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812 lines
30 KiB
Python
812 lines
30 KiB
Python
"""Wrapper around OpenAI APIs."""
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from __future__ import annotations
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import logging
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import sys
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import warnings
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from typing import (
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AbstractSet,
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Any,
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Callable,
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Collection,
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Dict,
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Generator,
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List,
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Literal,
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Mapping,
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Optional,
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Set,
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Tuple,
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Union,
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)
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from pydantic import Extra, Field, root_validator
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from tenacity import (
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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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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logger = logging.getLogger(__name__)
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def update_token_usage(
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keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
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) -> None:
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"""Update token usage."""
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_keys_to_use = keys.intersection(response["usage"])
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for _key in _keys_to_use:
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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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def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
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"""Update response from the stream response."""
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response["choices"][0]["text"] += stream_response["choices"][0]["text"]
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response["choices"][0]["finish_reason"] = stream_response["choices"][0][
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"finish_reason"
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]
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response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
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def _streaming_response_template() -> Dict[str, Any]:
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return {
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"choices": [
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{
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"text": "",
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"finish_reason": None,
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"logprobs": None,
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}
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]
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}
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def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]:
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import openai
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min_seconds = 4
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max_seconds = 10
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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return retry(
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reraise=True,
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stop=stop_after_attempt(llm.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(
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retry_if_exception_type(openai.error.Timeout)
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| retry_if_exception_type(openai.error.APIError)
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| retry_if_exception_type(openai.error.APIConnectionError)
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| retry_if_exception_type(openai.error.RateLimitError)
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| retry_if_exception_type(openai.error.ServiceUnavailableError)
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),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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return llm.client.create(**kwargs)
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return _completion_with_retry(**kwargs)
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async def acompletion_with_retry(
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llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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# Use OpenAI's async api https://github.com/openai/openai-python#async-api
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return await llm.client.acreate(**kwargs)
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return await _completion_with_retry(**kwargs)
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class BaseOpenAI(BaseLLM):
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"""Wrapper around OpenAI large language models."""
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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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openai_api_base: Optional[str] = None
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openai_organization: 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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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
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logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
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"""Adjust the probability of specific tokens being generated."""
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max_retries: int = 6
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
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"""Set of special tokens that are allowed。"""
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disallowed_special: Union[Literal["all"], Collection[str]] = "all"
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"""Set of special tokens that are not allowed。"""
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def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore
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"""Initialize the OpenAI object."""
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model_name = data.get("model_name", "")
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if model_name.startswith("gpt-3.5-turbo") or model_name.startswith("gpt-4"):
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warnings.warn(
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"You are trying to use a chat model. This way of initializing it is "
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"no longer supported. Instead, please use: "
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"`from langchain.chat_models import ChatOpenAI`"
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)
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return OpenAIChat(**data)
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return super().__new__(cls)
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.ignore
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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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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transfered to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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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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openai_api_base = get_from_dict_or_env(
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values,
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"openai_api_base",
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"OPENAI_API_BASE",
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default="",
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)
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openai_organization = get_from_dict_or_env(
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values,
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"openai_organization",
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"OPENAI_ORGANIZATION",
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default="",
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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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if openai_api_base:
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openai.api_base = openai_api_base
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if openai_organization:
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openai.organization = openai_organization
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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 install it with `pip install openai`."
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)
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if values["streaming"] and values["n"] > 1:
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raise ValueError("Cannot stream results when n > 1.")
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if values["streaming"] and values["best_of"] > 1:
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raise ValueError("Cannot stream results when best_of > 1.")
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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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"request_timeout": self.request_timeout,
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"logit_bias": self.logit_bias,
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}
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# Azure gpt-35-turbo doesn't support best_of
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# don't specify best_of if it is 1
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if self.best_of > 1:
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normal_params["best_of"] = self.best_of
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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._invocation_params
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sub_prompts = self.get_sub_prompts(params, prompts, stop)
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choices = []
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token_usage: Dict[str, int] = {}
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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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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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params["stream"] = True
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response = _streaming_response_template()
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for stream_resp in completion_with_retry(
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self, prompt=_prompts, **params
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):
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self.callback_manager.on_llm_new_token(
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stream_resp["choices"][0]["text"],
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verbose=self.verbose,
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logprobs=stream_resp["choices"][0]["logprobs"],
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)
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_update_response(response, stream_resp)
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choices.extend(response["choices"])
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else:
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response = completion_with_retry(self, prompt=_prompts, **params)
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choices.extend(response["choices"])
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if not self.streaming:
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# Can't update token usage if streaming
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update_token_usage(_keys, response, token_usage)
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return self.create_llm_result(choices, prompts, token_usage)
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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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"""Call out to OpenAI's endpoint async with k unique prompts."""
