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 if the input data cannot be parsed to form a valid model.

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