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
# Set up the unified parser - either a unified parser or fall back to
# separate parsers accessed through the parser interface
self.parser = ParserManager.get_parser(
tool_parser_name=tool_parser,
reasoning_parser_name=reasoning_parser,
enable_auto_tools=enable_auto_tools,
model_name=self.model_config.model,
)
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()
mc = self.model_config
self.override_max_tokens = (
self.default_sampling_params.get("max_tokens")
if mc.generation_config not in ("auto", "vllm")
else getattr(mc, "override_generation_config", {}).get("max_new_tokens")
)
# 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()
)
# Handle tool call ID type for Kimi K2 (supporting test mocking via overrides)
hf_overrides = getattr(self.model_config, "hf_overrides", None)
if self.model_config.hf_text_config.model_type == "kimi_k2" or (
isinstance(hf_overrides, dict)
and hf_overrides.get("model_type") == "kimi_k2"
):
self.tool_call_id_type = "kimi_k2"
else:
self.tool_call_id_type = "random"
self.enable_auto_tools = enable_auto_tools
# 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: ProcessorInputs,
) -> ErrorResponse | None:
"""Add validations to the input to the generator here."""
prompt_len = self._extract_prompt_len(engine_prompt)
max_model_len = self.model_config.max_model_len
if prompt_len >= max_model_len:
error_message = (
f"The engine prompt length {prompt_len} "
f"exceeds the max_model_len {max_model_len}. "
"Please reduce prompt."
)
return self.create_error_response(
err_type="invalid_request_error",
message=error_message,
status_code=HTTPStatus.BAD_REQUEST,
param="input",
)
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,
param="logprobs",
)
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,
param="background",
)
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,
param="previous_response_id",
)
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)
if self.use_harmony:
messages, engine_prompts = self._make_request_with_harmony(
request, prev_response
)
else:
messages, engine_prompts = await self._make_request(
request, prev_response
)
except (
ValueError,
TypeError,
RuntimeError,
jinja2.TemplateError,
NotImplementedError,
) as e:
logger.exception("Error in preprocessing prompt inputs")
return self.create_error_response(e)
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.
max_model_len = self.model_config.max_model_len
generators: list[AsyncGenerator[ConversationContext, None]] = []
# Only include builtin tools that the request actually asked for.
# Without this filter, tools registered on the server (e.g. via
# --tool-server demo) would be available for execution even when
# the request didn't enable them.
requested_tool_types = extract_tool_types(request.tools)
builtin_tool_list: list[str] = []
if self.tool_server is not None:
if (
self.tool_server.has_tool("browser")
and "web_search_preview" in requested_tool_types
):
builtin_tool_list.append("browser")
if (
self.tool_server.has_tool("python")
and "code_interpreter" in requested_tool_types
):
builtin_tool_list.append("python")
if (
self.tool_server.has_tool("container")
and "container" in requested_tool_types
):
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:
tokenizer = self.renderer.get_tokenizer()
for engine_prompt in engine_prompts:
maybe_error = self._validate_generator_input(engine_prompt)
if maybe_error is not None:
return maybe_error
default_max_tokens = get_max_tokens(
max_model_len,
request.max_output_tokens,
self._extract_prompt_len(engine_prompt),
self.default_sampling_params,
self.override_max_tokens,
)
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:
if envs.VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT:
# This is a feature in development for parsing
# tokens during generation instead of at the end
context = ParsableContext(
response_messages=messages,
tokenizer=tokenizer,
reasoning_parser_cls=self.parser.reasoning_parser_cls
if self.parser
else None,
request=request,
tool_parser_cls=self.parser.tool_parser_cls
if self.parser
else None,
available_tools=available_tools,
chat_template=self.chat_template,
chat_template_content_format=self.chat_template_content_format,
)
else:
context = SimpleContext()
if self.parser and self.parser.reasoning_parser_cls is not None:
reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
if (
isinstance(
struct_out := sampling_params.structured_outputs,
StructuredOutputsParams,
)
and struct_out.all_non_structural_tag_constraints_none()
):
sampling_params.structured_outputs = replace(
struct_out,
structural_tag=reasoning_parser.prepare_structured_tag(
struct_out.structural_tag, self.tool_server
),
)
generator = self._generate_with_builtin_tools(
request_id=request.request_id,
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:
return self.create_error_response(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 GenerationError as e:
return self._convert_generation_error_to_response(e)
except Exception as e:
return self.create_error_response(e)
async def _make_request(
self,
request: ResponsesRequest,
prev_response: ResponsesResponse | None,
):
tool_dicts = construct_tool_dicts(request.tools, request.tool_choice)