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params = self._invocation_params
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sub_prompts = self.get_sub_prompts(params, prompts, stop)
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choices = []
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token_usage: Dict[str, int] = {}
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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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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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params["stream"] = True
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response = _streaming_response_template()
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async for stream_resp in await acompletion_with_retry(
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self, prompt=_prompts, **params
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):
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if self.callback_manager.is_async:
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await self.callback_manager.on_llm_new_token(
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stream_resp["choices"][0]["text"],
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verbose=self.verbose,
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logprobs=stream_resp["choices"][0]["logprobs"],
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)
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else:
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self.callback_manager.on_llm_new_token(
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stream_resp["choices"][0]["text"],
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verbose=self.verbose,
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logprobs=stream_resp["choices"][0]["logprobs"],
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)
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_update_response(response, stream_resp)
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choices.extend(response["choices"])
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else:
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response = await acompletion_with_retry(self, prompt=_prompts, **params)
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choices.extend(response["choices"])
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if not self.streaming:
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# Can't update token usage if streaming
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update_token_usage(_keys, response, token_usage)
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return self.create_llm_result(choices, prompts, token_usage)
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def get_sub_prompts(
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self,
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params: Dict[str, Any],
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prompts: List[str],
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stop: Optional[List[str]] = None,
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) -> List[List[str]]:
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"""Get the sub prompts for llm call."""
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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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return sub_prompts
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def create_llm_result(
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self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
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) -> LLMResult:
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"""Create the LLMResult from the choices and prompts."""
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generations = []
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for i, _ 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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[
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Generation(
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text=choice["text"],
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generation_info=dict(
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finish_reason=choice.get("finish_reason"),
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logprobs=choice.get("logprobs"),
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),
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)
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for choice in sub_choices
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]
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)
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llm_output = {"token_usage": token_usage, "model_name": self.model_name}
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return LLMResult(generations=generations, llm_output=llm_output)
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def stream(self, prompt: str, stop: Optional[List[str]] = None) -> 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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stop: Optional list of stop words to use when generating.
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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.prep_streaming_params(stop)
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generator = self.client.create(prompt=prompt, **params)
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return generator
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def prep_streaming_params(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
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"""Prepare the params for streaming."""
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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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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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params["stream"] = True
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return params
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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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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 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
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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 install it with `pip install tiktoken`."
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)
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enc = tiktoken.encoding_for_model(self.model_name)
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tokenized_text = enc.encode(
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text,
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allowed_special=self.allowed_special,
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disallowed_special=self.disallowed_special,
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)
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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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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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model_token_mapping = {
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"gpt-4": 8192,
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"gpt-4-0314": 8192,
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"gpt-4-32k": 32768,
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"gpt-4-32k-0314": 32768,
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"gpt-3.5-turbo": 4096,
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"gpt-3.5-turbo-0301": 4096,
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"text-ada-001": 2049,
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"ada": 2049,
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"text-babbage-001": 2040,
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"babbage": 2049,
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"text-curie-001": 2049,
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"curie": 2049,
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"davinci": 2049,
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"text-davinci-003": 4097,
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"text-davinci-002": 4097,
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"code-davinci-002": 8001,
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"code-davinci-001": 8001,
|
|
"code-cushman-002": 2048,
|
|
"code-cushman-001": 2048,
|
|
}
|
|
|
|
context_size = model_token_mapping.get(modelname, None)
|
|
|
|
if context_size is None:
|
|
raise ValueError(
|
|
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
|
|
"Known models are: " + ", ".join(model_token_mapping.keys())
|
|
)
|
|
|
|
return context_size
|
|
|
|
def max_tokens_for_prompt(self, prompt: str) -> int:
|
|
"""Calculate the maximum number of tokens possible to generate for a prompt.
|
|
|
|
Args:
|
|
prompt: The prompt to pass into the model.
|
|
|
|
Returns:
|
|
The maximum number of tokens to generate for a prompt.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
|
|
"""
|
|
num_tokens = self.get_num_tokens(prompt)
|
|
|
|
# get max context size for model by name
|
|
max_size = self.modelname_to_contextsize(self.model_name)
|
|
return max_size - num_tokens
|
|
|
|
|
|
class OpenAI(BaseOpenAI):
|
|
"""Wrapper around OpenAI large language models.
