Source code for langchain_community.embeddings.gpt4all

from typing import Any, Dict, List, Optional

from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, model_validator



[docs]
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

            model_name = "all-MiniLM-L6-v2.gguf2.f16.gguf"
            gpt4all_kwargs = {'allow_download': 'True'}
            embeddings = GPT4AllEmbeddings(
                model_name=model_name,
                gpt4all_kwargs=gpt4all_kwargs
            )
    """

    model_name: Optional[str] = None
    n_threads: Optional[int] = None
    device: Optional[str] = "cpu"
    gpt4all_kwargs: Optional[dict] = {}
    client: Any  #: :meta private:

    model_config = ConfigDict(protected_namespaces=())

    @model_validator(mode="before")
    @classmethod
    def validate_environment(cls, values: Dict) -> Any:
        """Validate that GPT4All library is installed."""
        try:
            from gpt4all import Embed4All

            values["client"] = Embed4All(
                model_name=values.get("model_name"),
                n_threads=values.get("n_threads"),
                device=values.get("device"),
                **(values.get("gpt4all_kwargs") or {}),
            )
        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:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]