feat: simple criteria migrated to collections

src/ragas/metrics/collections/__init__.py

@@ -15,6 +15,7 @@
 from ragas.metrics.collections._context_entity_recall import ContextEntityRecall
 from ragas.metrics.collections._rouge_score import RougeScore
 from ragas.metrics.collections._semantic_similarity import SemanticSimilarity
+from ragas.metrics.collections._simple_criteria import SimpleCriteria
 from ragas.metrics.collections._string import (
     DistanceMeasure,
     ExactMatch,
@@ -36,6 +37,7 @@
     "NonLLMStringSimilarity",
     "RougeScore",
     "SemanticSimilarity",
+    "SimpleCriteria",
     "StringPresence",
     # AspectCritic helper functions
     "coherence",

src/ragas/metrics/collections/_simple_criteria.py

@@ -0,0 +1,156 @@
+"""SimpleCriteria metric for custom criteria-based evaluation."""
+
+import typing as t
+from collections import Counter
+
+from pydantic import BaseModel, Field
+
+from ragas.metrics.collections.base import BaseMetric
+from ragas.metrics.result import MetricResult
+
+if t.TYPE_CHECKING:
+    from ragas.llms.base import InstructorBaseRagasLLM
+
+
+class SimpleCriteriaOutput(BaseModel):
+    """Output for simple criteria evaluation."""
+
+    reason: str = Field(description="Reason for the scoring")
+    score: int = Field(description="The score for the submission")
+
+
+class SimpleCriteria(BaseMetric):
+    """
+    Judges submissions using custom criteria with configurable scoring.
+
+    Usage:
+        >>> from openai import AsyncOpenAI
+        >>> from ragas.llms import llm_factory
+        >>> from ragas.metrics.collections import SimpleCriteria
+        >>>
+        >>> # Setup dependencies
+        >>> client = AsyncOpenAI()
+        >>> llm = llm_factory("gpt-4o-mini", client=client)
+        >>>
+        >>> # Create metric instance
+        >>> metric = SimpleCriteria(
+        ...     name="clarity",
+        ...     definition="Is the response clear and easy to understand?",
+        ...     llm=llm,
+        ... )
+        >>>
+        >>> # Single evaluation
+        >>> result = await metric.ascore(
+        ...     user_input="What is machine learning?",
+        ...     response="Machine learning is a subset of artificial intelligence..."
+        ... )
+        >>> print(f"Score: {result.value}")
+
+    Attributes:
+        llm: Modern instructor-based LLM for evaluation
+        name: The metric name
+        definition: Criteria to judge the submission
+        strictness: Number of times self consistency checks is made (default: 1)
+        allowed_values: Score range for numeric validation
+    """
+
+    llm: "InstructorBaseRagasLLM"
+
+    def __init__(
+        self,
+        name: str,
+        definition: str,
+        llm: "InstructorBaseRagasLLM",
+        strictness: int = 1,
+        allowed_values: t.Tuple[float, float] = (0.0, 10.0),
+        **kwargs,
+    ):
+        """Initialize SimpleCriteria metric with required components."""
+        self.llm = llm
+        self.definition = definition
+        self.strictness = strictness if strictness % 2 != 0 else strictness + 1
+
+        super().__init__(name=name, allowed_values=allowed_values, **kwargs)
+
+    def _build_prompt(
+        self,
+        user_input: t.Optional[str] = None,
+        response: t.Optional[str] = None,
+        retrieved_contexts: t.Optional[t.List[str]] = None,
+        reference: t.Optional[str] = None,
+        reference_contexts: t.Optional[t.List[str]] = None,
+    ) -> str:
+        """Build the evaluation prompt from inputs."""
+        instruction = f"""Evaluate the input based on the criteria defined.
+Criteria Definition: {self.definition}
+
+Provide your evaluation in the following format:
+- reason: Brief explanation for your score
+- score: Integer score for the submission
+"""
+
+        input_parts = []
+        if user_input is not None:
+            input_parts.append(f"User Input: {user_input}")
+        if response is not None:
+            input_parts.append(f"Response: {response}")
+        if retrieved_contexts is not None and len(retrieved_contexts) > 0:
+            contexts_str = "\n".join(f"  - {ctx}" for ctx in retrieved_contexts)
+            input_parts.append(f"Retrieved Contexts:\n{contexts_str}")
+        if reference is not None:
+            input_parts.append(f"Reference: {reference}")
+        if reference_contexts is not None and len(reference_contexts) > 0:
+            ref_contexts_str = "\n".join(f"  - {ctx}" for ctx in reference_contexts)
+            input_parts.append(f"Reference Contexts:\n{ref_contexts_str}")
+
+        input_section = "\n\n".join(input_parts) if input_parts else ""
+
+        return f"{instruction}\n{input_section}"
+
+    async def ascore(
+        self,
+        user_input: t.Optional[str] = None,
+        response: t.Optional[str] = None,
+        retrieved_contexts: t.Optional[t.List[str]] = None,
+        reference: t.Optional[str] = None,
+        reference_contexts: t.Optional[t.List[str]] = None,
+    ) -> MetricResult:
+        """
+        Calculate simple criteria score asynchronously.
+
+        Args:
+            user_input: The input to the llm system (optional)
+            response: The response from the llm system (optional)
+            retrieved_contexts: The retrieved contexts from the llm system (optional)
+            reference: The reference answer for evaluation (optional)
+            reference_contexts: The reference contexts for evaluation (optional)
+
+        Returns:
+            MetricResult with score and reason
+        """
+        prompt = self._build_prompt(
+            user_input=user_input,
+            response=response,
+            retrieved_contexts=retrieved_contexts,
+            reference=reference,
+            reference_contexts=reference_contexts,
+        )
+
+        scores = []
+        reasons = []
+
+        for _ in range(self.strictness):
+            result = await self.llm.agenerate(prompt, SimpleCriteriaOutput)
+            scores.append(result.score)
+            reasons.append(result.reason)
+
+        if self.strictness > 1:
+            score = Counter(scores).most_common(1)[0][0]
+            majority_score = score
+            reason_idx = scores.index(majority_score)
+            reason = reasons[reason_idx]
+        else:
+            score = scores[0]
+            reason = reasons[0]
+
+        return MetricResult(value=float(score), reason=reason)