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serving.py 68.7 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import asyncio
import time
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import uuid
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from collections import deque
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from collections.abc import AsyncGenerator, AsyncIterator, Callable, Sequence
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from contextlib import AsyncExitStack
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from copy import copy
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from http import HTTPStatus
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from typing import Final
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from fastapi import Request
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from openai.types.responses import (
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    ResponseContentPartAddedEvent,
    ResponseContentPartDoneEvent,
    ResponseFunctionToolCall,
    ResponseOutputItem,
    ResponseOutputItemAddedEvent,
    ResponseOutputItemDoneEvent,
    ResponseOutputMessage,
    ResponseOutputText,
    ResponseReasoningItem,
    ResponseReasoningTextDeltaEvent,
    ResponseReasoningTextDoneEvent,
    ResponseStatus,
    ResponseTextDeltaEvent,
    ResponseTextDoneEvent,
    response_text_delta_event,
)
from openai.types.responses.response_output_text import Logprob, LogprobTopLogprob
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from openai.types.responses.response_reasoning_item import (
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    Content as ResponseReasoningTextContent,
)
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from openai.types.responses.tool import Mcp, Tool
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from openai_harmony import Message as OpenAIHarmonyMessage
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from pydantic import TypeAdapter
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from vllm import envs
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from vllm.config.utils import replace
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ChatTemplateContentFormatOption,
)
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.mcp.tool_server import ToolServer
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from vllm.entrypoints.openai.engine.protocol import (
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    DeltaMessage,
    ErrorResponse,
    RequestResponseMetadata,
)
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from vllm.entrypoints.openai.engine.serving import (
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    GenerationError,
    OpenAIServing,
)
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from vllm.entrypoints.openai.models.serving import OpenAIServingModels
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from vllm.entrypoints.openai.parser.harmony_utils import (
    get_developer_message,
    get_stop_tokens_for_assistant_actions,
    get_system_message,
    get_user_message,
    has_custom_tools,
    render_for_completion,
)
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from vllm.entrypoints.openai.responses.context import (
    ConversationContext,
    HarmonyContext,
    ParsableContext,
    SimpleContext,
    StreamingHarmonyContext,
)
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from vllm.entrypoints.openai.responses.harmony import (
    construct_harmony_previous_input_messages,
    harmony_to_response_output,
    parser_state_to_response_output,
    response_input_to_harmony,
)
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from vllm.entrypoints.openai.responses.protocol import (
    InputTokensDetails,
    OutputTokensDetails,
    ResponseCompletedEvent,
    ResponseCreatedEvent,
    ResponseInProgressEvent,
    ResponseInputOutputMessage,
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    ResponseReasoningPartAddedEvent,
    ResponseReasoningPartDoneEvent,
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    ResponsesRequest,
    ResponsesResponse,
    ResponseUsage,
    StreamingResponsesResponse,
)
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from vllm.entrypoints.openai.responses.streaming_events import (
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    StreamingState,
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    emit_content_delta_events,
    emit_previous_item_done_events,
    emit_tool_action_events,
)
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from vllm.entrypoints.openai.responses.utils import (
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    construct_input_messages,
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    construct_tool_dicts,
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    extract_tool_types,
)
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from vllm.entrypoints.utils import get_max_tokens
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from vllm.exceptions import VLLMValidationError
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from vllm.inputs.data import ProcessorInputs, token_inputs
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from vllm.logger import init_logger
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from vllm.logprobs import Logprob as SampleLogprob
from vllm.logprobs import SampleLogprobs
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from vllm.outputs import CompletionOutput
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from vllm.parser import ParserManager
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from vllm.sampling_params import SamplingParams, StructuredOutputsParams
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from vllm.tokenizers import TokenizerLike
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from vllm.utils import random_uuid

logger = init_logger(__name__)


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def _extract_allowed_tools_from_mcp_requests(
    tools: list[Tool],
) -> dict[str, list[str] | None]:
    """
    Extract allowed_tools mapping from MCP tool requests.

    Returns a dictionary mapping server_label to allowed_tools list.
    Handles both list format and McpAllowedToolsMcpToolFilter object format.

