serving_responses.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import asyncio
import json
import time
import uuid
from collections import deque
from collections.abc import AsyncGenerator, AsyncIterator, Sequence
from contextlib import AsyncExitStack
from copy import copy
from http import HTTPStatus
from typing import Callable, Final, Optional, Union

import jinja2
import openai.types.responses as openai_responses_types
from fastapi import Request
from openai import BaseModel
# yapf conflicts with isort for this block
# yapf: disable
from openai.types.responses import (ResponseCreatedEvent,
                                    ResponseFunctionToolCall,
                                    ResponseInProgressEvent,
                                    ResponseOutputItem,
                                    ResponseOutputItemDoneEvent,
                                    ResponseOutputMessage, ResponseOutputText,
                                    ResponseReasoningItem,
                                    ResponseReasoningTextDeltaEvent,
                                    ResponseReasoningTextDoneEvent,
                                    response_text_delta_event)
from openai.types.responses.response_output_text import (Logprob,
                                                         LogprobTopLogprob)
# yapf: enable
from openai.types.responses.response_reasoning_item import (
    Content as ResponseReasoningTextContent)
from openai_harmony import Message as OpenAIHarmonyMessage

from vllm import envs
from vllm.config import ModelConfig
from vllm.engine.protocol import EngineClient
from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
                                         ChatTemplateContentFormatOption)
from vllm.entrypoints.context import (ConversationContext, HarmonyContext,
                                      SimpleContext, StreamingHarmonyContext)
from vllm.entrypoints.harmony_utils import (
    get_developer_message, get_stop_tokens_for_assistant_actions,
    get_system_message, get_user_message, has_custom_tools,
    parse_output_message, parse_remaining_state, parse_response_input,
    render_for_completion)
from vllm.entrypoints.logger import RequestLogger
# yapf conflicts with isort for this block
# yapf: disable
from vllm.entrypoints.openai.protocol import (DeltaMessage, ErrorResponse,
                                              InputTokensDetails,
                                              OutputTokensDetails,
                                              RequestResponseMetadata,
                                              ResponsesRequest,
                                              ResponsesResponse, ResponseUsage)
# yapf: enable
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
from vllm.entrypoints.tool_server import ToolServer
from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
from vllm.logger import init_logger
from vllm.logprobs import Logprob as SampleLogprob
from vllm.logprobs import SampleLogprobs
from vllm.outputs import CompletionOutput
from vllm.reasoning import ReasoningParser, ReasoningParserManager
from vllm.sampling_params import SamplingParams
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import random_uuid

logger = init_logger(__name__)


class OpenAIServingResponses(OpenAIServing):

    def __init__(
        self,
        engine_client: EngineClient,
        model_config: ModelConfig,
        models: OpenAIServingModels,
        *,
        request_logger: Optional[RequestLogger],
        chat_template: Optional[str],
        chat_template_content_format: ChatTemplateContentFormatOption,
        return_tokens_as_token_ids: bool = False,
        reasoning_parser: str = "",
        enable_auto_tools: bool = False,
        tool_parser: Optional[str] = None,
        tool_server: Optional[ToolServer] = None,
        enable_prompt_tokens_details: bool = False,
        enable_force_include_usage: bool = False,
        enable_log_outputs: bool = False,
        log_error_stack: bool = False,
    ) -> None:
        super().__init__(
            engine_client=engine_client,
            model_config=model_config,
            models=models,
            request_logger=request_logger,
            return_tokens_as_token_ids=return_tokens_as_token_ids,
            enable_force_include_usage=enable_force_include_usage,
            log_error_stack=log_error_stack,
        )

        self.chat_template = chat_template
        self.chat_template_content_format: Final = chat_template_content_format
        self.enable_log_outputs = enable_log_outputs

        self.reasoning_parser: Optional[Callable[[AnyTokenizer],
                                                 ReasoningParser]] = None
        if reasoning_parser:
            try:
                self.reasoning_parser = (
                    ReasoningParserManager.get_reasoning_parser(
                        reasoning_parser))
                assert self.reasoning_parser is not None
            except Exception as e:
                raise TypeError(
                    f"{reasoning_parser=} has not been registered") from e

        self.enable_prompt_tokens_details = enable_prompt_tokens_details
        self.enable_force_include_usage = enable_force_include_usage
        self.default_sampling_params = (
            self.model_config.get_diff_sampling_param())
        if self.default_sampling_params:
            source = self.model_config.generation_config
            source = "model" if source == "auto" else source
            logger.info("Using default chat sampling params from %s: %s",
                        source, self.default_sampling_params)

        # If False (default), the "store" option is (silently) ignored and the
        # response is not stored. If True, the response is stored in memory.
        # NOTE(woosuk): This may not be intuitive for users, as the default
        # behavior in OpenAI's Responses API is to store the response, but
        # vLLM's default behavior is not.
        self.enable_store = envs.VLLM_ENABLE_RESPONSES_API_STORE
        if self.enable_store:
            logger.warning_once(
                "`VLLM_ENABLE_RESPONSES_API_STORE` is enabled. This may "
                "cause a memory leak since we never remove responses from "
                "the store.")

        self.use_harmony = model_config.hf_config.model_type == "gpt_oss"
        if self.use_harmony:
            logger.warning("For gpt-oss, we ignore --enable-auto-tool-choice "
                           "and always enable tool use.")
            # OpenAI models have two EOS-like tokens: <|return|> and <|call|>.
            # We need to add them to the stop token ids.
            if "stop_token_ids" not in self.default_sampling_params:
                self.default_sampling_params["stop_token_ids"] = []
            self.default_sampling_params["stop_token_ids"].extend(
                get_stop_tokens_for_assistant_actions())

        # set up tool use
        self.enable_auto_tools: bool = enable_auto_tools
        if self.enable_auto_tools:
            logger.info(
                "\"auto\" tool choice has been enabled please note that while"
                " the parallel_tool_calls client option is preset for "
                "compatibility reasons, it will be ignored.")

