serving_responses.py 84.8 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import asyncio
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import json
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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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import jinja2
from fastapi import Request
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from openai.types.responses import (
    ResponseCodeInterpreterCallCodeDeltaEvent,
    ResponseCodeInterpreterCallCodeDoneEvent,
    ResponseCodeInterpreterCallCompletedEvent,
    ResponseCodeInterpreterCallInProgressEvent,
    ResponseCodeInterpreterCallInterpretingEvent,
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    ResponseCodeInterpreterToolCallParam,
    ResponseContentPartAddedEvent,
    ResponseContentPartDoneEvent,
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    ResponseFunctionCallArgumentsDeltaEvent,
    ResponseFunctionCallArgumentsDoneEvent,
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    ResponseFunctionToolCall,
    ResponseFunctionWebSearch,
    ResponseOutputItem,
    ResponseOutputItemAddedEvent,
    ResponseOutputItemDoneEvent,
    ResponseOutputMessage,
    ResponseOutputText,
    ResponseReasoningItem,
    ResponseReasoningTextDeltaEvent,
    ResponseReasoningTextDoneEvent,
    ResponseStatus,
    ResponseTextDeltaEvent,
    ResponseTextDoneEvent,
    ResponseWebSearchCallCompletedEvent,
    ResponseWebSearchCallInProgressEvent,
    ResponseWebSearchCallSearchingEvent,
    response_function_web_search,
    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 vllm import envs
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ChatTemplateContentFormatOption,
)
from vllm.entrypoints.context import (
    ConversationContext,
    HarmonyContext,
    SimpleContext,
    StreamingHarmonyContext,
)
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from vllm.entrypoints.harmony_utils import (
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    construct_harmony_previous_input_messages,
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    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,
)
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (
    DeltaMessage,
    ErrorResponse,
    InputTokensDetails,
    OutputTokensDetails,
    RequestResponseMetadata,
    ResponseCompletedEvent,
    ResponseCreatedEvent,
    ResponseInProgressEvent,
    ResponseReasoningPartAddedEvent,
    ResponseReasoningPartDoneEvent,
    ResponsesRequest,
    ResponsesResponse,
    ResponseUsage,
    StreamingResponsesResponse,
)
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from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.entrypoints.responses_utils import (
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    construct_input_messages,
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    convert_tool_responses_to_completions_format,
    extract_tool_types,
)
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from vllm.entrypoints.tool_server import ToolServer
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from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
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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.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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        log_error_stack: 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,
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            log_error_stack=log_error_stack,
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        )

        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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        self.reasoning_parser = self._get_reasoning_parser(
            reasoning_parser_name=reasoning_parser
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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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        if self.default_sampling_params:
            source = self.model_config.generation_config
            source = "model" if source == "auto" else source
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            logger.info(
                "Using default chat sampling params from %s: %s",
                source,
                self.default_sampling_params,
            )
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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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        self.enable_auto_tools = enable_auto_tools
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        # set up tool use
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        self.tool_parser = self._get_tool_parser(
            tool_parser_name=tool_parser, enable_auto_tools=enable_auto_tools
        )
        self.exclude_tools_when_tool_choice_none = False
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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, engine_prompt: EngineTokensPrompt
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    ) -> ErrorResponse | None:
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        """Add validations to the input to the generator here."""
        if self.max_model_len <= len(engine_prompt["prompt_token_ids"]):
            error_message = (
                "The engine prompt length"
                f" {len(engine_prompt['prompt_token_ids'])} "
                f"exceeds the max_model_len {self.max_model_len}. "
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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,
            )
        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,
            )
        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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        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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        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

        try:
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            lora_request = self._maybe_get_adapters(request)
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            model_name = self.models.model_name(lora_request)
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            tokenizer = await self.engine_client.get_tokenizer()
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            if self.use_harmony:
                messages, request_prompts, engine_prompts = (
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                    self._make_request_with_harmony(request, prev_response)
                )
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            else:
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                messages, request_prompts, engine_prompts = await self._make_request(
                    request, prev_response, tokenizer
                )
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        except (
            ValueError,
            TypeError,
            RuntimeError,
            jinja2.TemplateError,
            NotImplementedError,
        ) as e:
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            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(f"{e} {e.__cause__}")