    Special handling:
    - If allowed_tools is None, returns None (allows all tools)
    - If allowed_tools contains "*", returns None (allows all tools)
    - Otherwise, returns the list of specific tool names

    This function can be reused for both harmony and non-harmony MCP calls.
    """
    allowed_tools_map: dict[str, list[str] | None] = {}
    for tool in tools:
        if not isinstance(tool, Mcp):
            continue

        # allowed_tools can be a list or an object with tool_names
        # Extract the actual list of tool names
        allowed_tools_val = None
        if tool.allowed_tools is not None:
            if isinstance(tool.allowed_tools, list):
                allowed_tools_val = tool.allowed_tools
            elif hasattr(tool.allowed_tools, "tool_names"):
                # It's an McpAllowedToolsMcpToolFilter object
                allowed_tools_val = tool.allowed_tools.tool_names

        # Normalize "*" to None (both mean "allow all tools")
        if allowed_tools_val is not None and "*" in allowed_tools_val:
            allowed_tools_val = None

        allowed_tools_map[tool.server_label] = allowed_tools_val
    return allowed_tools_map


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class OpenAIServingResponses(OpenAIServing):
    def __init__(
        self,
        engine_client: EngineClient,
        models: OpenAIServingModels,
        *,
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        request_logger: RequestLogger | None,
        chat_template: str | None,
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        chat_template_content_format: ChatTemplateContentFormatOption,
        return_tokens_as_token_ids: bool = False,
        reasoning_parser: str = "",
        enable_auto_tools: bool = False,
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        tool_parser: str | None = None,
        tool_server: ToolServer | None = None,
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        enable_prompt_tokens_details: bool = False,
        enable_force_include_usage: bool = False,
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        enable_log_outputs: bool = False,
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    ) -> None:
        super().__init__(
            engine_client=engine_client,
            models=models,
            request_logger=request_logger,
            return_tokens_as_token_ids=return_tokens_as_token_ids,
        )

        self.chat_template = chat_template
        self.chat_template_content_format: Final = chat_template_content_format
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        self.enable_log_outputs = enable_log_outputs
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        # 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,
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        )
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        self.enable_prompt_tokens_details = enable_prompt_tokens_details
        self.enable_force_include_usage = enable_force_include_usage
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        self.default_sampling_params = self.model_config.get_diff_sampling_param()
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        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")
        )
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        # 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.
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        self.enable_store = envs.VLLM_ENABLE_RESPONSES_API_STORE
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        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 "
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                "the store."
            )
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        self.use_harmony = self.model_config.hf_config.model_type == "gpt_oss"
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        if self.use_harmony:
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            logger.warning(
                "For gpt-oss, we ignore --enable-auto-tool-choice "
                "and always enable tool use."
            )
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            # 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(
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                get_stop_tokens_for_assistant_actions()
            )
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        # 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"

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        self.enable_auto_tools = enable_auto_tools
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        # HACK(woosuk): This is a hack. We should use a better store.
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        # FIXME: If enable_store=True, this may cause a memory leak since we
        # never remove responses from the store.
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        self.response_store: dict[str, ResponsesResponse] = {}
        self.response_store_lock = asyncio.Lock()

        # HACK(woosuk): This is a hack. We should use a better store.
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        # FIXME: If enable_store=True, this may cause a memory leak since we
        # never remove messages from the store.
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        self.msg_store: dict[str, list[ChatCompletionMessageParam]] = {}

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        # 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.
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        self.event_store: dict[
            str, tuple[deque[StreamingResponsesResponse], asyncio.Event]
        ] = {}
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        self.background_tasks: dict[str, asyncio.Task] = {}

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        self.tool_server = tool_server

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    def _validate_generator_input(
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        self,
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        engine_prompt: ProcessorInputs,
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    ) -> ErrorResponse | None:
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        """Add validations to the input to the generator here."""
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        prompt_len = self._extract_prompt_len(engine_prompt)
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        max_model_len = self.model_config.max_model_len

        if prompt_len >= max_model_len:
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            error_message = (
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                f"The engine prompt length {prompt_len} "
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                f"exceeds the max_model_len {max_model_len}. "
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                "Please reduce prompt."
            )
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            return self.create_error_response(
                err_type="invalid_request_error",
                message=error_message,
                status_code=HTTPStatus.BAD_REQUEST,
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                param="input",
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            )
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        return None

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    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,
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                param="logprobs",
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            )
        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,
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                param="background",
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            )
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        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,
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                param="previous_response_id",
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            )
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        return None

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    async def create_responses(
        self,
        request: ResponsesRequest,
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        raw_request: Request | None = None,
    ) -> (
        AsyncGenerator[StreamingResponsesResponse, None]
        | ResponsesResponse
        | ErrorResponse
    ):
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        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
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        maybe_validation_error = self._validate_create_responses_input(request)
        if maybe_validation_error is not None:
            return maybe_validation_error
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        # 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

