jAxddlmZddlmZmZmZmZddlmZm Z ddl Z ddl m Z ddl mZddlmZmZmZmZmZmZmZmZdd lmZmZmZdd lmZdd lmZm Z dd l!m"Z"m#Z#dd l$m%Z%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddgZ-GddeZ.Gdde Z/GddZ0GddZ1GddZ2GddZ3y)) annotations)DictUnionIterableOptional)LiteraloverloadN)_legacy_response)completion_create_params)BodyOmitQueryHeadersNotGivenSequenceNotStromit not_given) required_argsmaybe_transformasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)Stream AsyncStream)make_request_options) Completion) ChatCompletionStreamOptionsParam CompletionsAsyncCompletionsceZdZed dZeddZeeeeeeeeeeeeeeeeeddded ddZ eeeeeeeeeeeeeeeeddded dd Z eeeeeeeeeeeeeeeeddded dd Z e ddggd eeeeeeeeeeeeeeeeddded dd Z y)r"ct|Sa This property can be used as a prefix for any HTTP method call to return the raw response object instead of the parsed content. For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers )CompletionsWithRawResponseselfs f/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/openai/resources/completions.pywith_raw_responsezCompletions.with_raw_responses*$//ct|Sz An alternative to `.with_raw_response` that doesn't eagerly read the response body. For more information, see https://www.github.com/openai/openai-python#with_streaming_response ) CompletionsWithStreamingResponser(s r*with_streaming_responsez#Completions.with_streaming_response&s055r,Nbest_ofechofrequency_penalty logit_biaslogprobs max_tokensnpresence_penaltyseedstopstreamstream_optionssuffix temperaturetop_puser extra_headers extra_query extra_bodytimeoutmodelpromptcyu3 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) 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) 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) 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: Not supported with latest reasoning models `o3` and `o4-mini`. 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). stream_options: Options for streaming response. Only set this when you set `stream: true`. 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)rFrGr2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrEs r*createzCompletions.create/r r,r2r3r4r5r6r7r8r9r:r;r=r>r?r@rArBrCrDrEcyu3 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) 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) 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) 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: Not supported with latest reasoning models `o3` and `o4-mini`. Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence. stream_options: Options for streaming response. Only set this when you set `stream: true`. 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 NrJr)rFrGr<r2r3r4r5r6r7r8r9r:r;r=r>r?r@rArBrCrDrEs r*rLzCompletions.createrMr,cyrPrJrQs r*rLzCompletions.createerMr,rFrGr<c 2|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|i| rtjntjt ||||t | xsdtt SNz /completionsrFrGr2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rA)rBrCrDrEF)bodyoptionscast_tor< stream_cls)_postrr CompletionCreateParamsStreaming"CompletionCreateParamsNonStreamingrr rrKs r*rLzCompletions.createsC:zz  UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*)HH-PP/2)+Q[el?Uj)A! ! r,)returnr')r]r/.rFKUnion[str, Literal['gpt-3.5-turbo-instruct', 'davinci-002', 'babbage-002']]rGMUnion[str, SequenceNotStr[str], Iterable[int], Iterable[Iterable[int]], None]r2Optional[int] | Omitr3Optional[bool] | Omitr4Optional[float] | Omitr5Optional[Dict[str, int]] | Omitr6rar7rar8rar9rcr:rar;6Union[Optional[str], SequenceNotStr[str], None] | Omitr<zOptional[Literal[False]] | Omitr=1Optional[ChatCompletionStreamOptionsParam] | Omitr>Optional[str] | Omitr?rcr@rcrA str | OmitrBHeaders | NonerC Query | NonerD Body | NonerE'float | httpx.Timeout | None | NotGivenr]r ).rFr_rGr`r< Literal[True]r2rar3rbr4rcr5rdr6rar7rar8rar9rcr:rar;rer=rfr>rgr?rcr@rcrArhrBrirCrjrDrkrErlr]zStream[Completion]).rFr_rGr`r<boolr2rar3rbr4rcr5rdr6rar7rar8rar9rcr:rar;rer=rfr>rgr?rcr@rcrArhrBrirCrjrDrkrErlr]Completion | Stream[Completion]).rFr_rGr`r2rar3rbr4rcr5rdr6rar7rar8rar9rcr:rar;rer</Optional[Literal[False]] | Literal[True] | Omitr=rfr>rgr?rcr@rcrArhrBrirCrjrDrkrErlr]ro __name__ __module__ __qualname__rr+r0r rrrLrrJr,r*r"r"sw0066 )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&;D5X [X ^ X & X $ X 2X 4X 'X )X  X 1X #X EX 0X J!X "%#X $,%X &&'X ()X .&/X 0"1X 2 3X 495X 6 7X X t)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5X [X ^ X  X & X $X 2X 4X 'X )X  X 1X #X EX J!X "%#X $,%X &&'X ()X .&/X 0"1X 2 3X 495X 6 7X X t)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5X [X ^ X  X & X $X 2X 4X 'X )X  X 1X #X EX J!X "%#X $,%X &&'X ()X .&/X 0"1X 2 3X 495X 6 )7X X tGX&(EF )-&*486:)-+/"&37%)GKBFLP'+.2(,)-$("&;D5= [= ^ = & = $ = 2= 4= '= )=  = 1= #= E= @= J!= "%#= $,%= &&'= ()= .&/= 0"1= 2 3= 495= 6 )7= G= r,ceZdZed dZeddZeeeeeeeeeeeeeeeeeddded ddZ eeeeeeeeeeeeeeeeddded dd Z eeeeeeeeeeeeeeeeddded dd Z e ddggd eeeeeeeeeeeeeeeeddded dd Z y)r#ct|Sr&)AsyncCompletionsWithRawResponser(s r*r+z"AsyncCompletions.with_raw_responseBs/t44r,ct|Sr.)%AsyncCompletionsWithStreamingResponser(s r*r0z(AsyncCompletions.with_streaming_responseLs5T::r,Nr1rFrGc KywrIrJrKs r*rLzAsyncCompletions.createU r rNc KywrPrJrQs r*rLzAsyncCompletions.creater{r|c KywrPrJrQs r*rLzAsyncCompletions.creater{r|rSc bK|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|i| rtjntjd{t ||||t | xsdtt d{S757wrU)rZrr r[r\rr rrKs r*rLzAsyncCompletions.create&s\:ZZ ,UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*)HH-PP/2)+Q[el?U":.A ! ! ! ! s$A3B/5B+ 60B/&B-'B/-B/)r]rw)r]ryr^).rFr_rGr`r<rmr2rar3rbr4rcr5rdr6rar7rar8rar9rcr:rar;rer=rfr>rgr?rcr@rcrArhrBrirCrjrDrkrErlr]zAsyncStream[Completion]).rFr_rGr`r<rnr2rar3rbr4rcr5rdr6rar7rar8rar9rcr:rar;rer=rfr>rgr?rcr@rcrArhrBrirCrjrDrkrErlr]$Completion | AsyncStream[Completion]).rFr_rGr`r2rar3rbr4rcr5rdr6rar7rar8rar9rcr:rar;rer<rpr=rfr>rgr?rcr@rcrArhrBrirCrjrDrkrErlr]rrqrJr,r*r#r#Asw55;; )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&;D5X [X ^ X & X $ X 2X 4X 'X )X  X 1X #X EX 0X J!X "%#X $,%X &&'X ()X .&/X 0"1X 2 3X 495X 6 7X X t)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5X [X ^ X  X & X $X 2X 4X 'X )X  X 1X #X EX J!X "%#X $,%X &&'X ()X .&/X 0"1X 2 3X 495X 6 !7X X t)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5X [X ^ X  X & X $X 2X 4X 'X )X  X 1X #X EX J!X "%#X $,%X &&'X ()X .&/X 0"1X 2 3X 495X 6 .7X X tGX&(EF )-&*486:)-+/"&37%)GKBFLP'+.2(,)-$("&;D5= [= ^ = & = $ = 2= 4= '= )=  = 1= #= E= @= J!= "%#= $,%= &&'= ()= .&/= 0"1= 2 3= 495= 6 .7= G= r,ceZdZddZy)r'cZ||_tj|j|_yN) _completionsr to_raw_response_wrapperrLr) completionss r*__init__z#CompletionsWithRawResponse.__init__hs%'&>>     r,Nrr"r]NonerrrsrtrrJr,r*r'r'g r,r'ceZdZddZy)rwcZ||_tj|j|_yr)rr async_to_raw_response_wrapperrLrs r*rz(AsyncCompletionsWithRawResponse.__init__qs%'&DD     r,Nrr#r]rrrJr,r*rwrwprr,rwceZdZddZy)r/cF||_t|j|_yr)rrrLrs r*rz)CompletionsWithStreamingResponse.__init__zs'2     r,NrrrJr,r*r/r/yrr,r/ceZdZddZy)rycF||_t|j|_yr)rrrLrs r*rz.AsyncCompletionsWithStreamingResponse.__init__s'8     r,NrrrJr,r*ryryrr,ry)4 __future__rtypingrrrrtyping_extensionsrr httpxr typesr _typesr rrrrrrr_utilsrrr_compatr _resourcerr _responserr _streamingrr _base_clientrtypes.completionr /types.chat.chat_completion_stream_options_paramr!__all__r"r#r'rwr/ryrJr,r*rs#22/ ,ZZZJJ%9X,*^ , -c /c Lc 'c L        r,