1104 lines
56 KiB
Python
1104 lines
56 KiB
Python
# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
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from __future__ import annotations
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from typing import Dict, List, Union, Iterable, Optional, overload
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from typing_extensions import Literal
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import httpx
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from .. import _legacy_response
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from ..types import completion_create_params
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from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
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from .._utils import (
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required_args,
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maybe_transform,
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async_maybe_transform,
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)
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from .._compat import cached_property
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from .._resource import SyncAPIResource, AsyncAPIResource
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from .._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
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from .._streaming import Stream, AsyncStream
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from .._base_client import (
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make_request_options,
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)
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from ..types.completion import Completion
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__all__ = ["Completions", "AsyncCompletions"]
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class Completions(SyncAPIResource):
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@cached_property
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def with_raw_response(self) -> CompletionsWithRawResponse:
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return CompletionsWithRawResponse(self)
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@cached_property
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def with_streaming_response(self) -> CompletionsWithStreamingResponse:
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return CompletionsWithStreamingResponse(self)
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@overload
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def create(
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self,
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*,
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model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
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||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
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||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
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||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
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||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
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||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
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||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
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||
n: Optional[int] | NotGiven = NOT_GIVEN,
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||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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||
seed: Optional[int] | NotGiven = NOT_GIVEN,
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||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
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||
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
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||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
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||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
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||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
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||
user: str | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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||
# The extra values given here take precedence over values defined on the client or passed to this method.
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||
extra_headers: Headers | None = None,
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||
extra_query: Query | None = None,
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||
extra_body: Body | None = None,
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||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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||
) -> Completion:
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"""
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||
Creates a completion for the provided prompt and parameters.
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||
|
||
Args:
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model: ID of the model to use. You can use the
|
||
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
|
||
see all of your available models, or see our
|
||
[Model overview](https://platform.openai.com/docs/models/overview) for
|
||
descriptions of them.
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||
|
||
prompt: The prompt(s) to generate completions for, encoded as a string, array of
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||
strings, array of tokens, or array of token arrays.
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||
|
||
Note that <|endoftext|> is the document separator that the model sees during
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||
training, so if a prompt is not specified the model will generate as if from the
|
||
beginning of a new document.
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||
|
||
best_of: Generates `best_of` completions server-side and returns the "best" (the one with
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||
the highest log probability per token). Results cannot be streamed.
|
||
|
||
When used with `n`, `best_of` controls the number of candidate completions and
|
||
`n` specifies how many to return – `best_of` must be greater than `n`.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
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||
|
||
echo: Echo back the prompt in addition to the completion
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||
|
||
frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
|
||
existing frequency in the text so far, decreasing the model's likelihood to
|
||
repeat the same line verbatim.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
logit_bias: Modify the likelihood of specified tokens appearing in the completion.
|
||
|
||
Accepts a JSON object that maps tokens (specified by their token ID in the GPT
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||
tokenizer) to an associated bias value from -100 to 100. You can use this
|
||
[tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs.
|
||
Mathematically, the bias is added to the logits generated by the model prior to
|
||
sampling. The exact effect will vary per model, but values between -1 and 1
|
||
should decrease or increase likelihood of selection; values like -100 or 100
|
||
should result in a ban or exclusive selection of the relevant token.
|
||
|
||
As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token
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||
from being generated.
|
||
|
||
logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
|
||
well the chosen tokens. For example, if `logprobs` is 5, the API will return a
|
||
list of the 5 most likely tokens. The API will always return the `logprob` of
|
||
the sampled token, so there may be up to `logprobs+1` elements in the response.
|
||
|
||
The maximum value for `logprobs` is 5.
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||
|
||
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
|
||
completion.
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||
|
||
The token count of your prompt plus `max_tokens` cannot exceed the model's
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||
context length.
