o CjiB@sddlmZddlmZmZmZmZmZmZddl m Z ddl Z ddl m Z mZddlmZmZmZmZmZddlmZmZdd lmZmZdd lmZmZdd lmZm Z dd l!m"Z"erfdd l#m$Z$m%Z%ddgZ&GdddeZ'GdddeZ(GdddZ)GdddZ*dS)) annotations) TYPE_CHECKINGDictListUnionOptionaloverload)LiteralN) Completioncompletion_create_params) NOT_GIVENBodyQueryHeadersNotGiven) required_argsmaybe_transform)SyncAPIResourceAsyncAPIResource)to_raw_response_wrapperasync_to_raw_response_wrapper)Stream AsyncStream)make_request_options)OpenAI AsyncOpenAI CompletionsAsyncCompletionsceZdZUded<d;fdd Zeeeeeeeeeeeeeeeed d d ed dd7d0Zed d ggd8eeeeeeeeeeeeeeed d d ed d?d:d0ZZ S)@rCompletionsWithRawResponsewith_raw_responseclientrreturnNonect|t||_dSN)super__init__r r!selfr" __class__s/var/www/html/Resume-Parser/resume-parser-inhouse/venv/lib/python3.10/site-packages/openai/resources/completions.pyr( zCompletions.__init__Nbest_ofechofrequency_penalty logit_biaslogprobs max_tokensnpresence_penaltyseedstopstreamsuffix temperaturetop_puser extra_headers extra_query extra_bodytimeoutmodelUnion[str, Literal['babbage-002', 'davinci-002', 'gpt-3.5-turbo-instruct', 'text-davinci-003', 'text-davinci-002', 'text-davinci-001', 'code-davinci-002', 'text-curie-001', 'text-babbage-001', 'text-ada-001']]prompt7Union[str, List[str], List[int], List[List[int]], None]r1Optional[int] | NotGivenr2Optional[bool] | NotGivenr3Optional[float] | NotGivenr4#Optional[Dict[str, int]] | NotGivenr5r6r7r8r9r:0Union[Optional[str], List[str], None] | NotGivenr;#Optional[Literal[False]] | NotGivenr<Optional[str] | NotGivenr=r>r?str | NotGivenr@Headers | NonerA Query | NonerB Body | NonerC'float | httpx.Timeout | None | NotGivenr cCdSu\ 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/gpt/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) (which works for both GPT-2 and GPT-3) 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 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) to generate 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/gpt/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. 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-r*rDrFr1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCr-r-r.create!zCompletions.creater1r2r3r4r5r6r7r8r9r:r<r=r>r?r@rArBrC Literal[True]Stream[Completion]cCrTu\ 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/gpt/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) (which works for both GPT-2 and GPT-3) 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 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) to generate 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/gpt/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. 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-r*rDrFr;r1r2r3r4r5r6r7r8r9r:r<r=r>r?r@rArBrCr-r-r.rWrXboolCompletion | Stream[Completion]cCrTr\r-r]r-r-r.rWcrXrDrFr;3Optional[Literal[False]] | Literal[True] | NotGivenc Cs|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|tjt||||dt| pFdttdSNz /completionsrDrFr1r2r3r4r5r6r7r8r9r:r;r<r=r>r?)r@rArBrCF)bodyoptionscast_tor; stream_cls)_postrr CompletionCreateParamsrr rrVr-r-r.rWs^*     )r"rr#r$,rDrErFrGr1rHr2rIr3rJr4rKr5rHr6rHr7rHr8rJr9rHr:rLr;rMr<rNr=rJr>rJr?rOr@rPrArQrBrRrCrSr#r ),rDrErFrGr;rZr1rHr2rIr3rJr4rKr5rHr6rHr7rHr8rJr9rHr:rLr<rNr=rJr>rJr?rOr@rPrArQrBrRrCrSr#r[),rDrErFrGr;r^r1rHr2rIr3rJr4rKr5rHr6rHr7rHr8rJr9rHr:rLr<rNr=rJr>rJr?rOr@rPrArQrBrRrCrSr#r_),rDrErFrGr1rHr2rIr3rJr4rKr5rHr6rHr7rHr8rJr9rHr:rLr;rar<rNr=rJr>rJr?rOr@rPrArQrBrRrCrSr#r_ __name__ __module__ __qualname____annotations__r(rr rWr __classcell__r-r-r+r.r """cr)@rAsyncCompletionsWithRawResponser!r"rr#r$cr%r&)r'r(rqr!r)r+r-r.r(Sr/zAsyncCompletions.__init__Nr0rDrErFrGr1rHr2rIr3rJr4rKr5r6r7r8r9r:rLr;rMr<rNr=r>r?rOr@rPrArQrBrRrCrSr cdSrUr-rVr-r-r.rWW!zAsyncCompletions.createrYrZAsyncStream[Completion]crrr\r-r]r-r-r.rWrsr^$Completion | AsyncStream[Completion]crrr\r-r]r-r-r.rWrsr`rac s|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|tjt||||dt| pGdttdIdHSrb)rgrr rhrr rrVr-r-r.rW=s`*      )r"rr#r$ri),rDrErFrGr;rZr1rHr2rIr3rJr4rKr5rHr6rHr7rHr8rJr9rHr:rLr<rNr=rJr>rJr?rOr@rPrArQrBrRrCrSr#rt),rDrErFrGr;r^r1rHr2rIr3rJr4rKr5rHr6rHr7rHr8rJr9rHr:rLr<rNr=rJr>rJr?rOr@rPrArQrBrRrCrSr#ru),rDrErFrGr1rHr2rIr3rJr4rKr5rHr6rHr7rHr8rJr9rHr:rLr;rar<rNr=rJr>rJr?rOr@rPrArQrBrRrCrSr#rurjr-r-r+r.rPrpc@eZdZdddZdS) r completionsrr#r$cCt|j|_dSr&)rrWr*rwr-r-r.r( z#CompletionsWithRawResponse.__init__N)rwrr#r$rkrlrmr(r-r-r-r.r r c@rv) rqrwrr#r$cCrxr&)rrWryr-r-r.r(rzz(AsyncCompletionsWithRawResponse.__init__N)rwrr#r$r{r-r-r-r.rqr|rq)+ __future__rtypingrrrrrrtyping_extensionsr httpxtypesr r _typesr rrrr_utilsrr _resourcerr _responserr _streamingrr _base_clientr_clientrr__all__rrr rqr-r-r-r.s4    <<