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        if request.store and not self.enable_store:
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            # 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
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        # 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

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        lora_request = self._maybe_get_adapters(request)
        model_name = self.models.model_name(lora_request)
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        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)
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        request_metadata = RequestResponseMetadata(request_id=request.request_id)
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        if raw_request:
            raw_request.state.request_metadata = request_metadata

        # Schedule the request and get the result generator.
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        max_model_len = self.model_config.max_model_len
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        generators: list[AsyncGenerator[ConversationContext, None]] = []
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        # 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)
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        builtin_tool_list: list[str] = []
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        if self.tool_server is not None:
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            if (
                self.tool_server.has_tool("browser")
                and "web_search_preview" in requested_tool_types
            ):
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                builtin_tool_list.append("browser")
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            if (
                self.tool_server.has_tool("python")
                and "code_interpreter" in requested_tool_types
            ):
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                builtin_tool_list.append("python")
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            if (
                self.tool_server.has_tool("container")
                and "container" in requested_tool_types
            ):
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                builtin_tool_list.append("container")
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        if self.tool_server is not None:
            available_tools = builtin_tool_list
        else:
            assert len(builtin_tool_list) == 0
            available_tools = []
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        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,
            )
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            sampling_params = request.to_sampling_params(
                default_max_tokens, self.default_sampling_params
            )
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            trace_headers = (
                None
                if raw_request is None
                else await self._get_trace_headers(raw_request.headers)
            )
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            context: ConversationContext
            if self.use_harmony:
                if request.stream:
                    context = StreamingHarmonyContext(messages, available_tools)
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                else:
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                    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)
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        assert len(generators) == 1
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        (result_generator,) = generators
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        # Store the input messages.
        if request.store:
            self.msg_store[request.request_id] = messages
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        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
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            # Run the request in the background.
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            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}",
                )
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            # For cleanup.
            response_id = response.id
            self.background_tasks[response_id] = task
            task.add_done_callback(
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                lambda _: self.background_tasks.pop(response_id, None)
            )
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            if request.stream:
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                return self.responses_background_stream_generator(request.request_id)
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            return response

        if request.stream:
            return self.responses_stream_generator(
                request,
                sampling_params,
                result_generator,
                context,
                model_name,
                tokenizer,
                request_metadata,
            )

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        return await self.responses_full_generator(
            request,
            sampling_params,
            result_generator,
            context,
            model_name,
            tokenizer,
            request_metadata,
        )
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    async def _make_request(
        self,
        request: ResponsesRequest,
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        prev_response: ResponsesResponse | None,
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    ):
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        tool_dicts = construct_tool_dicts(request.tools, request.tool_choice)
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        # Construct the input messages.
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        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,
        )
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        _, engine_prompts = await self._preprocess_chat(
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            request,
            messages,
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            default_template=self.chat_template,
            default_template_content_format=self.chat_template_content_format,
            default_template_kwargs=None,
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            tool_dicts=tool_dicts,
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            tool_parser=self.parser.tool_parser_cls if self.parser else None,
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        )
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        return messages, engine_prompts
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    def _make_request_with_harmony(
        self,
        request: ResponsesRequest,
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        prev_response: ResponsesResponse | None,
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    ):
        if request.tool_choice != "auto":
            raise NotImplementedError(
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                "Only 'auto' tool_choice is supported in response API with Harmony"
            )
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        messages = self._construct_input_messages_with_harmony(request, prev_response)
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        prompt_token_ids = render_for_completion(messages)
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        engine_prompt = token_inputs(prompt_token_ids)
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        # Add cache_salt if provided in the request
        if request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

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        return messages, [engine_prompt]
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    async def _initialize_tool_sessions(
        self,
        request: ResponsesRequest,
        context: ConversationContext,
        exit_stack: AsyncExitStack,
    ):
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        # we should only initialize the tool session if the request needs tools
        if len(request.tools) == 0:
            return
        mcp_tools = {
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            tool.server_label: tool for tool in request.tools if tool.type == "mcp"
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        }
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        await context.init_tool_sessions(
            self.tool_server, exit_stack, request.request_id, mcp_tools
        )
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    async def responses_full_generator(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
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        result_generator: AsyncIterator[ConversationContext],
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        context: ConversationContext,
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        model_name: str,
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        tokenizer: TokenizerLike,
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        request_metadata: RequestResponseMetadata,
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        created_time: int | None = None,
    ) -> ErrorResponse | ResponsesResponse:
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        if created_time is None:
            created_time = int(time.time())

