Source code for langchain_community.embeddings.gpt4all

from typing import Any, Dict, List, Optional

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_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: str n_threads: Optional[int] = None device: Optional[str] = "cpu" gpt4all_kwargs: Optional[dict] = {} 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( model_name=values["model_name"], n_threads=values.get("n_threads"), device=values.get("device"), **values.get("gpt4all_kwargs"), ) 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]