langchain_community.embeddings.laser.LaserEmbeddings

class langchain_community.embeddings.laser.LaserEmbeddings[source]

Bases: BaseModel, Embeddings

LASER Language-Agnostic SEntence Representations. LASER is a Python library developed by the Meta AI Research team and used for creating multilingual sentence embeddings for over 147 languages as of 2/25/2024 See more documentation at: * https://github.com/facebookresearch/LASER/ * https://github.com/facebookresearch/LASER/tree/main/laser_encoders * https://arxiv.org/abs/2205.12654

To use this class, you must install the laser_encoders Python package.

pip install laser_encoders .. rubric:: Example

from laser_encoders import LaserEncoderPipeline encoder = LaserEncoderPipeline(lang=”eng_Latn”) embeddings = encoder.encode_sentences([“Hello”, “World”])

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param lang: Optional[str] = None

The language or language code you’d like to use If empty, this implementation will default to using a multilingual earlier LASER encoder model (called laser2) Find the list of supported languages at https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200

async aembed_documents(texts: List[str]) List[List[float]]

Asynchronous Embed search docs.

Parameters

texts (List[str]) –

Return type

List[List[float]]

async aembed_query(text: str) List[float]

Asynchronous Embed query text.

Parameters

text (str) –

Return type

List[float]

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

Generate embeddings for documents using LASER.

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]

Generate single query text embeddings using LASER.

Parameters

text (str) – The text to embed.

Returns

Embeddings for the text.

Return type

List[float]

Examples using LaserEmbeddings