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||
[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
||
for counting tokens.
|
||
|
||
n: How many completions to generate for each prompt.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
|
||
whether they appear in the text so far, increasing the model's likelihood to
|
||
talk about new topics.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
seed: If specified, our system will make a best effort to sample deterministically,
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||
such that repeated requests with the same `seed` and parameters should return
|
||
the same result.
|
||
|
||
Determinism is not guaranteed, and you should refer to the `system_fingerprint`
|
||
response parameter to monitor changes in the backend.
|
||
|
||
stop: Up to 4 sequences where the API will stop generating further tokens. The
|
||
returned text will not contain the stop sequence.
|
||
|
||
stream: Whether to stream back partial progress. If set, tokens will be sent as
|
||
data-only
|
||
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
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||
as they become available, with the stream terminated by a `data: [DONE]`
|
||
message.
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[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
|
||
|
||
suffix: The suffix that comes after a completion of inserted text.
|
||
|
||
This parameter is only supported for `gpt-3.5-turbo-instruct`.
|
||
|
||
temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
|
||
make the output more random, while lower values like 0.2 will make it more
|
||
focused and deterministic.
|
||
|
||
We generally recommend altering this or `top_p` but not both.
|
||
|
||
top_p: An alternative to sampling with temperature, called nucleus sampling, where the
|
||
model considers the results of the tokens with top_p probability mass. So 0.1
|
||
means only the tokens comprising the top 10% probability mass are considered.
|
||
|
||
We generally recommend altering this or `temperature` but not both.
|
||
|
||
user: A unique identifier representing your end-user, which can help OpenAI to monitor
|
||
and detect abuse.
|
||
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
|
||
|
||
extra_headers: Send extra headers
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||
|
||
extra_query: Add additional query parameters to the request
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||
|
||
extra_body: Add additional JSON properties to the request
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||
|
||
timeout: Override the client-level default timeout for this request, in seconds
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||
"""
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||
...
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||
|
||
@overload
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||
def create(
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||
self,
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||
*,
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||
model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
|
||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
|
||
stream: Literal[True],
|
||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
|
||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
|
||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
|
||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
user: str | NotGiven = NOT_GIVEN,
|
||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||
extra_headers: Headers | None = None,
|
||
extra_query: Query | None = None,
|
||
extra_body: Body | None = None,
|
||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||
) -> Stream[Completion]:
|
||
"""
|
||
Creates a completion for the provided prompt and parameters.
|
||
|
||
Args:
|
||
model: ID of the model to use. You can use the
|
||
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
|
||
see all of your available models, or see our
|
||
[Model overview](https://platform.openai.com/docs/models/overview) for
|
||
descriptions of them.
|
||
|
||
prompt: The prompt(s) to generate completions for, encoded as a string, array of
|
||
strings, array of tokens, or array of token arrays.
|
||
|
||
Note that <|endoftext|> is the document separator that the model sees during
|
||
training, so if a prompt is not specified the model will generate as if from the
|
||
beginning of a new document.
|
||
|
||
stream: Whether to stream back partial progress. If set, tokens will be sent as
|
||
data-only
|
||
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
|
||
as they become available, with the stream terminated by a `data: [DONE]`
|
||
message.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
|
||
|
||
best_of: Generates `best_of` completions server-side and returns the "best" (the one with
|
||
the highest log probability per token). Results cannot be streamed.
|
||
|
||
When used with `n`, `best_of` controls the number of candidate completions and
|
||
`n` specifies how many to return – `best_of` must be greater than `n`.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
echo: Echo back the prompt in addition to the completion
|
||
|
||
frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
|
||
existing frequency in the text so far, decreasing the model's likelihood to
|
||
repeat the same line verbatim.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
logit_bias: Modify the likelihood of specified tokens appearing in the completion.
|
||
|
||
Accepts a JSON object that maps tokens (specified by their token ID in the GPT
|
||
tokenizer) to an associated bias value from -100 to 100. You can use this
|
||
[tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs.
|
||
Mathematically, the bias is added to the logits generated by the model prior to
|
||
sampling. The exact effect will vary per model, but values between -1 and 1
|
||
should decrease or increase likelihood of selection; values like -100 or 100
|
||
should result in a ban or exclusive selection of the relevant token.
|
||
|
||
As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token
|
||
from being generated.
|
||
|
||
logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
|
||
well the chosen tokens. For example, if `logprobs` is 5, the API will return a
|
||
list of the 5 most likely tokens. The API will always return the `logprob` of
|
||
the sampled token, so there may be up to `logprobs+1` elements in the response.