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

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

        # HACK(wuhang): This is a hack. We should use a better store.
        # FIXME: If enable_store=True, this may cause a memory leak since we
        # never remove events from the store.
        self.event_store: dict[str, tuple[deque[str], asyncio.Event]] = {}

        self.background_tasks: dict[str, asyncio.Task] = {}

        self.tool_server = tool_server

    async def create_responses(
        self,
        request: ResponsesRequest,
        raw_request: Optional[Request] = None,
    ) -> Union[AsyncGenerator[str, None], ResponsesResponse, ErrorResponse]:
        error_check_ret = await self._check_model(request)
        if error_check_ret is not None:
            logger.error("Error with model %s", error_check_ret)
            return error_check_ret

        # If the engine is dead, raise the engine's DEAD_ERROR.
        # This is required for the streaming case, where we return a
        # success status before we actually start generating text :).
        if self.engine_client.errored:
            raise self.engine_client.dead_error

        if request.store and not self.enable_store:
            if request.background:
                return self.create_error_response(
                    err_type="invalid_request_error",
                    message=(
                        "This vLLM engine does not support `store=True` and "
                        "therefore does not support the background mode. To "
                        "enable these features, set the environment variable "
                        "`VLLM_ENABLE_RESPONSES_API_STORE=1` when launching "
                        "the vLLM server."),
                    status_code=HTTPStatus.BAD_REQUEST,
                )
            # Disable the store option.
            # NOTE(woosuk): Although returning an error is possible, we opted
            # to implicitly disable store and process the request anyway, as
            # we assume most users do not intend to actually store the response
            # (i.e., their request's `store=True` just because it's the default
            # value).
            request.store = False
        if self.use_harmony and request.is_include_output_logprobs():
            return self.create_error_response(
                err_type="invalid_request_error",
                message="logprobs are not supported with gpt-oss models",
                status_code=HTTPStatus.BAD_REQUEST,
            )

        # Handle the previous response ID.
        prev_response_id = request.previous_response_id
        if prev_response_id is not None:
            if not prev_response_id.startswith("resp_"):
                return self._make_invalid_id_error(prev_response_id)
            async with self.response_store_lock:
                prev_response = self.response_store.get(prev_response_id)
            if prev_response is None:
                return self._make_not_found_error(prev_response_id)
        else:
            prev_response = None

        try:
            lora_request = self._maybe_get_adapters(request)
            model_name = self._get_model_name(request.model, lora_request)
            tokenizer = await self.engine_client.get_tokenizer(lora_request)

            if self.use_harmony:
                messages, request_prompts, engine_prompts = (
                    self._make_request_with_harmony(request, prev_response))
            else:
                messages, request_prompts, engine_prompts = (
                    await self._make_request(request, prev_response,
                                             tokenizer))

        except (ValueError, TypeError, RuntimeError, jinja2.TemplateError,
                NotImplementedError) as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(f"{e} {e.__cause__}")

        request_metadata = RequestResponseMetadata(
            request_id=request.request_id)
        if raw_request:
            raw_request.state.request_metadata = request_metadata

        # Schedule the request and get the result generator.
        generators: list[AsyncGenerator[ConversationContext, None]] = []

        builtin_tool_list: list[str] = []
        if self.use_harmony and self.tool_server is not None:
            if self.tool_server.has_tool("browser"):
                builtin_tool_list.append("browser")
            if self.tool_server.has_tool("python"):
                builtin_tool_list.append("python")
            if self.tool_server.has_tool("container"):
                builtin_tool_list.append("container")

        if self.tool_server is not None:
            available_tools = builtin_tool_list
        else:
            assert len(builtin_tool_list) == 0
            available_tools = []
        try:
            for i, engine_prompt in enumerate(engine_prompts):
                default_max_tokens = self.max_model_len - len(
                    engine_prompt["prompt_token_ids"])
                sampling_params = request.to_sampling_params(
                    default_max_tokens, self.default_sampling_params)

                trace_headers = (None if raw_request is None else await
                                 self._get_trace_headers(raw_request.headers))

                context: ConversationContext
                if self.use_harmony:
                    if request.stream:
                        context = StreamingHarmonyContext(
                            messages, available_tools)
                    else:
                        context = HarmonyContext(messages, available_tools)
                else:
                    context = SimpleContext()
                generator = self._generate_with_builtin_tools(
                    request_id=request.request_id,
                    request_prompt=request_prompts[i],
                    engine_prompt=engine_prompt,
                    sampling_params=sampling_params,
                    context=context,
                    lora_request=lora_request,
                    priority=request.priority,
                    trace_headers=trace_headers,
                )
                generators.append(generator)
        except ValueError as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))

        assert len(generators) == 1
        result_generator, = generators

        # Store the input messages.
        if request.store:
            self.msg_store[request.request_id] = messages

        if request.background:
            created_time = int(time.time())
            response = ResponsesResponse.from_request(
                request,
                sampling_params,
                model_name=model_name,
                created_time=created_time,
                output=[],
                status="queued",
                usage=None,
            )
            async with self.response_store_lock:
                self.response_store[response.id] = response

            # Run the request in the background.
            if request.stream:
                task = asyncio.create_task(
                    self._run_background_request_stream(
                        request,
                        sampling_params,
                        result_generator,
                        context,
                        model_name,
                        tokenizer,
                        request_metadata,
                        created_time,
                    ),
                    name=f"create_{request.request_id}",
                )
            else:
                task = asyncio.create_task(
                    self._run_background_request(
                        request,
                        sampling_params,
                        result_generator,
                        context,
                        model_name,
                        tokenizer,
                        request_metadata,
                        created_time,
                    ),
                    name=f"create_{response.id}",
                )

