telnyx-ai-inference-python（telnyx AI inference Python 搜索）

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 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)

Create a chat completion

Chat with a language model. This endpoint is consistent with the OpenAI Chat Completions API 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)

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 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)

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 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.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}

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

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

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

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)

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}

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)

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}

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}

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)

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}

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

Get insights for a conversation

Retrieve insights for a specific conversation

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

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)

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

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

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)

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

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}

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}

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)

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

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}

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

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)

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}

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

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

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 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)

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

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)

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

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

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}

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}

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

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)

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

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

Returns: voices (array[object])