|
||
|
||
The maximum value for `logprobs` is 5.
|
||
|
||
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
|
||
completion.
|
||
|
||
The token count of your prompt plus `max_tokens` cannot exceed the model's
|
||
context length.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
||
for counting tokens.
|
||
|
||
n: How many completions to generate for each prompt.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
|
||
whether they appear in the text so far, increasing the model's likelihood to
|
||
talk about new topics.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
seed: If specified, our system will make a best effort to sample deterministically,
|
||
such that repeated requests with the same `seed` and parameters should return
|
||
the same result.
|
||
|
||
Determinism is not guaranteed, and you should refer to the `system_fingerprint`
|
||
response parameter to monitor changes in the backend.
|
||
|
||
stop: Up to 4 sequences where the API will stop generating further tokens. The
|
||
returned text will not contain the stop sequence.
|
||
|
||
suffix: The suffix that comes after a completion of inserted text.
|
||
|
||
This parameter is only supported for `gpt-3.5-turbo-instruct`.
|
||
|
||
temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
|
||
make the output more random, while lower values like 0.2 will make it more
|
||
focused and deterministic.
|
||
|
||
We generally recommend altering this or `top_p` but not both.
|
||
|
||
top_p: An alternative to sampling with temperature, called nucleus sampling, where the
|
||
model considers the results of the tokens with top_p probability mass. So 0.1
|
||
means only the tokens comprising the top 10% probability mass are considered.
|
||
|
||
We generally recommend altering this or `temperature` but not both.
|
||
|
||
user: A unique identifier representing your end-user, which can help OpenAI to monitor
|
||
and detect abuse.
|
||
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
|
||
|
||
extra_headers: Send extra headers
|
||
|
||
extra_query: Add additional query parameters to the request
|
||
|
||
extra_body: Add additional JSON properties to the request
|
||
|
||
timeout: Override the client-level default timeout for this request, in seconds
|
||
"""
|
||
...
|
||
|
||
@overload
|
||
def create(
|
||
self,
|
||
*,
|
||
model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
|
||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
|
||
stream: bool,
|
||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
|
||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
|
||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
|
||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
user: str | NotGiven = NOT_GIVEN,
|
||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||
extra_headers: Headers | None = None,
|
||
extra_query: Query | None = None,
|
||
extra_body: Body | None = None,
|
||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||
) -> Completion | Stream[Completion]:
|
||
"""
|
||
Creates a completion for the provided prompt and parameters.
|
||
|
||
Args:
|
||
model: ID of the model to use. You can use the
|
||
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
|
||
see all of your available models, or see our
|
||
[Model overview](https://platform.openai.com/docs/models/overview) for
|
||
descriptions of them.
|
||
|
||
prompt: The prompt(s) to generate completions for, encoded as a string, array of
|
||
strings, array of tokens, or array of token arrays.
|
||
|
||
Note that <|endoftext|> is the document separator that the model sees during
|
||
training, so if a prompt is not specified the model will generate as if from the
|
||
beginning of a new document.
|
||
|
||
stream: Whether to stream back partial progress. If set, tokens will be sent as
|
||
data-only
|
||
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
|
||
as they become available, with the stream terminated by a `data: [DONE]`
|
||
message.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
|
||
|
||
best_of: Generates `best_of` completions server-side and returns the "best" (the one with
|
||
the highest log probability per token). Results cannot be streamed.
|
||
|
||
When used with `n`, `best_of` controls the number of candidate completions and
|
||
`n` specifies how many to return – `best_of` must be greater than `n`.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
echo: Echo back the prompt in addition to the completion
|
||
|
||
frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
|
||
existing frequency in the text so far, decreasing the model's likelihood to
|
||
repeat the same line verbatim.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
logit_bias: Modify the likelihood of specified tokens appearing in the completion.
|
||
|
||
Accepts a JSON object that maps tokens (specified by their token ID in the GPT
|
||
tokenizer) to an associated bias value from -100 to 100. You can use this
|
||
[tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs.
|
||
Mathematically, the bias is added to the logits generated by the model prior to
|
||
sampling. The exact effect will vary per model, but values between -1 and 1
|
||
should decrease or increase likelihood of selection; values like -100 or 100
|
||
should result in a ban or exclusive selection of the relevant token.