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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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        generators: list[AsyncGenerator[ConversationContext, None]] = []
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        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")
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            if self.tool_server.has_tool("container"):
                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 = []
        try:
            for i, engine_prompt in enumerate(engine_prompts):
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                maybe_error = self._validate_generator_input(engine_prompt)
                if maybe_error is not None:
                    return maybe_error

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                default_max_tokens = self.max_model_len - len(
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                    engine_prompt["prompt_token_ids"]
                )
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                sampling_params = request.to_sampling_params(
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                    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:
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                        context = StreamingHarmonyContext(messages, available_tools)
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                    else:
                        context = HarmonyContext(messages, available_tools)
                else:
                    context = SimpleContext()
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                if self.reasoning_parser is not None:
                    reasoning_parser = self.reasoning_parser(tokenizer)
                    if sampling_params.structured_outputs is None:
                        sampling_params.structured_outputs = StructuredOutputsParams()
                    struct_out = sampling_params.structured_outputs
                    if struct_out.all_non_structural_tag_constraints_none():
                        sampling_params.structured_outputs.structural_tag = (
                            reasoning_parser.prepare_structured_tag(
                                sampling_params.structured_outputs.structural_tag,
                                self.tool_server,
                            )
                        )
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                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,
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                )
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                generators.append(generator)
        except ValueError as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))
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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,
            )

        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))
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    async def _make_request(
        self,
        request: ResponsesRequest,
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        prev_response: ResponsesResponse | None,
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        tokenizer: TokenizerLike,
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    ):
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        if request.tools is None or (
            request.tool_choice == "none" and self.exclude_tools_when_tool_choice_none
        ):
            tool_dicts = None
        else:
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            tool_dicts = [
                convert_tool_responses_to_completions_format(tool.model_dump())
                for tool in request.tools
            ]
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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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        _, request_prompts, engine_prompts = await self._preprocess_chat(
            request,
            tokenizer,
            messages,
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            tool_dicts=tool_dicts,
            tool_parser=self.tool_parser,
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            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,
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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"
            )
        messages = self._construct_input_messages_with_harmony(request, prev_response)
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        prompt_token_ids = render_for_completion(messages)
        engine_prompt = EngineTokensPrompt(prompt_token_ids=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, [prompt_token_ids], [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")
            except ValueError as e:
                # TODO: Use a vllm-specific Validation Error
                return self.create_error_response(str(e))
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        # NOTE: Implementation of stauts is still WIP, but for now
        # we guarantee that if the status is not "completed", it is accurate.
        # "completed" is implemented as the "catch-all" for now.
        status: ResponseStatus = "completed"

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        input_messages = None
        output_messages = 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"
            else:
                status = "incomplete"
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        else:
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            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]

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            output = self._make_response_output_items(request, final_output, tokenizer)
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            # TODO: context for non-gptoss models doesn't use messages
            # so we can't get them out yet
            if request.enable_response_messages:
                raise NotImplementedError(
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                    "enable_response_messages is currently only supported for gpt-oss"
                )
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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))
        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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        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 = (
                _logprob.decoded_token
                if _logprob.decoded_token is not None
                else tokenizer.decode([token_id])
            )
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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 = (
                token_logprob.decoded_token
                if token_logprob.decoded_token is not None
                else tokenizer.decode([token_id])
            )
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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]:
        if self.reasoning_parser:
            try:
                reasoning_parser = self.reasoning_parser(tokenizer)
            except RuntimeError as e:
                logger.exception("Error in reasoning parser creation.")
                raise e

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            reasoning, content = reasoning_parser.extract_reasoning(
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                final_output.text, request=request
            )
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        else:
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            reasoning = None
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            content = final_output.text

