serving.py

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

import asyncio
import json
import time
from collections.abc import AsyncGenerator, AsyncIterator
from collections.abc import Sequence as GenericSequence
from typing import Any, Final

import jinja2
import partial_json_parser
import regex as re
from fastapi import Request
from openai_harmony import Message as OpenAIMessage
from partial_json_parser.core.options import Allow

from vllm.engine.protocol import EngineClient
from vllm.entrypoints.chat_utils import (
    ChatTemplateContentFormatOption,
    ConversationMessage,
    get_history_tool_calls_cnt,
    make_tool_call_id,
)
from vllm.entrypoints.logger import RequestLogger
from vllm.entrypoints.openai.chat_completion.protocol import (
    ChatCompletionLogProb,
    ChatCompletionLogProbs,
    ChatCompletionLogProbsContent,
    ChatCompletionNamedToolChoiceParam,
    ChatCompletionRequest,
    ChatCompletionResponse,
    ChatCompletionResponseChoice,
    ChatCompletionResponseStreamChoice,
    ChatCompletionStreamResponse,
    ChatMessage,
)
from vllm.entrypoints.openai.chat_completion.stream_harmony import (
    TokenState,
    extract_harmony_streaming_delta,
)
from vllm.entrypoints.openai.engine.protocol import (
    DeltaFunctionCall,
    DeltaMessage,
    DeltaToolCall,
    ErrorResponse,
    FunctionCall,
    PromptTokenUsageInfo,
    RequestResponseMetadata,
    ToolCall,
    UsageInfo,
)
from vllm.entrypoints.openai.engine.serving import (
    GenerationError,
    OpenAIServing,
    clamp_prompt_logprobs,
)
from vllm.entrypoints.openai.models.serving import OpenAIServingModels
from vllm.entrypoints.openai.parser.harmony_utils import (
    get_developer_message,
    get_stop_tokens_for_assistant_actions,
    get_streamable_parser_for_assistant,
    get_system_message,
    parse_chat_inputs_to_harmony_messages,
    parse_chat_output,
    render_for_completion,
)
from vllm.entrypoints.openai.utils import maybe_filter_parallel_tool_calls
from vllm.entrypoints.utils import get_max_tokens, should_include_usage
from vllm.inputs.data import ProcessorInputs, TokensPrompt
from vllm.logger import init_logger
from vllm.logprobs import Logprob
from vllm.outputs import CompletionOutput, RequestOutput
from vllm.parser import ParserManager
from vllm.reasoning import ReasoningParser
from vllm.sampling_params import BeamSearchParams, SamplingParams
from vllm.tokenizers import TokenizerLike
from vllm.tokenizers.mistral import (
    MistralTokenizer,
    maybe_serialize_tool_calls,
    truncate_tool_call_ids,
    validate_request_params,
)
from vllm.tool_parsers import ToolParser
from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
from vllm.tool_parsers.utils import partial_json_loads
from vllm.utils.collection_utils import as_list

logger = init_logger(__name__)


class OpenAIServingChat(OpenAIServing):
    def __init__(
        self,
        engine_client: EngineClient,
        models: OpenAIServingModels,
        response_role: str,
        *,
        request_logger: RequestLogger | None,
        chat_template: str | None,
        chat_template_content_format: ChatTemplateContentFormatOption,
        trust_request_chat_template: bool = False,
        return_tokens_as_token_ids: bool = False,
        reasoning_parser: str = "",
        enable_auto_tools: bool = False,
        exclude_tools_when_tool_choice_none: bool = False,
        tool_parser: str | None = None,
        enable_prompt_tokens_details: bool = False,
        enable_force_include_usage: bool = False,
        enable_log_outputs: bool = False,
        enable_log_deltas: bool = True,
        log_error_stack: bool = False,
        default_chat_template_kwargs: dict[str, Any] | None = None,
    ) -> None:
        super().__init__(
            engine_client=engine_client,
            models=models,
            request_logger=request_logger,
            return_tokens_as_token_ids=return_tokens_as_token_ids,
            log_error_stack=log_error_stack,
        )

        self.response_role = response_role
        self.chat_template = chat_template
        self.chat_template_content_format: Final = chat_template_content_format
        self.trust_request_chat_template = trust_request_chat_template
        self.default_chat_template_kwargs = default_chat_template_kwargs or {}
        self.enable_log_outputs = enable_log_outputs
        self.enable_log_deltas = enable_log_deltas

        # set up reasoning parser
        self.reasoning_parser_cls = ParserManager.get_reasoning_parser(
            reasoning_parser_name=reasoning_parser
        )
        # set up tool use
        self.enable_auto_tools: bool = enable_auto_tools
        self.tool_parser = ParserManager.get_tool_parser(
            tool_parser_name=tool_parser,
            enable_auto_tools=enable_auto_tools,
            model_name=self.model_config.model,
        )
        self.exclude_tools_when_tool_choice_none = exclude_tools_when_tool_choice_none

        self.enable_prompt_tokens_details = enable_prompt_tokens_details
        self.enable_force_include_usage = enable_force_include_usage
        self.default_sampling_params = self.model_config.get_diff_sampling_param()
        mc = self.model_config
        self.override_max_tokens = (
            self.default_sampling_params.get("max_tokens")
            if mc.generation_config not in ("auto", "vllm")
            else getattr(mc, "override_generation_config", {}).get("max_new_tokens")
        )
        self.use_harmony = self.model_config.hf_config.model_type == "gpt_oss"
        if self.use_harmony:
            if "stop_token_ids" not in self.default_sampling_params:
                self.default_sampling_params["stop_token_ids"] = []
            self.default_sampling_params["stop_token_ids"].extend(
                get_stop_tokens_for_assistant_actions()
            )

        # Handle tool call ID type for Kimi K2 (supporting test mocking via overrides)
        hf_overrides = getattr(self.model_config, "hf_overrides", None)
        if self.model_config.hf_text_config.model_type == "kimi_k2" or (
            isinstance(hf_overrides, dict)
            and hf_overrides.get("model_type") == "kimi_k2"
        ):
            self.tool_call_id_type = "kimi_k2"
        else:
            self.tool_call_id_type = "random"

        # NOTE(woosuk): While OpenAI's chat completion API supports browsing
        # for some models, currently vLLM doesn't support it. Please use the
        # Responses API instead.
        self.supports_browsing = False
        self.browser_tool = None
        # NOTE(woosuk): Chat completion API does not support code interpreter.
        # Please use the Responses API instead.
        self.supports_code_interpreter = False
        self.python_tool = None

    async def warmup(self) -> None:
        """
        Warm up the chat template processing to avoid first-request latency.

        This method triggers Jinja2 template compilation and content format
        detection that would otherwise happen on the first real request,
        causing increased latency on the first request.
        """
        logger.info("Warming up chat template processing...")
        start_time = time.perf_counter()

        try:
            # Create a minimal dummy request
            dummy_request = ChatCompletionRequest(
                messages=[{"role": "user", "content": "warmup"}],
                model=None,
                max_completion_tokens=1,
            )

            # Call _preprocess_chat to trigger template compilation
            # This forces:
            # 1. Chat template content format detection
            # 2. Jinja2 template compilation
            # 3. Tokenizer initialization for chat
            await self._preprocess_chat(
                dummy_request,
                dummy_request.messages,
                default_template=self.chat_template,
                default_template_content_format=self.chat_template_content_format,
                default_template_kwargs=self.default_chat_template_kwargs,
            )

            elapsed = (time.perf_counter() - start_time) * 1000
            logger.info("Chat template warmup completed in %.1fms", elapsed)

        except Exception:
            # Log but don't fail server startup if warmup fails
            logger.exception("Chat template warmup failed")

    async def render_chat_request(
        self,
        request: ChatCompletionRequest,
    ) -> tuple[list[ConversationMessage], list[ProcessorInputs]] | ErrorResponse:
        """
        render chat request by validating and preprocessing inputs.

