Ej"ddlmZddlmZmZmZmZmZmZddl m Z ddl Z ddl m Z ddlmZmZmZmZmZddlmZmZmZdd lmZdd lmZmZdd lmZmZdd l m!Z!m"Z"dd l#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4ddgZ5GddeZ6GddeZ7GddZ8GddZ9GddZ:Gd d!Z;y)") annotations)DictListUnionIterableOptionaloverload)LiteralN)_legacy_response) NOT_GIVENBodyQueryHeadersNotGiven) required_argsmaybe_transformasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)Stream AsyncStream)completion_create_params)make_request_options) ChatModel)ChatCompletion)ChatCompletionChunk)ChatCompletionToolParam)ChatCompletionMessageParam) ChatCompletionStreamOptionsParam)#ChatCompletionToolChoiceOptionParam CompletionsAsyncCompletionsceZdZed dZeddZeeeeeeeeeeeeeeeeeeeeeeddded ddZeeeeeeeeeeeeeeeeeeeeeddded dd Zeeeeeeeeeeeeeeeeeeeeeddded dd Ze ddggd eeeeeeeeeeeeeeeeeeeeeddded dd Zy)r%ct|SN)CompletionsWithRawResponseselfs d/var/www/html/peoplehubportal/venv/lib/python3.12/site-packages/openai/resources/chat/completions.pywith_raw_responsezCompletions.with_raw_response%s )$//ct|Sr)) CompletionsWithStreamingResponser+s r-with_streaming_responsez#Completions.with_streaming_response)s /55r/Nfrequency_penalty function_call functions logit_biaslogprobs max_tokensnparallel_tool_callspresence_penaltyresponse_formatseed service_tierstopstreamstream_options temperature tool_choicetools top_logprobstop_puser extra_headers extra_query extra_bodytimeoutmessagesmodelcya! 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/text-generation/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. logprobs: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. max_tokens: The maximum number of [tokens](/tokenizer) that can be generated 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. Note that you will be charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to minimize costs. parallel_tool_calls: Whether to enable [parallel function calling](https://platform.openai.com/docs/guides/function-calling/parallel-function-calling) during tool use. 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) response_format: An object specifying the format that the model must output. Compatible with [GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and all GPT-3.5 Turbo models newer than `gpt-3.5-turbo-1106`. 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 a long-running and seemingly "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. service_tier: Specifies the latency tier to use for processing the request. This parameter is relevant for customers subscribed to the scale tier service: - If set to 'auto', the system will utilize scale tier credits until they are exhausted. - If set to 'default', the request will be processed in the shared cluster. When this parameter is set, the response body will include the `service_tier` utilized. 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). stream_options: Options for streaming response. Only set this when you set `stream: true`. 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) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `required` means the model must call one or more tools. Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}` forces the model to call that tool. `none` is the default when no tools are present. `auto` is the default if tools 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. A max of 128 functions are supported. top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. 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,rMrNr4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLs r-createzCompletions.create-r r/r4r5r6r7r8r9r:r;r<r=r>r?r@rBrCrDrErFrGrHrIrJrKrLcya! 