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        async with AsyncExitStack() as exit_stack:
            try:
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                await self._initialize_tool_sessions(request, context, exit_stack)
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                async for _ in result_generator:
                    pass
            except asyncio.CancelledError:
                return self.create_error_response("Client disconnected")
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        # NOTE: Implementation of status is still WIP, but for now
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        # we guarantee that if the status is not "completed", it is accurate.
        # "completed" is implemented as the "catch-all" for now.
        status: ResponseStatus = "completed"

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        input_messages: ResponseInputOutputMessage | None = None
        output_messages: ResponseInputOutputMessage | None = None
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        if self.use_harmony:
            assert isinstance(context, HarmonyContext)
            output = self._make_response_output_items_with_harmony(context)
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            if request.enable_response_messages:
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                input_messages = context.messages[: context.num_init_messages]
                output_messages = context.messages[context.num_init_messages :]
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            num_tool_output_tokens = context.num_tool_output_tokens
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            if len(output) > 0:
                if context.finish_reason == "length":
                    status = "incomplete"
                elif context.finish_reason == "abort":
                    status = "cancelled"
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                else:
                    self._raise_if_error(context.finish_reason, request.request_id)
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            else:
                status = "incomplete"
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        elif isinstance(context, ParsableContext):
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            output = context.parser.make_response_output_items_from_parsable_context()
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            if request.enable_response_messages:
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                input_messages = context.input_messages
                output_messages = context.output_messages
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            # TODO: Calculate usage.
            # assert final_res.prompt_token_ids is not None
            num_tool_output_tokens = 0
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            # Check finish reason from the parser
            if context.parser.finish_reason == "length":
                status = "incomplete"
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        else:
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            assert isinstance(context, SimpleContext)
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            # Use final_output which has accumulated text/token_ids/logprobs
            final_res = context.final_output
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            assert final_res is not None
            assert len(final_res.outputs) == 1
            final_output = final_res.outputs[0]

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            # finish_reason='error' indicates retryable internal error
            self._raise_if_error(final_output.finish_reason, request.request_id)

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            # Check if generation was stopped due to max_tokens
            if final_output.finish_reason == "length":
                status = "incomplete"

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            output = self._make_response_output_items(request, final_output, tokenizer)
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            if request.enable_response_messages:
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                input_messages = context.input_messages
                output_messages = context.output_messages

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            # Calculate usage.
            assert final_res.prompt_token_ids is not None
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            num_tool_output_tokens = 0

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        assert isinstance(context, (SimpleContext, HarmonyContext, ParsableContext))
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        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
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        # 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)
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        usage = ResponseUsage(
            input_tokens=num_prompt_tokens,
            output_tokens=num_generated_tokens,
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            total_tokens=num_prompt_tokens + num_generated_tokens,
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            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
                ],
            ),
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            output_tokens_details=OutputTokensDetails(
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                reasoning_tokens=num_reasoning_tokens,
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                tool_output_tokens=num_tool_output_tokens,
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                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
                ],
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            ),
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        )
        response = ResponsesResponse.from_request(
            request,
            sampling_params,
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            input_messages=input_messages,
            output_messages=output_messages,
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            model_name=model_name,
            created_time=created_time,
            output=output,
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            status=status,
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            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.
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                if stored_response is None or stored_response.status != "cancelled":
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                    self.response_store[response.id] = response
        return response

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    def _topk_logprobs(
        self,
        logprobs: dict[int, SampleLogprob],
        top_logprobs: int,
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        tokenizer: TokenizerLike,
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    ) -> list[LogprobTopLogprob]:
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        """Returns the top-k logprobs from the logprobs dictionary."""
        out = []
        for i, (token_id, _logprob) in enumerate(logprobs.items()):
            if i >= top_logprobs:
                break
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            text = self._get_decoded_token(
                logprob=_logprob,
                token_id=token_id,
                tokenizer=tokenizer,
                return_as_token_id=self.return_tokens_as_token_ids,
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            )
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            out.append(
                LogprobTopLogprob(
                    token=text,
                    logprob=max(_logprob.logprob, -9999.0),
                    bytes=list(text.encode("utf-8", errors="replace")),
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                )
            )
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        return out

