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fix(responses): merge bridged tool calls into the same choice as the text (#44346)
* revert(responses): revert "fix(responses): keep gpt-5.4/5.5 tool calls on chat and merge bridged tool calls into one choice" (#44295)
This reverts commit ca1994e403.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): merge bridged tool calls into the same choice as the text
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
devin-ai-integration[bot]
GitHub
parent
8e32d4568c
commit
5724117116
4 changed files with 744 additions and 2 deletions
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@ -187,7 +187,7 @@ def _reasoning_items_from_output_items(output_items: Sequence[object]) -> tuple[
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def _as_chat_reasoning_items(
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reasoning_items: Sequence[_BuiltReasoningItem],
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reasoning_items: Sequence[_BuiltReasoningItem | ChatCompletionReasoningItem],
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) -> list[ChatCompletionReasoningItem] | None:
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if not reasoning_items:
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return None
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@ -788,7 +788,32 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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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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# If we accumulated tool calls, create a single choice with all of them
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if accumulated_tool_calls and choices:
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last_choice: Final = choices[-1]
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last_reasoning_content: Final = getattr(last_choice.message, "reasoning_content", None)
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last_reasoning_items: Final = getattr(last_choice.message, "reasoning_items", None)
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merged_reasoning_content: Final = (
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" ".join(value for value in (last_reasoning_content, reasoning_content) if value) or None
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)
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merged_reasoning_items: Final = _as_chat_reasoning_items(
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(
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*(last_reasoning_items or ()),
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*(() if pending_reasoning_item is None else (pending_reasoning_item,)),
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)
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)
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merged_message: Final = Message(
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role=last_choice.message.role,
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content=last_choice.message.content,
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annotations=getattr(last_choice.message, "annotations", None),
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tool_calls=accumulated_tool_calls,
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reasoning_content=merged_reasoning_content,
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reasoning_items=merged_reasoning_items,
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)
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return [
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*choices[:-1],
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Choices(message=merged_message, finish_reason="tool_calls", index=last_choice.index),
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]
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if accumulated_tool_calls:
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msg = Message(
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content=None,
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@ -1,5 +1,6 @@
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import json
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import uuid
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from itertools import chain
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from typing import Final
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import pytest
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@ -64,3 +65,190 @@ def test_openai_chat_tool_choice_without_tools_is_not_forwarded(gateway: Gateway
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}
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]
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assert [(request.method, request.target) for request in wire.drain()] == [("POST", "/chat/completions")]
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def test_azure_gpt_6_bridged_stream_returns_text_and_tool_call_on_one_choice(gateway: Gateway) -> None:
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identity: Final = f"azure-gpt-6-sol-stream-{uuid.uuid4().hex}"
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expected_text: Final = "Let me check the weather."
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events: Final = (
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{
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"type": "response.created",
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"response": {
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"id": "resp_weather",
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"object": "response",
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"created_at": 1,
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"status": "in_progress",
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"model": "gpt-6-sol",
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},
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},
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{
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"type": "response.output_item.added",
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"output_index": 0,
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"item": {
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"id": "msg_weather",
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"type": "message",
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"status": "in_progress",
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"role": "assistant",
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"content": [],
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},
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},
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{
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"type": "response.output_text.delta",
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"item_id": "msg_weather",
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"output_index": 0,
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"content_index": 0,
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"delta": "Let me check ",
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},
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{
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"type": "response.output_text.delta",
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"item_id": "msg_weather",
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"output_index": 0,
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"content_index": 0,
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"delta": "the weather.",
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},
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{
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"type": "response.output_item.done",
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"output_index": 0,
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"item": {
