Add HuggingFaceInferenceAPIEmbeddingsComponent class

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

@@ -0,0 +1,42 @@
+from langflow import CustomComponent
+from typing import Optional, Dict
+from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
+
+
+class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
+    display_name = "HuggingFaceInferenceAPIEmbeddings"
+    description = "HuggingFace sentence_transformers embedding models, API version."
+    documentation = (
+        "https://github.com/huggingface/text-embeddings-inference"
+    )
+
+    def build_config(self):
+        return {
+            "api_key": {"display_name": "API Key", "password": True, "advanced": True},
+            "api_url": {"display_name": "API URL", "advanced": True},
+            "model_name": {"display_name": "Model Name"},
+            "cache_folder": {"display_name": "Cache Folder", "advanced": True},
+            "encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
+            "model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
+            "multi_process": {"display_name": "Multi Process", "advanced": True},
+        }
+
+    def build(
+        self,
+        api_key: Optional[str] = "",
+        api_url: str = "http://localhost:8080",
+        model_name: str = "BAAI/bge-large-en-v1.5",
+        cache_folder: Optional[str] = None,
+        encode_kwargs: Optional[Dict] = {},
+        model_kwargs: Optional[Dict] = {},
+        multi_process: bool = False,
+    ) -> HuggingFaceInferenceAPIEmbeddings:
+        return HuggingFaceInferenceAPIEmbeddings(
+            api_key=api_key,
+            api_url=api_url,
+            model_name=model_name,
+            cache_folder=cache_folder,
+            encode_kwargs=encode_kwargs,
+            model_kwargs=model_kwargs,
+            multi_process=multi_process,
+        )