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/text-generation/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. logprobs: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`. max_tokens: The maximum number of [tokens](/tokenizer) that can be generated 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. Note that you will be charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to minimize costs. parallel_tool_calls: Whether to enable [parallel function calling](https://platform.openai.com/docs/guides/function-calling/parallel-function-calling) during tool use. 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) response_format: An object specifying the format that the model must output. Compatible with [GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and all GPT-3.5 Turbo models newer than `gpt-3.5-turbo-1106`. 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 a long-running and seemingly "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. service_tier: Specifies the latency tier to use for processing the request. This parameter is relevant for customers subscribed to the scale tier service: - If set to 'auto', the system will utilize scale tier credits until they are exhausted. - If set to 'default', the request will be processed in the shared cluster. When this parameter is set, the response body will include the `service_tier` utilized. stop: Up to 4 sequences where the API will stop generating further tokens. stream_options: Options for streaming response. Only set this when you set `stream: true`. 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) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `required` means the model must call one or more tools. Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}` forces the model to call that tool. `none` is the default when no tools are present. `auto` is the default if tools 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. A max of 128 functions are supported. top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used. 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 NrQr,rMrNrAr4r5r6r7r8r9r:r;r<r=r>r?r@rBrCrDrErFrGrHrIrJrKrLs r-rSzCompletions.createrTr/cyrWrQrXs r-rSzCompletions.createrTr/rMrNrAc |jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|||||||dtjt ||||t |xsdt tSNz/chat/completionsrMrNr4r5r6r7r8r9r:r;r<r=r>r?r@rArB)rCrDrErFrGrH)rIrJrKrLF)bodyoptionscast_torA stream_cls)_postrrCompletionCreateParamsrrrr rRs r-rSzCompletions.create^sEDzz  U():$]     !* !**+>'(8&D#LD f!"%n#$$/#."$0" /2)??58)+Q[el#?U12G$ $ r/)returnr*)rcr18rM$Iterable[ChatCompletionMessageParam]rNUnion[str, ChatModel]r4Optional[float] | NotGivenr50completion_create_params.FunctionCall | NotGivenr66Iterable[completion_create_params.Function] | NotGivenr7#Optional[Dict[str, int]] | NotGivenr8Optional[bool] | NotGivenr9Optional[int] | NotGivenr:rlr;bool | NotGivenr<rgr=2completion_create_params.ResponseFormat | NotGivenr>rlr?/Optional[Literal['auto', 'default']] | NotGivenr@*Union[Optional[str], List[str]] | NotGivenrAz#Optional[Literal[False]] | NotGivenrB5Optional[ChatCompletionStreamOptionsParam] | NotGivenrCrgrD.ChatCompletionToolChoiceOptionParam | NotGivenrE,Iterable[ChatCompletionToolParam] | NotGivenrFrlrGrgrHstr | NotGivenrIHeaders | NonerJ Query | NonerK Body | NonerL'float | httpx.Timeout | None | NotGivenrcr)8rMrerNrfrA Literal[True]r4rgr5rhr6rir7rjr8rkr9rlr:rlr;rmr<rgr=rnr>rlr?ror@rprBrqrCrgrDrrrErsrFrlrGrgrHrtrIrurJrvrKrwrLrxrczStream[ChatCompletionChunk])8rMrerNrfrAboolr4rgr5rhr6rir7rjr8rkr9rlr:rlr;rmr<rgr=rnr>rlr?ror@rprBrqrCrgrDrrrErsrFrlrGrgrHrtrIrurJrvrKrwrLrxrc,ChatCompletion | Stream[ChatCompletionChunk])8rMrerNrfr4rgr5rhr6rir7rjr8rkr9rlr:rlr;rmr<rgr=rnr>rlr?ror@rprA3Optional[Literal[False]] | Literal[True] | NotGivenrBrqrCrgrDrrrErsrFrlrGrgrHrtrIrurJrvrKrwrLrxrcr{ __name__ __module__ __qualname__rr.r2r r rSrrQr/r-r%r%$s0066 9BJSLU:C.7/8&//87@NW)2HQ;D6?PY2;FO>G1:,5()-$("&;D?x 7x % x 6 x H x Jx 8x ,x -x  $x -x 5x Lx 'x F!x "9#x $4%x &N'x (0)x *D+x ,<-x .