# 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 contextlib import AsyncExitStack from copy import copy from http import HTTPStatus from typing import Any, Callable, Final, Optional, Union import jinja2 import openai.types.responses as openai_responses_types from fastapi import Request from openai import BaseModel # yapf conflicts with isort for this block # yapf: disable from openai.types.responses import (ResponseCreatedEvent, ResponseFunctionToolCall, ResponseInProgressEvent, ResponseOutputItem, ResponseOutputItemDoneEvent, ResponseOutputMessage, ResponseOutputText, ResponseReasoningItem, ResponseReasoningTextDeltaEvent, ResponseReasoningTextDoneEvent) # yapf: enable from openai.types.responses.response_reasoning_item import ( Content as ResponseReasoningTextContent) from openai_harmony import Message as OpenAIHarmonyMessage from vllm import envs from vllm.config import ModelConfig from vllm.engine.protocol import EngineClient from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam, ChatTemplateContentFormatOption) from vllm.entrypoints.context import (ConversationContext, HarmonyContext, SimpleContext, StreamingHarmonyContext) from vllm.entrypoints.harmony_utils import ( get_developer_message, get_stop_tokens_for_assistant_actions, get_system_message, get_user_message, parse_output_message, parse_remaining_state, parse_response_input, render_for_completion) from vllm.entrypoints.logger import RequestLogger # yapf conflicts with isort for this block # yapf: disable from vllm.entrypoints.openai.protocol import (ErrorResponse, InputTokensDetails, OutputTokensDetails, RequestResponseMetadata, ResponsesRequest, ResponsesResponse, ResponseUsage) # yapf: enable from vllm.entrypoints.openai.serving_engine import OpenAIServing from vllm.entrypoints.openai.serving_models import OpenAIServingModels from vllm.entrypoints.tool_server import MCPToolServer, ToolServer from vllm.inputs.data import TokensPrompt as EngineTokensPrompt from vllm.logger import init_logger from vllm.outputs import CompletionOutput from vllm.reasoning import ReasoningParser, ReasoningParserManager from vllm.sampling_params import SamplingParams from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.utils import random_uuid logger = init_logger(__name__) class OpenAIServingResponses(OpenAIServing): def __init__( self, engine_client: EngineClient, model_config: ModelConfig, models: OpenAIServingModels, *, request_logger: Optional[RequestLogger], chat_template: Optional[str], chat_template_content_format: ChatTemplateContentFormatOption, return_tokens_as_token_ids: bool = False, reasoning_parser: str = "", enable_auto_tools: bool = False, tool_parser: Optional[str] = None, tool_server: Optional[ToolServer] = None, enable_prompt_tokens_details: bool = False, enable_force_include_usage: bool = False, enable_log_outputs: bool = False, ) -> None: super().__init__( engine_client=engine_client, model_config=model_config, models=models, request_logger=request_logger, return_tokens_as_token_ids=return_tokens_as_token_ids, enable_force_include_usage=enable_force_include_usage, ) self.chat_template = chat_template self.chat_template_content_format: Final = chat_template_content_format self.enable_log_outputs = enable_log_outputs self.reasoning_parser: Optional[Callable[[AnyTokenizer], ReasoningParser]] = None if reasoning_parser: try: self.reasoning_parser = ( ReasoningParserManager.get_reasoning_parser( reasoning_parser)) assert self.reasoning_parser is not None except Exception as e: raise TypeError( f"{reasoning_parser=} has not been registered") from e self.enable_prompt_tokens_details = enable_prompt_tokens_details self.enable_force_include_usage = enable_force_include_usage self.default_sampling_params = ( self.model_config.get_diff_sampling_param()) if self.default_sampling_params: source = self.model_config.generation_config source = "model" if source == "auto" else source logger.info("Using default chat sampling params from %s: %s", source, self.default_sampling_params) # If False (default), the "store" option is (silently) ignored and the # response is not stored. If True, the response is stored in memory. # NOTE(woosuk): This may not be intuitive for users, as