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"id": "msg_weather",
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"type": "message",
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"status": "completed",
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"role": "assistant",
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"content": [{"type": "output_text", "text": expected_text, "annotations": []}],
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},
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},
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{
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"type": "response.output_item.added",
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"output_index": 1,
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"item": {
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"id": "fc_1",
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"type": "function_call",
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"status": "in_progress",
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"call_id": "call_1",
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"name": "get_weather",
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"arguments": "",
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},
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},
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{
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"type": "response.function_call_arguments.delta",
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"item_id": "fc_1",
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"output_index": 1,
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"delta": '{"city":',
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},
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{
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"type": "response.function_call_arguments.delta",
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"item_id": "fc_1",
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"output_index": 1,
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"delta": '"Paris"}',
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},
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{
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"type": "response.output_item.done",
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"output_index": 1,
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"item": {
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"id": "fc_1",
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"type": "function_call",
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"status": "completed",
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"call_id": "call_1",
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"name": "get_weather",
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"arguments": '{"city":"Paris"}',
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},
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},
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{
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"type": "response.completed",
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"response": {
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"id": "resp_weather",
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"object": "response",
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"created_at": 1,
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"status": "completed",
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"model": "gpt-6-sol",
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"output": [
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{
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"id": "msg_weather",
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"type": "message",
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"status": "completed",
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"role": "assistant",
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"content": [{"type": "output_text", "text": expected_text, "annotations": []}],
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},
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{
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"id": "fc_1",
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"type": "function_call",
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"status": "completed",
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"call_id": "call_1",
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"name": "get_weather",
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"arguments": '{"city":"Paris"}',
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},
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],
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"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
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},
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},
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)
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stream_chunks: Final = tuple(f"data: {json.dumps(event)}\n\n".encode() for event in events)
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def respond(request: Request) -> Reply:
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assert request.method == "POST"
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assert request.target == "/openai/responses?api-version=2025-04-01-preview"
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body: Final = _JSON_OBJECT.validate_json(request.body)
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assert body["model"] == "gpt-6-sol"
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return Reply(content_type="text/event-stream", chunks=stream_chunks)
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with wire_server(respond) as wire, gateway.scenario() as scenario:
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model: Final = scenario.model(
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model="azure/gpt-6-sol",
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api_base=wire.url,
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api_key=_API_KEY,
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api_version="2025-04-01-preview",
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)
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with gateway.client.stream(
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"POST",
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"/v1/chat/completions",
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headers={"Authorization": f"Bearer {gateway.key}"},
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json={
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"model": model,
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"messages": [{"role": "user", "content": f"What is the weather in Paris? {identity}"}],
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get the weather for a city.",
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"parameters": {
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"type": "object",
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"properties": {"city": {"type": "string"}},
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"required": ["city"],
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},
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},
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}
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],
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"stream": True,
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"cache": {"no-cache": True},
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},
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) as response:
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response_body: Final = response.read()