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

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        reasoning_item = None
        message_item = None
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        if reasoning:
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            reasoning_item = ResponseReasoningItem(
                id=f"rs_{random_uuid()}",
                summary=[],
                type="reasoning",
                content=[
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                    ResponseReasoningTextContent(text=reasoning, type="reasoning_text")
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                ],
                status=None,  # NOTE: Only the last output item has status.
            )
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        tool_calls, content = self._parse_tool_calls_from_content(
            request=request,
            tokenizer=tokenizer,
            content=content,
            enable_auto_tools=self.enable_auto_tools,
            tool_parser_cls=self.tool_parser,
        )
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        if content:
            output_text = ResponseOutputText(
                text=content,
                annotations=[],  # TODO
                type="output_text",
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                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
                ),
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            )
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            message_item = ResponseOutputMessage(
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                id=f"msg_{random_uuid()}",
                content=[output_text],
                role="assistant",
                status="completed",
                type="message",
            )
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        outputs = []

        if reasoning_item:
            outputs.append(reasoning_item)
        if message_item:
            outputs.append(message_item)
        if tool_calls:
            tool_call_items = [
                ResponseFunctionToolCall(
                    id=f"fc_{random_uuid()}",
                    call_id=f"call_{random_uuid()}",
                    type="function_call",
                    status="completed",
                    name=tool_call.name,
                    arguments=tool_call.arguments,
                )
                for tool_call in tool_calls
            ]
            outputs.extend(tool_call_items)
        return outputs
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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:]:
            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

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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:
        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(
            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]
            # 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
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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)
                        if msg.channel != "analysis":
                            prev_msgs.append(msg)
            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):
            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:
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                messages.append(parse_response_input(response_msg, prev_outputs))
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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 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

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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)
        response = None
        try:
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            generator = self.responses_stream_generator(request, *args, **kwargs)
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            async for event in generator:
                event_deque.append(event)
                new_event_signal.set()  # Signal new event available
        except Exception as e:
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            logger.exception("Background request failed for %s", request.request_id)
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            response = self.create_error_response(str(e))
        finally:
            new_event_signal.set()

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

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

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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]:
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        if response_id not in self.event_store:
            raise ValueError(f"Unknown response_id: {response_id}")

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

        while True:
            new_event_signal.clear()

            # Yield existing events from start_index
            while current_index < len(event_deque):
                event = event_deque[current_index]
                yield event
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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.",
                )

            # 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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    def _make_store_not_supported_error(self) -> ErrorResponse:
        return self.create_error_response(
            err_type="invalid_request_error",
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            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."
            ),
1175
1176
            status_code=HTTPStatus.BAD_REQUEST,
        )
1177

