/embeddings"
payload := strings.NewReader("{\n \"model\": \"<string>\",\n \"input\": {},\n \"encoding_format\": \"<string>\",\n \"dimensions\": 123\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "<authorization>")
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.gravitex.ai/v1/embeddings")
.header("Authorization", "<authorization>")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"<string>\",\n \"input\": {},\n \"encoding_format\": \"<string>\",\n \"dimensions\": 123\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.gravitex.ai/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<authorization>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"<string>\",\n \"input\": {},\n \"encoding_format\": \"<string>\",\n \"dimensions\": 123\n}"
response = http.request(request)
puts response.read_body文本补全和向量嵌入
嵌入(OpenAI格式)
POST /v1/embeddings 文本向量化
POST
/
v1
/
embeddings
原生 OpenAI 格式
curl --request POST \
--url https://api.gravitex.ai/v1/embeddings \
--header 'Authorization: <authorization>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "<string>",
"input": {},
"encoding_format": "<string>",
"dimensions": 123
}
'import requests
url = "https://api.gravitex.ai/v1/embeddings"
payload = {
"model": "<string>",
"input": {},
"encoding_format": "<string>",
"dimensions": 123
}
headers = {
"Authorization": "<authorization>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: '<authorization>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: '<string>', input: {}, encoding_format: '<string>', dimensions: 123})
};
fetch('https://api.gravitex.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.gravitex.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' => '<string>',
'input' => [
],
'encoding_format' => '<string>',
'dimensions' => 123
]),
CURLOPT_HTTPHEADER => [
"Authorization: <authorization>",
"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.gravitex.ai/v1/embeddings"
payload := strings.NewReader("{\n \"model\": \"<string>\",\n \"input\": {},\n \"encoding_format\": \"<string>\",\n \"dimensions\": 123\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "<authorization>")
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.gravitex.ai/v1/embeddings")
.header("Authorization", "<authorization>")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"<string>\",\n \"input\": {},\n \"encoding_format\": \"<string>\",\n \"dimensions\": 123\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.gravitex.ai/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<authorization>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"<string>\",\n \"input\": {},\n \"encoding_format\": \"<string>\",\n \"dimensions\": 123\n}"
response = http.request(request)
puts response.read_body简介
将文本转换为向量嵌入,适用于语义搜索、文本相似度计算、聚类分析等场景。兼容 OpenAI Embeddings API。认证
string
必填
Bearer Token,如
Bearer sk-xxxxxxxxxx请求参数
string
必填
模型名称,如
text-embedding-3-small、text-embedding-3-large、text-embedding-ada-002string | array
必填
要嵌入的文本,可以是字符串或字符串数组
string
默认值:"float"
返回格式:
float 或 base64integer
输出向量维度(仅部分模型支持)
请求示例
curl https://api.gravitex.ai/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-xxxxxxxxxx" \
-d '{
"model": "text-embedding-3-small",
"input": "你好,世界"
}'
Python 示例
from openai import OpenAI
client = OpenAI(
api_key="sk-xxxxxxxxxx",
base_url="https://api.gravitex.ai/v1"
)
response = client.embeddings.create(
model="text-embedding-3-small",
input="你好,世界"
)
print(response.data[0].embedding)
print(f"向量维度:{len(response.data[0].embedding)}")
响应示例
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0023064255, -0.009327292, 0.015797347]
}
],
"model": "text-embedding-3-small",
"usage": {
"prompt_tokens": 5,
"total_tokens": 5
}
}
支持的模型
| 模型 | 维度 | 说明 |
|---|---|---|
| text-embedding-3-small | 1536 | 高性价比,适合大多数场景 |
| text-embedding-3-large | 3072 | 高精度 |
| text-embedding-ada-002 | 1536 | 旧版模型 |
- 批量嵌入时,
input可传入字符串数组 - 部分模型支持通过
dimensions自定义输出维度
⌘I
