o #`^hX@sddlmZddlZddlmZmZmZmZmZddl m Z m Z ddl Z ddl Z ddlmZddlmZmZmZmZmZddlmZmZmZdd lmZdd lmZmZdd lm Z m!Z!dd l"m#Z#m$Z$dd l%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4ddl5m6Z6ddgZ7GdddeZ8GdddeZ9GdddZ:GdddZ;GdddZdS))) annotationsN)DictListUnionIterableOptional)Literaloverload)_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 CompletionsAsyncCompletionsc@ZeZdZedKddZedLddZeeeeeeeeeeeeeeeeeeeeeeeeeddded dMd?d@ZeeeeeeeeeeeeeeeeeeeeeeeedddedAdNdDd@ZeeeeeeeeeeeeeeeeeeeeeeeedddedAdOdGd@Ze d d ggdHeeeeeeeeeeeeeeeeeeeeeeeeddded dPdJd@ZdS)Qr$returnCompletionsWithRawResponsecCt|Sa This property can be used as a prefix for any HTTP method call to return the the raw response object instead of the parsed content. For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers )r(selfr-_/home/air/shanriGPT/back/venv/lib/python3.10/site-packages/openai/resources/chat/completions.pywith_raw_response%zCompletions.with_raw_response CompletionsWithStreamingResponsecCr)z An alternative to `.with_raw_response` that doesn't eagerly read the response body. For more information, see https://www.github.com/openai/openai-python#with_streaming_response )r1r+r-r-r.with_streaming_response/z#Completions.with_streaming_responseNfrequency_penalty function_call functions logit_biaslogprobsmax_completion_tokens max_tokensmetadatanparallel_tool_callspresence_penaltyresponse_formatseed service_tierstopstorestreamstream_options temperature tool_choicetools top_logprobstop_puser extra_headers extra_query extra_bodytimeoutmessages$Iterable[ChatCompletionMessageParam]modelUnion[str, ChatModel]r6Optional[float] | NotGivenr70completion_create_params.FunctionCall | NotGivenr86Iterable[completion_create_params.Function] | NotGivenr9#Optional[Dict[str, int]] | NotGivenr:Optional[bool] | NotGivenr;Optional[int] | NotGivenr<r=#Optional[Dict[str, str]] | NotGivenr>r?bool | NotGivenr@rA2completion_create_params.ResponseFormat | NotGivenrBrC/Optional[Literal['auto', 'default']] | NotGivenrD*Union[Optional[str], List[str]] | NotGivenrErF#Optional[Literal[False]] | NotGivenrG5Optional[ChatCompletionStreamOptionsParam] | NotGivenrHrI.ChatCompletionToolChoiceOptionParam | NotGivenrJ,Iterable[ChatCompletionToolParam] | NotGivenrKrLrMstr | NotGivenrNHeaders | NonerO Query | NonerP Body | NonerQ'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. Depending on the [model](https://platform.openai.com/docs/models) you use, different message types (modalities) are supported, like [text](https://platform.openai.com/docs/guides/text-generation), [images](https://platform.openai.com/docs/guides/vision), and [audio](https://platform.openai.com/docs/guides/audio). 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_completion_tokens: An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and [reasoning tokens](https://platform.openai.com/docs/guides/reasoning). max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the chat completion. This value can be used to control [costs](https://openai.com/api/pricing/) for text generated via API. This value is now deprecated in favor of `max_completion_tokens`, and is not compatible with [o1 series models](https://platform.openai.com/docs/guides/reasoning). metadata: Developer-defined tags and values used for filtering completions in the [dashboard](https://platform.openai.com/completions). 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-4o](https://platform.openai.com/docs/models/gpt-4o), [GPT-4o mini](https://platform.openai.com/docs/models/gpt-4o-mini), [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_schema", "json_schema": {...} }` enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs). Setting to `{ "type": "json_object" }` enables JSON mode, which ensures 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', and the Project is Scale tier enabled, the system will utilize scale tier credits until they are exhausted. - If set to 'auto', and the Project is not Scale tier enabled, the request will be processed using the default service tier with a lower uptime SLA and no latency guarentee. - If set to 'default', the request will be processed using the default service tier with a lower uptime SLA and no latency guarentee. - When not set, the default behavior is 'auto'. 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. store: Whether or not to store the output of this completion request for traffic logging in the [dashboard](https://platform.openai.com/completions). 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-r,rRrTr6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQr-r-r.create8WzCompletions.creater6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErGrHrIrJrKrLrMrNrOrPrQ Literal[True]Stream[ChatCompletionChunk]cCrja( Creates a model response for the given chat conversation. Args: messages: A list of messages comprising the conversation so far. Depending on the [model](https://platform.openai.com/docs/models) you use, different message types (modalities) are supported, like [text](https://platform.openai.com/docs/guides/text-generation), [images](https://platform.openai.com/docs/guides/vision), and [audio](https://platform.openai.com/docs/guides/audio). 