1178
    async def _process_simple_streaming_events(
1179
1180
1181
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
1182
        result_generator: AsyncIterator[ConversationContext | None],
1183
1184
        context: ConversationContext,
        model_name: str,
1185
        tokenizer: TokenizerLike,
1186
        request_metadata: RequestResponseMetadata,
1187
        created_time: int,
1188
        _increment_sequence_number_and_return: Callable[
1189
1190
            [StreamingResponsesResponse], StreamingResponsesResponse
        ],
1191
    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
        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:
1209
1210
1211
1212
1213
1214
1215
                    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,
1216
1217
                    )
                else:
1218
1219
1220
                    delta_message = DeltaMessage(
                        content=output.text,
                    )
1221
1222
1223
1224
1225
1226
                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())
1227
                    if delta_message.reasoning:
1228
                        yield _increment_sequence_number_and_return(
1229
1230
1231
1232
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
1233
                                item=ResponseReasoningItem(
1234
1235
1236
1237
1238
                                    type="reasoning",
                                    id=current_item_id,
                                    summary=[],
                                    status="in_progress",
                                ),
1239
1240
                            )
                        )
1241
                    else:
1242
                        yield _increment_sequence_number_and_return(
1243
1244
1245
1246
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
1247
                                item=ResponseOutputMessage(
1248
1249
1250
1251
1252
1253
                                    id=current_item_id,
                                    type="message",
                                    role="assistant",
                                    content=[],
                                    status="in_progress",
                                ),
1254
1255
                            )
                        )
1256
                    yield _increment_sequence_number_and_return(
1257
                        ResponseContentPartAddedEvent(
1258
1259
1260
1261
1262
                            type="response.content_part.added",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            content_index=current_content_index,
1263
                            part=ResponseOutputText(
1264
1265
1266
1267
1268
                                type="output_text",
                                text="",
                                annotations=[],
                                logprobs=[],
                            ),
1269
1270
                        )
                    )
1271
1272
1273
1274
1275
1276
                    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
1277
1278
                if (
                    previous_delta_messages
1279
                    and previous_delta_messages[-1].reasoning is not None
1280
1281
                    and delta_message.content is not None
                ):
1282
1283
                    # from reasoning to normal content, send done
                    # event for reasoning
1284
                    reason_content = "".join(
1285
                        pm.reasoning
1286
                        for pm in previous_delta_messages
1287
                        if pm.reasoning is not None
1288
                    )
1289
                    yield _increment_sequence_number_and_return(
1290
1291
1292
1293
1294
1295
1296
                        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,
1297
1298
                        )
                    )
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
                    current_content_index = 0
                    reasoning_item = ResponseReasoningItem(
                        type="reasoning",
                        content=[
                            ResponseReasoningTextContent(
                                text=reason_content,
                                type="reasoning_text",
                            ),
                        ],
                        status="completed",
                        id=current_item_id,
                        summary=[],
                    )
1312
                    yield _increment_sequence_number_and_return(
1313
1314
1315
1316
1317
                        ResponseOutputItemDoneEvent(
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=reasoning_item,
1318
1319
                        )
                    )
1320
                    yield _increment_sequence_number_and_return(
1321
                        ResponseOutputItemAddedEvent(
1322
1323
1324
                            type="response.output_item.added",
                            sequence_number=-1,
                            output_index=current_output_index,
1325
                            item=ResponseOutputMessage(
1326
1327
1328
1329
1330
1331
                                id=current_item_id,
                                type="message",
                                role="assistant",
                                content=[],
                                status="in_progress",
                            ),
1332
1333
                        )
                    )
1334
1335
                    current_output_index += 1
                    current_item_id = str(uuid.uuid4())
1336
                    yield _increment_sequence_number_and_return(
1337
                        ResponseContentPartAddedEvent(
1338
1339
1340
1341
1342
                            type="response.content_part.added",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            content_index=current_content_index,
1343
                            part=ResponseOutputText(
1344
1345
1346
1347
1348
                                type="output_text",
                                text="",
                                annotations=[],
                                logprobs=[],
                            ),
1349
1350
                        )
                    )
1351
1352
1353
                    current_content_index += 1
                    # reset previous delta messages
                    previous_delta_messages = []
1354

1355
                if delta_message.reasoning is not None:
1356
                    yield _increment_sequence_number_and_return(
1357
1358
1359
1360
1361
1362
                        ResponseReasoningTextDeltaEvent(
                            type="response.reasoning_text.delta",
                            sequence_number=-1,
                            content_index=current_content_index,
                            output_index=current_output_index,
                            item_id=current_item_id,
1363
                            delta=delta_message.reasoning,
1364
1365
                        )
                    )
1366
                elif delta_message.content is not None:
1367
                    yield _increment_sequence_number_and_return(
1368
                        ResponseTextDeltaEvent(
1369
1370
1371
1372
1373
1374
                            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,
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
                            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 []
                            ),
1385
1386
                        )
                    )
1387
1388
1389
1390
                current_content_index += 1

                previous_delta_messages.append(delta_message)
        if previous_delta_messages:
1391
            if previous_delta_messages[-1].reasoning is not None:
1392
                reason_content = "".join(
1393
                    pm.reasoning
1394
                    for pm in previous_delta_messages
1395
                    if pm.reasoning is not None
1396
                )
1397
                yield _increment_sequence_number_and_return(
1398
1399
1400
1401
1402
1403
1404
                    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,
1405
1406
                    )
                )
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
                current_content_index += 1
                reasoning_item = ResponseReasoningItem(
                    type="reasoning",
                    content=[
                        ResponseReasoningTextContent(
                            text=reason_content,
                            type="reasoning_text",
                        ),
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
1420
                yield _increment_sequence_number_and_return(
1421
1422
1423
1424
1425
                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=reasoning_item,
1426
1427
                    )
                )
1428
            elif previous_delta_messages[-1].content is not None:
1429
1430
1431
1432
1433
                final_content = "".join(
                    pm.content
                    for pm in previous_delta_messages
                    if pm.content is not None
                )
1434
                yield _increment_sequence_number_and_return(
1435
                    ResponseTextDoneEvent(
1436
1437
1438
1439
1440
1441
1442
                        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,
1443
1444
                    )
                )
1445
1446
1447
1448
1449
1450
                current_content_index += 1
                part = ResponseOutputText(
                    text=final_content,
                    type="output_text",
                    annotations=[],
                )
1451
                yield _increment_sequence_number_and_return(
1452
                    ResponseContentPartDoneEvent(
1453
1454
1455
1456
1457
1458
                        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,
1459
1460
                    )
                )
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
                current_content_index += 1
                item = ResponseOutputMessage(
                    type="message",
                    role="assistant",
                    content=[
                        part,
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
1472
                yield _increment_sequence_number_and_return(
1473
1474
1475
1476
1477
                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=item,
1478
1479
                    )
                )
1480
1481
1482
1483
1484

