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35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
from abc import ABC, abstractmethod
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from application.usage import gen_token_usage, stream_token_usage
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from application.cache import stream_cache, gen_cache
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class BaseLLM(ABC):
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def __init__(self):
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self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
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def _apply_decorator(self, method, decorators, *args, **kwargs):
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for decorator in decorators:
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method = decorator(method)
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return method(self, *args, **kwargs)
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@abstractmethod
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def _raw_gen(self, model, messages, stream, tools, *args, **kwargs):
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pass
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def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
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decorators = [gen_token_usage, gen_cache]
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return self._apply_decorator(self._raw_gen, decorators=decorators, model=model, messages=messages, stream=stream, tools=tools, *args, **kwargs)
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@abstractmethod
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def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
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pass
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def gen_stream(self, model, messages, stream=True, *args, **kwargs):
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decorators = [stream_cache, stream_token_usage]
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return self._apply_decorator(self._raw_gen_stream, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)
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def supports_tools(self):
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return hasattr(self, '_supports_tools') and callable(getattr(self, '_supports_tools'))
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def _supports_tools(self):
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raise NotImplementedError("Subclass must implement _supports_tools method") |