Telnyx Ai Inference - JavaScript

Installation

bash

npm install telnyx

Setup

javascript

import Telnyx from 'telnyx';

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

All examples below assume

client

is already initialized as shown above.

Error Handling

All API calls can fail with network errors, rate limits (429), validation errors (422), or authentication errors (401). Always handle errors in production code:

javascript

try {
  const result = await client.messages.send({ to: '+13125550001', from: '+13125550002', text: 'Hello' });
} catch (err) {
  if (err instanceof Telnyx.APIConnectionError) {
    console.error('Network error — check connectivity and retry');
  } else if (err instanceof Telnyx.RateLimitError) {
    // 429: rate limited — wait and retry with exponential backoff
    const retryAfter = err.headers?.['retry-after'] || 1;
    await new Promise(r => setTimeout(r, retryAfter * 1000));
  } else if (err instanceof Telnyx.APIError) {
    console.error(`API error ${err.status}: ${err.message}`);
    if (err.status === 422) {
      console.error('Validation error — check required fields and formats');
    }
  }
}

Common error codes:

invalid API key,

insufficient permissions,

resource not found,

validation error (check field formats),

rate limited (retry with exponential backoff).

Important Notes

Pagination: List methods return an auto-paginating iterator. Use
```
for await (const item of result) { ... }
```
to iterate through all pages automatically.

Transcribe speech to text

Transcribe speech to text. This endpoint is consistent with the OpenAI Transcription API and may be used with the OpenAI JS or Python SDK.

POST /ai/audio/transcriptions

javascript

const response = await client.ai.audio.transcribe({ model: 'distil-whisper/distil-large-v2' });

console.log(response.text);

Returns:

duration

(number),

segments

(array[object]),

text

(string)

Create a chat completion

Chat with a language model. This endpoint is consistent with the OpenAI Chat Completions API and may be used with the OpenAI JS or Python SDK.

POST /ai/chat/completions

— Required:

messages

Optional:

api_key_ref

(string),

best_of

(integer),

early_stopping

(boolean),

enable_thinking

(boolean),

frequency_penalty

(number),

guided_choice

(array[string]),

guided_json

(object),

guided_regex

(string),

length_penalty

(number),

logprobs

(boolean),

max_tokens

(integer),

min_p

(number),

model

(string),

(number),

presence_penalty

(number),

response_format

(object),

stream

(boolean),

temperature

(number),

tool_choice

(enum: none, auto, required),

tools

(array[object]),

top_logprobs

(integer),

top_p

(number),

use_beam_search

(boolean)

javascript

const response = await client.ai.chat.createCompletion({
  messages: [
    { role: 'system', content: 'You are a friendly chatbot.' },
    { role: 'user', content: 'Hello, world!' },
  ],
});

console.log(response);

List conversations

Retrieve a list of all AI conversations configured by the user. Supports PostgREST-style query parameters for filtering. Examples are included for the standard metadata fields, but you can filter on any field in the metadata JSON object.

GET /ai/conversations

javascript

const conversations = await client.ai.conversations.list();

console.log(conversations.data);

Returns:

created_at

(date-time),

id

(uuid),

last_message_at

(date-time),

metadata

(object),

name

(string)

Create a conversation

Create a new AI Conversation.

POST /ai/conversations

Optional:

metadata

(object),

name

(string)

javascript

const conversation = await client.ai.conversations.create();

console.log(conversation.id);

Returns:

created_at

(date-time),

id

(uuid),

last_message_at

(date-time),

metadata

(object),

name

(string)

Get Insight Template Groups

Get all insight groups

GET /ai/conversations/insight-groups

javascript

// Automatically fetches more pages as needed.
for await (const insightTemplateGroup of client.ai.conversations.insightGroups.retrieveInsightGroups()) {
  console.log(insightTemplateGroup.id);
}

Returns:

created_at

(date-time),

description

(string),

id

(uuid),

insights

(array[object]),

name

(string),

webhook

(string)

Create Insight Template Group

Create a new insight group

POST /ai/conversations/insight-groups

— Required:

name

Optional:

description

(string),

webhook

(string)

javascript

const insightTemplateGroupDetail = await client.ai.conversations.insightGroups.insightGroups({
  name: 'my-resource',
});

console.log(insightTemplateGroupDetail.data);

Returns:

created_at

(date-time),

description

(string),

id

(uuid),

insights

(array[object]),

name

(string),

webhook

(string)

Get Insight Template Group

Get insight group by ID

GET /ai/conversations/insight-groups/{group_id}

javascript

const insightTemplateGroupDetail = await client.ai.conversations.insightGroups.retrieve(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
);

console.log(insightTemplateGroupDetail.data);

