# File generated from our OpenAPI spec by Stainless. from __future__ import annotations from typing import TYPE_CHECKING, List, Union, overload from typing_extensions import Literal import httpx from ..types import Completion, completion_create_params from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven from .._utils import required_args, maybe_transform from .._resource import SyncAPIResource, AsyncAPIResource from .._response import to_raw_response_wrapper, async_to_raw_response_wrapper from .._streaming import Stream, AsyncStream from .._base_client import make_request_options if TYPE_CHECKING: from .._client import Anthropic, AsyncAnthropic __all__ = ["Completions", "AsyncCompletions"] class Completions(SyncAPIResource): with_raw_response: CompletionsWithRawResponse def __init__(self, client: Anthropic) -> None: super().__init__(client) self.with_raw_response = CompletionsWithRawResponse(self) @overload def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, stream: Literal[False] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> Completion: """ Create a Completion 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. As we improve Claude, we develop new versions of it that you can query. The `model` parameter controls which version of Claude responds to your request. Right now we offer two model families: Claude, and Claude Instant. You can use them by setting `model` to `"claude-2.1"` or `"claude-instant-1.2"`, respectively. See [models](https://docs.anthropic.com/claude/reference/selecting-a-model) 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 `\n\nHuman:` and `\n\nAssistant:` conversational turns. For example: ``` "\n\nHuman: {userQuestion}\n\nAssistant:" ``` See [prompt validation](https://anthropic.readme.io/claude/reference/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design) 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 `"\n\nHuman:"`, 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/claude/reference/streaming) for details. temperature: Amount of randomness injected into the response. Defaults to 1. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and closer to 1 for creative and generative tasks. 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). 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. 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 """ ... @overload def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, stream: Literal[True], metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> Stream[Completion]: """ Create a Completion 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. As we improve Claude, we develop new versions of it that you can query. The `model` parameter controls which version of Claude responds to your request. Right now we offer two model families: Claude, and Claude Instant. You can use them by setting `model` to `"claude-2.1"` or `"claude-instant-1.2"`, respectively. See [models](https://docs.anthropic.com/claude/reference/selecting-a-model) 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 `\n\nHuman:` and `\n\nAssistant:` conversational turns. For example: ``` "\n\nHuman: {userQuestion}\n\nAssistant:" ``` See [prompt validation](https://anthropic.readme.io/claude/reference/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design) for more details. stream: Whether to incrementally stream the response using server-sent events. See [streaming](https://docs.anthropic.com/claude/reference/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 `"\n\nHuman:"`, 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. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and closer to 1 for creative and generative tasks. 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). 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. 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 """ ... @overload def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, stream: bool, metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> Completion | Stream[Completion]: """ Create a Completion 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. As we improve Claude, we develop new versions of it that you can query. The `model` parameter controls which version of Claude responds to your request. Right now we offer two model families: Claude, and Claude Instant. You can use them by setting `model` to `"claude-2.1"` or `"claude-instant-1.2"`, respectively. See [models](https://docs.anthropic.com/claude/reference/selecting-a-model) 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 `\n\nHuman:` and `\n\nAssistant:` conversational turns. For example: ``` "\n\nHuman: {userQuestion}\n\nAssistant:" ``` See [prompt validation](https://anthropic.readme.io/claude/reference/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design) for more details. stream: Whether to incrementally stream the response using server-sent events. See [streaming](https://docs.anthropic.com/claude/reference/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 `"\n\nHuman:"`, 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. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and closer to 1 for creative and generative tasks. 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). 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. 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 """ ... @required_args(["max_tokens_to_sample", "model", "prompt"], ["max_tokens_to_sample", "model", "prompt", "stream"]) def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, stream: Literal[False] | Literal[True] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> Completion | Stream[Completion]: return self._post( "/v1/complete", body=maybe_transform( { "max_tokens_to_sample": max_tokens_to_sample, "model": model, "prompt": prompt, "metadata": metadata, "stop_sequences": stop_sequences, "stream": stream, "temperature": temperature, "top_k": top_k, "top_p": top_p, }, completion_create_params.CompletionCreateParams, ), options=make_request_options( extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout ), cast_to=Completion, stream=stream or False, stream_cls=Stream[Completion], ) class AsyncCompletions(AsyncAPIResource): with_raw_response: AsyncCompletionsWithRawResponse def __init__(self, client: AsyncAnthropic) -> None: super().