feat(validators): add SemanticValidator

src/veritext/validators/__init__.py

@@ -27,7 +27,12 @@ from veritext.validators.constraint import (
     LengthValidator,
     ReadabilityValidator,
 )
-from veritext.validators.metric import BleuValidator, LexicalValidator, RougeValidator
+from veritext.validators.metric import (
+    BleuValidator,
+    LexicalValidator,
+    RougeValidator,
+    SemanticValidator,
+)
 
 
 # Factory functions for clean API
@@ -187,6 +192,28 @@ def any_of(checks: list[Check]) -> AnyOf:
     return AnyOf(checks=checks)
 
 
+def semantic(
+    min_score: float,
+    model: str = "all-MiniLM-L6-v2",
+    cache_embeddings: bool = True,
+) -> SemanticValidator:
+    """Create a semantic similarity validator.
+
+    Requires the `veritext[semantic]` extra to be installed.
+
+    Args:
+        min_score: Minimum semantic similarity required (0.0 to 1.0).
+        model: Name of the sentence-transformers model to use.
+        cache_embeddings: Whether to cache embeddings for repeated texts.
+
+    Returns:
+        SemanticValidator instance.
+    """
+    return SemanticValidator(
+        min_score=min_score, model=model, cache_embeddings=cache_embeddings
+    )
+
+
 __all__ = [
     "AllOf",
     "AnyOf",
@@ -198,6 +225,7 @@ __all__ = [
     "LexicalValidator",
     "ReadabilityValidator",
     "RougeValidator",
+    "SemanticValidator",
     "all_of",
     "any_of",
     "bleu",
@@ -207,4 +235,5 @@ __all__ = [
     "lexical",
     "readability",
     "rouge",
+    "semantic",
 ]

src/veritext/validators/metric.py

@@ -286,3 +286,85 @@ class LexicalValidator:
             threshold=threshold,
             message=message,
         )
+
+
+class SemanticValidator:
+    """Validates that semantic similarity meets minimum threshold.
+
+    Requires the `veritext[semantic]` extra to be installed.
+    """
+
+    def __init__(
+        self,
+        min_score: float,
+        model: str = "all-MiniLM-L6-v2",
+        cache_embeddings: bool = True,
+    ) -> None:
+        """
+        Initialise the semantic validator.
+
+        Args:
+            min_score: Minimum semantic similarity required (0.0 to 1.0).
+            model: Name of the sentence-transformers model to use.
+            cache_embeddings: Whether to cache embeddings for repeated texts.
+
+        Raises:
+            InvalidThresholdError: If min_score is not in range [0.0, 1.0].
+            DependencyError: If sentence-transformers is not installed.
+        """
+        if not 0.0 <= min_score <= 1.0:
+            raise InvalidThresholdError(
+                f"min_score must be between 0.0 and 1.0, got {min_score}"
+            )
+
+        self._min_score = min_score
+        # Lazy import to avoid loading PyTorch unless needed
+        from veritext.semantic.similarity import SemanticSimilarity
+
+        self._metric: SemanticSimilarity = SemanticSimilarity(
+            model=model, cache_embeddings=cache_embeddings
+        )
+
+    @property
+    def name(self) -> str:
+        """Return the name of this check."""
+        return "semantic"
+
+    def check(self, text: str, context: ValidationContext) -> CheckResult:
+        """
+        Run the semantic similarity check.
+
+        Args:
+            text: The text to validate.
+            context: Validation context containing reference text.
+
+        Returns:
+            CheckResult with pass/fail status.
+
+        Raises:
+            ValidationError: If reference text is missing from context.
+        """
+        if context.reference is None:
+            raise ValidationError(f"{self.name} requires reference text in context")
+
+        result = self._metric.score(text, context.reference)
+        passed = result.similarity >= self._min_score
+
+        if passed:
+            message = (
+                f"Semantic similarity {result.similarity:.2f} "
+                f"meets minimum {self._min_score:.2f}"
+            )
+        else:
+            message = (
+                f"Semantic similarity {result.similarity:.2f} "
+                f"below minimum {self._min_score:.2f}"
+            )
+
+        return CheckResult(
+            name=self.name,
+            passed=passed,
+            actual=result.similarity,
+            threshold=self._min_score,
+            message=message,
+        )