            # For cleanup.
            response_id = response.id
            self.background_tasks[response_id] = task
            task.add_done_callback(
                lambda _: self.background_tasks.pop(response_id, None))

            if request.stream:
                return self.responses_background_stream_generator(
                    request.request_id)
            return response

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

        try:
            return await self.responses_full_generator(
                request,
                sampling_params,
                result_generator,
                context,
                model_name,
                tokenizer,
                request_metadata,
            )
        except Exception as e:
            return self.create_error_response(str(e))

    async def _make_request(
        self,
        request: ResponsesRequest,
        prev_response: Optional[ResponsesResponse],
        tokenizer: AnyTokenizer,
    ):
        if len(request.tools) > 0:
            raise NotImplementedError(
                "Tool use is not supported in Responses API without Harmony")
        # Construct the input messages.
        messages = self._construct_input_messages(request, prev_response)
        _, request_prompts, engine_prompts = await self._preprocess_chat(
            request,
            tokenizer,
            messages,
            chat_template=self.chat_template,
            chat_template_content_format=self.chat_template_content_format,
        )
        return messages, request_prompts, engine_prompts

    def _make_request_with_harmony(
        self,
        request: ResponsesRequest,
        prev_response: Optional[ResponsesResponse],
    ):
        if request.tool_choice != "auto":
            raise NotImplementedError(
                "Only 'auto' tool_choice is supported in "
                "response API with Harmony")
        messages = self._construct_input_messages_with_harmony(
            request, prev_response)
        prompt_token_ids = render_for_completion(messages)
        engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)

        # Add cache_salt if provided in the request
        if request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

        return messages, [prompt_token_ids], [engine_prompt]

    async def responses_full_generator(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
        result_generator: AsyncIterator[ConversationContext],
        context: ConversationContext,
        model_name: str,
        tokenizer: AnyTokenizer,
        request_metadata: RequestResponseMetadata,
        created_time: Optional[int] = None,
    ) -> Union[ErrorResponse, ResponsesResponse]:
        if created_time is None:
            created_time = int(time.time())

        async with AsyncExitStack() as exit_stack:
            try:
                await context.init_tool_sessions(self.tool_server, exit_stack,
                                                 request.request_id)
                async for _ in result_generator:
                    pass
            except asyncio.CancelledError:
                return self.create_error_response("Client disconnected")
            except ValueError as e:
                # TODO: Use a vllm-specific Validation Error
                return self.create_error_response(str(e))

        if self.use_harmony:
            assert isinstance(context, HarmonyContext)
            output = self._make_response_output_items_with_harmony(context)
            num_tool_output_tokens = context.num_tool_output_tokens
        else:
            assert isinstance(context, SimpleContext)
            final_res = context.last_output
            assert final_res is not None
            assert len(final_res.outputs) == 1
            final_output = final_res.outputs[0]

            output = self._make_response_output_items(request, final_output,
                                                      tokenizer)

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

        assert isinstance(context, (SimpleContext, HarmonyContext))
        num_prompt_tokens = context.num_prompt_tokens
        num_generated_tokens = context.num_output_tokens
        num_cached_tokens = context.num_cached_tokens
        num_reasoning_tokens = context.num_reasoning_tokens

        usage = ResponseUsage(
            input_tokens=num_prompt_tokens,
            output_tokens=num_generated_tokens,
            total_tokens=num_prompt_tokens + num_generated_tokens,
            input_tokens_details=InputTokensDetails(
                cached_tokens=num_cached_tokens),
            output_tokens_details=OutputTokensDetails(
                reasoning_tokens=num_reasoning_tokens,
                tool_output_tokens=num_tool_output_tokens),
        )
        response = ResponsesResponse.from_request(
            request,
            sampling_params,
            model_name=model_name,
            created_time=created_time,
            output=output,
            status="completed",
            usage=usage,
        )

        if request.store:
            async with self.response_store_lock:
                stored_response = self.response_store.get(response.id)
                # If the response is already cancelled, don't update it.
                if (stored_response is None
                        or stored_response.status != "cancelled"):
                    self.response_store[response.id] = response
        return response

    def _topk_logprobs(self, logprobs: dict[int,
                                            SampleLogprob], top_logprobs: int,
                       tokenizer: AnyTokenizer) -> list[LogprobTopLogprob]:
        """Returns the top-k logprobs from the logprobs dictionary."""
        out = []
        for i, (token_id, _logprob) in enumerate(logprobs.items()):
            if i >= top_logprobs:
                break
            text = _logprob.decoded_token if _logprob.decoded_token \
                is not None else tokenizer.decode([token_id])
            out.append(
                LogprobTopLogprob(
                    token=text,
                    logprob=max(_logprob.logprob, -9999.0),
                    bytes=list(text.encode("utf-8", errors="replace")),
                ))
        return out

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

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

    def _make_response_output_items(
        self,
        request: ResponsesRequest,
        final_output: CompletionOutput,
        tokenizer: AnyTokenizer,
    ) -> list[ResponseOutputItem]:
        if self.reasoning_parser:
            try:
                reasoning_parser = self.reasoning_parser(tokenizer)
            except RuntimeError as e:
                logger.exception("Error in reasoning parser creation.")
                raise e

            reasoning_content, content = (
                reasoning_parser.extract_reasoning_content(final_output.text,
                                                           request=request))
        else:
            reasoning_content = None
            content = final_output.text

