fixes ToolCallAccuracy raises ZeroDivisionError when called without a…

…ny arguments

src/ragas/metrics/_answer_similarity.py

@@ -8,11 +8,7 @@
 
 from ragas.dataset_schema import SingleTurnSample
 from ragas.embeddings.base import HuggingfaceEmbeddings
-from ragas.metrics.base import (
-    MetricType,
-    MetricWithEmbeddings,
-    SingleTurnMetric,
-)
+from ragas.metrics.base import MetricType, MetricWithEmbeddings, SingleTurnMetric
 
 if t.TYPE_CHECKING:
     from langchain_core.callbacks.base import Callbacks

src/ragas/metrics/_aspect_critic.py

@@ -286,7 +286,7 @@ class AspectCriticWithReference(AspectCritic):
     )
 
     async def _ascore(self, row: t.Dict, callbacks: Callbacks) -> float:
-        
+
         if self.llm is None:
             raise ValueError("LLM is not set")
 
@@ -320,10 +320,10 @@ async def _ascore(self, row: t.Dict, callbacks: Callbacks) -> float:
     async def _multi_turn_ascore(
         self, sample: MultiTurnSample, callbacks: Callbacks
     ) -> float:
-        
+
         if self.llm is None:
             raise ValueError("LLM is not set")
-        
+
         if sample.reference is None:
             raise ValueError("Reference is not set")
 

src/ragas/metrics/_noise_sensitivity.py

@@ -98,10 +98,7 @@ async def _decompose_answer_into_statements(
         assert self.sentence_segmenter is not None, "sentence_segmenter is not set"
 
         sentences = self.sentence_segmenter.segment(text)
-        sentences_with_index = {
-            i: sentence
-            for i, sentence in enumerate(sentences)
-        }
+        sentences_with_index = {i: sentence for i, sentence in enumerate(sentences)}
 
         statements_simplified = await self.statement_prompt.generate(
             llm=self.llm,

src/ragas/metrics/_tool_call_accuracy.py

@@ -35,6 +35,11 @@ def init(self, run_config):
     async def _get_arg_score(
         self, preds: t.Dict[str, t.Any], refs: t.Dict[str, t.Any], callbacks: Callbacks
     ) -> float:
+        if not refs and not preds:
+            return 1.0
+        if not refs:
+            return 0.0
+
         score = 0.0
         for arg in refs.keys():
             if arg in preds:

src/ragas/testset/transforms/__init__.py

@@ -1,4 +1,10 @@
-from .base import BaseGraphTransformation, Extractor, RelationshipBuilder, Splitter, NodeFilter
+from .base import (
+    BaseGraphTransformation,
+    Extractor,
+    NodeFilter,
+    RelationshipBuilder,
+    Splitter,
+)
 from .default import default_transforms
 from .engine import Parallel, Transforms, apply_transforms, rollback_transforms
 from .extractors import (
@@ -8,12 +14,12 @@
     SummaryExtractor,
     TitleExtractor,
 )
+from .filters import CustomNodeFilter
 from .relationship_builders.cosine import (
     CosineSimilarityBuilder,
     SummaryCosineSimilarityBuilder,
 )
 from .splitters import HeadlineSplitter
-from .filters import CustomNodeFilter
 
 __all__ = [
     # base

src/ragas/utils.py

@@ -233,6 +233,7 @@ def num_tokens_from_string(string: str, encoding_name: str = "cl100k_base") -> i
     num_tokens = len(encoding.encode(string))
     return num_tokens
 
+
 def batched(iterable: t.Iterable, n: int) -> t.Iterator[t.Tuple]:
     """Batch data from the iterable into tuples of length n. The last batch may be shorter than n."""
     # batched('ABCDEFG', 3) → ABC DEF G