        Returns:
            A tuple of (conversation, engine_prompts) on success,
            or an ErrorResponse on failure.
        """
        error_check_ret = await self._check_model(request)
        if error_check_ret is not None:
            logger.error("Error with model %s", error_check_ret)
            return error_check_ret

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

        try:
            tokenizer = self.renderer.tokenizer

            tool_parser = self.tool_parser

            if isinstance(tokenizer, MistralTokenizer):
                # because of issues with pydantic we need to potentially
                # re-serialize the tool_calls field of the request
                # for more info: see comment in `maybe_serialize_tool_calls`
                maybe_serialize_tool_calls(request)  # type: ignore[arg-type]
                truncate_tool_call_ids(request)  # type: ignore[arg-type]
                validate_request_params(request)

            # Check if tool parsing is unavailable (common condition)
            tool_parsing_unavailable = (
                tool_parser is None
                and not isinstance(tokenizer, MistralTokenizer)
                and not self.use_harmony
            )

            # Validate tool_choice when tool parsing is required but unavailable
            if tool_parsing_unavailable and request.tool_choice not in (
                None,
                "none",
            ):
                if request.tool_choice == "auto" and not self.enable_auto_tools:
                    # for hf tokenizers, "auto" tools requires
                    # --enable-auto-tool-choice and --tool-call-parser
                    return self.create_error_response(
                        '"auto" tool choice requires '
                        "--enable-auto-tool-choice and --tool-call-parser to be set"
                    )
                elif request.tool_choice != "auto":
                    # "required" or named tool requires tool parser
                    return self.create_error_response(
                        f'tool_choice="{request.tool_choice}" requires '
                        "--tool-call-parser to be set"
                    )

            if request.tools is None or (
                request.tool_choice == "none"
                and self.exclude_tools_when_tool_choice_none
            ):
                tool_dicts = None
            else:
                tool_dicts = [tool.model_dump() for tool in request.tools]

            if not self.use_harmony:
                # Common case.
                error_check_ret = self._validate_chat_template(
                    request_chat_template=request.chat_template,
                    chat_template_kwargs=request.chat_template_kwargs,
                    trust_request_chat_template=self.trust_request_chat_template,
                )
                if error_check_ret is not None:
                    return error_check_ret

                conversation, engine_prompts = await self._preprocess_chat(
                    request,
                    request.messages,
                    default_template=self.chat_template,
                    default_template_content_format=self.chat_template_content_format,
                    default_template_kwargs=self.default_chat_template_kwargs,
                    tool_dicts=tool_dicts,
                    tool_parser=tool_parser,
                )
            else:
                # For GPT-OSS.
                should_include_tools = tool_dicts is not None
                conversation, engine_prompts = self._make_request_with_harmony(
                    request, should_include_tools
                )
        except (ValueError, TypeError, RuntimeError, jinja2.TemplateError) as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(e)

        return conversation, engine_prompts

    async def create_chat_completion(
        self,
        request: ChatCompletionRequest,
        raw_request: Request | None = None,
    ) -> AsyncGenerator[str, None] | ChatCompletionResponse | ErrorResponse:
        """
        Chat Completion API similar to OpenAI's API.

        See https://platform.openai.com/docs/api-reference/chat/create
        for the API specification. This API mimics the OpenAI
        Chat Completion API.
        """
        # Streaming response
        tokenizer = self.renderer.tokenizer
        assert tokenizer is not None
        reasoning_parser: ReasoningParser | None = None
        try:
            if self.reasoning_parser_cls:
                # Pass the same chat template kwargs as used in tokenization
                chat_template_kwargs = self._prepare_extra_chat_template_kwargs(
                    request.chat_template_kwargs,
                    self.default_chat_template_kwargs,
                )
                reasoning_parser = self.reasoning_parser_cls(
                    tokenizer,
                    chat_template_kwargs=chat_template_kwargs,  # type: ignore[call-arg]
                )
        except RuntimeError as e:
            logger.exception("Error in reasoning parser creation.")
            return self.create_error_response(str(e))
        result = await self.render_chat_request(request)
        if isinstance(result, ErrorResponse):
            return result

        conversation, engine_prompts = result

        request_id = (
            f"chatcmpl-{self._base_request_id(raw_request, request.request_id)}"
        )

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

        try:
            lora_request = self._maybe_get_adapters(
                request, supports_default_mm_loras=True
            )

            model_name = self.models.model_name(lora_request)
        except (ValueError, TypeError, RuntimeError) as e:
            logger.exception("Error preparing request components")
            return self.create_error_response(e)

        # Extract data_parallel_rank from header (router can inject it)
        data_parallel_rank = self._get_data_parallel_rank(raw_request)

        # Schedule the request and get the result generator.
        max_model_len = self.model_config.max_model_len
        generators: list[AsyncGenerator[RequestOutput, None]] = []
        try:
            for i, engine_prompt in enumerate(engine_prompts):
                prompt_token_ids = self._extract_prompt_components(
                    engine_prompt
                ).token_ids

                # If we are creating sub requests for multiple prompts, ensure that they
                # have unique request ids.
                sub_request_id = (
                    request_id if len(engine_prompts) == 1 else f"{request_id}_{i}"
                )

                max_tokens = get_max_tokens(
                    max_model_len,
                    request.max_completion_tokens
                    if request.max_completion_tokens is not None
                    else request.max_tokens,
                    self._extract_prompt_len(engine_prompt),
                    self.default_sampling_params,
                    self.override_max_tokens,
                )

                sampling_params: SamplingParams | BeamSearchParams
                if request.use_beam_search:
                    sampling_params = request.to_beam_search_params(
                        max_tokens, self.default_sampling_params
                    )
                else:
                    sampling_params = request.to_sampling_params(
                        max_tokens,
                        self.default_sampling_params,
                    )

                self._log_inputs(
                    sub_request_id,
                    engine_prompt,
                    params=sampling_params,
                    lora_request=lora_request,
                )

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

                if isinstance(sampling_params, BeamSearchParams):
                    generator = self.beam_search(
                        prompt=engine_prompt,
                        request_id=sub_request_id,
                        params=sampling_params,
                        lora_request=lora_request,
                        trace_headers=trace_headers,
                    )
                else:
                    reasoning_ended = (
                        reasoning_parser.is_reasoning_end(prompt_token_ids or [])
                        if reasoning_parser
                        else None
                    )

                    generator = self.engine_client.generate(
                        engine_prompt,
                        sampling_params,
                        sub_request_id,
                        lora_request=lora_request,
                        trace_headers=trace_headers,
                        priority=request.priority,
                        data_parallel_rank=data_parallel_rank,
                        reasoning_ended=reasoning_ended,
                    )

                generators.append(generator)
        except ValueError as e:
            return self.create_error_response(e)

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

        if request.stream:
            return self.chat_completion_stream_generator(
                request,
                result_generator,
                request_id,
                model_name,
                conversation,
                tokenizer,
                request_metadata,
                reasoning_parser,
            )

        try:
            return await self.chat_completion_full_generator(
                request,
                result_generator,
                request_id,
                model_name,
                conversation,
                tokenizer,
                request_metadata,
                reasoning_parser,
            )
        except GenerationError as e:
            return self._convert_generation_error_to_response(e)
        except ValueError as e:
            return self.create_error_response(e)

    def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
        if request.add_generation_prompt:
            return self.response_role
        return request.messages[-1]["role"]

    @staticmethod
    def _bracket_level(s: str, opening="{", closing="}") -> int:
        """
        Calculate the current level of nested brackets in a given string.
        """
        level = 0
        for char in s:
            if char == opening:
                level += 1
            elif char == closing:
                level -= 1
        return level

