From the tutorials I am using the example that is provided

from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings

embeddings = HuggingFaceInferenceAPIEmbeddings(
    api_key=inference_api_key, model_name="sentence-transformers/all-MiniLM-l6-v2"
)
text = "this is a sample text"
query_result = embeddings.embed_query(text)
query_result[:3]

But this results in the error:

File ~/miniconda3/envs/rag/lib/python3.12/site-packages/langchain_community/embeddings/huggingface.py:373, in HuggingFaceInferenceAPIEmbeddings.embed_query(self, text)
    364 def embed_query(self, text: str) -> List[float]:
    365     """Compute query embeddings using a HuggingFace transformer model.
    366 
    367     Args:
   (...)
    371         Embeddings for the text.
    372     """
--> 373     return self.embed_documents([text])[0]

KeyError: 0

I can generate the text completions from LLMs hosted at the huggingface hub using the same inference_api_key so I guess maybe that is fine.(?)

Can you please help me understand how to use the inference API to get the embeddings from any embeddings model hosted on the huggingfacehub