# Construct the input messages.
messages = construct_input_messages(
request_instructions=request.instructions,
request_input=request.input,
prev_msg=self.msg_store.get(prev_response.id) if prev_response else None,
prev_response_output=prev_response.output if prev_response else None,
)
_, engine_prompts = await self._preprocess_chat(
request,
messages,
default_template=self.chat_template,
default_template_content_format=self.chat_template_content_format,
default_template_kwargs=None,
tool_dicts=tool_dicts,
tool_parser=self.parser.tool_parser_cls if self.parser else None,
)
return messages, 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 = token_inputs(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, [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: TokenizerLike,
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:
return self.create_error_response(e)
# NOTE: Implementation of status 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: ResponseInputOutputMessage | None = None
output_messages: ResponseInputOutputMessage | None = 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:
self._raise_if_error(context.finish_reason, request.request_id)
else:
status = "incomplete"
elif isinstance(context, ParsableContext):
output = context.parser.make_response_output_items_from_parsable_context()
if request.enable_response_messages:
input_messages = context.input_messages
output_messages = context.output_messages
# TODO: Calculate usage.
# assert final_res.prompt_token_ids is not None
num_tool_output_tokens = 0
# Check finish reason from the parser
if context.parser.finish_reason == "length":
status = "incomplete"
else:
assert isinstance(context, SimpleContext)
# Use final_output which has accumulated text/token_ids/logprobs
final_res = context.final_output
assert final_res is not None
assert len(final_res.outputs) == 1
final_output = final_res.outputs[0]
# finish_reason='error' indicates retryable internal error
self._raise_if_error(final_output.finish_reason, request.request_id)
# Check if generation was stopped due to max_tokens
if final_output.finish_reason == "length":
status = "incomplete"
output = self._make_response_output_items(request, final_output, tokenizer)
if request.enable_response_messages:
input_messages = context.input_messages
output_messages = context.output_messages
# Calculate usage.
assert final_res.prompt_token_ids is not None
num_tool_output_tokens = 0
assert isinstance(context, (SimpleContext, HarmonyContext, ParsableContext))
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
# For text-based reasoning parsers (e.g., <think>...</think>),
# HarmonyContext already counts reasoning tokens via channels.
# For Simple/Parsable contexts, derive reasoning_tokens from
# accumulated output token IDs using the parser if not already set.
if (
num_reasoning_tokens == 0
and self.parser is not None
and self.parser.reasoning_parser_cls is not None
and isinstance(context, (SimpleContext, ParsableContext))
):
reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
accumulated = getattr(context, "_accumulated_token_ids", []) or []
num_reasoning_tokens = reasoning_parser.count_reasoning_tokens(accumulated)
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: TokenizerLike,
) -> 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 = self._get_decoded_token(
logprob=_logprob,
token_id=token_id,
tokenizer=tokenizer,
return_as_token_id=self.return_tokens_as_token_ids,
)
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: TokenizerLike,
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 = self._get_decoded_token(
logprob=token_logprob,
token_id=token_id,
tokenizer=tokenizer,
return_as_token_id=self.return_tokens_as_token_ids,
)
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: TokenizerLike,
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: TokenizerLike,
) -> list[ResponseOutputItem]:
# Log complete response if output logging is enabled
if self.enable_log_outputs and self.request_logger:
self.request_logger.log_outputs(
request_id=request.request_id,
outputs=final_output.text,
output_token_ids=final_output.token_ids,
finish_reason=final_output.finish_reason,
is_streaming=False,
delta=False,
)
# Compute logprobs if requested
logprobs = None
if request.is_include_output_logprobs() and final_output.logprobs:
logprobs = self._create_response_logprobs(
token_ids=final_output.token_ids,
logprobs=final_output.logprobs,
tokenizer=tokenizer,
top_logprobs=request.top_logprobs,
)
# Use parser to extract and create response output items
if self.parser:
parser = self.parser(tokenizer)
return parser.extract_response_outputs(
model_output=final_output.text,
request=request,
enable_auto_tools=self.enable_auto_tools,
tool_call_id_type=self.tool_call_id_type,
logprobs=logprobs,
)
# Fallback when no parser is configured
return [
ResponseOutputMessage(
id=f"msg_{random_uuid()}",
content=[
ResponseOutputText(
text=final_output.text,
annotations=[],
type="output_text",
logprobs=logprobs,
)
]
if final_output.text
else [],
role="assistant",
status="completed",
type="message",
)
]
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 _extract_system_message_from_request(
self, request: ResponsesRequest
) -> str | None:
system_msg = None
if not isinstance(request.input, str):
for response_msg in request.input:
if (
isinstance(response_msg, dict)
and response_msg.get("role") == "system"
):
content = response_msg.get("content")
if isinstance(content, str):
system_msg = content
elif isinstance(content, list):
for param in content:
if (
isinstance(param, dict)
and param.get("type") == "input_text"
):
system_msg = param.get("text")
break
break
return system_msg
def _construct_harmony_system_input_message(
self, request: ResponsesRequest, with_custom_tools: bool, tool_types: set[str]
) -> OpenAIHarmonyMessage:
model_identity = self._extract_system_message_from_request(request)
reasoning_effort = request.reasoning.effort if request.reasoning else None
# Extract allowed_tools from MCP tool requests
allowed_tools_map = _extract_allowed_tools_from_mcp_requests(request.tools)
