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https://github.com/hwchase17/langchain
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Harrison/track token usage (#1382)
Co-authored-by: Zak King <zaking17@gmail.com>
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@ -23,7 +23,7 @@ from tenacity import (
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wait_exponential,
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)
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from langchain.llms.base import LLM, BaseLLM
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from langchain.llms.base import BaseLLM
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from langchain.schema import Generation, LLMResult
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from langchain.utils import get_from_dict_or_env
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@ -515,7 +515,7 @@ class AzureOpenAI(BaseOpenAI):
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return {**{"engine": self.deployment_name}, **super()._invocation_params}
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class OpenAIChat(LLM, BaseModel):
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class OpenAIChat(BaseLLM, BaseModel):
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"""Wrapper around OpenAI Chat large language models.
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To use, you should have the ``openai`` python package installed, and the
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@ -621,15 +621,30 @@ class OpenAIChat(LLM, BaseModel):
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return _completion_with_retry(**kwargs)
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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messages = self.prefix_messages + [{"role": "user", "content": prompt}]
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def _generate(
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self, prompts: List[str], stop: Optional[List[str]] = None
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) -> LLMResult:
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if len(prompts) > 1:
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raise ValueError(f"OpenAIChat only supports single prompts, got {prompts}")
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messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
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params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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response = self.completion_with_retry(messages=messages, **params)
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return response["choices"][0]["message"]["content"]
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return LLMResult(
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generations=[
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[Generation(text=response["choices"][0]["message"]["content"])]
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],
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llm_output={"token_usage": response["usage"]},
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)
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async def _agenerate(
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self, prompts: List[str], stop: Optional[List[str]] = None
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) -> LLMResult:
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"""Run the LLM on the given prompt and input."""
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raise NotImplementedError("Async generation not implemented for this LLM.")
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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