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RFC: azure simpler refactor

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Harrison Chase 2022-12-21 16:51:14 -05:00
parent bb76440bfa
commit 47181f9a7a
1 changed files with 36 additions and 7 deletions
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43
langchain/llms/openai.py
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@ -9,7 +9,7 @@ from langchain.schema import Generation
from langchain.utils import get_from_dict_or_env from langchain.utils import get_from_dict_or_env
class OpenAI(BaseLLM, BaseModel): class BaseOpenAI(BaseLLM, BaseModel):
"""Wrapper around OpenAI large language models. """Wrapper around OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the To use, you should have the ``openai`` python package installed, and the
@ -119,7 +119,7 @@ class OpenAI(BaseLLM, BaseModel):
response = openai.generate(["Tell me a joke."]) response = openai.generate(["Tell me a joke."])
""" """
# TODO: write a unit test for this # TODO: write a unit test for this
params = self._default_params params = self._invocation_params
if stop is not None: if stop is not None:
if "stop" in params: if "stop" in params:
raise ValueError("`stop` found in both the input and default params.") raise ValueError("`stop` found in both the input and default params.")
@ -141,9 +141,7 @@ class OpenAI(BaseLLM, BaseModel):
# Includes prompt, completion, and total tokens used. # Includes prompt, completion, and total tokens used.
_keys = ["completion_tokens", "prompt_tokens", "total_tokens"] _keys = ["completion_tokens", "prompt_tokens", "total_tokens"]
for _prompts in sub_prompts: for _prompts in sub_prompts:
response = self.client.create( response = self.client.create(prompt=_prompts, **params)
model=self.model_name, prompt=_prompts, **params
)
choices.extend(response["choices"]) choices.extend(response["choices"])
for _key in _keys: for _key in _keys:
if _key not in token_usage: if _key not in token_usage:
@ -179,14 +177,19 @@ class OpenAI(BaseLLM, BaseModel):
for token in generator: for token in generator:
yield token yield token
""" """
params = self._default_params params = self._invocation_params
if params["best_of"] != 1: if params["best_of"] != 1:
raise ValueError("OpenAI only supports best_of == 1 for streaming") raise ValueError("OpenAI only supports best_of == 1 for streaming")
params["stream"] = True params["stream"] = True
generator = self.client.create(model=self.model_name, prompt=prompt, **params) generator = self.client.create(prompt=prompt, **params)
return generator return generator
@property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
return self._default_params
@property @property
def _identifying_params(self) -> Mapping[str, Any]: def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters.""" """Get the identifying parameters."""
@ -274,3 +277,29 @@ class OpenAI(BaseLLM, BaseModel):
# get max context size for model by name # get max context size for model by name
max_size = self.modelname_to_contextsize(self.model_name) max_size = self.modelname_to_contextsize(self.model_name)
return max_size - num_tokens return max_size - num_tokens
class OpenAI(BaseOpenAI):
"""Generic OpenAI class that uses model name."""
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params}
class AzureOpenAI(BaseOpenAI):
"""Azure specific OpenAI class that uses deployment name."""
deployment_name: str = ""
"""Deployment name to use."""
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {
**{"deployment_name": self.deployment_name},
**super()._identifying_params,
}
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"engine": self.deployment_name}, **super()._invocation_params}