feat: Enhance HuggingFace Embeddings Inference component

src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py

@@ -1,32 +1,59 @@
 from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
 from pydantic.v1.types import SecretStr
+import requests
+from urllib.parse import urlparse
 
 from langflow.base.models.model import LCModelComponent
 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"
+    display_name = "HuggingFace Embeddings Inference"
+    description = "Generate embeddings using HuggingFace Text Embeddings Inference (TEI)"
+    documentation = "https://huggingface.co/docs/text-embeddings-inference/en/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"),
+        SecretStrInput(
+            name="api_key", display_name="API Key", advanced=True, info="The API key is required for non-local API URLs"
+        ),
+        MessageTextInput(name="api_url", display_name="API URL", required=True, value="http://localhost:8080"),
         MessageTextInput(name="model_name", display_name="Model Name", value="BAAI/bge-large-en-v1.5"),
     ]
 
     outputs = [
         Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
     ]
 
-    def build_embeddings(self) -> Embeddings:
-        if not self.api_key:
-            raise ValueError("API Key is required")
+    def validate_api_url(self, api_url: str) -> bool:
+        parsed_url = urlparse(api_url)
+        if not all([parsed_url.scheme, parsed_url.netloc]):
+            raise ValueError("Invalid API URL format")
+
+        try:
+            response = requests.get(f"{api_url}/health", timeout=5)
+            return response.status_code == 200
+        except requests.RequestException:
+            return False
 
-        api_key = SecretStr(self.api_key)
+    def build_embeddings(self) -> Embeddings:
+        if not self.api_url:
+            raise ValueError("API URL is required")
+
+        if not self.validate_api_url(self.api_url):
+            raise ValueError("API URL is invalid or the service is not responding")
+
+        # Check if the API URL is local
+        is_local_url = self.api_url.startswith(("http://localhost", "http://127.0.0.1"))
+
+        # Use a dummy key for local URLs if no key is provided
+        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 API URLs")
+        else:
+            api_key = SecretStr(self.api_key)
 
         return HuggingFaceInferenceAPIEmbeddings(api_key=api_key, api_url=self.api_url, model_name=self.model_name)