|
|
|
|
To use, you should have the ``openai`` python package installed, and the
|
|
environment variable ``OPENAI_API_KEY`` set with your API key.
|
|
|
|
Any parameters that are valid to be passed to the openai.create call can be passed
|
|
in, even if not explicitly saved on this class.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain.llms import OpenAI
|
|
openai = OpenAI(model_name="text-davinci-003")
|
|
"""
|
|
|
|
@property
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
return {**{"model": self.model_name}, **super()._invocation_params}
|
|
|
|
|
|
class AzureOpenAI(BaseOpenAI):
|
|
"""Wrapper around Azure-specific OpenAI large language models.
|
|
|
|
To use, you should have the ``openai`` python package installed, and the
|
|
environment variable ``OPENAI_API_KEY`` set with your API key.
|
|
|
|
Any parameters that are valid to be passed to the openai.create call can be passed
|
|
in, even if not explicitly saved on this class.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain.llms import AzureOpenAI
|
|
openai = AzureOpenAI(model_name="text-davinci-003")
|
|
"""
|
|
|
|
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}
|
|
|
|
|
|
class OpenAIChat(BaseLLM):
|
|
"""Wrapper around OpenAI Chat large language models.
|
|
|
|
To use, you should have the ``openai`` python package installed, and the
|
|
environment variable ``OPENAI_API_KEY`` set with your API key.
|
|
|
|
Any parameters that are valid to be passed to the openai.create call can be passed
|
|
in, even if not explicitly saved on this class.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain.llms import OpenAIChat
|
|
openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
|
|
"""
|
|
|
|
client: Any #: :meta private:
|
|
model_name: str = "gpt-3.5-turbo"
|
|
"""Model name to use."""
|
|
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
|
"""Holds any model parameters valid for `create` call not explicitly specified."""
|
|
openai_api_key: Optional[str] = None
|
|
openai_api_base: Optional[str] = None
|
|
max_retries: int = 6
|
|
"""Maximum number of retries to make when generating."""
|
|
prefix_messages: List = Field(default_factory=list)
|
|
"""Series of messages for Chat input."""
|
|
streaming: bool = False
|
|
"""Whether to stream the results or not."""
|
|
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
|
|
"""Set of special tokens that are allowed。"""
|
|
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
|
|
"""Set of special tokens that are not allowed。"""
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.ignore
|
|
|
|
@root_validator(pre=True)
|
|
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Build extra kwargs from additional params that were passed in."""
|
|
all_required_field_names = {field.alias for field in cls.__fields__.values()}
|
|
|
|
extra = values.get("model_kwargs", {})
|
|
for field_name in list(values):
|
|
if field_name not in all_required_field_names:
|
|
if field_name in extra:
|
|
raise ValueError(f"Found {field_name} supplied twice.")
|
|
extra[field_name] = values.pop(field_name)
|
|
values["model_kwargs"] = extra
|
|
return values
|
|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that api key and python package exists in environment."""
|
|
openai_api_key = get_from_dict_or_env(
|
|
values, "openai_api_key", "OPENAI_API_KEY"
|
|
)
|
|
openai_api_base = get_from_dict_or_env(
|
|
values,
|
|
"openai_api_base",
|
|
"OPENAI_API_BASE",
|
|
default="",
|
|
)
|
|
openai_organization = get_from_dict_or_env(
|
|
values, "openai_organization", "OPENAI_ORGANIZATION", default=""
|
|
)
|
|
try:
|
|
import openai
|
|
|
|
openai.api_key = openai_api_key
|
|
if openai_api_base:
|
|
openai.api_base = openai_api_base
|
|
if openai_organization:
|
|
openai.organization = openai_organization
|
|
except ImportError:
|
|
raise ValueError(
|
|
"Could not import openai python package. "
|
|
"Please install it with `pip install openai`."
|
|
)
|
|
try:
|
|
values["client"] = openai.ChatCompletion
|
|
except AttributeError:
|
|
raise ValueError(
|
|
"`openai` has no `ChatCompletion` attribute, this is likely "
|
|
"due to an old version of the openai package. Try upgrading it "
|
|
"with `pip install --upgrade openai`."
