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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
from __future__ import annotations
from typing import Any
from numpy import ndarray
from camel.embeddings.base import BaseEmbedding
class SentenceTransformerEncoder(BaseEmbedding[str]):
r"""This class provides functionalities to generate text
embeddings using `Sentence Transformers`.
References:
https://www.sbert.net/
"""
def __init__(
self,
model_name: str = "intfloat/e5-large-v2",
**kwargs,
):
r"""Initializes the: obj: `SentenceTransformerEmbedding` class
with the specified transformer model.
Args:
model_name (str, optional): The name of the model to use.
(default: :obj:`intfloat/e5-large-v2`)
**kwargs (optional): Additional arguments of
:class:`SentenceTransformer`, such as :obj:`prompts` etc.
"""
from sentence_transformers import SentenceTransformer
self.model = SentenceTransformer(model_name, **kwargs)
def embed_list(
self,
objs: list[str],
**kwargs: Any,
) -> list[list[float]]:
r"""Generates embeddings for the given texts using the model.
Args:
objs (list[str]): The texts for which to generate the
embeddings.
Returns:
list[list[float]]: A list that represents the generated embedding
as a list of floating-point numbers.
"""
if not objs:
raise ValueError("Input text list is empty")
embeddings = self.model.encode(
objs, normalize_embeddings=True, **kwargs
)
assert isinstance(embeddings, ndarray)
return embeddings.tolist()
def get_output_dim(self) -> int:
r"""Returns the output dimension of the embeddings.
Returns:
int: The dimensionality of the embeddings.
"""
output_dim = self.model.get_sentence_embedding_dimension()
assert isinstance(output_dim, int)
return output_dim
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