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68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any, Generic, TypeVar
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T = TypeVar('T')
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class BaseEmbedding(ABC, Generic[T]):
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r"""Abstract base class for text embedding functionalities."""
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@abstractmethod
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def embed_list(
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self,
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objs: list[T],
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**kwargs: Any,
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) -> list[list[float]]:
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r"""Generates embeddings for the given texts.
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Args:
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objs (list[T]): The objects for which to generate the embeddings.
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**kwargs (Any): Extra kwargs passed to the embedding API.
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Returns:
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list[list[float]]: A list that represents the
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generated embedding as a list of floating-point numbers.
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"""
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pass
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def embed(
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self,
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obj: T,
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**kwargs: Any,
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) -> list[float]:
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r"""Generates an embedding for the given text.
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Args:
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obj (T): The object for which to generate the embedding.
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**kwargs (Any): Extra kwargs passed to the embedding API.
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Returns:
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list[float]: A list of floating-point numbers representing the
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generated embedding.
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"""
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return self.embed_list([obj], **kwargs)[0]
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@abstractmethod
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def get_output_dim(self) -> int:
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r"""Returns the output dimension of the embeddings.
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Returns:
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int: The dimensionality of the embedding for the current model.
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"""
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pass
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