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assert response.status_code == 200, response.text
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chunks: Final = tuple(
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_JSON_OBJECT.validate_json(line.removeprefix("data: "))
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for line in response_body.decode().splitlines()
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if line.startswith("data: ") and line != "data: [DONE]"
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)
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choices: Final = tuple(chain.from_iterable(chunk["choices"] for chunk in chunks))
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assert choices, response.text
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assert all(choice["index"] == 0 for choice in choices), response.text
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assert "".join(str(choice["delta"].get("content") or "") for choice in choices) == expected_text, (
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response.text
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)
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tool_call_chunks: Final = tuple(
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chain.from_iterable(choice["delta"].get("tool_calls", []) for choice in choices)
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)
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assert (
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"".join(str(tool_call["function"].get("name") or "") for tool_call in tool_call_chunks) == "get_weather"
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), response.text
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assert (
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"".join(str(tool_call["function"].get("arguments") or "") for tool_call in tool_call_chunks)
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== '{"city":"Paris"}'
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), response.text
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assert tuple(
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choice.get("finish_reason") for choice in choices if choice.get("finish_reason") is not None
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) == ("tool_calls",), response.text
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assert [(request.method, request.target) for request in wire.drain()] == [
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("POST", "/openai/responses?api-version=2025-04-01-preview")
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]
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@ -194,3 +194,381 @@ def test_messages_over_responses_deployment_with_max_tokens_one_reaches_openai_a
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assert len(tuple(request for request in wire.drain() if request.method == "POST")) == 1
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assert body["content"] == [{"type": "text", "text": "ok"}], response.text
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assert body["usage"]["input_tokens"] == 9 and body["usage"]["output_tokens"] == 1, response.text
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def test_chat_over_responses_deployment_merges_message_and_function_call(gateway: Gateway) -> None:
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identity: Final = "responses-bridge-" + uuid.uuid4().hex
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def respond(request: Request) -> Reply:
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if request.method == "GET" and request.target == "/v1/models":
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return Reply(body=b'{"object":"list","data":[]}')
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assert request.method == "POST" and request.target == "/responses", request.target
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return Reply(
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body=json.dumps(
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{
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"id": "resp_weather",
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"object": "response",
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"created_at": 1789788253,
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"status": "completed",
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"model": "gpt-6-sol",
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"output": [
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{
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"type": "message",
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"id": "msg_weather",
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"status": "completed",
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"role": "assistant",
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"content": [
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{
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"type": "output_text",
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"text": "Let me check the weather.",
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"annotations": [],
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}
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],
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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": "get_weather",
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"arguments": '{"city":"Paris"}',
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"status": "completed",
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},
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],
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"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
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}
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).encode()
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)
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with wire_server(respond) as wire, gateway.scenario() as scenario:
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model: Final = scenario.model(
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model="openai/responses/gpt-6-sol", api_base=wire.url, api_key="synthetic-openai-key"
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)
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response: Final = gateway.request(
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"POST",
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"/v1/chat/completions",
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{
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"model": model,
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"messages": [{"role": "user", "content": f"What is the weather in Paris? {identity}"}],
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get the weather for a city.",
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"parameters": {
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"type": "object",
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"properties": {"city": {"type": "string"}},
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"required": ["city"],
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},
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},
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}
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],
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"cache": {"no-cache": True},
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},
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)
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assert response.status_code == 200, response.text
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body: Final = response.json()
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assert body["choices"] == [
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{
