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958 lines
34 KiB
958 lines
34 KiB
from __future__ import annotations
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import logging
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import sys
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from typing import (
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AbstractSet,
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Any,
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AsyncIterator,
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Callable,
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Collection,
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Dict,
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Iterator,
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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 langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.llms.base import BaseLLM, create_base_retry_decorator
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from langchain.pydantic_v1 import Field, root_validator
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from langchain.schema import Generation, LLMResult
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from langchain.schema.output import GenerationChunk
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from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
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from langchain.utils.utils import build_extra_kwargs
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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 _stream_response_to_generation_chunk(
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stream_response: Dict[str, Any],
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) -> GenerationChunk:
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"""Convert a stream response to a generation chunk."""
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return GenerationChunk(
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text=stream_response["choices"][0]["text"],
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generation_info=dict(
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finish_reason=stream_response["choices"][0].get("finish_reason", None),
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logprobs=stream_response["choices"][0].get("logprobs", None),
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),
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)
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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].get(
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"finish_reason", None
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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(
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llm: Union[BaseOpenAI, OpenAIChat],
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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import openai
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errors = [
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openai.error.Timeout,
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openai.error.APIError,
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openai.error.APIConnectionError,
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openai.error.RateLimitError,
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openai.error.ServiceUnavailableError,
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]
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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)
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def completion_with_retry(
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llm: Union[BaseOpenAI, OpenAIChat],
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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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],
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**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, run_manager=run_manager)
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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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"""Base OpenAI large language model class."""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"openai_api_key": "OPENAI_API_KEY"}
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@property
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def lc_attributes(self) -> Dict[str, Any]:
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attributes: Dict[str, Any] = {}
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if self.openai_api_base != "":
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attributes["openai_api_base"] = self.openai_api_base
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if self.openai_organization != "":
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attributes["openai_organization"] = self.openai_organization
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if self.openai_proxy != "":
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attributes["openai_proxy"] = self.openai_proxy
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return attributes
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@classmethod
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def is_lc_serializable(cls) -> bool:
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return True
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client: Any = None #: :meta private:
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model_name: str = Field(default="text-davinci-003", alias="model")
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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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# to support explicit proxy for OpenAI
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openai_proxy: 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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tiktoken_model_name: Optional[str] = None
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit. By default, when set to None, this will
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be the same as the embedding model name. However, there are some cases
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where you may want to use this Embedding class with a model name not
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supported by tiktoken. This can include when using Azure embeddings or
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when using one of the many model providers that expose an OpenAI-like
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API but with different models. In those cases, in order to avoid erroring
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when tiktoken is called, you can specify a model name to use here."""
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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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data.get("model_name", "")
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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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allow_population_by_field_name = True
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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 = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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values["model_kwargs"] = build_extra_kwargs(
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extra, values, all_required_field_names
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)
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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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values["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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values["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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values["openai_proxy"] = get_from_dict_or_env(
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values,
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"openai_proxy",
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"OPENAI_PROXY",
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default="",
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)
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values["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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values["client"] = openai.Completion
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except ImportError:
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raise ImportError(
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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 _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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params = {**self._invocation_params, **kwargs, "stream": True}
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self.get_sub_prompts(params, [prompt], stop) # this mutates params
