FjLxddlmZddlmZmZmZmZddlmZm Z ddl Z ddl m Z ddl mZddlmZmZmZmZmZmZmZmZdd lmZmZmZdd lmZdd lmZm Z dd l!m"Z"m#Z#dd l$m%Z%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddgZ-GddeZ.Gdde Z/GddZ0GddZ1GddZ2GddZ3y)) annotations)DictUnionIterableOptional)LiteraloverloadN)_legacy_response)completion_create_params)BodyOmitQueryHeadersNotGivenSequenceNotStromit not_given) required_argsmaybe_transformasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)Stream AsyncStream)make_request_options) Completion) ChatCompletionStreamOptionsParam CompletionsAsyncCompletionsceZdZdZeddZeddZeeeeeeeeeeeeeeeeeddde d ddZ eeeeeeeeeeeeeeeeddde d  dd Z eeeeeeeeeeeeeeeeddde d  dd Z e ddggd eeeeeeeeeeeeeeeeddde d dd Z y)r" Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. ct|Sa This property can be used as a prefix for any HTTP method call to return the raw response object instead of the parsed content. For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers )CompletionsWithRawResponseselfs Z/root/workspace/ytshorts/venv/lib/python3.12/site-packages/openai/resources/completions.pywith_raw_responsezCompletions.with_raw_responses*$//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_bodytimeoutmodelpromptcyu Creates a completion for the provided prompt and parameters. Returns a completion object, or a sequence of completion objects if the request is streamed. Args: model: ID of the model to use. You can use the [List models](https://developers.openai.com/api/reference/resources/models/methods/list) API to see all of your available models, or see our [Model overview](https://developers.openai.com/api/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://developers.openai.com/api/docs/guides/text) 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](https://platform.openai.com/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](https://platform.openai.com/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://developers.openai.com/api/docs/guides/text) 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://developers.openai.com/api/docs/guides/safety-best-practices#implement-safety-identifiers). 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+createzCompletions.create1z r-r3r4r5r6r7r8r9r:r;r<r>r?r@rArBrCrDrErFcyu Creates a completion for the provided prompt and parameters. Returns a completion object, or a sequence of completion objects if the request is streamed. Args: model: ID of the model to use. You can use the [List models](https://developers.openai.com/api/reference/resources/models/methods/list) API to see all of your available models, or see our [Model overview](https://developers.openai.com/api/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://developers.openai.com/api/docs/guides/text) 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](https://platform.openai.com/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](https://platform.openai.com/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://developers.openai.com/api/docs/guides/text) 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://developers.openai.com/api/docs/guides/safety-best-practices#implement-safety-identifiers). 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+rMzCompletions.createrNr-cyrQrKrRs r+rMzCompletions.createorNr-rGrHr=c 8|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|i| rtjntjt ||||ddit | xsdtt SNz /completionsrGrHr3r4r5r6r7r8r9r:r;r<r=r>r?r@rArB bearer_authT)rCrDrErFsecurityF)bodyoptionscast_tor= stream_cls)_postrr CompletionCreateParamsStreaming"CompletionCreateParamsNonStreamingrr rrLs r+rMzCompletions.createsL:zz  UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*)HH-PP/2)+'%'. ?Uj)I% % r-)returnr()r`r0.rGKUnion[str, Literal['gpt-3.5-turbo-instruct', 'davinci-002', 'babbage-002']]rHMUnion[str, SequenceNotStr[str], Iterable[int], Iterable[Iterable[int]], None]r3Optional[int] | Omitr4Optional[bool] | Omitr5Optional[float] | Omitr6Optional[Dict[str, int]] | Omitr7rdr8rdr9rdr:rfr;rdr<6Union[Optional[str], SequenceNotStr[str], None] | Omitr=zOptional[Literal[False]] | Omitr>1Optional[ChatCompletionStreamOptionsParam] | Omitr?Optional[str] | Omitr@rfrArfrB str | OmitrCHeaders | NonerD Query | NonerE Body | NonerF(float | httpx2.Timeout | None | NotGivenr`r ).rGrbrHrcr= Literal[True]r3rdr4rer5rfr6rgr7rdr8rdr9rdr:rfr;rdr<rhr>rir?rjr@rfrArfrBrkrCrlrDrmrErnrFror`zStream[Completion]).rGrbrHrcr=boolr3rdr4rer5rfr6rgr7rdr8rdr9rdr:rfr;rdr<rhr>rir?rjr@rfrArfrBrkrCrlrDrmrErnrFror`Completion | Stream[Completion]).rGrbrHrcr3rdr4rer5rfr6rgr7rdr8rdr9rdr:rfr;rdr<rhr=/Optional[Literal[False]] | Literal[True] | Omitr>rir?rjr@rfrArfrBrkrCrlrDrmrErnrFror`rr __name__ __module__ __qualname____doc__rr,r1r rrrMrrKr-r+r"r"s0066 )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&rir?rjr@rfrArfrBrkrCrlrDrmrErnrFror`zAsyncStream[Completion]).rGrbrHrcr=rqr3rdr4rer5rfr6rgr7rdr8rdr9rdr:rfr;rdr<rhr>rir?rjr@rfrArfrBrkrCrlrDrmrErnrFror`$Completion | AsyncStream[Completion]).rGrbrHrcr3rdr4rer5rfr6rgr7rdr8rdr9rdr:rfr;rdr<rhr=rsr>rir?rjr@rfrArfrBrkrCrlrDrmrErnrFror`rrtrKr-r+r#r#Ss55;; )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&>     r-Nrr"r`NonerurvrwrrKr-r+r(r( r-r(ceZdZddZy)r{cZ||_tj|j|_yr)rr async_to_raw_response_wrapperrMrs r+rz(AsyncCompletionsWithRawResponse.__init__s%'&DD     r-Nrr#r`rrrKr-r+r{r{rr-r{ceZdZddZy)r0cF||_t|j|_yr)rrrMrs r+rz)CompletionsWithStreamingResponse.__init__s'2     r-NrrrKr-r+r0r0rr-r0ceZdZddZy)r}cF||_t|j|_yr)rrrMrs r+rz.AsyncCompletionsWithStreamingResponse.__init__s'8     r-NrrrKr-r+r}r}rr-r})4 __future__rtypingrrrrtyping_extensionsrr httpx2r typesr _typesr rrrrrrr_utilsrrr_compatr _resourcerr _responserr _streamingrr _base_clientrtypes.completionr /types.chat.chat_completion_stream_options_paramr!__all__r"r#r(r{r0r}rKr-r+rs#22/ ,ZZZJJ%9X,/)^ , -w /w tw 'w t        r-