Source code for camel.models.model_factory
# ========= 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. =========
import json
from typing import Dict, Optional, Type, Union
import yaml
from camel.models.aiml_model import AIMLModel
from camel.models.anthropic_model import AnthropicModel
from camel.models.azure_openai_model import AzureOpenAIModel
from camel.models.base_model import BaseModelBackend
from camel.models.cohere_model import CohereModel
from camel.models.deepseek_model import DeepSeekModel
from camel.models.gemini_model import GeminiModel
from camel.models.groq_model import GroqModel
from camel.models.internlm_model import InternLMModel
from camel.models.litellm_model import LiteLLMModel
from camel.models.mistral_model import MistralModel
from camel.models.modelscope_model import ModelScopeModel
from camel.models.moonshot_model import MoonshotModel
from camel.models.nvidia_model import NvidiaModel
from camel.models.ollama_model import OllamaModel
from camel.models.openai_compatible_model import OpenAICompatibleModel
from camel.models.openai_model import OpenAIModel
from camel.models.openrouter_model import OpenRouterModel
from camel.models.ppio_model import PPIOModel
from camel.models.qwen_model import QwenModel
from camel.models.reka_model import RekaModel
from camel.models.samba_model import SambaModel
from camel.models.sglang_model import SGLangModel
from camel.models.siliconflow_model import SiliconFlowModel
from camel.models.stub_model import StubModel
from camel.models.togetherai_model import TogetherAIModel
from camel.models.vllm_model import VLLMModel
from camel.models.volcano_model import VolcanoModel
from camel.models.yi_model import YiModel
from camel.models.zhipuai_model import ZhipuAIModel
from camel.types import ModelPlatformType, ModelType, UnifiedModelType
from camel.utils import BaseTokenCounter
[docs]
class ModelFactory:
r"""Factory of backend models.
Raises:
ValueError: in case the provided model type is unknown.
"""
[docs]
@staticmethod
def create(
model_platform: ModelPlatformType,
model_type: Union[ModelType, str],
model_config_dict: Optional[Dict] = None,
token_counter: Optional[BaseTokenCounter] = None,
api_key: Optional[str] = None,
url: Optional[str] = None,
timeout: Optional[int] = None,
) -> BaseModelBackend:
r"""Creates an instance of `BaseModelBackend` of the specified type.
Args:
model_platform (ModelPlatformType): Platform from which the model
originates.
model_type (Union[ModelType, str]): Model for which a
backend is created. Can be a `str` for open source platforms.
model_config_dict (Optional[Dict]): A dictionary that will be fed
into the backend constructor. (default: :obj:`None`)
token_counter (Optional[BaseTokenCounter], optional): Token
counter to use for the model. If not provided,
:obj:`OpenAITokenCounter(ModelType.GPT_4O_MINI)`
will be used if the model platform didn't provide official
token counter. (default: :obj:`None`)
api_key (Optional[str], optional): The API key for authenticating
with the model service. (default: :obj:`None`)
url (Optional[str], optional): The url to the model service.
(default: :obj:`None`)
timeout (Optional[float], optional): The timeout value in seconds
for API calls. (default: :obj:`None`)
Returns:
BaseModelBackend: The initialized backend.
Raises:
ValueError: If there is no backend for the model.
