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Merge 95b0ca43bf into 9dda4d895f
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agustin18
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commit
7fcf92805b
2 changed files with 148 additions and 19 deletions
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@ -676,7 +676,6 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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from openai.types.responses import (
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ResponseFunctionToolCall,
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ResponseOutputMessage,
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ResponseReasoningItem,
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)
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try:
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@ -691,7 +690,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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choices: Final[list[Choices]] = []
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index = 0
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reasoning_content: str | None = None
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pending_reasoning_item: _BuiltReasoningItem | None = None
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pending_reasoning_items: Final[list[_BuiltReasoningItem]] = [] # mutable-ok: accumulator
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# Collect all tool calls to put them in a single choice
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# (Chat Completions API expects all tool calls in one message)
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@ -699,13 +698,13 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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tool_call_index = 0
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for item in output_items:
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if isinstance(item, ResponseReasoningItem):
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pending_reasoning_item = _build_reasoning_item(
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item_id=item.id,
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encrypted_content=getattr(item, "encrypted_content", None),
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summary_raw=item.summary,
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)
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reasoning_content = " ".join(s["text"] for s in pending_reasoning_item["summary"] if s.get("text"))
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reasoning_item = _reasoning_item_from_output_item(item)
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if reasoning_item is not None:
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pending_reasoning_items.append(reasoning_item)
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summary_texts = [s["text"] for s in reasoning_item["summary"] if s.get("text")]
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if summary_texts:
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step_text = " ".join(summary_texts)
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reasoning_content = f"{reasoning_content} {step_text}".strip() if reasoning_content else step_text
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elif isinstance(item, ResponseOutputMessage):
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for content in item.content:
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@ -720,10 +719,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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content=response_text if response_text else "",
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reasoning_content=reasoning_content,
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annotations=annotations,
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reasoning_items=cast(
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list[ChatCompletionReasoningItem] | None,
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([pending_reasoning_item] if pending_reasoning_item is not None else None),
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),
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reasoning_items=_as_chat_reasoning_items(pending_reasoning_items),
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)
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choices.append(
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@ -735,7 +731,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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)
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reasoning_content = None # flush
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pending_reasoning_item = None # flush
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pending_reasoning_items.clear() # flush
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index += 1
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elif isinstance(item, ResponseFunctionToolCall):
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@ -780,6 +776,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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elif handle_raw_dict_callback is not None:
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choice, index = handle_raw_dict_callback(item=raw_item, index=index)
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if choice is not None:
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if pending_reasoning_items:
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choice.message.reasoning_items = _as_chat_reasoning_items(pending_reasoning_items)
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if reasoning_content:
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choice.message.reasoning_content = reasoning_content
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pending_reasoning_items.clear()
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reasoning_content = None
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choices.append(choice)
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else:
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pass # don't fail request if item in list is not supported
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@ -790,14 +792,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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content=None,
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tool_calls=accumulated_tool_calls,
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reasoning_content=reasoning_content,
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reasoning_items=cast(
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list[ChatCompletionReasoningItem] | None,
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([pending_reasoning_item] if pending_reasoning_item is not None else None),
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),
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reasoning_items=_as_chat_reasoning_items(pending_reasoning_items),
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)
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choices.append(Choices(message=msg, finish_reason="tool_calls", index=index))
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reasoning_content = None
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pending_reasoning_item = None
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pending_reasoning_items.clear()
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return choices
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@ -4388,3 +4388,133 @@ def test_every_bridged_chunk_after_response_created_carries_the_served_service_t
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relayed = [iterator.chunk_parser(event).model_dump().get("service_tier") for event in events]
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assert relayed == ["default"] * len(events), relayed
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@pytest.mark.parametrize(
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"raw_output_items, expected_reasoning_ids, expected_summary_text, target_type",
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[
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pytest.param(
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[
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{
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"type": "reasoning",
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"id": "rs_1",
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"summary": [{"type": "summary_text", "text": "Step 1"}],
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"encrypted_content": "enc1",
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},
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{
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"type": "reasoning",
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"id": "rs_2",
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"summary": [{"type": "summary_text", "text": "Step 2"}],
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"encrypted_content": "enc2",
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},
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{
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"type": "function_call",
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"id": "fc_1",
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"call_id": "call_1",
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"name": "search",
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"arguments": "{}",
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"status": "completed",
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},
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],
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["rs_1", "rs_2"],
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"Step 1 Step 2",
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"tool_calls",
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id="multiple_reasoning_items_before_tool_call",
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),
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pytest.param(
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[
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{
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"type": "reasoning",
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"id": "rs_alpha",
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"summary": [{"type": "summary_text", "text": "Analysis"}],
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"encrypted_content": "enc_a",
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},
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{
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"type": "reasoning",
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"id": "rs_beta",
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"summary": [{"type": "summary_text", "text": "Conclusion"}],
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"encrypted_content": "enc_b",
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},
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],
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["rs_alpha", "rs_beta"],
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"Analysis Conclusion",
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"message",
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id="multiple_reasoning_items_before_output_message",
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),
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pytest.param(
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[
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{
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"type": "reasoning",
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"id": "rs_raw_1",
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"summary": [{"type": "summary_text", "text": "Raw Step 1"}],
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"encrypted_content": "enc_raw",
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},
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{
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"type": "message",
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"role": "assistant",
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"content": [{"type": "output_text", "text": "Raw response text"}],
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},
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],
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["rs_raw_1"],
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"Raw Step 1",
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"raw_dict_message",
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id="reasoning_items_before_raw_dict_message",
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),
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pytest.param(
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[
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{
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"type": "function_call",
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"id": "fc_solo",
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"call_id": "call_solo",
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"name": "lookup",
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"arguments": "{}",
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"status": "completed",
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}
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],
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None,
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None,
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"tool_calls",
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id="zero_reasoning_items_returns_none",
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),
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],
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)
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def test_convert_response_output_to_choices_preserves_all_reasoning_items(
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raw_output_items: list[dict[str, object]],
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expected_reasoning_ids: list[str] | None,
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expected_summary_text: str | None,
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target_type: str,
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) -> None:
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"""Verify non-streaming Responses API bridge retains all reasoning items across message and tool call turns."""
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from openai.types.responses import ResponseOutputMessage
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from openai.types.responses.response_output_message import ResponseOutputText
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items_to_pass: Final[list[object]] = list(raw_output_items) # mutable-ok: fixture setup
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if target_type == "message":
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items_to_pass.append(
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ResponseOutputMessage(
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id="msg_1",
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role="assistant",
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status="completed",
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type="message",
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content=[ResponseOutputText(annotations=[], text="Done", type="output_text", logprobs=[])],
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)
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)
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handler: Final = LiteLLMResponsesTransformationHandler()
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choices: Final = handler._convert_response_output_to_choices(
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items_to_pass,
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handle_raw_dict_callback=handler._handle_raw_dict_response_item,
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)
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assert len(choices) == 1
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msg = choices[0].message
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reasoning_items = getattr(msg, "reasoning_items", None)
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reasoning_content = getattr(msg, "reasoning_content", None)
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if expected_reasoning_ids is None:
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assert reasoning_items is None
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assert reasoning_content is None
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else:
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assert reasoning_items is not None
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assert [item["id"] for item in reasoning_items] == expected_reasoning_ids
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assert reasoning_content == expected_summary_text
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