2i-ddlmZddlmZmZmZmZddlmZm Z ddl Z ddl m Z ddl mZddlmZmZmZmZmZmZmZmZdd lmZmZmZdd lmZdd lmZm Z dd l!m"Z"m#Z#dd l$m%Z%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddgZ-GddeZ.Gdde Z/GddZ0GddZ1GddZ2GddZ3dS)) annotations)DictUnionIterableOptional)LiteraloverloadN)_legacy_response)completion_create_params)BodyOmitQueryHeadersNotGivenSequenceNotStromit not_given) required_argsmaybe_transformasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)Stream AsyncStream)make_request_options) Completion) ChatCompletionStreamOptionsParam CompletionsAsyncCompletionscjeZdZdZed:dZed;dZeeeeeeeeeeeeeeeeeddde ddd6Z e d d ggd7eeeeeeeeeeeeeeeeddde dd?d9Z dS)@r" Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. returnCompletionsWithRawResponsec t|Sa This property can be used as a prefix for any HTTP method call to return the raw response object instead of the parsed content. For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers )r'selfs c/root/.openclaw/workspace/.venv-openai/lib/python3.11/site-packages/openai/resources/completions.pywith_raw_responsezCompletions.with_raw_response s*$/// CompletionsWithStreamingResponsec t|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 )r/r*s r,with_streaming_responsez#Completions.with_streaming_response*s0555r.Nbest_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]r4Optional[int] | Omitr5Optional[bool] | Omitr6Optional[float] | Omitr7Optional[Dict[str, int]] | Omitr8r9r:r;r<r=6Union[Optional[str], SequenceNotStr[str], None] | Omitr>Optional[Literal[False]] | Omitr?1Optional[ChatCompletionStreamOptionsParam] | Omitr@Optional[str] | OmitrArBrC str | OmitrDHeaders | NonerE Query | NonerF Body | NonerG'float | httpx.Timeout | None | NotGivenr cdSu 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://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+rHrJr4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGs r,createzCompletions.create3 x r.r4r5r6r7r8r9r:r;r<r=r?r@rArBrCrDrErFrG Literal[True]Stream[Completion]cdSu 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://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+rHrJr>r4r5r6r7r8r9r:r;r<r=r?r@rArBrCrDrErFrGs r,r]zCompletions.creater^r.boolCompletion | Stream[Completion]cdSrcr[rds r,r]zCompletions.createor^r.rHrJr>/Optional[Literal[False]] | Literal[True] | Omitc B|dtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|i| r tjn tjt ||||t | pdtt SNz /completionsrHrJr4r5r6r7r8r9r:r;r<r=r>r?r@rArBrC)rDrErFrGF)bodyoptionscast_tor> stream_cls)_postrr CompletionCreateParamsStreaming"CompletionCreateParamsNonStreamingrr rr\s r,r]zCompletions.create s]:zz  UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*Q(HH-P/2)+Q[el?Uj)A! ! ! r.)r&r')r&r/.rHrIrJrKr4rLr5rMr6rNr7rOr8rLr9rLr:rLr;rNr<rLr=rPr>rQr?rRr@rSrArNrBrNrCrTrDrUrErVrFrWrGrXr&r ).rHrIrJrKr>r`r4rLr5rMr6rNr7rOr8rLr9rLr:rLr;rNr<rLr=rPr?rRr@rSrArNrBrNrCrTrDrUrErVrFrWrGrXr&ra).rHrIrJrKr>rer4rLr5rMr6rNr7rOr8rLr9rLr:rLr;rNr<rLr=rPr?rRr@rSrArNrBrNrCrTrDrUrErVrFrWrGrXr&rf).rHrIrJrKr4rLr5rMr6rNr7rOr8rLr9rLr:rLr;rNr<rLr=rPr>rir?rRr@rSrArNrBrNrCrTrDrUrErVrFrWrGrXr&rf __name__ __module__ __qualname____doc__rr-r2r rrr]rr[r.r,r"r"s/000_0666_6 )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&;D5[ [ [ [ [ X[ z)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5[ [ [ [ [ X[ z)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5[ [ [ [ [ X[ z]GX&(E(E(EFF )-&*486:)-+/"&37%)GKBFLP'+.2(,)-$("&;D5= = = = = GF= = = r.cjeZdZdZed:dZed;dZeeeeeeeeeeeeeeeeeddde ddd6Z e d d ggd7eeeeeeeeeeeeeeeeddde dd?d9Z dS)@r#r%r&AsyncCompletionsWithRawResponsec t|Sr))rzr*s r,r-z"AsyncCompletions.with_raw_responseSs/t444r.%AsyncCompletionsWithStreamingResponsec t|Sr1)r|r*s r,r2z(AsyncCompletions.with_streaming_response]s5T:::r.Nr3rHrIrJrKr4rLr5rMr6rNr7rOr8r9r:r;r<r=rPr>rQr?rRr@rSrArBrCrTrDrUrErVrFrWrGrXr c KdSrZr[r\s r,r]zAsyncCompletions.createfx r.r_r`AsyncStream[Completion]c KdSrcr[rds r,r]zAsyncCompletions.createrr.re$Completion | AsyncStream[Completion]c KdSrcr[rds r,r]zAsyncCompletions.createrr.rhric ^K|dtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|i| r tjn tjd{Vt ||||t | pdtt d{VSrk)rprr rqrrrr rr\s r,r]zAsyncCompletions.create@s:ZZ ,UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*Q(HH-P/2)+Q[el?U":.A ! ! ! ! ! ! ! ! ! r.)r&rz)r&r|rs).rHrIrJrKr>r`r4rLr5rMr6rNr7rOr8rLr9rLr:rLr;rNr<rLr=rPr?rRr@rSrArNrBrNrCrTrDrUrErVrFrWrGrXr&r).rHrIrJrKr>rer4rLr5rMr6rNr7rOr8rLr9rLr:rLr;rNr<rLr=rPr?rRr@rSrArNrBrNrCrTrDrUrErVrFrWrGrXr&r).rHrIrJrKr4rLr5rMr6rNr7rOr8rLr9rLr:rLr;rNr<rLr=rPr>rir?rRr@rSrArNrBrNrCrTrDrUrErVrFrWrGrXr&rrtr[r.r,r#r#Ns/555_5;;;_; )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&;D5[ [ [ [ [ X[ z)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5[ [ [ [ [ X[ z)-&*486:)-+/"&37%)GKLP'+.2(,)-$("&;D5[ [ [ [ [ X[ z]GX&(E(E(EFF )-&*486:)-+/"&37%)GKBFLP'+.2(,)-$("&;D5= = = = = GF= = = r.ceZdZddZdS)r' completionsr"r&NonecP||_tj|j|_dSN) _completionsr to_raw_response_wrapperr]r+rs r,__init__z#CompletionsWithRawResponse.__init__s('&>     r.Nrr"r&rrurvrwrr[r.r,r'r'(      r.r'ceZdZddZdS)rzrr#r&rcP||_tj|j|_dSr)rr async_to_raw_response_wrapperr]rs r,rz(AsyncCompletionsWithRawResponse.__init__s('&D     r.Nrr#r&rrr[r.r,rzrzrr.rzceZdZddZdS)r/rr"r&rcF||_t|j|_dSr)rrr]rs r,rz)CompletionsWithStreamingResponse.__init__s%'2     r.Nrrr[r.r,r/r/rr.r/ceZdZddZdS)r|rr#r&rcF||_t|j|_dSr)rrr]rs r,rz.AsyncCompletionsWithStreamingResponse.__init__s%'8     r.Nrrr[r.r,r|r|rr.r|)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'rzr/r|r[r.r,rs#"""""222222222222//////// ,,,,,,ZZZZZZZZZZZZZZZZZZZZJJJJJJJJJJ%%%%%%99999999XXXXXXXX,,,,,,,,*)))))^^^^^^ , -p p p p p /p p p fp p p p p 'p p p f                                  r.