the default # behavior in OpenAI's Responses API is to store the response, but # vLLM's default behavior is not. self.enable_store = envs.VLLM_ENABLE_RESPONSES_API_STORE if self.enable_store: logger.warning_once( "`VLLM_ENABLE_RESPONSES_API_STORE` is enabled. This may " "cause a memory leak since we never remove responses from " "the store.") self.use_harmony = model_config.hf_config.model_type == "gpt_oss" if self.use_harmony: logger.warning("For gpt-oss, we ignore --enable-auto-tool-choice " "and always enable tool use.") # OpenAI models have two EOS-like tokens: <|return|> and <|call|>. # We need to add them to the stop token ids. if "stop_token_ids" not in self.default_sampling_params: self.default_sampling_params["stop_token_ids"] = [] self.default_sampling_params["stop_token_ids"].extend( get_stop_tokens_for_assistant_actions()) # set up tool use self.enable_auto_tools: bool = enable_auto_tools if self.enable_auto_tools: logger.info( "\"auto\" tool choice has been enabled please note that while" " the parallel_tool_calls client option is preset for " "compatibility reasons, it will be ignored.") # HACK(woosuk): This is a hack. We should use a better store. # FIXME: If enable_store=True, this may cause a memory leak since we # never remove responses from the store. self.response_store: dict[str, ResponsesResponse] = {} self.response_store_lock = asyncio.Lock() # HACK(woosuk): This is a hack. We should use a better store. # FIXME: If enable_store=True, this may cause a memory leak since we # never remove messages from the store. self.msg_store: dict[str, list[ChatCompletionMessageParam]] = {} self.background_tasks: dict[str, asyncio.Task] = {} self.tool_server = tool_server async def create_responses( self, request: ResponsesRequest, raw_request: Optional[Request] = None, ) -> Union[AsyncGenerator[str, None], ResponsesResponse, ErrorResponse]: error_check_ret = await self._check_model(request) if error_check_ret is not None: logger.error("Error with model %s", error_check_ret) return error_check_ret # If the engine is dead, raise the engine's DEAD_ERROR. # This is required for the streaming case, where we return a # success status before we actually start generating text :). if self.engine_client.errored: raise self.engine_client.dead_error if request.store and not self.enable_store: if request.background: return self.create_error_response( err_type="invalid_request_error", message=( "This vLLM engine does not support `store=True` and " "therefore does not support the background mode. To " "enable these features, set the environment variable " "`VLLM_ENABLE_RESPONSES_API_STORE=1` when launching " "the vLLM server."), status_code=HTTPStatus.BAD_REQUEST, ) # Disable the store option. # NOTE(woosuk): Although returning an error is possible, we opted # to implicitly disable store and process the request anyway, as # we assume most users do not intend to actually store the response # (i.e., their request's `store=True` just because it's the default # value). request.store = False # Handle the previous response ID. prev_response_id = request.previous_response_id if prev_response_id is not None: if not prev_response_id.startswith("resp_"): return self._make_invalid_id_error(prev_response_id) async with self.response_store_lock: prev_response = self.response_store.get(prev_response_id) if prev_response is None: return self._make_not_found_error(prev_response_id) else: prev_response = None try: lora_request = self._maybe_get_adapters(request) model_name = self._get_model_name(request.model, lora_request) tokenizer = await self.engine_client.get_tokenizer(lora_request) if self.use_harmony: messages, request_prompts, engine_prompts = ( self._make_request_with_harmony(request, prev_response)) else: messages, request_prompts, engine_prompts = ( await self._make_request(request, prev_response, tokenizer)) except (ValueError, TypeError, RuntimeError, jinja2.TemplateError, NotImplementedError) as e: logger.exception("Error in preprocessing prompt inputs") return self.create_error_response(f"{e} {e.