diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py
index d0a32a88c..dbc4fdb64 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py
@@ -1,4 +1,4 @@
-from typing import ClassVar, Dict, List, Optional
+from typing import ClassVar, Dict, List, Optional, Union
 
 import numpy as np
 
@@ -81,7 +81,7 @@ def __init__(
     ):
         self.model = SparseTextEmbedding(model_name=model_name, cache_dir=cache_dir, threads=threads)
 
-    def embed(self, data: List[List[str]], **kwargs) -> List[Dict[str, np.ndarray]]:
+    def embed(self, data: List[List[str]], **kwargs) -> List[Dict[str, Union[List[int], List[float]]]]:
         # The embed method returns a Iterable[SparseEmbedding], so we convert it to a list of dictionaries.
         # Each dict contains an `indices` key containing a list of int and an `values` key containing a list of floats.
         sparse_embeddings = [sparse_embedding.as_object() for sparse_embedding in self.model.embed(data, **kwargs)]
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_SPLADE_embedder.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_SPLADE_embedder.py
index ef8c73803..33700ac12 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_SPLADE_embedder.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_SPLADE_embedder.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 import numpy as np
 from haystack import component, default_to_dict
 
@@ -94,7 +94,7 @@ def warm_up(self):
                 model_name=self.model_name, cache_dir=self.cache_dir, threads=self.threads
             )
 
-    @component.output_types(embedding=List[Dict[str, np.ndarray]])
+    @component.output_types(embedding=List[Dict[str, Union[List[int], List[float]]]])
     def run(self, text: str):
         """
         Embeds text using the Fastembed model.