# Model name string, or null to use whatever is provided in the chat completion request
model: ~
# JSON schema of the model's output
response_format:  |
  {
    "properties": {
      "answer_relevance_analysis": {
        "title": "Answer Relevance Analysis",
        "type": "string"
      },
      "answer_relevance_category": {
        "title": "Answer Relevance Category",
        "type": "string",
        "enum": [
          "Pertinent", 
          "Pertinent with relevant extra",
          "Excessive unnecessary information",
          "Unduly restrictive",
          "Too vague or generic",
          "Contextual misalignment",
          "Misinterpreted inquiry",
          "No attempt"
        ]
      },
      "answer_relevance_judgment": {
        "title": "Answer Relevance Judgment",
        "type": "boolean"
      }
    },
    "required": [
      "answer_relevance_analysis",
      "answer_relevance_category",
      "answer_relevance_judgment"
    ],
    "title": "AnswerRelevanceRawOutput",
    "type": "object"
  }
# Additional turn of instructions to add to the chat
instruction: "answer_relevance"
# Data transformations to perform during post-processing
transformations:
  # Convert categorical answer to continuous value by decoding logprobs
  - type: likelihood
    categories_to_values:
      true: 1.0
      false: 0.0
    input_path: ["answer_relevance_judgment"]
  # Rename answer_relevance_judgment column to reflect likelihood transformation
  - type: project
    input_path: []
    retained_fields:
      "answer_relevance_analysis": "answer_relevance_analysis"
      "answer_relevance_category": "answer_relevance_category"
      "answer_relevance_judgment": "answer_relevance_likelihood"
parameters:
  # Current LoRA can be quite verbose in its explanations.
  max_completion_tokens: 1024
sentence_boundaries: ~