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, 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:
extra = "forbid"
@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.")