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https://github.com/microsoft/agent-framework.git
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OpenAI Responses Agent Completeness
This commit is contained in:
Giles Odigwe
@@ -1,10 +1,10 @@
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# Copyright (c) Microsoft. All rights reserved.
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import sys
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from collections.abc import AsyncIterable, Callable, Mapping, MutableMapping, MutableSequence, Sequence
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from collections.abc import AsyncIterable, Mapping, MutableMapping, MutableSequence, Sequence
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from datetime import datetime
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from itertools import chain
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from typing import TYPE_CHECKING, Any, Literal, TypeVar
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from typing import TYPE_CHECKING, Any, TypeVar
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from openai import AsyncOpenAI, BadRequestError
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from openai.types.responses.file_search_tool_param import FileSearchToolParam
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@@ -68,14 +68,12 @@ from ._exceptions import OpenAIContentFilterException
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from ._shared import OpenAIBase, OpenAIConfigMixin, OpenAISettings, prepare_function_call_results
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if sys.version_info >= (3, 12):
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from typing import override # type: ignore # pragma: no cover
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pass # type: ignore # pragma: no cover
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else:
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from typing_extensions import override # type: ignore[import] # pragma: no cover
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pass # type: ignore[import] # pragma: no cover
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if TYPE_CHECKING:
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from openai.types.responses.response_includable import ResponseIncludable
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from .._types import ChatToolMode
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pass
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logger = get_logger("agent_framework.openai")
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@@ -90,192 +88,6 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
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FILE_SEARCH_MAX_RESULTS: int = 50
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def _filter_options(self, **kwargs: Any) -> dict[str, Any]:
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"""Filter options for the responses call."""
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# The responses call does not support all the options that the chat completion call does.
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# We filter out the unsupported options.
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return {key: value for key, value in kwargs.items() if value is not None}
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@override
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async def get_response(
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self,
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messages: str | ChatMessage | list[str] | list[ChatMessage],
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*,
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include: list["ResponseIncludable"] | None = None,
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instructions: str | None = None,
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max_tokens: int | None = None,
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parallel_tool_calls: bool | None = None,
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model: str | None = None,
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previous_response_id: str | None = None,
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reasoning: dict[str, str] | None = None,
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service_tier: str | None = None,
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response_format: type[BaseModel] | None = None,
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seed: int | None = None,
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store: bool | None = None,
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temperature: float | None = None,
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tool_choice: "ChatToolMode" | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
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tools: ToolProtocol
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| Callable[..., Any]
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| MutableMapping[str, Any]
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| list[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]]
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| None = None,
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top_p: float | None = None,
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user: str | None = None,
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truncation: str | None = None,
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timeout: float | None = None,
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additional_properties: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> ChatResponse:
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"""Get a response from the OpenAI API.
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Args:
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messages: the message or messages to send to the model
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include: additional output data to include in the model response.
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instructions: a system (or developer) message inserted into the model's context.
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max_tokens: The maximum number of tokens to generate.
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parallel_tool_calls: Whether to enable parallel tool calls.
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model: The model to use for the agent.
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previous_response_id: The ID of the previous response.
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reasoning: The reasoning to use for the response.
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service_tier: The service tier to use for the response.
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response_format: The format of the response.
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seed: The random seed to use for the response.
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store: whether to store the response.
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temperature: the sampling temperature to use.
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tool_choice: the tool choice for the request.
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tools: the tools to use for the request.
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top_p: the nucleus sampling probability to use.
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user: the user to associate with the request.
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truncation: the truncation strategy to use.
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timeout: the timeout for the request.
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additional_properties: additional properties to include in the request.
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kwargs: any additional keyword arguments,
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will only be passed to functions that are called.
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Returns:
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A chat response from the model.
