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# Source code for langchain_community.embeddings.awa

    from typing import Any, Dict, List

    from langchain_core.embeddings import Embeddings
    from langchain_core.pydantic_v1 import BaseModel, root_validator

    class AwaEmbeddings(BaseModel, Embeddings):
        """Embedding documents and queries with Awa DB.

        Attributes:
            client: The AwaEmbedding client.
            model: The name of the model used for embedding.
             Default is "all-mpnet-base-v2".
        """

        client: Any  #: :meta private:
        model: str = "all-mpnet-base-v2"

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that awadb library is installed."""

            try:
                from awadb import AwaEmbedding
            except ImportError as exc:
                raise ImportError(
                    "Could not import awadb library. "
                    "Please install it with `pip install awadb`"
                ) from exc
            values["client"] = AwaEmbedding()
            return values

        def set_model(self, model_name: str) -> None:
            """Set the model used for embedding.
            The default model used is all-mpnet-base-v2

            Args:
                model_name: A string which represents the name of model.
            """
            self.model = model_name
            self.client.model_name = model_name

        def embed_documents(self, texts: List[str]) -> List[List[float]]:
            """Embed a list of documents using AwaEmbedding.

            Args:
                texts: The list of texts need to be embedded

            Returns:
                List of embeddings, one for each text.
            """
            return self.client.EmbeddingBatch(texts)

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

            Args:
                text: The text to embed.

            Returns:
                Embeddings for the text.
            """
            return self.client.Embedding(text)