AXj|ddlmZddlmZddlmZmZddlZddlm Z ddl m Z ddl m Z mZmZmZmZmZmZmZdd lmZmZmZmZmZdd lmZdd lmZmZdd l m!Z!m"Z"dd l#m$Z$ddl%m&Z&m'Z'ddl(m)Z)ddl*m+Z+ddl,m-Z-ddl.m/Z/ddl0m1Z1ddgZ2GddeZ3GddeZ4GddZ5GddZ6GddZ7GddZ8y) ) annotations)List)LiteraloverloadN)_legacy_response)completion_create_params)BodyOmitQueryHeadersNotGivenSequenceNotStromit not_given)is_given required_argsmaybe_transformstrip_not_givenasync_maybe_transform)cached_property)SyncAPIResourceAsyncAPIResource)to_streamed_response_wrapper"async_to_streamed_response_wrapper)DEFAULT_TIMEOUT)Stream AsyncStream)make_request_options) Completion) ModelParam) MetadataParam)AnthropicBetaParam CompletionsAsyncCompletionsc eZdZed dZed dZeeeeeeeeddded ddZ eeeeeeeddded ddZ eeeeeeeddded ddZ e gd gd eeeeeeeddded dd Z y)r$ct|Sa This property can be used as a prefix for any HTTP method call to return the raw response object instead of the parsed content. For more information, see https://www.github.com/anthropics/anthropic-sdk-python#accessing-raw-response-data-eg-headers )CompletionsWithRawResponseselfs e/var/www/html/content_generation/venv/lib/python3.12/site-packages/anthropic/resources/completions.pywith_raw_responsezCompletions.with_raw_responses*$//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_pbetas extra_headers extra_query extra_bodytimeoutcyaS[Legacy] Create a Text Completion. The Text Completions API is a legacy API. We recommend using the [Messages API](https://platform.claude.com/docs/en/api/messages) going forward. Future models and features will not be compatible with Text Completions. See our [migration guide](https://platform.claude.com/docs/en/build-with-claude/working-with-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://platform.claude.com/docs/en/build-with-claude/working-with-messages) and our guide to [prompt design](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview) 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://platform.claude.com/docs/en/build-with-claude/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. 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`. Recommended for advanced use cases only. betas: Optional header to specify the beta version(s) you want to use. 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_samplemodelpromptr4r5r6r7r8r9r:r;r<r=r>s r,createzCompletions.create0R r. r4r5r7r8r9r:r;r<r=r>cyaS[Legacy] Create a Text Completion. The Text Completions API is a legacy API. We recommend using the [Messages API](https://platform.claude.com/docs/en/api/messages) going forward. Future models and features will not be compatible with Text Completions. See our [migration guide](https://platform.claude.com/docs/en/build-with-claude/working-with-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://platform.claude.com/docs/en/build-with-claude/working-with-messages) and our guide to [prompt design](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview) for more details. stream: Whether to incrementally stream the response using server-sent events. See [streaming](https://platform.claude.com/docs/en/build-with-claude/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. 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`. 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