# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import asyncio import contextlib import copy import json import logging from abc import ABC, abstractmethod from collections.abc import Callable from contextlib import AsyncExitStack from dataclasses import replace from typing import TYPE_CHECKING, Union from openai.types.responses.response_function_tool_call_output_item import ( ResponseFunctionToolCallOutputItem, ) from openai.types.responses.tool import Mcp from openai_harmony import Author, Message, Role, StreamState, TextContent from vllm import envs from vllm.entrypoints.chat_utils import ( ChatTemplateContentFormatOption, ) from vllm.entrypoints.constants import MCP_PREFIX from vllm.entrypoints.mcp.tool import Tool from vllm.entrypoints.mcp.tool_server import ToolServer from vllm.entrypoints.openai.engine.protocol import ( FunctionCall, ) from vllm.entrypoints.openai.parser.harmony_utils import ( get_encoding, get_streamable_parser_for_assistant, render_for_completion, ) from vllm.entrypoints.openai.parser.responses_parser import ( get_responses_parser_for_simple_context, ) from vllm.entrypoints.openai.responses.protocol import ( ResponseInputOutputItem, ResponseRawMessageAndToken, ResponsesRequest, ) from vllm.entrypoints.openai.responses.utils import construct_tool_dicts from vllm.outputs import RequestOutput from vllm.reasoning.abs_reasoning_parsers import ReasoningParser from vllm.renderers import RendererLike from vllm.tokenizers import TokenizerLike from vllm.tool_parsers.abstract_tool_parser import ToolParser from vllm.utils import random_uuid if TYPE_CHECKING: from mcp.client import ClientSession logger = logging.getLogger(__name__) # This is currently needed as the tool type doesn't 1:1 match the # tool namespace, which is what is used to look up the # connection to the tool server _TOOL_NAME_TO_TYPE_MAP = { "browser": "web_search_preview", "python": "code_interpreter", "container": "container", } def _map_tool_name_to_tool_type(tool_name: str) -> str: if tool_name not in _TOOL_NAME_TO_TYPE_MAP: available_tools = ", ".join(_TOOL_NAME_TO_TYPE_MAP.keys()) raise ValueError( f"Built-in tool name '{tool_name}' not defined in mapping. " f"Available tools: {available_tools}" ) return _TOOL_NAME_TO_TYPE_MAP[tool_name] class TurnMetrics: """Tracks token and toolcall details for a single conversation turn.""" def __init__( self, input_tokens: int = 0, output_tokens: int = 0, cached_input_tokens: int = 0, tool_output_tokens: int = 0, ) -> None: self.input_tokens = input_tokens self.output_tokens = output_tokens self.cached_input_tokens = cached_input_tokens self.tool_output_tokens = tool_output_tokens def reset(self) -> None: """Reset counters for a new turn.""" self.input_tokens = 0 self.output_tokens = 0 self.cached_input_tokens = 0 self.tool_output_tokens = 0 def copy(self) -> "TurnMetrics": """Create a copy of this turn's token counts.""" return TurnMetrics( self.input_tokens, self.output_tokens, self.cached_input_tokens, self.tool_output_tokens, ) class ConversationContext(ABC): @abstractmethod def append_output(self, output: RequestOutput) -> None: pass @abstractmethod def append_tool_output(self, output) -> None: pass @abstractmethod async def call_tool(self) -> list[Message]: pass @abstractmethod def need_builtin_tool_call(self) -> bool: pass @abstractmethod def render_for_completion(self) -> list[int]: pass @abstractmethod async def init_tool_sessions( self, tool_server: ToolServer | None, exit_stack: AsyncExitStack, request_id: str, mcp_tools: dict[str, Mcp], ) -> None: pass @abstractmethod async def cleanup_session(self) -> None: raise NotImplementedError("Should not be called.") def _create_json_parse_error_messages( last_msg: Message, e: json.JSONDecodeError ) -> list[Message]: """ Creates an error message when json parse failed. """ error_msg = ( f"Error parsing tool arguments as JSON: {str(e)}. " "Please ensure the tool call arguments are valid JSON and try again." ) content = TextContent(text=error_msg) author = Author(role=Role.TOOL, name=last_msg.recipient) return [ Message( author=author, content=[content], recipient=Role.ASSISTANT, channel=last_msg.channel, ) ] class SimpleContext(ConversationContext): """This is a context that cannot handle MCP tool calls""" def __init__(self): self.last_output = None # Accumulated final output for streaming mode self._accumulated_text: str = "" self._accumulated_token_ids: list[int] = [] self._accumulated_logprobs: list = [] self.num_prompt_tokens = 0 