team-telnyx 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
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
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}
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
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}
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}
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
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
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
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
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
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
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)
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
response = client.ai.summarize(
bucket="bucket",
filename="filename",
)
print(response.data)