    @staticmethod
    def _filter_delta_text(delta_text: str, previous_text: str) -> tuple[str, bool]:
        # remove last '},' of the tool definition stemming from the
        # "name"/"parameters" outer object or closing ']' of the tool list
        # count occurrences of opening and closing curly braces and
        # once level 0 is reached stop outputting text
        # if 0 is reached while parsing the delta_text we know the current
        # tool will finish in this current iteration
        bracket_level = OpenAIServingChat._bracket_level(previous_text)
        updated_delta, passed_zero = "", False
        for c in delta_text:
            if c == "{":
                bracket_level += 1
                passed_zero = bracket_level == 0
            elif c == "}":
                bracket_level -= 1
                passed_zero = bracket_level == 0

            if bracket_level != 0:
                updated_delta += c
            else:
                # if a comma is reached at level 0 we can stop
                if c == ",":
                    break
        return updated_delta, passed_zero

    def extract_tool_call_required_streaming(
        self,
        previous_text: str,
        current_text: str | None,
        delta_text: str,
        function_name_returned: bool,
        tool_call_idx: int | None = None,
    ) -> tuple[DeltaMessage | None, bool]:
        if current_text is None or current_text == "":
            # if the current text is empty, we cannot parse it
            return None, function_name_returned
        try:
            flags = Allow.ALL
            obj, _ = partial_json_loads(current_text, flags)
        except (
            partial_json_parser.core.exceptions.MalformedJSON,
            json.JSONDecodeError,
        ):
            logger.debug("not enough tokens to parse into JSON yet")
            obj = None

        # check if the current text is a valid array
        # containing a partial tool calling object
        # if not repeat
        if obj is None or not isinstance(obj, list) or not len(obj) > 0:
            function_name_returned = False
            delta_message = None
        else:
            _, finishes_previous_tool = OpenAIServingChat._filter_delta_text(
                delta_text, previous_text
            )
            # take the last tool call from the generated list
            current_tool_call = obj[-1]

            # once parameters have been generated the name is complete as well
            if not finishes_previous_tool and (
                "name" not in current_tool_call or "parameters" not in current_tool_call
            ):
                function_name_returned = False
                delta_message = None
            else:
                if not function_name_returned:
                    # get partly generated arguments from the latest tool call
                    param_match = re.search(
                        r'.*"parameters":\s*(.*)', current_text, re.DOTALL
                    )
                    arguments = param_match.group(1) if param_match else ""
                    arguments, _ = OpenAIServingChat._filter_delta_text(
                        arguments, previous_text
                    )

                    # if this iteration finishes a previous tool call but a
                    # new incomplete tool is already generated, take the
                    # previous from the list
                    if finishes_previous_tool and "parameters" not in current_tool_call:
                        current_tool_call = obj[-2]

                    function_name_returned = True
                    tool_call_id = make_tool_call_id(
                        id_type=self.tool_call_id_type,
                        func_name=current_tool_call["name"],
                        idx=tool_call_idx,
                    )
                    delta_message = DeltaMessage(
                        tool_calls=[
                            DeltaToolCall(
                                id=tool_call_id,
                                function=DeltaFunctionCall(
                                    name=current_tool_call["name"], arguments=arguments
                                ),
                                index=len(obj) - 1,
                                type="function",
                            )
                        ]
                    )

                else:
                    delta_text, _ = OpenAIServingChat._filter_delta_text(
                        delta_text, previous_text
                    )

                    if delta_text != "":
                        delta_message = DeltaMessage(
                            tool_calls=[
                                DeltaToolCall(
                                    function=DeltaFunctionCall(
                                        # OpenAI API returns None
                                        # instead of name every time
                                        name=None,
                                        arguments=delta_text,
                                    ),
                                    index=len(obj) - 1,
                                )
                            ]
                        )
                    else:
                        delta_message = None

        return delta_message, function_name_returned

    async def chat_completion_stream_generator(
        self,
        request: ChatCompletionRequest,
        result_generator: AsyncIterator[RequestOutput],
        request_id: str,
        model_name: str,
        conversation: list[ConversationMessage],
        tokenizer: TokenizerLike,
        request_metadata: RequestResponseMetadata,
        reasoning_parser: ReasoningParser | None = None,
    ) -> AsyncGenerator[str, None]:
        from vllm.tokenizers.mistral import MistralTokenizer

        created_time = int(time.time())
        chunk_object_type: Final = "chat.completion.chunk"
        first_iteration = True

        # Send response for each token for each request.n (index)
        num_choices = 1 if request.n is None else request.n
        previous_num_tokens = [0] * num_choices
        finish_reason_sent = [False] * num_choices
        num_prompt_tokens = 0
        num_cached_tokens = None
        if self.use_harmony:
            harmony_parsers = [
                get_streamable_parser_for_assistant() for _ in range(num_choices)
            ]
            harmony_tools_streamed = [False] * num_choices
        tools_streamed = [False] * num_choices

        if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
            tool_choice_function_name = request.tool_choice.function.name
        else:
            tool_choice_function_name = None

        # Determine whether tools are in use with "auto" tool choice
        tool_choice_auto = (
            not tool_choice_function_name
            and self._should_stream_with_auto_tool_parsing(request)
        )

        all_previous_token_ids: list[list[int]] | None
        function_name_returned = [False] * num_choices
        if self.tool_call_id_type == "kimi_k2":
            history_tool_call_cnt = get_history_tool_calls_cnt(conversation)
        else:
            history_tool_call_cnt = 0

        # Always track previous_texts for comprehensive output logging
        previous_texts = [""] * num_choices

        # Only one of these will be used, thus previous_texts and
        # all_previous_token_ids will not be used twice in the same iteration.
        if tool_choice_auto or reasoning_parser:
            # These are only required in "auto" tool choice case
            all_previous_token_ids = [[]] * num_choices
            # For reasoning parser and tool call all enabled
            added_content_delta_arr = [False] * num_choices
            reasoning_end_arr = [False] * num_choices
            prompt_is_reasoning_end_arr: list[bool | None] = [None] * num_choices
        else:
            all_previous_token_ids = None

        # Prepare the tool parser if it's needed
        try:
            if tool_choice_auto and self.tool_parser:
                if tokenizer is None:
                    raise ValueError(
                        "Tokenizer not available when `skip_tokenizer_init=True`"
                    )

                tool_parsers: list[ToolParser | None] = [
                    self.tool_parser(tokenizer)
                ] * num_choices
            else:
                tool_parsers = [None] * num_choices
        except Exception as e:
            logger.exception("Error in tool parser creation.")
            data = self.create_streaming_error_response(e)
            yield f"data: {data}\n\n"
            yield "data: [DONE]\n\n"
            return

        stream_options = request.stream_options
        include_usage, include_continuous_usage = should_include_usage(
            stream_options, self.enable_force_include_usage
        )

        try:
            async for res in result_generator:
                if res.prompt_token_ids is not None:
                    num_prompt_tokens = len(res.prompt_token_ids)
                    if res.encoder_prompt_token_ids is not None:
                        num_prompt_tokens += len(res.encoder_prompt_token_ids)

                # We need to do it here, because if there are exceptions in
                # the result_generator, it needs to be sent as the FIRST
                # response (by the try...catch).
                if first_iteration:
                    num_cached_tokens = res.num_cached_tokens
                    # Send first response for each request.n (index) with
                    # the role
                    role = self.get_chat_request_role(request)

                    # NOTE num_choices defaults to 1 so this usually executes
                    # once per request
                    for i in range(num_choices):
                        choice_data = ChatCompletionResponseStreamChoice(
                            index=i,
                            delta=DeltaMessage(
                                role=role,
                                content="",
                            ),
                            logprobs=None,
                            finish_reason=None,
                        )