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"finish_reason": "tool_calls",
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "Let me check the weather.",
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"tool_calls": [
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{
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"id": "fc_1",
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"type": "function",
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"function": {
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"name": "get_weather",
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"arguments": '{"city":"Paris"}',
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},
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"index": 0,
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}
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],
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},
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}
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], response.text
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assert [(request.method, request.target) for request in wire.drain()] == [("POST", "/responses")]
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def test_chat_over_responses_deployment_keeps_reasoning_with_merged_tool_call(gateway: Gateway) -> None:
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identity: Final = "responses-bridge-reasoning-" + uuid.uuid4().hex
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def respond(request: Request) -> Reply:
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if request.method == "GET" and request.target == "/v1/models":
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return Reply(body=b'{"object":"list","data":[]}')
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assert request.method == "POST" and request.target == "/responses", request.target
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return Reply(
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body=json.dumps(
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{
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"id": "resp_weather_reasoning",
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"object": "response",
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"created_at": 1789788253,
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"status": "completed",
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"model": "gpt-6-sol",
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"output": [
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{
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"type": "message",
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"id": "msg_weather_reasoning",
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"status": "completed",
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"role": "assistant",
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"content": [
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{
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"type": "output_text",
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"text": "Let me check the weather.",
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"annotations": [],
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}
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],
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},
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{
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"type": "reasoning",
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"id": "rs_weather",
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"summary": [{"type": "summary_text", "text": "Checking the forecast."}],
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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": "get_weather",
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"arguments": '{"city":"Paris"}',
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"status": "completed",
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},
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],
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"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
|
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}
|
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).encode()
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)
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with wire_server(respond) as wire, gateway.scenario() as scenario:
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model: Final = scenario.model(
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model="openai/responses/gpt-6-sol", api_base=wire.url, api_key="synthetic-openai-key"
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)
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response: Final = gateway.request(
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"POST",
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"/v1/chat/completions",
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{
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"model": model,
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"messages": [{"role": "user", "content": f"What is the weather in Paris? {identity}"}],
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"tools": [
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{
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"type": "function",
|
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"function": {
|
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"name": "get_weather",
|
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"description": "Get the weather for a city.",
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"parameters": {
|
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"type": "object",
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"properties": {"city": {"type": "string"}},
|
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"required": ["city"],
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},
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},
|
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}
|
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],
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"cache": {"no-cache": True},
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},
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)
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assert response.status_code == 200, response.text
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body: Final = response.json()
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assert body["choices"] == [
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{
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"finish_reason": "tool_calls",
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "Let me check the weather.",
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"reasoning_content": "Checking the forecast.",
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"reasoning_items": [
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{
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"type": "reasoning",
|
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"id": "rs_weather",
|
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"summary": [{"type": "summary_text", "text": "Checking the forecast."}],
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}
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],
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"tool_calls": [
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{
|
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"id": "fc_1",
|
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"type": "function",
|
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"function": {
|
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"name": "get_weather",
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"arguments": '{"city":"Paris"}',
