+hB,ddlmZddlmZmZmZmZmZmZddl m Z ddl Z ddl m Z mZddlmZmZmZmZmZddlmZmZdd lmZmZdd lmZmZdd lmZm Z dd l!m"Z"erdd l#m$Z$m%Z%ddgZ&GddeZ'GddeZ(GddZ)GddZ*y)) 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<dfd Zeeeeeeeeeeeeeeeeddded ddZeeeeeeeeeeeeeeeddded  dd Zeeeeeeeeeeeeeeeddded  dd Zeddggd eeeeeeeeeeeeeeeddded dd ZxZ S)rCompletionsWithRawResponsewith_raw_responsecDt||t||_yN)super__init__r!r"selfclient __class__s O/var/www/html/venv/lib/python3.12/site-packages/openai/resources/completions.pyr&zCompletions.__init__s  !;D!ANbest_ofechofrequency_penalty logit_biaslogprobs max_tokensnpresence_penaltyseedstopstreamsuffix temperaturetop_puser extra_headers extra_query extra_bodytimeoutmodelpromptcyu\ 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(rArBr.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@s r+createzCompletions.create@ r,r.r/r0r1r2r3r4r5r6r7r9r:r;r<r=r>r?r@cyu\ 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 NrEr(rArBr8r.r/r0r1r2r3r4r5r6r7r9r:r;r<r=r>r?r@s r+rGzCompletions.createrHr,cyrKrErLs r+rGzCompletions.createcrHr,rArBr8c |jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|tjt ||||t | xsdt t SNz /completionsrArBr.r/r0r1r2r3r4r5r6r7r8r9r:r;r<)r=r>r?r@F)bodyoptionscast_tor8 stream_cls)_postrr CompletionCreateParamsrr rrFs r+rGzCompletions.creates)Tzz  UfwD  ():  !* !*'(8DDff"; U!"D#&)??),)+Q[el?Uj);  r,)r)rreturnNone,rAUnion[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']]rB7Union[str, List[str], List[int], List[List[int]], None]r.Optional[int] | NotGivenr/Optional[bool] | NotGivenr0Optional[float] | NotGivenr1#Optional[Dict[str, int]] | NotGivenr2r\r3r\r4r\r5r^r6r\r70Union[Optional[str], List[str], None] | NotGivenr8z#Optional[Literal[False]] | NotGivenr9Optional[str] | NotGivenr:r^r;r^r<str | NotGivenr=Headers | Noner> Query | Noner? Body | Noner@'float | httpx.Timeout | None | NotGivenrWr ),rArZrBr[r8 Literal[True]r.r\r/r]r0r^r1r_r2r\r3r\r4r\r5r^r6r\r7r`r9rar:r^r;r^r<rbr=rcr>rdr?rer@rfrWzStream[Completion]),rArZrBr[r8boolr.r\r/r]r0r^r1r_r2r\r3r\r4r\r5r^r6r\r7r`r9rar:r^r;r^r<rbr=rcr>rdr?rer@rfrWCompletion | Stream[Completion]),rArZrBr[r.r\r/r]r0r^r1r_r2r\r3r\r4r\r5r^r6r\r7r`r83Optional[Literal[False]] | Literal[True] | NotGivenr9rar:r^r;r^r<rbr=rcr>rdr?rer@rfrWri __name__ __module__ __qualname____annotations__r&r rrGr __classcell__r*s@r+rrso11B(-6*38A:C-6/8&/7@)2AJ6?+42;,5()-$("&;DO_  _ $H%_ &*'_ (()_ *6+_ ,8-_ .+/_ 0-1_ 2 $3_ 455_ 6'7_ 8?9_ :4;_ <)=_ >0?_ @*A_ BC_ H&I_ J"K_ L M_ N9O_ P Q_ _ B*-6*38A:C-6/8&/7@)2AJ+42;,5()-$("&;DO_  _ $H%_ &'_ (*)_ *(+_ ,6-_ .8/_ 0+1_ 2-3_ 4 $5_ 657_ 8'9_ :?;_ <)=_ >0?_ @*A_ BC_ H&I_ J"K_ L M_ N9O_ P Q_ _ B*-6*38A:C-6/8&/7@)2AJ+42;,5()-$("&;DO_  _ $H%_ &'_ (*)_ *(+_ ,6-_ .8/_ 0+1_ 2-3_ 4 $5_ 657_ 8'9_ :?;_ <)=_ >0?_ @*A_ BC_ H&I_ J"K_ L M_ N9O_ P )Q_ _ BGX&(EF(-6*38A:C-6/8&/7@)2AJFO+42;,5()-$("&;DOG  G $H%G &*'G (()G *6+G ,8-G .+/G 0-1G 2 $3G 455G 6'7G 8?9G :D;G <)=G >0?G @*AG BCG H&IG J"KG L MG N9OG P )QG GG r,ceZdZUded<dfd Zeeeeeeeeeeeeeeeeddded ddZeeeeeeeeeeeeeeeddded  dd Zeeeeeeeeeeeeeeeddded  dd Zeddggd eeeeeeeeeeeeeeeddded dd ZxZ S)rAsyncCompletionsWithRawResponser"cDt||t||_yr$)r%r&rsr"r's r+r&zAsyncCompletions.__init__Ss  !@!Fr,Nr-rArBc KywrDrErFs r+rGzAsyncCompletions.createW @ rIc KywrKrErLs r+rGzAsyncCompletions.creatervrwc KywrKrErLs r+rGzAsyncCompletions.creatervrwrNc "K|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|tjt ||||t | xsdt t d{S7wrP)rUrr rVrr rrFs r+rGzAsyncCompletions.create=s7TZZ  UfwD  ():  !* !*'(8DDff"; U!"D#&)??),)+Q[el?U":.;     sBBB  B)r)rrWrXrY),rArZrBr[r8rgr.r\r/r]r0r^r1r_r2r\r3r\r4r\r5r^r6r\r7r`r9rar:r^r;r^r<rbr=rcr>rdr?rer@rfrWzAsyncStream[Completion]),rArZrBr[r8rhr.r\r/r]r0r^r1r_r2r\r3r\r4r\r5r^r6r\r7r`r9rar:r^r;r^r<rbr=rcr>rdr?rer@rfrW$Completion | AsyncStream[Completion]),rArZrBr[r.r\r/r]r0r^r1r_r2r\r3r\r4r\r5r^r6r\r7r`r8rjr9rar:r^r;r^r<rbr=rcr>rdr?rer@rfrWr{rkrqs@r+rrPso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r,ceZdZddZy)r!c8t|j|_yr$)rrGr( completionss r+r&z#CompletionsWithRawResponse.__init__s-     r,N)rrrWrXrlrmrnr&rEr,r+r!r! r,r!ceZdZddZy)rsc8t|j|_yr$)rrGr~s r+r&z(AsyncCompletionsWithRawResponse.__init__s3     r,N)rrrWrXrrEr,r+rsrsrr,rs)+ __future__rtypingrrrrrr typing_extensionsr httpxtypesr r _typesrrrrr_utilsrr _resourcerr _responserr _streamingrr _base_clientr_clientrr__all__rrr!rsrEr,r+rsr#GG% 8>>39N,/- , -u /u pu 'u p    r,