|
||
|
||
As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token
|
||
from being generated.
|
||
|
||
logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
|
||
well the chosen tokens. For example, if `logprobs` is 5, the API will return a
|
||
list of the 5 most likely tokens. The API will always return the `logprob` of
|
||
the sampled token, so there may be up to `logprobs+1` elements in the response.
|
||
|
||
The maximum value for `logprobs` is 5.
|
||
|
||
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
|
||
completion.
|
||
|
||
The token count of your prompt plus `max_tokens` cannot exceed the model's
|
||
context length.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
||
for counting tokens.
|
||
|
||
n: How many completions to generate for each prompt.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
|
||
whether they appear in the text so far, increasing the model's likelihood to
|
||
talk about new topics.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
seed: If specified, our system will make a best effort to sample deterministically,
|
||
such that repeated requests with the same `seed` and parameters should return
|
||
the same result.
|
||
|
||
Determinism is not guaranteed, and you should refer to the `system_fingerprint`
|
||
response parameter to monitor changes in the backend.
|
||
|
||
stop: Up to 4 sequences where the API will stop generating further tokens. The
|
||
returned text will not contain the stop sequence.
|
||
|
||
suffix: The suffix that comes after a completion of inserted text.
|
||
|
||
This parameter is only supported for `gpt-3.5-turbo-instruct`.
|
||
|
||
temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
|
||
make the output more random, while lower values like 0.2 will make it more
|
||
focused and deterministic.
|
||
|
||
We generally recommend altering this or `top_p` but not both.
|
||
|
||
top_p: An alternative to sampling with temperature, called nucleus sampling, where the
|
||
model considers the results of the tokens with top_p probability mass. So 0.1
|
||
means only the tokens comprising the top 10% probability mass are considered.
|
||
|
||
We generally recommend altering this or `temperature` but not both.
|
||
|
||
user: A unique identifier representing your end-user, which can help OpenAI to monitor
|
||
and detect abuse.
|
||
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
|
||
|
||
extra_headers: Send extra headers
|
||
|
||
extra_query: Add additional query parameters to the request
|
||
|
||
extra_body: Add additional JSON properties to the request
|
||
|
||
timeout: Override the client-level default timeout for this request, in seconds
|
||
"""
|
||
...
|
||
|
||
@required_args(["model", "prompt"], ["model", "prompt", "stream"])
|
||
def create(
|
||
self,
|
||
*,
|
||
model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
|
||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
|
||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
|
||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
|
||
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
|
||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
|
||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
user: str | NotGiven = NOT_GIVEN,
|
||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||
extra_headers: Headers | None = None,
|
||
extra_query: Query | None = None,
|
||
extra_body: Body | None = None,
|
||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||
) -> Completion | Stream[Completion]:
|
||
return self._post(
|
||
"/completions",
|
||
body=maybe_transform(
|
||
{
|
||
"model": model,
|
||
"prompt": prompt,
|
||
"best_of": best_of,
|
||
"echo": echo,
|
||
"frequency_penalty": frequency_penalty,
|
||
"logit_bias": logit_bias,
|
||
"logprobs": logprobs,
|
||
"max_tokens": max_tokens,
|
||
"n": n,
|
||
"presence_penalty": presence_penalty,
|
||
"seed": seed,
|
||
"stop": stop,
|
||
"stream": stream,
|
||
"suffix": suffix,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"user": user,
|
||
},
|
||
completion_create_params.CompletionCreateParams,
|
||
),
|
||
options=make_request_options(
|
||
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
|
||
),
|
||
cast_to=Completion,
|
||
stream=stream or False,
|
||
stream_cls=Stream[Completion],
|
||
)
|
||
|
||
|
||
class AsyncCompletions(AsyncAPIResource):
|
||
@cached_property
|
||
def with_raw_response(self) -> AsyncCompletionsWithRawResponse:
|
||
return AsyncCompletionsWithRawResponse(self)
|
||
|
||
@cached_property
|
||
def with_streaming_response(self) -> AsyncCompletionsWithStreamingResponse:
|
||
return AsyncCompletionsWithStreamingResponse(self)
|
||
|
||
@overload
|
||
async def create(
|
||
self,
|
||
*,
|
||
model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
|
||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
|
||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
|
||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
|
||
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
|
||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
|
||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
user: str | NotGiven = NOT_GIVEN,
|
||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||
extra_headers: Headers | None = None,
|
||
extra_query: Query | None = None,
|
||
extra_body: Body | None = None,
|
||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||
) -> Completion:
|
||
"""
|
||
Creates a completion for the provided prompt and parameters.
