/telnyx-ai-inference-ruby

name: telnyx-ai-inference-ruby
description: >-
  Access Telnyx LLM inference APIs, embeddings, and AI analytics for call
  insights and summaries. This skill provides Ruby SDK examples.
metadata:
  author: telnyx
  product: ai-inference
  language: ruby
  generated_by: telnyx-openapi-pipeline

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

Telnyx Ai Inference - Ruby

Installation

gem install telnyx

Setup

require "telnyx"

client = Telnyx::Client.new(
  api_key: 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:

begin
  result = client.messages.send_(to: "+13125550001", from: "+13125550002", text: "Hello")
rescue Telnyx::Errors::APIConnectionError
  puts "Network error — check connectivity and retry"
rescue Telnyx::Errors::RateLimitError
  # 429: rate limited — wait and retry with exponential backoff
  sleep(1) # Check Retry-After header for actual delay
rescue Telnyx::Errors::APIStatusError => e
  puts "API error #{e.status}: #{e.message}"
  if e.status == 422
    puts "Validation error — check required fields and formats"
  end
end

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:** Use `.auto_paging_each` for automatic iteration: `page.auto_paging_each { |item| puts item.id }`.

Transcribe speech to text

Transcribe speech to text. This endpoint is consistent with the [OpenAI Transcription API](https://platform.openai.com/docs/api-reference/audio/createTranscription) and may be used with the OpenAI JS or Python SDK.

`POST /ai/audio/transcriptions`

response = client.ai.audio.transcribe(model: :"distil-whisper/distil-large-v2")

puts(response)

Returns: `duration` (number), `segments` (array[object]), `text` (string), `words` (array[object])

Create a chat completion

**Deprecated**: Use `POST /v2/ai/openai/chat/completions` instead. Chat with a language model. This endpoint is consistent with the [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat) 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), `n` (number), `presence_penalty` (number), `response_format` (object), `seed` (integer), `stop` (object), `stream` (boolean), `temperature` (number), `tool_choice` (enum: none, auto, required), `tools` (array[object]), `top_logprobs` (integer), `top_p` (number), `use_beam_search` (boolean)

response = client.ai.chat.create_completion(
  messages: [{content: "You are a friendly chatbot.", role: :system}, {content: "Hello, world!", role: :user}]
)

puts(response)

List conversations

Retrieve a list of all AI conversations configured by the user. Supports [PostgREST-style query parameters](https://postgrest.org/en/stable/api.html#horizontal-filtering-rows) 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`

conversations = client.ai.conversations.list

puts(conversations)

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)

conversation = client.ai.conversations.create

puts(conversation)

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`

response = client.ai.conversations.conversation_insights.aggregate

puts(response)

Returns: `record_count` (integer)

Get Insight Template Groups

Get all insight groups

`GET /ai/conversations/insight-groups`

page = client.ai.conversations.insight_groups.retrieve_insight_groups

puts(page)

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)

insight_template_group_detail = client.ai.conversations.insight_groups.insight_groups(name: "my-resource")

puts(insight_template_group_detail)

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}`

insight_template_group_detail = client.ai.conversations.insight_groups.retrieve("182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e")

puts(insight_template_group_detail)

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)

insight_template_group_detail = client.ai.conversati