ޠjxSSKJr SSKJrJrJrJr SSKJrJ r SSK r SSK J r SSK Jr SSKJrJrJrJrJrJrJrJr SS KJrJrJr SS KJr SS KJrJ r SS K!J"r"J#r# SS K$J%r%J&r& SSK'J(r( SSK)J*r* SSK+J,r, SS/r-"SS\5r."SS\ 5r/"SS5r0"SS5r1"SS5r2"SS5r3g)) annotations)DictUnionIterableOptional)LiteraloverloadN)_legacy_response)completion_create_params)BodyOmitQueryHeadersNotGivenSequenceNotStromit not_given) required_argsmaybe_transformasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)Stream AsyncStream)make_request_options) Completion) ChatCompletionStreamOptionsParam CompletionsAsyncCompletionsc\rSrSrSr\SSj5r\SSj5r\\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ SSS\ S.SS jj5r \\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ SSS\ S .SS jj5r \\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ SSS\ S .SS jj5r \ "SS//S Q5\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ SSS\ S.SSjj5r Sr g)r" Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. c[U5$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 V/var/www/html/gaurav/venv/lib/python3.13/site-packages/openai/resources/completions.pywith_raw_responseCompletions.with_raw_responses*$//c[U5$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. 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+rJrKr6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIs r,createCompletions.create1x r/r6r7r8r9r:r;r<r=r>r?rArBrCrDrErFrGrHrIcgu 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 NrNr+rJrKr@r6r7r8r9r:r;r<r=r>r?rArBrCrDrErFrGrHrIs r,rPrQrRr/cgrUrNrVs r,rPrQmrRr/rJrKr@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[ UUUUSS0S9[ U =(d S[[ S9$Nz /completionsrJrKr6r7r8r9r:r;r<r=r>r?r@rArBrCrDrE bearer_authT)rFrGrHrIsecurityF)bodyoptionscast_tor@ stream_cls)_postrr CompletionCreateParamsStreaming"CompletionCreateParamsNonStreamingrr rrOs r,rPrQ sL:zz  UfwD  ():  !* !*'(8DDf%nf ";!"U#$D%*)HH-PP/2)+'%'. ?Uj)I% % r/rN)returnr))rdr2.rJKUnion[str, Literal['gpt-3.5-turbo-instruct', 'davinci-002', 'babbage-002']]rKMUnion[str, SequenceNotStr[str], Iterable[int], Iterable[Iterable[int]], None]r6Optional[int] | Omitr7Optional[bool] | Omitr8Optional[float] | Omitr9Optional[Dict[str, int]] | Omitr:rhr;rhr<rhr=rjr>rhr?6Union[Optional[str], SequenceNotStr[str], None] | Omitr@zOptional[Literal[False]] | OmitrA1Optional[ChatCompletionStreamOptionsParam] | OmitrBOptional[str] | OmitrCrjrDrjrE str | OmitrFHeaders | NonerG Query | NonerH Body | NonerI(float | httpx2.Timeout | None | NotGivenrdr ).rJrfrKrgr@ Literal[True]r6rhr7rir8rjr9rkr:rhr;rhr<rhr=rjr>rhr?rlrArmrBrnrCrjrDrjrErorFrprGrqrHrrrIrsrdzStream[Completion]).rJrfrKrgr@boolr6rhr7rir8rjr9rkr:rhr;rhr<rhr=rjr>rhr?rlrArmrBrnrCrjrDrjrErorFrprGrqrHrrrIrsrdCompletion | Stream[Completion]).rJrfrKrgr6rhr7rir8rjr9rkr:rhr;rhr<rhr=rjr>rhr?rlr@/Optional[Literal[False]] | Literal[True] | OmitrArmrBrnrCrjrDrjrErorFrprGrqrHrrrIrsrdrv__name__ __module__ __qualname____firstlineno____doc__rr-r3r rrrPr__static_attributes__rNr/r,r"r"s0066 )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&rhr?rlrArmrBrnrCrjrDrjrErorFrprGrqrHrrrIrsrdzAsyncStream[Completion]).rJrfrKrgr@rur6rhr7rir8rjr9rkr:rhr;rhr<rhr=rjr>rhr?rlrArmrBrnrCrjrDrjrErorFrprGrqrHrrrIrsrd$Completion | AsyncStream[Completion]).rJrfrKrgr6rhr7rir8rjr9rkr:rhr;rhr<rhr=rjr>rhr?rlr@rwrArmrBrnrCrjrDrjrErorFrprGrqrHrrrIrsrdrrxrNr/r,r#r#Ps55;; )-&*486:)-+/"&37%)GK26LP'+.2(,)-$("&>     r/rrPNrr"rdNoneryrzr{r|rr~rNr/r,r)r) r/r)c\rSrSrSSjrSrg)ricZXl[R"UR5Ulgr)rr async_to_raw_response_wrapperrPrs r,r(AsyncCompletionsWithRawResponse.__init__s#'&DD     r/rNrr#rdrrrNr/r,rrrr/rc\rSrSrSSjrSrg)r2icDXl[UR5Ulgr)rrrPrs r,r)CompletionsWithStreamingResponse.__init__s'2     r/rNrrrNr/r,r2r2rr/r2c\rSrSrSSjrSrg)ricDXl[UR5Ulgr)rrrPrs r,r.AsyncCompletionsWithStreamingResponse.__init__s'8     r/rNrrrNr/r,rrrr/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)rr2rrNr/r,rs#22/ ,ZZZJJ%9X,/)^ , -t /t nt 't n        r/