From feaf1a9fa6dc880364f97637768199edbb5f6ecc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Christian=20G=C3=B6rner?= Date: Thu, 15 Jan 2026 15:51:38 +0100 Subject: [PATCH 1/2] fix: Use embedding model variable in AnswerRelevancy sync runner --- py/autoevals/ragas.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/py/autoevals/ragas.py b/py/autoevals/ragas.py index 794ab03..ab99eee 100644 --- a/py/autoevals/ragas.py +++ b/py/autoevals/ragas.py @@ -1196,7 +1196,7 @@ def _run_eval_sync(self, output, expected=None, input=None, context=None, **kwar for _ in range(self.strictness) ] similarity = [ - EmbeddingSimilarity(client=self.client).eval(output=q["question"], expected=input, model=self.model) + EmbeddingSimilarity(client=self.client).eval(output=q["question"], expected=input, model=self.embedding_model) for q in questions ] From a036329189b21dc485bf24627f708b78d3324f51 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Christian=20G=C3=B6rner?= Date: Thu, 15 Jan 2026 17:17:03 +0100 Subject: [PATCH 2/2] fix: Pass model parameter on EmbeddingSimilarity init instead of eval call --- py/autoevals/ragas.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/py/autoevals/ragas.py b/py/autoevals/ragas.py index ab99eee..1133887 100644 --- a/py/autoevals/ragas.py +++ b/py/autoevals/ragas.py @@ -1176,8 +1176,8 @@ async def _run_eval_async(self, output, expected=None, input=None, context=None, ) similarity = await asyncio.gather( *[ - EmbeddingSimilarity(client=self.client).eval_async( - output=q["question"], expected=input, model=self.embedding_model + EmbeddingSimilarity(client=self.client, model=self.embedding_model).eval_async( + output=q["question"], expected=input ) for q in questions ] @@ -1196,7 +1196,7 @@ def _run_eval_sync(self, output, expected=None, input=None, context=None, **kwar for _ in range(self.strictness) ] similarity = [ - EmbeddingSimilarity(client=self.client).eval(output=q["question"], expected=input, model=self.embedding_model) + EmbeddingSimilarity(client=self.client, model=self.embedding_model).eval(output=q["question"], expected=input) for q in questions ]