self.num_output_tokens = 0 self.num_cached_tokens = 0 # todo num_reasoning_tokens is not implemented yet. self.num_reasoning_tokens = 0 # not implemented yet for SimpleContext self.all_turn_metrics = [] self.input_messages: list[ResponseRawMessageAndToken] = [] self.output_messages: list[ResponseRawMessageAndToken] = [] def append_output(self, output) -> None: self.last_output = output if not isinstance(output, RequestOutput): raise ValueError("SimpleContext only supports RequestOutput.") self.num_prompt_tokens = len(output.prompt_token_ids or []) self.num_cached_tokens = output.num_cached_tokens or 0 self.num_output_tokens += len(output.outputs[0].token_ids or []) # Accumulate text, token_ids, and logprobs for streaming mode delta_output = output.outputs[0] self._accumulated_text += delta_output.text self._accumulated_token_ids.extend(delta_output.token_ids) if delta_output.logprobs is not None: self._accumulated_logprobs.extend(delta_output.logprobs) if len(self.input_messages) == 0: output_prompt = output.prompt or "" output_prompt_token_ids = output.prompt_token_ids or [] self.input_messages.append( ResponseRawMessageAndToken( message=output_prompt, tokens=output_prompt_token_ids, ) ) self.output_messages.append( ResponseRawMessageAndToken( message=delta_output.text, tokens=delta_output.token_ids, ) ) @property def final_output(self) -> RequestOutput | None: """Return the final output, with complete text/token_ids/logprobs.""" if self.last_output is not None and self.last_output.outputs: assert isinstance(self.last_output, RequestOutput) final_output = copy.copy(self.last_output) # copy inner item to avoid modify last_output final_output.outputs = [replace(item) for item in self.last_output.outputs] final_output.outputs[0].text = self._accumulated_text final_output.outputs[0].token_ids = tuple(self._accumulated_token_ids) if self._accumulated_logprobs: final_output.outputs[0].logprobs = self._accumulated_logprobs return final_output return self.last_output def append_tool_output(self, output) -> None: raise NotImplementedError("Should not be called.") def need_builtin_tool_call(self) -> bool: return False async def call_tool(self) -> list[Message]: raise NotImplementedError("Should not be called.") def render_for_completion(self) -> list[int]: raise NotImplementedError("Should not be called.") async def init_tool_sessions( self, tool_server: ToolServer | None, exit_stack: AsyncExitStack, request_id: str, mcp_tools: dict[str, Mcp], ) -> None: pass async def cleanup_session(self) -> None: raise NotImplementedError("Should not be called.") class ParsableContext(ConversationContext): def __init__( self, *, response_messages: list[ResponseInputOutputItem], renderer: RendererLike, reasoning_parser_cls: Callable[[TokenizerLike], ReasoningParser] | None, request: ResponsesRequest, available_tools: list[str] | None, tool_parser_cls: Callable[[TokenizerLike], ToolParser] | None, chat_template: str | None, chat_template_content_format: ChatTemplateContentFormatOption, ): self.num_prompt_tokens = 0 self.num_output_tokens = 0 self.num_cached_tokens = 0 # TODO: num_reasoning_tokens is not implemented yet. self.num_reasoning_tokens = 0 # not implemented yet for ParsableContext self.all_turn_metrics: list[TurnMetrics] = [] if reasoning_parser_cls is None: raise ValueError("reasoning_parser_cls must be provided.") tokenizer = renderer.get_tokenizer() self.parser = get_responses_parser_for_simple_context( tokenizer=tokenizer, reasoning_parser_cls=reasoning_parser_cls, response_messages=response_messages, request=request, tool_parser_cls=tool_parser_cls, ) self.tool_parser_cls = tool_parser_cls self.request = request self.renderer = renderer self.tokenizer = tokenizer self.available_tools = available_tools or [] self._tool_sessions: dict[str, ClientSession | Tool] = {} self.called_tools: set[str] = set() self.tool_dicts = construct_tool_dicts(request.tools, request.tool_choice) self.chat_template = chat_template self.chat_template_content_format = chat_template_content_format self.input_messages: list[ResponseRawMessageAndToken] = [] self.output_messages: list[ResponseRawMessageAndToken] = [] def append_output(self, output: RequestOutput) -> None: self.num_prompt_tokens = len(output.prompt_token_ids or []) self.num_cached_tokens = output.num_cached_tokens or 0 self.num_output_tokens += len(output.outputs[0].token_ids or []) self.parser.process(output.outputs[0]) # only store if enable_response_messages is True, save memory if