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@ -9,7 +9,7 @@ from langchain.schema import Generation
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from langchain.utils import get_from_dict_or_env
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class OpenAI(BaseLLM, BaseModel):
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class BaseOpenAI(BaseLLM, BaseModel):
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"""Wrapper around OpenAI large language models.
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To use, you should have the ``openai`` python package installed, and the
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@ -119,7 +119,7 @@ class OpenAI(BaseLLM, BaseModel):
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response = openai.generate(["Tell me a joke."])
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"""
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# TODO: write a unit test for this
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params = self._default_params
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params = self._invocation_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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@ -141,9 +141,7 @@ class OpenAI(BaseLLM, BaseModel):
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# Includes prompt, completion, and total tokens used.
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_keys = ["completion_tokens", "prompt_tokens", "total_tokens"]
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for _prompts in sub_prompts:
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response = self.client.create(
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model=self.model_name, prompt=_prompts, **params
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)
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response = self.client.create(prompt=_prompts, **params)
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choices.extend(response["choices"])
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for _key in _keys:
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if _key not in token_usage:
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@ -179,14 +177,19 @@ class OpenAI(BaseLLM, BaseModel):
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for token in generator:
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yield token
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"""
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params = self._default_params
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params = self._invocation_params
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if params["best_of"] != 1:
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raise ValueError("OpenAI only supports best_of == 1 for streaming")
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params["stream"] = True
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generator = self.client.create(model=self.model_name, prompt=prompt, **params)
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generator = self.client.create(prompt=prompt, **params)
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return generator
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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"""Get the parameters used to invoke the model."""
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return self._default_params
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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@ -274,3 +277,29 @@ class OpenAI(BaseLLM, BaseModel):
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# get max context size for model by name
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max_size = self.modelname_to_contextsize(self.model_name)
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return max_size - num_tokens
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class OpenAI(BaseOpenAI):
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"""Generic OpenAI class that uses model name."""
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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return {**{"model": self.model_name}, **super()._invocation_params}
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class AzureOpenAI(BaseOpenAI):
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"""Azure specific OpenAI class that uses deployment name."""
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deployment_name: str = ""
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"""Deployment name to use."""
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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return {
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**{"deployment_name": self.deployment_name},
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**super()._identifying_params,
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}
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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return {**{"engine": self.deployment_name}, **super()._invocation_params}
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