__cause__}") request_metadata = RequestResponseMetadata( request_id=request.request_id) if raw_request: raw_request.state.request_metadata = request_metadata if self.tool_server is not None and isinstance( self.tool_server, MCPToolServer ) and (request.background or request.stream) and request.tools and any( tool.type in ["web_search_preview", "code_interpreter"] for tool in request.tools): return self.create_error_response( "MCP tool server is not supported in background mode and " "streaming mode") # Schedule the request and get the result generator. generators: list[AsyncGenerator[ConversationContext, None]] = [] builtin_tool_list: list[str] = [] if self.use_harmony and self.tool_server is not None: if self.tool_server.has_tool("browser"): builtin_tool_list.append("browser") if self.tool_server.has_tool("python"): builtin_tool_list.append("python") async with AsyncExitStack() as exit_stack: try: if self.tool_server is not None: # TODO: initialize tool sessions lazily when the session # is actually used. tool_session_ctxs: dict[str, Any] = { tool_name: exit_stack.enter_async_context( self.tool_server.new_session(tool_name)) for tool_name in builtin_tool_list } tool_sessions = {} for tool_name in builtin_tool_list: tool_sessions[tool_name] = ( await tool_session_ctxs[tool_name]) else: assert len(builtin_tool_list) == 0 tool_sessions = {} for i, engine_prompt in enumerate(engine_prompts): default_max_tokens = self.max_model_len - len( engine_prompt["prompt_token_ids"]) sampling_params = request.to_sampling_params( default_max_tokens, self.default_sampling_params) trace_headers = (None if raw_request is None else await self._get_trace_headers( raw_request.headers)) context: ConversationContext if self.use_harmony: if request.stream: context = StreamingHarmonyContext( messages, tool_sessions) else: context = HarmonyContext(messages, tool_sessions) else: context = SimpleContext() generator = self._generate_with_builtin_tools( request_id=request.request_id, request_prompt=request_prompts[i], engine_prompt=engine_prompt, sampling_params=sampling_params, context=context, lora_request=lora_request, priority=request.priority, trace_headers=trace_headers, ) generators.append(generator) except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) assert len(generators) == 1 result_generator, = generators # Store the input messages. if request.store: self.msg_store[request.request_id] = messages if request.background: created_time = int(time.time()) response = ResponsesResponse.from_request( request, sampling_params, model_name=model_name, created_time=created_time, output=[], status="queued", usage=None, ) async with self.response_store_lock: self.response_store[response.id] = response # Run the request in the background. task = asyncio.create_task( self._run_background_request( request, sampling_params, result_generator, context, model_name, tokenizer, request_metadata, created_time, ), name=f"create_{response.id}", ) # For cleanup. response_id = response.id self.background_tasks[response_id] = task task.add_done_callback( lambda _: self.background_tasks.pop(response_id, None)) return response if request.stream: return self.responses_stream_generator( request, sampling_params, result_generator, context, model_name, tokenizer, request_metadata, ) try: return await self.responses_full_generator( request, sampling_params, result_generator, context, model_name, tokenizer, request_metadata, ) except Exception as e: return self.create_error_response(str(e)) return self.create_error_response("Should not reach here") async def _make_request( self, request: ResponsesRequest, prev_response: Optional[ResponsesResponse], tokenizer: AnyTokenizer, ): if len(request.tools) > 0: raise NotImplementedError( "Tool use is not supported in Responses API without Harmony") # Construct the input messages. messages = self._construct_input_messages(request, prev_response) _, request_prompts, engine_prompts = await self._preprocess_chat( request, tokenizer, messages, chat_template=self.chat_template, chat_template_content_format=self.chat_template_content_format, ) return messages, request_prompts, engine_prompts def _make_request_with_harmony( self, request: ResponsesRequest, prev_response: Optional[ResponsesResponse], ): if request.tool_choice != "auto": raise NotImplementedError( "Only 'auto' tool_choice is supported