diff --git a/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py b/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
index b2b15c6ef37f..589043c6bc96 100644
--- a/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
@@ -1,32 +1,84 @@
+from urllib.parse import urlparse
+
+import requests
 from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
 from pydantic.v1.types import SecretStr
 
-from langflow.base.models.model import LCModelComponent
+from langflow.base.embeddings.model import LCEmbeddingsModel
 from langflow.field_typing import Embeddings
 from langflow.io import MessageTextInput, Output, SecretStrInput
 
 
-class HuggingFaceInferenceAPIEmbeddingsComponent(LCModelComponent):
-    display_name = "HuggingFace Embeddings"
-    description = "Generate embeddings using Hugging Face Inference API models."
-    documentation = "https://github.com/huggingface/text-embeddings-inference"
+class HuggingFaceInferenceAPIEmbeddingsComponent(LCEmbeddingsModel):
+    display_name = "HuggingFace Embeddings Inference"
+    description = "Generate embeddings using HuggingFace Text Embeddings Inference (TEI)"
+    documentation = "https://huggingface.co/docs/text-embeddings-inference/index"
     icon = "HuggingFace"
     name = "HuggingFaceInferenceAPIEmbeddings"
 
     inputs = [
-        SecretStrInput(name="api_key", display_name="API Key"),
-        MessageTextInput(name="api_url", display_name="API URL", advanced=True, value="http://localhost:8080"),
-        MessageTextInput(name="model_name", display_name="Model Name", value="BAAI/bge-large-en-v1.5"),
+        SecretStrInput(
+            name="api_key",
+            display_name="API Key",
+            advanced=True,
+            info="Required for non-local inference endpoints. Local inference does not require an API Key.",
+        ),
+        MessageTextInput(
+            name="inference_endpoint",
+            display_name="Inference Endpoint",
+            required=True,
+            value="http://localhost:8080",
+            info="Custom inference endpoint URL.",
+        ),
+        MessageTextInput(
+            name="model_name",
+            display_name="Model Name",
+            value="BAAI/bge-large-en-v1.5",
+            info="The name of the model to use for text embeddings.",
+        ),
     ]
 
     outputs = [
         Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
     ]
 
+    def validate_inference_endpoint(self, inference_endpoint: str) -> bool:
+        parsed_url = urlparse(inference_endpoint)
+        if not all([parsed_url.scheme, parsed_url.netloc]):
+            raise ValueError(
+                f"Invalid inference endpoint format: '{self.inference_endpoint}'. Please ensure the URL includes both a scheme (e.g., 'http://' or 'https://') and a domain name. Example: 'http://localhost:8080' or 'https://api.example.com'"
+            )
+
+        try:
+            response = requests.get(f"{inference_endpoint}/health", timeout=5)
+        except requests.RequestException:
+            raise ValueError(
+                f"Inference endpoint '{inference_endpoint}' is not responding. Please ensure the URL is correct and the service is running."
+            )
+
+        if response.status_code != 200:
+            raise ValueError(f"HuggingFace health check failed: {response.status_code}")
+        # returning True to solve linting error
+        return True
+
     def build_embeddings(self) -> Embeddings:
-        if not self.api_key:
-            raise ValueError("API Key is required")
+        if not self.inference_endpoint:
+            raise ValueError("Inference endpoint is required")
+
+        self.validate_inference_endpoint(self.inference_endpoint)
+
+        # Check if the inference endpoint is local
+        is_local_url = self.inference_endpoint.startswith(("http://localhost", "http://127.0.0.1"))
 
-        api_key = SecretStr(self.api_key)
+        # Use a dummy key for local URLs if no key is provided.
+        # Refer https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.huggingface.HuggingFaceInferenceAPIEmbeddings.html
+        if not self.api_key and is_local_url:
+            api_key = SecretStr("DummyAPIKeyForLocalDeployment")
+        elif not self.api_key:
+            raise ValueError("API Key is required for non-local inference endpoints")
+        else:
+            api_key = SecretStr(self.api_key)
 
-        return HuggingFaceInferenceAPIEmbeddings(api_key=api_key, api_url=self.api_url, model_name=self.model_name)
+        return HuggingFaceInferenceAPIEmbeddings(
+            api_key=api_key, api_url=self.inference_endpoint, model_name=self.model_name
+        )