o +ifX@sLddlmZddlmZmZmZmZmZmZddl m Z ddl Z ddl m Z ddlmZddlmZmZmZmZmZdd lmZmZmZdd lmZdd lmZmZdd lm Z m!Z!dd l"m#Z#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*ddgZ+GdddeZ,GdddeZ-GdddZ.GdddZ/GdddZ0GdddZ1dS)) annotations)DictListUnionIterableOptionaloverload)LiteralN)_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@eZdZedd0d1Zeeeeeeeeeeeeeeeeddded2d?d5d1Zeeeeeeeeeeeeeeeeddded2d@d8d1Ze d d ggd9eeeeeeeeeeeeeeeeddded dAd;d1ZdS)BrreturnCompletionsWithRawResponsecCt|SN)r#selfr(U/var/www/html/corbot_env/lib/python3.10/site-packages/openai/resources/completions.pywith_raw_response zCompletions.with_raw_response CompletionsWithStreamingResponsecCr$r%)r,r&r(r(r)with_streaming_response$r+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']]promptCUnion[str, List[str], Iterable[int], Iterable[Iterable[int]], None]r/Optional[int] | NotGivenr0Optional[bool] | NotGivenr1Optional[float] | NotGivenr2#Optional[Dict[str, int]] | NotGivenr3r4r5r6r7r80Union[Optional[str], List[str], None] | NotGivenr9#Optional[Literal[False]] | NotGivenr:5Optional[ChatCompletionStreamOptionsParam] | NotGivenr;Optional[str] | NotGivenr<r=r>str | NotGivenr?Headers | Noner@ Query | NonerA Body | NonerB'float | httpx.Timeout | None | NotGivenrcCdSu 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/text-generation/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) 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/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). 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'rCrEr/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBr(r(r)create(zCompletions.creater/r0r1r2r3r4r5r6r7r8r:r;r<r=r>r?r@rArB 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/text-generation/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) 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/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_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'rCrEr9r/r0r1r2r3r4r5r6r7r8r:r;r<r=r>r?r@rArBr(r(r)rWrXboolCompletion | Stream[Completion]cCrTr\r(r]r(r(r)rWZrXrCrEr93Optional[Literal[False]] | Literal[True] | NotGivenc Cs|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|itjt||||dt| pJdttdSNz /completionsrCrEr/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>)r?r@rArBF)bodyoptionscast_tor9 stream_cls)_postrr CompletionCreateParamsrrrrVr(r(r)rWsb     )r"r#)r"r,.rCrDrErFr/rGr0rHr1rIr2rJr3rGr4rGr5rGr6rIr7rGr8rKr9rLr:rMr;rNr<rIr=rIr>rOr?rPr@rQrArRrBrSr"r).rCrDrErFr9rZr/rGr0rHr1rIr2rJr3rGr4rGr5rGr6rIr7rGr8rKr:rMr;rNr<rIr=rIr>rOr?rPr@rQrArRrBrSr"r[).rCrDrErFr9r^r/rGr0rHr1rIr2rJr3rGr4rGr5rGr6rIr7rGr8rKr:rMr;rNr<rIr=rIr>rOr?rPr@rQrArRrBrSr"r_).rCrDrErFr/rGr0rHr1rIr2rJr3rGr4rGr5rGr6rIr7rGr8rKr9rar:rMr;rNr<rIr=rIr>rOr?rPr@rQrArRrBrSr"r_ __name__ __module__ __qualname__rr*r-rr rWrr(r(r(r)r  c@r!)Br r"AsyncCompletionsWithRawResponsecCr$r%)ror&r(r(r)r*3r+z"AsyncCompletions.with_raw_response%AsyncCompletionsWithStreamingResponsecCr$r%)rpr&r(r(r)r-7r+z(AsyncCompletions.with_streaming_responseNr.rCrDrErFr/rGr0rHr1rIr2rJr3r4r5r6r7r8rKr9rLr:rMr;rNr<r=r>rOr?rPr@rQrArRrBrSrcdSrUr(rVr(r(r)rW;zAsyncCompletions.createrYrZAsyncStream[Completion]crqr\r(r]r(r(r)rWrrr^$Completion | AsyncStream[Completion]crqr\r(r]r(r(r)rWmrrr`rac s|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|itjIdHt||||dt| pNdttdIdHSrb)rgrr rhrrrrVr(r(r)rWsd      )r"ro)r"rpri).rCrDrErFr9rZr/rGr0rHr1rIr2rJr3rGr4rGr5rGr6rIr7rGr8rKr:rMr;rNr<rIr=rIr>rOr?rPr@rQrArRrBrSr"rs).rCrDrErFr9r^r/rGr0rHr1rIr2rJr3rGr4rGr5rGr6rIr7rGr8rKr:rMr;rNr<rIr=rIr>rOr?rPr@rQrArRrBrSr"rt).rCrDrErFr/rGr0rHr1rIr2rJr3rGr4rGr5rGr6rIr7rGr8rKr9rar:rMr;rNr<rIr=rIr>rOr?rPr@rQrArRrBrSr"rtrjr(r(r(r)r 2rnc@eZdZdddZdS) r# completionsrr"NonecC||_t|j|_dSr%) _completionsr to_raw_response_wrapperrWr'rvr(r(r)__init__F z#CompletionsWithRawResponse.__init__Nrvrr"rwrkrlrmr|r(r(r(r)r#Er#c@ru) rorvr r"rwcCrxr%)ryr async_to_raw_response_wrapperrWr{r(r(r)r|Or}z(AsyncCompletionsWithRawResponse.__init__Nrvr r"rwrr(r(r(r)roNrroc@ru) r,rvrr"rwcC||_t|j|_dSr%)ryrrWr{r(r(r)r|X z)CompletionsWithStreamingResponse.__init__Nr~rr(r(r(r)r,Wrr,c@ru) rprvr r"rwcCrr%)ryrrWr{r(r(r)r|arz.AsyncCompletionsWithStreamingResponse.__init__Nrrr(r(r(r)rp`rrp)2 __future__rtypingrrrrrrtyping_extensionsr httpxr typesr _typesr rrrr_utilsrrr_compatr _resourcerr _responserr _streamingrr _base_clientrtypes.completionr/types.chat.chat_completion_stream_options_paramr__all__rr r#ror,rpr(r(r(r)s<