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34 lines
1.3 KiB
Python
34 lines
1.3 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 gen_cache, stream_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, *args, **kwargs):
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pass
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def gen(self, model, messages, stream=False, *args, **kwargs):
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decorators = [gen_cache, gen_token_usage]
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return self._apply_decorator(self._raw_gen, decorators=decorators, model=model, messages=messages, stream=stream, *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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"""
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Stream a response from the LLM with caching and token usage tracking.
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"""
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# List of decorators to apply for streaming generation
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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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