Dj|ddlmZddlmZddlmZmZddlZddlm Z ddl m Z ddl m Z mZmZmZmZdd lmZmZmZmZdd lmZdd lmZmZdd lmZmZdd lm Z ddl!m"Z"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)ddl*m+Z+ddgZ,GddeZ-GddeZ.GddZ/GddZ0GddZ1GddZ2y)) annotations)List)LiteraloverloadN)_legacy_response)completion_create_params) NOT_GIVENBodyQueryHeadersNotGiven)is_given required_argsmaybe_transformasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)DEFAULT_TIMEOUT)Stream AsyncStream)make_request_options) Completion) ModelParam) MetadataParam CompletionsAsyncCompletionsc eZdZed dZed dZeeeeeeeddded ddZeeeeeeddded ddZeeeeeeddded ddZe gd gd eeeeeeddded dd Zy)rct|Sa This property can be used as a prefix for any HTTP method call to return the the raw response object instead of the parsed content. For more information, see https://www.github.com/anthropics/anthropic-sdk-python#accessing-raw-response-data-eg-headers )CompletionsWithRawResponseselfs cC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\anthropic/resources/completions.pywith_raw_responsezCompletions.with_raw_response!s*$//ct|Sz An alternative to `.with_raw_response` that doesn't eagerly read the response body. For more information, see https://www.github.com/anthropics/anthropic-sdk-python#with_streaming_response ) CompletionsWithStreamingResponser%s r'with_streaming_responsez#Completions.with_streaming_response+s055r)N metadatastop_sequencesstream temperaturetop_ktop_p extra_headers extra_query extra_bodytimeoutc ya[[Legacy] Create a Text Completion. The Text Completions API is a legacy API. We recommend using the [Messages API](https://docs.anthropic.com/en/api/messages) going forward. Future models and features will not be compatible with Text Completions. See our [migration guide](https://docs.anthropic.com/en/api/migrating-from-text-completions-to-messages) for guidance in migrating from Text Completions to Messages. Args: max_tokens_to_sample: The maximum number of tokens to generate before stopping. Note that our models may stop _before_ reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate. model: The model that will complete your prompt. See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options. prompt: The prompt that you want Claude to complete. For proper response generation you will need to format your prompt using alternating ` Human:` and ` Assistant:` conversational turns. For example: ``` " Human: {userQuestion} Assistant:" ``` See [prompt validation](https://docs.anthropic.com/en/api/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/en/docs/intro-to-prompting) for more details. metadata: An object describing metadata about the request. stop_sequences: Sequences that will cause the model to stop generating. Our models stop on `" Human:"`, and may include additional built-in stop sequences in the future. By providing the stop_sequences parameter, you may include additional strings that will cause the model to stop generating. stream: Whether to incrementally stream the response using server-sent events. See [streaming](https://docs.anthropic.com/en/api/streaming) for details. temperature: Amount of randomness injected into the response. Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks. Note that even with `temperature` of `0.0`, the results will not be fully deterministic. top_k: Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). Recommended for advanced use cases only. You usually only need to use `temperature`. top_p: Use nucleus sampling. In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`. You should either alter `temperature` or `top_p`, but not both. Recommended for advanced use cases only. You usually only need to use `temperature`. 