o $iA@sTddlmZddlmZmZmZmZddlmZm Z ddl Z ddl m Z ddl mZddlmZmZmZmZmZmZmZmZdd lmZmZmZdd lmZdd lmZm Z dd l!m"Z"m#Z#dd l$m%Z%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddgZ-GdddeZ.Gddde Z/GdddZ0GdddZ1GdddZ2GdddZ3dS)) 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 CompletionsAsyncCompletionsc@eZdZedd0d1Z eeeeeeeeeeeeeeeeddded2d?d5d1Z eeeeeeeeeeeeeeeeddded2d@d8d1Z e d d ggd9eeeeeeeeeeeeeeeeddded dAd;d1Z dS)Br!returnCompletionsWithRawResponsecCt|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 )r%selfr*\/var/www/html/flask_server/venv/lib/python3.10/site-packages/openai/resources/completions.pywith_raw_responsezCompletions.with_raw_response CompletionsWithStreamingResponsecCr&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 )r.r(r*r*r+with_streaming_response&z#Completions.with_streaming_responseNbest_ofechofrequency_penalty logit_biaslogprobs max_tokensnpresence_penaltyseedstopstreamstream_optionssuffix temperaturetop_puser extra_headers extra_query extra_bodytimeoutmodelKUnion[str, Literal['gpt-3.5-turbo-instruct', 'davinci-002', 'babbage-002']]promptMUnion[str, SequenceNotStr[str], Iterable[int], Iterable[Iterable[int]], None]r3Optional[int] | Omitr4Optional[bool] | Omitr5Optional[float] | Omitr6Optional[Dict[str, int]] | Omitr7r8r9r:r;r<6Union[Optional[str], SequenceNotStr[str], None] | Omitr=Optional[Literal[False]] | Omitr>1Optional[ChatCompletionStreamOptionsParam] | Omitr?Optional[str] | Omitr@rArB str | OmitrCHeaders | NonerD Query | NonerE Body | NonerF'float | httpx.Timeout | None | NotGivenrcCdSu3 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*r)rGrIr3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFr*r*r+create/zCompletions.creater3r4r5r6r7r8r9r:r;r<r>r?r@rArBrCrDrErF Literal[True]Stream[Completion]cCrXu3 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 Nr*r)rGrIr=r3r4r5r6r7r8r9r:r;r<r>r?r@rArBrCrDrErFr*r*r+r[r\boolCompletion | Stream[Completion]cCrXr`r*rar*r*r+r[er\rGrIr=/Optional[Literal[False]] | Literal[True] | Omitc Cs|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|i| rAtjntjt||||dt| pOdttdSNz /completionsrGrIr3r4r5r6r7r8r9r:r;r<r=r>r?r@rArB)rCrDrErFF)bodyoptionscast_tor= stream_cls)_postrr CompletionCreateParamsStreaming"CompletionCreateParamsNonStreamingrrrrZr*r*r+r[sf     )r$r%)r$r..rGrHrIrJr3rKr4rLr5rMr6rNr7rKr8rKr9rKr:rMr;rKr<rOr=rPr>rQr?rRr@rMrArMrBrSrCrTrDrUrErVrFrWr$r).rGrHrIrJr=r^r3rKr4rLr5rMr6rNr7rKr8rKr9rKr:rMr;rKr<rOr>rQr?rRr@rMrArMrBrSrCrTrDrUrErVrFrWr$r_).rGrHrIrJr=rbr3rKr4rLr5rMr6rNr7rKr8rKr9rKr:rMr;rKr<rOr>rQr?rRr@rMrArMrBrSrCrTrDrUrErVrFrWr$rc).rGrHrIrJr3rKr4rLr5rMr6rNr7rKr8rKr9rKr:rMr;rKr<rOr=rer>rQr?rRr@rMrArMrBrSrCrTrDrUrErVrFrWr$rc __name__ __module__ __qualname__rr,r0rrrr[rr*r*r*r+r!  c@r#)Br"r$AsyncCompletionsWithRawResponsecCr&r')rtr(r*r*r+r,Br-z"AsyncCompletions.with_raw_response%AsyncCompletionsWithStreamingResponsecCr&r/)rur(r*r*r+r0Lr1z(AsyncCompletions.with_streaming_responseNr2rGrHrIrJr3rKr4rLr5rMr6rNr7r8r9r:r;r<rOr=rPr>rQr?rRr@rArBrSrCrTrDrUrErVrFrWrcdSrYr*rZr*r*r+r[UzAsyncCompletions.creater]r^AsyncStream[Completion]crvr`r*rar*r*r+r[rwrb$Completion | AsyncStream[Completion]crvr`r*rar*r*r+r[rwrdrec s|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|i| rBtjntjIdHt||||dt| pSdttdIdHSrf)rkrr rlrmrrrrZr*r*r+r[&sh      )r$rt)r$rurn).rGrHrIrJr=r^r3rKr4rLr5rMr6rNr7rKr8rKr9rKr:rMr;rKr<rOr>rQr?rRr@rMrArMrBrSrCrTrDrUrErVrFrWr$rx).rGrHrIrJr=rbr3rKr4rLr5rMr6rNr7rKr8rKr9rKr:rMr;rKr<rOr>rQr?rRr@rMrArMrBrSrCrTrDrUrErVrFrWr$ry).rGrHrIrJr3rKr4rLr5rMr6rNr7rKr8rKr9rKr:rMr;rKr<rOr=rer>rQr?rRr@rMrArMrBrSrCrTrDrUrErVrFrWr$ryror*r*r*r+r"Arsc@eZdZdddZdS) r% completionsr!r$NonecC||_t|j|_dSN) _completionsr to_raw_response_wrapperr[r)r{r*r*r+__init__h z#CompletionsWithRawResponse.__init__Nr{r!r$r|rprqrrrr*r*r*r+r%gr%c@rz) rtr{r"r$r|cCr}r~)rr async_to_raw_response_wrapperr[rr*r*r+rqrz(AsyncCompletionsWithRawResponse.__init__Nr{r"r$r|rr*r*r*r+rtprrtc@rz) r.r{r!r$r|cC||_t|j|_dSr~)rrr[rr*r*r+rz z)CompletionsWithStreamingResponse.__init__Nrrr*r*r*r+r.yrr.c@rz) rur{r"r$r|cCrr~)rrr[rr*r*r+rrz.AsyncCompletionsWithStreamingResponse.__init__Nrrr*r*r*r+rurru)4 __future__rtypingrrrrtyping_extensionsrrhttpxr typesr _typesr r rrrrrr_utilsrrr_compatr _resourcerr _responserr _streamingrr _base_clientrtypes.completionr/types.chat.chat_completion_stream_options_paramr __all__r!r"r%rtr.rur*r*r*r+s<   (    **