feat: answer similarity migrated to collections (#2358)

Author: anistark

src/ragas/metrics/collections/__init__.py

@@ -1,6 +1,7 @@
 """Collections of metrics using modern component architecture."""
 
 from ragas.metrics.collections._answer_relevancy import AnswerRelevancy
+from ragas.metrics.collections._answer_similarity import AnswerSimilarity
 from ragas.metrics.collections._bleu_score import BleuScore
 from ragas.metrics.collections._rouge_score import RougeScore
 from ragas.metrics.collections._string import (
@@ -14,6 +15,7 @@
 __all__ = [
     "BaseMetric",  # Base class
     "AnswerRelevancy",
+    "AnswerSimilarity",
     "BleuScore",
     "DistanceMeasure",
     "ExactMatch",

src/ragas/metrics/collections/_answer_similarity.py

@@ -0,0 +1,95 @@
+"""Answer Similarity metric."""
+
+import typing as t
+
+import numpy as np
+
+from ragas.metrics.collections.base import BaseMetric
+from ragas.metrics.result import MetricResult
+
+if t.TYPE_CHECKING:
+    from ragas.embeddings.base import BaseRagasEmbedding
+
+
+class AnswerSimilarity(BaseMetric):
+    """
+    Evaluate semantic similarity between reference and response using embeddings.
+
+    Usage:
+        >>> from openai import AsyncOpenAI
+        >>> from ragas.embeddings.base import embedding_factory
+        >>> from ragas.metrics.collections import AnswerSimilarity
+        >>>
+        >>> # Setup embeddings
+        >>> client = AsyncOpenAI()
+        >>> embeddings = embedding_factory("openai", model="text-embedding-ada-002", client=client, interface="modern")
+        >>>
+        >>> # Create metric instance
+        >>> metric = AnswerSimilarity(embeddings=embeddings)
+        >>>
+        >>> # Single evaluation
+        >>> result = await metric.ascore(
+        ...     reference="Paris is the capital of France.",
+        ...     response="The capital of France is Paris."
+        ... )
+        >>> print(f"Score: {result.value}")
+        >>>
+        >>> # Batch evaluation
+        >>> results = await metric.abatch_score([
+        ...     {"reference": "Text 1", "response": "Response 1"},
+        ...     {"reference": "Text 2", "response": "Response 2"},
+        ... ])
+
+    Attributes:
+        embeddings: Modern embeddings model with embed_text() method
+        name: The metric name
+        threshold: Optional threshold for binary classification
+        allowed_values: Score range (0.0 to 1.0)
+    """
+
+    embeddings: "BaseRagasEmbedding"
+
+    def __init__(
+        self,
+        embeddings: "BaseRagasEmbedding",
+        name: str = "answer_similarity",
+        threshold: t.Optional[float] = None,
+        **kwargs,
+    ):
+        """Initialize AnswerSimilarity metric with required embeddings."""
+        self.embeddings = embeddings
+        self.threshold = threshold
+
+        super().__init__(name=name, **kwargs)
+
+    async def ascore(self, reference: str, response: str) -> MetricResult:
+        """
+        Calculate semantic similarity score asynchronously.
+
+        Components are guaranteed to be validated and non-None by the base class.
+
+        Args:
+            reference: The reference/ground truth text
+            response: The response text to evaluate
+
+        Returns:
+            MetricResult with similarity score (0.0-1.0)
+        """
+        reference = reference or " "
+        response = response or " "
+
+        embedding_1 = np.array(self.embeddings.embed_text(reference))
+        embedding_2 = np.array(self.embeddings.embed_text(response))
+
+        norms_1 = np.linalg.norm(embedding_1, keepdims=True)
+        norms_2 = np.linalg.norm(embedding_2, keepdims=True)
+        embedding_1_normalized = embedding_1 / norms_1
+        embedding_2_normalized = embedding_2 / norms_2
+        similarity = embedding_1_normalized @ embedding_2_normalized.T
+        score = similarity.flatten()
+
+        assert isinstance(score, np.ndarray), "Expects ndarray"
+        if self.threshold:
+            score = score >= self.threshold
+
+        return MetricResult(value=float(score.item()))