|
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},
|
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"index": 0,
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}
|
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],
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},
|
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}
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], response.text
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assert [(request.method, request.target) for request in wire.drain()] == [("POST", "/responses")]
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def test_chat_over_responses_deployment_returns_tool_call_only_reply_as_one_choice(gateway: Gateway) -> None:
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identity: Final = "responses-bridge-tool-only-" + uuid.uuid4().hex
|
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|
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def respond(request: Request) -> Reply:
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if request.method == "GET" and request.target == "/v1/models":
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return Reply(body=b'{"object":"list","data":[]}')
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assert request.method == "POST" and request.target == "/responses", request.target
|
||||
return Reply(
|
||||
body=json.dumps(
|
||||
{
|
||||
"id": "resp_weather_tool_only",
|
||||
"object": "response",
|
||||
"created_at": 1789788253,
|
||||
"status": "completed",
|
||||
"model": "gpt-6-sol",
|
||||
"output": [
|
||||
{
|
||||
"type": "function_call",
|
||||
"id": "fc_1",
|
||||
"call_id": "call_1",
|
||||
"name": "get_weather",
|
||||
"arguments": '{"city":"Paris"}',
|
||||
"status": "completed",
|
||||
}
|
||||
],
|
||||
"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
|
||||
}
|
||||
).encode()
|
||||
)
|
||||
|
||||
with wire_server(respond) as wire, gateway.scenario() as scenario:
|
||||
model: Final = scenario.model(
|
||||
model="openai/responses/gpt-6-sol", api_base=wire.url, api_key="synthetic-openai-key"
|
||||
)
|
||||
response: Final = gateway.request(
|
||||
"POST",
|
||||
"/v1/chat/completions",
|
||||
{
|
||||
"model": model,
|
||||
"messages": [{"role": "user", "content": f"What is the weather in Paris? {identity}"}],
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the weather for a city.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"city": {"type": "string"}},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
}
|
||||
],
|
||||
"cache": {"no-cache": True},
|
||||
},
|
||||
)
|
||||
assert response.status_code == 200, response.text
|
||||
body: Final = response.json()
|
||||
assert body["choices"] == [
|
||||
{
|
||||
"finish_reason": "tool_calls",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "fc_1",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"arguments": '{"city":"Paris"}',
|
||||
},
|
||||
"index": 0,
|
||||
}
|
||||
],
|
||||
},
|
||||
}
|
||||
], response.text
|
||||
assert [(request.method, request.target) for request in wire.drain()] == [("POST", "/responses")]
|
||||
|
||||
|
||||
def test_chat_over_responses_deployment_merges_function_call_followed_by_message(gateway: Gateway) -> None:
|
||||
identity: Final = "responses-bridge-tool-then-message-" + uuid.uuid4().hex
|
||||
|
||||
def respond(request: Request) -> Reply:
|
||||
if request.method == "GET" and request.target == "/v1/models":
|
||||
return Reply(body=b'{"object":"list","data":[]}')
|
||||
assert request.method == "POST" and request.target == "/responses", request.target
|
||||
return Reply(
|
||||
body=json.dumps(
|
||||
{
|
||||
"id": "resp_weather_tool_then_message",
|
||||
"object": "response",
|
||||
"created_at": 1789788253,
|
||||
"status": "completed",
|
||||
"model": "gpt-6-sol",
|
||||
"output": [
|
||||
{
|
||||
"type": "function_call",
|
||||
"id": "fc_1",
|
||||
"call_id": "call_1",
|
||||
"name": "get_weather",
|
||||
"arguments": '{"city":"Paris"}',
|
||||
"status": "completed",
|
||||
},
|
||||
{
|
||||
"type": "message",
|
||||
"id": "msg_after_tool",
|
||||
"status": "completed",
|
||||
"role": "assistant",
|
||||
"content": [{"type": "output_text", "text": "After the tool.", "annotations": []}],
|
||||
},
|
||||
],
|
||||
"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
|
||||
}
|
||||
).encode()
|
||||
)
|
||||
|
||||
with wire_server(respond) as wire, gateway.scenario() as scenario:
|
||||
model: Final = scenario.model(
|
||||
model="openai/responses/gpt-6-sol", api_base=wire.url, api_key="synthetic-openai-key"
|
||||
)
|
||||
response: Final = gateway.request(
|
||||
"POST",
|
||||
"/v1/chat/completions",
|
||||
{
|
||||
"model": model,
|
||||
"messages": [{"role": "user", "content": f"What is the weather in Paris? {identity}"}],
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the weather for a city.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"city": {"type": "string"}},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
}
|
||||
],
|
||||
"cache": {"no-cache": True},
|
||||
},
|
||||
)
|
||||
assert response.status_code == 200, response.text
|
||||
body: Final = response.json()
|
||||
assert body["choices"] == [
|
||||
{
|
||||
"finish_reason": "tool_calls",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "After the tool.",
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "fc_1",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"arguments": '{"city":"Paris"}',
|
||||
},
|
||||
"index": 0,
|
||||
}
|
||||
],
|
||||
},
|
||||
}
|
||||
], response.text
|
||||
assert [(request.method, request.target) for request in wire.drain()] == [("POST", "/responses")]
|
||||
|
|
|
|||
|
|
@ -7,6 +7,15 @@ from unittest.mock import ANY, MagicMock, Mock, patch
|
|||
|
||||
import httpx
|
||||
import pytest
|
||||
from openai.types.responses import (
|
||||
ResponseFunctionToolCall,
|
||||
ResponseOutputMessage,
|
||||
ResponseOutputText,
|
||||
)
|
||||
from openai.types.responses.response_reasoning_item import (
|
||||
ResponseReasoningItem,
|
||||
Summary,
|
||||
)
|
||||
|
||||
import litellm
|
||||
from litellm.completion_extras.litellm_responses_transformation.transformation import (
|
||||
|
|
@ -3307,6 +3316,148 @@ def test_convert_response_output_generic_pydantic_message_item():
|
|||
assert choices[0].finish_reason == "stop"
|
||||
|
||||
|
||||
def test_convert_response_output_merges_message_reasoning_and_function_call() -> None:
|
||||
message: Final = ResponseOutputMessage(
|
||||
id="msg_weather",
|
||||
content=[
|
||||
ResponseOutputText(
|
||||
annotations=[
|
||||
{
|
||||
"type": "url_citation",
|
||||
"start_index": 0,
|
||||
"end_index": 5,
|
||||
"title": "Forecast",
|
||||
"url": "https://example.com/forecast",
|
||||
}
|
||||
],
|
||||
text="Sunny.",
|
||||
type="output_text",
|
||||
logprobs=[],
|
||||
)
|
||||
],
|
||||
role="assistant",
|
||||
status="completed",
|
||||
type="message",
|
||||
)
|
||||
reasoning: Final = ResponseReasoningItem(
|
||||
id="rs_before",
|
||||
summary=[Summary(type="summary_text", text="Checking the forecast.")],
|
||||
type="reasoning",
|
||||
content=None,
|
||||
encrypted_content=None,
|
||||
status=None,
|
||||
)
|
||||
pending_reasoning: Final = ResponseReasoningItem(
|
||||
id="rs_after",
|
||||
summary=[Summary(type="summary_text", text="The location is Paris.")],
|
||||
type="reasoning",
|
||||
content=None,
|
||||
encrypted_content=None,
|
||||
status=None,
|
||||
)
|
||||
function_call: Final = ResponseFunctionToolCall(
|
||||
id="fc_1",
|
||||
type="function_call",
|
||||
status="completed",
|
||||
arguments='{"city":"Paris"}',
|
||||
call_id="call_1",
|
||||
name="get_weather",
|
||||
)
|
||||
|
||||
message_and_call: Final = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices(
|
||||
(message, function_call)
|
||||
)
|
||||
assert len(message_and_call) == 1
|
||||
assert message_and_call[0].index == 0
|
||||
assert message_and_call[0].finish_reason == "tool_calls"
|
||||
assert message_and_call[0].message.role == "assistant"
|
||||
assert message_and_call[0].message.content == "Sunny."