                        # return prompt_token_ids at the first chunk ever
                        chunk = ChatCompletionStreamResponse(
                            id=request_id,
                            object=chunk_object_type,
                            created=created_time,
                            choices=[choice_data],
                            model=model_name,
                            prompt_token_ids=(
                                res.prompt_token_ids
                                if request.return_token_ids
                                else None
                            ),
                        )

                        # if continuous usage stats are requested, add it
                        if include_continuous_usage:
                            chunk.usage = UsageInfo(
                                prompt_tokens=num_prompt_tokens,
                                completion_tokens=0,
                                total_tokens=num_prompt_tokens,
                            )

                        data = chunk.model_dump_json(exclude_unset=True)
                        yield f"data: {data}\n\n"

                    # Send response to echo the input portion of the
                    # last message
                    if request.echo:
                        last_msg_content: str | list[dict[str, str]] = ""
                        if (
                            conversation
                            and "content" in conversation[-1]
                            and conversation[-1].get("role") == role
                        ):
                            last_msg_content = conversation[-1]["content"] or ""

                        if last_msg_content:
                            for i in range(num_choices):
                                choice_data = ChatCompletionResponseStreamChoice(
                                    index=i,
                                    delta=DeltaMessage(content=last_msg_content),
                                    logprobs=None,
                                    finish_reason=None,
                                )
                                chunk = ChatCompletionStreamResponse(
                                    id=request_id,
                                    object=chunk_object_type,
                                    created=created_time,
                                    choices=[choice_data],
                                    model=model_name,
                                )
                                if include_continuous_usage:
                                    chunk.usage = UsageInfo(
                                        prompt_tokens=num_prompt_tokens,
                                        completion_tokens=0,
                                        total_tokens=num_prompt_tokens,
                                    )

                                data = chunk.model_dump_json(exclude_unset=True)
                                yield f"data: {data}\n\n"
                    first_iteration = False

                for output in res.outputs:
                    i = output.index
                    tool_parser = tool_parsers[i]

                    if (
                        reasoning_parser
                        and res.prompt_token_ids
                        and prompt_is_reasoning_end_arr[i] is None
                    ):
                        # only check once per choice, because prompt_token_ids
                        # are the same for all deltas in that choice
                        prompt_is_reasoning_end_arr[i] = (
                            reasoning_parser.is_reasoning_end(res.prompt_token_ids)
                        )
                    if finish_reason_sent[i]:
                        continue

                    if request.logprobs and request.top_logprobs is not None:
                        assert output.logprobs is not None, "Did not output logprobs"
                        logprobs = self._create_chat_logprobs(
                            token_ids=output.token_ids,
                            top_logprobs=output.logprobs,
                            tokenizer=tokenizer,
                            num_output_top_logprobs=request.top_logprobs,
                            return_as_token_id=request.return_tokens_as_token_ids,
                        )
                    else:
                        logprobs = None

                    if self.use_harmony:
                        harmony_parser = harmony_parsers[i]
                        prev_recipient = harmony_parser.current_recipient

                        # Track accumulated content per token with their state
                        token_states: list[TokenState] = []
                        for token_id in output.token_ids:
                            harmony_parser.process(token_id)
                            token_delta = harmony_parser.last_content_delta or ""
                            token_states.append(
                                TokenState(
                                    harmony_parser.current_channel,
                                    harmony_parser.current_recipient,
                                    token_delta,
                                )
                            )
                        delta_text = "".join(delta for _, _, delta in token_states)
                        cur_channel = harmony_parser.current_channel

                        # handle the case where several tokens where generated at once
                        # including the final token, leading to a delta in the text
                        # but the current channel to be empty (start state)
                        if not cur_channel and delta_text:
                            cur_channel = "final"
                    else:
                        delta_text = output.text

                    if (
                        not delta_text
                        and not output.token_ids
                        and not previous_num_tokens[i]
                    ):
                        # Chunked prefill case, don't return empty chunks
                        continue

                    delta_message: DeltaMessage | None

                    # just update previous_texts and previous_token_ids
                    if tool_choice_auto or reasoning_parser:
                        assert previous_texts is not None
                        assert all_previous_token_ids is not None
                        previous_text = previous_texts[i]
                        previous_token_ids = all_previous_token_ids[i]
                        current_text = previous_text + delta_text
                        # avoid the None + list error.
                        if previous_token_ids:
                            current_token_ids = previous_token_ids + as_list(
                                output.token_ids
                            )
                        else:
                            current_token_ids = as_list(output.token_ids)

                    if self.use_harmony:
                        delta_message, tools_streamed_flag = (
                            extract_harmony_streaming_delta(
                                harmony_parser=harmony_parser,
                                token_states=token_states,
                                prev_recipient=prev_recipient,
                                include_reasoning=request.include_reasoning,
                            )
                        )
                        harmony_tools_streamed[i] |= tools_streamed_flag
                    # handle streaming deltas for tools with named tool_choice
                    elif tool_choice_function_name:
                        # When encountering think end id in prompt_token_ids
                        # i.e {"enable_thinking": False},
                        # check BEFORE calling the parser to avoid a spurious
                        # reasoning delta on the first chunk.
                        if (
                            reasoning_parser
                            and not reasoning_end_arr[i]
                            and prompt_is_reasoning_end_arr[i]
                        ):
                            reasoning_end_arr[i] = True

                        if (
                            reasoning_parser
                            and not reasoning_end_arr[i]
                            and not reasoning_parser.is_reasoning_end(
                                previous_token_ids
                            )
                        ):
                            assert reasoning_parser is not None
                            delta_message = (
                                reasoning_parser.extract_reasoning_streaming(
                                    previous_text,
                                    current_text,
                                    delta_text,
                                    previous_token_ids,
                                    current_token_ids,
                                    output.token_ids,
                                )
                            )
                            # When encountering think end id in delta_token_ids,
                            # set reasoning status to end.
                            # Only keep 'content', remove 'reasoning'.
                            if reasoning_parser.is_reasoning_end(
                                as_list(output.token_ids)
                            ):
                                reasoning_end_arr[i] = True
                                if delta_message and delta_message.content:
                                    # This need to be added to next `delta_text`
                                    current_text = delta_message.content
                                    delta_message.content = None
                                else:
                                    current_text = ""
                        else:
                            # Just to add remaining `content`
                            if reasoning_parser:
                                delta_text = previous_text + delta_text
                                current_text = ""

                            if function_name_returned[i]:
                                delta_tool_call = DeltaToolCall(
                                    function=DeltaFunctionCall(arguments=delta_text),
                                    index=i,
                                )
                            else:
                                # Generate ID based on tokenizer type
                                if isinstance(tokenizer, MistralTokenizer):
                                    tool_call_id = MistralToolCall.generate_random_id()
                                else:
                                    tool_call_id = make_tool_call_id(
                                        id_type=self.tool_call_id_type,
                                        func_name=tool_choice_function_name,
                                        idx=history_tool_call_cnt,
                                    )
                                delta_tool_call = DeltaToolCall(
                                    id=tool_call_id,
                                    type="function",
                                    function=DeltaFunctionCall(
                                        name=tool_choice_function_name,
                                        arguments=delta_text,
                                    ),
                                    index=i,
                                )
                                function_name_returned[i] = True
                                history_tool_call_cnt += 1

                            delta_message = DeltaMessage(
                                tool_calls=[
                                    delta_tool_call,
                                ]
                            )
                            tools_streamed[i] = True

                    elif request.tool_choice == "required":
                        assert previous_texts is not None
                        previous_text = previous_texts[i]
                        current_text = previous_text + delta_text
                        fn_name_returned = function_name_returned[i]
                        output_token_ids = as_list(output.token_ids)

                        if (
                            reasoning_parser is not None
                            and not reasoning_end_arr[i]
                            and prompt_is_reasoning_end_arr[i]
                        ):
                            reasoning_end_arr[i] = True