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for stream_resp in completion_with_retry(
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self, prompt=prompt, run_manager=run_manager, **params
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):
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chunk = _stream_response_to_generation_chunk(stream_resp)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(
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chunk.text,
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chunk=chunk,
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verbose=self.verbose,
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logprobs=chunk.generation_info["logprobs"]
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if chunk.generation_info
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else None,
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)
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async def _astream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[GenerationChunk]:
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params = {**self._invocation_params, **kwargs, "stream": True}
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self.get_sub_prompts(params, [prompt], stop) # this mutate params
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async for stream_resp in await acompletion_with_retry(
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self, prompt=prompt, run_manager=run_manager, **params
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):
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chunk = _stream_response_to_generation_chunk(stream_resp)
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(
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chunk.text,
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chunk=chunk,
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verbose=self.verbose,
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logprobs=chunk.generation_info["logprobs"]
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if chunk.generation_info
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else None,
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)
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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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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params = {**params, **kwargs}
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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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generation: Optional[GenerationChunk] = None
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for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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choices.append(
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{
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"text": generation.text,
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"finish_reason": generation.generation_info.get("finish_reason")
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if generation.generation_info
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else None,
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"logprobs": generation.generation_info.get("logprobs")
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if generation.generation_info
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else None,
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}
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)
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else:
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response = completion_with_retry(
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self, prompt=_prompts, run_manager=run_manager, **params
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)
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choices.extend(response["choices"])
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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,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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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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params = {**params, **kwargs}
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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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generation: Optional[GenerationChunk] = None
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async for chunk in self._astream(
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_prompts[0], stop, run_manager, **kwargs
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):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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choices.append(
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{
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"text": generation.text,
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"finish_reason": generation.generation_info.get("finish_reason")
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if generation.generation_info
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else None,
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"logprobs": generation.generation_info.get("logprobs")
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if generation.generation_info
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else None,
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}
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)
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else:
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response = await acompletion_with_retry(
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self, prompt=_prompts, run_manager=run_manager, **params
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)
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choices.extend(response["choices"])
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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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|
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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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@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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openai_creds: Dict[str, Any] = {
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"api_key": self.openai_api_key,
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"api_base": self.openai_api_base,
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"organization": self.openai_organization,
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}
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if self.openai_proxy:
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import openai
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openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # noqa: E501
|
|
return {**openai_creds, **self._default_params}
|
|
|
|
@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"
|
|
|
|
def get_token_ids(self, text: str) -> List[int]:
|
|
"""Get the token IDs using the 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 ImportError(
|
|
"Could not import tiktoken python package. "
|
|
"This is needed in order to calculate get_num_tokens. "
|
|
"Please install it with `pip install tiktoken`."
|
|
)
|
|
|
|
model_name = self.tiktoken_model_name or self.model_name
|
|
try:
|
|
enc = tiktoken.encoding_for_model(model_name)
|
|
except KeyError:
|
|
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
|
model = "cl100k_base"
|
|
enc = tiktoken.get_encoding(model)
|
|
|
|
return enc.encode(
|
|
text,
|
|
allowed_special=self.allowed_special,
|
|
disallowed_special=self.disallowed_special,
|
|
)
|
|
|
|
@staticmethod
|
|
def modelname_to_contextsize(modelname: str) -> int:
|
|
"""Calculate the maximum number of tokens possible to generate for a model.
|
|
|
|
Args:
|
|
modelname: The modelname we want to know the context size for.
|
|
|
|
Returns:
|
|
The maximum context size
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
|
|
"""
|
|
model_token_mapping = {
|
|
"gpt-4": 8192,
|
|
"gpt-4-0314": 8192,
|
|
"gpt-4-0613": 8192,
|
|
"gpt-4-32k": 32768,
|
|
"gpt-4-32k-0314": 32768,
|
|
"gpt-4-32k-0613": 32768,
|
|
"gpt-3.5-turbo": 4096,
|
|
"gpt-3.5-turbo-0301": 4096,
|
|
"gpt-3.5-turbo-0613": 4096,
|
|
"gpt-3.5-turbo-16k": 16385,
|
|
"gpt-3.5-turbo-16k-0613": 16385,
|
|
"gpt-3.5-turbo-instruct": 4096,
|
|
"text-ada-001": 2049,
|
|
"ada": 2049,
|
|
"text-babbage-001": 2040,
|
|
"babbage": 2049,
|
|
"text-curie-001": 2049,
|
|
"curie": 2049,
|
|
"davinci": 2049,
|
|
"text-davinci-003": 4097,
|
|
"text-davinci-002": 4097,
|
|
"code-davinci-002": 8001,
|
|
"code-davinci-001": 8001,
|
|
"code-cushman-002": 2048,
|
|
"code-cushman-001": 2048,
|
|
}
|
|
|
|
# handling finetuned models
|
|
if "ft-" in modelname:
|
|
modelname = modelname.split(":")[0]
|
|
|
|
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
|
|
|
|
@property
|
|
def max_context_size(self) -> int:
|
|
"""Get max context size for this model."""
