completions.py

# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

from __future__ import annotations

from typing import Dict, Union, Iterable, Optional
from typing_extensions import Literal, overload

import httpx

from ..._types import Body, Omit, Query, Headers, NotGiven, SequenceNotStr, omit, not_given
from ..._utils import required_args, maybe_transform, async_maybe_transform
from ..._compat import cached_property
from ..._resource import SyncAPIResource, AsyncAPIResource
from ..._response import (
    to_raw_response_wrapper,
    to_streamed_response_wrapper,
    async_to_raw_response_wrapper,
    async_to_streamed_response_wrapper,
)
from ..._streaming import Stream, AsyncStream
from ...types.chat import completion_create_params
from ..._base_client import make_request_options
from ...types.shared.chat_completion_chunk import ChatCompletionChunk
from ...types.chat.completion_create_response import CompletionCreateResponse

__all__ = ["CompletionsResource", "AsyncCompletionsResource"]


class CompletionsResource(SyncAPIResource):
    @cached_property
    def with_raw_response(self) -> CompletionsResourceWithRawResponse:
        """
        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/digitalocean/gradient-python#accessing-raw-response-data-eg-headers
        """
        return CompletionsResourceWithRawResponse(self)

    @cached_property
    def with_streaming_response(self) -> CompletionsResourceWithStreamingResponse:
        """
        An alternative to `.with_raw_response` that doesn't eagerly read the response body.

        For more information, see https://www.github.com/digitalocean/gradient-python#with_streaming_response
        """
        return CompletionsResourceWithStreamingResponse(self)

    @overload
    def create(
        self,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream: Optional[Literal[False]] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> CompletionCreateResponse:
        """
        Creates a model response for the given chat conversation.

        Args:
          messages: A list of messages comprising the conversation so far.

          model: Model ID used to generate the response.

          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.

          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: The maximum number of completion tokens that may be used over the course of the
              run. The run will make a best effort to use only the number of completion tokens
              specified, across multiple turns of the run.

          max_tokens: The maximum number of tokens that can be generated in the completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          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.

          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.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          stream: If set to true, the model response data will be streamed to the client as it is
              generated using server-sent events.

          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.

          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 DigitalOcean to
              monitor and detect abuse.

          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,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        stream: Literal[True],
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> Stream[ChatCompletionChunk]:
        """
        Creates a model response for the given chat conversation.

        Args:
          messages: A list of messages comprising the conversation so far.

          model: Model ID used to generate the response.

          stream: If set to true, the model response data will be streamed to the client as it is
              generated using server-sent events.

          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.

          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: The maximum number of completion tokens that may be used over the course of the
              run. The run will make a best effort to use only the number of completion tokens
              specified, across multiple turns of the run.

          max_tokens: The maximum number of tokens that can be generated in the completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          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.

          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.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          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.

          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 DigitalOcean to
              monitor and detect abuse.

          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,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        stream: bool,
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> CompletionCreateResponse | Stream[ChatCompletionChunk]:
        """
        Creates a model response for the given chat conversation.

        Args:
          messages: A list of messages comprising the conversation so far.

          model: Model ID used to generate the response.

          stream: If set to true, the model response data will be streamed to the client as it is
              generated using server-sent events.

          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.

          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: The maximum number of completion tokens that may be used over the course of the
              run. The run will make a best effort to use only the number of completion tokens
              specified, across multiple turns of the run.

          max_tokens: The maximum number of tokens that can be generated in the completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          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.

          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.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          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.

          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 DigitalOcean to
              monitor and detect abuse.