//x 0*1x 23x 8&9x :";x < =x >9?x @ Ax x t9BJSLU:C.7/8&//87@NW)2HQ;DPY2;FO>G1:,5()-$("&;D?x 7x % x  x 6 x Hx Jx 8x ,x -x  $x -x 5x Lx '!x "F#x $9%x &N'x (0)x *D+x ,<-x .//x 0*1x 23x 8&9x :";x < =x >9?x @ %Ax x t9BJSLU:C.7/8&//87@NW)2HQ;DPY2;FO>G1:,5()-$("&;D?x 7x % x  x 6 x Hx Jx 8x ,x -x  $x -x 5x Lx '!x "F#x $9%x &N'x (0)x *D+x ,<-x .//x 0*1x 23x 8&9x :";x < =x >9?x @ 6Ax x tJ(*IJ 9BJSLU:C.7/8&//87@NW)2HQ;DFOPY2;FO>G1:,5()-$("&;D?E 7E % E 6 E H E JE 8E ,E -E  $E -E 5E LE 'E F!E "9#E $D%E &N'E (0)E *D+E ,<-E .//E 0*1E 23E 8&9E :";E < =E >9?E @ 6AE KE r/ceZdZed dZeddZeeeeeeeeeeeeeeeeeeeeeeddded ddZeeeeeeeeeeeeeeeeeeeeeddded dd Zeeeeeeeeeeeeeeeeeeeeeddded dd Ze ddggd eeeeeeeeeeeeeeeeeeeeeddded dd Zy)r&ct|Sr))AsyncCompletionsWithRawResponser+s r-r.z"AsyncCompletions.with_raw_responses .t44r/ct|Sr))%AsyncCompletionsWithStreamingResponser+s r-r2z(AsyncCompletions.with_streaming_responses 4T::r/Nr3rMrNc KywrPrQrRs r-rSzAsyncCompletions.create r rUc KywrWrQrXs r-rSzAsyncCompletions.createkrrc KywrWrQrXs r-rSzAsyncCompletions.create&rrrZc HK|jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d|||||||dtjd{t ||||t |xsdt td{S757wr\)rarrrbrrrr rRs r-rSzAsyncCompletions.creates^DZZ ,U():$]     !* !**+>'(8&D#LD f!"%n#$$/#."$0" /2)??58)+Q[el#?U"#67G $ $ $ $ s$A&B"(B )0B"B B" B")rcr)rcrrd)8rMrerNrfrAryr4rgr5rhr6rir7rjr8rkr9rlr:rlr;rmr<rgr=rnr>rlr?ror@rprBrqrCrgrDrrrErsrFrlrGrgrHrtrIrurJrvrKrwrLrxrcz AsyncStream[ChatCompletionChunk])8rMrerNrfrArzr4rgr5rhr6rir7rjr8rkr9rlr:rlr;rmr<rgr=rnr>rlr?ror@rprBrqrCrgrDrrrErsrFrlrGrgrHrtrIrurJrvrKrwrLrxrc1ChatCompletion | AsyncStream[ChatCompletionChunk])8rMrerNrfr4rgr5rhr6rir7rjr8rkr9rlr:rlr;rmr<rgr=rnr>rlr?ror@rprAr|rBrqrCrgrDrrrErsrFrlrGrgrHrtrIrurJrvrKrwrLrxrcrr}rQr/r-r&r&s55;; 9BJSLU:C.7/8&//87@NW)2HQ;D6?PY2;FO>G1:,5()-$("&;D?x 7x % x 6 x H x Jx 8x ,x -x  $x -x 5x Lx 'x F!x "9#x $4%x &N'x (0)x *D+x ,<-x .//x 0*1x 23x 8&9x :";x < =x >9?x @ Ax x t9BJSLU:C.7/8&//87@NW)2HQ;DPY2;FO>G1:,5()-$("&;D?x 7x % x  x 6 x Hx Jx 8x ,x -x  $x -x 5x Lx '!x "F#x $9%x &N'x (0)x *D+x ,<-x .//x 0*1x 23x 8&9x :";x < =x >9?x @ *Ax x t9BJSLU:C.7/8&//87@NW)2HQ;DPY2;FO>G1:,5()-$("&;D?x 7x % x  x 6 x Hx Jx 8x ,x -x  $x -x 5x Lx '!x "F#x $9%x &N'x (0)x *D+x ,<-x .//x 0*1x 23x 8&9x :";x < =x >9?x @ ;Ax x tJ(*IJ 9BJSLU:C.7/8&//87@NW)2HQ;DFOPY2;FO>G1:,5()-$("&;D?E 7E % E 6 E H E JE 8E ,E -E  $E -E 5E LE 'E F!E "9#E $D%E &N'E (0)E *D+E ,<-E .//E 0*1E 23E 8&9E :";E < =E >9?E @ ;AE KE r/ceZdZddZy)r*cZ||_tj|j|_yr)) _completionsr to_raw_response_wrapperrSr, completionss r-__init__z#CompletionsWithRawResponse.__init__+s%'&>>     r/Nrr%rcNoner~rrrrQr/r-r*r** r/r*ceZdZddZy)rcZ||_tj|j|_yr))rr async_to_raw_response_wrapperrSrs r-rz(AsyncCompletionsWithRawResponse.__init__4s%'&DD     r/Nrr&rcrrrQr/r-rr3rr/rceZdZddZy)r1cF||_t|j|_yr))rrrSrs r-rz)CompletionsWithStreamingResponse.__init__=s'2     r/NrrrQr/r-r1r1<rr/r1ceZdZddZy)rcF||_t|j|_yr))rrrSrs r-rz.AsyncCompletionsWithStreamingResponse.__init__Fs'8     r/NrrrQr/r-rrErr/r)< __future__rtypingrrrrrr typing_extensionsr httpxr _typesr rrrr_utilsrrr_compatr _resourcerr _responserr _streamingrr types.chatr _base_clientrtypes.chat_modelrtypes.chat.chat_completionr types.chat.chat_completion_chunkr %types.chat.chat_completion_tool_paramr!(types.chat.chat_completion_message_paramr"/types.chat.chat_completion_stream_options_paramr#3types.chat.chat_completion_tool_choice_option_paramr$__all__r%r&r*rr1rrQr/r-rs#BB% ?? 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