o Cji@s$ddlmZddlmZmZmZmZmZmZddl m Z ddl Z ddl m Z mZmZmZmZddlmZmZddlmZmZdd lmZmZdd lmZmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&erndd l'm(Z(m)Z)ddgZ*GdddeZ+GdddeZ,GdddZ-GdddZ.dS)) annotations) TYPE_CHECKINGDictListUnionOptionaloverload)LiteralN) NOT_GIVENBodyQueryHeadersNotGiven) required_argsmaybe_transform)SyncAPIResourceAsyncAPIResource)to_raw_response_wrapperasync_to_raw_response_wrapper)Stream AsyncStream)ChatCompletionChatCompletionChunkChatCompletionToolParamChatCompletionMessageParam#ChatCompletionToolChoiceOptionParamcompletion_create_params)make_request_options)OpenAI AsyncOpenAI CompletionsAsyncCompletionsceZdZUded<d?fdd Zeeeeeeeeeeeeeeeeed d d ed d@d3d4Zeeeeeeeeeeeeeeeed d d ed5dAd8d4Zeeeeeeeeeeeeeeeed d d ed5dBd;d4Zed d ggd<eeeeeeeeeeeeeeeed d d ed dCd>d4ZZ S)Dr!CompletionsWithRawResponsewith_raw_responseclientrreturnNonect|t||_dSN)super__init__r$r%selfr& __class__x/var/www/html/Resume-Parser/resume-parser-inhouse/venv/lib/python3.10/site-packages/openai/resources/chat/completions.pyr," zCompletions.__init__Nfrequency_penalty function_call functions logit_bias max_tokensnpresence_penaltyresponse_formatseedstopstream temperature tool_choicetoolstop_puser extra_headers extra_query extra_bodytimeoutmessages List[ChatCompletionMessageParam]modelUnion[str, Literal['gpt-4-1106-preview', 'gpt-4-vision-preview', 'gpt-4', 'gpt-4-0314', 'gpt-4-0613', 'gpt-4-32k', 'gpt-4-32k-0314', 'gpt-4-32k-0613', 'gpt-3.5-turbo-1106', 'gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0301', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613']]r5Optional[float] | NotGivenr60completion_create_params.FunctionCall | NotGivenr72List[completion_create_params.Function] | NotGivenr8#Optional[Dict[str, int]] | NotGivenr9Optional[int] | NotGivenr:r;r<2completion_create_params.ResponseFormat | NotGivenr=r>*Union[Optional[str], List[str]] | NotGivenr?#Optional[Literal[False]] | NotGivenr@rA.ChatCompletionToolChoiceOptionParam | NotGivenrB(List[ChatCompletionToolParam] | NotGivenrCrDstr | NotGivenrEHeaders | NonerF Query | NonerG Body | NonerH'float | httpx.Timeout | None | NotGivenrcCdSa Creates a model response for the given chat conversation. Args: messages: A list of messages comprising the conversation so far. [Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models). model: ID of the model to use. See the [model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility) table for details on which models work with the Chat API. 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/gpt/parameter-details) function_call: Deprecated in favor of `tool_choice`. Controls which (if any) function is called by the model. `none` means the model will not call a function and instead generates a message. `auto` means the model can pick between generating a message or calling a function. Specifying a particular function via `{"name": "my_function"}` forces the model to call that function. `none` is the default when no functions are present. `auto`` is the default if functions are present. functions: Deprecated in favor of `tools`. A list of functions the model may generate JSON inputs for. 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 tokenizer) to an associated bias value from -100 to 100. 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. max_tokens: The maximum number of [tokens](/tokenizer) to generate in the chat completion. The total length of input tokens and generated tokens is limited by 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 chat completion choices to generate for each input message. 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/gpt/parameter-details) response_format: An object specifying the format that the model must output. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is valid JSON. **Important:** when using JSON mode, you **must** also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in increased latency and appearance of a "stuck" request. Also note that the message content may be partially cut off if `finish_reason="length"`, which indicates the generation exceeded `max_tokens` or the conversation exceeded the max context length. seed: This feature is in Beta. 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. stream: If set, partial message deltas will be sent, like in ChatGPT. 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). 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. tool_choice: Controls which (if any) function is called by the model. `none` means the model will not call a function and instead generates a message. `auto` means the model can pick between generating a message or calling a function. Specifying a particular function via `{"type: "function", "function": {"name": "my_function"}}` forces the model to call that function. `none` is the default when no functions are present. `auto` is the default if functions are present. tools: A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. 