metrics

Opik includes a number of pre-built metrics to help you evaluate your LLM application.

Each metric can be called as a standalone function using the score method:

from opik.evaluation.metrics import Hallucination

metric = Hallucination()

metric.score(
   input="What is the capital of France?",
   output="The capital of France is Paris. It is famous for its iconic Eiffel Tower and rich cultural heritage.",
   context=["France is a country in Western Europe. Its capital is Paris, which is known for landmarks like the Eiffel Tower."],
)

Or as part of an evaluation run using the evaluate function.

You can learn more about each metric in the following sections:

• Equals
• RegexMatch
• Contains
• IsJson
• LevenshteinRatio
• Hallucination
• GEval
• Moderation
• AnswerRelevance
• ContextPrecision
• ContextRecall
• BaseMetric
• ConversationThreadMetric
• Heuristic Metrics
• Conversation Heuristic Metrics
• Conversation LLM Judges
• LLM Judge Presets
• LLM Juries
• Utility Metrics

The pages above fall into two categories:

• Established metric guides (e.g., Equals, Hallucination) that remain the authoritative deep dives.

• Aggregation pages that collect the expanded metric families so every class exported via opik.evaluation.metrics has an accompanying API reference.

Use these aggregation pages to browse the extended catalog:

• Heuristic Metrics — sentence/word overlap, readability, sentiment, prompt safety, and distribution comparisons.

• Conversation Heuristic Metrics — fast heuristics for degeneracy and knowledge retention.

• Conversation LLM Judges — LLM-as-a-judge conversation evaluators and session quality metrics.

• LLM Judge Presets — pre-built GEval presets and bias checks.

• LLM Juries — multi-judge aggregation.

• Utility Metrics — helpers such as AggregatedMetric and RagasMetricWrapper.

Import any metric directly from opik.evaluation.metrics, and pair these API references with the Fern guides in apps/opik-documentation/documentation/fern/docs/evaluation/metrics for workflow context.