Add HuggingFaceInferenceAPIEmbeddingsComponent class (#1431)

Adds a new HuggingFaceInferenceAPIEmbeddingsComponent component class

<img width="658" alt="image"
src="https://github.com/logspace-ai/langflow/assets/763757/175ff5ec-9ea4-4232-8009-63f022132c51">

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,
+        )

src/backend/langflow/components/vectorstores/Qdrant.py

@@ -15,7 +15,7 @@ class QdrantComponent(CustomComponent):
         return {
             "documents": {"display_name": "Documents"},
             "embedding": {"display_name": "Embedding"},
-            "api_key": {"display_name": "API Key", "password": True},
+            "api_key": {"display_name": "API Key", "password": True, "advanced": True},
             "collection_name": {"display_name": "Collection Name"},
             "content_payload_key": {"display_name": "Content Payload Key", "advanced": True},
             "distance_func": {"display_name": "Distance Function", "advanced": True},
@@ -36,7 +36,7 @@ class QdrantComponent(CustomComponent):
     def build(
         self,
         embedding: Embeddings,
-        documents: List[Document],
+        documents: Optional[Document] = None,
         api_key: Optional[str] = None,
         collection_name: Optional[str] = None,
         content_payload_key: str = "page_content",
@@ -44,7 +44,7 @@ class QdrantComponent(CustomComponent):
         grpc_port: Optional[int] = 6334,
         host: Optional[str] = None,
         https: bool = False,
-        location: str = ":memory:",
+        location: Optional[str] = None,
         metadata_payload_key: str = "metadata",
         path: Optional[str] = None,
         port: Optional[int] = 6333,
@@ -54,7 +54,33 @@ class QdrantComponent(CustomComponent):
         timeout: Optional[float] = None,
         url: Optional[str] = None,
     ) -> Union[VectorStore, Qdrant, BaseRetriever]:
-        return Qdrant.from_documents(
+        if documents is None:
+            from qdrant_client import QdrantClient
+            client = QdrantClient(
+                location=location,
+                url=host, 
+                port=port,
+                grpc_port=grpc_port,
+                https=https,
+                prefix=prefix,
+                timeout=timeout,
+                prefer_grpc=prefer_grpc,
+                metadata_payload_key=metadata_payload_key,
+                content_payload_key=content_payload_key,
+                api_key=api_key,
+                collection_name=collection_name,
+                host=host,
+                path=path,
+                )
+            vs = Qdrant(client=client, 
+                        collection_name=collection_name, 
+                        embeddings=embedding,
+                        search_kwargs=search_kwargs,
+                        distance_func=distance_func,
+                        )
+            return vs
+        else:
+            vs = Qdrant.from_documents(
                 documents=documents,
                 embedding=embedding,
                 api_key=api_key,
@@ -74,3 +100,4 @@ class QdrantComponent(CustomComponent):
                 timeout=timeout,
                 url=url,
                 )
+        return vs