curl --request POST \
--url https://api.mixroute.ai/v1/embeddings \
--header 'Authorization: Bearer sk-xxxxxxxxxx' \
--header 'Content-Type: application/json' \
--data '{
"model": "text-embedding-3-small",
"input": "你好,世界"
}'
from openai import OpenAI
client = OpenAI(
api_key="sk-xxxxxxxxxx",
base_url="https://api.mixroute.ai/v1"
)
response = client.embeddings.create(
model="text-embedding-3-small",
input="你好,世界"
)
print(response.data[0].embedding)
const OpenAI = require('openai');
const client = new OpenAI({
apiKey: 'sk-xxxxxxxxxx',
baseURL: 'https://api.mixroute.ai/v1'
});
const response = await client.embeddings.create({
model: 'text-embedding-3-small',
input: '你好,世界'
});
console.log(response.data[0].embedding);
<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/embeddings', [
'headers' => [
'Authorization' => 'Bearer sk-xxxxxxxxxx',
'Content-Type' => 'application/json',
],
'json' => [
'model' => 'text-embedding-3-small',
'input' => '你好,世界'
]
]);
echo $response->getBody();
package main
import (
"context"
"fmt"
openai "github.com/sashabaranov/go-openai"
)
func main() {
config := openai.DefaultConfig("sk-xxxxxxxxxx")
config.BaseURL = "https://api.mixroute.ai/v1"
client := openai.NewClientWithConfig(config)
resp, _ := client.CreateEmbeddings(
context.Background(),
openai.EmbeddingRequest{
Model: "text-embedding-3-small",
Input: []string{"你好,世界"},
},
)
fmt.Println(resp.Data[0].Embedding)
}
import java.net.http.*;
import java.net.URI;
HttpClient client = HttpClient.newHttpClient();
String json = """
{
"model": "text-embedding-3-small",
"input": "你好,世界"
}
""";
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("https://api.mixroute.ai/v1/embeddings"))
.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/embeddings')
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 = {
model: 'text-embedding-3-small',
input: '你好,世界'
}.to_json
response = http.request(request)
puts response.body
{
"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
}
}
文本向量系列
文本向量化(Embedding)
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
POST
/
v1
/
embeddings
curl --request POST \
--url https://api.mixroute.ai/v1/embeddings \
--header 'Authorization: Bearer sk-xxxxxxxxxx' \
--header 'Content-Type: application/json' \
--data '{
"model": "text-embedding-3-small",
"input": "你好,世界"
}'
from openai import OpenAI
client = OpenAI(
api_key="sk-xxxxxxxxxx",
base_url="https://api.mixroute.ai/v1"
)
response = client.embeddings.create(
model="text-embedding-3-small",
input="你好,世界"
)
print(response.data[0].embedding)
const OpenAI = require('openai');
const client = new OpenAI({
apiKey: 'sk-xxxxxxxxxx',
baseURL: 'https://api.mixroute.ai/v1'
});
const response = await client.embeddings.create({
model: 'text-embedding-3-small',
input: '你好,世界'
});
console.log(response.data[0].embedding);
<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/embeddings', [
'headers' => [
'Authorization' => 'Bearer sk-xxxxxxxxxx',
'Content-Type' => 'application/json',
],
'json' => [
'model' => 'text-embedding-3-small',
'input' => '你好,世界'
]
]);
echo $response->getBody();
package main
import (
"context"
"fmt"
openai "github.com/sashabaranov/go-openai"
)
func main() {
config := openai.DefaultConfig("sk-xxxxxxxxxx")
config.BaseURL = "https://api.mixroute.ai/v1"
client := openai.NewClientWithConfig(config)
resp, _ := client.CreateEmbeddings(
context.Background(),
openai.EmbeddingRequest{
Model: "text-embedding-3-small",
Input: []string{"你好,世界"},
},
)
fmt.Println(resp.Data[0].Embedding)
}
import java.net.http.*;
import java.net.URI;
HttpClient client = HttpClient.newHttpClient();
String json = """
{
"model": "text-embedding-3-small",