    def _create_response_logprobs(
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        self,
        token_ids: Sequence[int],
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        logprobs: SampleLogprobs | None,
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        tokenizer: TokenizerLike,
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        top_logprobs: int | None = None,
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    ) -> list[Logprob]:
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        assert logprobs is not None, "logprobs must be provided"
        assert len(token_ids) == len(logprobs), (
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            "token_ids and logprobs.token_ids must have the same length"
        )
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        out = []
        for i, token_id in enumerate(token_ids):
            logprob = logprobs[i]
            token_logprob = logprob[token_id]
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            text = self._get_decoded_token(
                logprob=token_logprob,
                token_id=token_id,
                tokenizer=tokenizer,
                return_as_token_id=self.return_tokens_as_token_ids,
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            )
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            out.append(
                Logprob(
                    token=text,
                    logprob=max(token_logprob.logprob, -9999.0),
                    bytes=list(text.encode("utf-8", errors="replace")),
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                    top_logprobs=(
                        self._topk_logprobs(
                            logprob, top_logprobs=top_logprobs, tokenizer=tokenizer
                        )
                        if top_logprobs
                        else []
                    ),
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                )
            )
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        return out

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    def _create_stream_response_logprobs(
        self,
        token_ids: Sequence[int],
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        logprobs: SampleLogprobs | None,
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        tokenizer: TokenizerLike,
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        top_logprobs: int | None = None,
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    ) -> list[response_text_delta_event.Logprob]:
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        lgs = self._create_response_logprobs(
            token_ids=token_ids,
            logprobs=logprobs,
            tokenizer=tokenizer,
            top_logprobs=top_logprobs,
        )
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        return [
            response_text_delta_event.Logprob(
                token=lg.token,
                logprob=lg.logprob,
                top_logprobs=[
                    response_text_delta_event.LogprobTopLogprob(
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                        token=tl.token, logprob=tl.logprob
                    )
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                    for tl in lg.top_logprobs
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                ],
            )
            for lg in lgs
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        ]

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    def _make_response_output_items(
        self,
        request: ResponsesRequest,
        final_output: CompletionOutput,
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        tokenizer: TokenizerLike,
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    ) -> list[ResponseOutputItem]:
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        # Log complete response if output logging is enabled
        if self.enable_log_outputs and self.request_logger:
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            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,
            )
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        # 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,
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            )
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        # 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(
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                id=f"msg_{random_uuid()}",
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                content=[
                    ResponseOutputText(
                        text=final_output.text,
                        annotations=[],
                        type="output_text",
                        logprobs=logprobs,
                    )
                ]
                if final_output.text
                else [],
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                role="assistant",
                status="completed",
                type="message",
            )
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        ]
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    def _make_response_output_items_with_harmony(
        self,
        context: HarmonyContext,
    ) -> list[ResponseOutputItem]:
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        output_items: list[ResponseOutputItem] = []
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        num_init_messages = context.num_init_messages
        for msg in context.messages[num_init_messages:]:
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            output_items.extend(harmony_to_response_output(msg))
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        # Handle the generation stopped in the middle (if any).
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        last_items = parser_state_to_response_output(context.parser)
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        if last_items:
            output_items.extend(last_items)
        return output_items

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    def _extract_system_message_from_request(
        self, request: ResponsesRequest
    ) -> str | None:
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        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"
                ):
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                    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
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                    break
        return system_msg

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    def _construct_harmony_system_input_message(
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        self, request: ResponsesRequest, with_custom_tools: bool, tool_types: set[str]
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    ) -> OpenAIHarmonyMessage:
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        model_identity = self._extract_system_message_from_request(request)

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        reasoning_effort = request.reasoning.effort if request.reasoning else None
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        # 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
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            and self.tool_server is not None
            and self.tool_server.has_tool("browser")
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            else None
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        )
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        python_description = (
            self.tool_server.get_tool_description(
                "python", allowed_tools_map.get("code_interpreter")
            )
            if "code_interpreter" in tool_types
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            and self.tool_server is not None
            and self.tool_server.has_tool("python")
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            else None
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        )
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        container_description = (
            self.tool_server.get_tool_description(
                "container", allowed_tools_map.get("container")
            )
            if "container" in tool_types
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            and self.tool_server is not None
            and self.tool_server.has_tool("container")
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            else None
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        )
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        sys_msg = get_system_message(
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            model_identity=model_identity,
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            reasoning_effort=reasoning_effort,
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            browser_description=browser_description,
            python_description=python_description,
            container_description=container_description,
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            instructions=request.instructions,
            with_custom_tools=with_custom_tools,
        )
        return sys_msg

