/telnyx-ai-inference-python

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

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

Telnyx Ai Inference - Python

Installation

pip install telnyx

Setup

import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("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:

import telnyx

try:
    result = client.messages.send(to="+13125550001", from_="+13125550002", text="Hello")
except telnyx.APIConnectionError:
    print("Network error — check connectivity and retry")
except telnyx.RateLimitError:
    # 429: rate limited — wait and retry with exponential backoff
    import time
    time.sleep(1)  # Check Retry-After header for actual delay
except telnyx.APIStatusError as e:
    print(f"API error {e.status_code}: {e.message}")
    if e.status_code == 422:
        print("Validation error — check required fields and formats")

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 item in page_result:` to iterate through all pages automatically.

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",
)
print(response.text)

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=[{
        "role": "system",
        "content": "You are a friendly chatbot.",
    }, {
        "role": "user",
        "content": "Hello, world!",
    }],
)
print(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()
print(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)

conversation = client.ai.conversations.create()
print(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`

response = client.ai.conversations.conversation_insights.aggregate()
print(response.data)

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()
page = page.data[0]
print(page.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)

insight_template_group_detail = client.ai.conversations.insight_groups.insight_groups(
    name="my-resource",
)
print(insight_template_group_detail.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}`

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

Returns: `created_at` (date-time), `description` (string), `id` (uuid), `insights` (array[object]), `name` (string), `webhook` (string)

Update