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"""
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additional_properties = additional_properties or {}
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additional_properties.update(
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self._filter_options(
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include=include,
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instructions=instructions,
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parallel_tool_calls=parallel_tool_calls,
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model=model,
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previous_response_id=previous_response_id,
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reasoning=reasoning,
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service_tier=service_tier,
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truncation=truncation,
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timeout=timeout,
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)
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)
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return await super().get_response(
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messages=messages,
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max_tokens=max_tokens,
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response_format=response_format,
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seed=seed,
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store=store,
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temperature=temperature,
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tool_choice=tool_choice,
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tools=tools, # type: ignore
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top_p=top_p,
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user=user,
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additional_properties=additional_properties,
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**kwargs,
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)
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@override
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async def get_streaming_response(
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self,
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messages: str | ChatMessage | list[str] | list[ChatMessage],
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*,
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# TODO(peterychang): enable this option. background: bool | None = None,
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include: list["ResponseIncludable"] | None = None,
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instructions: str | None = None,
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max_tokens: int | None = None,
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parallel_tool_calls: bool | None = None,
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model: str | None = None,
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previous_response_id: str | None = None,
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reasoning: dict[str, str] | None = None,
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service_tier: str | None = None,
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response_format: type[BaseModel] | None = None,
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seed: int | None = None,
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store: bool | None = None,
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temperature: float | None = None,
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tool_choice: "ChatToolMode" | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
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tools: ToolProtocol
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| Callable[..., Any]
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| MutableMapping[str, Any]
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| list[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]]
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| None = None,
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top_p: float | None = None,
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user: str | None = None,
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truncation: str | None = None,
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timeout: float | None = None,
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additional_properties: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> AsyncIterable[ChatResponseUpdate]:
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"""Get a streaming response from the OpenAI API.
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Args:
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messages: the message or messages to send to the model
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include: additional output data to include in the model response.
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instructions: a system (or developer) message inserted into the model's context.
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max_tokens: The maximum number of tokens to generate.
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parallel_tool_calls: Whether to enable parallel tool calls.
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model: The model to use for the agent.
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previous_response_id: The ID of the previous response.
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reasoning: The reasoning to use for the response.
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service_tier: The service tier to use for the response.
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response_format: The format of the response.
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seed: The random seed to use for the response.
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store: whether to store the response.
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temperature: the sampling temperature to use.
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tool_choice: the tool choice for the request.
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tools: the tools to use for the request.
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top_p: the nucleus sampling probability to use.
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user: the user to associate with the request.
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truncation: the truncation strategy to use.
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timeout: the timeout for the request.
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additional_properties: additional properties to include in the request.
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kwargs: any additional keyword arguments,
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will only be passed to functions that are called.
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Returns:
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A stream representing the response(s) from the LLM.
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"""
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additional_properties = additional_properties or {}
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additional_properties.update(
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self._filter_options(
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include=include,
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instructions=instructions,
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parallel_tool_calls=parallel_tool_calls,
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model=model,
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previous_response_id=previous_response_id,
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reasoning=reasoning,
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service_tier=service_tier,
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truncation=truncation,
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timeout=timeout,
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)
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)
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async for update in super().get_streaming_response(
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messages=messages,
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max_tokens=max_tokens,
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response_format=response_format,
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seed=seed,
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store=store,
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temperature=temperature,
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tool_choice=tool_choice,
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tools=tools, # type: ignore
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top_p=top_p,
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user=user,
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additional_properties=additional_properties,
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**kwargs,
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):
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yield update
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# region Inner Methods
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async def _inner_get_response(
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@@ -474,18 +286,33 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
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return response_tools
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def _prepare_options(self, messages: MutableSequence[ChatMessage], chat_options: ChatOptions) -> dict[str, Any]:
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"""Take ChatOptions and create the specific options for Responses."""
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options_dict = chat_options.to_provider_settings(exclude={"response_format"})
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"""Take ChatOptions and create the specific options for Responses API."""
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options_dict: dict[str, Any] = {}
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if chat_options.max_tokens is not None:
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options_dict["max_output_tokens"] = chat_options.max_tokens
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if chat_options.temperature is not None:
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options_dict["temperature"] = chat_options.temperature
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if chat_options.top_p is not None:
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options_dict["top_p"] = chat_options.top_p
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if chat_options.user is not None:
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options_dict["user"] = chat_options.user
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# messages
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request_input = self._prepare_chat_messages_for_request(messages)
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if not request_input:
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raise ServiceInvalidRequestError("Messages are required for chat completions")
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options_dict["input"] = request_input
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# tools
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if chat_options.tools is None:
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options_dict.pop("parallel_tool_calls", None)
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else:
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options_dict["tools"] = self._tools_to_response_tools(chat_options.tools)
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# other settings
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if "store" not in options_dict:
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options_dict["store"] = False
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