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_completion_tokens: An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and [reasoning tokens](https://platform.openai.com/docs/guides/reasoning). max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the chat completion. This value can be used to control [costs](https://openai.com/api/pricing/) for text generated via API. This value is now deprecated in favor of `max_completion_tokens`, and is not compatible with [o1 series models](https://platform.openai.com/docs/guides/reasoning). metadata: Developer-defined tags and values used for filtering completions in the [dashboard](https://platform.openai.com/completions). 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-4o](https://platform.openai.com/docs/models/gpt-4o), [GPT-4o mini](https://platform.openai.com/docs/models/gpt-4o-mini), [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_schema", "json_schema": {...} }` enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs). Setting to `{ "type": "json_object" }` enables JSON mode, which ensures 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', and the Project is Scale tier enabled, the system will utilize scale tier credits until they are exhausted. - If set to 'auto', and the Project is not Scale tier enabled, the request will be processed using the default service tier with a lower uptime SLA and no latency guarentee. - If set to 'default', the request will be processed using the default service tier with a lower uptime SLA and no latency guarentee. - When not set, the default behavior is 'auto'. 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. store: Whether or not to store the output of this completion request for traffic logging in the [dashboard](https://platform.openai.com/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-r,rRrTrFr6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErGrHrIrJrKrLrMrNrOrPrQr-r-r.rmrnbool,ChatCompletion | Stream[ChatCompletionChunk]cCrjrrr-rsr-r-r.rmrnrRrTrF3Optional[Literal[False]] | Literal[True] | NotGivencCst||jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d||||||||||d tjt||||dt|pVdttdSNz/chat/completionsrRrTr6r7r8r9r:r;r<r=r>r?r@rArBrCrD) rErFrGrHrIrJrKrLrM)rNrOrPrQF)bodyoptionscast_torF stream_cls) validate_response_format_postrrCompletionCreateParamsrrrrrlr-r-r.rmst%     )r'r()r'r1>rRrSrTrUr6rVr7rWr8rXr9rYr:rZr;r[r<r[r=r\r>r[r?r]r@rVrAr^rBr[rCr_rDr`rErZrFrarGrbrHrVrIrcrJrdrKr[rLrVrMrerNrfrOrgrPrhrQrir'r)>rRrSrTrUrFrpr6rVr7rWr8rXr9rYr:rZr;r[r<r[r=r\r>r[r?r]r@rVrAr^rBr[rCr_rDr`rErZrGrbrHrVrIrcrJrdrKr[rLrVrMrerNrfrOrgrPrhrQrir'rq)>rRrSrTrUrFrtr6rVr7rWr8rXr9rYr:rZr;r[r<r[r=r\r>r[r?r]r@rVrAr^rBr[rCr_rDr`rErZrGrbrHrVrIrcrJrdrKr[rLrVrMrerNrfrOrgrPrhrQrir'ru)>rRrSrTrUr6rVr7rWr8rXr9rYr:rZr;r[r<r[r=r\r>r[r?r]r@rVrAr^rBr[rCr_rDr`rErZrFrwrGrbrHrVrIrcrJrdrKr[rLrVrMrerNrfrOrgrPrhrQrir'ru __name__ __module__ __qualname__rr/r3r r rmrr-r-r-r.r$$  XXXc@r&)Qr%r'AsyncCompletionsWithRawResponsecCr)r*)rr+r-r-r.r/r0z"AsyncCompletions.with_raw_response%AsyncCompletionsWithStreamingResponsecCr)r2)rr+r-r-r.r3r4z(AsyncCompletions.with_streaming_responseNr5rRrSrTrUr6rVr7rWr8rXr9rYr:rZr;r[r<r=r\r>r?r]r@rAr^rBrCr_rDr`rErFrarGrbrHrIrcrJrdrKrLrMrerNrfrOrgrPrhrQrircdSrkr-rlr-r-r.rm$WzAsyncCompletions.createrorp AsyncStream[ChatCompletionChunk]crrrr-rsr-r-r.rmrrt1ChatCompletion | AsyncStream[ChatCompletionChunk]crrrr-rsr-r-r.rmrrvrwcst||jdtid|d|d|d|d|d|d|d |d | d | d | d | d| d|d|d|d||||||||||d tjIdHt||||dt|pZdttdIdHSrx) r}r~rrrrrrrrlr-r-r.rmsv%      )r'r)r'rr)>rRrSrTrUrFrpr6rVr7rWr8rXr9rYr:rZr;r[r<r[r=r\r>r[r?r]r@rVrAr^rBr[rCr_rDr`rErZrGrbrHrVrIrcrJrdrKr[rLrVrMrerNrfrOrgrPrhrQrir'r)>rRrSrTrUrFrtr6rVr7rWr8rXr9rYr:rZr;r[r<r[r=r\r>r[r?r]r@rVrAr^rBr[rCr_rDr`rErZrGrbrHrVrIrcrJrdrKr[rLrVrMrerNrfrOrgrPrhrQrir'r)>rRrSrTrUr6rVr7rWr8rXr9rYr:rZr;r[r<r[r=r\r>r[r?r]r@rVrAr^rBr[rCr_rDr`rErZrFrwrGrbrHrVrIrcrJrdrKr[rLrVrMrerNrfrOrgrPrhrQrir'rrr-r-r-r.r%rc@eZdZdddZdS) r( completionsr$r'NonecC||_t|j|_dSN) _completionsr to_raw_response_wrapperrmr,rr-r-r.__init__ z#CompletionsWithRawResponse.__init__Nrr$r'rrrrrr-r-r-r.r(r(c@r) rrr%r'rcCrr)rr async_to_raw_response_wrapperrmrr-r-r.rrz(AsyncCompletionsWithRawResponse.__init__Nrr%r'rrr-r-r-r.rrrc@r) r1rr$r'rcC||_t|j|_dSr)rrrmrr-r-r.r z)CompletionsWithStreamingResponse.__init__Nrrr-r-r-r.r1rr1c@r) rrr%r'rcCrr)rrrmrr-r-r.rrz.AsyncCompletionsWithStreamingResponse.__init__Nrrr-r-r-r.rrrrAobjectr'rcCs&t|rt|tjrtddSdS)NzzYou tried to pass a `BaseModel` class to `chat.completions.create()`; You must use `beta.chat.completions.parse()` instead)inspectisclass issubclasspydantic BaseModel TypeError)rAr-r-r.r} s r})rArr'r)? __future__rrtypingrrrrrtyping_extensionsrr httpxrr _typesr r rrr_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(rr1rr}r-r-r-r.sP            qq