Source code for langchain_community.embeddings.spacy_embeddings

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

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. Attributes: model_name (str): Name of a spaCy model. 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. """ model_name: str = "en_core_web_sm" nlp: Optional[Any] = None 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 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 model are not installed. """ if values.get("model_name") is None: values["model_name"] = "en_core_web_sm" model_name = values.get("model_name") # 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 spaCy model import spacy values["nlp"] = spacy.load(model_name) except OSError: # If the model is not found, raise a ValueError raise ValueError( f"SpaCy model '{model_name}' not found. " f"Please install it with" f" `python -m spacy download {model_name}`" "or provide a valid spaCy model name." ) 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] # type: ignore[misc]
[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() # type: ignore[misc]
[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.")