hpSSKJr SSKJrJrJrJrJr SSKJ r J r SSK r SSK J r SSKJr SSKJrJrJrJrJr SS KJrJrJr SS KJr SS KJrJr SS KJ r J!r! SS K"J#r#J$r$ SSK%J&r& SSK'J(r( SSK)J*r* SS/r+"SS\5r,"SS\5r-"SS5r."SS5r/"SS5r0"SS5r1g)) annotations)DictListUnionIterableOptional)LiteraloverloadN)_legacy_response)completion_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) Completion) ChatCompletionStreamOptionsParam CompletionsAsyncCompletionsc\rSrSr\SSj5r\SSj5r\\\\\\\\\\\\\\\\\SSS\S.SSjj5r \\\\\\\\\\\\\\\\SSS\S .SS jj5r \\\\\\\\\\\\\\\\SSS\S .SS jj5r \ "SS//S Q5\\\\\\\\\\\\\\\\SSS\S.SS jj5r Sr g)r c[U5$z This property can be used as a prefix for any HTTP method call to return the raw response object instead of the parsed content. For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers )CompletionsWithRawResponseselfs N/var/www/html/env/lib/python3.13/site-packages/openai/resources/completions.pywith_raw_responseCompletions.with_raw_response s*$//c[U5$z 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_response#Completions.with_streaming_response*s055r,Nbest_ofechofrequency_penalty logit_biaslogprobs max_tokensnpresence_penaltyseedstopstreamstream_optionssuffix temperaturetop_puser extra_headers extra_query extra_bodytimeoutmodelpromptcgu 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(rGrHr3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFs r)createCompletions.create3r r,r3r4r5r6r7r8r9r:r;r<r>r?r@rArBrCrDrErFcgu 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 NrKr(rGrHr=r3r4r5r6r7r8r9r:r;r<r>r?r@rArBrCrDrErFs r)rMrNrOr,cgrRrKrSs r)rMrNirOr,rGrHr=c :URS[0SU_SU_SU_SU_SU_SU_SU_S U_S U _S U _S U _S U _SU _SU_SU_SU_SU_SU0EU (a[RO[R5[ UUUUS9[ U =(d S[[ S9$Nz /completionsrGrHr3r4r5r6r7r8r9r:r;r<r=r>r?r@rArB)rCrDrErFF)bodyoptionscast_tor= stream_cls)_postrr CompletionCreateParamsStreaming"CompletionCreateParamsNonStreamingrrrrLs r)rMrNsC:zz  UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*)HH-PP/2)+Q[el?Uj)A! ! r,rK)returnr&)r_r/.rGKUnion[str, Literal['gpt-3.5-turbo-instruct', 'davinci-002', 'babbage-002']]rHCUnion[str, List[str], Iterable[int], Iterable[Iterable[int]], None]r3Optional[int] | NotGivenr4Optional[bool] | NotGivenr5Optional[float] | NotGivenr6#Optional[Dict[str, int]] | NotGivenr7rcr8rcr9rcr:rer;rcr<0Union[Optional[str], List[str], None] | NotGivenr=z#Optional[Literal[False]] | NotGivenr>5Optional[ChatCompletionStreamOptionsParam] | NotGivenr?Optional[str] | NotGivenr@rerArerBstr | NotGivenrCHeaders | NonerD Query | NonerE Body | NonerF'float | httpx.Timeout | None | NotGivenr_r).rGrarHrbr= Literal[True]r3rcr4rdr5rer6rfr7rcr8rcr9rcr:rer;rcr<rgr>rhr?rir@rerArerBrjrCrkrDrlrErmrFrnr_zStream[Completion]).rGrarHrbr=boolr3rcr4rdr5rer6rfr7rcr8rcr9rcr:rer;rcr<rgr>rhr?rir@rerArerBrjrCrkrDrlrErmrFrnr_Completion | Stream[Completion]).rGrarHrbr3rcr4rdr5rer6rfr7rcr8rcr9rcr:rer;rcr<rgr=3Optional[Literal[False]] | Literal[True] | NotGivenr>rhr?rir@rerArerBrjrCrkrDrlrErmrFrnr_rq __name__ __module__ __qualname____firstlineno__rr*r0r rrMr__static_attributes__rKr,r)r r ss0066 -6*38A:C-6/8&/7@)2AJ6?PY+42;,5()-$("&;D5X [X T X * X ( X 6X 8X +X -X  $X 5X 'X ?X 4X N!X ")#X $0%X &*'X ()X .