TensorflowHubEmbeddings#

class langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings[source]#

Bases: BaseModel, Embeddings

TensorflowHub embedding models.

To use, you should have the tensorflow_text python package installed.

Example

from langchain_community.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)

Initialize the tensorflow_hub and tensorflow_text.

param model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'#: Model name to use.

async aembed_documents(texts: list[str]) → list[list[float]]#

Asynchronous Embed search docs.

Parameters:: texts (list[str]) – List of text to embed.
Returns:: List of embeddings.
Return type:: list[list[float]]

async aembed_query(text: str) → list[float]#

Asynchronous Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
Return type:: list[float]

embed_documents(texts: List[str]) → List[List[float]][source]#

Compute doc embeddings using a TensorflowHub embedding model.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

embed_query(text: str) → List[float][source]#

Compute query embeddings using a TensorflowHub embedding model.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using TensorflowHubEmbeddings

TensorFlow Hub