langchain_community.embeddings.tensorflow_hub.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¶