tf = TensorflowHubEmbeddings(model_url=url)
field model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'#
Model name to use.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a TensorflowHub embedding model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a TensorflowHub embedding model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
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By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html