telnyx-ai-inference-python

<!-- Auto-generated from Telnyx OpenAPI specs. Do not edit. --> # 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` ```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` ```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}` ```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` ```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}` ```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}` ```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` ```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` or `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` ```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` ```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](https://developers.telnyx.com/api-reference/embeddings/embed-documents) is clustered. `POST /ai/clusters` — Required: `bucket` ```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` ```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` ```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) ``` ## 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.