vllm.entrypoints.responses_utils ¶

def construct_chat_message_with_tool_call(
    item: ResponseInputOutputItem,
) -> ChatCompletionMessageParam:
    if isinstance(item, ResponseFunctionToolCall):
        # Append the function call as a tool call.
        return ChatCompletionAssistantMessageParam(
            role="assistant",
            tool_calls=[
                ChatCompletionMessageToolCallParam(
                    id=item.call_id,
                    function=FunctionCallTool(
                        name=item.name,
                        arguments=item.arguments,
                    ),
                    type="function",
                )
            ],
        )
    elif item.get("type") == "function_call_output":
        # Append the function call output as a tool message.
        return ChatCompletionToolMessageParam(
            role="tool",
            content=item.get("output"),
            tool_call_id=item.get("call_id"),
        )
    return item  # type: ignore

def convert_tool_responses_to_completions_format(tool: dict) -> dict:
    """
    Convert a flat tool schema:
        {"type": "function", "name": "...", "description": "...", "parameters": {...}}
    into:
        {"type": "function", "function": {...}}
    """
    return {
        "type": "function",
        "function": tool,
    }

def extract_tool_types(tools: list[Tool]) -> set[str]:
    """
    Extracts the tool types from the given tools.
    """
    tool_types: set[str] = set()
    for tool in tools:
        if tool.type == "mcp":
            # Allow the MCP Tool type to enable built in tools if the
            # server_label is allowlisted in
            # envs.VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS
            if tool.server_label in envs.VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS:
                tool_types.add(tool.server_label)
        else:
            tool_types.add(tool.type)
    return tool_types