          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(["messages", "model"], ["messages", "model", "stream"])
    def create(
        self,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream: Optional[Literal[False]] | Literal[True] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> CompletionCreateResponse | Stream[ChatCompletionChunk]:
        # This method requires an model_access_key to be set via client argument or environment variable
        if not self._client.model_access_key:
            raise TypeError(
                "Could not resolve authentication method. Expected model_access_key to be set for chat completions."
            )
        headers = extra_headers or {}
        headers = {
            "Authorization": f"Bearer {self._client.model_access_key}",
            **headers,
        }

        return self._post(
            "/chat/completions"
            if self._client._base_url_overridden
            else f"{self._client.inference_endpoint}/v1/chat/completions",
            body=maybe_transform(
                {
                    "messages": messages,
                    "model": model,
                    "frequency_penalty": frequency_penalty,
                    "logit_bias": logit_bias,
                    "logprobs": logprobs,
                    "max_completion_tokens": max_completion_tokens,
                    "max_tokens": max_tokens,
                    "metadata": metadata,
                    "n": n,
                    "presence_penalty": presence_penalty,
                    "stop": stop,
                    "stream": stream,
                    "stream_options": stream_options,
                    "temperature": temperature,
                    "tool_choice": tool_choice,
                    "tools": tools,
                    "top_logprobs": top_logprobs,
                    "top_p": top_p,
                    "user": user,
                },
                (
                    completion_create_params.CompletionCreateParamsStreaming
                    if stream
                    else completion_create_params.CompletionCreateParamsNonStreaming
                ),
            ),
            options=make_request_options(
                extra_headers=headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
            ),
            cast_to=CompletionCreateResponse,
            stream=stream or False,
            stream_cls=Stream[ChatCompletionChunk],
        )


class AsyncCompletionsResource(AsyncAPIResource):
    @cached_property
    def with_raw_response(self) -> AsyncCompletionsResourceWithRawResponse:
        """
        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/digitalocean/gradient-python#accessing-raw-response-data-eg-headers
        """
        return AsyncCompletionsResourceWithRawResponse(self)

    @cached_property
    def with_streaming_response(self) -> AsyncCompletionsResourceWithStreamingResponse:
        """
        An alternative to `.with_raw_response` that doesn't eagerly read the response body.

        For more information, see https://www.github.com/digitalocean/gradient-python#with_streaming_response
        """
        return AsyncCompletionsResourceWithStreamingResponse(self)

    @overload
    async def create(
        self,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream: Optional[Literal[False]] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> CompletionCreateResponse:
        """
        Creates a model response for the given chat conversation.

        Args:
          messages: A list of messages comprising the conversation so far.

          model: Model ID used to generate the response.

          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.

          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: The maximum number of completion tokens that may be used over the course of the
              run. The run will make a best effort to use only the number of completion tokens
              specified, across multiple turns of the run.

          max_tokens: The maximum number of tokens that can be generated in the completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          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.

          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.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          stream: If set to true, the model response data will be streamed to the client as it is
              generated using server-sent events.

          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.

          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 DigitalOcean to
              monitor and detect abuse.

          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,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        stream: Literal[True],
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> AsyncStream[ChatCompletionChunk]:
        """
        Creates a model response for the given chat conversation.

        Args:
          messages: A list of messages comprising the conversation so far.

          model: Model ID used to generate the response.

          stream: If set to true, the model response data will be streamed to the client as it is
              generated using server-sent events.

          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.

          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: The maximum number of completion tokens that may be used over the course of the
              run. The run will make a best effort to use only the number of completion tokens
              specified, across multiple turns of the run.

          max_tokens: The maximum number of tokens that can be generated in the completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          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.

          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.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          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.

          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 DigitalOcean to
              monitor and detect abuse.

          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,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        stream: bool,
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> CompletionCreateResponse | AsyncStream[ChatCompletionChunk]:
        """
        Creates a model response for the given chat conversation.

        Args:
          messages: A list of messages comprising the conversation so far.

          model: Model ID used to generate the response.

          stream: If set to true, the model response data will be streamed to the client as it is
              generated using server-sent events.

          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.

          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: The maximum number of completion tokens that may be used over the course of the
              run. The run will make a best effort to use only the number of completion tokens
              specified, across multiple turns of the run.

          max_tokens: The maximum number of tokens that can be generated in the completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          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.