self.request.enable_response_messages: output_prompt = output.prompt or "" output_prompt_token_ids = output.prompt_token_ids or [] if len(self.input_messages) == 0: self.input_messages.append( ResponseRawMessageAndToken( message=output_prompt, tokens=output_prompt_token_ids, ) ) else: self.output_messages.append( ResponseRawMessageAndToken( message=output_prompt, tokens=output_prompt_token_ids, ) ) self.output_messages.append( ResponseRawMessageAndToken( message=output.outputs[0].text, tokens=output.outputs[0].token_ids, ) ) def append_tool_output(self, output: list[ResponseInputOutputItem]) -> None: self.parser.response_messages.extend(output) def need_builtin_tool_call(self) -> bool: """Return true if the last message is a MCP tool call""" last_message = self.parser.response_messages[-1] # TODO(qandrew): figure out which tools are MCP tools if last_message.type == "function_call": # noqa: SIM102 if last_message.name in ( "code_interpreter", "python", "web_search_preview", ) or last_message.name.startswith("container"): return True return False async def call_python_tool( self, tool_session: Union["ClientSession", Tool], last_msg: FunctionCall ) -> list[ResponseInputOutputItem]: self.called_tools.add("python") if isinstance(tool_session, Tool): return await tool_session.get_result_parsable_context(self) args = json.loads(last_msg.arguments) param = { "code": args["code"], } result = await tool_session.call_tool("python", param) result_str = result.content[0].text message = ResponseFunctionToolCallOutputItem( id=f"mcpo_{random_uuid()}", type="function_call_output", call_id=f"call_{random_uuid()}", output=result_str, status="completed", ) return [message] async def call_search_tool( self, tool_session: Union["ClientSession", Tool], last_msg: FunctionCall ) -> list[ResponseInputOutputItem]: self.called_tools.add("browser") if isinstance(tool_session, Tool): return await tool_session.get_result_parsable_context(self) if envs.VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: try: args = json.loads(last_msg.arguments) except json.JSONDecodeError as e: return _create_json_parse_error_messages(last_msg, e) else: args = json.loads(last_msg.arguments) result = await tool_session.call_tool("search", args) result_str = result.content[0].text message = ResponseFunctionToolCallOutputItem( id=f"fco_{random_uuid()}", type="function_call_output", call_id=f"call_{random_uuid()}", output=result_str, status="completed", ) return [message] async def call_container_tool( self, tool_session: Union["ClientSession", Tool], last_msg: Message ) -> list[Message]: """ Call container tool. Expect this to be run in a stateful docker with command line terminal. The official container tool would at least expect the following format: - for tool name: exec - args: { "cmd":List[str] "command to execute", "workdir":optional[str] "current working directory", "env":optional[object/dict] "environment variables", "session_name":optional[str] "session name", "timeout":optional[int] "timeout in seconds", "user":optional[str] "user name", } """ self.called_tools.add("container") if isinstance(tool_session, Tool): return await tool_session.get_result_parsable_context(self) # tool_name = last_msg.recipient.split(".")[1].split(" ")[0] if envs.VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: try: args = json.loads(last_msg.arguments) except json.JSONDecodeError as e: return _create_json_parse_error_messages(last_msg, e) else: args = json.loads(last_msg.arguments) result = await tool_session.call_tool("exec", args) result_str = result.content[0].text message = ResponseFunctionToolCallOutputItem( id=f"fco_{random_uuid()}", type="function_call_output", call_id=f"call_{random_uuid()}", output=result_str, status="completed", ) return [message] async def call_tool(self) -> list[ResponseInputOutputItem]: if not self.parser.response_messages: return [] last_msg = self.parser.response_messages[-1] # change this to a mcp_ function call last_msg.id = f"{MCP_PREFIX}{random_uuid()}" self.parser.response_messages[-1] = last_msg if last_msg.name == "code_interpreter": return await self.call_python_tool(self._tool_sessions["python"], last_msg) elif last_msg.name == "web_search_preview": return await self.call_search_tool(self._tool_sessions["browser"], last_msg) elif last_msg.name.startswith("container"): return await self.call_container_tool( self._tool_sessions["container"], last_msg ) return [] def render_for_completion(self): raise NotImplementedError("Should not be