tests/e2e/metrics_migration/test_answer_similarity_migration.py

@@ -0,0 +1,245 @@
+"""E2E tests for Answer Similarity metric migration from v1 to v2 (class-based)."""
+
+import pytest
+
+from ragas.dataset_schema import SingleTurnSample
+from ragas.metrics import AnswerSimilarity as LegacyAnswerSimilarity, MetricResult
+from ragas.metrics.collections import AnswerSimilarity
+
+
+class TestAnswerSimilarityE2EMigration:
+    """E2E test compatibility between legacy AnswerSimilarity and new V2 AnswerSimilarity with automatic validation."""
+
+    @pytest.fixture
+    def sample_data(self):
+        """Real-world test cases for answer similarity evaluation."""
+        return [
+            {
+                "reference": "Paris is the capital of France.",
+                "response": "The capital of France is Paris.",
+                "description": "Semantically similar with word reordering",
+            },
+            {
+                "reference": "Python is a high-level programming language known for its simplicity and readability.",
+                "response": "Python is a programming language that emphasizes code readability.",
+                "description": "Similar content with paraphrasing",
+            },
+            {
+                "reference": "Machine learning is a subset of artificial intelligence.",
+                "response": "Deep learning uses neural networks with multiple layers.",
+                "description": "Related but different concepts",
+            },
+            {
+                "reference": "The quick brown fox jumps over the lazy dog.",
+                "response": "A slow red cat walks under the active mouse.",
+                "description": "Different content with similar structure",
+            },
+            {
+                "reference": "",
+                "response": "Some response text",
+                "description": "Empty reference",
+            },
+        ]
+
+    @pytest.fixture
+    def test_legacy_embeddings(self):
+        """Create legacy embeddings for legacy implementation."""
+        try:
+            from ragas.embeddings.base import embedding_factory
+
+            return embedding_factory("text-embedding-ada-002")
+        except ImportError as e:
+            pytest.skip(f"Embedding factory not available: {e}")
+        except Exception as e:
+            pytest.skip(
+                f"Could not create legacy embeddings (API key may be missing): {e}"
+            )
+
+    @pytest.fixture
+    def test_modern_embeddings(self):
+        """Create modern embeddings for v2 implementation."""
+        try:
+            import openai
+
+            from ragas.embeddings.base import embedding_factory
+
+            client = openai.AsyncOpenAI()
+
+            return embedding_factory(
+                provider="openai",
+                model="text-embedding-ada-002",
+                client=client,
+                interface="modern",
+            )
+        except ImportError as e:
+            pytest.skip(f"OpenAI or embedding factory not available: {e}")
+        except Exception as e:
+            pytest.skip(
+                f"Could not create modern embeddings (API key may be missing): {e}"
+            )
+
+    @pytest.mark.asyncio
+    async def test_legacy_answer_similarity_vs_v2_answer_similarity_e2e_compatibility(
+        self,
+        sample_data,
+        test_legacy_embeddings,
+        test_modern_embeddings,
+    ):
+        """E2E test that legacy and v2 implementations produce identical scores with real embeddings."""
+
+        if test_legacy_embeddings is None or test_modern_embeddings is None:
+            pytest.skip("Embeddings required for E2E testing")
+
+        for i, data in enumerate(sample_data):
+            print(
+                f"\n🧪 Testing Answer Similarity - Case {i + 1}: {data['description']}"
+            )
+            print(f"   Reference: {data['reference'][:50]}...")
+            print(f"   Response:  {data['response'][:50]}...")
+
+            legacy_answer_similarity = LegacyAnswerSimilarity(
+                embeddings=test_legacy_embeddings
+            )
+            legacy_sample = SingleTurnSample(
+                user_input="dummy",
+                response=data["response"],
+                reference=data["reference"],
+            )
+            legacy_score = await legacy_answer_similarity._single_turn_ascore(
+                legacy_sample, None
+            )
+
+            v2_answer_similarity = AnswerSimilarity(embeddings=test_modern_embeddings)
+            v2_answer_similarity_result = await v2_answer_similarity.ascore(
+                reference=data["reference"],
+                response=data["response"],
+            )
+
+            score_diff = abs(legacy_score - v2_answer_similarity_result.value)
+            print(f"   Legacy:    {legacy_score:.6f}")
+            print(f"   V2 Class:  {v2_answer_similarity_result.value:.6f}")
+            print(f"   Diff:      {score_diff:.10f}")
+
+            assert score_diff < 1e-6, (