|
||
|
||
Args:
|
||
model: ID of the model to use. You can use the
|
||
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
|
||
see all of your available models, or see our
|
||
[Model overview](https://platform.openai.com/docs/models/overview) for
|
||
descriptions of them.
|
||
|
||
prompt: The prompt(s) to generate completions for, encoded as a string, array of
|
||
strings, array of tokens, or array of token arrays.
|
||
|
||
Note that <|endoftext|> is the document separator that the model sees during
|
||
training, so if a prompt is not specified the model will generate as if from the
|
||
beginning of a new document.
|
||
|
||
best_of: Generates `best_of` completions server-side and returns the "best" (the one with
|
||
the highest log probability per token). Results cannot be streamed.
|
||
|
||
When used with `n`, `best_of` controls the number of candidate completions and
|
||
`n` specifies how many to return – `best_of` must be greater than `n`.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
echo: Echo back the prompt in addition to the completion
|
||
|
||
frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
|
||
existing frequency in the text so far, decreasing the model's likelihood to
|
||
repeat the same line verbatim.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
logit_bias: Modify the likelihood of specified tokens appearing in the completion.
|
||
|
||
Accepts a JSON object that maps tokens (specified by their token ID in the GPT
|
||
tokenizer) to an associated bias value from -100 to 100. You can use this
|
||
[tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs.
|
||
Mathematically, the bias is added to the logits generated by the model prior to
|
||
sampling. The exact effect will vary per model, but values between -1 and 1
|
||
should decrease or increase likelihood of selection; values like -100 or 100
|
||
should result in a ban or exclusive selection of the relevant token.
|
||
|
||
As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token
|
||
from being generated.
|
||
|
||
logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
|
||
well the chosen tokens. For example, if `logprobs` is 5, the API will return a
|
||
list of the 5 most likely tokens. The API will always return the `logprob` of
|
||
the sampled token, so there may be up to `logprobs+1` elements in the response.
|
||
|
||
The maximum value for `logprobs` is 5.
|
||
|
||
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
|
||
completion.
|
||
|
||
The token count of your prompt plus `max_tokens` cannot exceed the model's
|
||
context length.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
||
for counting tokens.
|
||
|
||
n: How many completions to generate for each prompt.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
|
||
whether they appear in the text so far, increasing the model's likelihood to
|
||
talk about new topics.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
seed: If specified, our system will make a best effort to sample deterministically,
|
||
such that repeated requests with the same `seed` and parameters should return
|
||
the same result.
|
||
|
||
Determinism is not guaranteed, and you should refer to the `system_fingerprint`
|
||
response parameter to monitor changes in the backend.
|
||
|
||
stop: Up to 4 sequences where the API will stop generating further tokens. The
|
||
returned text will not contain the stop sequence.
|
||
|
||
stream: Whether to stream back partial progress. If set, tokens will be sent as
|
||
data-only
|
||
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
|
||
as they become available, with the stream terminated by a `data: [DONE]`
|
||
message.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
|
||
|
||
suffix: The suffix that comes after a completion of inserted text.
|
||
|
||
This parameter is only supported for `gpt-3.5-turbo-instruct`.
|
||
|
||
temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
|
||
make the output more random, while lower values like 0.2 will make it more
|
||
focused and deterministic.
|
||
|
||
We generally recommend altering this or `top_p` but not both.
|
||
|
||
top_p: An alternative to sampling with temperature, called nucleus sampling, where the
|
||
model considers the results of the tokens with top_p probability mass. So 0.1
|
||
means only the tokens comprising the top 10% probability mass are considered.
|
||
|
||
We generally recommend altering this or `temperature` but not both.
|
||
|
||
user: A unique identifier representing your end-user, which can help OpenAI to monitor
|
||
and detect abuse.