called.") async def init_tool_sessions( self, tool_server: ToolServer | None, exit_stack: AsyncExitStack, request_id: str, mcp_tools: dict[str, Mcp], ): if tool_server: for tool_name in self.available_tools: if tool_name in self._tool_sessions: continue tool_type = _map_tool_name_to_tool_type(tool_name) headers = ( mcp_tools[tool_type].headers if tool_type in mcp_tools else None ) tool_session = await exit_stack.enter_async_context( tool_server.new_session(tool_name, request_id, headers) ) self._tool_sessions[tool_name] = tool_session exit_stack.push_async_exit(self.cleanup_session) async def cleanup_session(self, *args, **kwargs) -> None: """Can be used as coro to used in __aexit__""" async def cleanup_tool_session(tool_session): if not isinstance(tool_session, Tool): logger.info( "Cleaning up tool session for %s", tool_session._client_info ) with contextlib.suppress(Exception): await tool_session.call_tool("cleanup_session", {}) await asyncio.gather( *( cleanup_tool_session(self._tool_sessions[tool]) for tool in self.called_tools ) ) class HarmonyContext(ConversationContext): def __init__( self, messages: list, available_tools: list[str], ): self._messages = messages self.finish_reason: str | None = None self.available_tools = available_tools self._tool_sessions: dict[str, ClientSession | Tool] = {} self.called_tools: set[str] = set() self.parser = get_streamable_parser_for_assistant() self.num_init_messages = len(messages) self.num_prompt_tokens = 0 self.num_output_tokens = 0 self.num_cached_tokens = 0 self.num_reasoning_tokens = 0 self.num_tool_output_tokens = 0 # Turn tracking - replaces multiple individual tracking variables self.current_turn_metrics = TurnMetrics() # Track metrics for all turns self.all_turn_metrics: list[TurnMetrics] = [] self.is_first_turn = True self.first_tok_of_message = True # For streaming support def _update_num_reasoning_tokens(self): # Count all analysis and commentary channels as reasoning tokens if self.parser.current_channel in {"analysis", "commentary"}: self.num_reasoning_tokens += 1 def append_output(self, output: RequestOutput) -> None: output_token_ids = output.outputs[0].token_ids self.parser = get_streamable_parser_for_assistant() for token_id in output_token_ids: self.parser.process(token_id) # Check if the current token is part of reasoning content self._update_num_reasoning_tokens() self._update_prefill_token_usage(output) self._update_decode_token_usage(output) # Append current turn to all turn list for next turn's calculations self.all_turn_metrics.append(self.current_turn_metrics.copy()) self.current_turn_metrics.reset() # append_output is called only once before tool calling # in non-streaming case # so we can append all the parser messages to _messages output_msgs = self.parser.messages # The responses finish reason is set in the last message self.finish_reason = output.outputs[0].finish_reason self._messages.extend(output_msgs) def append_tool_output(self, output: list[Message]) -> None: output_msgs = output self._messages.extend(output_msgs) def _update_prefill_token_usage(self, output: RequestOutput) -> None: """Update token usage statistics for the prefill phase of generation. The prefill phase processes the input prompt tokens. This method: 1. Counts the prompt tokens for this turn 2. Calculates tool output tokens for multi-turn conversations 3. Updates cached token counts 4. Tracks state for next turn calculations Tool output tokens are calculated as: current_prompt_tokens - last_turn_prompt_tokens - last_turn_output_tokens This represents tokens added between turns (typically tool responses). Args: output: The RequestOutput containing prompt token information """ if output.prompt_token_ids is not None: this_turn_input_tokens = len(output.prompt_token_ids) else: this_turn_input_tokens = 0 logger.error("RequestOutput appended contains no prompt_token_ids.") # Update current turn input tokens self.current_turn_metrics.input_tokens = this_turn_input_tokens self.num_prompt_tokens += this_turn_input_tokens # Calculate tool tokens (except on first turn) if self.is_first_turn: self.is_first_turn = False else: previous_turn = self.all_turn_metrics[-1] # start counting tool after first turn # tool tokens = this turn prefill - last turn prefill - # last turn decode this_turn_tool_tokens = ( self.current_turn_metrics.input_tokens - previous_turn.input_tokens - previous_turn.output_tokens ) # Handle negative tool token counts (shouldn't happen in normal # cases) if