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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 abc import ABC, abstractmethod
from typing import Any, Generic, TypeVar
T = TypeVar('T')
class BaseEmbedding(ABC, Generic[T]):
r"""Abstract base class for text embedding functionalities."""
@abstractmethod
def embed_list(
self,
objs: list[T],
**kwargs: Any,
) -> list[list[float]]:
r"""Generates embeddings for the given texts.
Args:
objs (list[T]): The objects for which to generate the embeddings.
**kwargs (Any): Extra kwargs passed to the embedding API.
Returns:
list[list[float]]: A list that represents the
generated embedding as a list of floating-point numbers.
"""
pass
def embed(
self,
obj: T,
**kwargs: Any,
) -> list[float]:
r"""Generates an embedding for the given text.
Args:
obj (T): The object for which to generate the embedding.
**kwargs (Any): Extra kwargs passed to the embedding API.
Returns:
list[float]: A list of floating-point numbers representing the
generated embedding.
"""
return self.embed_list([obj], **kwargs)[0]
@abstractmethod
def get_output_dim(self) -> int:
r"""Returns the output dimension of the embeddings.
Returns:
int: The dimensionality of the embedding for the current model.
"""
pass
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