+                f"Case {i + 1} ({data['description']}): Mismatch: {legacy_score} vs {v2_answer_similarity_result.value}"
+            )
+
+            assert isinstance(legacy_score, float)
+            assert isinstance(v2_answer_similarity_result, MetricResult)
+            assert 0.0 <= legacy_score <= 1.0
+            assert 0.0 <= v2_answer_similarity_result.value <= 1.0
+
+            print("   ✅ Scores match!")
+
+    @pytest.mark.asyncio
+    async def test_answer_similarity_with_threshold(
+        self, test_legacy_embeddings, test_modern_embeddings
+    ):
+        """Test that both implementations correctly handle threshold parameter."""
+
+        if test_legacy_embeddings is None or test_modern_embeddings is None:
+            pytest.skip("Embeddings required for E2E testing")
+
+        test_cases = [
+            {
+                "reference": "Paris is the capital of France.",
+                "response": "The capital of France is Paris.",
+                "threshold": 0.9,
+                "description": "High similarity with high threshold",
+            },
+            {
+                "reference": "Machine learning is a subset of artificial intelligence.",
+                "response": "Deep learning uses neural networks.",
+                "threshold": 0.5,
+                "description": "Different content with medium threshold",
+            },
+        ]
+
+        for case in test_cases:
+            print(f"\n🎯 Testing threshold: {case['description']}")
+
+            legacy_answer_similarity = LegacyAnswerSimilarity(
+                embeddings=test_legacy_embeddings, threshold=case["threshold"]
+            )
+            legacy_sample = SingleTurnSample(
+                user_input="dummy",
+                response=case["response"],
+                reference=case["reference"],
+            )
+            legacy_score = await legacy_answer_similarity._single_turn_ascore(
+                legacy_sample, None
+            )
+
+            v2_answer_similarity = AnswerSimilarity(
+                embeddings=test_modern_embeddings, threshold=case["threshold"]
+            )
+            v2_result = await v2_answer_similarity.ascore(
+                reference=case["reference"],
+                response=case["response"],
+            )
+
+            print(f"   Reference: {case['reference']}")
+            print(f"   Response:  {case['response']}")
+            print(f"   Threshold: {case['threshold']}")
+            print(f"   Legacy:    {legacy_score:.6f}")
+            print(f"   V2 Class:  {v2_result.value:.6f}")
+
+            score_diff = abs(legacy_score - v2_result.value)
+            assert score_diff < 1e-6, (
+                f"Threshold test failed: {legacy_score} vs {v2_result.value}"
+            )
+
+            assert legacy_score in [0.0, 1.0]
+            assert v2_result.value in [0.0, 1.0]
+
+            print("   ✅ Threshold handling matches!")
+
+    @pytest.mark.asyncio
+    async def test_v2_class_batch_processing(self, sample_data, test_modern_embeddings):
+        """Test V2 class-based AnswerSimilarity batch processing."""
+
+        if test_modern_embeddings is None:
+            pytest.skip("Modern embeddings required for V2 testing")
+
+        metric = AnswerSimilarity(embeddings=test_modern_embeddings)
+
+        batch_inputs = [
+            {"reference": case["reference"], "response": case["response"]}
+            for case in sample_data[:3]
+        ]
+
+        print(f"\n📦 Testing V2 class batch processing with {len(batch_inputs)} items:")
+
+        results = await metric.abatch_score(batch_inputs)
+
+        assert len(results) == len(batch_inputs)
+
+        for i, (case, result) in enumerate(zip(sample_data[:3], results)):
+            print(f"   Case {i + 1}: {result.value:.6f} - {case['description']}")
+            assert isinstance(result.value, float)
+            assert 0.0 <= result.value <= 1.0
+            assert result.reason is None
+
+        print("   ✅ V2 class batch processing works correctly!")
+
+    def test_answer_similarity_migration_requirements_documented(self):
+        """Document the requirements for running full E2E answer similarity tests."""
+
+        requirements = {
+            "embeddings": "OpenAI embeddings, HuggingFace embeddings, or similar",
+            "environment": "API keys configured for embedding providers",
+            "purpose": "Verify that v2 class-based implementation produces identical results to legacy implementation",
+        }
+
+        print("\n📋 Answer Similarity E2E Test Requirements:")
+        for key, value in requirements.items():
+            print(f"   {key.capitalize()}: {value}")
+
+        print("\n🚀 To enable full E2E testing:")
+        print("   1. Configure embedding provider (e.g., export OPENAI_API_KEY=...)")
+        print("   2. Remove @pytest.mark.skip decorators")
+        print(
+            "   3. Run: pytest tests/e2e/metrics_migration/test_answer_similarity_migration.py -v -s"
+        )
+
+        assert True