                        if reasoning_parser and not reasoning_end_arr[i]:
                            delta_message = (
                                reasoning_parser.extract_reasoning_streaming(
                                    previous_text,
                                    current_text,
                                    delta_text,
                                    previous_token_ids,
                                    current_token_ids,
                                    output_token_ids,
                                )
                            )
                            if reasoning_parser.is_reasoning_end(output_token_ids):
                                reasoning_end_arr[i] = True
                                if delta_message and delta_message.content:
                                    current_text = delta_message.content
                                    delta_message.content = None
                                else:
                                    # reasoning ended
                                    current_text = ""

                        else:
                            # either finished reasoning or no reasoning at all
                            content = current_text

                            delta_message, function_name_returned[i] = (
                                self.extract_tool_call_required_streaming(
                                    previous_text=previous_text,
                                    current_text=content,
                                    delta_text=delta_text,
                                    function_name_returned=fn_name_returned,
                                    tool_call_idx=history_tool_call_cnt,
                                )
                            )
                            if (
                                delta_message
                                and delta_message.tool_calls
                                and delta_message.tool_calls[0].id is not None
                            ):
                                history_tool_call_cnt += 1
                                tools_streamed[i] = True

                    # handle streaming deltas for tools with "auto" tool choice
                    # and reasoning parser
                    elif tool_choice_auto and reasoning_parser:
                        assert tool_parser is not None
                        assert added_content_delta_arr is not None
                        assert reasoning_end_arr is not None
                        output_token_ids = as_list(output.token_ids)
                        if not reasoning_end_arr[i]:
                            # When encountering think end id in prompt_token_ids
                            # i.e {"enable_thinking": False},
                            # set reasoning status to end.
                            if prompt_is_reasoning_end_arr[i]:
                                reasoning_end_arr[i] = True
                                current_token_ids = output_token_ids
                                # Don't update current_text, keep it as is from delta
                            else:
                                delta_message = (
                                    reasoning_parser.extract_reasoning_streaming(
                                        previous_text,
                                        current_text,
                                        delta_text,
                                        previous_token_ids,
                                        current_token_ids,
                                        output_token_ids,
                                    )
                                )

                                # When encountering think end id in delta_token_ids,
                                # set reasoning status to end.
                                # Remove the text and token ids related
                                # to 'reasoning'.
                                if reasoning_parser.is_reasoning_end(output_token_ids):
                                    reasoning_end_arr[i] = True
                                    current_token_ids = (
                                        reasoning_parser.extract_content_ids(
                                            output_token_ids
                                        )
                                    )
                                    if delta_message and delta_message.content:
                                        current_text = delta_message.content
                                        delta_message.content = None
                                    else:
                                        current_text = ""

                        # handle tool calls only after reasoning is done,
                        if reasoning_end_arr[i]:
                            delta_token_ids = output_token_ids
                            # First time to tool call,
                            # add the remaining text and token ids
                            # to delta from previous
                            if not added_content_delta_arr[i]:
                                added_content_delta_arr[i] = True
                                previous_text = ""
                                previous_token_ids = []
                                delta_text = current_text
                                delta_token_ids = current_token_ids

                            delta_message = tool_parser.extract_tool_calls_streaming(
                                previous_text=previous_text,
                                current_text=current_text,
                                delta_text=delta_text,
                                previous_token_ids=previous_token_ids,
                                current_token_ids=current_token_ids,
                                delta_token_ids=delta_token_ids,
                                request=request,
                            )
                            if delta_message and delta_message.tool_calls:
                                tools_streamed[i] = True
                    # when only tool calls
                    elif tool_choice_auto:
                        assert tool_parser is not None
                        delta_message = tool_parser.extract_tool_calls_streaming(
                            previous_text=previous_text,
                            current_text=current_text,
                            delta_text=delta_text,
                            previous_token_ids=previous_token_ids,
                            current_token_ids=current_token_ids,
                            delta_token_ids=output.token_ids,
                            request=request,
                        )
                        if delta_message and delta_message.tool_calls:
                            tools_streamed[i] = True

                    # when only reasoning
                    elif reasoning_parser:
                        # When encountering think end id in prompt_token_ids
                        # i.e {"enable_thinking": False},
                        # set reasoning status to end.
                        # Route all generated tokens as content directly.
                        if prompt_is_reasoning_end_arr[i]:
                            delta_message = DeltaMessage(content=delta_text)
                        else:
                            delta_message = (
                                reasoning_parser.extract_reasoning_streaming(
                                    previous_text,
                                    current_text,
                                    delta_text,
                                    previous_token_ids,
                                    current_token_ids,
                                    output.token_ids,
                                )
                            )
                    # handle streaming just a content delta
                    else:
                        delta_message = DeltaMessage(content=delta_text)

                    # update the previous values for the next iteration
                    if (tool_choice_auto or reasoning_parser) and not self.use_harmony:
                        assert previous_texts is not None
                        assert all_previous_token_ids is not None
                        previous_texts[i] = current_text
                        all_previous_token_ids[i] = current_token_ids
                    else:
                        # Update for comprehensive logging even in simple case
                        assert previous_texts is not None
                        previous_texts[i] += delta_text

                    # set the previous values for the next iteration
                    previous_num_tokens[i] += len(output.token_ids)

                    # if the message delta is None (e.g. because it was a
                    # "control token" for tool calls or the parser otherwise
                    # wasn't ready to send a token, then
                    #   get the next token without streaming a chunk
                    if delta_message is None:
                        # NOTE: If return_token_ids is enabled, we still need to
                        # send a chunk with token_ids even if delta_message is None
                        # to ensure all tokens are included in the response
                        if (
                            output.finish_reason is None
                            and not request.return_token_ids
                        ):
                            continue
                        delta_message = DeltaMessage()

                    # Log streaming delta if output logging is enabled
                    if self.enable_log_outputs and self.request_logger:
                        delta_content_parts = []
                        if delta_message.content:
                            delta_content_parts.append(delta_message.content)
                        if delta_message.reasoning:
                            reasoning = delta_message.reasoning
                            delta_content_parts.append(f"[reasoning: {reasoning}]")
                        if delta_message.tool_calls:
                            tool_args = "".join(
                                tc.function.arguments
                                for tc in delta_message.tool_calls
                                if tc.function and tc.function.arguments
                            )
                            if tool_args:
                                delta_content_parts.append(f"[tool_calls: {tool_args}]")

                        if delta_content_parts and self.enable_log_deltas:
                            delta_content = " ".join(delta_content_parts)
                            self.request_logger.log_outputs(
                                request_id=request_id,
                                outputs=delta_content,
                                output_token_ids=as_list(output.token_ids),
                                finish_reason=output.finish_reason,
                                is_streaming=True,
                                delta=True,
                            )

                    if output.finish_reason is None:
                        # Send token-by-token response for each request.n
                        choice_data = ChatCompletionResponseStreamChoice(
                            index=i,
                            delta=delta_message,
                            logprobs=logprobs,
                            finish_reason=None,
                            token_ids=(
                                as_list(output.token_ids)
                                if request.return_token_ids
                                else None
                            ),
                        )

                    # if the model is finished generating
                    else:
                        # check for error finish reason and abort streaming
                        # finish_reason='error' indicates a retryable error
                        self._raise_if_error(output.finish_reason, request_id)

                        # check to make sure we haven't "forgotten" to stream
                        #   any tokens that were generated but previously
                        #   matched by partial json parsing
                        # only happens if we are NOT using structured outputs
                        auto_tools_called = False
                        if tool_parser:
                            auto_tools_called = len(tool_parser.prev_tool_call_arr) > 0
                            index = (
                                len(tool_parser.prev_tool_call_arr) - 1
                                if auto_tools_called
                                else 0
                            )
                        else:
                            index = 0