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&max_tokens_to_samplemodelpromptr/r0r1r2r3r4r5r6r7r8s r'createzCompletions.create4L r) r/r0r2r3r4r5r6r7r8c ya[[Legacy] Create a Text Completion. The Text Completions API is a legacy API. We recommend using the [Messages API](https://docs.anthropic.com/en/api/messages) going forward. Future models and features will not be compatible with Text Completions. See our [migration guide](https://docs.anthropic.com/en/api/migrating-from-text-completions-to-messages) for guidance in migrating from Text Completions to Messages. Args: max_tokens_to_sample: The maximum number of tokens to generate before stopping. Note that our models may stop _before_ reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate. model: The model that will complete your prompt. See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options. prompt: The prompt that you want Claude to complete. For proper response generation you will need to format your prompt using alternating ` Human:` and ` Assistant:` conversational turns. For example: ``` " Human: {userQuestion} Assistant:" ``` See [prompt validation](https://docs.anthropic.com/en/api/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/en/docs/intro-to-prompting) for more details. stream: Whether to incrementally stream the response using server-sent events. See [streaming](https://docs.anthropic.com/en/api/streaming) for details. metadata: An object describing metadata about the request. stop_sequences: Sequences that will cause the model to stop generating. Our models stop on `" Human:"`, and may include additional built-in stop sequences in the future. By providing the stop_sequences parameter, you may include additional strings that will cause the model to stop generating. temperature: Amount of randomness injected into the response. Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks. Note that even with `temperature` of `0.0`, the results will not be fully deterministic. top_k: Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). Recommended for advanced use cases only. You usually only need to use `temperature`. top_p: Use nucleus sampling. In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`. You should either alter `temperature` or `top_p`, but not both. Recommended for advanced use cases only. You usually only need to use `temperature`. 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&r=r>r?r1r/r0r2r3r4r5r6r7r8s r'r@zCompletions.createrAr)c yrDr;rEs r'r@zCompletions.createrAr)r=r>r?r=r>r?r1c t| s|jjtk(rd} |j dt ||||||||| d t jt| | | | t|xsdttSNiXz /v1/complete) r=r>r?r/r0r1r2r3r4)r5r6r7r8F)bodyoptionscast_tor1 stream_cls) r_clientr8r_postrr CompletionCreateParamsrrrr<s r'r@zCompletions.createls( T\\%9%9_%LGzz  ,@"$ (&4$#."" )?? )+Q[el?Uj)+  r))returnr$)rRr,r=intr>rr?strr/MetadataParam | NotGivenr0List[str] | NotGivenr1zLiteral[False] | NotGivenr2float | NotGivenr3int | NotGivenr4rXr5Headers | Noner6 Query | Noner7 Body | Noner8'float | httpx.Timeout | None | NotGivenrRr)r=rTr>rr?rUr1 Literal[True]r/rVr0rWr2rXr3rYr4rXr5rZr6r[r7r\r8r]rRzStream[Completion])r=rTr>rr?rUr1boolr/rVr0rWr2rXr3rYr4rXr5rZr6r[r7r\r8r]rRCompletion | Stream[Completion])r=rTr>rr?rUr/rVr0rWr1)Literal[False] | Literal[True] | NotGivenr2rXr3rYr4rXr5rZr6r[r7r\r8r]rRr` __name__ __module__ __qualname__rr(r-rr r@rr;r)r'rr sg0066.7/8,5(1 )"+)-$("&;D#e "e  e  e + e -e *e &e e  e &e "e  !e "9#e $ %e e N.7/8(1 )"+)-$("&;D#e "e  e  e  e +e -e &e e  e &e "e  !e "9#e $ %e e N.7/8(1 )"+)-$("&;D#e "e  e  e  e +e -e &e e  e &e "e  !e "9#e $ )%e e N>@uv.7/8rr?rUr1r^r/rVr0rWr2rXr3rYr4rXr5rZr6r[r7r\r8r]rRzAsyncStream[Completion])r=rTr>rr?rUr1r_r/rVr0rWr2rXr3rYr4rXr5rZr6r[r7r\r8r]rR$Completion | AsyncStream[Completion])r=rTr>rr?rUr/rVr0rWr1rar2rXr3rYr4rXr5rZr6r[r7r\r8r]rRrqrbr;r)r'r r sg55;;.7/8,5(1 )"+)-$("&;D#e "e  e  e + e -e *e &e e  e &e "e  !e "9#e $ %e e N.7/8(1 )"+)-$("&;D#e "e  e  e  e +e -e &e e  e &e "e  !e "9#e $ !%e e N.7/8(1 )"+)-$("&;D#e "e  e  e  e +e -e &e e  e &e "e  !e "9#e $ .%e e N>@uv.7/8>     r)NrxrrRNonercrdreryr;r)r'r$r$ r)r$ceZdZddZy)rhcZ||_tj|j|_yrt)rurasync_to_raw_response_wrapperr@rws r'ryz(AsyncCompletionsWithRawResponse.__init__ s%'&DD     r)Nrxr rRr{r|r;r)r'rhrhr}r)rhceZdZddZy)r,cF||_t|j|_yrt)rurr@rws r'ryz)CompletionsWithStreamingResponse.__init__)s'2     r)Nrzr|r;r)r'r,r,(r}r)r,ceZdZddZy)rjcF||_t|j|_yrt)rurr@rws r'ryz.AsyncCompletionsWithStreamingResponse.__init__2s'8     r)Nrr|r;r)r'rjrj1r}r)rj)3 __future__rtypingrtyping_extensionsrrhttpxrtypesr _typesr r r r r_utilsrrrr_compatr _resourcerr _responserr _constantsr _streamingrr _base_clientrtypes.completionrtypes.model_paramrtypes.metadata_paramr__all__rr r$rhr,rjr;r)r'rs#/ ,>> &9X(,/)*0 , -x /x v x 'x v         r)