diff --git a/src/backend/base/langflow/components/embeddings/text_embedder.py b/src/backend/base/langflow/components/embeddings/text_embedder.py
index 5c66a9372..a9efb1ffe 100644
--- a/src/backend/base/langflow/components/embeddings/text_embedder.py
+++ b/src/backend/base/langflow/components/embeddings/text_embedder.py
@@ -38,46 +38,26 @@ class TextEmbedderComponent(Component):
             embedding_model: Embeddings = self.embedding_model
             message: Message = self.message
 
-            # Validate embedding model
-            if not embedding_model:
-                msg = "Embedding model not provided"
+            # Combine validation checks to reduce nesting
+            if not embedding_model or not hasattr(embedding_model, "embed_documents"):
+                msg = "Invalid or incompatible embedding model"
                 raise ValueError(msg)
 
-            # Extract the text content from the message
             text_content = message.text if message and message.text else ""
             if not text_content:
                 msg = "No text content found in message"
                 raise ValueError(msg)
 
-            # Check if the embedding model has the required attributes
-            if not hasattr(embedding_model, "client") or not embedding_model.client:
-                msg = "Embedding model client not properly initialized"
-                raise ValueError(msg)
-
-            # Ensure the base URL has proper protocol
-            if hasattr(embedding_model.client, "base_url"):
-                base_url = embedding_model.client.base_url
-                if not base_url.startswith(("http://", "https://")):
-                    embedding_model.client.base_url = f"https://{base_url}"
-
-            # Generate embeddings using the provided embedding model
             embeddings = embedding_model.embed_documents([text_content])
-
-            # Validate embeddings output
             if not embeddings or not isinstance(embeddings, list):
                 msg = "Invalid embeddings generated"
                 raise ValueError(msg)
 
             embedding_vector = embeddings[0]
-
+            self.status = {"text": text_content, "embeddings": embedding_vector}
+            return Data(data={"text": text_content, "embeddings": embedding_vector})
         except Exception as e:
             logging.exception("Error generating embeddings")
-            # Return empty data with error status
             error_data = Data(data={"text": "", "embeddings": [], "error": str(e)})
             self.status = {"error": str(e)}
             return error_data
-
-        # Create a Data object to encapsulate the results
-        result_data = Data(data={"text": text_content, "embeddings": embedding_vector})
-        self.status = {"text": text_content, "embeddings": embedding_vector}
-        return result_data