                        if (
                            self._should_check_for_unstreamed_tool_arg_tokens(
                                delta_message, output
                            )
                            and tool_parser
                        ):
                            latest_delta_len = 0
                            if (
                                isinstance(
                                    delta_message.tool_calls[0].function,
                                    DeltaFunctionCall,
                                )
                            ) and isinstance(
                                delta_message.tool_calls[0].function.arguments, str
                            ):
                                latest_delta_len = len(
                                    delta_message.tool_calls[0].function.arguments
                                )

                            # get the expected call based on partial JSON
                            # parsing which "autocompletes" the JSON
                            expected_call = json.dumps(
                                tool_parser.prev_tool_call_arr[index].get(
                                    "arguments", {}
                                ),
                                ensure_ascii=False,
                            )

                            # get what we've streamed so far for arguments
                            # for the current tool
                            actual_call = tool_parser.streamed_args_for_tool[index]
                            if latest_delta_len > 0:
                                actual_call = actual_call[:-latest_delta_len]

                            # check to see if there's anything left to stream
                            remaining_call = expected_call.replace(actual_call, "", 1)
                            # set that as a delta message
                            delta_message = self._create_remaining_args_delta(
                                delta_message, remaining_call, index
                            )

                        # Send the finish response for each request.n only once
                        # In OpenAI's API, when a tool is called, the
                        # finish_reason is:
                        # "tool_calls" for "auto" or "required" tool calls,
                        # and "stop" for named tool calls.
                        if (
                            auto_tools_called
                            or (tools_streamed[i] and not tool_choice_function_name)
                            or (self.use_harmony and harmony_tools_streamed[i])
                        ):
                            finish_reason_ = "tool_calls"
                        else:
                            finish_reason_ = (
                                output.finish_reason if output.finish_reason else "stop"
                            )
                        choice_data = ChatCompletionResponseStreamChoice(
                            index=i,
                            delta=delta_message,
                            logprobs=logprobs,
                            finish_reason=finish_reason_,
                            stop_reason=output.stop_reason,
                            token_ids=(
                                as_list(output.token_ids)
                                if request.return_token_ids
                                else None
                            ),
                        )

                        finish_reason_sent[i] = True

                    choice_data = maybe_filter_parallel_tool_calls(choice_data, request)
                    chunk = ChatCompletionStreamResponse(
                        id=request_id,
                        object=chunk_object_type,
                        created=created_time,
                        choices=[choice_data],
                        model=model_name,
                    )

                    # handle usage stats if requested & if continuous
                    if include_continuous_usage:
                        completion_tokens = previous_num_tokens[i]
                        chunk.usage = UsageInfo(
                            prompt_tokens=num_prompt_tokens,
                            completion_tokens=completion_tokens,
                            total_tokens=num_prompt_tokens + completion_tokens,
                        )

                    data = chunk.model_dump_json(exclude_unset=True)
                    yield f"data: {data}\n\n"

            # once the final token is handled, if stream_options.include_usage
            # is sent, send the usage
            if include_usage:
                completion_tokens = sum(previous_num_tokens)
                final_usage = UsageInfo(
                    prompt_tokens=num_prompt_tokens,
                    completion_tokens=completion_tokens,
                    total_tokens=num_prompt_tokens + completion_tokens,
                )
                if self.enable_prompt_tokens_details and num_cached_tokens:
                    final_usage.prompt_tokens_details = PromptTokenUsageInfo(
                        cached_tokens=num_cached_tokens
                    )

                final_usage_chunk = ChatCompletionStreamResponse(
                    id=request_id,
                    object=chunk_object_type,
                    created=created_time,
                    choices=[],
                    model=model_name,
                    usage=final_usage,
                )
                final_usage_data = final_usage_chunk.model_dump_json(
                    exclude_unset=True, exclude_none=True
                )
                yield f"data: {final_usage_data}\n\n"

            # report to FastAPI middleware aggregate usage across all choices
            num_completion_tokens = sum(previous_num_tokens)
            request_metadata.final_usage_info = UsageInfo(
                prompt_tokens=num_prompt_tokens,
                completion_tokens=num_completion_tokens,
                total_tokens=num_prompt_tokens + num_completion_tokens,
            )

            # Log complete streaming response if output logging is enabled
            if self.enable_log_outputs and self.request_logger:
                # Log the complete response for each choice
                for i in range(num_choices):
                    full_text = (
                        previous_texts[i]
                        if previous_texts and i < len(previous_texts)
                        else f"<streaming_complete: {previous_num_tokens[i]} tokens>"
                    )
                    self.request_logger.log_outputs(
                        request_id=request_id,
                        outputs=full_text,
                        output_token_ids=None,  # Consider also logging all token IDs
                        finish_reason="streaming_complete",
                        is_streaming=True,
                        delta=False,
                    )

        except GenerationError as e:
            yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n"
        except Exception as e:
            logger.exception("Error in chat completion stream generator.")
            data = self.create_streaming_error_response(e)
            yield f"data: {data}\n\n"
        # Send the final done message after all response.n are finished
        yield "data: [DONE]\n\n"

    async def chat_completion_full_generator(
        self,
        request: ChatCompletionRequest,
        result_generator: AsyncIterator[RequestOutput],
        request_id: str,
        model_name: str,
        conversation: list[ConversationMessage],
        tokenizer: TokenizerLike,
        request_metadata: RequestResponseMetadata,
        reasoning_parser: ReasoningParser | None = None,
    ) -> ErrorResponse | ChatCompletionResponse:
        from vllm.tokenizers.mistral import MistralTokenizer

        created_time = int(time.time())
        final_res: RequestOutput | None = None

        try:
            async for res in result_generator:
                final_res = res
        except asyncio.CancelledError:
            return self.create_error_response("Client disconnected")
        except ValueError as e:
            return self.create_error_response(e)

        assert final_res is not None

        choices: list[ChatCompletionResponseChoice] = []
        if self.tool_call_id_type == "kimi_k2":
            history_tool_call_cnt = get_history_tool_calls_cnt(conversation)
        else:
            history_tool_call_cnt = 0

        role = self.get_chat_request_role(request)
        for output in final_res.outputs:
            # check for error finish reason and raise GenerationError
            # finish_reason='error' indicates a retryable request-level internal error
            self._raise_if_error(output.finish_reason, request_id)
            token_ids = output.token_ids
            out_logprobs = output.logprobs
            tool_call_info = None

            if request.logprobs and request.top_logprobs is not None:
                assert out_logprobs is not None, "Did not output logprobs"
                logprobs = self._create_chat_logprobs(
                    token_ids=token_ids,
                    top_logprobs=out_logprobs,
                    num_output_top_logprobs=request.top_logprobs,
                    tokenizer=tokenizer,
                    return_as_token_id=request.return_tokens_as_token_ids,
                )
            else:
                logprobs = None

            if self.use_harmony:
                reasoning, content, _ = parse_chat_output(token_ids)
                if not request.include_reasoning:
                    reasoning = None

                if self.tool_parser is not None:
                    if tokenizer is None:
                        raise ValueError(
                            "Tokenizer not available when `skip_tokenizer_init=True`"
                        )

                    tool_parser = self.tool_parser(tokenizer)
                    # NOTE: We use token_ids for openai tool parser
                    tool_call_info = tool_parser.extract_tool_calls(
                        "",
                        request=request,
                        token_ids=token_ids,  # type: ignore
                    )
                    content = tool_call_info.content
                    message = ChatMessage(
                        role=role,
                        reasoning=reasoning,
                        content=content,
                        tool_calls=tool_call_info.tool_calls,
                    )
                else:
                    message = ChatMessage(
                        role=role,
                        reasoning=reasoning,
                        content=content,
                    )