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 Nr1r.rIrKr5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHr1r1r2create&*zCompletions.creater5r6r7r8r9r:r;r<r=r>r@rArBrCrDrErFrGrH Literal[True]Stream[ChatCompletionChunk]cCr\a Creates a model response for the given chat conversation. Args: messages: A list of messages comprising the conversation so far. [Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models). model: ID of the model to use. See the [model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility) table for details on which models work with the Chat API. stream: If set, partial message deltas will be sent, like in ChatGPT. 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). 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/gpt/parameter-details) function_call: Deprecated in favor of `tool_choice`. Controls which (if any) function is called by the model. `none` means the model will not call a function and instead generates a message. `auto` means the model can pick between generating a message or calling a function. Specifying a particular function via `{"name": "my_function"}` forces the model to call that function. `none` is the default when no functions are present. `auto`` is the default if functions are present. functions: Deprecated in favor of `tools`. A list of functions the model may generate JSON inputs for. 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 tokenizer) to an associated bias value from -100 to 100. 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. max_tokens: The maximum number of [tokens](/tokenizer) to generate in the chat completion. The total length of input tokens and generated tokens is limited by 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 chat completion choices to generate for each input message. 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/gpt/parameter-details) response_format: An object specifying the format that the model must output. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is valid JSON. **Important:** when using JSON mode, you **must** also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in increased latency and appearance of a "stuck" request. Also note that the message content may be partially cut off if `finish_reason="length"`, which indicates the generation exceeded `max_tokens` or the conversation exceeded the max context length. seed: This feature is in Beta. 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. 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. tool_choice: Controls which (if any) function is called by the model. `none` means the model will not call a function and instead generates a message. `auto` means the model can pick between generating a message or calling a function. Specifying a particular function via `{"type: "function", "function": {"name": "my_function"}}` forces the model to call that function. `none` is the default when no functions are present. `auto` is the default if functions are present. tools: A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. 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 Nr1r.rIrKr?r5r6r7r8r9r:r;r<r=r>r@rArBrCrDrErFrGrHr1r1r2r_r`bool,ChatCompletion | Stream[ChatCompletionChunk]cCr\rdr1rer1r1r2r_|r`rIrKr?3Optional[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/chat/completionsrIrKr5r6r7r8r9r:r;r<r=r>r?r@rArBrCrD)rErFrGrHF)bodyoptionscast_tor? stream_cls)_postrrCompletionCreateParamsrrrrr^r1r1r2r_'sb/     )r&rr'r(.rIrJrKrLr5rMr6rNr7rOr8rPr9rQr:rQr;rMr<rRr=rQr>rSr?rTr@rMrArUrBrVrCrMrDrWrErXrFrYrGrZrHr[r'r).rIrJrKrLr?rbr5rMr6rNr7rOr8rPr9rQr:rQr;rMr<rRr=rQr>rSr@rMrArUrBrVrCrMrDrWrErXrFrYrGrZrHr[r'rc).rIrJrKrLr?rfr5rMr6rNr7rOr8rPr9rQr:rQr;rMr<rRr=rQr>rSr@rMrArUrBrVrCrMrDrWrErXrFrYrGrZrHr[r'rg).rIrJrKrLr5rMr6rNr7rOr8rPr9rQr:rQr;rMr<rRr=rQr>rSr?rir@rMrArUrBrVrCrMrDrWrErXrFrYrGrZrHr[r'rg __name__ __module__ __qualname____annotations__r,rr r_r __classcell__r1r1r/r2r! +++cr#)Dr"AsyncCompletionsWithRawResponser%r&r r'r(cr)r*)r+r,ryr%r-r/r1r2r,{r3zAsyncCompletions.__init__Nr4rIrJrKrLr5rMr6rNr7rOr8rPr9rQr:r;r<rRr=r>rSr?rTr@rArUrBrVrCrDrWrErXrFrYrGrZrHr[rcdSr]r1r^r1r1r2r_*zAsyncCompletions.createrarb AsyncStream[ChatCompletionChunk]crzrdr1rer1r1r2r_*r{rf1ChatCompletion | AsyncStream[ChatCompletionChunk]crzrdr1rer1r1r2r_r{rhric s|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|d|itjt||||dt| pKdttdIdHSrj)rorrrprrrrr^r1r1r2r_sd/      )r&r r'r(rq).rIrJrKrLr?rbr5rMr6rNr7rOr8rPr9rQr:rQr;rMr<rRr=rQr>rSr@rMrArUrBrVrCrMrDrWrErXrFrYrGrZrHr[r'r|).rIrJrKrLr?rfr5rMr6rNr7rOr8rPr9rQr:rQr;rMr<rRr=rQr>rSr@rMrArUrBrVrCrMrDrWrErXrFrYrGrZrHr[r'r}).rIrJrKrLr5rMr6rNr7rOr8rPr9rQr:rQr;rMr<rRr=rQr>rSr?rir@rMrArUrBrVrCrMrDrWrErXrFrYrGrZrHr[r'r}rrr1r1r/r2r"xrxc@eZdZdddZdS) r$ completionsr!r'r(cCt|j|_dSr*)rr_r.rr1r1r2r, z#CompletionsWithRawResponse.__init__N)rr!r'r(rsrtrur,r1r1r1r2r$r$c@r~) ryrr"r'r(cCrr*)rr_rr1r1r2r,rz(AsyncCompletionsWithRawResponse.__init__N)rr"r'r(rr1r1r1r2ryrry)/ __future__rtypingrrrrrrtyping_extensionsr httpx_typesr r r rr_utilsrr _resourcerr _responserr _streamingrr types.chatrrrrrr _base_clientr_clientrr __all__r!r"r$ryr1r1r1r2s4     ]]