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.

List conversations

Retrieve a list of all AI conversations configured by the user.

GET /ai/conversations

python

conversations = client.ai.conversations.list()
print(conversations.data)

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)

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)

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="name",
)
print(insight_template_group_detail.data)

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)

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)

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)

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="instructions",
    name="name",
)
print(insight_template_detail.data)

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)

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)

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)

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="conversation_id",
)
print(conversation.data)

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)

Create Message

Add a new message to the conversation.

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="role",
)

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)

Get Tasks by Status

Retrieve tasks for the user that are either

queued

processing

failed

success

partial_success

based on the query string.

GET /ai/embeddings

python

embeddings = client.ai.embeddings.list()
print(embeddings.data)

Embed documents

Perform embedding on a Telnyx Storage Bucket using an embedding model.

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="bucket_name",
)
print(embedding_response.data)

List embedded buckets

Get all embedding buckets for a user.

GET /ai/embeddings/buckets

python

buckets = client.ai.embeddings.buckets.list()
print(buckets.data)

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)

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.

POST /ai/embeddings/similarity-search

— Required:

bucket_name

query

Optional:

num_of_docs

(integer)

python

response = client.ai.embeddings.similarity_search(
    bucket_name="bucket_name",
    query="query",
)
print(response.data)

Embed URL content

Embed website content from a specified URL, including child pages up to 5 levels deep within the same domain.

POST /ai/embeddings/url

— Required:

url

bucket_name

python

embedding_response = client.ai.embeddings.url(
    bucket_name="bucket_name",
    url="url",
)
print(embedding_response.data)

Get an embedding task's status

Check the status of a current embedding task.

GET /ai/embeddings/{task_id}

python

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

List all clusters

GET /ai/clusters

python

page = client.ai.clusters.list()
page = page.data[0]
print(page.task_id)

Compute new clusters

Starts a background task to compute how the data in an embedded storage bucket is clustered.

POST /ai/clusters

— Required:

bucket

Optional:

files

(array[string]),

min_cluster_size

(integer),

min_subcluster_size

(integer),

prefix

(string)

python

response = client.ai.clusters.compute(
    bucket="bucket",
)
print(response.data)

Fetch a cluster

GET /ai/clusters/{task_id}

python

cluster = client.ai.clusters.retrieve(
    task_id="task_id",
)
print(cluster.data)

Delete a cluster

DELETE /ai/clusters/{task_id}

python

client.ai.clusters.delete(
    "task_id",
)

Fetch a cluster visualization

GET /ai/clusters/{task_id}/graph

python

response = client.ai.clusters.fetch_graph(
    task_id="task_id",
)
print(response)
content = response.read()
print(content)

Transcribe speech to text

Transcribe speech to text.

POST /ai/audio/transcriptions

python

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

Create a chat completion

Chat with a language model.

POST /ai/chat/completions

— Required:

messages

Optional:

api_key_ref

(string),

best_of

(integer),

early_stopping

(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),

(number),

presence_penalty

(number),

response_format

(object),

stream

(boolean),

temperature

(number),

tool_choice

(enum),

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 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)

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="model",
    training_file="training_file",
)
print(fine_tuning_job.id)

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)

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)

Create embeddings

Creates an embedding vector representing the input text.

POST /ai/openai/embeddings

— Required:

input

model

Optional:

dimensions

(integer),

encoding_format

(enum),

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)

List embedding models

Returns a list of available embedding models.

GET /ai/openai/embeddings/models

python

response = client.ai.openai.embeddings.list_embedding_models()
print(response.data)

Get available models

This endpoint returns a list of Open Source and OpenAI models that are available for use.

GET /ai/models

python

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

Summarize file content

Generate a summary of a file's contents.

POST /ai/summarize

— Required:

bucket

filename

Optional:

system_prompt

(string)

python

response = client.ai.summarize(
    bucket="bucket",
    filename="filename",
)
print(response.data)

telnyx-ai-inference-python

NPX Install

Tags

SKILL.md Content

Telnyx Ai Inference - Python

Installation

Setup

List conversations

Create a conversation

Get Insight Template Groups

Create Insight Template Group

Get Insight Template Group

Update Insight Template Group

Delete Insight Template Group

Assign Insight Template To Group

Unassign Insight Template From Group

Get Insight Templates

Create Insight Template

Get Insight Template

Update Insight Template

Delete Insight Template

Get a conversation

Update conversation metadata

Delete a conversation

Get insights for a conversation

Create Message

Get conversation messages

Get Tasks by Status

Embed documents

List embedded buckets

Get file-level embedding statuses for a bucket

Disable AI for an Embedded Bucket

Search for documents

Embed URL content

Get an embedding task's status

List all clusters

Compute new clusters

Fetch a cluster

Delete a cluster

Fetch a cluster visualization

Transcribe speech to text

Create a chat completion

List fine tuning jobs

Create a fine tuning job

Get a fine tuning job

Cancel a fine tuning job

Create embeddings

List embedding models

Get available models

Summarize file content