Embed documents

Perform embedding on a Telnyx Storage Bucket using the a embedding model. The current supported file types are:

PDF
HTML
txt/unstructured text files
json
csv
audio / video (mp3, mp4, mpeg, mpga, m4a, wav, or webm ) - Max of 100mb file size.

Any files not matching the above types will be attempted to be embedded as unstructured text.

This process can be slow, so it runs in the background and the user can check the status of the task using the endpoint /ai/embeddings/{task_id}.

Important Note: When you update documents in a Telnyx Storage bucket, their associated embeddings are automatically kept up to date. If you add or update a file, it is automatically embedded. If you delete a file, the embeddings are deleted for that particular file.

You can also specify a custom loader param. Currently the only supported loader value is intercom which loads Intercom article jsons as specified by the Intercom article API This loader will split each article into paragraphs and save additional parameters relevant to Intercom docs, such as article_url and heading. These values will be returned by the /v2/ai/embeddings/similarity-search endpoint in the loader_metadata field.

POST

embeddings

JavaScript

import Telnyx from 'telnyx';

const client = new Telnyx({
  apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted
});

const embeddingResponse = await client.ai.embeddings.create({ bucket_name: 'bucket_name' });

console.log(embeddingResponse.data);

import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("TELNYX_API_KEY"),  # This is the default and can be omitted
)
embedding_response = client.ai.embeddings.create(
    bucket_name="bucket_name",
)
print(embedding_response.data)

package main

import (
	"context"
	"fmt"

	"github.com/team-telnyx/telnyx-go"
	"github.com/team-telnyx/telnyx-go/option"
)

func main() {
	client := telnyx.NewClient(
		option.WithAPIKey("My API Key"),
	)
	embeddingResponse, err := client.AI.Embeddings.New(context.TODO(), telnyx.AIEmbeddingNewParams{
		BucketName: "bucket_name",
	})
	if err != nil {
		panic(err.Error())
	}
	fmt.Printf("%+v\n", embeddingResponse.Data)
}

package com.telnyx.sdk.example;

import com.telnyx.sdk.client.TelnyxClient;
import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingCreateParams;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingResponse;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        TelnyxClient client = TelnyxOkHttpClient.fromEnv();

        EmbeddingCreateParams params = EmbeddingCreateParams.builder()
            .bucketName("bucket_name")
            .build();
        EmbeddingResponse embeddingResponse = client.ai().embeddings().create(params);
    }
}

require "telnyx"

telnyx = Telnyx::Client.new(api_key: "My API Key")

embedding_response = telnyx.ai.embeddings.create(bucket_name: "bucket_name")

puts(embedding_response)

<?php

require_once dirname(__DIR__) . '/vendor/autoload.php';

use Telnyx\Client;
use Telnyx\Core\Exceptions\APIException;

$client = new Client(apiKey: getenv('TELNYX_API_KEY') ?: 'My API Key');

try {
  $embeddingResponse = $client->ai->embeddings->create(
    bucketName: 'bucket_name',
    documentChunkOverlapSize: 0,
    documentChunkSize: 0,
    embeddingModel: 'thenlper/gte-large',
    loader: 'default',
  );

  var_dump($embeddingResponse);
} catch (APIException $e) {
  echo $e->getMessage();
}

telnyx ai:embeddings create \
  --api-key 'My API Key' \
  --bucket-name bucket_name

curl --request POST \
  --url https://api.telnyx.com/v2/ai/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "bucket_name": "<string>",
  "document_chunk_size": 1024,
  "document_chunk_overlap_size": 512,
  "embedding_model": "thenlper/gte-large",
  "loader": "default"
}
'

{
  "data": {
    "task_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "task_name": "<string>",
    "status": "<string>",
    "created_at": "<string>",
    "finished_at": "<string>",
    "user_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
  }
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

bucket_name

string

required

document_chunk_size

integer

default:1024

document_chunk_overlap_size

integer

default:512

embedding_model

enum<string>

default:thenlper/gte-large

Supported models to vectorize and embed documents.

Available options:

thenlper/gte-large,

intfloat/multilingual-e5-large

loader

enum<string>

default:default

Supported types of custom document loaders for embeddings.

Available options:

default,

intercom

Response

Successful Response

data

object

required

Show child attributes

Get Tasks by Status

List embedded buckets

⌘I

JavaScript

import Telnyx from 'telnyx';

const client = new Telnyx({
  apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted
});

const embeddingResponse = await client.ai.embeddings.create({ bucket_name: 'bucket_name' });

console.log(embeddingResponse.data);

import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("TELNYX_API_KEY"),  # This is the default and can be omitted
)
embedding_response = client.ai.embeddings.create(
    bucket_name="bucket_name",
)
print(embedding_response.data)

package main

import (
	"context"
	"fmt"

	"github.com/team-telnyx/telnyx-go"
	"github.com/team-telnyx/telnyx-go/option"
)

func main() {
	client := telnyx.NewClient(
		option.WithAPIKey("My API Key"),
	)
	embeddingResponse, err := client.AI.Embeddings.New(context.TODO(), telnyx.AIEmbeddingNewParams{
		BucketName: "bucket_name",
	})
	if err != nil {
		panic(err.Error())
	}
	fmt.Printf("%+v\n", embeddingResponse.Data)
}

package com.telnyx.sdk.example;

import com.telnyx.sdk.client.TelnyxClient;
import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingCreateParams;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingResponse;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        TelnyxClient client = TelnyxOkHttpClient.fromEnv();

        EmbeddingCreateParams params = EmbeddingCreateParams.builder()
            .bucketName("bucket_name")
            .build();
        EmbeddingResponse embeddingResponse = client.ai().embeddings().create(params);
    }
}

require "telnyx"

telnyx = Telnyx::Client.new(api_key: "My API Key")

embedding_response = telnyx.ai.embeddings.create(bucket_name: "bucket_name")

puts(embedding_response)

<?php

require_once dirname(__DIR__) . '/vendor/autoload.php';

use Telnyx\Client;
use Telnyx\Core\Exceptions\APIException;

$client = new Client(apiKey: getenv('TELNYX_API_KEY') ?: 'My API Key');

try {
  $embeddingResponse = $client->ai->embeddings->create(
    bucketName: 'bucket_name',
    documentChunkOverlapSize: 0,
    documentChunkSize: 0,
    embeddingModel: 'thenlper/gte-large',
    loader: 'default',
  );

  var_dump($embeddingResponse);
} catch (APIException $e) {
  echo $e->getMessage();
}

telnyx ai:embeddings create \
  --api-key 'My API Key' \
  --bucket-name bucket_name

curl --request POST \
  --url https://api.telnyx.com/v2/ai/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "bucket_name": "<string>",
  "document_chunk_size": 1024,
  "document_chunk_overlap_size": 512,
  "embedding_model": "thenlper/gte-large",
  "loader": "default"
}
'

{
  "data": {
    "task_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "task_name": "<string>",
    "status": "<string>",
    "created_at": "<string>",
    "finished_at": "<string>",
    "user_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
  }
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

Overview

Conversation History

Conversation Histories API

Embedding

For AI Agents

Embed documents

Authorizations

Body

Response