Gemini Text Embedding (embedContent)

curl --request POST \
  --url https://api.leapx-hub.com/v1/models/{model}:embedContent \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "content": {},
  "outputDimensionality": 123,
  "taskType": "<string>"
}
'

import requests

url = "https://api.leapx-hub.com/v1/models/{model}:embedContent"

payload = {
    "content": {},
    "outputDimensionality": 123,
    "taskType": "<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({content: {}, outputDimensionality: 123, taskType: '<string>'})
};

fetch('https://api.leapx-hub.com/v1/models/{model}:embedContent', 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.leapx-hub.com/v1/models/{model}:embedContent",
  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([
    'content' => [
        
    ],
    'outputDimensionality' => 123,
    'taskType' => '<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.leapx-hub.com/v1/models/{model}:embedContent"

	payload := strings.NewReader("{\n  \"content\": {},\n  \"outputDimensionality\": 123,\n  \"taskType\": \"<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.leapx-hub.com/v1/models/{model}:embedContent")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"content\": {},\n  \"outputDimensionality\": 123,\n  \"taskType\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.leapx-hub.com/v1/models/{model}:embedContent")

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  \"content\": {},\n  \"outputDimensionality\": 123,\n  \"taskType\": \"<string>\"\n}"

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

{
  "embedding": {
    "values": [0.0023064255, -0.009327292, 0.015797347, ...]
  },
  "metadata": {
    "usage": {
      "prompt_tokens": 6,
      "total_tokens": 6
    }
  }
}

POST

models

{model}

:embedContent

Gemini Text Embedding (embedContent)

curl --request POST \
  --url https://api.leapx-hub.com/v1/models/{model}:embedContent \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "content": {},
  "outputDimensionality": 123,
  "taskType": "<string>"
}
'

import requests

url = "https://api.leapx-hub.com/v1/models/{model}:embedContent"

payload = {
    "content": {},
    "outputDimensionality": 123,
    "taskType": "<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({content: {}, outputDimensionality: 123, taskType: '<string>'})
};

fetch('https://api.leapx-hub.com/v1/models/{model}:embedContent', 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.leapx-hub.com/v1/models/{model}:embedContent",
  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([
    'content' => [
        
    ],
    'outputDimensionality' => 123,
    'taskType' => '<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.leapx-hub.com/v1/models/{model}:embedContent"

	payload := strings.NewReader("{\n  \"content\": {},\n  \"outputDimensionality\": 123,\n  \"taskType\": \"<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.leapx-hub.com/v1/models/{model}:embedContent")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"content\": {},\n  \"outputDimensionality\": 123,\n  \"taskType\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.leapx-hub.com/v1/models/{model}:embedContent")

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  \"content\": {},\n  \"outputDimensionality\": 123,\n  \"taskType\": \"<string>\"\n}"

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

{
  "embedding": {
    "values": [0.0023064255, -0.009327292, 0.015797347, ...]
  },
  "metadata": {
    "usage": {
      "prompt_tokens": 6,
      "total_tokens": 6
    }
  }
}

Introduction

Use the Gemini native API to convert text to vector embeddings. The model is specified in the URL path (e.g. gemini-embedding-001). Use this when you need Google embedding models or alignment with the Gemini API.

This complements the Embeddings (OpenAI-style) endpoint: this doc describes the Gemini native path; the same capability is also available via POST /v1/embeddings.

Authentication

string

required

Bearer token, e.g. Bearer sk-xxxxxxxxxx

Path Parameters

string

required

Embedding model name, e.g. gemini-embedding-001. Do not send model in the request body.

Request Parameters

object

required

Content to embed. Must include a parts array; each item is { "text": "your text" }.

integer

Output vector dimension (supported only by some models, e.g. gemini-embedding-001, text-embedding-004).

string

Task type, e.g. RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY (optional).

cURL Example

curl -X POST "https://api.leapx-hub.com/v1/models/gemini-embedding-001:embedContent" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-XyLy**************************mIqSt" \
  -d '{
    "content": {
      "parts": [
        { "text": "Text to embed" }
      ]
    }
  }'

With dimension:

curl -X POST "https://api.leapx-hub.com/v1/models/gemini-embedding-001:embedContent" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-XyLy**************************mIqSt" \
  -d '{
    "content": {
      "parts": [
        { "text": "Text to embed" }
      ]
    },
    "outputDimensionality": 768
  }'

Python Example

import requests

url = "https://api.leapx-hub.com/v1/models/gemini-embedding-001:embedContent"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer sk-XyLy**************************mIqSt"
}
payload = {
    "content": {
        "parts": [
            { "text": "Text to embed" }
        ]
    }
}

response = requests.post(url, json=payload, headers=headers)
data = response.json()
embedding = data["embedding"]["values"]
print(f"Dimension: {len(embedding)}")

{
  "embedding": {
    "values": [0.0023064255, -0.009327292, 0.015797347, ...]
  },
  "metadata": {
    "usage": {
      "prompt_tokens": 6,
      "total_tokens": 6
    }
  }
}

Batch (batchEmbedContents)

For batch embedding use: POST /v1/models/{model}:batchEmbedContents with a requests array; each item has the same shape as a single request (including content.parts). Do not include model in each item.

curl -X POST "https://api.leapx-hub.com/v1/models/gemini-embedding-001:batchEmbedContents" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-XyLy**************************mIqSt" \
  -d '{
    "requests": [
      { "content": { "parts": [{ "text": "First text" }] } },
      { "content": { "parts": [{ "text": "Second text" }] } }
    ]
  }'

Supported Models

Model	Description
gemini-embedding-001	General-purpose embedding model; supports `outputDimensionality`
text-embedding-004	High-accuracy embedding model

Notes

The model is specified in the URL path; do not include model in the request body
content.parts is required with at least one non-empty text
Usage is returned in metadata.usage (prompt_tokens, total_tokens)

API documentation

Text Series

Text Embedding Series

Image Series

Video Series

Realtime Voice

Gemini Text Embedding (embedContent)

Introduction

Authentication

Path Parameters

Request Parameters

cURL Example

Python Example

Batch (batchEmbedContents)

Supported Models

Notes

​Introduction

​Authentication

​Path Parameters

​Request Parameters

​cURL Example

​Python Example

​Batch (batchEmbedContents)

​Supported Models

​Notes

Introduction

Authentication

Path Parameters

Request Parameters

cURL Example

Python Example

Batch (batchEmbedContents)

Supported Models

Notes