---
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-ext-skills-generator
---

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

# Telnyx Ai Inference - Python

## Installation

```bash
pip install telnyx
```

## Setup

```python
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:

```python
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`

```python
response = client.ai.audio.transcribe(
    model="distil-whisper/distil-large-v2",
)
print(response.text)
```

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

## Create a chat completion

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), `stream` (boolean), `temperature` (number), `tool_choice` (enum: none, auto, required), `tools` (array[object]), `top_logprobs` (integer), `top_p` (number), `use_beam_search` (boolean)

```python
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`

```python
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)

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

Returns: `created_at` (date-time), `id` (uuid), `last_message_at` (date-time), `metadata` (object), `name` (string)

## Get Insight Template Groups

Get all insight groups

`GET /ai/conversations/insight-groups`

```python
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)

```python
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}`

```python
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

Update an insight template group

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

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

```python
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[object]), `name` (string), `webhook` (string)

## Delete Insight Template Group

Delete insight group by ID

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

```python
client.ai.conversations.insight_groups.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`

```python
client.ai.conversations.insight_groups.insights.assign(
    insight_id="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`

```python
client.ai.conversations.insight_groups.insights.delete_unassign(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    group_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
```

## Get Insight Templates

Get all insights

`GET /ai/conversations/insights`

```python
page = client.ai.conversations.insights.list()
page = page.data[0]
print(page.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

Create a new insight

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

Optional: `json_schema` (object), `webhook` (string)

```python
insight_template_detail = client.ai.conversations.insights.create(
    instructions="You are a helpful assistant.",
    name="my-resource",
)
print(insight_template_detail.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

Get insight by ID

`GET /ai/conversations/insights/{insight_id}`

```python
insight_template_detail = client.ai.conversations.insights.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.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

Update an insight template

`PUT /ai/conversations/insights/{insight_id}`

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

```python
insight_template_detail = client.ai.conversations.insights.update(
    insight_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(insight_template_detail.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

Delete insight by ID

`DELETE /ai/conversations/insights/{insight_id}`

```python
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}`

```python
conversation = client.ai.conversations.retrieve(
    "conversation_id",
)
print(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)

```python
conversation = client.ai.conversations.update(
    conversation_id="550e8400-e29b-41d4-a716-446655440000",
)
print(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}`

```python
client.ai.conversations.delete(
    "conversation_id",
)
```

## Get insights for a conversation

Retrieve insights for a specific conversation

`GET /ai/conversations/{conversation_id}/conversations-insights`

```python
response = client.ai.conversations.retrieve_conversations_insights(
    "conversation_id",
)
print(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)

```python
client.ai.conversations.add_message(
    conversation_id="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`

```python
messages = client.ai.conversations.messages.list(
    "conversation_id",
)
print(messages.data)
```

Returns: `created_at` (date-time), `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`

```python
embeddings = client.ai.embeddings.list()
print(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)

```python
embedding_response = client.ai.embeddings.create(
    bucket_name="my-bucket",
)
print(embedding_response.data)
```

Returns: `created_at` (string), `finished_at` (string | null), `status` (string), `task_id` (uuid), `task_name` (string), `user_id` (uuid)

## List embedded buckets

Get all embedding buckets for a user.

`GET /ai/embeddings/buckets`

```python
buckets = client.ai.embeddings.buckets.list()
print(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}`

```python
bucket = client.ai.embeddings.buckets.retrieve(
    "bucket_name",
)
print(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}`

```python
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)

```python
response = client.ai.embeddings.similarity_search(
    bucket_name="my-bucket",
    query="What is Telnyx?",
)
print(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`

```python
embedding_response = client.ai.embeddings.url(
    bucket_name="my-bucket",
    url="https://example.com/resource",
)
print(embedding_response.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}`

```python
embedding = client.ai.embeddings.retrieve(
    "task_id",
)
print(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`

```python
jobs = client.ai.fine_tuning.jobs.list()
print(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

Create a new fine tuning job.

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

Optional: `hyperparameters` (object), `suffix` (string)

```python
fine_tuning_job = client.ai.fine_tuning.jobs.create(
    model="openai/gpt-4o",
    training_file="training-data.jsonl",
)
print(fine_tuning_job.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

Retrieve a fine tuning job by `job_id`.

`GET /ai/fine_tuning/jobs/{job_id}`

```python
fine_tuning_job = client.ai.fine_tuning.jobs.retrieve(
    "job_id",
)
print(fine_tuning_job.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

Cancel a fine tuning job.

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

```python
fine_tuning_job = client.ai.fine_tuning.jobs.cancel(
    "job_id",
)
print(fine_tuning_job.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 available models

This endpoint returns a list of Open Source and OpenAI models that are available for use.    **Note**: Model `id`'s will be in the form `{source}/{model_name}`. For example `openai/gpt-4` or `mistralai/Mistral-7B-Instruct-v0.1` consistent with HuggingFace naming conventions.

`GET /ai/models`

```python
response = client.ai.retrieve_models()
print(response.data)
```

Returns: `created` (integer), `id` (string), `object` (string), `owned_by` (string)

## Create embeddings

Creates an embedding vector representing the input text. This endpoint is compatible with the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) 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)

```python
response = client.ai.openai.embeddings.create_embeddings(
    input="The quick brown fox jumps over the lazy dog",
    model="thenlper/gte-large",
)
print(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`

```python
response = client.ai.openai.embeddings.list_embedding_models()
print(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)

```python
response = client.ai.summarize(
    bucket="my-bucket",
    filename="data.csv",
)
print(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`

```python
speech_to_texts = client.legacy.reporting.batch_detail_records.speech_to_text.list()
print(speech_to_texts.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`

```python
from datetime import datetime

speech_to_text = client.legacy.reporting.batch_detail_records.speech_to_text.create(
    end_date=datetime.fromisoformat("2020-07-01T00:00:00-06:00"),
    start_date=datetime.fromisoformat("2020-07-01T00:00:00-06:00"),
)
print(speech_to_text.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}`

```python
speech_to_text = client.legacy.reporting.batch_detail_records.speech_to_text.retrieve(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(speech_to_text.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}`

```python
speech_to_text = client.legacy.reporting.batch_detail_records.speech_to_text.delete(
    "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(speech_to_text.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`

```python
response = client.legacy.reporting.usage_reports.retrieve_speech_to_text()
print(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), `language` (string), `minimax` (object), `output_type` (enum: binary_output, base64_output), `provider` (enum: aws, telnyx, azure, elevenlabs, minimax, rime, resemble), `resemble` (object), `rime` (object), `telnyx` (object), `text` (string), `text_type` (enum: text, ssml), `voice` (string), `voice_settings` (object)

```python
response = client.text_to_speech.generate()
print(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`

```python
response = client.text_to_speech.list_voices()
print(response.voices)
```

Returns: `voices` (array[object])