<!-- Auto-generated from Telnyx OpenAPI specs. Do not edit. -->

Telnyx Ai Inference - JavaScript

Installation

npm install telnyx@6.74.2

Setup

import Telnyx from 'telnyx';

const client = new Telnyx({
  apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted
  publicKey: process.env['TELNYX_PUBLIC_KEY'], // Required for client.webhooks.unwrap() (webhook verification)
});

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:

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, then retry the request (bounded exponential backoff)
    const retryAfter = err.headers?.['retry-after'] || 1;
    await new Promise(r => setTimeout(r, retryAfter * 1000));
    // ...retry the call here (give up after a few attempts)
  } 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');
    }
  }
  throw err; // Re-throw: a failed request must never look like success to the caller
}

Common error codes: 401 invalid API key, 403 insufficient permissions, 404 resource not found, 422 validation error (check field formats), 429 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

import fs from 'fs';

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), words (array[object])

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

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

Creates a new AI conversation, the container for messages exchanged with an assistant, and returns the created conversation.

POST /ai/conversations

Optional: metadata (object), name (string)

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)

Aggregate Conversation Insights

Aggregate conversation insights by specified fields

GET /ai/conversations/conversation-insights/aggregates

const response = await client.ai.conversations.conversationInsights.retrieveAggregates();

console.log(response.data);

Returns: record_count (integer)

Get Insight Template Groups

Returns a paginated list of your insight template groups. Groups organize related insight templates that are applied together when analyzing conversations.

GET /ai/conversations/insight-groups

// 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

Creates a new insight template group for organizing related insight templates, and returns the created group.

POST /ai/conversations/insight-groups — Required: name

Optional: description (string), webhook (string)

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

Returns the details of a single insight template group, including the insight templates assigned to it.

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

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

Updates the specified insight template group and returns the updated group.

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

Optional: description (string), name (string), webhook (string)

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

Permanently deletes the specified insight template group by its ID.

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

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

Assign Insight Template To Group

Assigns the specified insight template to the specified insight template group.

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

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

Unassign Insight Template From Group

Removes the specified insight template from the specified group. The insight template itself is not deleted.

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

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

Get Insight Templates

Returns a paginated list of your insight templates. Insight templates define analyses that run over AI conversations to extract structured findings.

GET /ai/conversations/insights

// 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

Creates a new insight template defining an analysis to run over conversations, and returns the created template.

POST /ai/conversations/insights — Required: instructions, name

Optional: json_schema (object), webhook (string)

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

Returns the details of a single insight template by its ID, including its configuration.

GET /ai/conversations/insights/{insight_id}

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

Updates the specified insight template and returns the updated template.

PUT /ai/conversations/insights/{insight_id}

Optional: instructions (string), json_schema (object), name (string), webhook (string)

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

Permanently deletes the specified insight template by its ID.

DELETE /ai/conversations/insights/{insight_id}

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}

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)

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}

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

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)

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

// Automatically fetches more pages as needed.
for await (const messageListResponse of client.ai.conversations.messages.list('550e8400-e29b-41d4-a716-446655440000')) {
  console.log(messageListResponse.role);
}

Returns: created_at (date-time), metadata (object), 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 or partial_success based on the query string. Defaults to queued and processing.

GET /ai/embeddings

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)

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

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

Returns the list of storage buckets that have been embedded for your account, for use with similarity search.

GET /ai/embeddings/buckets

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}

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}

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)

const response = await client.ai.embeddings.similaritySearch({
  bucket_name: 'my-bucket',
  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

const embeddingResponse = await client.ai.embeddings.url({
  bucket_name: 'my-bucket',
  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}

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

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

Creates a new fine-tuning job that trains a model on the provided dataset, and returns the created job.

POST /ai/fine_tuning/jobs — Required: model, training_file

Optional: hyperparameters (object), suffix (string)

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

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

Returns the details of a single fine-tuning job by its job_id, including its current status.

GET /ai/fine_tuning/jobs/{job_id}

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

Cancels the specified in-progress fine-tuning job and returns the updated job.

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

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)

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)

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

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)

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

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

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}

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}

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

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), humain (object), language (string), minimax (object), output_type (enum: binary_output, base64_output), provider (enum: aws, telnyx, azure, elevenlabs, minimax, resemble, xai, humain, soniox), resemble (object), soniox (object), telnyx (object), text (string), text_type (enum: text, ssml), voice (string), voice_settings (object), xai (object)

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

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

console.log(response.voices);

Returns: voices (array[object])