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": "Hello, world"
  }'

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="Hello, world"
)
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: 'Hello, world'
});
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' => 'Hello, world'
    ]
]);
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{"Hello, world"},
        },
    )
    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": "Hello, world"
    }
    """;
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: 'Hello, world'
}.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
  }
}

POST

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": "Hello, world"
  }'

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="Hello, world"
)
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: 'Hello, world'
});
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' => 'Hello, world'
    ]
]);
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{"Hello, world"},
        },
    )
    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": "Hello, world"
    }
    """;
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: 'Hello, world'
}.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
  }
}

Text Embeddings

The Embeddings API converts text into high-dimensional vector representations, useful for semantic search, clustering, and RAG applications.

Endpoint

POST https://api.mixroute.ai/v1/embeddings

Authentication

Authorization: Bearer sk-xxxxxxxxxx

Request Body

Parameter	Type	Required	Description
`model`	string	Yes	Embedding model ID
`input`	string/array	Yes	Text(s) to embed
`encoding_format`	string	No	Output format: `float` or `base64`
`dimensions`	integer	No	Output dimensions (model-dependent)

Example Request

Single Text

curl -X POST https://api.mixroute.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog."
  }'

Batch Texts

curl -X POST https://api.mixroute.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "text-embedding-3-small",
    "input": [
      "First document text",
      "Second document text",
      "Third document text"
    ]
  }'

Custom Dimensions

Reduce embedding dimensions for efficiency:

curl -X POST https://api.mixroute.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "text-embedding-3-large",
    "input": "Sample text for embedding",
    "dimensions": 512
  }'

Supported Models

Model	Dimensions	Description
`text-embedding-3-small`	1536	Fast, cost-effective
`text-embedding-3-large`	3072	Higher accuracy
`text-embedding-ada-002`	1536	Legacy model

Use Cases

Semantic Search

import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Compare query embedding with document embeddings
similarities = [cosine_similarity(query_embedding, doc) for doc in doc_embeddings]

Document Clustering

from sklearn.cluster import KMeans

# Cluster documents by embedding similarity
kmeans = KMeans(n_clusters=5)
clusters = kmeans.fit_predict(embeddings)

Best Practices

Batch requests: Send multiple texts in one request for efficiency
Normalize vectors: Normalize embeddings for consistent similarity scores
Choose dimensions: Use smaller dimensions when storage/speed matters
Chunk long texts: Split long documents into meaningful chunks before embedding

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": "Hello, world"
  }'

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="Hello, world"
)
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: 'Hello, world'
});
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' => 'Hello, world'
    ]
]);
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{"Hello, world"},
        },
    )
    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": "Hello, world"
    }
    """;
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: 'Hello, world'
}.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
  }
}

Count Tokens (Claude)Gemini Text Embedding (embedContent)

Popular Models

Smart Routing

OpenAI SDK Usage

Text Series

Embedding Series

Image Series

Video Series

Others

Text Embeddings

Text Embeddings

Endpoint

Authentication

Request Body

Example Request

Single Text

Batch Texts

Custom Dimensions

Supported Models

Use Cases

Semantic Search

Document Clustering

Best Practices

​Text Embeddings

​Endpoint

​Authentication

​Request Body

​Example Request

​Single Text

​Batch Texts

​Custom Dimensions

​Supported Models

​Use Cases

​Semantic Search

​Document Clustering

​Best Practices

Text Embeddings

Endpoint

Authentication

Request Body

Example Request

Single Text

Batch Texts

Custom Dimensions

Supported Models

Use Cases

Semantic Search

Document Clustering

Best Practices