Returns:

created_at

(date-time),

description

(string),

id

(uuid),

insights

(array[object]),

name

(string),

webhook

(string)

Update Insight Template Group

Update an insight template group

PUT /ai/conversations/insight-groups/{group_id}

Optional:

description

(string),

name

(string),

webhook

(string)

javascript

const insightTemplateGroupDetail = await client.ai.conversations.insightGroups.update(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
);

console.log(insightTemplateGroupDetail.data);

Returns:

created_at

(date-time),

description

(string),

id

(uuid),

insights

(array[object]),

name

(string),

webhook

(string)

Delete Insight Template Group

Delete insight group by ID

DELETE /ai/conversations/insight-groups/{group_id}

javascript

await client.ai.conversations.insightGroups.delete('182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e');

Assign Insight Template To Group

Assign an insight to a group

POST /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/assign

javascript

await client.ai.conversations.insightGroups.insights.assign(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
  { group_id: '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e' },
);

Unassign Insight Template From Group

Remove an insight from a group

DELETE /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/unassign

javascript

await client.ai.conversations.insightGroups.insights.deleteUnassign(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
  { group_id: '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e' },
);

Get Insight Templates

Get all insights

GET /ai/conversations/insights

javascript

// Automatically fetches more pages as needed.
for await (const insightTemplate of client.ai.conversations.insights.list()) {
  console.log(insightTemplate.id);
}

Returns:

created_at

(date-time),

id

(uuid),

insight_type

(enum: custom, default),

instructions

(string),

json_schema

(object),

name

(string),

webhook

(string)

Create Insight Template

Create a new insight

POST /ai/conversations/insights

— Required:

instructions

name

Optional:

json_schema

(object),

webhook

(string)

javascript

const insightTemplateDetail = await client.ai.conversations.insights.create({
  instructions: 'You are a helpful assistant.',
  name: 'my-resource',
});

console.log(insightTemplateDetail.data);

Returns:

created_at

(date-time),

id

(uuid),

insight_type

(enum: custom, default),

instructions

(string),

json_schema

(object),

name

(string),

webhook

(string)

Get Insight Template

Get insight by ID

GET /ai/conversations/insights/{insight_id}

javascript

const insightTemplateDetail = await client.ai.conversations.insights.retrieve(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
);

console.log(insightTemplateDetail.data);

Returns:

created_at

(date-time),

id

(uuid),

insight_type

(enum: custom, default),

instructions

(string),

json_schema

(object),

name

(string),

webhook

(string)

Update Insight Template

Update an insight template

PUT /ai/conversations/insights/{insight_id}

Optional:

instructions

(string),

json_schema

(object),

name

(string),

webhook

(string)

javascript

const insightTemplateDetail = await client.ai.conversations.insights.update(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
);

console.log(insightTemplateDetail.data);

Returns:

created_at

(date-time),

id

(uuid),

insight_type

(enum: custom, default),

instructions

(string),

json_schema

(object),

name

(string),

webhook

(string)

Delete Insight Template

Delete insight by ID

DELETE /ai/conversations/insights/{insight_id}

javascript

await client.ai.conversations.insights.delete('182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e');

Get a conversation

Retrieve a specific AI conversation by its ID.

GET /ai/conversations/{conversation_id}

javascript

const conversation = await client.ai.conversations.retrieve('550e8400-e29b-41d4-a716-446655440000');

console.log(conversation.data);

Returns:

created_at

(date-time),

id

(uuid),

last_message_at

(date-time),

metadata

(object),

name

(string)

Update conversation metadata

Update metadata for a specific conversation.

PUT /ai/conversations/{conversation_id}

Optional:

metadata

(object)

javascript

const conversation = await client.ai.conversations.update('550e8400-e29b-41d4-a716-446655440000');

console.log(conversation.data);

Returns:

created_at

(date-time),

id

(uuid),

last_message_at

(date-time),

metadata

(object),

name

(string)

Delete a conversation

Delete a specific conversation by its ID.

