SpacyEmbeddings#

class langchain_community.embeddings.spacy_embeddings.SpacyEmbeddings[source]#

Bases: BaseModel, Embeddings

Embeddings by spaCy models.

model_name#

Name of a spaCy model.

nlp#

The spaCy model loaded into memory.

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.

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.

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.

Examples using SpacyEmbeddings