in " "response API with Harmony") messages = self._construct_input_messages_with_harmony( request, prev_response) prompt_token_ids = render_for_completion(messages) engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids) return messages, [prompt_token_ids], [engine_prompt] async def responses_full_generator( self, request: ResponsesRequest, sampling_params: SamplingParams, result_generator: AsyncIterator[ConversationContext], context: ConversationContext, model_name: str, tokenizer: AnyTokenizer, request_metadata: RequestResponseMetadata, created_time: Optional[int] = None, ) -> Union[ErrorResponse, ResponsesResponse]: if created_time is None: created_time = int(time.time()) try: async for _ in result_generator: pass except asyncio.CancelledError: return self.create_error_response("Client disconnected") except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) if self.use_harmony: assert isinstance(context, HarmonyContext) output = self._make_response_output_items_with_harmony(context) # TODO: these are all 0 for now! num_prompt_tokens = context.num_prompt_tokens num_generated_tokens = context.num_output_tokens num_cached_tokens = context.num_cached_tokens num_reasoning_tokens = context.num_reasoning_tokens else: assert isinstance(context, SimpleContext) final_res = context.last_output assert final_res is not None assert len(final_res.outputs) == 1 final_output = final_res.outputs[0] output = self._make_response_output_items(request, final_output, tokenizer) # Calculate usage. assert final_res.prompt_token_ids is not None num_prompt_tokens = len(final_res.prompt_token_ids) num_generated_tokens = len(final_output.token_ids) num_cached_tokens = final_res.num_cached_tokens num_reasoning_tokens = 0 usage = ResponseUsage( input_tokens=num_prompt_tokens, output_tokens=num_generated_tokens, total_tokens=num_prompt_tokens + num_generated_tokens, input_tokens_details=InputTokensDetails( cached_tokens=num_cached_tokens), output_tokens_details=OutputTokensDetails( reasoning_tokens=num_reasoning_tokens), ) response = ResponsesResponse.from_request( request, sampling_params, model_name=model_name, created_time=created_time, output=output, status="completed", usage=usage, ) if request.store: async with self.response_store_lock: stored_response = self.response_store.get(response.id) # If the response is already cancelled, don't update it. if (stored_response is None or stored_response.status != "cancelled"): self.response_store[response.id] = response return response def _make_response_output_items( self, request: ResponsesRequest, final_output: CompletionOutput, tokenizer: AnyTokenizer, ) -> list[ResponseOutputItem]: if self.reasoning_parser: try: reasoning_parser = self.reasoning_parser(tokenizer) except RuntimeError as e: logger.exception("Error in reasoning parser creation.") raise e reasoning_content, content = ( reasoning_parser.extract_reasoning_content(final_output.text, request=request)) else: reasoning_content = None content = final_output.text # Log complete response if output logging is enabled if self.enable_log_outputs and self.request_logger: output_text = "" if content: output_text = content elif reasoning_content: output_text = f"[reasoning: {reasoning_content}]" if output_text: self.request_logger.log_outputs( request_id=request.request_id, outputs=output_text, output_token_ids=final_output.token_ids, finish_reason=final_output.finish_reason, is_streaming=False, delta=False, ) output = [] if reasoning_content: reasoning_item = ResponseReasoningItem( id=f"rs_{random_uuid()}", summary=[], type="reasoning", content=[ ResponseReasoningTextContent(text=reasoning_content, type="reasoning_text") ], status=None, # NOTE: Only the last output item has status. ) output.append(reasoning_item) if content: output_text = ResponseOutputText( text=content, annotations=[], # TODO type="output_text", logprobs=None, # TODO ) message = ResponseOutputMessage( id=f"msg_{random_uuid()}", content=[output_text], role="assistant", status="completed", type="message", ) output.append(message) return output def _make_response_output_items_with_harmony( self, context: HarmonyContext, ) -> list[ResponseOutputItem]: output_items = [] num_init_messages = context.num_init_messages for msg in context.messages[num_init_messages:]: output_items.extend(parse_output_message(msg)) # Handle the generation stopped in the middle (if any). last_items = parse_remaining_state(context.parser) if last_items: output_items.extend(last_items) return output_items def _construct_input_messages( self, request: ResponsesRequest, prev_response: Optional[ResponsesResponse] = None, ) -> list[ChatCompletionMessageParam]: messages: list[ChatCompletionMessageParam] = [] if request.instructions: messages.append({ "role": "system", "content": request.instructions, }) # Prepend the conversation history. if prev_response is not None: # Add the previous messages. prev_msg = self.msg_store[prev_response.id] messages.extend(prev_msg) # Add the previous output. for output_item in prev_response.output: # NOTE: We skip the reasoning output. if isinstance(output_item, ResponseOutputMessage): for content in output_item.content: messages.append({ "role": "assistant", "content": content.text, }) # Append the new input. # Responses API supports simple text inputs without chat format. if isinstance(request.input, str): messages.append({"role": "user", "content": request.input}) else: messages.extend(request.input) # type: ignore return messages def _construct_input_messages_with_harmony( self, request: ResponsesRequest, prev_response: Optional[ResponsesResponse], ) -> list[OpenAIHarmonyMessage]: messages: list[OpenAIHarmonyMessage] = [] if prev_response is None: # New conversation. reasoning_effort = (request.reasoning.effort if request.reasoning else None) tool_types = [tool.type for tool in request.tools] enable_browser = ("web_search_preview" in tool_types and self.tool_server is not None and self.tool_server.has_tool("browser")) enable_code_interpreter = ("code_interpreter" in tool_types and self.tool_server is not None and self.tool_server.has_tool("python")) sys_msg = get_system_message( reasoning_effort=reasoning_effort, browser_description=self.tool_server.get_tool_description( "browser") if enable_browser and self.tool_server is not None else None, python_description=self.tool_server.get_tool_description( "python") if enable_code_interpreter and self.tool_server is not None else None, ) messages.append(sys_msg) dev_msg = get_developer_message(request.instructions, request.tools) messages.append(dev_msg) else: # Continue the previous conversation. # FIXME(woosuk): Currently, request params like reasoning and # instructions are ignored. prev_msgs = self.msg_store[prev_response.id] # Remove the previous chain-of-thoughts if there is a new "final" # message. Note that this also removes these messages from the # msg_store. if len(prev_msgs) > 0: last_msg = prev_msgs[-1] assert isinstance(last_msg, OpenAIHarmonyMessage) if last_msg.channel == "final": prev_final_msg_idx = -1 for i in range(len(prev_msgs) - 2, -1, -1): prev_msg_i = prev_msgs[i] assert isinstance(prev_msg_i, OpenAIHarmonyMessage) if prev_msg_i.channel == "final": prev_final_msg_idx = i break recent_turn_msgs = prev_msgs[prev_final_msg_idx + 1:] del prev_msgs[prev_final_msg_idx + 1:] for msg in recent_turn_msgs: assert isinstance(msg, OpenAIHarmonyMessage) if msg.channel != "analysis": prev_msgs.append(msg) messages.extend(prev_msgs) # Append the new input. # Reponses API supports simple text inputs without chat format. if isinstance(request.input, str): messages.append(get_user_message(request.input)) else: if prev_response is not None: prev_outputs = copy(prev_response.output) else: prev_outputs = [] for response_msg in request.input: messages.append( parse_response_input(response_msg, prev_outputs)) # User passes in a a tool call request and its output. We need # to add the tool call request to prev_outputs so that the # parse_response_input can find the tool call request when # parsing the tool call output. if isinstance(response_msg, ResponseFunctionToolCall): prev_outputs.append(response_msg) return messages async def _run_background_request( self, request: ResponsesRequest, *args, **kwargs, ): try: response = await self.responses_full_generator( request, *args, **kwargs) except Exception as e: logger.exception("Background request failed for %s", request.request_id) response = self.create_error_response(str(e)) if isinstance(response, ErrorResponse): # If the request has failed, update the status to "failed". response_id = request.request_id async with self.response_store_lock: stored_response = self.response_store.get(response_id) assert stored_response is not None if stored_response.status not in ("completed", "cancelled"): stored_response.status = "failed" async def retrieve_responses( self, response_id: str, ) -> Union[ErrorResponse, ResponsesResponse]: if not response_id.startswith("resp_"): return self._make_invalid_id_error(response_id) async with self.response_store_lock: response = self.response_store.get(response_id) if response is None: return self._make_not_found_error(response_id) return response async def cancel_responses( self, response_id: str, ) -> Union[ErrorResponse, ResponsesResponse]: if not response_id.startswith("resp_"): return self._make_invalid_id_error(response_id) async with self.response_store_lock: response = self.response_store.get(response_id) if response is None: return self._make_not_found_error(response_id) prev_status = response.status if prev_status not in ("queued", "in_progress"): return self.create_error_response( err_type="invalid_request_error", message="Cannot cancel a synchronous response.", ) # Update the status to "cancelled". response.status = "cancelled" # Abort the request. if (task := self.background_tasks.get(response_id)): task.cancel() try: await task except asyncio.CancelledError: logger.exception("Background task for %s was cancelled", response_id) return response def _make_invalid_id_error(self, response_id: str) -> ErrorResponse: return self.create_error_response( err_type="invalid_request_error", message=(f"Invalid 'response_id': '{response_id}'. " "Expected an ID that begins with 'resp'."), ) def _make_not_found_error(self, response_id: str) -> ErrorResponse: return self.create_error_response( err_type="invalid_request_error", message=f"Response with id '{response_id}' not found.", status_code=HTTPStatus.NOT_FOUND, ) def _make_store_not_supported_error(self) -> ErrorResponse: return self.create_error_response( err_type="invalid_request_error", message=("`store=True` (default) is not supported. Please set " "`store=False` in Responses API or set " "`VLLM_ENABLE_RESPONSES_API_STORE=1` in the env var when " "starting the vLLM server."), status_code=HTTPStatus.BAD_REQUEST, ) async def responses_stream_generator( self, request: ResponsesRequest, sampling_params: SamplingParams, result_generator: AsyncIterator[Optional[ConversationContext]], context: ConversationContext, model_name: str, tokenizer: AnyTokenizer, request_metadata: RequestResponseMetadata, created_time: Optional[int] = None, ) -> AsyncGenerator[str, None]: # TODO: # 1. Handle disconnect if not isinstance(context, StreamingHarmonyContext): raise NotImplementedError( "Streaming is not supported for responses API without Harmony." ) created_time = created_time or int(time.time()) sequence_number = 0 def _send_event(event: BaseModel): nonlocal sequence_number # Set sequence_number if the event has this attribute if hasattr(event, 'sequence_number'): event.sequence_number = sequence_number sequence_number += 1 # Get event type from the event's type field if it exists event_type = getattr(event, 'type', 'unknown') return (f"event: {event_type}\n" f"data: {event.model_dump_json(indent=None)}\n\n") current_content_index = 0 # FIXME: this number is never changed current_output_index = 0 current_item_id = "" # FIXME: this number is never changed sent_output_item_added = False initial_response = ResponsesResponse.from_request( request, sampling_params, model_name=model_name, created_time=created_time, output=[], status="in_progress", usage=None, ).model_dump() yield _send_event( ResponseCreatedEvent( type="response.created", sequence_number=-1, response=initial_response, )) yield _send_event( ResponseInProgressEvent( type="response.in_progress", sequence_number=-1, response=initial_response, )) async for ctx in result_generator: assert isinstance(ctx, StreamingHarmonyContext) if ctx.is_expecting_start(): current_output_index += 1 sent_output_item_added = False if len(ctx.parser.messages) > 