class OpenAIServingResponses(OpenAIServing):
def __init__(
self,
engine_client: EngineClient,
model_config: ModelConfig,
models: OpenAIServingModels,
*,
request_logger: Optional[RequestLogger],
chat_template: Optional[str],
chat_template_content_format: ChatTemplateContentFormatOption,
return_tokens_as_token_ids: bool = False,
reasoning_parser: str = "",
enable_auto_tools: bool = False,
tool_parser: Optional[str] = None,
tool_server: Optional[ToolServer] = 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,
model_config=model_config,
models=models,
request_logger=request_logger,
return_tokens_as_token_ids=return_tokens_as_token_ids,
enable_force_include_usage=enable_force_include_usage,
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: Optional[Callable[[AnyTokenizer],
ReasoningParser]] = None
if reasoning_parser:
try:
self.reasoning_parser = (
ReasoningParserManager.get_reasoning_parser(
reasoning_parser))
assert self.reasoning_parser is not None
except Exception as e:
raise TypeError(
f"{reasoning_parser=} has not been registered") from e
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 = 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
async def create_responses(
self,
request: ResponsesRequest,
raw_request: Optional[Request] = None,
) -> Union[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
# 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:
if 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,
)
# 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
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,
)
# 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):
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()
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: Optional[ResponsesResponse],
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: Optional[ResponsesResponse],
):
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 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: Optional[int] = None,
) -> Union[ErrorResponse, ResponsesResponse]:
if created_time is None:
created_time = int(time.time())
async with AsyncExitStack() as exit_stack:
try:
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 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),
output_tokens_details=OutputTokensDetails(
reasoning_tokens=num_reasoning_tokens,
tool_output_tokens=num_tool_output_tokens),
)
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: Optional[SampleLogprobs],
tokenizer: AnyTokenizer,
top_logprobs: Optional[int] = 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: Optional[SampleLogprobs],
tokenizer: AnyTokenizer,
top_logprobs: Optional[int] = 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: Optional[ResponsesResponse] = 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_input_messages_with_harmony(
self,
request: ResponsesRequest,
prev_response: Optional[ResponsesResponse],
) -> list[OpenAIHarmonyMessage]:
messages: list[OpenAIHarmonyMessage] = []
if prev_response is None:
# New conversation.
reasoning_effort = (request.reasoning.effort
if request.reasoning else None)
tool_types = [tool.type for tool in request.tools]
# Allow the MCP Tool type to enable built in tools if the
# server_label is allowlisted in
# envs.GPT_OSS_SYSTEM_TOOL_MCP_LABELS
if envs.GPT_OSS_SYSTEM_TOOL_MCP_LABELS:
for tool in request.tools:
if (tool.type == "mcp" and tool.server_label
in envs.GPT_OSS_SYSTEM_TOOL_MCP_LABELS):
tool_types.append(tool.server_label)
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"))
with_custom_tools = has_custom_tools(tool_types)
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,
)
messages.append(sys_msg)
if with_custom_tools:
dev_msg = get_developer_message(
instructions=request.instructions, tools=request.tools)
messages.append(dev_msg)
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, 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: Optional[int] = 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: Optional[int],
stream: Optional[bool],
) -> Union[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,
) -> Union[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[Optional[ConversationContext]],
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[Optional[ConversationContext]],
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
async for ctx in result_generator:
assert isinstance(ctx, StreamingHarmonyContext)
if ctx.is_expecting_start():
current_output_index += 1
sent_output_item_added = False
if len(ctx.parser.messages) > 0:
previous_item = ctx.parser.messages[-1]
if previous_item.recipient is not None:
# Deal with tool call here
pass
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",
),
))
async def responses_stream_generator(
self,
request: ResponsesRequest,
sampling_params: SamplingParams,
result_generator: AsyncIterator[Optional[ConversationContext]],
context: ConversationContext,
model_name: str,
tokenizer: AnyTokenizer,
request_metadata: RequestResponseMetadata,
created_time: Optional[int] = 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:
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)
processer = self._process_harmony_streaming_events
else:
processer = self._process_simple_streaming_events
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.model_dump(),
))