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.

List conversations

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

GET /ai/conversations

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

Create a conversation

Create a new AI Conversation.

POST /ai/conversations

Optional: metadata (object), name (string)

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

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)

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

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)

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)

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)

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

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)

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)

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)

Update conversation metadata

Update metadata for a specific conversation.

PUT /ai/conversations/{conversation_id}

Optional: metadata (object)

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}

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)

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)

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

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 or partial_success based on the query string.

GET /ai/embeddings

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)

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

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}

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}

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)

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

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}

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

List all clusters

GET /ai/clusters

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)

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

Fetch a cluster

GET /ai/clusters/{task_id}

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

Delete a cluster

DELETE /ai/clusters/{task_id}

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

Fetch a cluster visualization

GET /ai/clusters/{task_id}/graph

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

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), n (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)

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

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)

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}

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

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)

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

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

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)

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