    async def _process_harmony_streaming_events(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
1485
        result_generator: AsyncIterator[ConversationContext | None],
1486
1487
        context: ConversationContext,
        model_name: str,
1488
        tokenizer: TokenizerLike,
1489
1490
        request_metadata: RequestResponseMetadata,
        created_time: int,
1491
        _increment_sequence_number_and_return: Callable[
1492
1493
            [StreamingResponsesResponse], StreamingResponsesResponse
        ],
1494
    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1495
        current_content_index = -1
1496
        current_output_index = 0
1497
        current_item_id: str = ""
1498
        sent_output_item_added = False
1499
        is_first_function_call_delta = False
1500
1501
1502
1503
1504
1505
        async for ctx in result_generator:
            assert isinstance(ctx, StreamingHarmonyContext)

            if ctx.is_expecting_start():
                current_output_index += 1
                sent_output_item_added = False
1506
                is_first_function_call_delta = False
1507
1508
1509
                if len(ctx.parser.messages) > 0:
                    previous_item = ctx.parser.messages[-1]
                    if previous_item.recipient is not None:
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
                        # Deal with tool call
                        if previous_item.recipient.startswith("functions."):
                            function_name = previous_item.recipient[len("functions.") :]
                            yield _increment_sequence_number_and_return(
                                ResponseFunctionCallArgumentsDoneEvent(
                                    type="response.function_call_arguments.done",
                                    arguments=previous_item.content[0].text,
                                    name=function_name,
                                    item_id=current_item_id,
                                    output_index=current_output_index,
                                    sequence_number=-1,
                                )
                            )
                            function_call_item = ResponseFunctionToolCall(
                                type="function_call",
                                arguments=previous_item.content[0].text,
                                name=function_name,
                                item_id=current_item_id,
                                output_index=current_output_index,
                                sequence_number=-1,
                                call_id=f"fc_{random_uuid()}",
                                status="completed",
                            )
                            yield _increment_sequence_number_and_return(
                                ResponseOutputItemDoneEvent(
                                    type="response.output_item.done",
                                    sequence_number=-1,
                                    output_index=current_output_index,
                                    item=function_call_item,
                                )
                            )
1541
                    elif previous_item.channel == "analysis":
1542
1543
1544
1545
                        content = ResponseReasoningTextContent(
                            text=previous_item.content[0].text,
                            type="reasoning_text",
                        )
1546
1547
                        reasoning_item = ResponseReasoningItem(
                            type="reasoning",
1548
                            content=[content],
1549
                            status="completed",
1550
1551
                            id=current_item_id,
                            summary=[],
1552
                        )
1553
                        yield _increment_sequence_number_and_return(
1554
1555
1556
1557
1558
1559
1560
                            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,
1561
1562
                            )
                        )
1563
1564
1565
1566
1567
1568
1569
1570
                        yield _increment_sequence_number_and_return(
                            ResponseReasoningPartDoneEvent(
                                type="response.reasoning_part.done",
                                sequence_number=-1,
                                item_id=current_item_id,
                                output_index=current_output_index,
                                content_index=current_content_index,
                                part=content,
1571
1572
                            )
                        )
1573
                        yield _increment_sequence_number_and_return(
1574
1575
1576
1577
1578
                            ResponseOutputItemDoneEvent(
                                type="response.output_item.done",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item=reasoning_item,
1579
1580
                            )
                        )
1581
1582
1583
1584
1585
1586
                    elif previous_item.channel == "final":
                        text_content = ResponseOutputText(
                            type="output_text",
                            text=previous_item.content[0].text,
                            annotations=[],
                        )
1587
                        yield _increment_sequence_number_and_return(
1588
                            ResponseTextDoneEvent(
1589
1590
1591
1592
1593
1594
1595
                                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,
1596
1597
                            )
                        )
1598
                        yield _increment_sequence_number_and_return(
1599
1600
1601
1602
1603
1604
1605
                            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,
1606
1607
                            )
                        )
1608
                        yield _increment_sequence_number_and_return(
1609
                            ResponseOutputItemDoneEvent(
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
                                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",
                                ),
1620
1621
                            )
                        )
1622