|
|
)
|
|
warnings.warn(
|
|
"You are trying to use a chat model. This way of initializing it is "
|
|
"no longer supported. Instead, please use: "
|
|
"`from langchain.chat_models import ChatOpenAI`"
|
|
)
|
|
return values
|
|
|
|
@property
|
|
def _default_params(self) -> Dict[str, Any]:
|
|
"""Get the default parameters for calling OpenAI API."""
|
|
return self.model_kwargs
|
|
|
|
def _get_chat_params(
|
|
self, prompts: List[str], stop: Optional[List[str]] = None
|
|
) -> Tuple:
|
|
if len(prompts) > 1:
|
|
raise ValueError(
|
|
f"OpenAIChat currently only supports single prompt, got {prompts}"
|
|
)
|
|
messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
|
|
params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
|
|
if stop is not None:
|
|
if "stop" in params:
|
|
raise ValueError("`stop` found in both the input and default params.")
|
|
params["stop"] = stop
|
|
if params.get("max_tokens") == -1:
|
|
# for ChatGPT api, omitting max_tokens is equivalent to having no limit
|
|
del params["max_tokens"]
|
|
return messages, params
|
|
|
|
def _generate(
|
|
self, prompts: List[str], stop: Optional[List[str]] = None
|
|
) -> LLMResult:
|
|
messages, params = self._get_chat_params(prompts, stop)
|
|
if self.streaming:
|
|
response = ""
|
|
params["stream"] = True
|
|
for stream_resp in completion_with_retry(self, messages=messages, **params):
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
response += token
|
|
self.callback_manager.on_llm_new_token(
|
|
token,
|
|
verbose=self.verbose,
|
|
)
|
|
return LLMResult(
|
|
generations=[[Generation(text=response)]],
|
|
)
|
|
else:
|
|
full_response = completion_with_retry(self, messages=messages, **params)
|
|
llm_output = {
|
|
"token_usage": full_response["usage"],
|
|
"model_name": self.model_name,
|
|
}
|
|
return LLMResult(
|
|
generations=[
|
|
[Generation(text=full_response["choices"][0]["message"]["content"])]
|
|
],
|
|
llm_output=llm_output,
|
|
)
|
|
|
|
async def _agenerate(
|
|
self, prompts: List[str], stop: Optional[List[str]] = None
|
|
) -> LLMResult:
|
|
messages, params = self._get_chat_params(prompts, stop)
|
|
if self.streaming:
|
|
response = ""
|
|
params["stream"] = True
|
|
async for stream_resp in await acompletion_with_retry(
|
|
self, messages=messages, **params
|
|
):
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
response += token
|
|
if self.callback_manager.is_async:
|
|
await self.callback_manager.on_llm_new_token(
|
|
token,
|
|
verbose=self.verbose,
|
|
)
|
|
else:
|
|
self.callback_manager.on_llm_new_token(
|
|
token,
|
|
verbose=self.verbose,
|
|
)
|
|
return LLMResult(
|
|
generations=[[Generation(text=response)]],
|
|
)
|
|
else:
|
|
full_response = await acompletion_with_retry(
|
|
self, messages=messages, **params
|
|
)
|
|
llm_output = {
|
|
"token_usage": full_response["usage"],
|
|
"model_name": self.model_name,
|
|
}
|
|
return LLMResult(
|
|
generations=[
|
|
[Generation(text=full_response["choices"][0]["message"]["content"])]
|
|
],
|
|
llm_output=llm_output,
|
|
)
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {**{"model_name": self.model_name}, **self._default_params}
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of llm."""
|
|
return "openai-chat"
|
|
|
|
def get_num_tokens(self, text: str) -> int:
|
|
"""Calculate num tokens with tiktoken package."""
|
|
# tiktoken NOT supported for Python < 3.8
|
|
if sys.version_info[1] < 8:
|
|
return super().get_num_tokens(text)
|
|
try:
|
|
import tiktoken
|
|
except ImportError:
|
|
raise ValueError(
|
|
"Could not import tiktoken python package. "
|
|
"This is needed in order to calculate get_num_tokens. "
|
|
"Please install it with `pip install tiktoken`."
|
|
)
|
|
# create a GPT-3.5-Turbo encoder instance
|
|
enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
|
|
|
|
# encode the text using the GPT-3.5-Turbo encoder
|
|
tokenized_text = enc.encode(
|
|
text,
|
|
allowed_special=self.allowed_special,
|
|
disallowed_special=self.disallowed_special,
|
|
)
|
|
|
|
# calculate the number of tokens in the encoded text
|
|
return len(tokenized_text)
|