# 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, 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 TokensPrompt from vllm.logger import init_logger from vllm.logprobs import Logprob from vllm.outputs import CompletionOutput, RequestOutput 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 from vllm.v1.sample.logits_processor import validate_logits_processors_parameters 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 logits processors self.logits_processors = self.model_config.logits_processors # set up reasoning parser self.reasoning_parser = self._get_reasoning_parser( reasoning_parser_name=reasoning_parser ) # set up tool use self.enable_auto_tools: bool = enable_auto_tools 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 = 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() if self.model_config.hf_config.model_type == "kimi_k2": self.tool_call_id_type = "kimi_k2" else: self.tool_call_id_type = "random" 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() ) # 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: renderer = self.engine_client.renderer # 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, renderer, dummy_request.messages, chat_template=self.chat_template, chat_template_content_format=self.chat_template_content_format, add_generation_prompt=True, continue_final_message=False, tool_dicts=None, documents=None, chat_template_kwargs=None, default_chat_template_kwargs=self.default_chat_template_kwargs, tool_parser=None, add_special_tokens=False, ) 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[Any]] | 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: renderer = self.engine_client.renderer tokenizer = 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) truncate_tool_call_ids(request) 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 chat_template_kwargs = request.chat_template_kwargs or {} chat_template_kwargs.update(reasoning_effort=request.reasoning_effort) conversation, engine_prompts = await self._preprocess_chat( request, renderer, request.messages, chat_template=request.chat_template or self.chat_template, chat_template_content_format=self.chat_template_content_format, add_generation_prompt=request.add_generation_prompt, continue_final_message=request.continue_final_message, tool_dicts=tool_dicts, documents=request.documents, chat_template_kwargs=chat_template_kwargs, default_chat_template_kwargs=self.default_chat_template_kwargs, tool_parser=tool_parser, add_special_tokens=request.add_special_tokens, ) 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. """ 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. generators: list[AsyncGenerator[RequestOutput, None]] = [] try: for i, engine_prompt in enumerate(engine_prompts): prompt_text, _, _ = self._get_prompt_components(engine_prompt) # 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}" ) if self.default_sampling_params is None: self.default_sampling_params = {} max_tokens = get_max_tokens( max_model_len=self.max_model_len, request=request, input_length=len(engine_prompt["prompt_token_ids"]), default_sampling_params=self.default_sampling_params, ) 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.model_config.logits_processor_pattern, self.default_sampling_params, ) validate_logits_processors_parameters( self.logits_processors, 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: engine_request, tokenization_kwargs = await self._process_inputs( sub_request_id, engine_prompt, sampling_params, lora_request=lora_request, trace_headers=trace_headers, priority=request.priority, data_parallel_rank=data_parallel_rank, ) generator = self.engine_client.generate( engine_request, sampling_params, sub_request_id, lora_request=lora_request, trace_headers=trace_headers, priority=request.priority, prompt_text=prompt_text, tokenization_kwargs=tokenization_kwargs, data_parallel_rank=data_parallel_rank, ) generators.append(generator) except ValueError as e: return self.create_error_response(e) assert len(generators) == 1 (result_generator,) = generators # Streaming response tokenizer = self.renderer.tokenizer if request.stream: return self.chat_completion_stream_generator( request, result_generator, request_id, model_name, conversation, tokenizer, request_metadata, ) try: return await self.chat_completion_full_generator( request, result_generator, request_id, model_name, conversation, tokenizer, request_metadata, ) 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 | None, request_metadata: RequestResponseMetadata, ) -> AsyncGenerator[str, None]: 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 self.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 else: all_previous_token_ids = None try: if self.reasoning_parser: if tokenizer is None: raise ValueError( "Tokenizer not available when `skip_tokenizer_init=True`" ) # 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( tokenizer, chat_template_kwargs=chat_template_kwargs, # type: ignore[call-arg] ) except RuntimeError as e: logger.exception("Error in reasoning parser creation.") data = self.create_streaming_error_response(str(e)) yield f"data: {data}\n\n" yield "data: [DONE]\n\n" return # 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 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 self.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: if ( self.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 # or think end id in prompt_token_ids # i.e {"enable_thinking": False}, # set reasoning status to end. # Only keep 'content', remove 'reasoning'. if reasoning_parser.is_reasoning_end( as_list(output.token_ids) ) or ( res.prompt_token_ids and reasoning_parser.is_reasoning_end( res.prompt_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 self.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: delta_tool_call = DeltaToolCall( id=make_tool_call_id(), type="function", function=DeltaFunctionCall( name=tool_choice_function_name, arguments=delta_text, ), index=i, ) function_name_returned[i] = True 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 ( self.reasoning_parser is not None and not reasoning_end_arr[i] and res.prompt_token_ids and reasoning_parser.is_reasoning_end(res.prompt_token_ids) ): reasoning_end_arr[i] = True if self.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 self.reasoning_parser: assert tool_parser is not None assert reasoning_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 ( res.prompt_token_ids and reasoning_parser.is_reasoning_end( res.prompt_token_ids ) ): 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 self.reasoning_parser: 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 self.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_content: reasoning = delta_message.reasoning_content 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"" ) 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 | None, request_metadata: RequestResponseMetadata, ) -> ErrorResponse | ChatCompletionResponse: 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 self.reasoning_parser: try: if tokenizer is None: raise ValueError( "Tokenizer not available when `skip_tokenizer_init=True`" ) # 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( 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)) # 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 (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) # if the request uses tools and specified a tool choice elif ( request.tool_choice and type(request.tool_choice) is ChatCompletionNamedToolChoiceParam ): assert tool_calls is not None and len(tool_calls) > 0 message = ChatMessage( role=role, reasoning=reasoning, content="", tool_calls=[tool_call_class(function=tc) for tc in tool_calls], ) 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 tool_call in tool_calls: tool_call_class_items.append( tool_call_class( id=make_tool_call_id( id_type=self.tool_call_id_type, func_name=tool_call.name, idx=history_tool_call_cnt, ), 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: message = ChatMessage( role=role, reasoning=reasoning, content=content, tool_calls=[ ToolCall( function=tc, type="function", ) for tc in tool_calls ], ) 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: if hasattr(tc.function, "name") and hasattr( tc.function, "arguments" ): tool_call_descriptions.append( f"{tc.function.name}({tc.function.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) # 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 ) 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]