        # Log complete response if output logging is enabled
        if self.enable_log_outputs and self.request_logger:
            output_text = ""
            if content:
                output_text = content
            elif reasoning_content:
                output_text = f"[reasoning: {reasoning_content}]"

            if output_text:
                self.request_logger.log_outputs(
                    request_id=request.request_id,
                    outputs=output_text,
                    output_token_ids=final_output.token_ids,
                    finish_reason=final_output.finish_reason,
                    is_streaming=False,
                    delta=False,
                )

        output = []
        if reasoning_content:
            reasoning_item = ResponseReasoningItem(
                id=f"rs_{random_uuid()}",
                summary=[],
                type="reasoning",
                content=[
                    ResponseReasoningTextContent(text=reasoning_content,
                                                 type="reasoning_text")
                ],
                status=None,  # NOTE: Only the last output item has status.
            )
            output.append(reasoning_item)
        if content:
            output_text = ResponseOutputText(
                text=content,
                annotations=[],  # TODO
                type="output_text",
                logprobs=self._create_response_logprobs(
                    token_ids=final_output.token_ids,
                    logprobs=final_output.logprobs,
                    tokenizer=tokenizer,
                    top_logprobs=request.top_logprobs,
                ) if request.is_include_output_logprobs() else None,
            )
            message = ResponseOutputMessage(
                id=f"msg_{random_uuid()}",
                content=[output_text],
                role="assistant",
                status="completed",
                type="message",
            )
            output.append(message)
        return output

    def _make_response_output_items_with_harmony(
        self,
        context: HarmonyContext,
    ) -> list[ResponseOutputItem]:
        output_items = []
        num_init_messages = context.num_init_messages
        for msg in context.messages[num_init_messages:]:
            output_items.extend(parse_output_message(msg))
        # Handle the generation stopped in the middle (if any).
        last_items = parse_remaining_state(context.parser)
        if last_items:
            output_items.extend(last_items)
        return output_items

    def _construct_input_messages(
        self,
        request: ResponsesRequest,
        prev_response: Optional[ResponsesResponse] = None,
    ) -> list[ChatCompletionMessageParam]:
        messages: list[ChatCompletionMessageParam] = []
        if request.instructions:
            messages.append({
                "role": "system",
                "content": request.instructions,
            })

        # Prepend the conversation history.
        if prev_response is not None:
            # Add the previous messages.
            prev_msg = self.msg_store[prev_response.id]
            messages.extend(prev_msg)

            # Add the previous output.
            for output_item in prev_response.output:
                # NOTE: We skip the reasoning output.
                if isinstance(output_item, ResponseOutputMessage):
                    for content in output_item.content:
                        messages.append({
                            "role": "assistant",
                            "content": content.text,
                        })

        # Append the new input.
        # Responses API supports simple text inputs without chat format.
        if isinstance(request.input, str):
            messages.append({"role": "user", "content": request.input})
        else:
            messages.extend(request.input)  # type: ignore
        return messages

    def _construct_input_messages_with_harmony(
        self,
        request: ResponsesRequest,
        prev_response: Optional[ResponsesResponse],
    ) -> list[OpenAIHarmonyMessage]:
        messages: list[OpenAIHarmonyMessage] = []
        if prev_response is None:
            # New conversation.
            reasoning_effort = (request.reasoning.effort
                                if request.reasoning else None)
            # Temporary: OpenAI types doesn't have container tool
            # so we used MCP to cover that, up for change
            tool_types = [tool.type for tool in request.tools]
            if envs.VLLM_GPT_OSS_USE_CONTAINER_TOOL:
                tool_types.append("container")
            enable_browser = ("web_search_preview" in tool_types
                              and self.tool_server is not None
                              and self.tool_server.has_tool("browser"))
            enable_code_interpreter = ("code_interpreter" in tool_types
                                       and self.tool_server is not None
                                       and self.tool_server.has_tool("python"))
            enable_container = ("container" in tool_types
                                and self.tool_server is not None
                                and self.tool_server.has_tool("container"))
            with_custom_tools = has_custom_tools(tool_types)
            sys_msg = get_system_message(
                reasoning_effort=reasoning_effort,
                browser_description=self.tool_server.get_tool_description(
                    "browser")
                if enable_browser and self.tool_server is not None else None,
                python_description=self.tool_server.get_tool_description(
                    "python") if enable_code_interpreter
                and self.tool_server is not None else None,
                container_description=self.tool_server.get_tool_description(
                    "container")
                if enable_container and self.tool_server is not None else None,
                instructions=request.instructions,
                with_custom_tools=with_custom_tools,
            )
            messages.append(sys_msg)
            if with_custom_tools:
                dev_msg = get_developer_message(
                    instructions=request.instructions, tools=request.tools)
                messages.append(dev_msg)
        else:
            # Continue the previous conversation.
            # FIXME(woosuk): Currently, request params like reasoning and
            # instructions are ignored.
            prev_msgs = self.msg_store[prev_response.id]
            # Remove the previous chain-of-thoughts if there is a new "final"
            # message. Note that this also removes these messages from the
            # msg_store.
            if len(prev_msgs) > 0:
                last_msg = prev_msgs[-1]
                assert isinstance(last_msg, OpenAIHarmonyMessage)
                if last_msg.channel == "final":
                    prev_final_msg_idx = -1
                    for i in range(len(prev_msgs) - 2, -1, -1):
                        prev_msg_i = prev_msgs[i]
                        assert isinstance(prev_msg_i, OpenAIHarmonyMessage)
                        if prev_msg_i.channel == "final":
                            prev_final_msg_idx = i
                            break
                    recent_turn_msgs = prev_msgs[prev_final_msg_idx + 1:]
                    del prev_msgs[prev_final_msg_idx + 1:]
                    for msg in recent_turn_msgs:
                        assert isinstance(msg, OpenAIHarmonyMessage)
                        if msg.channel != "analysis":
                            prev_msgs.append(msg)
            messages.extend(prev_msgs)
        # Append the new input.
        # Responses API supports simple text inputs without chat format.
        if isinstance(request.input, str):
            messages.append(get_user_message(request.input))
        else:
            if prev_response is not None:
                prev_outputs = copy(prev_response.output)
            else:
                prev_outputs = []
            for response_msg in request.input:
                messages.append(
                    parse_response_input(response_msg, prev_outputs))
                # User passes in a tool call request and its output. We need
                # to add the tool call request to prev_outputs so that the
                # parse_response_input can find the tool call request when
                # parsing the tool call output.
                if isinstance(response_msg, ResponseFunctionToolCall):
                    prev_outputs.append(response_msg)
        return messages

