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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, | |
| ) | |
| 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 | |