&/X 0"1X 2 3X 495X 6 7X X t-6*38A:C-6/8&/7@)2AJPY+42;,5()-$("&;D5X [X T X  X * X (X 6X 8X +X -X  $X 5X 'X ?X N!X ")#X $0%X &*'X ()X .&/X 0"1X 2 3X 495X 6 7X X t-6*38A:C-6/8&/7@)2AJPY+42;,5()-$("&;D5X [X T X  X * X (X 6X 8X +X -X  $X 5X 'X ?X N!X ")#X $0%X &*'X ()X .&/X 0"1X 2 3X 495X 6 )7X X tGX&(EF -6*38A:C-6/8&/7@)2AJFOPY+42;,5()-$("&;D5= [= T = * = ( = 6= 8= += -=  $= 5= '= ?= D= N!= ")#= $0%= &*'= ()= .&/= 0"1= 2 3= 495= 6 )7= G= r,c\rSrSr\SSj5r\SSj5r\\\\\\\\\\\\\\\\\SSS\S.SSjj5r \\\\\\\\\\\\\\\\SSS\S .SS jj5r \\\\\\\\\\\\\\\\SSS\S .SS jj5r \ "SS//S Q5\\\\\\\\\\\\\\\\SSS\S.SS jj5r Sr g)r!iEc[U5$r%)AsyncCompletionsWithRawResponser's r)r*"AsyncCompletions.with_raw_responseFs/t44r,c[U5$r.)%AsyncCompletionsWithStreamingResponser's r)r0(AsyncCompletions.with_streaming_responsePs5T::r,Nr2rGrHc # g7frJrKrLs r)rMAsyncCompletions.createY r rPc # g7frRrKrSs r)rMrrrc # g7frRrKrSs r)rMrrrrUc j# URS[0SU_SU_SU_SU_SU_SU_SU_S U_S U _S U _S U _S U _SU _SU_SU_SU_SU_SU0EU (a[RO[R5IShvN [ UUUUS9[ U =(d S[[ S9IShvN $N4N7frW)r\rr r]r^rrrrLs r)rMr*sZ:ZZ ,UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*)HH-PP/2)+Q[el?U":.A ! ! ! ! s$A8B3:B/ ;/B3*B1+B31B3rK)r_r{)r_r~r`).rGrarHrbr=ror3rcr4rdr5rer6rfr7rcr8rcr9rcr:rer;rcr<rgr>rhr?rir@rerArerBrjrCrkrDrlrErmrFrnr_zAsyncStream[Completion]).rGrarHrbr=rpr3rcr4rdr5rer6rfr7rcr8rcr9rcr:rer;rcr<rgr>rhr?rir@rerArerBrjrCrkrDrlrErmrFrnr_$Completion | AsyncStream[Completion]).rGrarHrbr3rcr4rdr5rer6rfr7rcr8rcr9rcr:rer;rcr<rgr=rrr>rhr?rir@rerArerBrjrCrkrDrlrErmrFrnr_rrsrKr,r)r!r!Ess55;; -6*38A:C-6/8&/7@)2AJ6?PY+42;,5()-$("&;D5X [X T X * X ( X 6X 8X +X -X  $X 5X 'X ?X 4X N!X ")#X $0%X &*'X ()X .&/X 0"1X 2 3X 495X 6 7X X t-6*38A:C-6/8&/7@)2AJPY+42;,5()-$("&;D5X [X T X  X * X (X 6X 8X +X -X  $X 5X 'X ?X N!X ")#X $0%X &*'X ()X .&/X 0"1X 2 3X 495X 6 !7X X t-6*38A:C-6/8&/7@)2AJPY+42;,5()-$("&;D5X [X T X  X * X (X 6X 8X +X -X  $X 5X 'X ?X N!X ")#X $0%X &*'X ()X .&/X 0"1X 2 3X 495X 6 .7X X tGX&(EF -6*38A:C-6/8&/7@)2AJFOPY+42;,5()-$("&;D5= [= T = * = ( = 6= 8= += -=  $= 5= '= ?= D= N!= ")#= $0%= &*'= ()= .&/= 0"1= 2 3= 495= 6 .7= G= r,c\rSrSrSSjrSrg)r&ikcZXl[R"UR5UlgN) _completionsr to_raw_response_wrapperrMr( completionss r)__init__#CompletionsWithRawResponse.__init__ls#'&>>     r,rrMNrr r_NonertrurvrwrrxrKr,r)r&r&k r,r&c\rSrSrSSjrSrg)r{itcZXl[R"UR5Ulgr)rr async_to_raw_response_wrapperrMrs r)r(AsyncCompletionsWithRawResponse.__init__us#'&DD     r,rNrr!r_rrrKr,r)r{r{trr,r{c\rSrSrSSjrSrg)r/i}cDXl[UR5Ulgr)rrrMrs r)r)CompletionsWithStreamingResponse.__init__~s'2     r,rNrrrKr,r)r/r/}rr,r/c\rSrSrSSjrSrg)r~icDXl[UR5Ulgr)rrrMrs r)r.AsyncCompletionsWithStreamingResponse.__init__s'8     r,rNrrrKr,r)r~r~rr,r~)2 __future__rtypingrrrrrtyping_extensionsr r httpxr typesr _typesrrrrr_utilsrrr_compatr _resourcerr _responserr _streamingrr _base_clientrtypes.completionr/types.chat.chat_completion_stream_options_paramr__all__r r!r&r{r/r~rKr,r)rs#88/ ,>> &9X,*^ , -c /c Lc 'c L        r,