|
||
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
|
||
|
||
extra_headers: Send extra headers
|
||
|
||
extra_query: Add additional query parameters to the request
|
||
|
||
extra_body: Add additional JSON properties to the request
|
||
|
||
timeout: Override the client-level default timeout for this request, in seconds
|
||
"""
|
||
...
|
||
|
||
@overload
|
||
async def create(
|
||
self,
|
||
*,
|
||
model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
|
||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
|
||
stream: Literal[True],
|
||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
|
||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
|
||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
|
||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
user: str | NotGiven = NOT_GIVEN,
|
||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||
extra_headers: Headers | None = None,
|
||
extra_query: Query | None = None,
|
||
extra_body: Body | None = None,
|
||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||
) -> AsyncStream[Completion]:
|
||
"""
|
||
Creates a completion for the provided prompt and parameters.
|
||
|
||
Args:
|
||
model: ID of the model to use. You can use the
|
||
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
|
||
see all of your available models, or see our
|
||
[Model overview](https://platform.openai.com/docs/models/overview) for
|
||
descriptions of them.
|
||
|
||
prompt: The prompt(s) to generate completions for, encoded as a string, array of
|
||
strings, array of tokens, or array of token arrays.
|
||
|
||
Note that <|endoftext|> is the document separator that the model sees during
|
||
training, so if a prompt is not specified the model will generate as if from the
|
||
beginning of a new document.
|
||
|
||
stream: Whether to stream back partial progress. If set, tokens will be sent as
|
||
data-only
|
||
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
|
||
as they become available, with the stream terminated by a `data: [DONE]`
|
||
message.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
|
||
|
||
best_of: Generates `best_of` completions server-side and returns the "best" (the one with
|
||
the highest log probability per token). Results cannot be streamed.
|
||
|
||
When used with `n`, `best_of` controls the number of candidate completions and
|
||
`n` specifies how many to return – `best_of` must be greater than `n`.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
echo: Echo back the prompt in addition to the completion
|
||
|
||
frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
|
||
existing frequency in the text so far, decreasing the model's likelihood to
|
||
repeat the same line verbatim.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
logit_bias: Modify the likelihood of specified tokens appearing in the completion.
|
||
|
||
Accepts a JSON object that maps tokens (specified by their token ID in the GPT
|
||
tokenizer) to an associated bias value from -100 to 100. You can use this
|
||
[tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs.
|
||
Mathematically, the bias is added to the logits generated by the model prior to
|
||
sampling. The exact effect will vary per model, but values between -1 and 1
|
||
should decrease or increase likelihood of selection; values like -100 or 100
|
||
should result in a ban or exclusive selection of the relevant token.
|
||
|
||
As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token
|
||
from being generated.
|
||
|
||
logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
|
||
well the chosen tokens. For example, if `logprobs` is 5, the API will return a
|
||
list of the 5 most likely tokens. The API will always return the `logprob` of
|
||
the sampled token, so there may be up to `logprobs+1` elements in the response.
|
||
|
||
The maximum value for `logprobs` is 5.
|
||
|
||
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
|
||
completion.
|
||
|
||
The token count of your prompt plus `max_tokens` cannot exceed the model's
|
||
context length.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
||
for counting tokens.
|
||
|
||
n: How many completions to generate for each prompt.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
|
||
whether they appear in the text so far, increasing the model's likelihood to
|
||
talk about new topics.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
seed: If specified, our system will make a best effort to sample deterministically,
|
||
such that repeated requests with the same `seed` and parameters should return
|
||
the same result.
|
||
|
||
Determinism is not guaranteed, and you should refer to the `system_fingerprint`
|
||
response parameter to monitor changes in the backend.
|
||
|
||
stop: Up to 4 sequences where the API will stop generating further tokens. The
|
||
returned text will not contain the stop sequence.
|
||
|
||
suffix: The suffix that comes after a completion of inserted text.
|
||
|
||
This parameter is only supported for `gpt-3.5-turbo-instruct`.
|
||
|
||
temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
|
||
make the output more random, while lower values like 0.2 will make it more
|
||
focused and deterministic.
|
||
|
||
We generally recommend altering this or `top_p` but not both.
