# Unit

JudgeUnit(DiscreteScale((1, 5)), name="CXEmpathyJudge")
    .via('gpt-4o', retries=3, temperature=1.4)
    .prompt("""
        Score the following customer support conversation on empathy.

        <conversation>
        ...
        </conversation>
    """)
    .extract(RegexExtractor(pattern=RegexExtractor.FIRST_INT))
    .propagate(lambda output: Schema.of(score=output.score / 5))

Verdict comes bundled with common LLM-as-a-Judge system building blocks. These wrappers, or units,

Have well-defined inputs and outputs (see Schema)
Support arbitrary dependency structures with other units using the >> operator
Are configured using chainable directives for
- Model selection with .via
- Prompt specification (templatable with dependency outputs) with .prompt
- Structured extraction (regex, uncertainty quantification, etc.) with .extract
- Arbitrary transformation with .propagate
Are executed in parallel with client-side rate-limiting
Output validation and post-processing

# Anatomy of a Unit

At its heart, a Unit is simply a wrapper around an LLM inference call with a well-specified input and output. We break this requirement into three components.

(optional) InputSchema, which dependency units must provide (we perform append-only name-casting)
(required) ResponseSchema: the raw response from the LLM inference call
(optional) OutputSchema: by default, this is the unprocessed ResponseSchema

Refer to the Unit Execution Lifecycle section for specific details.

# Unit Registry

Reusing the same Unit name across different files is not permitted. This is to avoid ambiguity when using the previous context variable.

# Built-Ins

Unit	Description	Example Notebook
`JudgeUnit`	Direct score judge with customizable score `Scale` and optional preceeding explanation/CoT.
`PairwiseJudgeUnit`	Pairwise judge with customizable score `Scale` and optional preceeding explanation/CoT.
`BestOfKJudgeUnit`	Best of `k` inputs with customizable score `Scale` and optional preceeding explanation/CoT.
`CategoricalJudgeUnit`	Judge unit for categorical decisions (e.g., 'Harmful' or 'Not Harmful', 'Hallucination' or 'No Hallucination').
`RankerUnit`	Rank `k` inputs with optional preceeding explanation/CoT.
`ConversationalUnit`	Supports roles and a shared conversation history. Useful for debate, etc.
`CoTUnit`	Simple reasoning unit with single string field `thinking`.

# Defining a Custom Unit

Subclass Unit to define your own units. We'll walk through the implementation of the PairwiseJudgeUnit as a case study.

from verdict import Unit
from verdict.schema import Schema

class PairwiseJudgeUnit(Unit):
    _char: str = "PairwiseJudge"

    class InputSchema(Schema):
        A: str
        B: str

    # This Unit's default Prompt template
    _prompt: Prompt = Prompt.from_template("""
        You must choose the better option between the following two options based on how well they satisfy the following single criteria:

        A:
        {input.A}

        B:
        {input.B}
    """)

    # This Unit's response produced from LLM inference
    #   * validate(ResponseSchema) must pass
    #   * process(ResponseSchema, InputSchema) -> OutputSchema
    class ResponseSchema(Schema):
        winner: DiscreteScale = DiscreteScale(['A', 'B'])

    # This Unit's output
    class OutputSchema(Schema):
        winner: str

    # Validate the ResponseSchema. This is called after the LLM inference call.
    def validate(self, input: InputSchema, response: ResponseSchema) -> None:
        pass

    # Post-process the ResponseSchema into the OutputSchema.
    def process(self, input: InputSchema, response: ResponseSchema) -> OutputSchema:
        return self.OutputSchema(winner=input.A if response.winner == 'A' else input.B)

# Subclass Checklist

You can override the following components when creating a custom Unit.

Component	Optional?	Description
InputSchema	✅	Previous `Unit`'s `OutputSchema` casted to the current `Unit`'s `InputSchema`.
ResponseSchema	❌	Schema of response generated via inference using the specified extractor.
validate	✅	Validates that the `ResponseSchema` is valid. Will trigger a retry if validation fails.
process	✅	Populates the `OutputSchema` from the `ResponseSchema`.
OutputSchema	✅	Passed to the next `Unit`.
_char	✅	Unique identifier for the `Unit`.