/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
    public_key=os.environ.get("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:

import telnyx

try:
    result = client.messages.send(to="+13125550001", from_="+13125550002", text="Hello")
except telnyx.APIConnectionError:
    print("Network error — check connectivity and retry")
    raise
except telnyx.RateLimitError:
    # 429: rate limited — wait, then retry the request (bounded exponential backoff)
    import time
    time.sleep(1)  # Check Retry-After header for actual delay
    # ...retry the call here (give up after a few attempts); otherwise re-raise:
    raise
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")
    raise  # 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 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])

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

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)

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.retrieve_aggregates()
print(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`

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

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)

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

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

`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 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)

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

Returns: `created_at` (date-time), `description` (string), `id` (uuid), `insights` (array[objec