together-chat-completions

To get structured outputs from chat completion models, you can enforce JSON schema, json_object, or regex formats for deterministic responses. This ensures the chat completion API returns data strictly matching your specified structured output constraints.

Yes, you can use function calling with Together AI chat completions. The skill supports six distinct tool calling patterns, enabling seamless tooling integration and multi-turn conversations within your chat applications.

The best way to access multiple open-source models for serverless inference is via Together AI's OpenAI-compatible API. It provides serverless chat completion endpoints across 100+ models with pay-per-token pricing.

Yes, this skill supports reasoning models like DeepSeek R1, DeepSeek V3.1, and Kimi K2.5 for multi-turn conversations. These thinking models enable advanced reasoning capabilities within your chat completion workflows.

You can implement streaming chat completions in Python and TypeScript using the provided runnable examples in the scripts and references directories. These examples demonstrate usage across both languages with the Together AI API.

When using regex formats for deterministic chat responses, you are constrained to outputs matching that specific regex pattern. The skill supports regex alongside json_schema and json_object to enforce strict deterministic response structures.