fjddlmZddlmZmZmZmZmZmZddl m Z ddl Z ddl m Z ddlmZmZddlmZmZmZmZmZdd lmZmZmZdd lmZdd lmZmZdd l m!Z!m"Z"dd l#m$Z$m%Z%ddl&m'Z'ddgZ(GddeZ)GddeZ*GddZ+GddZ,GddZ-GddZ.dS)) annotations)DictListUnionIterableOptionaloverload)LiteralN)_legacy_response) Completioncompletion_create_params) NOT_GIVENBodyQueryHeadersNotGiven) required_argsmaybe_transformasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)Stream AsyncStream)make_request_options CompletionsAsyncCompletionsc^eZdZed7dZed8dZeeeeeeeeeeeeeeeedddedd9d,Zeeeeeeeeeeeeeeeddded-d:d0Zeeeeeeeeeeeeeeeddded-d;d3Ze dd ggd4eeeeeeeeeeeeeeedddeddHeaders | Noner? Query | Noner@ Body | NonerA'float | httpx.Timeout | None | NotGivenr cdSu 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 Nr'rBrDr/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rAs r(createzCompletions.create& h r*r/r0r1r2r3r4r5r6r7r8r:r;r<r=r>r?r@rA Literal[True]Stream[Completion]cdSu 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 NrTr'rBrDr9r/r0r1r2r3r4r5r6r7r8r:r;r<r=r>r?r@rAs r(rVzCompletions.createrWr*boolCompletion | Stream[Completion]cdSr\rTr]s r(rVzCompletions.createRrWr*rBrDr93Optional[Literal[False]] | Literal[True] | NotGivenc |dtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|tjt ||||t | pdt t SNz /completionsrBrDr/r0r1r2r3r4r5r6r7r8r9r:r;r<r=)r>r?r@rAF)bodyoptionscast_tor9 stream_cls)_postrrCompletionCreateParamsrr rrUs r(rVzCompletions.creates98zz  UfwD  ():  !* !*'(8DDff"; U!"D#&)?),)+Q[el?Uj);   r*)r"r#)r"r+,rBrCrDrEr/rFr0rGr1rHr2rIr3rFr4rFr5rFr6rHr7rFr8rJr9rKr:rLr;rHr<rHr=rMr>rNr?rOr@rPrArQr"r ),rBrCrDrEr9rYr/rFr0rGr1rHr2rIr3rFr4rFr5rFr6rHr7rFr8rJr:rLr;rHr<rHr=rMr>rNr?rOr@rPrArQr"rZ),rBrCrDrEr9r^r/rFr0rGr1rHr2rIr3rFr4rFr5rFr6rHr7rFr8rJr:rLr;rHr<rHr=rMr>rNr?rOr@rPrArQr"r_),rBrCrDrEr/rFr0rGr1rHr2rIr3rFr4rFr5rFr6rHr7rFr8rJr9rbr:rLr;rHr<rHr=rMr>rNr?rOr@rPrArQr"r_ __name__ __module__ __qualname__rr)r-r rrVrrTr*r(rrs000_0666_6 -6*38A:C-6/8&/7@)2AJ6?+42;,5()-$("&;D3S S S S S XS j-6*38A:C-6/8&/7@)2AJ+42;,5()-$("&;D3S S S S S XS j-6*38A:C-6/8&/7@)2AJ+42;,5()-$("&;D3S S S S S XS j]GX&(E(E(EFF -6*38A:C-6/8&/7@)2AJFO+42;,5()-$("&;D39 9 9 9 9 GF9 9 9 r*c^eZdZed7dZed8dZeeeeeeeeeeeeeeeedddedd9d,Zeeeeeeeeeeeeeeeddded-d:d0Zeeeeeeeeeeeeeeeddded-d;d3Ze dd ggd4eeeeeeeeeeeeeeedddeddrNr?rOr@rPrArQr c KdSrSrTrUs r(rVzAsyncCompletions.create.h r*rXrYAsyncStream[Completion]c KdSr\rTr]s r(rVzAsyncCompletions.creatervr*r^$Completion | AsyncStream[Completion]c KdSr\rTr]s r(rVzAsyncCompletions.createZrvr*rarbc :K|dtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|tjd{Vt ||||t | pdt t d{VSrd)rirrrjrr rrUs r(rVzAsyncCompletions.createsy8ZZ ,UfwD  ():  !* !*'(8DDff"; U!"D#&)?),)+Q[el?U":.;          r*)r"rq)r"rsrk),rBrCrDrEr9rYr/rFr0rGr1rHr2rIr3rFr4rFr5rFr6rHr7rFr8rJr:rLr;rHr<rHr=rMr>rNr?rOr@rPrArQr"rw),rBrCrDrEr9r^r/rFr0rGr1rHr2rIr3rFr4rFr5rFr6rHr7rFr8rJr:rLr;rHr<rHr=rMr>rNr?rOr@rPrArQr"ry),rBrCrDrEr/rFr0rGr1rHr2rIr3rFr4rFr5rFr6rHr7rFr8rJr9rbr:rLr;rHr<rHr=rMr>rNr?rOr@rPrArQr"ryrlrTr*r(r r %s555_5;;;_; -6*38A:C-6/8&/7@)2AJ6?+42;,5()-$("&;D3S S S S S XS j-6*38A:C-6/8&/7@)2AJ+42;,5()-$("&;D3S S S S S XS j-6*38A:C-6/8&/7@)2AJ+42;,5()-$("&;D3S S S S S XS j]GX&(E(E(EFF -6*38A:C-6/8&/7@)2AJFO+42;,5()-$("&;D39 9 9 9 9 GF9 9 9 r*ceZdZddZdS)r# completionsrr"NonecP||_tj|j|_dSr%) _completionsr to_raw_response_wrapperrVr'r}s r(__init__z#CompletionsWithRawResponse.__init__.s('&>     r*Nr}rr"r~rmrnrorrTr*r(r#r#-(      r*r#ceZdZddZdS)rqr}r r"r~cP||_tj|j|_dSr%)rr async_to_raw_response_wrapperrVrs r(rz(AsyncCompletionsWithRawResponse.__init__7s('&D     r*Nr}r r"r~rrTr*r(rqrq6rr*rqceZdZddZdS)r+r}rr"r~cF||_t|j|_dSr%)rrrVrs r(rz)CompletionsWithStreamingResponse.__init__@s%'2     r*NrrrTr*r(r+r+?rr*r+ceZdZddZdS)rsr}r r"r~cF||_t|j|_dSr%)rrrVrs r(rz.AsyncCompletionsWithStreamingResponse.__init__Is%'8     r*NrrrTr*r(rsrsHrr*rs)/ __future__rtypingrrrrrr typing_extensionsr httpxr typesr r_typesrrrrr_utilsrrr_compatr _resourcerr _responserr _streamingrr _base_clientr__all__rr r#rqr+rsrTr*r(rs#"""""BBBBBBBBBBBBBBBB%%%%%% 88888888>>>>>>>>>>>>>> &%%%%%99999999XXXXXXXX,,,,,,,, , -E E E E E /E E E PE E E E E 'E E E P                                  r*