1623
            # stream the output of a harmony message
1624
            if ctx.parser.last_content_delta:
1625
1626
1627
1628
                if (
                    ctx.parser.current_channel == "final"
                    and ctx.parser.current_recipient is None
                ):
1629
1630
                    if not sent_output_item_added:
                        sent_output_item_added = True
1631
                        current_item_id = f"msg_{random_uuid()}"
1632
                        yield _increment_sequence_number_and_return(
1633
1634
1635
1636
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
1637
                                item=ResponseOutputMessage(
1638
1639
1640
1641
1642
1643
                                    id=current_item_id,
                                    type="message",
                                    role="assistant",
                                    content=[],
                                    status="in_progress",
                                ),
1644
1645
                            )
                        )
1646
                        current_content_index += 1
1647
                        yield _increment_sequence_number_and_return(
1648
1649
1650
1651
1652
1653
                            ResponseContentPartAddedEvent(
                                type="response.content_part.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item_id=current_item_id,
                                content_index=current_content_index,
1654
                                part=ResponseOutputText(
1655
1656
1657
1658
1659
                                    type="output_text",
                                    text="",
                                    annotations=[],
                                    logprobs=[],
                                ),
1660
1661
                            )
                        )
1662
                    yield _increment_sequence_number_and_return(
1663
                        ResponseTextDeltaEvent(
1664
1665
1666
1667
1668
1669
1670
1671
                            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=[],
1672
1673
1674
1675
1676
1677
                        )
                    )
                elif (
                    ctx.parser.current_channel == "analysis"
                    and ctx.parser.current_recipient is None
                ):
1678
1679
                    if not sent_output_item_added:
                        sent_output_item_added = True
1680
                        current_item_id = f"msg_{random_uuid()}"
1681
                        yield _increment_sequence_number_and_return(
1682
1683
1684
1685
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
1686
                                item=ResponseReasoningItem(
1687
1688
1689
1690
1691
                                    type="reasoning",
                                    id=current_item_id,
                                    summary=[],
                                    status="in_progress",
                                ),
1692
1693
                            )
                        )
1694
                        current_content_index += 1
1695
                        yield _increment_sequence_number_and_return(
1696
1697
                            ResponseReasoningPartAddedEvent(
                                type="response.reasoning_part.added",
1698
1699
1700
1701
                                sequence_number=-1,
                                output_index=current_output_index,
                                item_id=current_item_id,
                                content_index=current_content_index,
1702
                                part=ResponseReasoningTextContent(
1703
                                    text="",
1704
                                    type="reasoning_text",
1705
                                ),
1706
1707
                            )
                        )
1708
                    yield _increment_sequence_number_and_return(
1709
1710
1711
1712
1713
1714
1715
                        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,
1716
1717
                        )
                    )
1718
1719
1720
                # built-in tools will be triggered on the analysis channel
                # However, occasionally built-in tools will
                # still be output to commentary.
1721
1722
1723
1724
                elif (
                    ctx.parser.current_channel == "commentary"
                    or ctx.parser.current_channel == "analysis"
                ) and ctx.parser.current_recipient == "python":
1725
1726
                    if not sent_output_item_added:
                        sent_output_item_added = True
1727
                        current_item_id = f"tool_{random_uuid()}"
1728
                        yield _increment_sequence_number_and_return(
1729
1730
1731
1732
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
1733
                                item=ResponseCodeInterpreterToolCallParam(
1734
1735
1736
1737
1738
1739
1740
                                    type="code_interpreter_call",
                                    id=current_item_id,
                                    code=None,
                                    container_id="auto",
                                    outputs=None,
                                    status="in_progress",
                                ),
1741
1742
                            )
                        )
1743
                        yield _increment_sequence_number_and_return(
1744
                            ResponseCodeInterpreterCallInProgressEvent(
1745
                                type="response.code_interpreter_call.in_progress",
1746
1747
1748
                                sequence_number=-1,
                                output_index=current_output_index,
                                item_id=current_item_id,
1749
1750
                            )
                        )
1751
                    yield _increment_sequence_number_and_return(
1752
1753
1754
1755
1756
1757
                        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,
1758
1759
                        )
                    )
1760
1761

