chore: import upstream snapshot with attribution

apps/opik-documentation/python-sdk-docs/source/evaluation/Dataset.rst

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+Dataset
+=======
+
+.. autoclass:: opik.Dataset
+    :members:
+    :special-members: __init__

apps/opik-documentation/python-sdk-docs/source/evaluation/evaluate.rst

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+evaluate
+========
+
+.. autofunction:: opik.evaluation.evaluate

apps/opik-documentation/python-sdk-docs/source/evaluation/evaluate_experiment.rst

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+evaluate_experiment
+===================
+
+.. autofunction:: opik.evaluation.evaluate_experiment

apps/opik-documentation/python-sdk-docs/source/evaluation/evaluate_prompt.rst

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+evaluate_prompt
+===============
+
+.. autofunction:: opik.evaluation.evaluate_prompt

apps/opik-documentation/python-sdk-docs/source/evaluation/evaluate_threads.rst

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+evaluate_threads
+================
+
+.. autofunction:: opik.evaluation.evaluate_threads
+

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/AnswerRelevance.rst

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+AnswerRelevance
+===============
+
+.. autoclass:: opik.evaluation.metrics.AnswerRelevance
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/BaseMetric.rst

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+BaseMetric  
+==========
+
+.. autoclass:: opik.evaluation.metrics.BaseMetric
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/Contains.rst

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+Contains
+========
+
+.. autoclass:: opik.evaluation.metrics.Contains
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/ContextPrecision.rst

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+ContextPrecision
+================
+
+.. autoclass:: opik.evaluation.metrics.ContextPrecision
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/ContextRecall.rst

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+ContextRecall
+=============
+
+.. autoclass:: opik.evaluation.metrics.ContextRecall
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/ConversationHeuristicMetrics.rst

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+Conversation Heuristic Metrics
+==============================
+
+.. currentmodule:: opik.evaluation.metrics
+
+Use these fast, rule-based metrics when you want lightweight signals about dialogue
+quality without invoking an LLM judge. They operate over full conversation
+transcripts and surface issues such as repetition and missing context.
+
+.. autoclass:: ConversationDegenerationMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: KnowledgeRetentionMetric
+    :special-members: __init__
+    :members: score

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/ConversationLLMJudges.rst

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+Conversation LLM Judges
+=======================
+
+.. currentmodule:: opik.evaluation.metrics
+
+These evaluators wrap GEval-style LLM judges so you can score full conversations
+without manually extracting turns. They expect transcripts in the same format used
+by :class:`~opik.evaluation.metrics.ConversationThreadMetric` and typically rely on
+an OpenAI- or Azure-compatible backend. Refer to the relevant Fern guides for API
+keys, rate limits, and pricing considerations.
+
+Core Conversation Judges
+------------------------
+
+.. autoclass:: GEvalConversationMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ConversationalCoherenceMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: SessionCompletenessQuality
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: UserFrustrationMetric
+    :special-members: __init__
+    :members: score
+
+Specialized Variants
+--------------------
+
+.. autoclass:: ConversationComplianceRiskMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ConversationDialogueHelpfulnessMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ConversationQARelevanceMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ConversationSummarizationCoherenceMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ConversationSummarizationConsistencyMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ConversationPromptUncertaintyMetric
+    :special-members: __init__
+    :members: score

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/ConversationThreadMetric.rst

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+ConversationThreadMetric
+========================
+
+.. autoclass:: opik.evaluation.metrics.conversation.conversation_thread_metric.ConversationThreadMetric
+    :members:
+    :inherited-members:
+

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/Equals.rst

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+Equals  
+======
+
+.. autoclass:: opik.evaluation.metrics.Equals
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/GEval.rst

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+GEval
+=====
+
+.. autoclass:: opik.evaluation.metrics.GEval
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/Hallucination.rst

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+Hallucination
+=============
+
+.. autoclass:: opik.evaluation.metrics.Hallucination
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/HeuristicMetrics.rst

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+Heuristic Metrics
+=================
+
+.. currentmodule:: opik.evaluation.metrics
+
+This section lists the text-similarity, readability, safety, and sentiment metrics
+that can be composed into larger test suites. Several metrics rely on optional
+dependencies (for example, ``bert-score``, ``sacrebleu``, ``fasttext``, ``nltk``);
+install the relevant packages before scoring.
+
+Sentence & Token Overlap
+------------------------
+
+.. autoclass:: SentenceBLEU
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: CorpusBLEU
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: GLEU
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ROUGE
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ChrF
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: METEOR
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: BERTScore
+    :special-members: __init__
+    :members: score
+
+Distribution Comparisons
+------------------------
+
+.. autoclass:: JSDivergence
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: JSDistance
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: KLDivergence
+    :special-members: __init__
+    :members: score
+
+Rank & Readability
+------------------
+
+.. autoclass:: SpearmanRanking
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: Readability
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: Tone
+    :special-members: __init__
+    :members: score
+
+Prompt Safety & Sentiment
+-------------------------
+
+.. autoclass:: PromptInjection
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: LanguageAdherenceMetric
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: Sentiment
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: VADERSentiment
+    :special-members: __init__
+    :members: score