    async def _run_background_request_stream(
        self,
        request: ResponsesRequest,
        *args,
        **kwargs,
    ):
        event_deque: deque[str] = deque()
        new_event_signal = asyncio.Event()
        self.event_store[request.request_id] = (event_deque, new_event_signal)
        response = None
        try:
            generator = self.responses_stream_generator(
                request, *args, **kwargs)
            async for event in generator:
                event_deque.append(event)
                new_event_signal.set()  # Signal new event available
        except Exception as e:
            logger.exception("Background request failed for %s",
                             request.request_id)
            response = self.create_error_response(str(e))
        finally:
            # Mark as finished with a special marker
            event_deque.append("__STREAM_END__")
            new_event_signal.set()

        if response is not None and isinstance(response, ErrorResponse):
            # If the request has failed, update the status to "failed".
            response_id = request.request_id
            async with self.response_store_lock:
                stored_response = self.response_store.get(response_id)
                assert stored_response is not None
                if stored_response.status not in ("completed", "cancelled"):
                    stored_response.status = "failed"

    async def _run_background_request(
        self,
        request: ResponsesRequest,
        *args,
        **kwargs,
    ):
        try:
            response = await self.responses_full_generator(
                request, *args, **kwargs)
        except Exception as e:
            logger.exception("Background request failed for %s",
                             request.request_id)
            response = self.create_error_response(str(e))

        if isinstance(response, ErrorResponse):
            # If the request has failed, update the status to "failed".
            response_id = request.request_id
            async with self.response_store_lock:
                stored_response = self.response_store.get(response_id)
                assert stored_response is not None
                if stored_response.status not in ("completed", "cancelled"):
                    stored_response.status = "failed"

    async def responses_background_stream_generator(
        self,
        response_id: str,
        starting_after: Optional[int] = None,
    ):
        if response_id not in self.event_store:
            raise ValueError(f"Unknown response_id: {response_id}")

        event_deque, new_event_signal = self.event_store[response_id]
        start_index = 0 if starting_after is None else starting_after + 1
        current_index = start_index

        while True:
            new_event_signal.clear()

            # Yield existing events from start_index
            while current_index < len(event_deque):
                event = event_deque[current_index]
                if event == "__STREAM_END__":
                    return
                yield event
                current_index += 1

            await new_event_signal.wait()

    async def retrieve_responses(
        self,
        response_id: str,
        starting_after: Optional[int],
        stream: Optional[bool],
    ) -> Union[ErrorResponse, ResponsesResponse]:
        if not response_id.startswith("resp_"):
            return self._make_invalid_id_error(response_id)

        async with self.response_store_lock:
            response = self.response_store.get(response_id)

        if response is None:
            return self._make_not_found_error(response_id)

        if stream:
            return self.responses_background_stream_generator(
                response_id,
                starting_after,
            )
        return response

    async def cancel_responses(
        self,
        response_id: str,
    ) -> Union[ErrorResponse, ResponsesResponse]:
        if not response_id.startswith("resp_"):
            return self._make_invalid_id_error(response_id)

        async with self.response_store_lock:
            response = self.response_store.get(response_id)
            if response is None:
                return self._make_not_found_error(response_id)

            prev_status = response.status
            if prev_status not in ("queued", "in_progress"):
                return self.create_error_response(
                    err_type="invalid_request_error",
                    message="Cannot cancel a synchronous response.",
                )

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

        # Abort the request.
        if (task := self.background_tasks.get(response_id)):
            task.cancel()
            try:
                await task
            except asyncio.CancelledError:
                logger.exception("Background task for %s was cancelled",
                                 response_id)
        return response

    def _make_invalid_id_error(self, response_id: str) -> ErrorResponse:
        return self.create_error_response(
            err_type="invalid_request_error",
            message=(f"Invalid 'response_id': '{response_id}'. "
                     "Expected an ID that begins with 'resp'."),
        )

    def _make_not_found_error(self, response_id: str) -> ErrorResponse:
        return self.create_error_response(
            err_type="invalid_request_error",
            message=f"Response with id '{response_id}' not found.",
            status_code=HTTPStatus.NOT_FOUND,
        )

    def _make_store_not_supported_error(self) -> ErrorResponse:
        return self.create_error_response(
            err_type="invalid_request_error",
            message=("`store=True` (default) is not supported. Please set "
                     "`store=False` in Responses API or set "
                     "`VLLM_ENABLE_RESPONSES_API_STORE=1` in the env var when "
                     "starting the vLLM server."),
            status_code=HTTPStatus.BAD_REQUEST,
        )

