Generate Video Embedding

curl --request POST \
  --url https://mavi-backend.memories.ai/serve/api/v2/embeddings/video \
  --header 'Authorization: <api-key>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "asset_id": "re_657745568997527552",
  "model": "multimodalembedding@001"
}
'

import requests

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

payload = {
    "asset_id": "re_657745568997527552",
    "model": "multimodalembedding@001"
}
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({asset_id: 're_657745568997527552', model: 'multimodalembedding@001'})
};

fetch('https://mavi-backend.memories.ai/serve/api/v2/embeddings/video', 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/video",
  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([
    'asset_id' => 're_657745568997527552',
    'model' => 'multimodalembedding@001'
  ]),
  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/video"

	payload := strings.NewReader("{\n  \"asset_id\": \"re_657745568997527552\",\n  \"model\": \"multimodalembedding@001\"\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/video")
  .header("Authorization", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"asset_id\": \"re_657745568997527552\",\n  \"model\": \"multimodalembedding@001\"\n}")
  .asString();

require 'uri'
require 'net/http'

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

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  \"asset_id\": \"re_657745568997527552\",\n  \"model\": \"multimodalembedding@001\"\n}"

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

{
  "code": 200,
  "msg": "success",
  "data": {
    "embedding": [
      0.0156250,
      -0.0234375,
      0.0312500,
      0.0078125,
      -0.0156250,
      "... (continues for vector length)"
    ]
  },
  "success": true,
  "failed": false
}

POST

embeddings

video

Generate Video Embedding

curl --request POST \
  --url https://mavi-backend.memories.ai/serve/api/v2/embeddings/video \
  --header 'Authorization: <api-key>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "asset_id": "re_657745568997527552",
  "model": "multimodalembedding@001"
}
'

import requests

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

payload = {
    "asset_id": "re_657745568997527552",
    "model": "multimodalembedding@001"
}
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({asset_id: 're_657745568997527552', model: 'multimodalembedding@001'})
};

fetch('https://mavi-backend.memories.ai/serve/api/v2/embeddings/video', 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/video",
  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([
    'asset_id' => 're_657745568997527552',
    'model' => 'multimodalembedding@001'
  ]),
  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/video"

	payload := strings.NewReader("{\n  \"asset_id\": \"re_657745568997527552\",\n  \"model\": \"multimodalembedding@001\"\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/video")
  .header("Authorization", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"asset_id\": \"re_657745568997527552\",\n  \"model\": \"multimodalembedding@001\"\n}")
  .asString();

require 'uri'
require 'net/http'

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

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  \"asset_id\": \"re_657745568997527552\",\n  \"model\": \"multimodalembedding@001\"\n}"

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

{
  "code": 200,
  "msg": "success",
  "data": {
    "embedding": [
      0.0156250,
      -0.0234375,
      0.0312500,
      0.0078125,
      -0.0156250,
      "... (continues for vector length)"
    ]
  },
  "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 a single vector embedding representing the entire video content. The video must be previously uploaded and referenced by its asset ID.

Pricing:

$0.002/second of video

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/video`, {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': API_KEY
  },
  body: JSON.stringify({
    asset_id: 're_657745568997527552',
    model: 'multimodalembedding@001'
  })
});

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/video`, {
  asset_id: 're_657745568997527552',
  model: 'multimodalembedding@001'
}, {
  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 video_embedding(asset_id, model="multimodalembedding@001"):
    url = f"{BASE_URL}/embeddings/video"
    data = {"asset_id": asset_id, "model": model}
    response = requests.post(url, headers=HEADERS, json=data)
    return response.json()

# Usage example
result = video_embedding("re_657745568997527552", model="multimodalembedding@001")
print(result)

Request Body

Field	Type	Required	Description
asset_id	string	Yes	Video asset ID from a previous upload
model	string	Yes	Embedding model name

Supported Models:

multimodalembedding@001 - Google’s multimodal embedding model supporting video

Response

Returns a single embedding vector representing the entire video.

{
  "code": 200,
  "msg": "success",
  "data": {
    "embedding": [
      0.0156250,
      -0.0234375,
      0.0312500,
      0.0078125,
      -0.0156250,
      "... (continues for vector length)"
    ]
  },
  "success": true,
  "failed": false
}

Response Parameters

Parameter	Type	Description
code	string	Response code indicating the result status (200 indicates success)
msg	string	Response message describing the operation result
data	object	Response data object containing the embedding
data.embedding	array[number]	Vector embedding array representing the entire video
success	boolean	Indicates whether the operation was successful
failed	boolean	Indicates whether the operation failed

Notes

Video embeddings are returned synchronously in the response
The embedding represents the entire video content as a single vector
Videos must be uploaded first using the /upload endpoint to obtain an asset_id
multimodalembedding@001 produces 1408-dimensional vectors
Supports various video formats: MP4, MOV, AVI, WebM, MKV, etc.
Processing time depends on video length and complexity
Use video embeddings for:
- Video similarity search
- Content-based video retrieval
- Video classification and categorization
- Duplicate video detection

Authorizations

Authorization

string

header

required

Body

application/json

asset_id

string

required

Video asset ID

Example:

"re_657745568997527552"

model

enum<string>

required

Embedding model name

Available options:

multimodalembedding@001

Example:

"multimodalembedding@001"

Response

200 - application/json

Embedding generated successfully

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 Image Embedding Generate Text Embedding

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 Video 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