Source code for langchain_community.embeddings.spacy_embeddings

import importlib.util
from typing import Any, Dict, List

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator


[docs]class SpacyEmbeddings(BaseModel, Embeddings): """Embeddings by SpaCy models. It only supports the 'en_core_web_sm' model. Attributes: nlp (Any): The Spacy model loaded into memory. Methods: 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. """ nlp: Any # The Spacy model loaded into memory class Config: """Configuration for this pydantic object.""" extra = Extra.forbid # Forbid extra attributes during model initialization @root_validator(pre=True) def validate_environment(cls, values: Dict) -> Dict: """ Validates that the Spacy package and the 'en_core_web_sm' model are installed. Args: values (Dict): The values provided to the class constructor. Returns: The validated values. Raises: ValueError: If the Spacy package or the 'en_core_web_sm' model are not installed. """ # Check if the Spacy package is installed if importlib.util.find_spec("spacy") is None: raise ValueError( "Spacy package not found. " "Please install it with `pip install spacy`." ) try: # Try to load the 'en_core_web_sm' Spacy model import spacy values["nlp"] = spacy.load("en_core_web_sm") except OSError: # If the model is not found, raise a ValueError raise ValueError( "Spacy model 'en_core_web_sm' not found. " "Please install it with" " `python -m spacy download en_core_web_sm`." ) return values # Return the validated values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """ Generates embeddings for a list of documents. Args: texts (List[str]): The documents to generate embeddings for. Returns: A list of embeddings, one for each document. """ return [self.nlp(text).vector.tolist() for text in texts]
[docs] def embed_query(self, text: str) -> List[float]: """ Generates an embedding for a single piece of text. Args: text (str): The text to generate an embedding for. Returns: The embedding for the text. """ return self.nlp(text).vector.tolist()
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """ Asynchronously generates embeddings for a list of documents. This method is not implemented and raises a NotImplementedError. Args: texts (List[str]): The documents to generate embeddings for. Raises: NotImplementedError: This method is not implemented. """ raise NotImplementedError("Asynchronous embedding generation is not supported.")
[docs] async def aembed_query(self, text: str) -> List[float]: """ Asynchronously generates an embedding for a single piece of text. This method is not implemented and raises a NotImplementedError. Args: text (str): The text to generate an embedding for. Raises: NotImplementedError: This method is not implemented. """ raise NotImplementedError("Asynchronous embedding generation is not supported.")