this_turn_tool_tokens < 0: logger.error( "Negative tool output tokens calculated: %d " "(current_input=%d, previous_input=%d, " "previous_output=%d). Setting to 0.", this_turn_tool_tokens, self.current_turn_metrics.input_tokens, previous_turn.input_tokens, previous_turn.output_tokens, ) this_turn_tool_tokens = 0 self.num_tool_output_tokens += this_turn_tool_tokens self.current_turn_metrics.tool_output_tokens = this_turn_tool_tokens # Update cached tokens num_cached_token = output.num_cached_tokens if num_cached_token is not None: self.num_cached_tokens += num_cached_token self.current_turn_metrics.cached_input_tokens = num_cached_token def _update_decode_token_usage(self, output: RequestOutput) -> int: """Update token usage statistics for the decode phase of generation. The decode phase processes the generated output tokens. This method: 1. Counts output tokens from all completion outputs 2. Updates the total output token count 3. Tracks tokens generated in the current turn In streaming mode, this is called for each token generated. In non-streaming mode, this is called once with all output tokens. Args: output: The RequestOutput containing generated token information Returns: int: Number of output tokens processed in this call """ updated_output_token_count = 0 if output.outputs: for completion_output in output.outputs: # only keep last round updated_output_token_count += len(completion_output.token_ids) self.num_output_tokens += updated_output_token_count self.current_turn_metrics.output_tokens += updated_output_token_count return updated_output_token_count @property def messages(self) -> list: return self._messages def need_builtin_tool_call(self) -> bool: last_msg = self.messages[-1] recipient = last_msg.recipient return recipient is not None and ( recipient.startswith("browser.") or recipient.startswith("python") or recipient.startswith("container.") ) async def call_tool(self) -> list[Message]: if not self.messages: return [] last_msg = self.messages[-1] recipient = last_msg.recipient if recipient is not None: if recipient.startswith("browser."): return await self.call_search_tool( self._tool_sessions["browser"], last_msg ) elif recipient.startswith("python"): return await self.call_python_tool( self._tool_sessions["python"], last_msg ) elif recipient.startswith("container."): return await self.call_container_tool( self._tool_sessions["container"], last_msg ) raise ValueError("No tool call found") def render_for_completion(self) -> list[int]: return render_for_completion(self.messages) async def call_search_tool( self, tool_session: Union["ClientSession", Tool], last_msg: Message ) -> list[Message]: self.called_tools.add("browser") if isinstance(tool_session, Tool): return await tool_session.get_result(self) tool_name = last_msg.recipient.split(".")[1] if envs.VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: try: args = json.loads(last_msg.content[0].text) except json.JSONDecodeError as e: return _create_json_parse_error_messages(last_msg, e) else: args = json.loads(last_msg.content[0].text) result = await tool_session.call_tool(tool_name, args) result_str = result.content[0].text content = TextContent(text=result_str) author = Author(role=Role.TOOL, name=last_msg.recipient) return [ Message( author=author, content=[content], recipient=Role.ASSISTANT, channel=last_msg.channel, ) ] async def call_python_tool( self, tool_session: Union["ClientSession", Tool], last_msg: Message ) -> list[Message]: self.called_tools.add("python") if isinstance(tool_session, Tool): return await tool_session.get_result(self) param = { "code": last_msg.content[0].text, } result = await tool_session.call_tool("python", param) result_str = result.content[0].text content = TextContent(text=result_str) author = Author(role=Role.TOOL, name="python") return [ Message( author=author, content=[content], channel=last_msg.channel, recipient=Role.ASSISTANT, ) ] async def init_tool_sessions( self, tool_server: ToolServer | None, exit_stack: AsyncExitStack, request_id: str, mcp_tools: dict[str, Mcp], ): if tool_server: for tool_name in self.available_tools: if tool_name not in self._tool_sessions: tool_type = _map_tool_name_to_tool_type(tool_name) headers = ( mcp_tools[tool_type].headers if tool_type in mcp_tools else None ) tool_session = await exit_stack.enter_async_context( tool_server.new_session(tool_name, request_id, headers) ) self._tool_sessions[tool_name] = tool_session exit_stack.push_async_exit(self.cleanup_session) async def