            # stream tool call outputs
1762
1763
            if ctx.is_assistant_action_turn() and len(ctx.parser.messages) > 0:
                previous_item = ctx.parser.messages[-1]
1764
1765
1766
1767
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                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.") :]
1771
1772
1773
                    action = None
                    parsed_args = json.loads(previous_item.content[0].text)
                    if function_name == "search":
1774
                        action = response_function_web_search.ActionSearch(
1775
1776
                            type="search",
                            query=parsed_args["query"],
1777
                        )
1778
                    elif function_name == "open":
1779
1780
1781
1782
1783
                        action = response_function_web_search.ActionOpenPage(
                            type="open_page",
                            # TODO: translate to url
                            url=f"cursor:{parsed_args.get('cursor', '')}",
                        )
1784
                    elif function_name == "find":
1785
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                        action = response_function_web_search.ActionFind(
                            type="find",
                            pattern=parsed_args["pattern"],
                            # TODO: translate to url
                            url=f"cursor:{parsed_args.get('cursor', '')}",
                        )
1791
                    else:
1792
                        raise ValueError(f"Unknown function name: {function_name}")
1793

1794
                    current_item_id = f"tool_{random_uuid()}"
1795
                    yield _increment_sequence_number_and_return(
1796
                        ResponseOutputItemAddedEvent(
1797
1798
1799
                            type="response.output_item.added",
                            sequence_number=-1,
                            output_index=current_output_index,
1800
                            item=response_function_web_search.ResponseFunctionWebSearch(
1801
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                                # TODO: generate a unique id for web search call
                                type="web_search_call",
                                id=current_item_id,
                                action=action,
                                status="in_progress",
                            ),
1807
1808
                        )
                    )
1809
                    yield _increment_sequence_number_and_return(
1810
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1812
1813
1814
                        ResponseWebSearchCallInProgressEvent(
                            type="response.web_search_call.in_progress",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
1815
1816
                        )
                    )
1817
                    yield _increment_sequence_number_and_return(
1818
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1820
1821
1822
                        ResponseWebSearchCallSearchingEvent(
                            type="response.web_search_call.searching",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
1823
1824
                        )
                    )
1825
1826

                    # enqueue
1827
                    yield _increment_sequence_number_and_return(
1828
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1830
1831
1832
                        ResponseWebSearchCallCompletedEvent(
                            type="response.web_search_call.completed",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
1833
1834
                        )
                    )
1835
                    yield _increment_sequence_number_and_return(
1836
                        ResponseOutputItemDoneEvent(
1837
1838
1839
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
1840
                            item=ResponseFunctionWebSearch(
1841
1842
1843
1844
1845
                                type="web_search_call",
                                id=current_item_id,
                                action=action,
                                status="completed",
                            ),
1846
1847
                        )
                    )
1848