|
||
|
||
top_p: An alternative to sampling with temperature, called nucleus sampling, where the
|
||
model considers the results of the tokens with top_p probability mass. So 0.1
|
||
means only the tokens comprising the top 10% probability mass are considered.
|
||
|
||
We generally recommend altering this or `temperature` but not both.
|
||
|
||
user: A unique identifier representing your end-user, which can help OpenAI to monitor
|
||
and detect abuse.
|
||
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
|
||
|
||
extra_headers: Send extra headers
|
||
|
||
extra_query: Add additional query parameters to the request
|
||
|
||
extra_body: Add additional JSON properties to the request
|
||
|
||
timeout: Override the client-level default timeout for this request, in seconds
|
||
"""
|
||
...
|
||
|
||
@overload
|
||
async def create(
|
||
self,
|
||
*,
|
||
model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
|
||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
|
||
stream: bool,
|
||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
|
||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
|
||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
|
||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
user: str | NotGiven = NOT_GIVEN,
|
||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||
extra_headers: Headers | None = None,
|
||
extra_query: Query | None = None,
|
||
extra_body: Body | None = None,
|
||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||
) -> Completion | AsyncStream[Completion]:
|
||
"""
|
||
Creates a completion for the provided prompt and parameters.
|
||
|
||
Args:
|
||
model: ID of the model to use. You can use the
|
||
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
|
||
see all of your available models, or see our
|
||
[Model overview](https://platform.openai.com/docs/models/overview) for
|
||
descriptions of them.
|
||
|
||
prompt: The prompt(s) to generate completions for, encoded as a string, array of
|
||
strings, array of tokens, or array of token arrays.
|
||
|
||
Note that <|endoftext|> is the document separator that the model sees during
|
||
training, so if a prompt is not specified the model will generate as if from the
|
||
beginning of a new document.
|
||
|
||
stream: Whether to stream back partial progress. If set, tokens will be sent as
|
||
data-only
|
||
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
|
||
as they become available, with the stream terminated by a `data: [DONE]`
|
||
message.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
|
||
|
||
best_of: Generates `best_of` completions server-side and returns the "best" (the one with
|
||
the highest log probability per token). Results cannot be streamed.
|
||
|
||
When used with `n`, `best_of` controls the number of candidate completions and
|
||
`n` specifies how many to return – `best_of` must be greater than `n`.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
echo: Echo back the prompt in addition to the completion
|
||
|
||
frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
|
||
existing frequency in the text so far, decreasing the model's likelihood to
|
||
repeat the same line verbatim.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
logit_bias: Modify the likelihood of specified tokens appearing in the completion.
|
||
|
||
Accepts a JSON object that maps tokens (specified by their token ID in the GPT
|
||
tokenizer) to an associated bias value from -100 to 100. You can use this
|
||
[tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs.
|
||
Mathematically, the bias is added to the logits generated by the model prior to
|
||
sampling. The exact effect will vary per model, but values between -1 and 1
|
||
should decrease or increase likelihood of selection; values like -100 or 100
|
||
should result in a ban or exclusive selection of the relevant token.
|
||
|
||
As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token
|
||
from being generated.
|
||
|
||
logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
|
||
well the chosen tokens. For example, if `logprobs` is 5, the API will return a
|
||
list of the 5 most likely tokens. The API will always return the `logprob` of
|
||
the sampled token, so there may be up to `logprobs+1` elements in the response.
|
||
|
||
The maximum value for `logprobs` is 5.
|
||
|
||
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
|
||
completion.
|
||
|
||
The token count of your prompt plus `max_tokens` cannot exceed the model's
|
||
context length.
|
||
[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
||
for counting tokens.
|
||
|
||
n: How many completions to generate for each prompt.
|
||
|
||
**Note:** Because this parameter generates many completions, it can quickly
|
||
consume your token quota. Use carefully and ensure that you have reasonable
|
||
settings for `max_tokens` and `stop`.
|
||
|
||
presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
|
||
whether they appear in the text so far, increasing the model's likelihood to
|
||
talk about new topics.
|
||
|
||
[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
|
||
|
||
seed: If specified, our system will make a best effort to sample deterministically,
|
||
such that repeated requests with the same `seed` and parameters should return
|
||
the same result.
