GPT4AllEmbeddings#

class langchain_community.embeddings.gpt4all.GPT4AllEmbeddings[source]#

Bases: BaseModel, Embeddings

GPT4All embedding models.

To use, you should have the gpt4all python package installed

Example

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
)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param device: str | None = 'cpu'#

param gpt4all_kwargs: dict | None = {}#

param model_name: str | None = None#

param n_threads: int | None = None#

async aembed_documents(texts: list[str]) → list[list[float]]#

Asynchronous Embed search docs.

Parameters:: texts (list[str]) – List of text to embed.
Returns:: List of embeddings.
Return type:: list[list[float]]

async aembed_query(text: str) → list[float]#

Asynchronous Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
Return type:: list[float]

embed_documents(texts: List[str]) → List[List[float]][source]#

Embed a list of documents using GPT4All.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

embed_query(text: str) → List[float][source]#

Embed a query using GPT4All.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using GPT4AllEmbeddings