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Added a note to the documentation stating that the similarity method does not support embeddings other than non-quantized ones. #3131

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Dec 12, 2024
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Added a note to the documentation stating that the similarity method does not support embeddings other than non-quantized ones. #3131

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16 changes: 11 additions & 5 deletions sentence_transformers/SentenceTransformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,11 +20,11 @@
from typing import Any, Callable, Literal, overload

import numpy as np
import numpy.typing as npt
import torch
import torch.multiprocessing as mp
import transformers
from huggingface_hub import HfApi
from numpy import ndarray
from torch import Tensor, device, nn
from tqdm.autonotebook import trange
from transformers import is_torch_npu_available
Expand Down Expand Up @@ -723,14 +723,15 @@ def similarity_fn_name(
def similarity(self, embeddings1: Tensor, embeddings2: Tensor) -> Tensor: ...

@overload
def similarity(self, embeddings1: ndarray, embeddings2: ndarray) -> Tensor: ...
def similarity(self, embeddings1: npt.NDArray[np.float32], embeddings2: npt.NDArray[np.float32]) -> Tensor: ...
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Nice, looks to be the correct usage. I wasn't aware of these.
https://numpy.org/devdocs/reference/typing.html#numpy.typing.NDArray


@property
def similarity(self) -> Callable[[Tensor | ndarray, Tensor | ndarray], Tensor]:
def similarity(self) -> Callable[[Tensor | npt.NDArray[np.float32], Tensor | npt.NDArray[np.float32]], Tensor]:
"""
Compute the similarity between two collections of embeddings. The output will be a matrix with the similarity
scores between all embeddings from the first parameter and all embeddings from the second parameter. This
differs from `similarity_pairwise` which computes the similarity between each pair of embeddings.
This method supports only embeddings with fp32 precision and does not accommodate quantized embeddings.

Args:
embeddings1 (Union[Tensor, ndarray]): [num_embeddings_1, embedding_dim] or [embedding_dim]-shaped numpy array or torch tensor.
Expand Down Expand Up @@ -772,13 +773,18 @@ def similarity(self) -> Callable[[Tensor | ndarray, Tensor | ndarray], Tensor]:
def similarity_pairwise(self, embeddings1: Tensor, embeddings2: Tensor) -> Tensor: ...

@overload
def similarity_pairwise(self, embeddings1: ndarray, embeddings2: ndarray) -> Tensor: ...
def similarity_pairwise(
self, embeddings1: npt.NDArray[np.float32], embeddings2: npt.NDArray[np.float32]
) -> Tensor: ...

@property
def similarity_pairwise(self) -> Callable[[Tensor | ndarray, Tensor | ndarray], Tensor]:
def similarity_pairwise(
self,
) -> Callable[[Tensor | npt.NDArray[np.float32], Tensor | npt.NDArray[np.float32]], Tensor]:
"""
Compute the similarity between two collections of embeddings. The output will be a vector with the similarity
scores between each pair of embeddings.
This method supports only embeddings with fp32 precision and does not accommodate quantized embeddings.

Args:
embeddings1 (Union[Tensor, ndarray]): [num_embeddings, embedding_dim] or [embedding_dim]-shaped numpy array or torch tensor.
Expand Down
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