Hugging Face · OpenAPI Overlay 1.0.0

API Evangelist conversational phrasing for Hugging Face Inference Providers Chat Completions API

2 actions 2 updates phrasing extends openapi/hugging-face-chat-completions-api-openapi.yml

Generated by API Evangelist Written by API Evangelist tooling for Hugging Face's API. It is a proposal applied on top of the contract, not a document Hugging Face publishes.

View Overlay File View on GitHub Overlay Specification

What the actions change

x-apievangelist-phrasing

Targets 2

$.info

$.paths['/v1/chat/completions'].post

OpenAPI Overlay

# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand.
overlay: 1.0.0
info:
  title: API Evangelist conversational phrasing for Hugging Face Inference Providers Chat Completions API
  version: 1.0.0
extends: openapi/hugging-face-chat-completions-api-openapi.yml
actions:
- target: $.info
  update:
    x-apievangelist-phrasing:
      method: generated
      generated: '2026-10-01'
      generator: build-phrasing.py
      label: Generated by API Evangelist
      operations: 1
- target: $.paths['/v1/chat/completions'].post
  update:
    x-apievangelist-phrasing:
      intent: Get a chat completion via the inference router
      effect: write
      questions:
      - Can I send chat messages to a Hub model and have the request routed to the best inference provider automatically?
      - Does the router chat endpoint work with vision-language models that take images in the conversation?
      - Can I set a reasoning effort level for models that think before answering?
      instructions:
      - text: Ask model {model} to respond to these {messages}.
        slots:
          model: requestBody.model
          messages: requestBody.messages
      - text: Send {messages} to {model} with reasoning effort {reasoning_effort}.
        slots:
          messages: requestBody.messages
          model: requestBody.model
          reasoning_effort: requestBody.reasoning_effort
      - text: Get a reproducible chat reply from {model} for {messages} using seed {seed} and temperature {temperature}.
        slots:
          model: requestBody.model
          messages: requestBody.messages
          seed: requestBody.seed
          temperature: requestBody.temperature
      method: generated
      generated: '2026-10-01'

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