    async def _process_simple_streaming_events(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
        result_generator: AsyncIterator[Optional[ConversationContext]],
        context: ConversationContext,
        model_name: str,
        tokenizer: AnyTokenizer,
        request_metadata: RequestResponseMetadata,
        created_time: int,
        _send_event: Callable[[BaseModel], str],
    ) -> AsyncGenerator[str, None]:
        current_content_index = 0
        current_output_index = 0
        current_item_id = ""
        reasoning_parser = None
        if self.reasoning_parser:
            reasoning_parser = self.reasoning_parser(tokenizer)
        previous_text = ""
        previous_token_ids: list[int] = []
        first_delta_sent = False
        previous_delta_messages: list[DeltaMessage] = []
        async for ctx in result_generator:
            assert isinstance(ctx, SimpleContext)
            if ctx.last_output is None:
                continue
            if ctx.last_output.outputs:
                output = ctx.last_output.outputs[0]
                if reasoning_parser:
                    delta_message = \
                        reasoning_parser.extract_reasoning_content_streaming(
                        previous_text=previous_text,
                        current_text=previous_text + output.text,
                        delta_text=output.text,
                        previous_token_ids=previous_token_ids,
                        current_token_ids=previous_token_ids +
                        output.token_ids,
                        delta_token_ids=output.token_ids,
                    )
                else:
                    delta_message = DeltaMessage(content=output.text, )
                previous_text += output.text
                previous_token_ids += output.token_ids
                if not delta_message:
                    continue
                if not first_delta_sent:
                    current_item_id = str(uuid.uuid4())
                    if delta_message.reasoning_content:
                        yield _send_event(
                            openai_responses_types.
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=openai_responses_types.
                                ResponseReasoningItem(
                                    type="reasoning",
                                    id=current_item_id,
                                    summary=[],
                                    status="in_progress",
                                ),
                            ))
                    else:
                        yield _send_event(
                            openai_responses_types.
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=openai_responses_types.
                                ResponseOutputMessage(
                                    id=current_item_id,
                                    type="message",
                                    role="assistant",
                                    content=[],
                                    status="in_progress",
                                ),
                            ))
                    yield _send_event(
                        openai_responses_types.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=openai_responses_types.ResponseOutputText(
                                type="output_text",
                                text="",
                                annotations=[],
                                logprobs=[],
                            ),
                        ))
                    current_content_index += 1
                    first_delta_sent = True
                # todo(kebe7jun) tool call support

                # check delta message and previous delta message are
                # same as content or reasoning content
                if (previous_delta_messages
                        and previous_delta_messages[-1].reasoning_content
                        is not None and delta_message.content is not None):
                    # from reasoning to normal content, send done
                    # event for reasoning
                    reason_content = ''.join(
                        pm.reasoning_content for pm in previous_delta_messages
                        if pm.reasoning_content is not None)
                    yield _send_event(
                        ResponseReasoningTextDoneEvent(
                            type="response.reasoning_text.done",
                            item_id=current_item_id,
                            sequence_number=-1,
                            output_index=current_output_index,
                            content_index=current_content_index,
                            text=reason_content,
                        ))
                    current_content_index = 0
                    reasoning_item = ResponseReasoningItem(
                        type="reasoning",
                        content=[
                            ResponseReasoningTextContent(
                                text=reason_content,
                                type="reasoning_text",
                            ),
                        ],
                        status="completed",
                        id=current_item_id,
                        summary=[],
                    )
                    yield _send_event(
                        ResponseOutputItemDoneEvent(
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=reasoning_item,
                        ))
                    yield _send_event(
                        openai_responses_types.ResponseOutputItemAddedEvent(
                            type="response.output_item.added",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=openai_responses_types.ResponseOutputMessage(
                                id=current_item_id,
                                type="message",
                                role="assistant",
                                content=[],
                                status="in_progress",
                            ),
                        ))
                    current_output_index += 1
                    current_item_id = str(uuid.uuid4())
                    yield _send_event(
                        openai_responses_types.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=openai_responses_types.ResponseOutputText(
                                type="output_text",
                                text="",
                                annotations=[],
                                logprobs=[],
                            ),
                        ))
                    current_content_index += 1
                    # reset previous delta messages
                    previous_delta_messages = []

                if delta_message.reasoning_content is not None:
                    yield _send_event(
                        ResponseReasoningTextDeltaEvent(
                            type="response.reasoning_text.delta",
                            sequence_number=-1,
                            content_index=current_content_index,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            delta=delta_message.reasoning_content,
                        ))
                elif delta_message.content is not None:
                    yield _send_event(
                        openai_responses_types.ResponseTextDeltaEvent(
                            type="response.output_text.delta",
                            sequence_number=-1,
                            content_index=current_content_index,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            delta=delta_message.content,
                            logprobs=self._create_stream_response_logprobs(
                                token_ids=output.token_ids,
                                logprobs=output.logprobs,
                                tokenizer=tokenizer,
                                top_logprobs=request.top_logprobs,
                            ) if request.is_include_output_logprobs() else [],
                        ))
                current_content_index += 1

                previous_delta_messages.append(delta_message)
        if previous_delta_messages:
            if previous_delta_messages[-1].reasoning_content is not None:
                reason_content = ''.join(pm.reasoning_content
                                         for pm in previous_delta_messages
                                         if pm.reasoning_content is not None)
                yield _send_event(
                    ResponseReasoningTextDoneEvent(
                        type="response.reasoning_text.done",
                        item_id=current_item_id,
                        sequence_number=-1,
                        output_index=current_output_index,
                        content_index=current_content_index,
                        text=reason_content,
                    ))
                current_content_index += 1
                reasoning_item = ResponseReasoningItem(
                    type="reasoning",
                    content=[
                        ResponseReasoningTextContent(
                            text=reason_content,
                            type="reasoning_text",
                        ),
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
                yield _send_event(
                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=reasoning_item,
                    ))
            elif previous_delta_messages[-1].content is not None:
                final_content = ''.join(pm.content
                                        for pm in previous_delta_messages
                                        if pm.content is not None)
                yield _send_event(
                    openai_responses_types.ResponseTextDoneEvent(
                        type="response.output_text.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        content_index=current_content_index,
                        text=final_content,
                        logprobs=[],
                        item_id=current_item_id,
                    ))
                current_content_index += 1
                part = ResponseOutputText(
                    text=final_content,
                    type="output_text",
                    annotations=[],
                )
                yield _send_event(
                    openai_responses_types.ResponseContentPartDoneEvent(
                        type="response.content_part.done",
                        sequence_number=-1,
                        item_id=current_item_id,
                        output_index=current_output_index,
                        content_index=current_content_index,
                        part=part,
                    ))
                current_content_index += 1
                item = ResponseOutputMessage(
                    type="message",
                    role="assistant",
                    content=[
                        part,
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
                yield _send_event(
                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=item,
                    ))