                choice_data = ChatCompletionResponseChoice(
                    index=output.index,
                    message=message,
                    logprobs=logprobs,
                    finish_reason=(
                        "tool_calls"
                        if (tool_call_info is not None and tool_call_info.tools_called)
                        else output.finish_reason
                        if output.finish_reason
                        else "stop"
                    ),
                    stop_reason=output.stop_reason,
                    token_ids=(
                        as_list(output.token_ids) if request.return_token_ids else None
                    ),
                )
                choices.append(choice_data)
                continue

            if reasoning_parser:
                # If the reasoning parser is enabled,
                # tool calls are extracted exclusively from the content.
                reasoning, content = reasoning_parser.extract_reasoning(
                    output.text, request=request
                )
                if not request.include_reasoning:
                    reasoning = None
            else:
                reasoning = None
                content = output.text

            auto_tools_called = False
            # if auto tools are not enabled, and a named tool choice using
            #   outlines is not being used
            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,
            )
            tool_call_class = (
                MistralToolCall if isinstance(tokenizer, MistralTokenizer) else ToolCall
            )
            if self.use_harmony:
                # Harmony models already have parsed content and tool_calls
                # through parse_chat_output. Respect its output directly.
                message = ChatMessage(
                    role=role,
                    reasoning=reasoning,
                    content=content,
                    tool_calls=tool_calls if tool_calls else [],
                )

            elif (not self.enable_auto_tools or not self.tool_parser) and (
                not isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam)
                and request.tool_choice != "required"
            ):
                message = ChatMessage(role=role, reasoning=reasoning, content=content)

            elif (
                request.tool_choice
                and type(request.tool_choice) is ChatCompletionNamedToolChoiceParam
            ):
                assert tool_calls is not None and len(tool_calls) > 0
                tool_call_class_items = []
                for idx, tc in enumerate(tool_calls):
                    # Use native ID if available (e.g., Kimi K2),
                    # otherwise generate ID with correct id_type
                    if tc.id:
                        tool_call_class_items.append(
                            tool_call_class(id=tc.id, function=tc)
                        )
                    else:
                        # Generate ID using the correct format (kimi_k2 or random),
                        # but leave it to the class if it's Mistral to preserve
                        # 9-char IDs
                        if isinstance(tokenizer, MistralTokenizer):
                            tool_call_class_items.append(tool_call_class(function=tc))
                        else:
                            generated_id = make_tool_call_id(
                                id_type=self.tool_call_id_type,
                                func_name=tc.name,
                                idx=history_tool_call_cnt,
                            )
                            tool_call_class_items.append(
                                tool_call_class(id=generated_id, function=tc)
                            )
                    history_tool_call_cnt += 1
                message = ChatMessage(
                    role=role,
                    reasoning=reasoning,
                    content="",
                    tool_calls=tool_call_class_items,
                )

            elif request.tool_choice and request.tool_choice == "required":
                tool_call_class_items = []
                assert tool_calls is not None and len(tool_calls) > 0
                for idx, tool_call in enumerate(tool_calls):
                    # Use native ID if available,
                    # otherwise generate ID with correct id_type
                    if tool_call.id:
                        tool_call_class_items.append(
                            tool_call_class(id=tool_call.id, function=tool_call)
                        )
                    else:
                        # Generate ID using the correct format (kimi_k2 or random),
                        # but leave it to the class if it's Mistral to preserve
                        # 9-char IDs
                        if isinstance(tokenizer, MistralTokenizer):
                            tool_call_class_items.append(
                                tool_call_class(function=tool_call)
                            )
                        else:
                            generated_id = make_tool_call_id(
                                id_type=self.tool_call_id_type,
                                func_name=tool_call.name,
                                idx=history_tool_call_cnt,
                            )
                            tool_call_class_items.append(
                                tool_call_class(id=generated_id, function=tool_call)
                            )
                    history_tool_call_cnt += 1
                message = ChatMessage(
                    role=role,
                    content="",
                    tool_calls=tool_call_class_items,
                    reasoning=reasoning,
                )

            # if the request doesn't use tool choice
            # OR specifies to not use a tool
            elif not request.tool_choice or request.tool_choice == "none":
                message = ChatMessage(role=role, reasoning=reasoning, content=content)

            # handle when there are tools and tool choice is auto
            elif (
                request.tools
                and (request.tool_choice == "auto" or request.tool_choice is None)
                and self.enable_auto_tools
                and self.tool_parser
            ):
                # In the OpenAI API the finish_reason is "tools_called"
                # if the tool choice is auto and the model produced a tool
                # call. The same is not true for named function calls
                auto_tools_called = tool_calls is not None and len(tool_calls) > 0
                if tool_calls:
                    tool_call_items = []
                    for idx, tc in enumerate(tool_calls):
                        # Use native ID if available (e.g., Kimi K2),
                        # otherwise generate ID with correct id_type
                        if tc.id:
                            tool_call_items.append(
                                tool_call_class(id=tc.id, function=tc)
                            )
                        else:
                            # Generate ID using the correct format (kimi_k2 or random),
                            # but leave it to the class if it's Mistral to preserve
                            # 9-char IDs
                            if isinstance(tokenizer, MistralTokenizer):
                                tool_call_items.append(tool_call_class(function=tc))
                            else:
                                generated_id = make_tool_call_id(
                                    id_type=self.tool_call_id_type,
                                    func_name=tc.name,
                                    idx=history_tool_call_cnt,
                                )
                                tool_call_items.append(
                                    tool_call_class(id=generated_id, function=tc)
                                )
                        history_tool_call_cnt += 1
                    message = ChatMessage(
                        role=role,
                        reasoning=reasoning,
                        content=content,
                        tool_calls=tool_call_items,
                    )

                else:
                    # FOR NOW make it a chat message; we will have to detect
                    # the type to make it later.
                    ret_content = content

                    # try to use content return from tool parser first,
                    # tool parser may do some modify for the content.
                    if content and len(content) > 0:
                        ret_content = content
                    message = ChatMessage(
                        role=role,
                        reasoning=reasoning,
                        content=ret_content,
                    )

            # undetermined case that is still important to handle
            else:
                logger.error(
                    "Error in chat_completion_full_generator - cannot determine"
                    " if tools should be extracted. Returning a standard chat "
                    "completion."
                )
                message = ChatMessage(role=role, reasoning=reasoning, content=content)
            # In OpenAI's API, when a tool is called, the finish_reason is:
            # "tool_calls" for "auto" or "required" tool calls,
            # and "stop" for named tool calls.
            is_finish_reason_tool_calls = auto_tools_called or (
                request.tool_choice
                and request.tool_choice == "required"
                and output.finish_reason == "stop"
            )

            choice_data = ChatCompletionResponseChoice(
                index=output.index,
                message=message,
                logprobs=logprobs,
                finish_reason="tool_calls"
                if is_finish_reason_tool_calls
                else output.finish_reason
                if output.finish_reason
                else "stop",
                stop_reason=output.stop_reason,
                token_ids=(
                    as_list(output.token_ids) if request.return_token_ids else None
                ),
            )
            choice_data = maybe_filter_parallel_tool_calls(choice_data, request)

            choices.append(choice_data)

        if request.echo:
            last_msg_content: str | list[dict[str, str]] = ""
            if (
                conversation
                and "content" in conversation[-1]
                and conversation[-1].get("role") == role
            ):
                last_msg_content = conversation[-1]["content"] or ""
            if isinstance(last_msg_content, list):
                last_msg_content = "\n".join(msg["text"] for msg in last_msg_content)

            for choice in choices:
                full_message = last_msg_content + (choice.message.content or "")
                choice.message.content = full_message

        assert final_res.prompt_token_ids is not None
        num_prompt_tokens = len(final_res.prompt_token_ids)
        if final_res.encoder_prompt_token_ids is not None:
            num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
        num_generated_tokens = sum(
            len(output.token_ids) for output in final_res.outputs
        )
        usage = UsageInfo(
            prompt_tokens=num_prompt_tokens,
            completion_tokens=num_generated_tokens,
            total_tokens=num_prompt_tokens + num_generated_tokens,
        )
        if self.enable_prompt_tokens_details and final_res.num_cached_tokens:
            usage.prompt_tokens_details = PromptTokenUsageInfo(
                cached_tokens=final_res.num_cached_tokens
            )

        request_metadata.final_usage_info = usage

        response = ChatCompletionResponse(
            id=request_id,
            created=created_time,
            model=model_name,
            choices=choices,
            usage=usage,
            prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),
            prompt_token_ids=(
                final_res.prompt_token_ids if request.return_token_ids else None
            ),
            kv_transfer_params=final_res.kv_transfer_params,
        )

