630 lines
28 KiB
Python
630 lines
28 KiB
Python
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# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
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from __future__ import annotations
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from typing import Dict, List, Type, Union, Iterable, Optional, cast
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from functools import partial
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from typing_extensions import Literal
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import httpx
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from .... import _legacy_response
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from ...._types import NOT_GIVEN, Body, Query, Headers, NotGiven
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from ...._utils import maybe_transform, async_maybe_transform
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from ...._compat import cached_property
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from ...._resource import SyncAPIResource, AsyncAPIResource
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from ...._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
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from ...._streaming import Stream
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from ....types.chat import (
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ChatCompletionReasoningEffort,
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completion_create_params,
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)
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from ...._base_client import make_request_options
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from ....lib._parsing import (
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ResponseFormatT,
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validate_input_tools as _validate_input_tools,
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parse_chat_completion as _parse_chat_completion,
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type_to_response_format_param as _type_to_response_format,
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)
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from ....types.chat_model import ChatModel
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from ....lib.streaming.chat import ChatCompletionStreamManager, AsyncChatCompletionStreamManager
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from ....types.chat.chat_completion import ChatCompletion
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from ....types.chat.chat_completion_chunk import ChatCompletionChunk
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from ....types.chat.parsed_chat_completion import ParsedChatCompletion
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from ....types.chat.chat_completion_modality import ChatCompletionModality
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from ....types.chat.chat_completion_tool_param import ChatCompletionToolParam
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from ....types.chat.chat_completion_audio_param import ChatCompletionAudioParam
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from ....types.chat.chat_completion_message_param import ChatCompletionMessageParam
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from ....types.chat.chat_completion_stream_options_param import ChatCompletionStreamOptionsParam
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from ....types.chat.chat_completion_prediction_content_param import ChatCompletionPredictionContentParam
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from ....types.chat.chat_completion_tool_choice_option_param import ChatCompletionToolChoiceOptionParam
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__all__ = ["Completions", "AsyncCompletions"]
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class Completions(SyncAPIResource):
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@cached_property
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def with_raw_response(self) -> CompletionsWithRawResponse:
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"""
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This property can be used as a prefix for any HTTP method call to return the
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the raw response object instead of the parsed content.
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For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers
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"""
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return CompletionsWithRawResponse(self)
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@cached_property
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def with_streaming_response(self) -> CompletionsWithStreamingResponse:
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"""
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An alternative to `.with_raw_response` that doesn't eagerly read the response body.
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For more information, see https://www.github.com/openai/openai-python#with_streaming_response
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"""
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return CompletionsWithStreamingResponse(self)
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def parse(
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self,
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*,
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messages: Iterable[ChatCompletionMessageParam],
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model: Union[str, ChatModel],
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audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
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response_format: type[ResponseFormatT] | NotGiven = NOT_GIVEN,
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frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
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functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
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logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
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logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
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max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
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max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
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metadata: Optional[Dict[str, str]] | NotGiven = NOT_GIVEN,
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modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
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n: Optional[int] | NotGiven = NOT_GIVEN,
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parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
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prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
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presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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reasoning_effort: ChatCompletionReasoningEffort | NotGiven = NOT_GIVEN,
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seed: Optional[int] | NotGiven = NOT_GIVEN,
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service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
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stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
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store: Optional[bool] | NotGiven = NOT_GIVEN,
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stream_options: Optional[ChatCompletionStreamOptionsParam] | NotGiven = NOT_GIVEN,
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temperature: Optional[float] | NotGiven = NOT_GIVEN,
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tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
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tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
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top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
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top_p: Optional[float] | NotGiven = NOT_GIVEN,
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user: str | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> ParsedChatCompletion[ResponseFormatT]:
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"""Wrapper over the `client.chat.completions.create()` method that provides richer integrations with Python specific types
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& returns a `ParsedChatCompletion` object, which is a subclass of the standard `ChatCompletion` class.
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You can pass a pydantic model to this method and it will automatically convert the model
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into a JSON schema, send it to the API and parse the response content back into the given model.