    async def _process_harmony_streaming_events(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
        result_generator: AsyncIterator[Optional[ConversationContext]],
        context: ConversationContext,
        model_name: str,
        tokenizer: AnyTokenizer,
        request_metadata: RequestResponseMetadata,
        created_time: int,
        _send_event: Callable[[BaseModel], str],
    ) -> AsyncGenerator[str, None]:
        current_content_index = 0  # FIXME: this number is never changed
        current_output_index = 0
        current_item_id = ""  # FIXME: this number is never changed
        sent_output_item_added = False

        async for ctx in result_generator:

            assert isinstance(ctx, StreamingHarmonyContext)

            if ctx.is_expecting_start():
                current_output_index += 1
                sent_output_item_added = False

                if len(ctx.parser.messages) > 0:
                    previous_item = ctx.parser.messages[-1]
                    if previous_item.recipient is not None:
                        # Deal with tool call here
                        pass
                    elif previous_item.channel == "analysis":
                        reasoning_item = ResponseReasoningItem(
                            type="reasoning",
                            content=[
                                ResponseReasoningTextContent(
                                    text=previous_item.content[0].text,
                                    type="reasoning_text",
                                ),
                            ],
                            status="completed",
                            id=current_item_id,
                            summary=[],
                        )
                        yield _send_event(
                            ResponseReasoningTextDoneEvent(
                                type="response.reasoning_text.done",
                                item_id=current_item_id,
                                sequence_number=-1,
                                output_index=current_output_index,
                                content_index=current_content_index,
                                text=previous_item.content[0].text,
                            ))
                        yield _send_event(
                            ResponseOutputItemDoneEvent(
                                type="response.output_item.done",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=reasoning_item,
                            ))
                    elif previous_item.channel == "final":
                        text_content = ResponseOutputText(
                            type="output_text",
                            text=previous_item.content[0].text,
                            annotations=[],
                        )
                        yield _send_event(
                            openai_responses_types.ResponseTextDoneEvent(
                                type="response.output_text.done",
                                sequence_number=-1,
                                output_index=current_output_index,
                                content_index=current_content_index,
                                text=previous_item.content[0].text,
                                logprobs=[],
                                item_id=current_item_id,
                            ))
                        yield _send_event(
                            openai_responses_types.
                            ResponseContentPartDoneEvent(
                                type="response.content_part.done",
                                sequence_number=-1,
                                item_id=current_item_id,
                                output_index=current_output_index,
                                content_index=current_content_index,
                                part=text_content,
                            ))
                        yield _send_event(
                            openai_responses_types.ResponseOutputItemDoneEvent(
                                type="response.output_item.done",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=ResponseOutputMessage(
                                    id=current_item_id,
                                    type="message",
                                    role="assistant",
                                    content=[text_content],
                                    status="completed",
                                ),
                            ))

            if ctx.parser.last_content_delta:
                if (ctx.parser.current_channel == "final"
                        and ctx.parser.current_recipient is None):
                    if not sent_output_item_added:
                        sent_output_item_added = True
                        yield _send_event(
                            openai_responses_types.
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=openai_responses_types.
                                ResponseOutputMessage(
                                    id=current_item_id,
                                    type="message",
                                    role="assistant",
                                    content=[],
                                    status="in_progress",
                                ),
                            ))
                        yield _send_event(
                            openai_responses_types.
                            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=openai_responses_types.ResponseOutputText(
                                    type="output_text",
                                    text="",
                                    annotations=[],
                                    logprobs=[],
                                ),
                            ))
                    yield _send_event(
                        openai_responses_types.ResponseTextDeltaEvent(
                            type="response.output_text.delta",
                            sequence_number=-1,
                            content_index=current_content_index,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            delta=ctx.parser.last_content_delta,
                            # TODO, use logprobs from ctx.last_request_output
                            logprobs=[],
                        ))
                elif (ctx.parser.current_channel == "analysis"
                      and ctx.parser.current_recipient is None):
                    if not sent_output_item_added:
                        sent_output_item_added = True
                        yield _send_event(
                            openai_responses_types.
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=openai_responses_types.
                                ResponseReasoningItem(
                                    type="reasoning",
                                    id=current_item_id,
                                    summary=[],
                                    status="in_progress",
                                ),
                            ))
                        yield _send_event(
                            openai_responses_types.
                            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=openai_responses_types.ResponseOutputText(
                                    type="output_text",
                                    text="",
                                    annotations=[],
                                    logprobs=[],
                                ),
                            ))
                    yield _send_event(
                        ResponseReasoningTextDeltaEvent(
                            type="response.reasoning_text.delta",
                            item_id=current_item_id,
                            output_index=current_output_index,
                            content_index=current_content_index,
                            delta=ctx.parser.last_content_delta,
                            sequence_number=-1,
                        ))
                # built-in tools will be triggered on the analysis channel
                # However, occasionally built-in tools will
                # still be output to commentary.
                elif (ctx.parser.current_channel == "commentary"
                      or ctx.parser.current_channel == "analysis"
                      ) and ctx.parser.current_recipient == "python":
                    if not sent_output_item_added:
                        sent_output_item_added = True
                        yield _send_event(
                            openai_responses_types.
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=openai_responses_types.
                                ResponseCodeInterpreterToolCallParam(
                                    type="code_interpreter_call",
                                    id=current_item_id,
                                    code=None,
                                    container_id="auto",
                                    outputs=None,
                                    status="in_progress",
                                ),
                            ))
                        yield _send_event(
                            openai_responses_types.
                            ResponseCodeInterpreterCallInProgressEvent(
                                type=
                                "response.code_interpreter_call.in_progress",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item_id=current_item_id,
                            ))
                    yield _send_event(
                        openai_responses_types.
                        ResponseCodeInterpreterCallCodeDeltaEvent(
                            type="response.code_interpreter_call_code.delta",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            delta=ctx.parser.last_content_delta,
                        ))
            if ctx.is_assistant_action_turn() and len(ctx.parser.messages) > 0:
                previous_item = ctx.parser.messages[-1]
                if (self.tool_server is not None
                        and self.tool_server.has_tool("browser")
                        and previous_item.recipient is not None
                        and previous_item.recipient.startswith("browser.")):
                    function_name = previous_item.recipient[len("browser."):]
                    action = None
                    parsed_args = json.loads(previous_item.content[0].text)
                    if function_name == "search":
                        action = (openai_responses_types.
                                  response_function_web_search.ActionSearch(
                                      type="search",
                                      query=parsed_args["query"],
                                  ))
                    elif function_name == "open":
                        action = (
                            openai_responses_types.
                            response_function_web_search.ActionOpenPage(
                                type="open_page",
                                # TODO: translate to url
                                url=f"cursor:{parsed_args.get('cursor', '')}",
                            ))
                    elif function_name == "find":
                        action = (
                            openai_responses_types.
                            response_function_web_search.ActionFind(
                                type="find",
                                pattern=parsed_args["pattern"],
                                # TODO: translate to url
                                url=f"cursor:{parsed_args.get('cursor', '')}",
                            ))
                    else:
                        raise ValueError(
                            f"Unknown function name: {function_name}")