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    def _construct_input_messages_with_harmony(
        self,
        request: ResponsesRequest,
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        prev_response: ResponsesResponse | None,
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    ) -> list[OpenAIHarmonyMessage]:
        messages: list[OpenAIHarmonyMessage] = []
        if prev_response is None:
            # New conversation.
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            tool_types = extract_tool_types(request.tools)
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            with_custom_tools = has_custom_tools(tool_types)
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            sys_msg = self._construct_harmony_system_input_message(
                request, with_custom_tools, tool_types
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            )
            messages.append(sys_msg)
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            if with_custom_tools:
                dev_msg = get_developer_message(
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                    instructions=request.instructions, tools=request.tools
                )
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                messages.append(dev_msg)
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            messages += construct_harmony_previous_input_messages(request)

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        else:
            # Continue the previous conversation.
            # FIXME(woosuk): Currently, request params like reasoning and
            # instructions are ignored.
            prev_msgs = self.msg_store[prev_response.id]
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            # 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.
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            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
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                    recent_turn_msgs = prev_msgs[prev_final_msg_idx + 1 :]
                    del prev_msgs[prev_final_msg_idx + 1 :]
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                    for msg in recent_turn_msgs:
                        assert isinstance(msg, OpenAIHarmonyMessage)
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                        prev_msgs.append(msg)
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            messages.extend(prev_msgs)
        # Append the new input.
co63oc's avatar
co63oc committed
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        # Responses API supports simple text inputs without chat format.
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        if isinstance(request.input, str):
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            # 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))
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        else:
            if prev_response is not None:
                prev_outputs = copy(prev_response.output)
            else:
                prev_outputs = []
            for response_msg in request.input:
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                new_msg = response_input_to_harmony(response_msg, prev_outputs)
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                if new_msg.author.role != "system":
                    messages.append(new_msg)

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                # User passes in a tool call request and its output. We need
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                # to add the tool call request to prev_outputs so that
                # response_input_to_harmony can find the tool call request when
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                # parsing the tool call output.
                if isinstance(response_msg, ResponseFunctionToolCall):
                    prev_outputs.append(response_msg)
        return messages

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    async def _run_background_request_stream(
        self,
        request: ResponsesRequest,
        *args,
        **kwargs,
    ):
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        event_deque: deque[StreamingResponsesResponse] = deque()
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        new_event_signal = asyncio.Event()
        self.event_store[request.request_id] = (event_deque, new_event_signal)
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        generator = self.responses_stream_generator(request, *args, **kwargs)
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        try:
            async for event in generator:
                event_deque.append(event)
                new_event_signal.set()  # Signal new event available
        finally:
            new_event_signal.set()

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    async def _run_background_request(
        self,
        request: ResponsesRequest,
        *args,
        **kwargs,
    ):
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        response = await self.responses_full_generator(request, *args, **kwargs)
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        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"

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    async def responses_background_stream_generator(
        self,
        response_id: str,
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        starting_after: int | None = None,
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    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1135
        if response_id not in self.event_store:
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            raise VLLMValidationError(
                f"Unknown response_id: {response_id}",
                parameter="response_id",
                value=response_id,
            )
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        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
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                if getattr(event, "type", "unknown") == "response.completed":
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                    return
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                current_index += 1

            await new_event_signal.wait()

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    async def retrieve_responses(
        self,
        response_id: str,
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        starting_after: int | None,
        stream: bool | None,
    ) -> (
        ErrorResponse
        | ResponsesResponse
        | AsyncGenerator[StreamingResponsesResponse, None]
    ):
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        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)
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        if stream:
            return self.responses_background_stream_generator(
                response_id,
                starting_after,
            )
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        return response

    async def cancel_responses(
        self,
        response_id: str,
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    ) -> ErrorResponse | ResponsesResponse:
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        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.",
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                    param="response_id",
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                )

