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telnyx-ai-inference-ruby Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Ruby SDK examples.
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Télécharger Zip Téléchargement... Plus depuis ce dépôt Complete Telnyx toolkit — ready-to-use tools (STT, TTS, RAG, Networking, 10DLC) plus SDK documentation for JavaScript, Python, Go, Java, and Ruby.
Automated Telnyx bot account signup via challenge-response
Track agent activities using the Telnyx AI Missions API. Use this skill when executing multi-step tasks that should be logged and tracked. Supports creating voice/SMS agents, scheduling calls, and retrieving conversation insights. Use when tasks involve calling people, sending SMS, or any substantial tracked work.
Métiers associés SOC
Basé sur la classification professionnelle SOC
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 /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 /ai/conversations/insights
page = client.ai.conversations.insights.list
puts(page)
Create Insight Template 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 /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 /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.
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 page = client.ai.clusters.list
puts(page)
Compute new clusters 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.
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.
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)
