| name | telnyx-ai-inference-ruby |
| description | Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Ruby SDK examples. |
| metadata | {"author":"telnyx","product":"ai-inference","language":"ruby","generated_by":"telnyx-ext-skills-generator"} |
Telnyx Ai Inference - Ruby
Installation
gem install telnyx
Setup
require "telnyx"
client = Telnyx::Client.new(
api_key: ENV["TELNYX_API_KEY"],
)
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
puts(conversations)
Create a conversation
Create a new AI Conversation.
POST /ai/conversations
conversation = client.ai.conversations.create
puts(conversation)
Get Insight Template Groups
Get all insight groups
GET /ai/conversations/insight-groups
page = client.ai.conversations.insight_groups.retrieve_insight_groups
puts(page)
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")
puts(insight_template_group_detail)
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")
puts(insight_template_group_detail)
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("182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e")
puts(insight_template_group_detail)
Delete Insight Template Group
Delete insight group by ID
DELETE /ai/conversations/insight-groups/{group_id}
result = client.ai.conversations.insight_groups.delete("182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e")
puts(result)
Assign Insight Template To Group
Assign an insight to a group
POST /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/assign
result = client.ai.conversations.insight_groups.insights.assign(
"182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
group_id: "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
)
puts(result)
Unassign Insight Template From Group
Remove an insight from a group
DELETE /ai/conversations/insight-groups/{group_id}/insights/{insight_id}/unassign
result = client.ai.conversations.insight_groups.insights.delete_unassign(
"182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
group_id: "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e"
)
puts(result)
Get Insight Templates
Get all insights
GET /ai/conversations/insights
page = client.ai.conversations.insights.list
puts(page)
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")
puts(insight_template_detail)
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")
puts(insight_template_detail)
Update Insight Template
Update an insight template
PUT /ai/conversations/insights/{insight_id}
insight_template_detail = client.ai.conversations.insights.update("182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e")
puts(insight_template_detail)
Delete Insight Template
Delete insight by ID
DELETE /ai/conversations/insights/{insight_id}
result = client.ai.conversations.insights.delete("182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e")
puts(result)
Get a conversation
Retrieve a specific AI conversation by its ID.
GET /ai/conversations/{conversation_id}
conversation = client.ai.conversations.retrieve("conversation_id")
puts(conversation)
Update conversation metadata
Update metadata for a specific conversation.
PUT /ai/conversations/{conversation_id}
conversation = client.ai.conversations.update("conversation_id")
puts(conversation)
Delete a conversation
Delete a specific conversation by its ID.
DELETE /ai/conversations/{conversation_id}
result = client.ai.conversations.delete("conversation_id")
puts(result)
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")
puts(response)
Create Message
Add a new message to the conversation.
POST /ai/conversations/{conversation_id}/message — Required: role
result = client.ai.conversations.add_message("182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", role: "role")
puts(result)
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")
puts(messages)
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
puts(embeddings)
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")
puts(embedding_response)
List embedded buckets
Get all embedding buckets for a user.
GET /ai/embeddings/buckets
buckets = client.ai.embeddings.buckets.list
puts(buckets)
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")
puts(bucket)
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}
result = client.ai.embeddings.buckets.delete("bucket_name")
puts(result)
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")
puts(response)
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")
puts(embedding_response)
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")
puts(embedding)
List all clusters
GET /ai/clusters
page = client.ai.clusters.list
puts(page)
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")
puts(response)
Fetch a cluster
GET /ai/clusters/{task_id}
cluster = client.ai.clusters.retrieve("task_id")
puts(cluster)
Delete a cluster
DELETE /ai/clusters/{task_id}
result = client.ai.clusters.delete("task_id")
puts(result)
Fetch a cluster visualization
GET /ai/clusters/{task_id}/graph
response = client.ai.clusters.fetch_graph("task_id")
puts(response)
Transcribe speech to text
Transcribe speech to text.
POST /ai/audio/transcriptions
response = client.ai.audio.transcribe(model: :"distil-whisper/distil-large-v2")
puts(response)
Create a chat completion
Chat with a language model.
POST /ai/chat/completions — Required: messages
response = client.ai.chat.create_completion(
messages: [{content: "You are a friendly chatbot.", role: :system}, {content: "Hello, world!", role: :user}]
)
puts(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
puts(jobs)
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")
puts(fine_tuning_job)
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")
puts(fine_tuning_job)
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")
puts(fine_tuning_job)
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
puts(response)
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")
puts(response)