__init__(client) self.with_raw_response = AsyncCompletionsWithRawResponse(self) @overload async def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, stream: Literal[False] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> Completion: """ Create a Completion 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. As we improve Claude, we develop new versions of it that you can query. The `model` parameter controls which version of Claude responds to your request. Right now we offer two model families: Claude, and Claude Instant. You can use them by setting `model` to `"claude-2.1"` or `"claude-instant-1.2"`, respectively. See [models](https://docs.anthropic.com/claude/reference/selecting-a-model) 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 `\n\nHuman:` and `\n\nAssistant:` conversational turns. For example: ``` "\n\nHuman: {userQuestion}\n\nAssistant:" ``` See [prompt validation](https://anthropic.readme.io/claude/reference/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design) 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 `"\n\nHuman:"`, 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/claude/reference/streaming) for details. temperature: Amount of randomness injected into the response. Defaults to 1. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and closer to 1 for creative and generative tasks. 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). 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. 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 """ ... @overload async def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, stream: Literal[True], metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> AsyncStream[Completion]: """ Create a Completion 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. As we improve Claude, we develop new versions of it that you can query. The `model` parameter controls which version of Claude responds to your request. Right now we offer two model families: Claude, and Claude Instant. You can use them by setting `model` to `"claude-2.1"` or `"claude-instant-1.2"`, respectively. See [models](https://docs.anthropic.com/claude/reference/selecting-a-model) 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 `\n\nHuman:` and `\n\nAssistant:` conversational turns. For example: ``` "\n\nHuman: {userQuestion}\n\nAssistant:" ``` See [prompt validation](https://anthropic.readme.io/claude/reference/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design) for more details. stream: Whether to incrementally stream the response using server-sent events. See [streaming](https://docs.anthropic.com/claude/reference/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 `"\n\nHuman:"`, 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. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and closer to 1 for creative and generative tasks. 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). 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. 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 """ ... @overload async def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, stream: bool, metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> Completion | AsyncStream[Completion]: """ Create a Completion 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. As we improve Claude, we develop new versions of it that you can query. The `model` parameter controls which version of Claude responds to your request. Right now we offer two model families: Claude, and Claude Instant. You can use them by setting `model` to `"claude-2.1"` or `"claude-instant-1.2"`, respectively. See [models](https://docs.anthropic.com/claude/reference/selecting-a-model) 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 `\n\nHuman:` and `\n\nAssistant:` conversational turns. For example: ``` "\n\nHuman: {userQuestion}\n\nAssistant:" ``` See [prompt validation](https://anthropic.readme.io/claude/reference/prompt-validation) and our guide to [prompt design](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design) for more details. stream: Whether to incrementally stream the response using server-sent events. See [streaming](https://docs.anthropic.com/claude/reference/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 `"\n\nHuman:"`, 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. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and closer to 1 for creative and generative tasks. 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). 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. 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 """ ... @required_args(["max_tokens_to_sample", "model", "prompt"], ["max_tokens_to_sample", "model", "prompt", "stream"]) async def create( self, *, max_tokens_to_sample: int, model: Union[str, Literal["claude-2.1", "claude-instant-1"]], prompt: str, metadata: completion_create_params.Metadata | NotGiven = NOT_GIVEN, stop_sequences: List[str] | NotGiven = NOT_GIVEN, stream: Literal[False] | Literal[True] | NotGiven = NOT_GIVEN, temperature: float | NotGiven = NOT_GIVEN, top_k: int | NotGiven = NOT_GIVEN, top_p: float | NotGiven = NOT_GIVEN, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. extra_headers: Headers | None = None, extra_query: Query | None = None, extra_body: Body | None = None, timeout: float | httpx.Timeout | None | NotGiven = 600, ) -> Completion | AsyncStream[Completion]: return await self._post( "/v1/complete", body=maybe_transform( { "max_tokens_to_sample": max_tokens_to_sample, "model": model, "prompt": prompt, "metadata": metadata, "stop_sequences": stop_sequences, "stream": stream, "temperature": temperature, "top_k": top_k, "top_p": top_p, }, completion_create_params.CompletionCreateParams, ), options=make_request_options( extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout ), cast_to=Completion, stream=stream or False, stream_cls=AsyncStream[Completion], ) class CompletionsWithRawResponse: def __init__(self, completions: Completions) -> None: self.create = to_raw_response_wrapper( completions.create, ) class AsyncCompletionsWithRawResponse: def __init__(self, completions: AsyncCompletions) -> None: self.create = async_to_raw_response_wrapper( completions.create, )