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# Source code for langchain_community.embeddings.gpt4all

    from typing import Any, Dict, List

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

    class GPT4AllEmbeddings(BaseModel, Embeddings):
        """GPT4All embedding models.

        To use, you should have the gpt4all python package installed

        Example:
            .. code-block:: python

                from langchain_community.embeddings import GPT4AllEmbeddings

                embeddings = GPT4AllEmbeddings()
        """

        client: Any  #: :meta private:

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that GPT4All library is installed."""

            try:
                from gpt4all import Embed4All

                values["client"] = Embed4All()
            except ImportError:
                raise ImportError(
                    "Could not import gpt4all library. "
                    "Please install the gpt4all library to "
                    "use this embedding model: pip install gpt4all"
                )
            return values

        def embed_documents(self, texts: List[str]) -> List[List[float]]:
            """Embed a list of documents using GPT4All.

            Args:
                texts: The list of texts to embed.

            Returns:
                List of embeddings, one for each text.
            """

            embeddings = [self.client.embed(text) for text in texts]
            return [list(map(float, e)) for e in embeddings]

        def embed_query(self, text: str) -> List[float]:
            """Embed a query using GPT4All.

            Args:
                text: The text to embed.

            Returns:
                Embeddings for the text.
            """
            return self.embed_documents([text])[0]