Short answer: Drop the "DeepInfra → DeepInfra Chat Completion" action anywhere in your workflow, map the inputs from upstream nodes, and publish.
Every field can be mapped from an upstream trigger, AI step, table row, or hard-coded literal.
| Field | Type | Required | Description |
|---|---|---|---|
Model model | options | Required | Which model to use |
User Message message | string | Required | Message sent to the model as user role |
System Prompt system_prompt | string | Optional | Optional system instructions |
Temperature temperature | string | Optional | 0-2, higher = more random |
Max Tokens max_tokens | string | Optional | Maximum tokens to generate |
{"model": "{{trigger.model}}","message": "Your prompt","system_prompt": "e.g. You are a helpful assistant","temperature": "0.7","max_tokens": "1024"}
{"id": "chatcmpl_abc","model": "meta-llama/Llama-3.3-70B-Instruct","usage": {"total_tokens": 60,"prompt_tokens": 10,"completion_tokens": 50},"choices": [{"message": {"role": "assistant","content": "Sample"},"finish_reason": "stop"}]}
Use these fields in downstream nodes for routing, logging, or error handling.
Any of these apps can fire this action as part of a workflow.
Triggered by anything in the catalog. Free tier available. No credit card.