Embeddings

curl --request POST \
  --url https://api.redpill.ai/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "<string>",
  "input": [
    "<string>"
  ],
  "encoding_format": "<string>",
  "dimensions": 123
}
'

import requests

url = "https://api.redpill.ai/v1/embeddings"

payload = {
    "model": "<string>",
    "input": ["<string>"],
    "encoding_format": "<string>",
    "dimensions": 123
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: '<string>',
    input: ['<string>'],
    encoding_format: '<string>',
    dimensions: 123
  })
};

fetch('https://api.redpill.ai/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.redpill.ai/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([
    'model' => '<string>',
    'input' => [
        '<string>'
    ],
    'encoding_format' => '<string>',
    'dimensions' => 123
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "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.redpill.ai/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"<string>\",\n  \"input\": [\n    \"<string>\"\n  ],\n  \"encoding_format\": \"<string>\",\n  \"dimensions\": 123\n}")

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

	req.Header.Add("Authorization", "Bearer <token>")
	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.redpill.ai/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"<string>\",\n  \"input\": [\n    \"<string>\"\n  ],\n  \"encoding_format\": \"<string>\",\n  \"dimensions\": 123\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.redpill.ai/v1/embeddings")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"<string>\",\n  \"input\": [\n    \"<string>\"\n  ],\n  \"encoding_format\": \"<string>\",\n  \"dimensions\": 123\n}"

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

POST

embeddings

Embeddings

curl --request POST \
  --url https://api.redpill.ai/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "<string>",
  "input": [
    "<string>"
  ],
  "encoding_format": "<string>",
  "dimensions": 123
}
'

import requests

url = "https://api.redpill.ai/v1/embeddings"

payload = {
    "model": "<string>",
    "input": ["<string>"],
    "encoding_format": "<string>",
    "dimensions": 123
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: '<string>',
    input: ['<string>'],
    encoding_format: '<string>',
    dimensions: 123
  })
};

fetch('https://api.redpill.ai/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.redpill.ai/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([
    'model' => '<string>',
    'input' => [
        '<string>'
    ],
    'encoding_format' => '<string>',
    'dimensions' => 123
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "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.redpill.ai/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"<string>\",\n  \"input\": [\n    \"<string>\"\n  ],\n  \"encoding_format\": \"<string>\",\n  \"dimensions\": 123\n}")

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

	req.Header.Add("Authorization", "Bearer <token>")
	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.redpill.ai/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"<string>\",\n  \"input\": [\n    \"<string>\"\n  ],\n  \"encoding_format\": \"<string>\",\n  \"dimensions\": 123\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.redpill.ai/v1/embeddings")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"<string>\",\n  \"input\": [\n    \"<string>\"\n  ],\n  \"encoding_format\": \"<string>\",\n  \"dimensions\": 123\n}"

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

Create Embeddings

Generate vector embeddings from text for semantic search, similarity, clustering, and retrieval. Requests are served through the attested TEE gateway, which does not retain request bodies. Embedding requests route to a third-party provider; no embedding model runs on a verified confidential upstream today.

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

Request Body

string

required

Embedding model id. See Supported models below.

string | string[]

required

Text to embed. Pass a single string or an array of strings for batch embedding.

string

float (default) or base64.

integer

Optional output dimension count. Supported by openai/text-embedding-3-small and openai/text-embedding-3-large, which can return fewer dimensions than their default.

Example

curl https://api.redpill.ai/v1/embeddings \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": "Confidential AI you can verify."
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1",
)

resp = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="Confidential AI you can verify.",
)
print(len(resp.data[0].embedding))  # 1536

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.redpill.ai/v1",
  apiKey: process.env.API_KEY,
});

const resp = await client.embeddings.create({
  model: "openai/text-embedding-3-small",
  input: "Confidential AI you can verify.",
});
console.log(resp.data[0].embedding.length); // 1536

Batch input

Pass an array to embed several strings in one request. Results are returned in input order, each with its index.

curl https://api.redpill.ai/v1/embeddings \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": ["first document", "second document"]
  }'

Response

{
  "object": "list",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0167, -0.0557, "…"] }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": { "prompt_tokens": 8, "total_tokens": 8 }
}

Supported models

Model id	Dimensions
`openai/text-embedding-3-small`	1536
`openai/text-embedding-3-large`	3072
`openai/text-embedding-ada-002`	1536
`qwen/qwen3-embedding-8b`	4096

If a model id returns model_not_found, it is not currently routed. The live catalog is GET /v1/embeddings/models. It accepts the same zdr filter as the chat model catalog, so you can list the embedding models reachable under zero data retention:

curl -s "https://api.redpill.ai/v1/embeddings/models?zdr=true" \
  -H "Authorization: Bearer $REDPILL_API_KEY" \
  | jq -r '.data[].id'

Embedding requests accept the same provider routing block as chat completions, including zdr, aci_verified, and aci_session_ids. See Zero data retention.

Custom dimensions

openai/text-embedding-3-small and openai/text-embedding-3-large accept a dimensions parameter to return shorter vectors, which reduces storage and speeds up similarity search at some cost to quality.

curl https://api.redpill.ai/v1/embeddings \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-large",
    "input": "shorter vector",
    "dimensions": 1024
  }'

Keep one model and one dimension count per index. Vectors from different models are not comparable.

Models

Chat and confidential models.

Trust boundary

What the gateway protects.

Endpoints

Verification

Legacy

Embeddings

Create Embeddings

Request Body

Example

Batch input

Response

Supported models

Custom dimensions

Models

Trust boundary

​Create Embeddings

​Request Body

​Example

​Batch input

​Response

​Supported models

​Custom dimensions

​Related

Models

Trust boundary

Create Embeddings

Request Body

Example

Batch input

Response

Supported models

Custom dimensions

Related