# 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: |
  {
    "$defs": {
      "HallucinationOutputEntry": {
        "properties": {
          "r": {
            "minimum": 0,
            "title": "Sentence Num",
            "type": "integer"
          },
          "f": {
            "title": "Is Faithful",
            "type": "string",
            "enum": ["faithful", "partial", "unfaithful"]
          },
          "e": {
            "title": "Reasoning",
            "type": "string"
          }
        },
        "required": [
          "r",
          "e",
          "f"
        ],
        "title": "HallucinationOutputEntry",
        "type": "object"
      }
    },
    "items": {
      "$ref": "#/$defs/HallucinationOutputEntry"
    },
    "title": "HallucinationOutput",
    "type": "array"
  }
transformations:
  # Use logprobs to replace "f" flag with a probability
  - type: likelihood
    categories_to_values:
      "faithful": 1.0
      "partial": 0.5
      "unfaithful": 0.0
    input_path: [~, "f"]  # Null in path means wildcard
  # Replace sentence number with sentence location and contents
  - type: decode_sentences
    source: "last_message"
    input_path: [~, "r"]  # Null in path means wildcard
    # New fields to add for each sentence
    output_names:
      begin: "response_begin"
      end: "response_end"
      text: "response_text"
  # Remove fields that we no longer need and rename some of the fields.
  - type: project
    input_path: []
    retained_fields:
      "response_begin": "response_begin"
      "response_end": "response_end"
      "response_text": "response_text"
      "f": "faithfulness_likelihood"
      "e": "explanation"
instruction: >
  Split the last assistant response into individual sentences.
  For each sentence in the last assistant response, identify the faithfulness
  by comparing with the provided documents and generate the faithfulness reasoning
  and faithfulness decision.
  Ensure that your output includes all response sentence IDs,
  and for each response sentence ID, provide the corresponding faithfulness
  reasoning and faithfulness decision.
  The output must be a json structure.
parameters:
  # Current LoRA can be quite verbose in its explanations.
  max_completion_tokens: 4096
sentence_boundaries:
  last_message: "i"