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This method will also automatically parse `function` tool calls if:
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- You use the `openai.pydantic_function_tool()` helper method
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- You mark your tool schema with `"strict": True`
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Example usage:
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```py
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from pydantic import BaseModel
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from openai import OpenAI
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class Step(BaseModel):
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explanation: str
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output: str
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class MathResponse(BaseModel):
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steps: List[Step]
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final_answer: str
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client = OpenAI()
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completion = client.beta.chat.completions.parse(
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model="gpt-4o-2024-08-06",
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messages=[
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{"role": "system", "content": "You are a helpful math tutor."},
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{"role": "user", "content": "solve 8x + 31 = 2"},
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],
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response_format=MathResponse,
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)
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message = completion.choices[0].message
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if message.parsed:
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print(message.parsed.steps)
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print("answer: ", message.parsed.final_answer)
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```
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"""
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_validate_input_tools(tools)
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extra_headers = {
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"X-Stainless-Helper-Method": "beta.chat.completions.parse",
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**(extra_headers or {}),
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}
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def parser(raw_completion: ChatCompletion) -> ParsedChatCompletion[ResponseFormatT]:
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return _parse_chat_completion(
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response_format=response_format,
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chat_completion=raw_completion,
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input_tools=tools,
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)
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return self._post(
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"/chat/completions",
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body=maybe_transform(
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{
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"messages": messages,
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"model": model,
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"audio": audio,
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"frequency_penalty": frequency_penalty,
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"function_call": function_call,
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"functions": functions,
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"logit_bias": logit_bias,
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"logprobs": logprobs,
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"max_completion_tokens": max_completion_tokens,
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"max_tokens": max_tokens,
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"metadata": metadata,
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"modalities": modalities,
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"n": n,
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"parallel_tool_calls": parallel_tool_calls,
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"prediction": prediction,
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"presence_penalty": presence_penalty,
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"reasoning_effort": reasoning_effort,
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"response_format": _type_to_response_format(response_format),
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"seed": seed,
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"service_tier": service_tier,
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"stop": stop,
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"store": store,
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"stream": False,
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"stream_options": stream_options,
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"temperature": temperature,
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"tool_choice": tool_choice,
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"tools": tools,
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"top_logprobs": top_logprobs,
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"top_p": top_p,
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"user": user,
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},
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completion_create_params.CompletionCreateParams,
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),
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options=make_request_options(
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extra_headers=extra_headers,
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extra_query=extra_query,
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extra_body=extra_body,
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timeout=timeout,
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post_parser=parser,
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),
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# we turn the `ChatCompletion` instance into a `ParsedChatCompletion`
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# in the `parser` function above
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cast_to=cast(Type[ParsedChatCompletion[ResponseFormatT]], ChatCompletion),
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stream=False,
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)
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def stream(
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self,
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*,
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messages: Iterable[ChatCompletionMessageParam],
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model: Union[str, ChatModel],
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audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
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response_format: completion_create_params.ResponseFormat | type[ResponseFormatT] | NotGiven = NOT_GIVEN,
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frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
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functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
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logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
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logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
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max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
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max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
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metadata: Optional[Dict[str, str]] | NotGiven = NOT_GIVEN,
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modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
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n: Optional[int] | NotGiven = NOT_GIVEN,
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parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
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prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
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presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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reasoning_effort: ChatCompletionReasoningEffort | NotGiven = NOT_GIVEN,
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seed: Optional[int] | NotGiven = NOT_GIVEN,
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service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
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stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
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store: Optional[bool] | NotGiven = NOT_GIVEN,
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stream_options: Optional[ChatCompletionStreamOptionsParam] | NotGiven = NOT_GIVEN,
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temperature: Optional[float] | NotGiven = NOT_GIVEN,
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tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
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tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
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top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
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top_p: Optional[float] | NotGiven = NOT_GIVEN,
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user: str | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
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) -> ChatCompletionStreamManager[ResponseFormatT]:
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"""Wrapper over the `client.chat.completions.create(stream=True)` method that provides a more granular event API
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and automatic accumulation of each delta.
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This also supports all of the parsing utilities that `.parse()` does.
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Unlike `.create(stream=True)`, the `.stream()` method requires usage within a context manager to prevent accidental leakage of the response:
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```py
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with client.beta.chat.completions.stream(
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model="gpt-4o-2024-08-06",
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messages=[...],
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) as stream:
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for event in stream:
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if event.type == "content.delta":
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print(event.delta, flush=True, end="")
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```
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When the context manager is entered, a `ChatCompletionStream` instance is returned which, like `.create(stream=True)` is an iterator. The full list of events that are yielded by the iterator are outlined in [these docs](https://github.com/openai/openai-python/blob/main/helpers.md#chat-completions-events).
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When the context manager exits, the response will be closed, however the `stream` instance is still available outside
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the context manager.