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/IsJson.rst

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+IsJson
+======
+
+.. autoclass:: opik.evaluation.metrics.IsJson
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/LLMJudgePresets.rst

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+LLM Judge Presets
+=================
+
+.. currentmodule:: opik.evaluation.metrics
+
+These presets wrap common GEval prompt templates so you can instantiate judges with
+one line of code. Provide an appropriate ``model`` (for example, ``"gpt-4o"``) and
+ensure the backing provider is configured via ``opik.configure``.
+
+Agent & Task Presets
+--------------------
+
+.. autoclass:: AgentTaskCompletionJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: AgentToolCorrectnessJudge
+    :special-members: __init__
+    :members: score
+
+Conversation Quality Presets
+----------------------------
+
+.. autoclass:: DialogueHelpfulnessJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: QARelevanceJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: SummarizationCoherenceJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: SummarizationConsistencyJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: PromptUncertaintyJudge
+    :special-members: __init__
+    :members: score
+
+Risk & Bias Presets
+-------------------
+
+.. autoclass:: ComplianceRiskJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: DemographicBiasJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: GenderBiasJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: PoliticalBiasJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: RegionalBiasJudge
+    :special-members: __init__
+    :members: score
+
+.. autoclass:: ReligiousBiasJudge
+    :special-members: __init__
+    :members: score

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/LLMJuries.rst

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+LLM Juries
+==========
+
+.. currentmodule:: opik.evaluation.metrics
+
+Use :class:`LLMJuriesJudge` when you want to call multiple judges and aggregate the
+results into a single decision. Provide a list of judge instances plus an
+aggregation strategy (majority vote, max confidence, etc.).
+
+.. autoclass:: LLMJuriesJudge
+    :special-members: __init__
+    :members: score

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/LevenshteinRatio.rst

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+LevenshteinRatio  
+================
+
+.. autoclass:: opik.evaluation.metrics.LevenshteinRatio
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/Moderation.rst

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+Moderation
+==========
+
+.. autoclass:: opik.evaluation.metrics.Moderation
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/RegexMatch.rst

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+RegexMatch  
+==========
+
+.. autoclass:: opik.evaluation.metrics.RegexMatch
+    :members:
+    :inherited-members:

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/UtilityMetrics.rst

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+Utility Metrics
+===============
+
+.. currentmodule:: opik.evaluation.metrics
+
+Helper components that adapt or combine other metrics. Use them to stitch existing
+evaluators together without rewriting orchestration logic.
+
+.. autoclass:: AggregatedMetric
+    :special-members: __init__
+    :members: score, validate_score_arguments
+
+.. autoclass:: RagasMetricWrapper
+    :special-members: __init__
+    :members: score

apps/opik-documentation/python-sdk-docs/source/evaluation/metrics/index.rst

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+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:
+
+.. toctree::
+   :maxdepth: 4
+   :titlesonly:
+
+   Equals
+   RegexMatch
+   Contains
+   IsJson
+   LevenshteinRatio
+
+   Hallucination
+   GEval
+   Moderation
+
+   AnswerRelevance
+   ContextPrecision
+   ContextRecall
+
+   BaseMetric
+   ConversationThreadMetric
+   HeuristicMetrics
+   ConversationHeuristicMetrics
+   ConversationLLMJudges
+   LLMJudgePresets
+   LLMJuries
+   UtilityMetrics
+
+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 :mod:`opik.evaluation.metrics` has an accompanying API reference.
+
+Use these aggregation pages to browse the extended catalog:
+
+- :doc:`HeuristicMetrics` — sentence/word overlap, readability, sentiment, prompt safety, and distribution comparisons.
+- :doc:`ConversationHeuristicMetrics` — fast heuristics for degeneracy and knowledge retention.
+- :doc:`ConversationLLMJudges` — LLM-as-a-judge conversation evaluators and session quality metrics.
+- :doc:`LLMJudgePresets` — pre-built GEval presets and bias checks.
+- :doc:`LLMJuries` — multi-judge aggregation.
+- :doc:`UtilityMetrics` — helpers such as ``AggregatedMetric`` and ``RagasMetricWrapper``.
+
+Import any metric directly from :mod:`opik.evaluation.metrics`, and pair these API
+references with the Fern guides in
+``apps/opik-documentation/documentation/fern/docs/evaluation/metrics`` for workflow
+context.