Create Embeddings

curl --request POST \
  --url https://api.mor.org/api/v1/embeddings \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": "<string>",
  "model": "<string>",
  "encoding_format": "<string>",
  "user": "<string>"
}
'

import requests

url = "https://api.mor.org/api/v1/embeddings"

payload = {
    "input": "<string>",
    "model": "<string>",
    "encoding_format": "<string>",
    "user": "<string>"
}
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({
    input: '<string>',
    model: '<string>',
    encoding_format: '<string>',
    user: '<string>'
  })
};

fetch('https://api.mor.org/api/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.mor.org/api/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([
    'input' => '<string>',
    'model' => '<string>',
    'encoding_format' => '<string>',
    'user' => '<string>'
  ]),
  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.mor.org/api/v1/embeddings"

	payload := strings.NewReader("{\n  \"input\": \"<string>\",\n  \"model\": \"<string>\",\n  \"encoding_format\": \"<string>\",\n  \"user\": \"<string>\"\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.mor.org/api/v1/embeddings")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": \"<string>\",\n  \"model\": \"<string>\",\n  \"encoding_format\": \"<string>\",\n  \"user\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.mor.org/api/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  \"input\": \"<string>\",\n  \"model\": \"<string>\",\n  \"encoding_format\": \"<string>\",\n  \"user\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body

{
  "object": "<string>",
  "data": [
    {
      "object": "<string>",
      "embedding": [
        {}
      ],
      "index": 123
    }
  ],
  "model": "<string>",
  "usage": {
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}

POST

api

embeddings

Create Embeddings

curl --request POST \
  --url https://api.mor.org/api/v1/embeddings \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": "<string>",
  "model": "<string>",
  "encoding_format": "<string>",
  "user": "<string>"
}
'

import requests

url = "https://api.mor.org/api/v1/embeddings"

payload = {
    "input": "<string>",
    "model": "<string>",
    "encoding_format": "<string>",
    "user": "<string>"
}
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({
    input: '<string>',
    model: '<string>',
    encoding_format: '<string>',
    user: '<string>'
  })
};

fetch('https://api.mor.org/api/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.mor.org/api/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([
    'input' => '<string>',
    'model' => '<string>',
    'encoding_format' => '<string>',
    'user' => '<string>'
  ]),
  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.mor.org/api/v1/embeddings"

	payload := strings.NewReader("{\n  \"input\": \"<string>\",\n  \"model\": \"<string>\",\n  \"encoding_format\": \"<string>\",\n  \"user\": \"<string>\"\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.mor.org/api/v1/embeddings")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": \"<string>\",\n  \"model\": \"<string>\",\n  \"encoding_format\": \"<string>\",\n  \"user\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.mor.org/api/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  \"input\": \"<string>\",\n  \"model\": \"<string>\",\n  \"encoding_format\": \"<string>\",\n  \"user\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body

{
  "object": "<string>",
  "data": [
    {
      "object": "<string>",
      "embedding": [
        {}
      ],
      "index": 123
    }
  ],
  "model": "<string>",
  "usage": {
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}

Create embeddings for the given input text(s). This endpoint creates vector embeddings, typically for the purpose of “RAG” (Retrieval Augemented Generation) storage. It automatically manages sessions and routes requests to the appropriate embedding model.

Headers

string

required

API key in format: Bearer sk-xxxxxx

Body

string

required

Input text to generate embeddings for. Can be a single string or an array of strings.Single text example:

"input": "The quick brown fox jumps over the lazy dog"

Multiple texts example (batch processing):

"input": ["First text", "Second text", "Third text"]

When using the interactive playground, it may show "input": {} - ignore this and manually edit the generated cURL command to use either a string "text" or array ["text1", "text2"] format.

string

required

Model ID to use for embedding generation (blockchain hex address or name)

Use the List Models endpoint to see available embedding models.

string

default:"float"

Format for the embedding vectors. Options: float or base64

string

Unique identifier representing your end-user for monitoring and abuse detection

Response

string

Always returns "list"

array

Array of embedding objects

Show Embedding Object

string

Always "embedding"

array

Vector representation of the input text as an array of floats (or base64 if specified)

integer

Index of the embedding in the array (corresponds to the input array position)

string

Model used for generating the embeddings

object

Token usage statistics

Show Usage Object

integer

Number of tokens in the input

integer

Total tokens processed

Example Request

import openai

client = openai.OpenAI(
    api_key="sk-xxxxxx",
    base_url="https://api.mor.org/api/v1"
)

response = client.embeddings.create(
    model="text-embedding-model",
    input="The quick brown fox jumps over the lazy dog"
)

print(response.data[0].embedding)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-xxxxxx',
  baseURL: 'https://api.mor.org/api/v1'
});

const response = await client.embeddings.create({
  model: 'text-embedding-model',
  input: 'The quick brown fox jumps over the lazy dog'
});

console.log(response.data[0].embedding);

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

Use Cases

Semantic Search

Generate embeddings for documents and queries to enable similarity-based search

Clustering

Group similar texts together by comparing embedding vectors

Recommendations

Build recommendation systems by finding similar items based on embeddings

Classification

Use embeddings as features for text classification models

The API is fully compatible with the OpenAI SDK. Simply change the base_url to point to the Morpheus Gateway.

Embedding dimensions vary by model. Ensure your application can handle different vector sizes when switching models.

Getting Started

Models

Chat

Embeddings

Audio

Billing

Utility

Create Embeddings

Headers

Body

Response

Example Request

Use Cases

Semantic Search

Clustering

Recommendations

Classification

​Headers

​Body

​Response

​Example Request

​Use Cases

Semantic Search

Clustering

Recommendations

Classification

Headers

Body

Response

Example Request

Use Cases