Merge pull request #166 from Vela-zz/Vela-zz/issue160

Async call of OpenAISimilarityScorer is slower than sync version

src/langcheck/metrics/eval_clients/_openai.py

@@ -8,6 +8,7 @@
 
 import torch
 from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
+from openai.types.create_embedding_response import CreateEmbeddingResponse
 
 from langcheck.utils.progress_bar import tqdm_wrapper
 
@@ -409,28 +410,32 @@ def __init__(
         self.openai_args = openai_args
         self._use_async = use_async
 
+    async def _async_embed(self, inputs: list[str]) -> CreateEmbeddingResponse:
+      """Embed the inputs using the OpenAI API in async mode."""
+      assert isinstance(self.openai_client, AsyncOpenAI)
+      if self.openai_args:
+          responses = await self.openai_client.embeddings.create(
+              input=inputs, **self.openai_args
+          )
+      else:
+          responses = await self.openai_client.embeddings.create(
+              input=inputs, model="text-embedding-3-small"
+          )
+      return responses
+
     def _embed(self, inputs: list[str]) -> torch.Tensor:
         """Embed the inputs using the OpenAI API."""
 
         # TODO: Fix that this async call could be much slower than the sync
         # version. https://github.com/citadel-ai/langcheck/issues/160
         if self._use_async:
-
-            async def _call_async_api() -> Any:
-                assert isinstance(self.openai_client, AsyncOpenAI)
-                if self.openai_args:
-                    responses = await self.openai_client.embeddings.create(
-                        input=inputs, **self.openai_args
-                    )
-                else:
-                    responses = await self.openai_client.embeddings.create(
-                        input=inputs, model="text-embedding-3-small"
-                    )
-                return responses
-
-            embed_response = asyncio.run(_call_async_api())
-            embeddings = [item.embedding for item in embed_response.data]
-
+          try:
+            loop = asyncio.get_event_loop()
+          except RuntimeError:  # pragma: py-lt-310
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+          embed_response = loop.run_until_complete(self._async_embed(inputs))
+          embeddings = [item.embedding for item in embed_response.data]
         else:
             assert isinstance(self.openai_client, OpenAI)