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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
import os
from typing import Any
from openai import OpenAI
from camel.embeddings.base import BaseEmbedding
from camel.types import NOT_GIVEN, EmbeddingModelType, NotGiven
from camel.utils import api_keys_required
class OpenAIEmbedding(BaseEmbedding[str]):
r"""Provides text embedding functionalities using OpenAI's models.
Args:
model_type (EmbeddingModelType, optional): The model type to be
used for text embeddings.
(default: :obj:`TEXT_EMBEDDING_3_SMALL`)
api_key (str, optional): The API key for authenticating with the
OpenAI service. (default: :obj:`None`)
dimensions (int, optional): The text embedding output dimensions.
(default: :obj:`NOT_GIVEN`)
Raises:
RuntimeError: If an unsupported model type is specified.
"""
def __init__(
self,
model_type: EmbeddingModelType = (
EmbeddingModelType.TEXT_EMBEDDING_3_SMALL
),
api_key: str | None = None,
dimensions: int | NotGiven = NOT_GIVEN,
) -> None:
if not model_type.is_openai:
raise ValueError("Invalid OpenAI embedding model type.")
self.model_type = model_type
if dimensions == NOT_GIVEN:
self.output_dim = model_type.output_dim
else:
assert isinstance(dimensions, int)
self.output_dim = dimensions
self._api_key = api_key or os.environ.get("OPENAI_API_KEY")
self.client = OpenAI(timeout=60, max_retries=3, api_key=self._api_key)
@api_keys_required("OPENAI_API_KEY")
def embed_list(
self,
objs: list[str],
**kwargs: Any,
) -> list[list[float]]:
r"""Generates embeddings for the given texts.
Args:
objs (list[str]): The texts 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.
"""
# TODO: count tokens
if self.model_type == EmbeddingModelType.TEXT_EMBEDDING_ADA_2:
response = self.client.embeddings.create(
input=objs,
model=self.model_type.value,
**kwargs,
)
else:
response = self.client.embeddings.create(
input=objs,
model=self.model_type.value,
dimensions=self.output_dim,
**kwargs,
)
return [data.embedding for data in response.data]
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.
"""
return self.output_dim
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