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124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
"""Wrapper around OpenAI APIs."""
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from typing import Any, Dict, List, Mapping, Optional
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from pydantic import BaseModel, Extra, Field, root_validator
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from langchain.llms.base import LLM
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from langchain.utils import get_from_dict_or_env
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class OpenAI(LLM, 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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environment variable ``OPENAI_API_KEY`` set with your API key.
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Example:
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.. code-block:: python
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from langchain import OpenAI
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openai = OpenAI(model="text-davinci-002")
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"""
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client: Any #: :meta private:
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model_name: str = "text-davinci-002"
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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max_tokens: int = 256
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"""The maximum number of tokens to generate in the completion."""
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top_p: int = 1
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"""Total probability mass of tokens to consider at each step."""
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frequency_penalty: int = 0
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"""Penalizes repeated tokens according to frequency."""
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presence_penalty: int = 0
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"""Penalizes repeated tokens."""
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n: int = 1
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"""How many completions to generate for each prompt."""
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best_of: int = 1
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"""Generates best_of completions server-side and returns the "best"."""
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model_kwargs: dict = Field(default_factory=dict)
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openai_api_key: Optional[str] = None
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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openai_api_key = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY"
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)
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try:
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import openai
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openai.api_key = openai_api_key
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values["client"] = openai.Completion
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except ImportError:
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raise ValueError(
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"Could not import openai python package. "
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"Please it install it with `pip install openai`."
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)
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return values
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@root_validator()
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def validate_model_kwargs(cls, values: Dict) -> Dict:
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named_params = {
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"temperature",
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"max_tokens",
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"top_p",
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"frequency_penalty",
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"presence_penalty",
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"n",
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"best_of",
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}
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overlap = named_params.intersection(values["model_kwargs"])
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if overlap:
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raise ValueError(
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"Found named params in model_kwargs, "
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f"should be specified separately: {overlap}"
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)
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return values
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@property
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def _default_params(self) -> Mapping[str, Any]:
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"""Get the default parameters for calling OpenAI API."""
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named_params = {
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"temperature": self.temperature,
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"max_tokens": self.max_tokens,
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"top_p": self.top_p,
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"frequency_penalty": self.frequency_penalty,
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"presence_penalty": self.presence_penalty,
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"n": self.n,
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"best_of": self.best_of,
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}
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return {**named_params, **self.model_kwargs}
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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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return {**{"model": self.model_name}, **self._default_params}
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def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to OpenAI's create endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = openai("Tell me a joke.")
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"""
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response = self.client.create(
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model=self.model_name, prompt=prompt, stop=stop, **self._default_params
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)
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return response["choices"][0]["text"]
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