# Get filtered tool descriptions first.
# If get_tool_description returns None (due to filtering), the tool is disabled.
browser_description = (
self.tool_server.get_tool_description(
"browser", allowed_tools_map.get("web_search_preview")
)
if "web_search_preview" in tool_types
and self.tool_server is not None
and self.tool_server.has_tool("browser")
else None
)
python_description = (
self.tool_server.get_tool_description(
"python", allowed_tools_map.get("code_interpreter")
)
if "code_interpreter" in tool_types
and self.tool_server is not None
and self.tool_server.has_tool("python")
else None
)
container_description = (
self.tool_server.get_tool_description(
"container", allowed_tools_map.get("container")
)
if "container" in tool_types
and self.tool_server is not None
and self.tool_server.has_tool("container")
else None
)
sys_msg = get_system_message(
model_identity=model_identity,
reasoning_effort=reasoning_effort,
browser_description=browser_description,
python_description=python_description,
container_description=container_description,
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]
# FIXME(woosuk): The slice-delete-reappend cycle below is
# currently a no-op --- it removes messages then puts them all
# back unfiltered. It may be intentionally deferred (see FIXME
# above) or redundant if the Harmony encoder already strips
# analysis messages at render time. If analysis messages need
# to be dropped here, add a channel != "analysis" filter when
# re-appending, similar to auto_drop_analysis_messages in
# harmony_utils.py.
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)
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):
# Skip empty string input when previous_input_messages supplies
# the full conversation history --- an empty trailing user message
# confuses the model into thinking nothing was sent.
if request.input or not request.previous_input_messages:
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:
new_msg = parse_response_input(response_msg, prev_outputs)
if new_msg.author.role != "system":
messages.append(new_msg)
# 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 GenerationError as e:
response = self._convert_generation_error_to_response(e)
except Exception as e:
logger.exception("Background request failed for %s", request.request_id)
response = self.create_error_response(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 GenerationError as e:
response = self._convert_generation_error_to_response(e)
except Exception as e:
logger.exception("Background request failed for %s", request.request_id)
response = self.create_error_response(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 VLLMValidationError(
f"Unknown response_id: {response_id}",
parameter="response_id",
value=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.",
param="response_id",
)
# 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,
param="response_id",
)
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,
param="store",
)
async def _process_simple_streaming_events(
self,
request: ResponsesRequest,
sampling_params: SamplingParams,
result_generator: AsyncIterator[ConversationContext | None],
context: ConversationContext,
model_name: str,
tokenizer: TokenizerLike,
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.parser and self.parser.reasoning_parser_cls:
reasoning_parser = self.parser.reasoning_parser_cls(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]
# finish_reason='error' indicates a retryable error
self._raise_if_error(output.finish_reason, request.request_id)
if reasoning_parser:
delta_message = reasoning_parser.extract_reasoning_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:
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 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
for pm in previous_delta_messages
if pm.reasoning 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 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,
)
)
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 is not None:
reason_content = "".join(
pm.reasoning
for pm in previous_delta_messages
if pm.reasoning 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: TokenizerLike,
request_metadata: RequestResponseMetadata,
created_time: int,
_increment_sequence_number_and_return: Callable[
[StreamingResponsesResponse], StreamingResponsesResponse
],
) -> AsyncGenerator[StreamingResponsesResponse, None]:
state = HarmonyStreamingState()
async for ctx in result_generator:
assert isinstance(ctx, StreamingHarmonyContext)
# finish_reason='error' indicates a retryable error
self._raise_if_error(ctx.finish_reason, request.request_id)
if ctx.is_expecting_start():
if len(ctx.parser.messages) > 0:
previous_item = ctx.parser.messages[-1]
for event in emit_previous_item_done_events(previous_item, state):
yield _increment_sequence_number_and_return(event)
state.reset_for_new_item()
# Stream the output of a harmony message
for event in emit_content_delta_events(ctx, state):
yield _increment_sequence_number_and_return(event)
# Stream tool call outputs
for event in emit_tool_action_events(ctx, state, self.tool_server):
yield _increment_sequence_number_and_return(event)
async def responses_stream_generator(
self,
request: ResponsesRequest,
sampling_params: SamplingParams,
result_generator: AsyncIterator[ConversationContext | None],
context: ConversationContext,
model_name: str,
tokenizer: TokenizerLike,
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:
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,
)
)
try:
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
except GenerationError as e:
error_json = self._convert_generation_error_to_streaming_response(e)
yield _increment_sequence_number_and_return(
TypeAdapter(StreamingResponsesResponse).validate_json(error_json)
)
return
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,
)
)