"""
model_class: Optional[Type[BaseModelBackend]] = None
model_type = UnifiedModelType(model_type)
if model_platform.is_ollama:
model_class = OllamaModel
elif model_platform.is_vllm:
model_class = VLLMModel
elif model_platform.is_sglang:
model_class = SGLangModel
elif model_platform.is_openai_compatible_model:
model_class = OpenAICompatibleModel
elif model_platform.is_samba:
model_class = SambaModel
elif model_platform.is_together:
model_class = TogetherAIModel
elif model_platform.is_litellm:
model_class = LiteLLMModel
elif model_platform.is_nvidia:
model_class = NvidiaModel
elif model_platform.is_siliconflow:
model_class = SiliconFlowModel
elif model_platform.is_aiml:
model_class = AIMLModel
elif model_platform.is_volcano:
model_class = VolcanoModel
elif model_platform.is_openai and model_type.is_openai:
model_class = OpenAIModel
elif model_platform.is_azure and model_type.is_azure_openai:
model_class = AzureOpenAIModel
elif model_platform.is_anthropic and model_type.is_anthropic:
model_class = AnthropicModel
elif model_platform.is_groq and model_type.is_groq:
model_class = GroqModel
elif model_platform.is_openrouter and model_type.is_openrouter:
model_class = OpenRouterModel
elif model_platform.is_zhipuai and model_type.is_zhipuai:
model_class = ZhipuAIModel
elif model_platform.is_gemini and model_type.is_gemini:
model_class = GeminiModel
elif model_platform.is_mistral and model_type.is_mistral:
model_class = MistralModel
elif model_platform.is_reka and model_type.is_reka:
model_class = RekaModel
elif model_platform.is_cohere and model_type.is_cohere:
model_class = CohereModel
elif model_platform.is_yi and model_type.is_yi:
model_class = YiModel
elif model_platform.is_qwen and model_type.is_qwen:
model_class = QwenModel
elif model_platform.is_deepseek:
model_class = DeepSeekModel
elif model_platform.is_ppio:
model_class = PPIOModel
elif model_platform.is_internlm and model_type.is_internlm:
model_class = InternLMModel
elif model_platform.is_moonshot and model_type.is_moonshot:
model_class = MoonshotModel
elif model_platform.is_modelscope:
model_class = ModelScopeModel
elif model_type == ModelType.STUB:
model_class = StubModel
if model_class is None:
raise ValueError(
f"Unknown pair of model platform `{model_platform}` "
f"and model type `{model_type}`."
)
return model_class(
model_type=model_type,
model_config_dict=model_config_dict,
api_key=api_key,
url=url,
token_counter=token_counter,
timeout=timeout,
)
@classmethod
def __parse_model_platform(
cls, model_platform_str: str
) -> ModelPlatformType:
r"""Parses a string and returns the corresponding ModelPlatformType
enum.
Args:
model_platform_str (str): The platform name as a string. Can be in
the form "ModelPlatformType.<NAME>" or simply "<NAME>".
Returns:
ModelPlatformType: The matching enum value.
Raises:
ValueError: If the platform name is not a valid member of
ModelPlatformType.
"""
try:
if model_platform_str.startswith("ModelPlatformType."):
platform_name = model_platform_str.split('.')[-1]
else:
platform_name = model_platform_str.upper()
if platform_name not in ModelPlatformType.__members__:
raise ValueError(
f"Invalid model platform: {platform_name}. "
f"Valid options: "
f"{', '.join(ModelPlatformType.__members__.keys())}"
)
return ModelPlatformType[platform_name]
except KeyError:
raise KeyError(f"Invalid model platform: {model_platform_str}")
@classmethod
def __load_yaml(cls, filepath: str) -> Dict:
r"""Loads and parses a YAML file into a dictionary.
Args:
filepath (str): Path to the YAML configuration file.
Returns:
Dict: The parsed YAML content as a dictionary.
"""
with open(filepath, 'r') as file:
config = yaml.safe_load(file)
return config
@classmethod
def __load_json(cls, filepath: str) -> Dict:
r"""Loads and parses a JSON file into a dictionary.
Args:
filepath (str): Path to the JSON configuration file.
Returns:
Dict: The parsed JSON content as a dictionary.
"""
with open(filepath, 'r') as file:
config = json.load(file)
return config
[docs]
@classmethod
def create_from_yaml(cls, filepath: str) -> BaseModelBackend:
r"""Creates and returns a model base backend instance
from a YAML configuration file.
Args:
filepath (str): Path to the YAML file containing model
configuration.
Returns:
BaseModelBackend: An instance of the model backend based on the
configuration.
"""
config = cls.__load_yaml(filepath)
config["model_platform"] = cls.__parse_model_platform(
config["model_platform"]
)
model = ModelFactory.create(**config)
return model
[docs]
@classmethod
def create_from_json(cls, filepath: str) -> BaseModelBackend:
r"""Creates and returns a base model backend instance
from a JSON configuration file.
Args:
filepath (str): Path to the JSON file containing model
configuration.
Returns:
BaseModelBackend: An instance of the model backend based on the
configuration.
"""
config = cls.__load_json(filepath)
config["model_platform"] = cls.__parse_model_platform(
config["model_platform"]
)
model = ModelFactory.create(**config)
return model