"input": "你好,世界"
}
""";
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("https://api.mixroute.ai/v1/embeddings"))
.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/embeddings')
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 = {
model: 'text-embedding-3-small',
input: '你好,世界'
}.to_json
response = http.request(request)
puts response.body
{
"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
}
}
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。认证
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-largestring | array
必填
要嵌入的文本,可以是字符串或字符串数组
string
返回格式:
float 或 base64integer
用于开放此字段的模型/路由,例如 OpenAI text-embedding-3;不要假设 Cohere 原生 output_dimension 与此字段映射一致。
string
Cohere 必填:search_document、search_query、classification 或 clustering。
cURL 示例
curl https://api.mixroute.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.mixroute.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)}")
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,适合对精度要求高的场景 |
| text-embedding-ada-002 | 1536 | 旧版模型 |
embed-v4.0 | 1536 | Cohere 多语言向量模型,支持长文本检索。 |
embed-english-v3.0 | 1024 | Cohere 面向英语检索优化的文本向量模型。 |
embed-multilingual-v3.0 | 1024 | Cohere 面向跨语言检索的文本向量模型。 |
Cohere 向量
入库文档使用input_type=search_document,查询使用 search_query;两侧保持相同模型和维度。文本兼容接口从 data[].embedding 返回向量;不要将 Cohere 原生图像/PDF 嵌入对象直接放入此文本输入结构。
curl --request POST "https://api.mixroute.ai/v1/embeddings" \
--header "Authorization: Bearer $MIXROUTE_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "embed-v4.0",
"input": [
"An API reference for developers."
],
"input_type": "search_document",
"encoding_format": "float"
}'
注意事项
- 部分模型支持通过
dimensions参数自定义输出维度 - 批量嵌入时,
input可传入字符串数组
curl --request POST \
--url https://api.mixroute.ai/v1/embeddings \
--header 'Authorization: Bearer sk-xxxxxxxxxx' \
--header 'Content-Type: application/json' \
--data '{
"model": "text-embedding-3-small",
"input": "你好,世界"
}'
from openai import OpenAI
client = OpenAI(
api_key="sk-xxxxxxxxxx",
base_url="https://api.mixroute.ai/v1"
)
response = client.embeddings.create(
model="text-embedding-3-small",
input="你好,世界"
)
print(response.data[0].embedding)
const OpenAI = require('openai');
const client = new OpenAI({
apiKey: 'sk-xxxxxxxxxx',
baseURL: 'https://api.mixroute.ai/v1'
});
const response = await client.embeddings.create({
model: 'text-embedding-3-small',
input: '你好,世界'
});
console.log(response.data[0].embedding);
<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/embeddings', [
'headers' => [
'Authorization' => 'Bearer sk-xxxxxxxxxx',
'Content-Type' => 'application/json',
],
'json' => [
'model' => 'text-embedding-3-small',
'input' => '你好,世界'
]
]);
echo $response->getBody();
package main
import (
"context"
"fmt"
openai "github.com/sashabaranov/go-openai"
)
func main() {
config := openai.DefaultConfig("sk-xxxxxxxxxx")
config.BaseURL = "https://api.mixroute.ai/v1"
client := openai.NewClientWithConfig(config)
resp, _ := client.CreateEmbeddings(
context.Background(),
openai.EmbeddingRequest{
Model: "text-embedding-3-small",
Input: []string{"你好,世界"},
},
)
fmt.Println(resp.Data[0].Embedding)
}
import java.net.http.*;
import java.net.URI;
HttpClient client = HttpClient.newHttpClient();
String json = """
{
"model": "text-embedding-3-small",
"input": "你好,世界"
}
""";
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("https://api.mixroute.ai/v1/embeddings"))
.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/embeddings')
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 = {
model: 'text-embedding-3-small',
input: '你好,世界'
}.to_json
response = http.request(request)
puts response.body
{
"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
}
}