Source code for langchain.embeddings.gpt4all
from typing import Any, Dict, List
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class GPT4AllEmbeddings(BaseModel, Embeddings):
    """GPT4All embedding models.
    To use, you should have the gpt4all python package installed
    Example:
        .. code-block:: python
            from langchain.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
[docs]    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]
[docs]    def embed_query(self, text: str) -> List[float]:
        """Embed a query using GPT4All.
        Args:
            text: The text to embed.
        Returns: