langchain_community.embeddings.spacy_embeddings.SpacyEmbeddings

class langchain_community.embeddings.spacy_embeddings.SpacyEmbeddings[source]

Bases: BaseModel, Embeddings

Embeddings by spaCy models.

model_name

Name of a spaCy model.

Type

str

nlp

The spaCy model loaded into memory.

Type

Any

embed_documents(texts

List[str]) -> List[List[float]]: Generates embeddings for a list of documents.

embed_query(text

str) -> List[float]: Generates an embedding for a single piece of text.

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 model_name: str = 'en_core_web_sm'
param nlp: Optional[Any] = None
async aembed_documents(texts: List[str]) List[List[float]][source]

Asynchronously generates embeddings for a list of documents. This method is not implemented and raises a NotImplementedError.

Parameters

texts (List[str]) – The documents to generate embeddings for.

Raises

NotImplementedError – This method is not implemented.

Return type

List[List[float]]

async aembed_query(text: str) List[float][source]

Asynchronously generates an embedding for a single piece of text. This method is not implemented and raises a NotImplementedError.

Parameters

text (str) – The text to generate an embedding for.

Raises

NotImplementedError – This method is not implemented.

Return type

List[float]

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

Generates embeddings for a list of documents.

Parameters

texts (List[str]) – The documents to generate embeddings for.

Returns

A list of embeddings, one for each document.

Return type

List[List[float]]

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

Generates an embedding for a single piece of text.

Parameters

text (str) – The text to generate an embedding for.

Returns

The embedding for the text.

Return type

List[float]

Examples using SpacyEmbeddings