            # Update the status to "cancelled".
            response.status = "cancelled"

        # Abort the request.
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        if task := self.background_tasks.get(response_id):
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            task.cancel()
            try:
                await task
            except asyncio.CancelledError:
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                logger.exception("Background task for %s was cancelled", response_id)
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        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,
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            param="response_id",
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        )
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    async def _process_simple_streaming_events(
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        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
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        result_generator: AsyncIterator[ConversationContext | None],
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        context: ConversationContext,
        model_name: str,
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        tokenizer: TokenizerLike,
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        request_metadata: RequestResponseMetadata,
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        created_time: int,
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        _increment_sequence_number_and_return: Callable[
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            [StreamingResponsesResponse], StreamingResponsesResponse
        ],
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    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
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        current_content_index = 0
        current_output_index = 0
        current_item_id = ""
        reasoning_parser = None
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        if self.parser and self.parser.reasoning_parser_cls:
            reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
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        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]
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                # finish_reason='error' indicates a retryable error
                self._raise_if_error(output.finish_reason, request.request_id)
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                if reasoning_parser:
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                    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,
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                    )
                else:
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                    delta_message = DeltaMessage(
                        content=output.text,
                    )
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                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())
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                    if delta_message.reasoning:
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                        yield _increment_sequence_number_and_return(
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                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
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                                item=ResponseReasoningItem(
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                                    type="reasoning",
                                    id=current_item_id,
                                    summary=[],
                                    status="in_progress",
                                ),
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                            )
                        )
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                        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",
                                ),
                            )
                        )
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                    else:
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                        yield _increment_sequence_number_and_return(
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                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
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                                item=ResponseOutputMessage(
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                                    id=current_item_id,
                                    type="message",
                                    role="assistant",
                                    content=[],
                                    status="in_progress",
                                ),
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                            )
                        )
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                        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=[],
                                ),
                            )
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                        )
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                    first_delta_sent = True
                # todo(kebe7jun) tool call support

                # check delta message and previous delta message are
                # same as content or reasoning content
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                if (
                    previous_delta_messages
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                    and previous_delta_messages[-1].reasoning is not None
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                    and delta_message.content is not None
                ):
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                    # from reasoning to normal content, send done
                    # event for reasoning
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                    reason_content = "".join(
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                        pm.reasoning
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                        for pm in previous_delta_messages
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                        if pm.reasoning is not None
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                    )
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                    # delta message could have both reasoning and
                    # content. Include current delta's reasoning in the
                    # finalization since it may carry the tail end of
                    # reasoning text (e.g. when reasoning end and
                    # content start arrive in the same delta).
                    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,
                            )
                        )
                        reason_content += delta_message.reasoning
                        delta_message = DeltaMessage(content=delta_message.content)

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                    yield _increment_sequence_number_and_return(
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                        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,
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                        )
                    )
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                    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=ResponseReasoningTextContent(
                                text=reason_content,
                                type="reasoning_text",
                            ),
                        )
                    )
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                    current_content_index = 0
                    reasoning_item = ResponseReasoningItem(
                        type="reasoning",
                        content=[
                            ResponseReasoningTextContent(
                                text=reason_content,
                                type="reasoning_text",
                            ),
                        ],
                        status="completed",
                        id=current_item_id,
                        summary=[],
                    )
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                    yield _increment_sequence_number_and_return(
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                        ResponseOutputItemDoneEvent(
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=reasoning_item,
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                        )
                    )
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                    current_output_index += 1
                    current_item_id = str(uuid.uuid4())
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                    yield _increment_sequence_number_and_return(
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                        ResponseOutputItemAddedEvent(
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                            type="response.output_item.added",
                            sequence_number=-1,
                            output_index=current_output_index,
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                            item=ResponseOutputMessage(
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                                id=current_item_id,
                                type="message",
                                role="assistant",
                                content=[],
                                status="in_progress",
                            ),
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                        )
                    )
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                    yield _increment_sequence_number_and_return(
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                        ResponseContentPartAddedEvent(
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                            type="response.content_part.added",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            content_index=current_content_index,
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                            part=ResponseOutputText(
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                                type="output_text",
                                text="",
                                annotations=[],
                                logprobs=[],
                            ),
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                        )
                    )
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                    # reset previous delta messages
                    previous_delta_messages = []
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                if delta_message.reasoning is not None:
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                    yield _increment_sequence_number_and_return(
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                        ResponseReasoningTextDeltaEvent(
                            type="response.reasoning_text.delta",
                            sequence_number=-1,
                            content_index=current_content_index,
                            output_index=current_output_index,
                            item_id=current_item_id,
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                            delta=delta_message.reasoning,
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                        )
                    )
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                elif delta_message.content is not None:
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                    yield _increment_sequence_number_and_return(
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                        ResponseTextDeltaEvent(
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                            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,
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                            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 []
                            ),
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                        )
                    )
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                previous_delta_messages.append(delta_message)
        if previous_delta_messages:
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            if previous_delta_messages[-1].reasoning is not None:
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                reason_content = "".join(
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                    pm.reasoning
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                    for pm in previous_delta_messages
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                    if pm.reasoning is not None
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                )
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                yield _increment_sequence_number_and_return(
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                    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,
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                    )
                )
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                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=ResponseReasoningTextContent(
                            text=reason_content,
                            type="reasoning_text",
                        ),
                    )
                )
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                reasoning_item = ResponseReasoningItem(
                    type="reasoning",
                    content=[
                        ResponseReasoningTextContent(
                            text=reason_content,
                            type="reasoning_text",
                        ),
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
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                yield _increment_sequence_number_and_return(
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                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=reasoning_item,
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                    )
                )
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            elif previous_delta_messages[-1].content is not None:
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                final_content = "".join(
                    pm.content
                    for pm in previous_delta_messages
                    if pm.content is not None
                )
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                yield _increment_sequence_number_and_return(
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                    ResponseTextDoneEvent(
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                        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,
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                    )
                )
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                part = ResponseOutputText(
                    text=final_content,
                    type="output_text",
                    annotations=[],
                )
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                yield _increment_sequence_number_and_return(
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                    ResponseContentPartDoneEvent(
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                        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,
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                    )
                )
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                item = ResponseOutputMessage(
                    type="message",
                    role="assistant",
                    content=[
                        part,
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
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                yield _increment_sequence_number_and_return(
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                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=item,
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                    )
                )
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    async def _process_harmony_streaming_events(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
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        result_generator: AsyncIterator[ConversationContext | None],
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        context: ConversationContext,
        model_name: str,
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        tokenizer: TokenizerLike,
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        request_metadata: RequestResponseMetadata,
        created_time: int,
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        _increment_sequence_number_and_return: Callable[
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            [StreamingResponsesResponse], StreamingResponsesResponse
        ],
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    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
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        state = StreamingState()
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        async for ctx in result_generator:
            assert isinstance(ctx, StreamingHarmonyContext)