|
|
return self.modelname_to_contextsize(self.model_name)
|
|
|
|
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)
|
|
return self.max_context_size - num_tokens
|
|
|
|
|
|
class OpenAI(BaseOpenAI):
|
|
"""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):
|
|
"""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."""
|
|
openai_api_type: str = ""
|
|
openai_api_version: str = ""
|
|
|
|
@root_validator()
|
|
def validate_azure_settings(cls, values: Dict) -> Dict:
|
|
values["openai_api_version"] = get_from_dict_or_env(
|
|
values,
|
|
"openai_api_version",
|
|
"OPENAI_API_VERSION",
|
|
)
|
|
values["openai_api_type"] = get_from_dict_or_env(
|
|
values, "openai_api_type", "OPENAI_API_TYPE", "azure"
|
|
)
|
|
return values
|
|
|
|
@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]:
|
|
openai_params = {
|
|
"engine": self.deployment_name,
|
|
"api_type": self.openai_api_type,
|
|
"api_version": self.openai_api_version,
|
|
}
|
|
return {**openai_params, **super()._invocation_params}
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of llm."""
|
|
return "azure"
|
|
|
|
@property
|
|
def lc_attributes(self) -> Dict[str, Any]:
|
|
return {
|
|
"openai_api_type": self.openai_api_type,
|
|
"openai_api_version": self.openai_api_version,
|
|
}
|
|
|
|
|
|
class OpenAIChat(BaseLLM):
|
|
"""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
|
|
# to support explicit proxy for OpenAI
|
|
openai_proxy: 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。"""
|
|
|
|
@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_proxy = get_from_dict_or_env(
|
|
values,
|
|
"openai_proxy",
|
|
"OPENAI_PROXY",
|
|
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
|
|
if openai_proxy:
|
|
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # noqa: E501
|
|
except ImportError:
|
|
raise ImportError(
|
|
"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`."
|
|
)
|
|
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 _stream(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[GenerationChunk]:
|
|
messages, params = self._get_chat_params([prompt], stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
for stream_resp in completion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
):
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
chunk = GenerationChunk(text=token)
|
|
yield chunk
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(token, chunk=chunk)
|
|
|
|
async def _astream(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[GenerationChunk]:
|
|
messages, params = self._get_chat_params([prompt], stop)
|
|
params = {**params, **kwargs, "stream": True}
|
|
async for stream_resp in await acompletion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
):
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
chunk = GenerationChunk(text=token)
|
|
yield chunk
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(token, chunk=chunk)
|
|
|
|
def _generate(
|
|
self,
|
|
prompts: List[str],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
if self.streaming:
|
|
generation: Optional[GenerationChunk] = None
|
|
for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
|
|
if generation is None:
|
|
generation = chunk
|
|
else:
|
|
generation += chunk
|
|
assert generation is not None
|
|
return LLMResult(generations=[[generation]])
|
|
|
|
messages, params = self._get_chat_params(prompts, stop)
|
|
params = {**params, **kwargs}
|
|
full_response = completion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **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,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
if self.streaming:
|
|
generation: Optional[GenerationChunk] = None
|
|
async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
|
|
if generation is None:
|
|
generation = chunk
|
|
else:
|
|
generation += chunk
|
|
assert generation is not None
|
|
return LLMResult(generations=[[generation]])
|
|
|
|
messages, params = self._get_chat_params(prompts, stop)
|
|
params = {**params, **kwargs}
|
|
full_response = await acompletion_with_retry(
|
|
self, messages=messages, run_manager=run_manager, **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_token_ids(self, text: str) -> List[int]:
|
|
"""Get the token IDs using the tiktoken package."""
|
|
# tiktoken NOT supported for Python < 3.8
|
|
if sys.version_info[1] < 8:
|
|
return super().get_token_ids(text)
|
|
try:
|
|
import tiktoken
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import tiktoken python package. "
|
|
"This is needed in order to calculate get_num_tokens. "
|
|
"Please install it with `pip install tiktoken`."
|
|
)
|
|
|
|
enc = tiktoken.encoding_for_model(self.model_name)
|
|
return enc.encode(
|
|
text,
|
|
allowed_special=self.allowed_special,
|
|
disallowed_special=self.disallowed_special,
|
|
)
|