DELETE /ai/conversations/{conversation_id}

javascript

await client.ai.conversations.delete('550e8400-e29b-41d4-a716-446655440000');

Get insights for a conversation

Retrieve insights for a specific conversation

GET /ai/conversations/{conversation_id}/conversations-insights

javascript

const response = await client.ai.conversations.retrieveConversationsInsights('550e8400-e29b-41d4-a716-446655440000');

console.log(response.data);

Returns:

conversation_insights

(array[object]),

created_at

(date-time),

id

(string),

status

(enum: pending, in_progress, completed, failed)

Create Message

Add a new message to the conversation. Used to insert a new messages to a conversation manually ( without using chat endpoint )

POST /ai/conversations/{conversation_id}/message

— Required:

role

Optional:

content

(string),

metadata

(object),

name

(string),

sent_at

(date-time),

tool_call_id

(string),

tool_calls

(array[object]),

tool_choice

(object)

javascript

await client.ai.conversations.addMessage('182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e', { role: 'user' });

Get conversation messages

Retrieve messages for a specific conversation, including tool calls made by the assistant.

GET /ai/conversations/{conversation_id}/messages

javascript

const messages = await client.ai.conversations.messages.list('550e8400-e29b-41d4-a716-446655440000');

console.log(messages.data);

Returns:

created_at

(date-time),

role

(enum: user, assistant, tool),

sent_at

(date-time),

text

(string),

tool_calls

(array[object])

Get Tasks by Status

Retrieve tasks for the user that are either

queued

processing

failed

success

partial_success

based on the query string. Defaults to

queued

and

processing

GET /ai/embeddings

javascript

const embeddings = await client.ai.embeddings.list();

console.log(embeddings.data);

Returns:

bucket

(string),

created_at

(date-time),

finished_at

(date-time),

status

(enum: queued, processing, success, failure, partial_success),

task_id

(string),

task_name

(string),

user_id

(string)

Embed documents

Perform embedding on a Telnyx Storage Bucket using an 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.

POST /ai/embeddings

— Required:

bucket_name

Optional:

document_chunk_overlap_size

(integer),

document_chunk_size

(integer),

embedding_model

(object),

loader

(object)

javascript

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

console.log(embeddingResponse.data);

Returns:

created_at

(string),

finished_at

(string | null),

status

(string),

task_id

(uuid),

task_name

(string),

user_id

(uuid)

List embedded buckets

Get all embedding buckets for a user.

GET /ai/embeddings/buckets

javascript

const buckets = await client.ai.embeddings.buckets.list();

console.log(buckets.data);

Returns:

buckets

(array[string])

Get file-level embedding statuses for a bucket

Get all embedded files for a given user bucket, including their processing status.

GET /ai/embeddings/buckets/{bucket_name}

javascript

const bucket = await client.ai.embeddings.buckets.retrieve('bucket_name');

console.log(bucket.data);

Returns:

created_at

(date-time),

error_reason

(string),

filename

(string),

last_embedded_at

(date-time),

status

(string),

updated_at

(date-time)

Disable AI for an Embedded Bucket

Deletes an entire bucket's embeddings and disables the bucket for AI-use, returning it to normal storage pricing.

DELETE /ai/embeddings/buckets/{bucket_name}

javascript

await client.ai.embeddings.buckets.delete('bucket_name');

Search for documents

Perform a similarity search on a Telnyx Storage Bucket, returning the most similar

num_docs

document chunks to the query. Currently the only available distance metric is cosine similarity which will return a

distance

between 0 and 1. The lower the distance, the more similar the returned document chunks are to the query.

POST /ai/embeddings/similarity-search

— Required:

bucket_name

query

Optional:

num_of_docs

(integer)

javascript

const response = await client.ai.embeddings.similaritySearch({
  bucket_name: 'bucket_name',
  query: 'What is Telnyx?',
});

console.log(response.data);

Returns:

distance

(number),

document_chunk

(string),

metadata

(object)

Embed URL content

Embed website content from a specified URL, including child pages up to 5 levels deep within the same domain. The process crawls and loads content from the main URL and its linked pages into a Telnyx Cloud Storage bucket.

POST /ai/embeddings/url

— Required:

url

bucket_name

javascript

const embeddingResponse = await client.ai.embeddings.url({
  bucket_name: 'bucket_name',
  url: 'https://example.com/resource',
});

console.log(embeddingResponse.data);

Returns:

created_at

(string),

finished_at

(string | null),

status

(string),

task_id

(uuid),

task_name

(string),

user_id

(uuid)

Get an embedding task's status

Check the status of a current embedding task. Will be one of the following:

```
queued
```
- Task is waiting to be picked up by a worker
```
processing
```
- The embedding task is running
```
success
```
- Task completed successfully and the bucket is embedded
```
failure
```
- Task failed and no files were embedded successfully
```
partial_success
```
- Some files were embedded successfully, but at least one failed

GET /ai/embeddings/{task_id}

javascript

const embedding = await client.ai.embeddings.retrieve('task_id');

console.log(embedding.data);

Returns:

created_at

(string),

finished_at

(string),

status

(enum: queued, processing, success, failure, partial_success),

task_id

(uuid),

task_name

(string)

List fine tuning jobs

Retrieve a list of all fine tuning jobs created by the user.

