/telnyx-ai-inference-java

name: telnyx-ai-inference-java
description: >-
  Access Telnyx LLM inference APIs, embeddings, and AI analytics for call
  insights and summaries. This skill provides Java SDK examples.
metadata:
  author: telnyx
  product: ai-inference
  language: java
  generated_by: telnyx-openapi-pipeline

<!-- Auto-generated from Telnyx OpenAPI specs. Do not edit. -->

Telnyx Ai Inference - Java

Installation

<!-- Maven -->
<dependency>
    <groupId>com.telnyx.sdk</groupId>
    <artifactId>telnyx</artifactId>
    <version>6.84.0</version>
</dependency>

// Gradle
implementation("com.telnyx.sdk:telnyx:6.84.0")

Setup

import com.telnyx.sdk.client.TelnyxClient;
import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient;

TelnyxClient client = TelnyxOkHttpClient.fromEnv();

All examples below assume `client` is already initialized as shown above.

Error Handling

All API calls can fail with network errors, rate limits (429), validation errors (422), or authentication errors (401). Always handle errors in production code:

import com.telnyx.sdk.errors.TelnyxServiceException;

try {
    var result = client.messages().send(params);
} catch (TelnyxServiceException e) {
    System.err.println("API error " + e.statusCode() + ": " + e.getMessage());
    if (e.statusCode() == 422) {
        System.err.println("Validation error — check required fields and formats");
    } else if (e.statusCode() == 429) {
        // Rate limited — wait and retry with exponential backoff
        Thread.sleep(1000);
    }
}

Common error codes: `401` invalid API key, `403` insufficient permissions, `404` resource not found, `422` validation error (check field formats), `429` rate limited (retry with exponential backoff).

Important Notes

• **Pagination:** List methods return a page. Use `.autoPager()` for automatic iteration: `for (var item : page.autoPager()) { ... }`. For manual control, use `.hasNextPage()` and `.nextPage()`.

Transcribe speech to text

Transcribe speech to text. This endpoint is consistent with the [OpenAI Transcription API](https://platform.openai.com/docs/api-reference/audio/createTranscription) and may be used with the OpenAI JS or Python SDK.

`POST /ai/audio/transcriptions`

import com.telnyx.sdk.models.ai.audio.AudioTranscribeParams;
import com.telnyx.sdk.models.ai.audio.AudioTranscribeResponse;

AudioTranscribeParams params = AudioTranscribeParams.builder()
    .model(AudioTranscribeParams.Model.DISTIL_WHISPER_DISTIL_LARGE_V2)
    .build();
AudioTranscribeResponse response = client.ai().audio().transcribe(params);

Returns: `duration` (number), `segments` (array[object]), `text` (string), `words` (array[object])

Create a chat completion

**Deprecated**: Use `POST /v2/ai/openai/chat/completions` instead. Chat with a language model. This endpoint is consistent with the [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat) and may be used with the OpenAI JS or Python SDK.

`POST /ai/chat/completions` — Required: `messages`

Optional: `api_key_ref` (string), `best_of` (integer), `early_stopping` (boolean), `enable_thinking` (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), `seed` (integer), `stop` (object), `stream` (boolean), `temperature` (number), `tool_choice` (enum: none, auto, required), `tools` (array[object]), `top_logprobs` (integer), `top_p` (number), `use_beam_search` (boolean)

import com.telnyx.sdk.models.ai.chat.ChatCreateCompletionParams;
import com.telnyx.sdk.models.ai.chat.ChatCreateCompletionResponse;

ChatCreateCompletionParams params = ChatCreateCompletionParams.builder()
    .addMessage(ChatCreateCompletionParams.Message.builder()
        .content("You are a friendly chatbot.")
        .role(ChatCreateCompletionParams.Message.Role.SYSTEM)
        .build())
    .addMessage(ChatCreateCompletionParams.Message.builder()
        .content("Hello, world!")
        .role(ChatCreateCompletionParams.Message.Role.USER)
        .build())
    .build();
ChatCreateCompletionResponse response = client.ai().chat().createCompletion(params);

List conversations

Retrieve a list of all AI conversations configured by the user. Supports [PostgREST-style query parameters](https://postgrest.org/en/stable/api.html#horizontal-filtering-rows) for filtering. Examples are included for the standard metadata fields, but you can filter on any field in the metadata JSON object.

`GET /ai/conversations`

import com.telnyx.sdk.models.ai.conversations.ConversationListParams;
import com.telnyx.sdk.models.ai.conversations.ConversationListResponse;

ConversationListResponse conversations = client.ai().conversations().list();

Returns: `created_at` (date-time), `id` (uuid), `last_message_at` (date-time), `metadata` (object), `name` (string)

Create a conversation

Create a new AI Conversation.

`POST /ai/conversations`

Optional: `metadata` (object), `name` (string)

import com.telnyx.sdk.models.ai.conversations.Conversation;
import com.telnyx.sdk.models.ai.conversations.ConversationCreateParams;

Conversation conversation = client.ai().conversations().create();

Returns: `created_at` (date-time), `id` (uuid), `last_message_at` (date-time), `metadata` (object), `name` (string)

Aggregate Conversation Insights

Aggregate conversation insights by specified fields

`GET /ai/conversations/conversation-insights/aggregates`

import com.telnyx.sdk.models.ai.conversations.conversationinsights.ConversationInsightAggregateParams;
import com.telnyx.sdk.models.ai.conversations.conversationinsights.ConversationInsightAggregateResponse;

ConversationInsightAggregateResponse response = client.ai().conversations().conversationInsights().a