|
||
|
||
Determinism is not guaranteed, and you should refer to the `system_fingerprint`
|
||
response parameter to monitor changes in the backend.
|
||
|
||
stop: Up to 4 sequences where the API will stop generating further tokens. The
|
||
returned text will not contain the stop sequence.
|
||
|
||
suffix: The suffix that comes after a completion of inserted text.
|
||
|
||
This parameter is only supported for `gpt-3.5-turbo-instruct`.
|
||
|
||
temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
|
||
make the output more random, while lower values like 0.2 will make it more
|
||
focused and deterministic.
|
||
|
||
We generally recommend altering this or `top_p` but not both.
|
||
|
||
top_p: An alternative to sampling with temperature, called nucleus sampling, where the
|
||
model considers the results of the tokens with top_p probability mass. So 0.1
|
||
means only the tokens comprising the top 10% probability mass are considered.
|
||
|
||
We generally recommend altering this or `temperature` but not both.
|
||
|
||
user: A unique identifier representing your end-user, which can help OpenAI to monitor
|
||
and detect abuse.
|
||
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
|
||
|
||
extra_headers: Send extra headers
|
||
|
||
extra_query: Add additional query parameters to the request
|
||
|
||
extra_body: Add additional JSON properties to the request
|
||
|
||
timeout: Override the client-level default timeout for this request, in seconds
|
||
"""
|
||
...
|
||
|
||
@required_args(["model", "prompt"], ["model", "prompt", "stream"])
|
||
async def create(
|
||
self,
|
||
*,
|
||
model: Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]],
|
||
prompt: Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None],
|
||
best_of: Optional[int] | NotGiven = NOT_GIVEN,
|
||
echo: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
|
||
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
|
||
suffix: Optional[str] | NotGiven = NOT_GIVEN,
|
||
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
user: str | NotGiven = NOT_GIVEN,
|
||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||
extra_headers: Headers | None = None,
|
||
extra_query: Query | None = None,
|
||
extra_body: Body | None = None,
|
||
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
|
||
) -> Completion | AsyncStream[Completion]:
|
||
return await self._post(
|
||
"/completions",
|
||
body=await async_maybe_transform(
|
||
{
|
||
"model": model,
|
||
"prompt": prompt,
|
||
"best_of": best_of,
|
||
"echo": echo,
|
||
"frequency_penalty": frequency_penalty,
|
||
"logit_bias": logit_bias,
|
||
"logprobs": logprobs,
|
||
"max_tokens": max_tokens,
|
||
"n": n,
|
||
"presence_penalty": presence_penalty,
|
||
"seed": seed,
|
||
"stop": stop,
|
||
"stream": stream,
|
||
"suffix": suffix,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"user": user,
|
||
},
|
||
completion_create_params.CompletionCreateParams,
|
||
),
|
||
options=make_request_options(
|
||
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
|
||
),
|
||
cast_to=Completion,
|
||
stream=stream or False,
|
||
stream_cls=AsyncStream[Completion],
|
||
)
|
||
|
||
|
||
class CompletionsWithRawResponse:
|
||
def __init__(self, completions: Completions) -> None:
|
||
self._completions = completions
|
||
|
||
self.create = _legacy_response.to_raw_response_wrapper(
|
||
completions.create,
|
||
)
|
||
|
||
|
||
class AsyncCompletionsWithRawResponse:
|
||
def __init__(self, completions: AsyncCompletions) -> None:
|
||
self._completions = completions
|
||
|
||
self.create = _legacy_response.async_to_raw_response_wrapper(
|
||
completions.create,
|
||
)
|
||
|
||
|
||
class CompletionsWithStreamingResponse:
|
||
def __init__(self, completions: Completions) -> None:
|
||
self._completions = completions
|
||
|
||
self.create = to_streamed_response_wrapper(
|
||
completions.create,
|
||
)
|
||
|
||
|
||
class AsyncCompletionsWithStreamingResponse:
|
||
def __init__(self, completions: AsyncCompletions) -> None:
|
||
self._completions = completions
|
||
|
||
self.create = async_to_streamed_response_wrapper(
|
||
completions.create,
|
||
)
|