          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.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          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.

          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 DigitalOcean to
              monitor and detect abuse.

          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(["messages", "model"], ["messages", "model", "stream"])
    async def create(
        self,
        *,
        messages: Iterable[completion_create_params.Message],
        model: str,
        frequency_penalty: Optional[float] | Omit = omit,
        logit_bias: Optional[Dict[str, int]] | Omit = omit,
        logprobs: Optional[bool] | Omit = omit,
        max_completion_tokens: Optional[int] | Omit = omit,
        max_tokens: Optional[int] | Omit = omit,
        metadata: Optional[Dict[str, str]] | Omit = omit,
        n: Optional[int] | Omit = omit,
        presence_penalty: Optional[float] | Omit = omit,
        stop: Union[Optional[str], SequenceNotStr[str], None] | Omit = omit,
        stream: Optional[Literal[False]] | Literal[True] | Omit = omit,
        stream_options: Optional[completion_create_params.StreamOptions] | Omit = omit,
        temperature: Optional[float] | Omit = omit,
        tool_choice: completion_create_params.ToolChoice | Omit = omit,
        tools: Iterable[completion_create_params.Tool] | Omit = omit,
        top_logprobs: Optional[int] | Omit = omit,
        top_p: Optional[float] | Omit = omit,
        user: str | Omit = omit,
        # 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 = not_given,
    ) -> CompletionCreateResponse | AsyncStream[ChatCompletionChunk]:
        # This method requires an model_access_key to be set via client argument or environment variable
        if not hasattr(self._client, "model_access_key") or not self._client.model_access_key:
            raise TypeError(
                "Could not resolve authentication method. Expected model_access_key to be set for chat completions."
            )
        headers = extra_headers or {}
        headers = {
            "Authorization": f"Bearer {self._client.model_access_key}",
            **headers,
        }

        return await self._post(
            "/chat/completions"
            if self._client._base_url_overridden
            else f"{self._client.inference_endpoint}/v1/chat/completions",
            body=await async_maybe_transform(
                {
                    "messages": messages,
                    "model": model,
                    "frequency_penalty": frequency_penalty,
                    "logit_bias": logit_bias,
                    "logprobs": logprobs,
                    "max_completion_tokens": max_completion_tokens,
                    "max_tokens": max_tokens,
                    "metadata": metadata,
                    "n": n,
                    "presence_penalty": presence_penalty,
                    "stop": stop,
                    "stream": stream,
                    "stream_options": stream_options,
                    "temperature": temperature,
                    "tool_choice": tool_choice,
                    "tools": tools,
                    "top_logprobs": top_logprobs,
                    "top_p": top_p,
                    "user": user,
                },
                (
                    completion_create_params.CompletionCreateParamsStreaming
                    if stream
                    else completion_create_params.CompletionCreateParamsNonStreaming
                ),
            ),
            options=make_request_options(
                extra_headers=headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
            ),
            cast_to=CompletionCreateResponse,
            stream=stream or False,
            stream_cls=AsyncStream[ChatCompletionChunk],
        )


class CompletionsResourceWithRawResponse:
    def __init__(self, completions: CompletionsResource) -> None:
        self._completions = completions

        self.create = to_raw_response_wrapper(
            completions.create,
        )


class AsyncCompletionsResourceWithRawResponse:
    def __init__(self, completions: AsyncCompletionsResource) -> None:
        self._completions = completions

        self.create = async_to_raw_response_wrapper(
            completions.create,
        )


class CompletionsResourceWithStreamingResponse:
    def __init__(self, completions: CompletionsResource) -> None:
        self._completions = completions

        self.create = to_streamed_response_wrapper(
            completions.create,
        )


class AsyncCompletionsResourceWithStreamingResponse:
    def __init__(self, completions: AsyncCompletionsResource) -> None:
        self._completions = completions

        self.create = async_to_streamed_response_wrapper(
            completions.create,
        )