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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, Optional
from openai import OpenAI
from camel.embeddings.base import BaseEmbedding
from camel.utils import api_keys_required
class OpenAICompatibleEmbedding(BaseEmbedding[str]):
r"""Provides text embedding functionalities supporting OpenAI
compatibility.
Args:
model_type (str): The model type to be used for text embeddings.
api_key (str): The API key for authenticating with the model service.
url (str): The url to the model service.
"""
def __init__(
self,
model_type: str,
api_key: Optional[str] = None,
url: Optional[str] = None,
) -> None:
self.model_type = model_type
self.output_dim: Optional[int] = None
self._api_key = api_key or os.environ.get(
"OPENAI_COMPATIBILIY_API_KEY"
)
self._url = url or os.environ.get("OPENAI_COMPATIBILIY_API_BASE_URL")
self._client = OpenAI(
timeout=60,
max_retries=3,
api_key=self._api_key,
base_url=self._url,
)
@api_keys_required("OPENAI_COMPATIBILIY_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.
"""
response = self._client.embeddings.create(
input=objs,
model=self.model_type,
**kwargs,
)
self.output_dim = len(response.data[0].embedding)
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.
"""
if self.output_dim is None:
raise ValueError(
"Output dimension is not yet determined. Call "
"'embed_list' first."
)
return self.output_dim
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