                    yield _send_event(
                        openai_responses_types.ResponseOutputItemAddedEvent(
                            type="response.output_item.added",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=openai_responses_types.
                            response_function_web_search.
                            ResponseFunctionWebSearch(
                                # TODO: generate a unique id for web search call
                                type="web_search_call",
                                id=current_item_id,
                                action=action,
                                status="in_progress",
                            ),
                        ))
                    yield _send_event(
                        openai_responses_types.
                        ResponseWebSearchCallInProgressEvent(
                            type="response.web_search_call.in_progress",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                        ))
                    yield _send_event(
                        openai_responses_types.
                        ResponseWebSearchCallSearchingEvent(
                            type="response.web_search_call.searching",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                        ))

                    # enqueue
                    yield _send_event(
                        openai_responses_types.
                        ResponseWebSearchCallCompletedEvent(
                            type="response.web_search_call.completed",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                        ))
                    yield _send_event(
                        openai_responses_types.ResponseOutputItemDoneEvent(
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=openai_responses_types.
                            ResponseFunctionWebSearch(
                                type="web_search_call",
                                id=current_item_id,
                                action=action,
                                status="completed",
                            ),
                        ))

                if (self.tool_server is not None
                        and self.tool_server.has_tool("python")
                        and previous_item.recipient is not None
                        and previous_item.recipient.startswith("python")):
                    yield _send_event(
                        openai_responses_types.
                        ResponseCodeInterpreterCallCodeDoneEvent(
                            type="response.code_interpreter_call_code.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            code=previous_item.content[0].text,
                        ))
                    yield _send_event(
                        openai_responses_types.
                        ResponseCodeInterpreterCallInterpretingEvent(
                            type="response.code_interpreter_call.interpreting",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                        ))
                    yield _send_event(
                        openai_responses_types.
                        ResponseCodeInterpreterCallCompletedEvent(
                            type="response.code_interpreter_call.completed",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                        ))
                    yield _send_event(
                        openai_responses_types.ResponseOutputItemDoneEvent(
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=openai_responses_types.
                            ResponseCodeInterpreterToolCallParam(
                                type="code_interpreter_call",
                                id=current_item_id,
                                code=previous_item.content[0].text,
                                container_id="auto",
                                # TODO: add outputs here
                                outputs=[],
                                status="completed",
                            ),
                        ))

    async def responses_stream_generator(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
        result_generator: AsyncIterator[Optional[ConversationContext]],
        context: ConversationContext,
        model_name: str,
        tokenizer: AnyTokenizer,
        request_metadata: RequestResponseMetadata,
        created_time: Optional[int] = None,
    ) -> AsyncGenerator[str, None]:
        # TODO:
        # 1. Handle disconnect

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

        sequence_number = 0

        def _send_event(event: BaseModel):
            nonlocal sequence_number
            # Set sequence_number if the event has this attribute
            if hasattr(event, 'sequence_number'):
                event.sequence_number = sequence_number
            sequence_number += 1
            # Get event type from the event's type field if it exists
            event_type = getattr(event, 'type', 'unknown')
            return (f"event: {event_type}\n"
                    f"data: {event.model_dump_json(indent=None)}\n\n")

        async with AsyncExitStack() as exit_stack:
            processer = None
            if self.use_harmony:
                await context.init_tool_sessions(self.tool_server, exit_stack,
                                                 request.request_id)
                processer = self._process_harmony_streaming_events
            else:
                processer = self._process_simple_streaming_events

            initial_response = ResponsesResponse.from_request(
                request,
                sampling_params,
                model_name=model_name,
                created_time=created_time,
                output=[],
                status="in_progress",
                usage=None,
            ).model_dump()
            yield _send_event(
                ResponseCreatedEvent(
                    type="response.created",
                    sequence_number=-1,
                    response=initial_response,
                ))
            yield _send_event(
                ResponseInProgressEvent(
                    type="response.in_progress",
                    sequence_number=-1,
                    response=initial_response,
                ))

            async for event_data in processer(request, sampling_params,
                                              result_generator, context,
                                              model_name, tokenizer,
                                              request_metadata, created_time,
                                              _send_event):
                yield event_data

            async def empty_async_generator():
                # A hack to trick Python to think this is a generator but
                # in fact it immediately returns.
                if False:
                    yield

            final_response = await self.responses_full_generator(
                request,
                sampling_params,
                empty_async_generator(),
                context,
                model_name,
                tokenizer,
                request_metadata,
                created_time=created_time,
            )
            yield _send_event(
                openai_responses_types.ResponseCompletedEvent(
                    type="response.completed",
                    sequence_number=-1,
                    response=final_response.model_dump(),
                ))