|
||||
assert message_and_call[0].message.annotations == [
|
||||
{
|
||||
"type": "url_citation",
|
||||
"start_index": 0,
|
||||
"end_index": 5,
|
||||
"title": "Forecast",
|
||||
"url": "https://example.com/forecast",
|
||||
}
|
||||
]
|
||||
function_calls: Final = message_and_call[0].message.tool_calls
|
||||
assert function_calls is not None
|
||||
assert len(function_calls) == 1
|
||||
assert function_calls[0].function.name == "get_weather"
|
||||
assert function_calls[0].function.arguments == '{"city":"Paris"}'
|
||||
|
||||
reasoning_before_message: Final = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices(
|
||||
(reasoning, message, function_call)
|
||||
)
|
||||
assert len(reasoning_before_message) == 1
|
||||
assert reasoning_before_message[0].message.reasoning_content == "Checking the forecast."
|
||||
reasoning_before_items: Final = reasoning_before_message[0].message.reasoning_items
|
||||
assert reasoning_before_items is not None
|
||||
assert reasoning_before_items[0]["id"] == "rs_before"
|
||||
|
||||
reasoning_after_message: Final = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices(
|
||||
(message, pending_reasoning, function_call)
|
||||
)
|
||||
assert len(reasoning_after_message) == 1
|
||||
assert reasoning_after_message[0].message.reasoning_content == "The location is Paris."
|
||||
reasoning_after_items: Final = reasoning_after_message[0].message.reasoning_items
|
||||
assert reasoning_after_items is not None
|
||||
assert reasoning_after_items[0]["id"] == "rs_after"
|
||||
|
||||
merged_reasoning: Final = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices(
|
||||
(reasoning, message, pending_reasoning, function_call)
|
||||
)
|
||||
assert len(merged_reasoning) == 1
|
||||
assert merged_reasoning[0].message.reasoning_content == "Checking the forecast. The location is Paris."
|
||||
merged_reasoning_items: Final = merged_reasoning[0].message.reasoning_items
|
||||
assert merged_reasoning_items is not None
|
||||
assert [item["id"] for item in merged_reasoning_items] == ["rs_before", "rs_after"]
|
||||
|
||||
tool_only: Final = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices((function_call,))
|
||||
assert len(tool_only) == 1
|
||||
assert tool_only[0].index == 0
|
||||
assert tool_only[0].finish_reason == "tool_calls"
|
||||
assert tool_only[0].message.content is None
|
||||
assert tool_only[0].message.tool_calls is not None
|
||||
assert len(tool_only[0].message.tool_calls) == 1
|
||||
|
||||
message_only: Final = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices((message,))
|
||||
assert len(message_only) == 1
|
||||
assert message_only[0].index == 0
|
||||
assert message_only[0].finish_reason == "stop"
|
||||
assert message_only[0].message.content == "Sunny."
|
||||
assert message_only[0].message.tool_calls is None
|
||||
|
||||
|
||||
def test_convert_response_output_merges_raw_dict_message_and_function_call() -> None:
|
||||
handler: Final = LiteLLMResponsesTransformationHandler()
|
||||
raw_message: Final = {
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": [{"type": "output_text", "text": "Let me check.", "annotations": []}],
|
||||
}
|
||||
raw_function_call: Final = {
|
||||
"type": "function_call",
|
||||
"id": "fc_1",
|
||||
"call_id": "call_1",
|
||||
"name": "get_weather",
|
||||
"arguments": '{"city":"Paris"}',
|
||||
}
|
||||
choices: Final = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices(
|
||||
(raw_message, raw_function_call),
|
||||
handle_raw_dict_callback=handler._handle_raw_dict_response_item,
|
||||
)
|
||||
|
||||
assert len(choices) == 1
|
||||
assert choices[0].index == 0
|
||||
assert choices[0].finish_reason == "tool_calls"
|
||||
assert choices[0].message.role == "assistant"
|
||||
assert choices[0].message.content == "Let me check."
|
||||
assert choices[0].message.tool_calls is not None
|
||||
assert len(choices[0].message.tool_calls) == 1
|
||||
|
||||
|
||||
def test_convert_tools_to_responses_format_flattens_nested_custom_tool():
|
||||
from litellm.completion_extras.litellm_responses_transformation.transformation import (
|
||||
LiteLLMResponsesTransformationHandler,
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue