class OpenAIServingResponses(OpenAIServing):
def __init__(
self,
engine_client: EngineClient,
models: OpenAIServingModels,
*,
request_logger: RequestLogger | None,
chat_template: str | None,
chat_template_content_format: ChatTemplateContentFormatOption,
return_tokens_as_token_ids: bool = False,
reasoning_parser: str = "",
enable_auto_tools: bool = False,
tool_parser: str | None = None,
tool_server: ToolServer | None = None,
enable_prompt_tokens_details: bool = False,
enable_force_include_usage: bool = False,
enable_log_outputs: bool = False,
log_error_stack: bool = False,
) -> None:
super().__init__(
engine_client=engine_client,
models=models,
request_logger=request_logger,
return_tokens_as_token_ids=return_tokens_as_token_ids,
log_error_stack=log_error_stack,
)
self.chat_template = chat_template
self.chat_template_content_format: Final = chat_template_content_format
self.enable_log_outputs = enable_log_outputs
self.reasoning_parser = self._get_reasoning_parser(
reasoning_parser_name=reasoning_parser
)
self.enable_prompt_tokens_details = enable_prompt_tokens_details
self.enable_force_include_usage = enable_force_include_usage
self.default_sampling_params = self.model_config.get_diff_sampling_param()
if self.default_sampling_params:
source = self.model_config.generation_config
source = "model" if source == "auto" else source
logger.info(
"Using default chat sampling params from %s: %s",
source,
self.default_sampling_params,
)
# If False (default), the "store" option is (silently) ignored and the
# response is not stored. If True, the response is stored in memory.
# NOTE(woosuk): This may not be intuitive for users, as the default
# behavior in OpenAI's Responses API is to store the response, but
# vLLM's default behavior is not.
self.enable_store = envs.VLLM_ENABLE_RESPONSES_API_STORE
if self.enable_store:
logger.warning_once(
"`VLLM_ENABLE_RESPONSES_API_STORE` is enabled. This may "
"cause a memory leak since we never remove responses from "
"the store."
)
self.use_harmony = self.model_config.hf_config.model_type == "gpt_oss"
if self.use_harmony:
logger.warning(
"For gpt-oss, we ignore --enable-auto-tool-choice "
"and always enable tool use."
)
# OpenAI models have two EOS-like tokens: <|return|> and <|call|>.
# We need to add them to the stop token ids.
if "stop_token_ids" not in self.default_sampling_params:
self.default_sampling_params["stop_token_ids"] = []
self.default_sampling_params["stop_token_ids"].extend(
get_stop_tokens_for_assistant_actions()
)
# set up tool use
self.enable_auto_tools: bool = enable_auto_tools
if self.enable_auto_tools:
logger.info(
'"auto" tool choice has been enabled please note that while'
" the parallel_tool_calls client option is preset for "
"compatibility reasons, it will be ignored."
)
# HACK(woosuk): This is a hack. We should use a better store.
# FIXME: If enable_store=True, this may cause a memory leak since we
# never remove responses from the store.
self.response_store: dict[str, ResponsesResponse] = {}
self.response_store_lock = asyncio.Lock()
# HACK(woosuk): This is a hack. We should use a better store.
# FIXME: If enable_store=True, this may cause a memory leak since we
# never remove messages from the store.
self.msg_store: dict[str, list[ChatCompletionMessageParam]] = {}
# HACK(wuhang): This is a hack. We should use a better store.
# FIXME: If enable_store=True, this may cause a memory leak since we
# never remove events from the store.
self.event_store: dict[
str, tuple[deque[StreamingResponsesResponse], asyncio.Event]
] = {}
self.background_tasks: dict[str, asyncio.Task] = {}
self.tool_server = tool_server
def _validate_generator_input(
self, engine_prompt: EngineTokensPrompt
) -> ErrorResponse | None:
"""Add validations to the input to the generator here."""
if self.max_model_len <= len(engine_prompt["prompt_token_ids"]):
error_message = (
"The engine prompt length"
f" {len(engine_prompt['prompt_token_ids'])} "
f"exceeds the max_model_len {self.max_model_len}. "
"Please reduce prompt."
)
return self.create_error_response(
err_type="invalid_request_error",
message=error_message,
status_code=HTTPStatus.BAD_REQUEST,
)
return None
def _validate_create_responses_input(
self, request: ResponsesRequest
) -> ErrorResponse | None:
if self.use_harmony and request.is_include_output_logprobs():
return self.create_error_response(
err_type="invalid_request_error",
message="logprobs are not supported with gpt-oss models",
status_code=HTTPStatus.BAD_REQUEST,
)
if request.store and not self.enable_store and request.background:
return self.create_error_response(
err_type="invalid_request_error",
message=(
"This vLLM engine does not support `store=True` and "
"therefore does not support the background mode. To "
"enable these features, set the environment variable "
"`VLLM_ENABLE_RESPONSES_API_STORE=1` when launching "
"the vLLM server."
),
status_code=HTTPStatus.BAD_REQUEST,
)
if request.previous_input_messages and request.previous_response_id:
return self.create_error_response(
err_type="invalid_request_error",
message="Only one of `previous_input_messages` and "
"`previous_response_id` can be set.",
status_code=HTTPStatus.BAD_REQUEST,
)
return None
async def create_responses(
self,
request: ResponsesRequest,
raw_request: Request | None = None,
) -> (
AsyncGenerator[StreamingResponsesResponse, None]
| ResponsesResponse
| ErrorResponse
):
error_check_ret = await self._check_model(request)
if error_check_ret is not None:
logger.error("Error with model %s", error_check_ret)
return error_check_ret
maybe_validation_error = self._validate_create_responses_input(request)
if maybe_validation_error is not None:
return maybe_validation_error
# If the engine is dead, raise the engine's DEAD_ERROR.
# This is required for the streaming case, where we return a
# success status before we actually start generating text :).
if self.engine_client.errored:
raise self.engine_client.dead_error
if request.store and not self.enable_store:
# Disable the store option.
# NOTE(woosuk): Although returning an error is possible, we opted
# to implicitly disable store and process the request anyway, as
# we assume most users do not intend to actually store the response
# (i.e., their request's `store=True` just because it's the default
# value).
request.store = False
# Handle the previous response ID.
prev_response_id = request.previous_response_id
if prev_response_id is not None:
async with self.response_store_lock:
prev_response = self.response_store.get(prev_response_id)
if prev_response is None:
return self._make_not_found_error(prev_response_id)
else:
prev_response = None
try:
lora_request = self._maybe_get_adapters(request)
model_name = self.models.model_name(lora_request)
tokenizer = await self.engine_client.get_tokenizer()
if self.use_harmony:
messages, request_prompts, engine_prompts = (
self._make_request_with_harmony(request, prev_response)
)
else:
messages, request_prompts, engine_prompts = await self._make_request(
request, prev_response, tokenizer
)
except (
ValueError,
TypeError,
RuntimeError,
jinja2.TemplateError,
NotImplementedError,
) as e:
logger.exception("Error in preprocessing prompt inputs")
return self.create_error_response(f"{e} {e.__cause__}")
request_metadata = RequestResponseMetadata(request_id=request.request_id)
if raw_request:
raw_request.state.request_metadata = request_metadata
# Schedule the request and get the result generator.
generators: list[AsyncGenerator[ConversationContext, None]] = []
builtin_tool_list: list[str] = []
if self.use_harmony and self.tool_server is not None:
if self.tool_server.has_tool("browser"):
builtin_tool_list.append("browser")
if self.tool_server.has_tool("python"):
builtin_tool_list.append("python")
if self.tool_server.has_tool("container"):
builtin_tool_list.append("container")
if self.tool_server is not None:
available_tools = builtin_tool_list
else:
assert len(builtin_tool_list) == 0
available_tools = []
try:
for i, engine_prompt in enumerate(engine_prompts):
maybe_error = self._validate_generator_input(engine_prompt)
if maybe_error is not None:
return maybe_error
default_max_tokens = self.max_model_len - len(
engine_prompt["prompt_token_ids"]
)
sampling_params = request.to_sampling_params(
default_max_tokens, self.default_sampling_params
)
trace_headers = (
None
if raw_request is None
else await self._get_trace_headers(raw_request.headers)
)
context: ConversationContext
if self.use_harmony:
if request.stream:
context = StreamingHarmonyContext(messages, available_tools)
else:
context = HarmonyContext(messages, available_tools)
else:
context = SimpleContext()
if self.reasoning_parser is not None:
reasoning_parser = self.reasoning_parser(tokenizer)
if sampling_params.structured_outputs is None:
sampling_params.structured_outputs = StructuredOutputsParams()
struct_out = sampling_params.structured_outputs
if struct_out.all_non_structural_tag_constraints_none():
sampling_params.structured_outputs.structural_tag = (
reasoning_parser.prepare_structured_tag(
sampling_params.structured_outputs.structural_tag,
self.tool_server,
)
)
generator = self._generate_with_builtin_tools(
request_id=request.request_id,
request_prompt=request_prompts[i],
engine_prompt=engine_prompt,
sampling_params=sampling_params,
context=context,
lora_request=lora_request,
priority=request.priority,
trace_headers=trace_headers,
)
generators.append(generator)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
assert len(generators) == 1
(result_generator,) = generators
# Store the input messages.
if request.store:
self.msg_store[request.request_id] = messages
if request.background:
created_time = int(time.time())
response = ResponsesResponse.from_request(
request,
sampling_params,
model_name=model_name,
created_time=created_time,
output=[],
status="queued",
usage=None,
)
async with self.response_store_lock:
self.response_store[response.id] = response
# Run the request in the background.
if request.stream:
task = asyncio.create_task(
self._run_background_request_stream(
request,
sampling_params,
result_generator,
context,
model_name,
tokenizer,
request_metadata,
created_time,
),
name=f"create_{request.request_id}",
)
else:
task = asyncio.create_task(
self._run_background_request(
request,
sampling_params,
result_generator,
context,
model_name,
tokenizer,
request_metadata,
created_time,
),
name=f"create_{response.id}",
)
# For cleanup.
response_id = response.id
self.background_tasks[response_id] = task
task.add_done_callback(
lambda _: self.background_tasks.pop(response_id, None)
)
if request.stream:
return self.responses_background_stream_generator(request.request_id)
return response
if request.stream:
return self.responses_stream_generator(
request,
sampling_params,
result_generator,
context,
model_name,
tokenizer,
request_metadata,
)
try:
return await self.responses_full_generator(
request,
sampling_params,
result_generator,
context,
model_name,
tokenizer,
request_metadata,
)
except Exception as e:
return self.create_error_response(str(e))
async def _make_request(
self,
request: ResponsesRequest,
prev_response: ResponsesResponse | None,
tokenizer: AnyTokenizer,
):
if len(request.tools) > 0:
raise NotImplementedError(
"Tool use is not supported in Responses API without Harmony"
)
# Construct the input messages.
messages = self._construct_input_messages(request, prev_response)
_, request_prompts, engine_prompts = await self._preprocess_chat(
request,
tokenizer,
messages,
chat_template=self.chat_template,
chat_template_content_format=self.chat_template_content_format,
)
return messages, request_prompts, engine_prompts
def _make_request_with_harmony(
self,
request: ResponsesRequest,
prev_response: ResponsesResponse | None,
):
if request.tool_choice != "auto":
raise NotImplementedError(
"Only 'auto' tool_choice is supported in response API with Harmony"
)
messages = self._construct_input_messages_with_harmony(request, prev_response)
prompt_token_ids = render_for_completion(messages)
engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)
# Add cache_salt if provided in the request
if request.cache_salt is not None:
engine_prompt["cache_salt"] = request.cache_salt
return messages, [prompt_token_ids], [engine_prompt]
async def _initialize_tool_sessions(
self,
request: ResponsesRequest,
context: ConversationContext,
exit_stack: AsyncExitStack,
):
# we should only initialize the tool session if the request needs tools
if len(request.tools) == 0:
return
mcp_tools = {
tool.server_label: tool for tool in request.tools if tool.type == "mcp"
}
await context.init_tool_sessions(
self.tool_server, exit_stack, request.request_id, mcp_tools
)
async def responses_full_generator(
self,
request: ResponsesRequest,
sampling_params: SamplingParams,
result_generator: AsyncIterator[ConversationContext],
context: ConversationContext,
model_name: str,
tokenizer: AnyTokenizer,
request_metadata: RequestResponseMetadata,
created_time: int | None = None,
) -> ErrorResponse | ResponsesResponse:
if created_time is None:
created_time = int(time.time())
async with AsyncExitStack() as exit_stack:
try:
await self._initialize_tool_sessions(request, context, exit_stack)
async for _ in result_generator:
pass
except asyncio.CancelledError:
return self.create_error_response("Client disconnected")
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
# NOTE: Implementation of stauts is still WIP, but for now
# we guarantee that if the status is not "completed", it is accurate.
# "completed" is implemented as the "catch-all" for now.
status: ResponseStatus = "completed"
input_messages = None
output_messages = None
if self.use_harmony:
assert isinstance(context, HarmonyContext)
output = self._make_response_output_items_with_harmony(context)
if request.enable_response_messages:
input_messages = context.messages[: context.num_init_messages]
output_messages = context.messages[context.num_init_messages :]
num_tool_output_tokens = context.num_tool_output_tokens
if len(output) > 0:
if context.finish_reason == "length":
status = "incomplete"
elif context.finish_reason == "abort":
status = "cancelled"
else:
status = "incomplete"
else:
assert isinstance(context, SimpleContext)
final_res = context.last_output
assert final_res is not None
assert len(final_res.outputs) == 1
final_output = final_res.outputs[0]
output = self._make_response_output_items(request, final_output, tokenizer)
# TODO: context for non-gptoss models doesn't use messages
# so we can't get them out yet
if request.enable_response_messages:
raise NotImplementedError(
"enable_response_messages is currently only supported for gpt-oss"
)
# Calculate usage.
assert final_res.prompt_token_ids is not None
num_tool_output_tokens = 0
assert isinstance(context, (SimpleContext, HarmonyContext))
num_prompt_tokens = context.num_prompt_tokens
num_generated_tokens = context.num_output_tokens
num_cached_tokens = context.num_cached_tokens
num_reasoning_tokens = context.num_reasoning_tokens
usage = ResponseUsage(
input_tokens=num_prompt_tokens,
output_tokens=num_generated_tokens,
total_tokens=num_prompt_tokens + num_generated_tokens,
input_tokens_details=InputTokensDetails(
cached_tokens=num_cached_tokens,
input_tokens_per_turn=[
turn.input_tokens for turn in context.all_turn_metrics
],
cached_tokens_per_turn=[
turn.cached_input_tokens for turn in context.all_turn_metrics
],
),
output_tokens_details=OutputTokensDetails(
reasoning_tokens=num_reasoning_tokens,
tool_output_tokens=num_tool_output_tokens,
output_tokens_per_turn=[
turn.output_tokens for turn in context.all_turn_metrics
],
tool_output_tokens_per_turn=[
turn.tool_output_tokens for turn in context.all_turn_metrics
],
),
)
response = ResponsesResponse.from_request(
request,
sampling_params,
input_messages=input_messages,
output_messages=output_messages,
model_name=model_name,
created_time=created_time,
output=output,
status=status,
usage=usage,
)
if request.store:
async with self.response_store_lock:
stored_response = self.response_store.get(response.id)
# If the response is already cancelled, don't update it.
if stored_response is None or stored_response.status != "cancelled":
self.response_store[response.id] = response
return response
def _topk_logprobs(
self,
logprobs: dict[int, SampleLogprob],
top_logprobs: int,
tokenizer: AnyTokenizer,
) -> list[LogprobTopLogprob]:
"""Returns the top-k logprobs from the logprobs dictionary."""
out = []
for i, (token_id, _logprob) in enumerate(logprobs.items()):
if i >= top_logprobs:
break
text = (
_logprob.decoded_token
if _logprob.decoded_token is not None
else tokenizer.decode([token_id])
)
out.append(
LogprobTopLogprob(
token=text,
logprob=max(_logprob.logprob, -9999.0),
bytes=list(text.encode("utf-8", errors="replace")),
)
)
return out
def _create_response_logprobs(
self,
token_ids: Sequence[int],
logprobs: SampleLogprobs | None,
tokenizer: AnyTokenizer,
top_logprobs: int | None = None,
) -> list[Logprob]:
assert logprobs is not None, "logprobs must be provided"
assert len(token_ids) == len(logprobs), (
"token_ids and logprobs.token_ids must have the same length"
)
out = []
for i, token_id in enumerate(token_ids):
logprob = logprobs[i]
token_logprob = logprob[token_id]
text = (
token_logprob.decoded_token
if token_logprob.decoded_token is not None
else tokenizer.decode([token_id])
)
out.append(
Logprob(
token=text,
logprob=max(token_logprob.logprob, -9999.0),
bytes=list(text.encode("utf-8", errors="replace")),
top_logprobs=(
self._topk_logprobs(
logprob, top_logprobs=top_logprobs, tokenizer=tokenizer
)
if top_logprobs
else []
),
)
)
return out
def _create_stream_response_logprobs(
self,
token_ids: Sequence[int],
logprobs: SampleLogprobs | None,
tokenizer: AnyTokenizer,
top_logprobs: int | None = None,
) -> list[response_text_delta_event.Logprob]:
lgs = self._create_response_logprobs(
token_ids=token_ids,
logprobs=logprobs,
tokenizer=tokenizer,
top_logprobs=top_logprobs,
)
return [
response_text_delta_event.Logprob(
token=lg.token,
logprob=lg.logprob,
top_logprobs=[
response_text_delta_event.LogprobTopLogprob(
token=tl.token, logprob=tl.logprob
)
for tl in lg.top_logprobs
],
)
for lg in lgs
]
def _make_response_output_items(
self,
request: ResponsesRequest,
final_output: CompletionOutput,
tokenizer: AnyTokenizer,
) -> list[ResponseOutputItem]:
if self.reasoning_parser:
try:
reasoning_parser = self.reasoning_parser(tokenizer)
except RuntimeError as e:
logger.exception("Error in reasoning parser creation.")
raise e
reasoning_content, content = reasoning_parser.extract_reasoning_content(
final_output.text, request=request
)
else:
reasoning_content = None
content = final_output.text
# Log complete response if output logging is enabled
if self.enable_log_outputs and self.request_logger:
output_text = ""
if content:
output_text = content
elif reasoning_content:
output_text = f"[reasoning: {reasoning_content}]"
if output_text:
self.request_logger.log_outputs(
request_id=request.request_id,
outputs=output_text,
output_token_ids=final_output.token_ids,
finish_reason=final_output.finish_reason,
is_streaming=False,
delta=False,
)
output = []
if reasoning_content:
reasoning_item = ResponseReasoningItem(
id=f"rs_{random_uuid()}",
summary=[],
type="reasoning",
content=[
ResponseReasoningTextContent(
text=reasoning_content, type="reasoning_text"
)
],
status=None, # NOTE: Only the last output item has status.
)
output.append(reasoning_item)
if content:
output_text = ResponseOutputText(
text=content,
annotations=[], # TODO
type="output_text",
logprobs=(
self._create_response_logprobs(
token_ids=final_output.token_ids,
logprobs=final_output.logprobs,
tokenizer=tokenizer,
top_logprobs=request.top_logprobs,
)
if request.is_include_output_logprobs()
else None
),
)
message = ResponseOutputMessage(
id=f"msg_{random_uuid()}",
content=[output_text],
role="assistant",
status="completed",
type="message",
)
output.append(message)
return output
def _make_response_output_items_with_harmony(
self,
context: HarmonyContext,
) -> list[ResponseOutputItem]:
output_items: list[ResponseOutputItem] = []
num_init_messages = context.num_init_messages
for msg in context.messages[num_init_messages:]:
output_items.extend(parse_output_message(msg))
# Handle the generation stopped in the middle (if any).
last_items = parse_remaining_state(context.parser)
if last_items:
output_items.extend(last_items)
return output_items
def _construct_input_messages(
self,
request: ResponsesRequest,
prev_response: ResponsesResponse | None = None,
) -> list[ChatCompletionMessageParam]:
messages: list[ChatCompletionMessageParam] = []
if request.instructions:
messages.append(
{
"role": "system",
"content": request.instructions,
}
)
# Prepend the conversation history.
if prev_response is not None:
# Add the previous messages.
prev_msg = self.msg_store[prev_response.id]
messages.extend(prev_msg)
# Add the previous output.
for output_item in prev_response.output:
# NOTE: We skip the reasoning output.
if isinstance(output_item, ResponseOutputMessage):
for content in output_item.content:
messages.append(
{
"role": "assistant",
"content": content.text,
}
)
# Append the new input.
# Responses API supports simple text inputs without chat format.
if isinstance(request.input, str):
messages.append({"role": "user", "content": request.input})
else:
messages.extend(request.input) # type: ignore
return messages
def _construct_harmony_system_input_message(
self, request: ResponsesRequest, with_custom_tools: bool, tool_types: set[str]
) -> OpenAIHarmonyMessage:
reasoning_effort = request.reasoning.effort if request.reasoning else None
enable_browser = (
"web_search_preview" in tool_types
and self.tool_server is not None
and self.tool_server.has_tool("browser")
)
enable_code_interpreter = (
"code_interpreter" in tool_types
and self.tool_server is not None
and self.tool_server.has_tool("python")
)
enable_container = (
"container" in tool_types
and self.tool_server is not None
and self.tool_server.has_tool("container")
)
sys_msg = get_system_message(
reasoning_effort=reasoning_effort,
browser_description=(
self.tool_server.get_tool_description("browser")
if enable_browser and self.tool_server is not None
else None
),
python_description=(
self.tool_server.get_tool_description("python")
if enable_code_interpreter and self.tool_server is not None
else None
),
container_description=(
self.tool_server.get_tool_description("container")
if enable_container and self.tool_server is not None
else None
),
instructions=request.instructions,
with_custom_tools=with_custom_tools,
)
return sys_msg
def _construct_input_messages_with_harmony(
self,
request: ResponsesRequest,
prev_response: ResponsesResponse | None,
) -> list[OpenAIHarmonyMessage]:
messages: list[OpenAIHarmonyMessage] = []
if prev_response is None:
# New conversation.
tool_types = extract_tool_types(request.tools)
with_custom_tools = has_custom_tools(tool_types)
sys_msg = self._construct_harmony_system_input_message(
request, with_custom_tools, tool_types
)
messages.append(sys_msg)
if with_custom_tools:
dev_msg = get_developer_message(
instructions=request.instructions, tools=request.tools
)
messages.append(dev_msg)
messages += construct_harmony_previous_input_messages(request)
else:
# Continue the previous conversation.
# FIXME(woosuk): Currently, request params like reasoning and
# instructions are ignored.
prev_msgs = self.msg_store[prev_response.id]
# Remove the previous chain-of-thoughts if there is a new "final"
# message. Note that this also removes these messages from the
# msg_store.
if len(prev_msgs) > 0:
last_msg = prev_msgs[-1]
assert isinstance(last_msg, OpenAIHarmonyMessage)
if last_msg.channel == "final":
prev_final_msg_idx = -1
for i in range(len(prev_msgs) - 2, -1, -1):
prev_msg_i = prev_msgs[i]
assert isinstance(prev_msg_i, OpenAIHarmonyMessage)
if prev_msg_i.channel == "final":
prev_final_msg_idx = i
break
recent_turn_msgs = prev_msgs[prev_final_msg_idx + 1 :]
del prev_msgs[prev_final_msg_idx + 1 :]
for msg in recent_turn_msgs:
assert isinstance(msg, OpenAIHarmonyMessage)
if msg.channel != "analysis":
prev_msgs.append(msg)
messages.extend(prev_msgs)
# Append the new input.
# Responses API supports simple text inputs without chat format.
if isinstance(request.input, str):
messages.append(get_user_message(request.input))
else:
if prev_response is not None:
prev_outputs = copy(prev_response.output)
else:
prev_outputs = []
for response_msg in request.input:
messages.append(parse_response_input(response_msg, prev_outputs))
# User passes in a tool call request and its output. We need
# to add the tool call request to prev_outputs so that the
# parse_response_input can find the tool call request when
# parsing the tool call output.
if (
isinstance(response_msg, dict)
and response_msg.get("type") == "function_call"
):
response_msg = ResponseFunctionToolCall.model_validate(response_msg)
if isinstance(response_msg, ResponseFunctionToolCall):
prev_outputs.append(response_msg)
return messages
async def _run_background_request_stream(
self,
request: ResponsesRequest,
*args,
**kwargs,
):
event_deque: deque[StreamingResponsesResponse] = deque()
new_event_signal = asyncio.Event()
self.event_store[request.request_id] = (event_deque, new_event_signal)
response = None
try:
generator = self.responses_stream_generator(request, *args, **kwargs)
async for event in generator:
event_deque.append(event)
new_event_signal.set() # Signal new event available
except Exception as e:
logger.exception("Background request failed for %s", request.request_id)
response = self.create_error_response(str(e))
finally:
new_event_signal.set()
if response is not None and isinstance(response, ErrorResponse):
# If the request has failed, update the status to "failed".
response_id = request.request_id
async with self.response_store_lock:
stored_response = self.response_store.get(response_id)
assert stored_response is not None
if stored_response.status not in ("completed", "cancelled"):
stored_response.status = "failed"
async def _run_background_request(
self,
request: ResponsesRequest,
*args,
**kwargs,
):
try:
response = await self.responses_full_generator(request, *args, **kwargs)
except Exception as e:
logger.exception("Background request failed for %s", request.request_id)
response = self.create_error_response(str(e))
if isinstance(response, ErrorResponse):
# If the request has failed, update the status to "failed".
response_id = request.request_id
async with self.response_store_lock:
stored_response = self.response_store.get(response_id)
assert stored_response is not None
if stored_response.status not in ("completed", "cancelled"):
stored_response.status = "failed"
async def responses_background_stream_generator(
self,
response_id: str,
starting_after: int | None = None,
) -> AsyncGenerator[StreamingResponsesResponse, None]:
if response_id not in self.event_store:
raise ValueError(f"Unknown response_id: {response_id}")
event_deque, new_event_signal = self.event_store[response_id]
start_index = 0 if starting_after is None else starting_after + 1
current_index = start_index
while True:
new_event_signal.clear()
# Yield existing events from start_index
while current_index < len(event_deque):
event = event_deque[current_index]
yield event
if getattr(event, "type", "unknown") == "response.completed":
return
current_index += 1
await new_event_signal.wait()
async def retrieve_responses(
self,
response_id: str,
starting_after: int | None,
stream: bool | None,
) -> (
ErrorResponse
| ResponsesResponse
| AsyncGenerator[StreamingResponsesResponse, None]
):
async with self.response_store_lock:
response = self.response_store.get(response_id)
if response is None:
return self._make_not_found_error(response_id)
if stream:
return self.responses_background_stream_generator(
response_id,
starting_after,
)
return response
async def cancel_responses(
self,
response_id: str,
) -> ErrorResponse | ResponsesResponse:
async with self.response_store_lock:
response = self.response_store.get(response_id)
if response is None:
return self._make_not_found_error(response_id)
prev_status = response.status
if prev_status not in ("queued", "in_progress"):
return self.create_error_response(
err_type="invalid_request_error",
message="Cannot cancel a synchronous response.",
)
# Update the status to "cancelled".
response.status = "cancelled"
# Abort the request.
if task := self.background_tasks.get(response_id):
task.cancel()
try:
await task
except asyncio.CancelledError:
logger.exception("Background task for %s was cancelled", response_id)
return response
def _make_not_found_error(self, response_id: str) -> ErrorResponse:
return self.create_error_response(
err_type="invalid_request_error",
message=f"Response with id '{response_id}' not found.",
status_code=HTTPStatus.NOT_FOUND,
)
def _make_store_not_supported_error(self) -> ErrorResponse:
return self.create_error_response(
err_type="invalid_request_error",
message=(
"`store=True` (default) is not supported. Please set "
"`store=False` in Responses API or set "
"`VLLM_ENABLE_RESPONSES_API_STORE=1` in the env var when "
"starting the vLLM server."
),
status_code=HTTPStatus.BAD_REQUEST,
)
async def _process_simple_streaming_events(
self,
request: ResponsesRequest,
sampling_params: SamplingParams,
result_generator: AsyncIterator[ConversationContext | None],
context: ConversationContext,
model_name: str,
tokenizer: AnyTokenizer,
request_metadata: RequestResponseMetadata,
created_time: int,
_increment_sequence_number_and_return: Callable[
[StreamingResponsesResponse], StreamingResponsesResponse
],
) -> AsyncGenerator[StreamingResponsesResponse, None]:
current_content_index = 0
current_output_index = 0
current_item_id = ""
reasoning_parser = None
if self.reasoning_parser:
reasoning_parser = self.reasoning_parser(tokenizer)
previous_text = ""
previous_token_ids: list[int] = []
first_delta_sent = False
previous_delta_messages: list[DeltaMessage] = []
async for ctx in result_generator:
assert isinstance(ctx, SimpleContext)
if ctx.last_output is None:
continue
if ctx.last_output.outputs:
output = ctx.last_output.outputs[0]
if reasoning_parser:
delta_message = (
reasoning_parser.extract_reasoning_content_streaming(
previous_text=previous_text,
current_text=previous_text + output.text,
delta_text=output.text,
previous_token_ids=previous_token_ids,
current_token_ids=previous_token_ids + output.token_ids,
delta_token_ids=output.token_ids,
)
)
else:
delta_message = DeltaMessage(
content=output.text,
)
previous_text += output.text
previous_token_ids += output.token_ids
if not delta_message:
continue
if not first_delta_sent:
current_item_id = str(uuid.uuid4())
if delta_message.reasoning_content:
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=ResponseReasoningItem(
type="reasoning",
id=current_item_id,
summary=[],
status="in_progress",
),
)
)
else:
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=ResponseOutputMessage(
id=current_item_id,
type="message",
role="assistant",
content=[],
status="in_progress",
),
)
)
yield _increment_sequence_number_and_return(
ResponseContentPartAddedEvent(
type="response.content_part.added",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
content_index=current_content_index,
part=ResponseOutputText(
type="output_text",
text="",
annotations=[],
logprobs=[],
),
)
)
current_content_index += 1
first_delta_sent = True
# todo(kebe7jun) tool call support
# check delta message and previous delta message are
# same as content or reasoning content
if (
previous_delta_messages
and previous_delta_messages[-1].reasoning_content is not None
and delta_message.content is not None
):
# from reasoning to normal content, send done
# event for reasoning
reason_content = "".join(
pm.reasoning_content
for pm in previous_delta_messages
if pm.reasoning_content is not None
)
yield _increment_sequence_number_and_return(
ResponseReasoningTextDoneEvent(
type="response.reasoning_text.done",
item_id=current_item_id,
sequence_number=-1,
output_index=current_output_index,
content_index=current_content_index,
text=reason_content,
)
)
current_content_index = 0
reasoning_item = ResponseReasoningItem(
type="reasoning",
content=[
ResponseReasoningTextContent(
text=reason_content,
type="reasoning_text",
),
],
status="completed",
id=current_item_id,
summary=[],
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=reasoning_item,
)
)
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=ResponseOutputMessage(
id=current_item_id,
type="message",
role="assistant",
content=[],
status="in_progress",
),
)
)
current_output_index += 1
current_item_id = str(uuid.uuid4())
yield _increment_sequence_number_and_return(
ResponseContentPartAddedEvent(
type="response.content_part.added",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
content_index=current_content_index,
part=ResponseOutputText(
type="output_text",
text="",
annotations=[],
logprobs=[],
),
)
)
current_content_index += 1
# reset previous delta messages
previous_delta_messages = []
if delta_message.reasoning_content is not None:
yield _increment_sequence_number_and_return(
ResponseReasoningTextDeltaEvent(
type="response.reasoning_text.delta",
sequence_number=-1,
content_index=current_content_index,
output_index=current_output_index,
item_id=current_item_id,
delta=delta_message.reasoning_content,
)
)
elif delta_message.content is not None:
yield _increment_sequence_number_and_return(
ResponseTextDeltaEvent(
type="response.output_text.delta",
sequence_number=-1,
content_index=current_content_index,
output_index=current_output_index,
item_id=current_item_id,
delta=delta_message.content,
logprobs=(
self._create_stream_response_logprobs(
token_ids=output.token_ids,
logprobs=output.logprobs,
tokenizer=tokenizer,
top_logprobs=request.top_logprobs,
)
if request.is_include_output_logprobs()
else []
),
)
)
current_content_index += 1
previous_delta_messages.append(delta_message)
if previous_delta_messages:
if previous_delta_messages[-1].reasoning_content is not None:
reason_content = "".join(
pm.reasoning_content
for pm in previous_delta_messages
if pm.reasoning_content is not None
)
yield _increment_sequence_number_and_return(
ResponseReasoningTextDoneEvent(
type="response.reasoning_text.done",
item_id=current_item_id,
sequence_number=-1,
output_index=current_output_index,
content_index=current_content_index,
text=reason_content,
)
)
current_content_index += 1
reasoning_item = ResponseReasoningItem(
type="reasoning",
content=[
ResponseReasoningTextContent(
text=reason_content,
type="reasoning_text",
),
],
status="completed",
id=current_item_id,
summary=[],
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=reasoning_item,
)
)
elif previous_delta_messages[-1].content is not None:
final_content = "".join(
pm.content
for pm in previous_delta_messages
if pm.content is not None
)
yield _increment_sequence_number_and_return(
ResponseTextDoneEvent(
type="response.output_text.done",
sequence_number=-1,
output_index=current_output_index,
content_index=current_content_index,
text=final_content,
logprobs=[],
item_id=current_item_id,
)
)
current_content_index += 1
part = ResponseOutputText(
text=final_content,
type="output_text",
annotations=[],
)
yield _increment_sequence_number_and_return(
ResponseContentPartDoneEvent(
type="response.content_part.done",
sequence_number=-1,
item_id=current_item_id,
output_index=current_output_index,
content_index=current_content_index,
part=part,
)
)
current_content_index += 1
item = ResponseOutputMessage(
type="message",
role="assistant",
content=[
part,
],
status="completed",
id=current_item_id,
summary=[],
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=item,
)
)
async def _process_harmony_streaming_events(
self,
request: ResponsesRequest,
sampling_params: SamplingParams,
result_generator: AsyncIterator[ConversationContext | None],
context: ConversationContext,
model_name: str,
tokenizer: AnyTokenizer,
request_metadata: RequestResponseMetadata,
created_time: int,
_increment_sequence_number_and_return: Callable[
[StreamingResponsesResponse], StreamingResponsesResponse
],
) -> AsyncGenerator[StreamingResponsesResponse, None]:
current_content_index = -1
current_output_index = 0
current_item_id: str = ""
sent_output_item_added = False
is_first_function_call_delta = False
async for ctx in result_generator:
assert isinstance(ctx, StreamingHarmonyContext)
if ctx.is_expecting_start():
current_output_index += 1
sent_output_item_added = False
is_first_function_call_delta = False
if len(ctx.parser.messages) > 0:
previous_item = ctx.parser.messages[-1]
if previous_item.recipient is not None:
# Deal with tool call
if previous_item.recipient.startswith("functions."):
function_name = previous_item.recipient[len("functions.") :]
yield _increment_sequence_number_and_return(
ResponseFunctionCallArgumentsDoneEvent(
type="response.function_call_arguments.done",
arguments=previous_item.content[0].text,
name=function_name,
item_id=current_item_id,
output_index=current_output_index,
sequence_number=-1,
)
)
function_call_item = ResponseFunctionToolCall(
type="function_call",
arguments=previous_item.content[0].text,
name=function_name,
item_id=current_item_id,
output_index=current_output_index,
sequence_number=-1,
call_id=f"fc_{random_uuid()}",
status="completed",
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=function_call_item,
)
)
elif previous_item.channel == "analysis":
content = ResponseReasoningTextContent(
text=previous_item.content[0].text,
type="reasoning_text",
)
reasoning_item = ResponseReasoningItem(
type="reasoning",
content=[content],
status="completed",
id=current_item_id,
summary=[],
)
yield _increment_sequence_number_and_return(
ResponseReasoningTextDoneEvent(
type="response.reasoning_text.done",
item_id=current_item_id,
sequence_number=-1,
output_index=current_output_index,
content_index=current_content_index,
text=previous_item.content[0].text,
)
)
yield _increment_sequence_number_and_return(
ResponseReasoningPartDoneEvent(
type="response.reasoning_part.done",
sequence_number=-1,
item_id=current_item_id,
output_index=current_output_index,
content_index=current_content_index,
part=content,
)
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=reasoning_item,
)
)
elif previous_item.channel == "final":
text_content = ResponseOutputText(
type="output_text",
text=previous_item.content[0].text,
annotations=[],
)
yield _increment_sequence_number_and_return(
ResponseTextDoneEvent(
type="response.output_text.done",
sequence_number=-1,
output_index=current_output_index,
content_index=current_content_index,
text=previous_item.content[0].text,
logprobs=[],
item_id=current_item_id,
)
)
yield _increment_sequence_number_and_return(
ResponseContentPartDoneEvent(
type="response.content_part.done",
sequence_number=-1,
item_id=current_item_id,
output_index=current_output_index,
content_index=current_content_index,
part=text_content,
)
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=ResponseOutputMessage(
id=current_item_id,
type="message",
role="assistant",
content=[text_content],
status="completed",
),
)
)
# stream the output of a harmony message
if ctx.parser.last_content_delta:
if (
ctx.parser.current_channel == "final"
and ctx.parser.current_recipient is None
):
if not sent_output_item_added:
sent_output_item_added = True
current_item_id = f"msg_{random_uuid()}"
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=ResponseOutputMessage(
id=current_item_id,
type="message",
role="assistant",
content=[],
status="in_progress",
),
)
)
current_content_index += 1
yield _increment_sequence_number_and_return(
ResponseContentPartAddedEvent(
type="response.content_part.added",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
content_index=current_content_index,
part=ResponseOutputText(
type="output_text",
text="",
annotations=[],
logprobs=[],
),
)
)
yield _increment_sequence_number_and_return(
ResponseTextDeltaEvent(
type="response.output_text.delta",
sequence_number=-1,
content_index=current_content_index,
output_index=current_output_index,
item_id=current_item_id,
delta=ctx.parser.last_content_delta,
# TODO, use logprobs from ctx.last_request_output
logprobs=[],
)
)
elif (
ctx.parser.current_channel == "analysis"
and ctx.parser.current_recipient is None
):
if not sent_output_item_added:
sent_output_item_added = True
current_item_id = f"msg_{random_uuid()}"
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=ResponseReasoningItem(
type="reasoning",
id=current_item_id,
summary=[],
status="in_progress",
),
)
)
current_content_index += 1
yield _increment_sequence_number_and_return(
ResponseReasoningPartAddedEvent(
type="response.reasoning_part.added",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
content_index=current_content_index,
part=ResponseReasoningTextContent(
text="",
type="reasoning_text",
),
)
)
yield _increment_sequence_number_and_return(
ResponseReasoningTextDeltaEvent(
type="response.reasoning_text.delta",
item_id=current_item_id,
output_index=current_output_index,
content_index=current_content_index,
delta=ctx.parser.last_content_delta,
sequence_number=-1,
)
)
# built-in tools will be triggered on the analysis channel
# However, occasionally built-in tools will
# still be output to commentary.
elif (
ctx.parser.current_channel == "commentary"
or ctx.parser.current_channel == "analysis"
) and ctx.parser.current_recipient == "python":
if not sent_output_item_added:
sent_output_item_added = True
current_item_id = f"tool_{random_uuid()}"
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=ResponseCodeInterpreterToolCallParam(
type="code_interpreter_call",
id=current_item_id,
code=None,
container_id="auto",
outputs=None,
status="in_progress",
),
)
)
yield _increment_sequence_number_and_return(
ResponseCodeInterpreterCallInProgressEvent(
type="response.code_interpreter_call.in_progress",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
)
)
yield _increment_sequence_number_and_return(
ResponseCodeInterpreterCallCodeDeltaEvent(
type="response.code_interpreter_call_code.delta",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
delta=ctx.parser.last_content_delta,
)
)
# stream tool call outputs
if ctx.is_assistant_action_turn() and len(ctx.parser.messages) > 0:
previous_item = ctx.parser.messages[-1]
if (
self.tool_server is not None
and self.tool_server.has_tool("browser")
and previous_item.recipient is not None
and previous_item.recipient.startswith("browser.")
):
function_name = previous_item.recipient[len("browser.") :]
action = None
parsed_args = json.loads(previous_item.content[0].text)
if function_name == "search":
action = response_function_web_search.ActionSearch(
type="search",
query=parsed_args["query"],
)
elif function_name == "open":
action = response_function_web_search.ActionOpenPage(
type="open_page",
# TODO: translate to url
url=f"cursor:{parsed_args.get('cursor', '')}",
)
elif function_name == "find":
action = response_function_web_search.ActionFind(
type="find",
pattern=parsed_args["pattern"],
# TODO: translate to url
url=f"cursor:{parsed_args.get('cursor', '')}",
)
else:
raise ValueError(f"Unknown function name: {function_name}")
current_item_id = f"tool_{random_uuid()}"
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=response_function_web_search.ResponseFunctionWebSearch(
# TODO: generate a unique id for web search call
type="web_search_call",
id=current_item_id,
action=action,
status="in_progress",
),
)
)
yield _increment_sequence_number_and_return(
ResponseWebSearchCallInProgressEvent(
type="response.web_search_call.in_progress",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
)
)
yield _increment_sequence_number_and_return(
ResponseWebSearchCallSearchingEvent(
type="response.web_search_call.searching",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
)
)
# enqueue
yield _increment_sequence_number_and_return(
ResponseWebSearchCallCompletedEvent(
type="response.web_search_call.completed",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
)
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=ResponseFunctionWebSearch(
type="web_search_call",
id=current_item_id,
action=action,
status="completed",
),
)
)
if (
self.tool_server is not None
and self.tool_server.has_tool("python")
and previous_item.recipient is not None
and previous_item.recipient.startswith("python")
):
yield _increment_sequence_number_and_return(
ResponseCodeInterpreterCallCodeDoneEvent(
type="response.code_interpreter_call_code.done",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
code=previous_item.content[0].text,
)
)
yield _increment_sequence_number_and_return(
ResponseCodeInterpreterCallInterpretingEvent(
type="response.code_interpreter_call.interpreting",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
)
)
yield _increment_sequence_number_and_return(
ResponseCodeInterpreterCallCompletedEvent(
type="response.code_interpreter_call.completed",
sequence_number=-1,
output_index=current_output_index,
item_id=current_item_id,
)
)
yield _increment_sequence_number_and_return(
ResponseOutputItemDoneEvent(
type="response.output_item.done",
sequence_number=-1,
output_index=current_output_index,
item=ResponseCodeInterpreterToolCallParam(
type="code_interpreter_call",
id=current_item_id,
code=previous_item.content[0].text,
container_id="auto",
# TODO: add outputs here
outputs=[],
status="completed",
),
)
)
# developer tools will be triggered on the commentary channel
# and recipient starts with "functions.TOOL_NAME"
if (
ctx.parser.current_channel == "commentary"
and ctx.parser.current_recipient
and ctx.parser.current_recipient.startswith("functions.")
):
if is_first_function_call_delta is False:
is_first_function_call_delta = True
fc_name = ctx.parser.current_recipient[len("functions.") :]
tool_call_item = ResponseFunctionToolCall(
name=fc_name,
type="function_call",
id=current_item_id,
call_id=f"call_{random_uuid()}",
arguments="",
status="in_progress",
)
current_item_id = f"fc_{random_uuid()}"
yield _increment_sequence_number_and_return(
ResponseOutputItemAddedEvent(
type="response.output_item.added",
sequence_number=-1,
output_index=current_output_index,
item=tool_call_item,
)
)
else:
yield _increment_sequence_number_and_return(
ResponseFunctionCallArgumentsDeltaEvent(
item_id=current_item_id,
delta=ctx.parser.last_content_delta,
output_index=current_output_index,
sequence_number=-1,
type="response.function_call_arguments.delta",
)
)
async def responses_stream_generator(
self,
request: ResponsesRequest,
sampling_params: SamplingParams,
result_generator: AsyncIterator[ConversationContext | None],
context: ConversationContext,
model_name: str,
tokenizer: AnyTokenizer,
request_metadata: RequestResponseMetadata,
created_time: int | None = None,
) -> AsyncGenerator[StreamingResponsesResponse, None]:
# TODO:
# 1. Handle disconnect
created_time = created_time or int(time.time())
sequence_number = 0
def _increment_sequence_number_and_return(
event: StreamingResponsesResponse,
) -> StreamingResponsesResponse:
nonlocal sequence_number
# Set sequence_number if the event has this attribute
if hasattr(event, "sequence_number"):
event.sequence_number = sequence_number
sequence_number += 1
return event
async with AsyncExitStack() as exit_stack:
processer = None
if self.use_harmony:
# TODO: in streaming, we noticed this bug:
# https://github.com/vllm-project/vllm/issues/25697
await self._initialize_tool_sessions(request, context, exit_stack)
processer = self._process_harmony_streaming_events
else:
processer = self._process_simple_streaming_events
# TODO Hanchen make sampling params to include the structural tag
initial_response = ResponsesResponse.from_request(
request,
sampling_params,
model_name=model_name,
created_time=created_time,
output=[],
status="in_progress",
usage=None,
).model_dump()
yield _increment_sequence_number_and_return(
ResponseCreatedEvent(
type="response.created",
sequence_number=-1,
response=initial_response,
)
)
yield _increment_sequence_number_and_return(
ResponseInProgressEvent(
type="response.in_progress",
sequence_number=-1,
response=initial_response,
)
)
async for event_data in processer(
request,
sampling_params,
result_generator,
context,
model_name,
tokenizer,
request_metadata,
created_time,
_increment_sequence_number_and_return,
):
yield event_data
async def empty_async_generator():
# A hack to trick Python to think this is a generator but
# in fact it immediately returns.
if False:
yield
final_response = await self.responses_full_generator(
request,
sampling_params,
empty_async_generator(),
context,
model_name,
tokenizer,
request_metadata,
created_time=created_time,
)
yield _increment_sequence_number_and_return(
ResponseCompletedEvent(
type="response.completed",
sequence_number=-1,
response=final_response,
)
)