telnyx-ai-inference-javascript

name	telnyx-ai-inference-javascript
description	Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides JavaScript SDK examples.
metadata	{"author":"telnyx","product":"ai-inference","language":"javascript","generated_by":"telnyx-ext-skills-generator"}

Telnyx Ai Inference - JavaScript

Installation

npm install telnyx

Setup

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.

List conversations

Retrieve a list of all AI conversations configured by the user.

GET /ai/conversations

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

console.log(conversations.data);

Create a conversation

Create a new AI Conversation.

POST /ai/conversations

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

console.log(conversation.id);

Get Insight Template Groups

Get all insight groups

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);
}

Create Insight Template Group

Create a new insight group

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

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

console.log(insightTemplateGroupDetail.data);

Get Insight Template Group

Get insight group by ID

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

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

console.log(insightTemplateGroupDetail.data);

Update Insight Template Group

Update an insight template group

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

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

console.log(insightTemplateGroupDetail.data);

Delete Insight Template Group

Delete insight group by ID

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

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

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

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

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

Create Insight Template

Create a new insight

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

const insightTemplateDetail = await client.ai.conversations.insights.create({
  instructions: 'instructions',
  name: 'name',
});

console.log(insightTemplateDetail.data);

Get Insight Template

Get insight by ID

GET /ai/conversations/insights/{insight_id}

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

console.log(insightTemplateDetail.data);

Update Insight Template

Update an insight template

PUT /ai/conversations/insights/{insight_id}

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

console.log(insightTemplateDetail.data);

Delete Insight Template

Delete insight by 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('conversation_id');

console.log(conversation.data);

Update conversation metadata

Update metadata for a specific conversation.

PUT /ai/conversations/{conversation_id}

const conversation = await client.ai.conversations.update('conversation_id');

console.log(conversation.data);

Delete a conversation

Delete a specific conversation by its ID.

DELETE /ai/conversations/{conversation_id}

await client.ai.conversations.delete('conversation_id');

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('conversation_id');

console.log(response.data);

Create Message

Add a new message to the conversation.

POST /ai/conversations/{conversation_id}/message — Required: role

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

Get conversation messages

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

GET /ai/conversations/{conversation_id}/messages

const messages = await client.ai.conversations.messages.list('conversation_id');

console.log(messages.data);

Get Tasks by Status

Retrieve tasks for the user that are either queued, processing, failed, success or partial_success based on the query string.

GET /ai/embeddings

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

console.log(embeddings.data);

Embed documents

Perform embedding on a Telnyx Storage Bucket using an embedding model.

POST /ai/embeddings — Required: bucket_name

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

console.log(embeddingResponse.data);

List embedded buckets

Get all embedding buckets for a user.

GET /ai/embeddings/buckets

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

console.log(buckets.data);

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);

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.

POST /ai/embeddings/similarity-search — Required: bucket_name, query

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

console.log(response.data);

Embed URL content

Embed website content from a specified URL, including child pages up to 5 levels deep within the same domain.

POST /ai/embeddings/url — Required: url, bucket_name

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

console.log(embeddingResponse.data);

Get an embedding task's status

Check the status of a current embedding task.

GET /ai/embeddings/{task_id}

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

console.log(embedding.data);

List all clusters

GET /ai/clusters

// Automatically fetches more pages as needed.
for await (const clusterListResponse of client.ai.clusters.list()) {
  console.log(clusterListResponse.task_id);
}

Compute new clusters

Starts a background task to compute how the data in an embedded storage bucket is clustered.

POST /ai/clusters — Required: bucket

const response = await client.ai.clusters.compute({ bucket: 'bucket' });

console.log(response.data);

Fetch a cluster

GET /ai/clusters/{task_id}

const cluster = await client.ai.clusters.retrieve('task_id');

console.log(cluster.data);

Delete a cluster

DELETE /ai/clusters/{task_id}

await client.ai.clusters.delete('task_id');

Fetch a cluster visualization

GET /ai/clusters/{task_id}/graph

const response = await client.ai.clusters.fetchGraph('task_id');

console.log(response);

const content = await response.blob();
console.log(content);

Transcribe speech to text

Transcribe speech to text.

POST /ai/audio/transcriptions

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

console.log(response.text);

Create a chat completion

Chat with a language model.

POST /ai/chat/completions — Required: messages

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 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);

Create a fine tuning job

Create a new fine tuning job.

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

const fineTuningJob = await client.ai.fineTuning.jobs.create({
  model: 'model',
  training_file: 'training_file',
});

console.log(fineTuningJob.id);

Get a fine tuning job

Retrieve a fine tuning job by job_id.

GET /ai/fine_tuning/jobs/{job_id}

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

console.log(fineTuningJob.id);

Cancel a fine tuning job

Cancel a fine tuning job.

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

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

console.log(fineTuningJob.id);

Get available models

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

GET /ai/models

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

console.log(response.data);

Summarize file content

Generate a summary of a file's contents.

POST /ai/summarize — Required: bucket, filename

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

console.log(response.data);