0: previous_item = ctx.parser.messages[-1] if previous_item.recipient is not None: # Deal with tool call here pass elif previous_item.channel == "analysis": reasoning_item = ResponseReasoningItem( type="reasoning", content=[ ResponseReasoningTextContent( text=previous_item.content[0].text, type="reasoning_text", ), ], status="completed", id=current_item_id, summary=[], ) yield _send_event( ResponseReasoningTextDoneEvent( type="response.reasoning_text.done", item_id=current_item_id, sequence_number=-1, output_index=current_output_index, content_index=current_content_index, text=previous_item.content[0].text, )) yield _send_event( ResponseOutputItemDoneEvent( type="response.output_item.done", sequence_number=-1, output_index=current_output_index, item=reasoning_item, )) elif previous_item.channel == "final": text_content = ResponseOutputText( type="output_text", text=previous_item.content[0].text, annotations=[], ) yield _send_event( openai_responses_types.ResponseTextDoneEvent( type="response.output_text.done", sequence_number=-1, output_index=current_output_index, content_index=current_content_index, text=previous_item.content[0].text, logprobs=[], item_id=current_item_id, )) yield _send_event( openai_responses_types. ResponseContentPartDoneEvent( type="response.content_part.done", sequence_number=-1, item_id=current_item_id, output_index=current_output_index, content_index=current_content_index, part=text_content, )) yield _send_event( openai_responses_types.ResponseOutputItemDoneEvent( type="response.output_item.done", sequence_number=-1, output_index=current_output_index, item=ResponseOutputMessage( id=current_item_id, type="message", role="assistant", content=[text_content], status="completed", ), )) if ctx.parser.last_content_delta: if (ctx.parser.current_channel == "final" and ctx.parser.current_recipient is None): if not sent_output_item_added: sent_output_item_added = True yield _send_event( openai_responses_types. ResponseOutputItemAddedEvent( type="response.output_item.added", sequence_number=-1, output_index=current_output_index, item=openai_responses_types. ResponseOutputMessage( id=current_item_id, type="message", role="assistant", content=[], status="in_progress", ), )) yield _send_event( openai_responses_types. ResponseContentPartAddedEvent( type="response.content_part.added", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, content_index=current_content_index, part=openai_responses_types.ResponseOutputText( type="output_text", text="", annotations=[], logprobs=[], ), )) yield _send_event( openai_responses_types.ResponseTextDeltaEvent( type="response.output_text.delta", sequence_number=-1, content_index=current_content_index, output_index=current_output_index, item_id=current_item_id, delta=ctx.parser.last_content_delta, # TODO, use logprobs from ctx.last_request_output logprobs=[], )) elif (ctx.parser.current_channel == "analysis" and ctx.parser.current_recipient is None): if not sent_output_item_added: sent_output_item_added = True yield _send_event( openai_responses_types. ResponseOutputItemAddedEvent( type="response.output_item.added", sequence_number=-1, output_index=current_output_index, item=openai_responses_types. ResponseReasoningItem( type="reasoning", id=current_item_id, summary=[], status="in_progress", ), )) yield _send_event( openai_responses_types. ResponseContentPartAddedEvent( type="response.content_part.added", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, content_index=current_content_index, part=openai_responses_types.ResponseOutputText( type="output_text", text="", annotations=[], logprobs=[], ), )) yield _send_event( ResponseReasoningTextDeltaEvent( type="response.reasoning_text.delta", item_id=current_item_id, output_index=current_output_index, content_index=current_content_index, delta=ctx.parser.last_content_delta, sequence_number=-1, )) if ctx.is_assistant_action_turn() and len(ctx.parser.messages) > 0: previous_item = ctx.parser.messages[-1] if (self.tool_server is not None and self.tool_server.has_tool("browser") and previous_item.recipient is not None and previous_item.recipient.startswith("browser.")): function_name = previous_item.recipient[len("browser."):] action = None