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112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
# ========= Copyright 2023-2026 @ 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 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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from __future__ import annotations
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import os
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from typing import Any, Optional, Union
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from openai import OpenAI
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from camel.embeddings.base import BaseEmbedding
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from camel.types import NOT_GIVEN, EmbeddingModelType, NotGiven
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from camel.utils import api_keys_required
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class OpenAIEmbedding(BaseEmbedding[str]):
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r"""Provides text embedding functionalities using OpenAI's models.
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Args:
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model_type (EmbeddingModelType): The model type to be used for text
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embeddings.
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(default: :obj:`TEXT_EMBEDDING_3_SMALL`)
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url (Optional[str]): The url to the OpenAI service.
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(default: :obj:`None`)
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api_key (Optional[str]): The API key for authenticating with
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the OpenAI service. (default: :obj:`None`)
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dimensions (Union[int, NotGiven]): The text embedding output
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dimensions. (default: :obj:`NOT_GIVEN`)
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Raises:
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RuntimeError: If an unsupported model type is specified.
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"""
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@api_keys_required(
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[
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("api_key", 'OPENAI_API_KEY'),
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]
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)
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def __init__(
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self,
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model_type: EmbeddingModelType = (
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EmbeddingModelType.TEXT_EMBEDDING_3_SMALL
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),
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url: Optional[str] = None,
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api_key: Optional[str] = None,
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dimensions: Union[int, NotGiven] = NOT_GIVEN,
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) -> None:
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if not model_type.is_openai:
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raise ValueError("Invalid OpenAI embedding model type.")
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self.model_type = model_type
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if dimensions == NOT_GIVEN:
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self.output_dim = model_type.output_dim
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else:
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assert isinstance(dimensions, int)
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self.output_dim = dimensions
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self._api_key = api_key or os.environ.get("OPENAI_API_KEY")
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self._url = url or os.environ.get("OPENAI_API_BASE_URL")
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self.client = OpenAI(
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timeout=180,
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max_retries=3,
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base_url=self._url,
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api_key=self._api_key,
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)
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def embed_list(
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self,
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objs: list[str],
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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[str]): The texts 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 generated embedding
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as a list of floating-point numbers.
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"""
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# TODO: count tokens
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if self.model_type == EmbeddingModelType.TEXT_EMBEDDING_ADA_2:
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response = self.client.embeddings.create(
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input=objs,
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model=self.model_type.value,
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**kwargs,
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)
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else:
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response = self.client.embeddings.create(
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input=objs,
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model=self.model_type.value,
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dimensions=self.output_dim,
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**kwargs,
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)
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return [data.embedding for data in response.data]
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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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return self.output_dim
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