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# Source code for langchain_community.embeddings.laser

    from typing import Any, Dict, List, Optional

    import numpy as np
    from langchain_core.embeddings import Embeddings
    from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator

    LASER_MULTILINGUAL_MODEL: str = "laser2"

    class LaserEmbeddings(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`
        Example:
            from laser_encoders import LaserEncoderPipeline
            encoder = LaserEncoderPipeline(lang="eng_Latn")
            embeddings = encoder.encode_sentences(["Hello", "World"])
        """

        lang: Optional[str]
        """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
        """

        _encoder_pipeline: Any  # : :meta private:

        class Config:
            """Configuration for this pydantic object."""

            extra = Extra.forbid

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that laser_encoders has been installed."""
            try:
                from laser_encoders import LaserEncoderPipeline

                lang = values.get("lang")
                if lang:
                    encoder_pipeline = LaserEncoderPipeline(lang=lang)
                else:
                    encoder_pipeline = LaserEncoderPipeline(laser=LASER_MULTILINGUAL_MODEL)
                values["_encoder_pipeline"] = encoder_pipeline

            except ImportError as e:
                raise ImportError(
                    "Could not import 'laser_encoders' Python package. "
                    "Please install it with `pip install laser_encoders`."
                ) from e
            return values

        def embed_documents(self, texts: List[str]) -> List[List[float]]:
            """Generate embeddings for documents using LASER.

            Args:
                texts: The list of texts to embed.

            Returns:
                List of embeddings, one for each text.
            """
            embeddings: np.ndarray
            embeddings = self._encoder_pipeline.encode_sentences(texts)

            return embeddings.tolist()

        def embed_query(self, text: str) -> List[float]:
            """Generate single query text embeddings using LASER.

            Args:
                text: The text to embed.

            Returns:
                Embeddings for the text.
            """
            query_embeddings: np.ndarray
            query_embeddings = self._encoder_pipeline.encode_sentences([text])
            return query_embeddings.tolist()[0]