1849
1850
1851
1852
1853
1854
                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")
                ):
1855
                    yield _increment_sequence_number_and_return(
1856
1857
1858
1859
1860
                        ResponseCodeInterpreterCallCodeDoneEvent(
                            type="response.code_interpreter_call_code.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
1861
                            code=previous_item.content[0].text,
1862
1863
                        )
                    )
1864
                    yield _increment_sequence_number_and_return(
1865
1866
1867
1868
1869
                        ResponseCodeInterpreterCallInterpretingEvent(
                            type="response.code_interpreter_call.interpreting",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
1870
1871
                        )
                    )
1872
                    yield _increment_sequence_number_and_return(
1873
1874
1875
1876
1877
                        ResponseCodeInterpreterCallCompletedEvent(
                            type="response.code_interpreter_call.completed",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
1878
1879
                        )
                    )
1880
                    yield _increment_sequence_number_and_return(
1881
                        ResponseOutputItemDoneEvent(
1882
1883
1884
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
1885
                            item=ResponseCodeInterpreterToolCallParam(
1886
1887
1888
1889
1890
1891
1892
1893
                                type="code_interpreter_call",
                                id=current_item_id,
                                code=previous_item.content[0].text,
                                container_id="auto",
                                # TODO: add outputs here
                                outputs=[],
                                status="completed",
                            ),
1894
1895
                        )
                    )
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
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1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
            # developer tools will be triggered on the commentary channel
            # and recipient starts with "functions.TOOL_NAME"
            if (
                ctx.parser.current_channel == "commentary"
                and ctx.parser.current_recipient
                and ctx.parser.current_recipient.startswith("functions.")
            ):
                if is_first_function_call_delta is False:
                    is_first_function_call_delta = True
                    fc_name = ctx.parser.current_recipient[len("functions.") :]
                    tool_call_item = ResponseFunctionToolCall(
                        name=fc_name,
                        type="function_call",
                        id=current_item_id,
                        call_id=f"call_{random_uuid()}",
                        arguments="",
                        status="in_progress",
                    )
                    current_item_id = f"fc_{random_uuid()}"
                    yield _increment_sequence_number_and_return(
                        ResponseOutputItemAddedEvent(
                            type="response.output_item.added",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=tool_call_item,
                        )
                    )
                else:
                    yield _increment_sequence_number_and_return(
                        ResponseFunctionCallArgumentsDeltaEvent(
                            item_id=current_item_id,
                            delta=ctx.parser.last_content_delta,
                            output_index=current_output_index,
                            sequence_number=-1,
                            type="response.function_call_arguments.delta",
                        )
                    )
1933

1934
1935
1936
1937
    async def responses_stream_generator(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
1938
        result_generator: AsyncIterator[ConversationContext | None],
1939
1940
        context: ConversationContext,
        model_name: str,
1941
        tokenizer: TokenizerLike,
1942
        request_metadata: RequestResponseMetadata,
1943
        created_time: int | None = None,
1944
    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1945
1946
1947
1948
1949
        # TODO:
        # 1. Handle disconnect

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

1950
1951
        sequence_number = 0

1952
        def _increment_sequence_number_and_return(
1953
            event: StreamingResponsesResponse,
1954
        ) -> StreamingResponsesResponse:
1955
1956
            nonlocal sequence_number
            # Set sequence_number if the event has this attribute
1957
            if hasattr(event, "sequence_number"):
1958
1959
                event.sequence_number = sequence_number
            sequence_number += 1
1960
            return event
1961

1962
        async with AsyncExitStack() as exit_stack:
1963
1964
            processer = None
            if self.use_harmony:
1965
1966
                # TODO: in streaming, we noticed this bug:
                # https://github.com/vllm-project/vllm/issues/25697
1967
                await self._initialize_tool_sessions(request, context, exit_stack)
1968
1969
1970
                processer = self._process_harmony_streaming_events
            else:
                processer = self._process_simple_streaming_events
1971
            # TODO Hanchen make sampling params to include the structural tag
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981

            initial_response = ResponsesResponse.from_request(
                request,
                sampling_params,
                model_name=model_name,
                created_time=created_time,
                output=[],
                status="in_progress",
                usage=None,
            ).model_dump()
1982
            yield _increment_sequence_number_and_return(
1983
1984
1985
1986
                ResponseCreatedEvent(
                    type="response.created",
                    sequence_number=-1,
                    response=initial_response,
1987
1988
                )
            )
1989
            yield _increment_sequence_number_and_return(
1990
1991
1992
1993
                ResponseInProgressEvent(
                    type="response.in_progress",
                    sequence_number=-1,
                    response=initial_response,
1994
1995
                )
            )
1996

1997
            async for event_data in processer(
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
                request,
                sampling_params,
                result_generator,
                context,
                model_name,
                tokenizer,
                request_metadata,
                created_time,
                _increment_sequence_number_and_return,
            ):
2008
                yield event_data
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025

            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,
            )
2026
            yield _increment_sequence_number_and_return(
2027
                ResponseCompletedEvent(
2028
2029
                    type="response.completed",
                    sequence_number=-1,
2030
                    response=final_response,
2031
2032
                )
            )