Generate Text Embedding

curl --request POST \
  --url https://mavi-backend.memories.ai/serve/api/v2/embeddings/text \
  --header 'Authorization: <api-key>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": [
    "Sample text to embed",
    "Another text string"
  ],
  "model": "gemini-embedding-001",
  "dimensionality": 512
}
'

import requests

url = "https://mavi-backend.memories.ai/serve/api/v2/embeddings/text"

payload = {
    "input": ["Sample text to embed", "Another text string"],
    "model": "gemini-embedding-001",
    "dimensionality": 512
}
headers = {
    "Authorization": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    input: ['Sample text to embed', 'Another text string'],
    model: 'gemini-embedding-001',
    dimensionality: 512
  })
};

fetch('https://mavi-backend.memories.ai/serve/api/v2/embeddings/text', 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://mavi-backend.memories.ai/serve/api/v2/embeddings/text",
  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' => [
        'Sample text to embed',
        'Another text string'
    ],
    'model' => 'gemini-embedding-001',
    'dimensionality' => 512
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: <api-key>",
    "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://mavi-backend.memories.ai/serve/api/v2/embeddings/text"

	payload := strings.NewReader("{\n  \"input\": [\n    \"Sample text to embed\",\n    \"Another text string\"\n  ],\n  \"model\": \"gemini-embedding-001\",\n  \"dimensionality\": 512\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "<api-key>")
	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://mavi-backend.memories.ai/serve/api/v2/embeddings/text")
  .header("Authorization", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": [\n    \"Sample text to embed\",\n    \"Another text string\"\n  ],\n  \"model\": \"gemini-embedding-001\",\n  \"dimensionality\": 512\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://mavi-backend.memories.ai/serve/api/v2/embeddings/text")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"input\": [\n    \"Sample text to embed\",\n    \"Another text string\"\n  ],\n  \"model\": \"gemini-embedding-001\",\n  \"dimensionality\": 512\n}"

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

{
  "code": 200,
  "msg": "success",
  "data": [
    {
      "embedding": [
        -0.031649195,
        0.00069497403,
        0.011988669,
        "... (continues for `dimensionality` length)"
      ]
    },
    {
      "embedding": [
        0.012345,
        -0.067890,
        "... (one entry per input string)"
      ]
    }
  ],
  "success": true,
  "failed": false
}

POST

embeddings

text

Generate Text Embedding

curl --request POST \
  --url https://mavi-backend.memories.ai/serve/api/v2/embeddings/text \
  --header 'Authorization: <api-key>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": [
    "Sample text to embed",
    "Another text string"
  ],
  "model": "gemini-embedding-001",
  "dimensionality": 512
}
'

import requests

url = "https://mavi-backend.memories.ai/serve/api/v2/embeddings/text"

payload = {
    "input": ["Sample text to embed", "Another text string"],
    "model": "gemini-embedding-001",
    "dimensionality": 512
}
headers = {
    "Authorization": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    input: ['Sample text to embed', 'Another text string'],
    model: 'gemini-embedding-001',
    dimensionality: 512
  })
};

fetch('https://mavi-backend.memories.ai/serve/api/v2/embeddings/text', 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://mavi-backend.memories.ai/serve/api/v2/embeddings/text",
  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' => [
        'Sample text to embed',
        'Another text string'
    ],
    'model' => 'gemini-embedding-001',
    'dimensionality' => 512
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: <api-key>",
    "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://mavi-backend.memories.ai/serve/api/v2/embeddings/text"

	payload := strings.NewReader("{\n  \"input\": [\n    \"Sample text to embed\",\n    \"Another text string\"\n  ],\n  \"model\": \"gemini-embedding-001\",\n  \"dimensionality\": 512\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "<api-key>")
	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://mavi-backend.memories.ai/serve/api/v2/embeddings/text")
  .header("Authorization", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": [\n    \"Sample text to embed\",\n    \"Another text string\"\n  ],\n  \"model\": \"gemini-embedding-001\",\n  \"dimensionality\": 512\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://mavi-backend.memories.ai/serve/api/v2/embeddings/text")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"input\": [\n    \"Sample text to embed\",\n    \"Another text string\"\n  ],\n  \"model\": \"gemini-embedding-001\",\n  \"dimensionality\": 512\n}"

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

{
  "code": 200,
  "msg": "success",
  "data": [
    {
      "embedding": [
        -0.031649195,
        0.00069497403,
        0.011988669,
        "... (continues for `dimensionality` length)"
      ]
    },
    {
      "embedding": [
        0.012345,
        -0.067890,
        "... (one entry per input string)"
      ]
    }
  ],
  "success": true,
  "failed": false
}

Product: Visual Intelligence — Embeddings Use case: Generate vector embeddings for image, video, or text inputs for semantic search and similarity tasks Host: https://mavi-backend.memories.ai/serve/api/v2 Auth: Authorization: sk-mavi-... (no Bearer prefix)

This endpoint generates vector embeddings for text strings. You can embed single or multiple text inputs in one request, and optionally specify the output dimensionality.