parsed_args = json.loads(previous_item.content[0].text) if function_name == "search": action = (openai_responses_types. response_function_web_search.ActionSearch( type="search", query=parsed_args["query"], )) elif function_name == "open": action = ( openai_responses_types. response_function_web_search.ActionOpenPage( type="open_page", # TODO: translate to url url=f"cursor:{parsed_args.get('cursor', '')}", )) elif function_name == "find": action = ( openai_responses_types. response_function_web_search.ActionFind( type="find", pattern=parsed_args["pattern"], # TODO: translate to url url=f"cursor:{parsed_args.get('cursor', '')}", )) else: raise ValueError( f"Unknown function name: {function_name}") yield _send_event( openai_responses_types.ResponseOutputItemAddedEvent( type="response.output_item.added", sequence_number=-1, output_index=current_output_index, item=openai_responses_types. response_function_web_search. ResponseFunctionWebSearch( # TODO: generate a unique id for web search call type="web_search_call", id=current_item_id, action=action, status="in_progress", ), )) yield _send_event( openai_responses_types. ResponseWebSearchCallInProgressEvent( type="response.web_search_call.in_progress", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, )) yield _send_event( openai_responses_types. ResponseWebSearchCallSearchingEvent( type="response.web_search_call.searching", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, )) # enqueue yield _send_event( openai_responses_types. ResponseWebSearchCallCompletedEvent( type="response.web_search_call.completed", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, )) yield _send_event( openai_responses_types.ResponseOutputItemDoneEvent( type="response.output_item.done", sequence_number=-1, output_index=current_output_index, item=openai_responses_types. ResponseFunctionWebSearch( type="web_search_call", id=current_item_id, action=action, status="completed", ), )) if (self.tool_server is not None and self.tool_server.has_tool("python") and previous_item.recipient is not None and previous_item.recipient.startswith("python")): yield _send_event( openai_responses_types.ResponseOutputItemAddedEvent( type="response.output_item.added", sequence_number=-1, output_index=current_output_index, item=openai_responses_types. ResponseCodeInterpreterToolCallParam( type="code_interpreter_call", id=current_item_id, code="", container_id="auto", outputs=[], status="in_progress", ), )) yield _send_event( openai_responses_types. ResponseCodeInterpreterCallInProgressEvent( type="response.code_interpreter_call.in_progress", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, )) # TODO: do we need to add delta event here? yield _send_event( openai_responses_types. ResponseCodeInterpreterCallCodeDoneEvent( type="response.code_interpreter_call_code.done", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, code=previous_item.content[0].text)) yield _send_event( openai_responses_types. ResponseCodeInterpreterCallInterpretingEvent( type="response.code_interpreter_call.interpreting", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, )) yield _send_event( openai_responses_types. ResponseCodeInterpreterCallCompletedEvent( type="response.code_interpreter_call.completed", sequence_number=-1, output_index=current_output_index, item_id=current_item_id, )) yield _send_event( openai_responses_types.ResponseOutputItemDoneEvent( type="response.output_item.done", sequence_number=-1, output_index=current_output_index, item=openai_responses_types. ResponseCodeInterpreterToolCallParam( type="code_interpreter_call", id=current_item_id, code=previous_item.content[0].text, container_id="auto", # TODO: add outputs here outputs=[], status="completed", ), )) async def empty_async_generator(): # A hack to trick Python to think this is a generator but in fact # it immediately returns. if False: yield final_response = await self.responses_full_generator( request, sampling_params, empty_async_generator(), context, model_name, tokenizer, request_metadata, created_time=created_time, ) yield _send_event( openai_responses_types.ResponseCompletedEvent( type="response.completed", sequence_number=-1, response=final_response.model_dump(), ))