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5e028bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | from typing import Dict, Optional
from importlib import import_module
from lightmem.configs.topic_segmenter.base import TopicSegmenterConfig
class TopicSegmenterFactory:
_MODEL_MAPPING: Dict[str, str] = {
"identity": "lightmem.factory.topic_segmenter.identity.IdentitySegmenter",
"llmlingua-2": "lightmem.factory.topic_segmenter.llmlingua_2.LlmLingua2Segmenter",
}
@classmethod
def from_config(cls, config: TopicSegmenterConfig, shared: bool, compressor: None):
"""
Instantiate a topic segmenter by dynamically importing the class based on config.
Args:
config: TopicSegmenterConfig containing model name and specific configs
Returns:
An instance of the requested segmenter model
Raises:
ValueError: If model name is not supported or instantiation fails
ImportError: If the module or class cannot be imported
"""
model_name = config.model_name
if model_name not in cls._MODEL_MAPPING:
raise ValueError(
f"Unsupported segmenter model: {model_name}. "
f"Supported models are: {list(cls._MODEL_MAPPING.keys())}"
)
class_path = cls._MODEL_MAPPING[model_name]
try:
module_path, class_name = class_path.rsplit('.', 1)
module = import_module(module_path)
segmenter_class = getattr(module, class_name)
if config.configs is None:
return segmenter_class()
else:
return segmenter_class(config=config.configs, shared = shared, compressor = compressor)
except ImportError as e:
raise ImportError(
f"Could not import segmenter class '{class_path}': {str(e)}"
) from e
except AttributeError as e:
raise ImportError(
f"Maybe class '{class_name}' not found in module '{module_path}': {str(e)}"
) from e
except Exception as e:
raise ValueError(
f"Failed to instantiate {model_name} segmenter: {str(e)}"
) from e
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