Pricing:

$0.0000002 per token (2e-7/token)

Code Examples

const BASE_URL = "https://mavi-backend.memories.ai/serve/api/v2";
const API_KEY = "sk-mavi-...";

const response = await fetch(`${BASE_URL}/embeddings/text`, {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': API_KEY
  },
  body: JSON.stringify({
    input: ['Sample text to embed', 'Another text string'],
    model: 'gemini-embedding-001',
    dimensionality: 512
  })
});

const data = await response.json();
console.log(data);

import axios from 'axios';

const BASE_URL = "https://mavi-backend.memories.ai/serve/api/v2";
const API_KEY = "sk-mavi-...";

const response = await axios.post(`${BASE_URL}/embeddings/text`, {
  input: ['Sample text to embed', 'Another text string'],
  model: 'gemini-embedding-001',
  dimensionality: 512
}, {
  headers: {
    'Authorization': API_KEY
  }
});

console.log(response.data);

import requests

BASE_URL = "https://mavi-backend.memories.ai/serve/api/v2"
API_KEY = "sk-mavi-..."
HEADERS = {
    "Authorization": f"{API_KEY}"
}

def text_embedding(input_list, model="gemini-embedding-001", dimensionality=512):
    url = f"{BASE_URL}/embeddings/text"
    data = {
        "input": input_list,
        "model": model,
        "dimensionality": dimensionality
    }
    response = requests.post(url, headers=HEADERS, json=data)
    return response.json()

# Usage example
result = text_embedding(
    ["Sample text to embed", "Another text string"],
    model="gemini-embedding-001",
    dimensionality=512
)
print(result)

Request Body

Field	Type	Required	Description
input	array[string]	Yes	List of text strings to embed (can be a single string or multiple strings)
model	string	Yes	Embedding model name
dimensionality	integer	No	Output embedding dimension size (e.g., 256, 512, 768). Model-dependent.

Supported Models:

gemini-embedding-001 - Google’s Gemini text embedding model

Response

Returns embedding vectors for each input text.

The live response shape does not match the previously documented “singular vs plural” split. Live response is always data: [ {embedding: [...]} ] — an array of objects, each carrying its own embedding (singular) key — regardless of whether you sent one input or many. There is no top-level data.embedding / data.embeddings field. Verified live with gemini-embedding-001.

{
  "code": 200,
  "msg": "success",
  "data": [
    {
      "embedding": [
        -0.031649195,
        0.00069497403,
        0.011988669,
        "... (continues for `dimensionality` length)"
      ]
    },
    {
      "embedding": [
        0.012345,
        -0.067890,
        "... (one entry per input string)"
      ]
    }
  ],
  "success": true,
  "failed": false
}

Response Parameters

Parameter	Type	Description
code	integer	Response code (`200` on success)
msg	string	Response message describing the operation result
data	array[object]	One entry per `input` string, in the same order.
data[].embedding	array[number]	Vector for that input. Length equals `dimensionality` (or the model default).
success	boolean	Indicates whether the operation was successful
failed	boolean	Indicates whether the operation failed

Notes

Text embeddings are returned synchronously in the response.
The response is always an array — index into data[i].embedding to get the vector for input[i]. There is no singular-vs-plural variant.
The dimensionality parameter allows you to control the output vector size
Supported dimensionality depends on the model (common values: 256, 512, 768, 1024)
Lower dimensionality results in faster processing and reduced storage, but may have lower accuracy
Use text embeddings for:
- Semantic search and similarity matching
- Text classification and clustering
- Question answering and information retrieval
- Recommendation systems
- Duplicate detection

Authorizations

Authorization

string

header

required

Body

application/json

input

string[]

required

List of text strings to embed

Example:

[
  "Sample text to embed",
  "Another text string"
]

model

enum<string>

required

Embedding model name

Available options:

gemini-embedding-001

Example:

"gemini-embedding-001"

dimensionality

integer

Output embedding dimension size

Example:

512

Response

200 - application/json

Embeddings generated successfully

Option 1
Option 2

Response for single input

code

string

Response code indicating the result status

Example:

200

msg

string

Response message describing the operation result

Example:

"success"

data

object

Response data object containing the embedding

Show child attributes

success

boolean

Indicates whether the operation was successful

Example:

true

failed

boolean

Indicates whether the operation failed

Example:

false

Generate Video Embedding Video Caption

Get Started

Asset Management

Social Media Scraping

Audio File Transcription

Live Audio Transcription

Video Model APIs

Video Task APIs

Live Video Content Moderation

Live Video Understanding

Image Model APIs

Embeddings

Human ReID & Caption

Reference

Generate Text Embedding

Code Examples

Request Body

Response

Response Parameters

Notes

Authorizations

Body

Response

​Code Examples

​Request Body

​Response

​Response Parameters

​Notes

Authorizations

Body

Response

Code Examples

Request Body

Response

Response Parameters

Notes