        # Log complete response if output logging is enabled
        if self.enable_log_outputs and self.request_logger:
            for choice in choices:
                output_text = ""
                if choice.message.content:
                    output_text = choice.message.content
                elif choice.message.tool_calls:
                    # For tool calls, log the function name and arguments
                    tool_call_descriptions = []
                    for tc in choice.message.tool_calls:  # type: ignore
                        function_call: FunctionCall = tc.function  # type: ignore
                        tool_call_descriptions.append(
                            f"{function_call.name}({function_call.arguments})"
                        )
                    tool_calls_str = ", ".join(tool_call_descriptions)
                    output_text = f"[tool_calls: {tool_calls_str}]"

                if output_text:
                    # Get the corresponding output token IDs
                    output_token_ids = None
                    if choice.index < len(final_res.outputs):
                        output_token_ids = final_res.outputs[choice.index].token_ids

                    self.request_logger.log_outputs(
                        request_id=request_id,
                        outputs=output_text,
                        output_token_ids=output_token_ids,
                        finish_reason=choice.finish_reason,
                        is_streaming=False,
                        delta=False,
                    )

        return response

    def _get_top_logprobs(
        self,
        logprobs: dict[int, Logprob],
        top_logprobs: int | None,
        tokenizer: TokenizerLike | None,
        should_return_as_token_id: bool,
    ) -> list[ChatCompletionLogProb]:
        return [
            ChatCompletionLogProb(
                token=(
                    token := self._get_decoded_token(
                        p[1],
                        p[0],
                        tokenizer,
                        return_as_token_id=should_return_as_token_id,
                    )
                ),
                logprob=max(p[1].logprob, -9999.0),
                bytes=list(token.encode("utf-8", errors="replace")),
            )
            for i, p in enumerate(logprobs.items())
            if (top_logprobs and i < top_logprobs or top_logprobs == -1)
        ]

    def _create_chat_logprobs(
        self,
        token_ids: GenericSequence[int],
        top_logprobs: GenericSequence[dict[int, Logprob] | None],
        tokenizer: TokenizerLike | None,
        num_output_top_logprobs: int | None = None,
        return_as_token_id: bool | None = None,
    ) -> ChatCompletionLogProbs:
        """Create OpenAI-style logprobs."""
        logprobs_content: list[ChatCompletionLogProbsContent] = []

        should_return_as_token_id = (
            return_as_token_id
            if return_as_token_id is not None
            else self.return_tokens_as_token_ids
        )
        for i, token_id in enumerate(token_ids):
            step_top_logprobs = top_logprobs[i]
            if step_top_logprobs is None or step_top_logprobs.get(token_id) is None:
                if should_return_as_token_id:
                    token = f"token_id:{token_id}"
                else:
                    if tokenizer is None:
                        raise ValueError(
                            "Unable to get tokenizer because `skip_tokenizer_init=True`"
                        )

                    token = tokenizer.decode(token_id)

                logprobs_content.append(
                    ChatCompletionLogProbsContent(
                        token=token,
                        bytes=list(token.encode("utf-8", errors="replace")),
                    )
                )
            else:
                step_token = step_top_logprobs[token_id]
                step_decoded = step_token.decoded_token

                logprobs_content.append(
                    ChatCompletionLogProbsContent(
                        token=self._get_decoded_token(
                            step_token,
                            token_id,
                            tokenizer,
                            should_return_as_token_id,
                        ),
                        logprob=max(step_token.logprob, -9999.0),
                        bytes=(
                            None
                            if step_decoded is None
                            else list(step_decoded.encode("utf-8", errors="replace"))
                        ),
                        top_logprobs=self._get_top_logprobs(
                            step_top_logprobs,
                            num_output_top_logprobs,
                            tokenizer,
                            should_return_as_token_id,
                        ),
                    )
                )

        return ChatCompletionLogProbs(content=logprobs_content)

    def _should_stream_with_auto_tool_parsing(self, request: ChatCompletionRequest):
        """
        Utility function to check if streamed tokens should go through the tool
        call parser that was configured.

        We only want to do this IF user-provided tools are set, a tool parser
        is configured, "auto" tool choice is enabled, and the request's tool
        choice field indicates that "auto" tool choice should be used.
        """
        return (
            request.tools
            and self.tool_parser
            and self.enable_auto_tools
            and request.tool_choice in ["auto", None]
        )

    def _should_check_for_unstreamed_tool_arg_tokens(
        self,
        delta_message: DeltaMessage | None,
        output: CompletionOutput,
    ) -> bool:
        """
        Check to see if we should check for unstreamed tool arguments tokens.
        This is only applicable when auto tool parsing is enabled, the delta
        is a tool call with arguments.
        """

        return bool(
            # if there is a delta message that includes tool calls which
            # include a function that has arguments
            output.finish_reason is not None
            and self.enable_auto_tools
            and self.tool_parser
            and delta_message
            and delta_message.tool_calls
            and delta_message.tool_calls[0]
            and delta_message.tool_calls[0].function
            and delta_message.tool_calls[0].function.arguments is not None
        )

    @staticmethod
    def _create_remaining_args_delta(
        delta_message: DeltaMessage,
        remaining_call: str,
        index: int,
    ) -> DeltaMessage:
        """
        Create a delta message for remaining tool arguments, preserving
        id/type/name from the original delta.
        """
        original_tc = next(
            (tc for tc in delta_message.tool_calls if tc.index == index),
            None,
        )
        original_fn = original_tc.function if original_tc else None
        return DeltaMessage(
            tool_calls=[
                DeltaToolCall(
                    index=index,
                    id=original_tc.id if original_tc else None,
                    type=original_tc.type if original_tc else None,
                    function=DeltaFunctionCall(
                        name=original_fn.name if original_fn else None,
                        arguments=remaining_call,
                    ),
                )
            ]
        )

    def _make_request_with_harmony(
        self,
        request: ChatCompletionRequest,
        should_include_tools: bool = True,
    ):
        messages: list[OpenAIMessage] = []

        # because of issues with pydantic we need to potentially
        # re-serialize the tool_calls field of the request
        # for more info: see comment in `maybe_serialize_tool_calls`
        maybe_serialize_tool_calls(request)  # type: ignore[arg-type]

        # Add system message.
        # NOTE: In Chat Completion API, browsing is enabled by default
        # if the model supports it. TODO: Support browsing.
        assert not self.supports_browsing
        assert not self.supports_code_interpreter
        sys_msg = get_system_message(
            reasoning_effort=request.reasoning_effort,
            browser_description=None,
            python_description=None,
            with_custom_tools=should_include_tools,
        )
        messages.append(sys_msg)

        # Add developer message.
        if request.tools:
            dev_msg = get_developer_message(
                tools=request.tools if should_include_tools else None  # type: ignore[arg-type]
            )
            messages.append(dev_msg)

        # Add user message.
        messages.extend(parse_chat_inputs_to_harmony_messages(request.messages))

        # Render prompt token ids.
        prompt_token_ids = render_for_completion(messages)
        engine_prompt = TokensPrompt(prompt_token_ids=prompt_token_ids)

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

        return messages, [engine_prompt]