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feat: add return type hints to LLM class methods (#5173)
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@ -369,16 +369,16 @@ class LLM(RetryMixin, DebugMixin):
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):
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self.config.max_output_tokens = self.model_info['max_tokens']
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def vision_is_active(self):
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def vision_is_active(self) -> bool:
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with warnings.catch_warnings():
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warnings.simplefilter('ignore')
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return not self.config.disable_vision and self._supports_vision()
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def _supports_vision(self):
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def _supports_vision(self) -> bool:
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"""Acquire from litellm if model is vision capable.
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Returns:
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bool: True if model is vision capable. If model is not supported by litellm, it will return False.
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bool: True if model is vision capable. Return False if model not supported by litellm.
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"""
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# litellm.supports_vision currently returns False for 'openai/gpt-...' or 'anthropic/claude-...' (with prefixes)
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# but model_info will have the correct value for some reason.
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@ -476,7 +476,7 @@ class LLM(RetryMixin, DebugMixin):
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if stats:
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logger.debug(stats)
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def get_token_count(self, messages):
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def get_token_count(self, messages) -> int:
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"""Get the number of tokens in a list of messages.
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Args:
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@ -491,7 +491,7 @@ class LLM(RetryMixin, DebugMixin):
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# TODO: this is to limit logspam in case token count is not supported
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return 0
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def _is_local(self):
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def _is_local(self) -> bool:
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"""Determines if the system is using a locally running LLM.
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Returns:
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@ -506,7 +506,7 @@ class LLM(RetryMixin, DebugMixin):
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return True
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return False
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def _completion_cost(self, response):
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def _completion_cost(self, response) -> float:
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"""Calculate the cost of a completion response based on the model. Local models are treated as free.
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Add the current cost into total cost in metrics.
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@ -555,7 +555,7 @@ class LLM(RetryMixin, DebugMixin):
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def __repr__(self):
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return str(self)
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def reset(self):
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def reset(self) -> None:
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self.metrics.reset()
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def format_messages_for_llm(self, messages: Message | list[Message]) -> list[dict]:
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