Bases: BaseEvaluator
Embedding similarity evaluator.
Evaluate the quality of a question answering system by
comparing the similarity between embeddings of the generated answer
and the reference answer.
Inspired by this paper:
- Semantic Answer Similarity for Evaluating Question Answering Models
https://arxiv.org/pdf/2108.06130.pdf
Parameters:
| Name |
Type |
Description |
Default |
service_context |
Optional[ServiceContext]
|
|
None
|
similarity_threshold |
float
|
Embedding similarity threshold for "passing".
Defaults to 0.8.
|
0.8
|
Source code in llama-index-core/llama_index/core/evaluation/semantic_similarity.py
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84 | class SemanticSimilarityEvaluator(BaseEvaluator):
"""Embedding similarity evaluator.
Evaluate the quality of a question answering system by
comparing the similarity between embeddings of the generated answer
and the reference answer.
Inspired by this paper:
- Semantic Answer Similarity for Evaluating Question Answering Models
https://arxiv.org/pdf/2108.06130.pdf
Args:
service_context (Optional[ServiceContext]): Service context.
similarity_threshold (float): Embedding similarity threshold for "passing".
Defaults to 0.8.
"""
def __init__(
self,
embed_model: Optional[BaseEmbedding] = None,
similarity_fn: Optional[Callable[..., float]] = None,
similarity_mode: Optional[SimilarityMode] = None,
similarity_threshold: float = 0.8,
# deprecated
service_context: Optional[ServiceContext] = None,
) -> None:
self._embed_model = embed_model or embed_model_from_settings_or_context(
Settings, service_context
)
if similarity_fn is None:
similarity_mode = similarity_mode or SimilarityMode.DEFAULT
self._similarity_fn = lambda x, y: similarity(x, y, mode=similarity_mode)
else:
if similarity_mode is not None:
raise ValueError(
"Cannot specify both similarity_fn and similarity_mode"
)
self._similarity_fn = similarity_fn
self._similarity_threshold = similarity_threshold
def _get_prompts(self) -> PromptDictType:
"""Get prompts."""
return {}
def _update_prompts(self, prompts: PromptDictType) -> None:
"""Update prompts."""
async def aevaluate(
self,
query: Optional[str] = None,
response: Optional[str] = None,
contexts: Optional[Sequence[str]] = None,
reference: Optional[str] = None,
**kwargs: Any,
) -> EvaluationResult:
del query, contexts, kwargs # Unused
if response is None or reference is None:
raise ValueError("Must specify both response and reference")
response_embedding = await self._embed_model.aget_text_embedding(response)
reference_embedding = await self._embed_model.aget_text_embedding(reference)
similarity_score = self._similarity_fn(response_embedding, reference_embedding)
passing = similarity_score >= self._similarity_threshold
return EvaluationResult(
score=similarity_score,
passing=passing,
feedback=f"Similarity score: {similarity_score}",
)
|