NLPCloudEmbeddings#

class langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings[source]#

Bases: BaseModel, Embeddings

NLP Cloud embedding models.

To use, you should have the nlpcloud python package installed

Example

from langchain_community.embeddings import NLPCloudEmbeddings

embeddings = NLPCloudEmbeddings()

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 gpu: bool [Required]#
param model_name: str [Required]#
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 NLP Cloud.

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 NLP Cloud.

Parameters:

text (str) – The text to embed.

Returns:

Embeddings for the text.

Return type:

List[float]

classmethod validate_environment(values: Dict) → Dict[source]#

Validate that api key and python package exists in environment.

Parameters:

values (Dict)

Return type:

Dict

Examples using NLPCloudEmbeddings