GET /ai/fine_tuning/jobs

javascript

const jobs = await client.ai.fineTuning.jobs.list();

console.log(jobs.data);

Returns:

created_at

(integer),

finished_at

(integer | null),

hyperparameters

(object),

id

(string),

model

(string),

organization_id

(string),

status

(enum: queued, running, succeeded, failed, cancelled),

trained_tokens

(integer | null),

training_file

(string)

Create a fine tuning job

Create a new fine tuning job.

POST /ai/fine_tuning/jobs

— Required:

model

training_file

Optional:

hyperparameters

(object),

suffix

(string)

javascript

const fineTuningJob = await client.ai.fineTuning.jobs.create({
  model: 'openai/gpt-4o',
  training_file: 'training_file',
});

console.log(fineTuningJob.id);

Returns:

created_at

(integer),

finished_at

(integer | null),

hyperparameters

(object),

id

(string),

model

(string),

organization_id

(string),

status

(enum: queued, running, succeeded, failed, cancelled),

trained_tokens

(integer | null),

training_file

(string)

Get a fine tuning job

Retrieve a fine tuning job by

job_id

GET /ai/fine_tuning/jobs/{job_id}

javascript

const fineTuningJob = await client.ai.fineTuning.jobs.retrieve('job_id');

console.log(fineTuningJob.id);

Returns:

created_at

(integer),

finished_at

(integer | null),

hyperparameters

(object),

id

(string),

model

(string),

organization_id

(string),

status

(enum: queued, running, succeeded, failed, cancelled),

trained_tokens

(integer | null),

training_file

(string)

Cancel a fine tuning job

Cancel a fine tuning job.

POST /ai/fine_tuning/jobs/{job_id}/cancel

javascript

const fineTuningJob = await client.ai.fineTuning.jobs.cancel('job_id');

console.log(fineTuningJob.id);

Returns:

created_at

(integer),

finished_at

(integer | null),

hyperparameters

(object),

id

(string),

model

(string),

organization_id

(string),

status

(enum: queued, running, succeeded, failed, cancelled),

trained_tokens

(integer | null),

training_file

(string)

Get available models

This endpoint returns a list of Open Source and OpenAI models that are available for use. Note: Model

id

's will be in the form

{source}/{model_name}

. For example

openai/gpt-4

mistralai/Mistral-7B-Instruct-v0.1

consistent with HuggingFace naming conventions.

GET /ai/models

javascript

const response = await client.ai.retrieveModels();

console.log(response.data);

Returns:

created

(integer),

id

(string),

object

(string),

owned_by

(string)

Create embeddings

Creates an embedding vector representing the input text. This endpoint is compatible with the OpenAI Embeddings API and may be used with the OpenAI JS or Python SDK by setting the base URL to

https://api.telnyx.com/v2/ai/openai

POST /ai/openai/embeddings

— Required:

input

model

Optional:

dimensions

(integer),

encoding_format

(enum: float, base64),

user

(string)

javascript

const response = await client.ai.openai.embeddings.createEmbeddings({
  input: 'The quick brown fox jumps over the lazy dog',
  model: 'thenlper/gte-large',
});

console.log(response.data);

Returns:

data

(array[object]),

model

(string),

object

(string),

usage

(object)

List embedding models

Returns a list of available embedding models. This endpoint is compatible with the OpenAI Models API format.

GET /ai/openai/embeddings/models

javascript

const response = await client.ai.openai.embeddings.listEmbeddingModels();

console.log(response.data);

Returns:

created

(integer),

id

(string),

object

(string),

owned_by

(string)

Summarize file content

Generate a summary of a file's contents. Supports the following text formats:

PDF, HTML, txt, json, csv

Supports the following media formats (billed for both the transcription and summary):

flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm
Up to 100 MB

POST /ai/summarize

— Required:

bucket

filename

Optional:

system_prompt

(string)

javascript

const response = await client.ai.summarize({ bucket: 'my-bucket', filename: 'data.csv' });

console.log(response.data);

Returns:

summary

(string)

Get all Speech to Text batch report requests

Retrieves all Speech to Text batch report requests for the authenticated user

GET /legacy/reporting/batch_detail_records/speech_to_text

javascript

const speechToTexts = await client.legacy.reporting.batchDetailRecords.speechToText.list();

console.log(speechToTexts.data);