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"""
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extra_headers = {
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"X-Stainless-Helper-Method": "beta.chat.completions.stream",
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**(extra_headers or {}),
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}
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api_request: partial[Stream[ChatCompletionChunk]] = partial(
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self._client.chat.completions.create,
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messages=messages,
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model=model,
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audio=audio,
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stream=True,
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response_format=_type_to_response_format(response_format),
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frequency_penalty=frequency_penalty,
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function_call=function_call,
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functions=functions,
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logit_bias=logit_bias,
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logprobs=logprobs,
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max_completion_tokens=max_completion_tokens,
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max_tokens=max_tokens,
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metadata=metadata,
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modalities=modalities,
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n=n,
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parallel_tool_calls=parallel_tool_calls,
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prediction=prediction,
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presence_penalty=presence_penalty,
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reasoning_effort=reasoning_effort,
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seed=seed,
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service_tier=service_tier,
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store=store,
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stop=stop,
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stream_options=stream_options,
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temperature=temperature,
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tool_choice=tool_choice,
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tools=tools,
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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extra_headers=extra_headers,
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extra_query=extra_query,
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extra_body=extra_body,
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timeout=timeout,
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)
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return ChatCompletionStreamManager(
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api_request,
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response_format=response_format,
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input_tools=tools,
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)
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class AsyncCompletions(AsyncAPIResource):
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@cached_property
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def with_raw_response(self) -> AsyncCompletionsWithRawResponse:
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"""
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This property can be used as a prefix for any HTTP method call to return the
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the raw response object instead of the parsed content.
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For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers
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"""
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return AsyncCompletionsWithRawResponse(self)
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@cached_property
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def with_streaming_response(self) -> AsyncCompletionsWithStreamingResponse:
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"""
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An alternative to `.with_raw_response` that doesn't eagerly read the response body.
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For more information, see https://www.github.com/openai/openai-python#with_streaming_response
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"""
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|
|
return AsyncCompletionsWithStreamingResponse(self)
|
||
|
|
|
||
|
|
async def parse(
|
||
|
|
self,
|
||
|
|
*,
|
||
|
|
messages: Iterable[ChatCompletionMessageParam],
|
||
|
|
model: Union[str, ChatModel],
|
||
|
|
audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
|
||
|
|
response_format: type[ResponseFormatT] | NotGiven = NOT_GIVEN,
|
||
|
|
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
|
||
|
|
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
|
||
|
|
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
|
|
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
|
|
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
metadata: Optional[Dict[str, str]] | NotGiven = NOT_GIVEN,
|
||
|
|
modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
|
||
|
|
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
|
||
|
|
prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
|
||
|
|
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
reasoning_effort: ChatCompletionReasoningEffort | NotGiven = NOT_GIVEN,
|
||
|
|
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
|
||
|
|
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
|
||
|
|
store: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
|
|
stream_options: Optional[ChatCompletionStreamOptionsParam] | NotGiven = NOT_GIVEN,
|
||
|
|
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
|
||
|
|
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
|
||
|
|
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
user: str | 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 = NOT_GIVEN,
|
||
|
|
) -> ParsedChatCompletion[ResponseFormatT]:
|
||
|
|
"""Wrapper over the `client.chat.completions.create()` method that provides richer integrations with Python specific types
|
||
|
|
& returns a `ParsedChatCompletion` object, which is a subclass of the standard `ChatCompletion` class.
|
||
|
|
|
||
|
|
You can pass a pydantic model to this method and it will automatically convert the model
|
||
|
|
into a JSON schema, send it to the API and parse the response content back into the given model.
