# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand.
overlay: 1.0.0
info:
  title: API Evangelist conversational phrasing for Thehiveryiq Com Compute API
  version: 1.0.0
extends: openapi/thehiveryiq-com-compute-api-openapi.yml
actions:
- target: $.info
  update:
    x-apievangelist-phrasing:
      method: generated
      generated: '2026-10-02'
      generator: build-phrasing.py
      label: Generated by API Evangelist
      operations: 5
- target: $.paths['/v1/compute/chat/completions'].post
  update:
    x-apievangelist-phrasing:
      intent: Generate a chat reply from a model
      effect: write
      questions:
      - How do I send a conversation of messages to a hosted model and get the assistant's reply?
      - Can I use a standard chat completions request format with HiveCompute?
      - What endpoint gives multi-turn chat responses rather than plain text continuation?
      instructions:
      - text: Send my chat messages to a model and return the assistant's reply.
      - text: Run a chat completion for this conversation history.
      method: generated
      generated: '2026-10-02'
- target: $.paths['/v1/compute/completions'].post
  update:
    x-apievangelist-phrasing:
      intent: Generate a text completion from a prompt
      effect: write
      questions:
      - Can I get a model to continue a single raw text prompt without chat messages?
      - Which call does plain prompt-in, text-out completion?
      instructions:
      - text: Complete this raw text prompt with a model.
      - text: Generate a plain text continuation for my prompt, not a chat reply.
      method: generated
      generated: '2026-10-02'
- target: $.paths['/v1/compute/embeddings'].post
  update:
    x-apievangelist-phrasing:
      intent: Create vector embeddings for text
      effect: write
      questions:
      - How do I turn text into embedding vectors for semantic search?
      - Can I generate embeddings to store in a vector database?
      instructions:
      - text: Create embeddings for these pieces of text.
      - text: Generate vector embeddings I can use for similarity search.
      method: generated
      generated: '2026-10-02'
- target: $.paths['/v1/compute/models'].get
  update:
    x-apievangelist-phrasing:
      intent: List the models available for inference
      effect: read
      questions:
      - Which models can I run inference against?
      - What model names are available to choose from on the compute API?
      instructions:
      - text: List all models available for compute.
      - text: Show me which inference models I can use.
      method: generated
      generated: '2026-10-02'
- target: $.paths['/v1/compute/estimate'].post
  update:
    x-apievangelist-phrasing:
      intent: Estimate the cost of an inference request
      effect: read
      questions:
      - How much will an inference request cost before I actually run it?
      - Can I get a price estimate for a model call up front?
      instructions:
      - text: Estimate what this inference request will cost.
      - text: Give me a cost estimate before running the model call.
      method: generated
      generated: '2026-10-02'