feat: ✨ remove api key from advanced and update HuggingFace components (#3397)

* feat: ✨ remove api key from advanced

* refactor: 🎨 improve naming and descriptions

* fix: 🐛 Fix hf api component

* [autofix.ci] apply automated fixes

* feat: 🎨 Add default values

* [autofix.ci] apply automated fixes

* fix: 🐛 fix hf api component

* [autofix.ci] apply automated fixes

* feat: 🔥 remove hugging face embeddings (local)

* refactor: Removed HuggingFaceEmbeddingsComponent from __init__.py

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

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

@@ -1,36 +0,0 @@
-from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
-
-from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Embeddings
-from langflow.io import BoolInput, DictInput, MessageTextInput, Output
-
-
-class HuggingFaceEmbeddingsComponent(LCModelComponent):
-    display_name = "Hugging Face Embeddings"
-    description = "Generate embeddings using HuggingFace models."
-    documentation = (
-        "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
-    )
-    icon = "HuggingFace"
-    name = "HuggingFaceEmbeddings"
-
-    inputs = [
-        MessageTextInput(name="cache_folder", display_name="Cache Folder", advanced=True),
-        DictInput(name="encode_kwargs", display_name="Encode Kwargs", advanced=True),
-        DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
-        MessageTextInput(name="model_name", display_name="Model Name", value="sentence-transformers/all-mpnet-base-v2"),
-        BoolInput(name="multi_process", display_name="Multi Process", advanced=True),
-    ]
-
-    outputs = [
-        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
-    ]
-
-    def build_embeddings(self) -> Embeddings:
-        return HuggingFaceEmbeddings(
-            cache_folder=self.cache_folder,
-            encode_kwargs=self.encode_kwargs,
-            model_kwargs=self.model_kwargs,
-            model_name=self.model_name,
-            multi_process=self.multi_process,
-        )

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

@@ -7,14 +7,14 @@ from langflow.io import MessageTextInput, Output, SecretStrInput
 
 
 class HuggingFaceInferenceAPIEmbeddingsComponent(LCModelComponent):
-    display_name = "Hugging Face API Embeddings"
+    display_name = "HuggingFace Embeddings"
     description = "Generate embeddings using Hugging Face Inference API models."
     documentation = "https://github.com/huggingface/text-embeddings-inference"
     icon = "HuggingFace"
     name = "HuggingFaceInferenceAPIEmbeddings"
 
     inputs = [
-        SecretStrInput(name="api_key", display_name="API Key", advanced=True),
+        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"),
     ]

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

@@ -3,7 +3,6 @@ from .AmazonBedrockEmbeddings import AmazonBedrockEmbeddingsComponent
 from .AstraVectorize import AstraVectorizeComponent
 from .AzureOpenAIEmbeddings import AzureOpenAIEmbeddingsComponent
 from .CohereEmbeddings import CohereEmbeddingsComponent
-from .HuggingFaceEmbeddings import HuggingFaceEmbeddingsComponent
 from .HuggingFaceInferenceAPIEmbeddings import HuggingFaceInferenceAPIEmbeddingsComponent
 from .OllamaEmbeddings import OllamaEmbeddingsComponent
 from .OpenAIEmbeddings import OpenAIEmbeddingsComponent
@@ -15,7 +14,6 @@ __all__ = [
     "AstraVectorizeComponent",
     "AzureOpenAIEmbeddingsComponent",
     "CohereEmbeddingsComponent",
-    "HuggingFaceEmbeddingsComponent",
     "HuggingFaceInferenceAPIEmbeddingsComponent",
     "OllamaEmbeddingsComponent",
     "OpenAIEmbeddingsComponent",

src/backend/base/langflow/components/models/HuggingFaceModel.py

@@ -1,6 +1,4 @@
 from tenacity import retry, stop_after_attempt, wait_fixed
-
-from langchain_community.chat_models.huggingface import ChatHuggingFace
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 
 from langflow.base.models.model import LCModelComponent
@@ -9,53 +7,54 @@ from langflow.io import DictInput, DropdownInput, SecretStrInput, StrInput, IntI
 
 
 class HuggingFaceEndpointsComponent(LCModelComponent):
-    display_name: str = "Hugging Face API"
+    display_name: str = "HuggingFace"
     description: str = "Generate text using Hugging Face Inference APIs."
     icon = "HuggingFace"
     name = "HuggingFaceModel"
 
     inputs = LCModelComponent._base_inputs + [
-        SecretStrInput(name="endpoint_url", display_name="Endpoint URL", password=True),
         StrInput(
             name="model_id",
-            display_name="Model Id",
+            display_name="Model ID",
-            info="Id field of endpoint_url response.",
+            value="openai-community/gpt2",
         ),
         DropdownInput(
             name="task",
             display_name="Task",
-            options=["text2text-generation", "text-generation", "summarization"],
+            options=["text2text-generation", "text-generation", "summarization", "translation"],
+            value="text-generation",
         ),
-        SecretStrInput(name="huggingfacehub_api_token", display_name="API token", password=True),
+        SecretStrInput(name="huggingfacehub_api_token", display_name="API Token", password=True),
         DictInput(name="model_kwargs", display_name="Model Keyword Arguments", advanced=True),
-        IntInput(name="retry_attempts", display_name="Retry Attempts", value=1),
+        IntInput(name="retry_attempts", display_name="Retry Attempts", value=1, advanced=True),
     ]
 
-    def create_huggingface_endpoint(self, endpoint_url, task, huggingfacehub_api_token, model_kwargs):
+    def create_huggingface_endpoint(
-        @retry(stop=stop_after_attempt(self.retry_attempts), wait=wait_fixed(2))
+        self, model_id: str, task: str, huggingfacehub_api_token: str, model_kwargs: dict
+    ) -> HuggingFaceEndpoint:
+        retry_attempts = self.retry_attempts  # Access the retry attempts input
+        endpoint_url = f"https://api-inference.huggingface.co/models/{model_id}"
+
+        @retry(stop=stop_after_attempt(retry_attempts), wait=wait_fixed(2))
         def _attempt_create():
-            try:
+            return HuggingFaceEndpoint(
-                return HuggingFaceEndpoint(  # type: ignore
                 endpoint_url=endpoint_url,
                 task=task,
                 huggingfacehub_api_token=huggingfacehub_api_token,
                 model_kwargs=model_kwargs,
             )
-            except Exception as e:
-                raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
 
         return _attempt_create()
 
     def build_model(self) -> LanguageModel:  # type: ignore[type-var]
-        endpoint_url = self.endpoint_url
+        model_id = self.model_id
         task = self.task
         huggingfacehub_api_token = self.huggingfacehub_api_token
         model_kwargs = self.model_kwargs or {}
 
         try:
-            llm = self.create_huggingface_endpoint(endpoint_url, task, huggingfacehub_api_token, model_kwargs)
+            llm = self.create_huggingface_endpoint(model_id, task, huggingfacehub_api_token, model_kwargs)
         except Exception as e:
             raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
 
-        output = ChatHuggingFace(llm=llm, model_id=self.model_id)
+        return llm
-        return output  # type: ignore