|
||
|
|
|
||
|
|
This method will also automatically parse `function` tool calls if:
|
||
|
|
- You use the `openai.pydantic_function_tool()` helper method
|
||
|
|
- You mark your tool schema with `"strict": True`
|
||
|
|
|
||
|
|
Example usage:
|
||
|
|
```py
|
||
|
|
from pydantic import BaseModel
|
||
|
|
from openai import AsyncOpenAI
|
||
|
|
|
||
|
|
|
||
|
|
class Step(BaseModel):
|
||
|
|
explanation: str
|
||
|
|
output: str
|
||
|
|
|
||
|
|
|
||
|
|
class MathResponse(BaseModel):
|
||
|
|
steps: List[Step]
|
||
|
|
final_answer: str
|
||
|
|
|
||
|
|
|
||
|
|
client = AsyncOpenAI()
|
||
|
|
completion = await client.beta.chat.completions.parse(
|
||
|
|
model="gpt-4o-2024-08-06",
|
||
|
|
messages=[
|
||
|
|
{"role": "system", "content": "You are a helpful math tutor."},
|
||
|
|
{"role": "user", "content": "solve 8x + 31 = 2"},
|
||
|
|
],
|
||
|
|
response_format=MathResponse,
|
||
|
|
)
|
||
|
|
|
||
|
|
message = completion.choices[0].message
|
||
|
|
if message.parsed:
|
||
|
|
print(message.parsed.steps)
|
||
|
|
print("answer: ", message.parsed.final_answer)
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
_validate_input_tools(tools)
|
||
|
|
|
||
|
|
extra_headers = {
|
||
|
|
"X-Stainless-Helper-Method": "beta.chat.completions.parse",
|
||
|
|
**(extra_headers or {}),
|
||
|
|
}
|
||
|
|
|
||
|
|
def parser(raw_completion: ChatCompletion) -> ParsedChatCompletion[ResponseFormatT]:
|
||
|
|
return _parse_chat_completion(
|
||
|
|
response_format=response_format,
|
||
|
|
chat_completion=raw_completion,
|
||
|
|
input_tools=tools,
|
||
|
|
)
|
||
|
|
|
||
|
|
return await self._post(
|
||
|
|
"/chat/completions",
|
||
|
|
body=await async_maybe_transform(
|
||
|
|
{
|
||
|
|
"messages": messages,
|
||
|
|
"model": model,
|
||
|
|
"audio": audio,
|
||
|
|
"frequency_penalty": frequency_penalty,
|
||
|
|
"function_call": function_call,
|
||
|
|
"functions": functions,
|
||
|
|
"logit_bias": logit_bias,
|
||
|
|
"logprobs": logprobs,
|
||
|
|
"max_completion_tokens": max_completion_tokens,
|
||
|
|
"max_tokens": max_tokens,
|
||
|
|
"metadata": metadata,
|
||
|
|
"modalities": modalities,
|
||
|
|
"n": n,
|
||
|
|
"parallel_tool_calls": parallel_tool_calls,
|
||
|
|
"prediction": prediction,
|
||
|
|
"presence_penalty": presence_penalty,
|
||
|
|
"reasoning_effort": reasoning_effort,
|
||
|
|
"response_format": _type_to_response_format(response_format),
|
||
|
|
"seed": seed,
|
||
|
|
"service_tier": service_tier,
|
||
|
|
"store": store,
|
||
|
|
"stop": stop,
|
||
|
|
"stream": False,
|
||
|
|
"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.CompletionCreateParams,
|
||
|
|
),
|
||
|
|
options=make_request_options(
|
||
|
|
extra_headers=extra_headers,
|
||
|
|
extra_query=extra_query,
|
||
|
|
extra_body=extra_body,
|
||
|
|
timeout=timeout,
|
||
|
|
post_parser=parser,
|
||
|
|
),
|
||
|
|
# we turn the `ChatCompletion` instance into a `ParsedChatCompletion`
|
||
|
|
# in the `parser` function above
|
||
|
|
cast_to=cast(Type[ParsedChatCompletion[ResponseFormatT]], ChatCompletion),
|
||
|
|
stream=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
def stream(
|
||
|
|
self,
|
||
|
|
*,
|
||
|
|
messages: Iterable[ChatCompletionMessageParam],
|
||
|
|
model: Union[str, ChatModel],
|
||
|
|
audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
|
||
|
|
response_format: completion_create_params.ResponseFormat | type[ResponseFormatT] | NotGiven = NOT_GIVEN,
|
||
|
|
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
|
||
|
|
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
|
||
|
|
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
|
||
|
|
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
|
|
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
metadata: Optional[Dict[str, str]] | NotGiven = NOT_GIVEN,
|
||
|
|
modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
|
||
|
|
n: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
|
||
|
|
prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
|
||
|
|
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
reasoning_effort: ChatCompletionReasoningEffort | NotGiven = NOT_GIVEN,
|
||
|
|
seed: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
|
||
|
|
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
|
||
|
|
store: Optional[bool] | NotGiven = NOT_GIVEN,
|
||
|
|
stream_options: Optional[ChatCompletionStreamOptionsParam] | NotGiven = NOT_GIVEN,
|
||
|
|
temperature: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
|
||
|
|
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
|
||
|
|
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
|
||
|
|
top_p: Optional[float] | NotGiven = NOT_GIVEN,
|
||
|
|
user: str | 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 = NOT_GIVEN,
|
||
|
|
) -> AsyncChatCompletionStreamManager[ResponseFormatT]:
|
||
|
|
"""Wrapper over the `client.chat.completions.create(stream=True)` method that provides a more granular event API
|
||
|
|
and automatic accumulation of each delta.
