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294 lines
10 KiB
Python
294 lines
10 KiB
Python
"""Wrapper around OpenAI APIs."""
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import sys
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from typing import Any, Dict, Generator, 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, LLMResult
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from langchain.schema import Generation
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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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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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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-003")
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"""
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client: Any #: :meta private:
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model_name: str = "text-davinci-003"
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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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-1 returns as many tokens as possible given the prompt and
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the models maximal context size."""
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top_p: float = 1
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"""Total probability mass of tokens to consider at each step."""
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frequency_penalty: float = 0
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"""Penalizes repeated tokens according to frequency."""
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presence_penalty: float = 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[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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openai_api_key: Optional[str] = None
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batch_size: int = 20
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"""Batch size to use when passing multiple documents to generate."""
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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(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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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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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling OpenAI API."""
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normal_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 {**normal_params, **self.model_kwargs}
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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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"""Call out to OpenAI's endpoint with k unique prompts.
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Args:
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prompts: The prompts 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 full LLM output.
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Example:
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.. code-block:: python
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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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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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if params["max_tokens"] == -1:
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if len(prompts) != 1:
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raise ValueError(
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"max_tokens set to -1 not supported for multiple inputs."
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)
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params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
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sub_prompts = [
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prompts[i : i + self.batch_size]
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for i in range(0, len(prompts), self.batch_size)
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]
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choices = []
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token_usage = {}
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# Get the token usage from the response.
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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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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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token_usage[_key] = response["usage"][_key]
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else:
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token_usage[_key] += response["usage"][_key]
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generations = []
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for i, prompt in enumerate(prompts):
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sub_choices = choices[i * self.n : (i + 1) * self.n]
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generations.append(
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[Generation(text=choice["text"]) for choice in sub_choices]
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)
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return LLMResult(
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generations=generations, llm_output={"token_usage": token_usage}
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)
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def stream(self, prompt: str) -> Generator:
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"""Call OpenAI with streaming flag and return the resulting generator.
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BETA: this is a beta feature while we figure out the right abstraction.
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Once that happens, this interface could change.
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Args:
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prompt: The prompts to pass into the model.
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Returns:
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A generator representing the stream of tokens from OpenAI.
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Example:
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.. code-block:: python
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generator = openai.stream("Tell me a joke.")
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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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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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return generator
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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_name": self.model_name}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "openai"
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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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return self.generate([prompt], stop=stop).generations[0][0].text
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def get_num_tokens(self, text: str) -> int:
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"""Calculate num tokens with tiktoken package."""
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# tiktoken NOT supported for Python 3.8 or below
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if sys.version_info[1] <= 8:
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return super().get_num_tokens(text)
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try:
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import tiktoken
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except ImportError:
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raise ValueError(
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"Could not import tiktoken python package. "
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"This is needed in order to calculate get_num_tokens. "
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"Please it install it with `pip install tiktoken`."
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)
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# create a GPT-3 encoder instance
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enc = tiktoken.get_encoding("gpt2")
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# encode the text using the GPT-3 encoder
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tokenized_text = enc.encode(text)
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# calculate the number of tokens in the encoded text
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return len(tokenized_text)
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def modelname_to_contextsize(self, modelname: str) -> int:
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"""Calculate the maximum number of tokens possible to generate for a model.
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text-davinci-003: 4,000 tokens
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text-curie-001: 2,048 tokens
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text-babbage-001: 2,048 tokens
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text-ada-001: 2,048 tokens
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code-davinci-002: 8,000 tokens
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code-cushman-001: 2,048 tokens
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Args:
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modelname: The modelname we want to know the context size for.
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Returns:
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The maximum context size
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Example:
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.. code-block:: python
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max_tokens = openai.modelname_to_contextsize("text-davinci-003")
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"""
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if modelname == "text-davinci-003":
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return 4000
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elif modelname == "text-curie-001":
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return 2048
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elif modelname == "text-babbage-001":
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return 2048
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elif modelname == "text-ada-001":
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return 2048
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elif modelname == "code-davinci-002":
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return 8000
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elif modelname == "code-cushman-001":
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return 2048
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else:
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return 4000
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def max_tokens_for_prompt(self, prompt: str) -> int:
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"""Calculate the maximum number of tokens possible to generate for a prompt.
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Args:
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prompt: The prompt to pass into the model.
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Returns:
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The maximum number of tokens to generate for a prompt.
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Example:
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.. code-block:: python
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max_tokens = openai.max_token_for_prompt("Tell me a joke.")
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"""
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num_tokens = self.get_num_tokens(prompt)
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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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