Auto call warm_up

Author: sjrl

integrations/optimum/src/haystack_integrations/components/embedders/optimum/optimum_document_embedder.py

@@ -200,14 +200,12 @@ def run(self, documents: list[Document]) -> dict[str, list[Document]]:
             A list of Documents to embed.
         :returns:
             The updated Documents with their embeddings.
-        :raises RuntimeError:
-            If the component was not initialized.
         :raises TypeError:
             If the input is not a list of Documents.
         """
         if not self._initialized:
-            msg = "The embedding model has not been loaded. Please call warm_up() before running."
-            raise RuntimeError(msg)
+            self.warm_up()
+
         if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "OptimumDocumentEmbedder expects a list of Documents as input."

integrations/optimum/src/haystack_integrations/components/embedders/optimum/optimum_text_embedder.py

@@ -162,14 +162,11 @@ def run(self, text: str) -> dict[str, list[float]]:
             The text to embed.
         :returns:
             The embeddings of the text.
-        :raises RuntimeError:
-            If the component was not initialized.
         :raises TypeError:
             If the input is not a string.
         """
         if not self._initialized:
-            msg = "The embedding model has not been loaded. Please call warm_up() before running."
-            raise RuntimeError(msg)
+            self.warm_up()
 
         if not isinstance(text, str):
             msg = (

integrations/optimum/tests/test_optimum_document_embedder.py

@@ -371,7 +371,6 @@ def test_run(self, opt_config, quant_config):
                 optimizer_settings=opt_config,
                 quantizer_settings=quant_config,
             )
-            embedder.warm_up()
 
             result = embedder.run(documents=docs)
             _ = [embedder.run([d]) for d in docs_copy]

integrations/optimum/tests/test_optimum_text_embedder.py

@@ -252,7 +252,6 @@ def test_run(self):
                 suffix=" suffix",
                 pooling_mode=pooling_mode,
             )
-            embedder.warm_up()
 
             result = embedder.run(text="The food was delicious")