call_container_tool( self, tool_session: Union["ClientSession", Tool], last_msg: Message ) -> list[Message]: """ Call container tool. Expect this to be run in a stateful docker with command line terminal. The official container tool would at least expect the following format: - for tool name: exec - args: { "cmd":List[str] "command to execute", "workdir":optional[str] "current working directory", "env":optional[object/dict] "environment variables", "session_name":optional[str] "session name", "timeout":optional[int] "timeout in seconds", "user":optional[str] "user name", } """ self.called_tools.add("container") if isinstance(tool_session, Tool): return await tool_session.get_result(self) tool_name = last_msg.recipient.split(".")[1].split(" ")[0] if envs.VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: try: args = json.loads(last_msg.content[0].text) except json.JSONDecodeError as e: return _create_json_parse_error_messages(last_msg, e) else: args = json.loads(last_msg.content[0].text) result = await tool_session.call_tool(tool_name, args) result_str = result.content[0].text content = TextContent(text=result_str) author = Author(role=Role.TOOL, name=last_msg.recipient) return [ Message( author=author, content=[content], recipient=Role.ASSISTANT, channel=last_msg.channel, ) ] async def cleanup_session(self, *args, **kwargs) -> None: """Can be used as coro to used in __aexit__""" async def cleanup_tool_session(tool_session): if not isinstance(tool_session, Tool): logger.info( "Cleaning up tool session for %s", tool_session._client_info ) with contextlib.suppress(Exception): await tool_session.call_tool("cleanup_session", {}) await asyncio.gather( *( cleanup_tool_session(self._tool_sessions[tool]) for tool in self.called_tools ) ) class StreamingHarmonyContext(HarmonyContext): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.last_output = None self.parser = get_streamable_parser_for_assistant() self.encoding = get_encoding() self.last_tok = None self.first_tok_of_message = True self.last_content_delta = None @property def messages(self) -> list: return self._messages def append_output(self, output: RequestOutput) -> None: # append_output is called for each output token in streaming case, # so we only want to add the prompt tokens once for each message. self.last_content_delta = None if self.first_tok_of_message: self._update_prefill_token_usage(output) # Reset self.first_tok_of_message if needed: # if the current token is the last one of the current message # (finished=True), then the next token processed will mark the # beginning of a new message self.first_tok_of_message = output.finished last_delta_text = "" for tok in output.outputs[0].token_ids: self.parser.process(tok) last_delta_text += self.parser.last_content_delta or "" if last_delta_text: self.last_content_delta = last_delta_text self._update_decode_token_usage(output) # For streaming, update previous turn when message is complete if output.finished: self.all_turn_metrics.append(self.current_turn_metrics.copy()) self.current_turn_metrics.reset() # Check if the current token is part of reasoning content self._update_num_reasoning_tokens() self.last_tok = tok if len(self._messages) - self.num_init_messages < len(self.parser.messages): self._messages.extend( self.parser.messages[len(self._messages) - self.num_init_messages :] ) def append_tool_output(self, output: list[Message]) -> None: # Handle the case of tool output in direct message format assert len(output) == 1, "Tool output should be a single message" msg = output[0] # Sometimes the recipient is not set for tool messages, # so we set it to "assistant" if msg.author.role == Role.TOOL and msg.recipient is None: msg.recipient = "assistant" toks = self.encoding.render(msg) for tok in toks: self.parser.process(tok) self.last_tok = toks[-1] # TODO: add tool_output messages to self._messages def is_expecting_start(self) -> bool: return self.parser.state == StreamState.EXPECT_START def is_assistant_action_turn(self) -> bool: return self.last_tok in self.encoding.stop_tokens_for_assistant_actions() def render_for_completion(self) -> list[int]: # now this list of tokens as next turn's starting tokens # `<|start|>assistant`, # we need to process them in parser. rendered_tokens = super().render_for_completion() last_n = -1 to_process = [] while rendered_tokens[last_n] != self.last_tok: to_process.append(rendered_tokens[last_n]) last_n -= 1 for tok in reversed(to_process): self.parser.process(tok) return rendered_tokens