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            # finish_reason='error' indicates a retryable error
            self._raise_if_error(ctx.finish_reason, request.request_id)

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            if ctx.is_expecting_start():
                if len(ctx.parser.messages) > 0:
                    previous_item = ctx.parser.messages[-1]
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                    for event in emit_previous_item_done_events(previous_item, state):
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                        yield _increment_sequence_number_and_return(event)
                state.reset_for_new_item()

            # Stream the output of a harmony message
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            for event in emit_content_delta_events(ctx, state):
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                yield _increment_sequence_number_and_return(event)

            # Stream tool call outputs
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            for event in emit_tool_action_events(ctx, state, self.tool_server):
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                yield _increment_sequence_number_and_return(event)
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    async def responses_stream_generator(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
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        result_generator: AsyncIterator[ConversationContext | None],
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        context: ConversationContext,
        model_name: str,
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        tokenizer: TokenizerLike,
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        request_metadata: RequestResponseMetadata,
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        created_time: int | None = None,
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    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
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        # TODO:
        # 1. Handle disconnect

        created_time = created_time or int(time.time())

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        sequence_number = 0

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        def _increment_sequence_number_and_return(
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            event: StreamingResponsesResponse,
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        ) -> StreamingResponsesResponse:
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            nonlocal sequence_number
            # Set sequence_number if the event has this attribute
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            if hasattr(event, "sequence_number"):
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                event.sequence_number = sequence_number
            sequence_number += 1
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            return event
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        async with AsyncExitStack() as exit_stack:
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            if self.use_harmony:
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                # TODO: in streaming, we noticed this bug:
                # https://github.com/vllm-project/vllm/issues/25697
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                await self._initialize_tool_sessions(request, context, exit_stack)
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                processor = self._process_harmony_streaming_events
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            else:
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                processor = self._process_simple_streaming_events
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            # TODO Hanchen make sampling params to include the structural tag
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            initial_response = ResponsesResponse.from_request(
                request,
                sampling_params,
                model_name=model_name,
                created_time=created_time,
                output=[],
                status="in_progress",
                usage=None,
            ).model_dump()
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            yield _increment_sequence_number_and_return(
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                ResponseCreatedEvent(
                    type="response.created",
                    sequence_number=-1,
                    response=initial_response,
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                )
            )
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            yield _increment_sequence_number_and_return(
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                ResponseInProgressEvent(
                    type="response.in_progress",
                    sequence_number=-1,
                    response=initial_response,
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                )
            )
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            try:
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                async for event_data in processor(
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                    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
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            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,
            )
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            yield _increment_sequence_number_and_return(
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                ResponseCompletedEvent(
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                    type="response.completed",
                    sequence_number=-1,
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                    response=final_response,
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                )
            )