|
||
|
|
|
||
|
|
This also supports all of the parsing utilities that `.parse()` does.
|
||
|
|
|
||
|
|
Unlike `.create(stream=True)`, the `.stream()` method requires usage within a context manager to prevent accidental leakage of the response:
|
||
|
|
|
||
|
|
```py
|
||
|
|
async with client.beta.chat.completions.stream(
|
||
|
|
model="gpt-4o-2024-08-06",
|
||
|
|
messages=[...],
|
||
|
|
) as stream:
|
||
|
|
async for event in stream:
|
||
|
|
if event.type == "content.delta":
|
||
|
|
print(event.delta, flush=True, end="")
|
||
|
|
```
|
||
|
|
|
||
|
|
When the context manager is entered, an `AsyncChatCompletionStream` instance is returned which, like `.create(stream=True)` is an async iterator. The full list of events that are yielded by the iterator are outlined in [these docs](https://github.com/openai/openai-python/blob/main/helpers.md#chat-completions-events).
|
||
|
|
|
||
|
|
When the context manager exits, the response will be closed, however the `stream` instance is still available outside
|
||
|
|
the context manager.
|
||
|
|
"""
|
||
|
|
_validate_input_tools(tools)
|
||
|
|
|
||
|
|
extra_headers = {
|
||
|
|
"X-Stainless-Helper-Method": "beta.chat.completions.stream",
|
||
|
|
**(extra_headers or {}),
|
||
|
|
}
|
||
|
|
|
||
|
|
api_request = self._client.chat.completions.create(
|
||
|
|
messages=messages,
|
||
|
|
model=model,
|
||
|
|
audio=audio,
|
||
|
|
stream=True,
|
||
|
|
response_format=_type_to_response_format(response_format),
|
||
|
|
frequency_penalty=frequency_penalty,
|
||
|
|
function_call=function_call,
|
||
|
|
functions=functions,
|
||
|
|
logit_bias=logit_bias,
|
||
|
|
logprobs=logprobs,
|
||
|
|
max_completion_tokens=max_completion_tokens,
|
||
|
|
max_tokens=max_tokens,
|
||
|
|
metadata=metadata,
|
||
|
|
modalities=modalities,
|
||
|
|
n=n,
|
||
|
|
parallel_tool_calls=parallel_tool_calls,
|
||
|
|
prediction=prediction,
|
||
|
|
presence_penalty=presence_penalty,
|
||
|
|
reasoning_effort=reasoning_effort,
|
||
|
|
seed=seed,
|
||
|
|
service_tier=service_tier,
|
||
|
|
stop=stop,
|
||
|
|
store=store,
|
||
|
|
stream_options=stream_options,
|
||
|
|
temperature=temperature,
|
||
|
|
tool_choice=tool_choice,
|
||
|
|
tools=tools,
|
||
|
|
top_logprobs=top_logprobs,
|
||
|
|
top_p=top_p,
|
||
|
|
user=user,
|
||
|
|
extra_headers=extra_headers,
|
||
|
|
extra_query=extra_query,
|
||
|
|
extra_body=extra_body,
|
||
|
|
timeout=timeout,
|
||
|
|
)
|
||
|
|
return AsyncChatCompletionStreamManager(
|
||
|
|
api_request,
|
||
|
|
response_format=response_format,
|
||
|
|
input_tools=tools,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class CompletionsWithRawResponse:
|
||
|
|
def __init__(self, completions: Completions) -> None:
|
||
|
|
self._completions = completions
|
||
|
|
|
||
|
|
self.parse = _legacy_response.to_raw_response_wrapper(
|
||
|
|
completions.parse,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class AsyncCompletionsWithRawResponse:
|
||
|
|
def __init__(self, completions: AsyncCompletions) -> None:
|
||
|
|
self._completions = completions
|
||
|
|
|
||
|
|
self.parse = _legacy_response.async_to_raw_response_wrapper(
|
||
|
|
completions.parse,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class CompletionsWithStreamingResponse:
|
||
|
|
def __init__(self, completions: Completions) -> None:
|
||
|
|
self._completions = completions
|
||
|
|
|
||
|
|
self.parse = to_streamed_response_wrapper(
|
||
|
|
completions.parse,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class AsyncCompletionsWithStreamingResponse:
|
||
|
|
def __init__(self, completions: AsyncCompletions) -> None:
|
||
|
|
self._completions = completions
|
||
|
|
|
||
|
|
self.parse = async_to_streamed_response_wrapper(
|
||
|
|
completions.parse,
|
||
|
|
)
|