Returns:

created_at

(date-time),

download_link

(string),

end_date

(date-time),

id

(string),

record_type

(string),

start_date

(date-time),

status

(enum: PENDING, COMPLETE, FAILED, EXPIRED)

Create a new Speech to Text batch report request

Creates a new Speech to Text batch report request with the specified filters

POST /legacy/reporting/batch_detail_records/speech_to_text

— Required:

start_date

end_date

javascript

const speechToText = await client.legacy.reporting.batchDetailRecords.speechToText.create({
  end_date: '2020-07-01T00:00:00-06:00',
  start_date: '2020-07-01T00:00:00-06:00',
});

console.log(speechToText.data);

Returns:

created_at

(date-time),

download_link

(string),

end_date

(date-time),

id

(string),

record_type

(string),

start_date

(date-time),

status

(enum: PENDING, COMPLETE, FAILED, EXPIRED)

Get a specific Speech to Text batch report request

Retrieves a specific Speech to Text batch report request by ID

GET /legacy/reporting/batch_detail_records/speech_to_text/{id}

javascript

const speechToText = await client.legacy.reporting.batchDetailRecords.speechToText.retrieve(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
);

console.log(speechToText.data);

Returns:

created_at

(date-time),

download_link

(string),

end_date

(date-time),

id

(string),

record_type

(string),

start_date

(date-time),

status

(enum: PENDING, COMPLETE, FAILED, EXPIRED)

Delete a Speech to Text batch report request

Deletes a specific Speech to Text batch report request by ID

DELETE /legacy/reporting/batch_detail_records/speech_to_text/{id}

javascript

const speechToText = await client.legacy.reporting.batchDetailRecords.speechToText.delete(
  '182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
);

console.log(speechToText.data);

Returns:

created_at

(date-time),

download_link

(string),

end_date

(date-time),

id

(string),

record_type

(string),

start_date

(date-time),

status

(enum: PENDING, COMPLETE, FAILED, EXPIRED)

Get speech to text usage report

Generate and fetch speech to text usage report synchronously. This endpoint will both generate and fetch the speech to text report over a specified time period.

GET /legacy/reporting/usage_reports/speech_to_text

javascript

const response = await client.legacy.reporting.usageReports.retrieveSpeechToText();

console.log(response.data);

Returns:

data

(object)

Generate speech from text

Generate synthesized speech audio from text input. Returns audio in the requested format (binary audio stream, base64-encoded JSON, or an audio URL for later retrieval). Authentication is provided via the standard

Authorization: Bearer

header.

POST /text-to-speech/speech

Optional:

aws

(object),

azure

(object),

disable_cache

(boolean),

elevenlabs

(object),

language

(string),

minimax

(object),

output_type

(enum: binary_output, base64_output),

provider

(enum: aws, telnyx, azure, elevenlabs, minimax, rime, resemble),

resemble

(object),

rime

(object),

telnyx

(object),

text

(string),

text_type

(enum: text, ssml),

voice

(string),

voice_settings

(object)

javascript

const response = await client.textToSpeech.generate();

console.log(response.base64_audio);

Returns:

base64_audio

(string)

List available voices

Retrieve a list of available voices from one or all TTS providers. When

provider

is specified, returns voices for that provider only. Otherwise, returns voices from all providers.

GET /text-to-speech/voices

javascript

const response = await client.textToSpeech.listVoices();

console.log(response.voices);

Returns:

voices

(array[object])

telnyx-ai-inference-javascript

NPX Install

Tags

SKILL.md Content

Telnyx Ai Inference - JavaScript

Installation

Setup

Error Handling

Important Notes

Transcribe speech to text

Create a chat completion

List conversations

Create a conversation

Get Insight Template Groups

Create Insight Template Group

Get Insight Template Group

Update Insight Template Group

Delete Insight Template Group

Assign Insight Template To Group

Unassign Insight Template From Group

Get Insight Templates

Create Insight Template

Get Insight Template

Update Insight Template

Delete Insight Template

Get a conversation

Update conversation metadata

Delete a conversation

Get insights for a conversation

Create Message

Get conversation messages

Get Tasks by Status

Embed documents

List embedded buckets

Get file-level embedding statuses for a bucket

Disable AI for an Embedded Bucket

Search for documents

Embed URL content

Get an embedding task's status

List fine tuning jobs

Create a fine tuning job

Get a fine tuning job

Cancel a fine tuning job

Get available models

Create embeddings

List embedding models

Summarize file content

Get all Speech to Text batch report requests

Create a new Speech to Text batch report request

Get a specific Speech to Text batch report request

Delete a Speech to Text batch report request

Get speech to text usage report

Generate speech from text

List available voices