# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ This file contains the command line arguments for the vLLM's OpenAI-compatible server. It is kept in a separate file for documentation purposes. """ import argparse import json import ssl from collections.abc import Sequence from dataclasses import field from typing import Any, Literal from pydantic.dataclasses import dataclass import vllm.envs as envs from vllm.config import config from vllm.engine.arg_utils import AsyncEngineArgs, optional_type from vllm.entrypoints.chat_utils import ( ChatTemplateContentFormatOption, validate_chat_template, ) from vllm.entrypoints.constants import ( H11_MAX_HEADER_COUNT_DEFAULT, H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT, ) from vllm.entrypoints.openai.models.protocol import LoRAModulePath from vllm.logger import init_logger from vllm.tool_parsers import ToolParserManager from vllm.utils.argparse_utils import FlexibleArgumentParser logger = init_logger(__name__) class LoRAParserAction(argparse.Action): def __call__( self, parser: argparse.ArgumentParser, namespace: argparse.Namespace, values: str | Sequence[str] | None, option_string: str | None = None, ): if values is None: values = [] if isinstance(values, str): raise TypeError("Expected values to be a list") lora_list: list[LoRAModulePath] = [] for item in values: if item in [None, ""]: # Skip if item is None or empty string continue if "=" in item and "," not in item: # Old format: name=path name, path = item.split("=") lora_list.append(LoRAModulePath(name, path)) else: # Assume JSON format try: lora_dict = json.loads(item) lora = LoRAModulePath(**lora_dict) lora_list.append(lora) except json.JSONDecodeError: parser.error(f"Invalid JSON format for --lora-modules: {item}") except TypeError as e: parser.error( f"Invalid fields for --lora-modules: {item} - {str(e)}" ) setattr(namespace, self.dest, lora_list) @config @dataclass class FrontendArgs: """Arguments for the OpenAI-compatible frontend server.""" host: str | None = None """Host name.""" port: int = 8000 """Port number.""" uds: str | None = None """Unix domain socket path. If set, host and port arguments are ignored.""" uvicorn_log_level: Literal[ "critical", "error", "warning", "info", "debug", "trace" ] = "info" """Log level for uvicorn.""" disable_uvicorn_access_log: bool = False """Disable uvicorn access log.""" allow_credentials: bool = False """Allow credentials.""" allowed_origins: list[str] = field(default_factory=lambda: ["*"]) """Allowed origins.""" allowed_methods: list[str] = field(default_factory=lambda: ["*"]) """Allowed methods.""" allowed_headers: list[str] = field(default_factory=lambda: ["*"]) """Allowed headers.""" api_key: list[str] | None = None """If provided, the server will require one of these keys to be presented in the header.""" lora_modules: list[LoRAModulePath] | None = None """LoRA modules configurations in either 'name=path' format or JSON format or JSON list format. Example (old format): `'name=path'` Example (new format): `{\"name\": \"name\", \"path\": \"lora_path\", \"base_model_name\": \"id\"}`""" chat_template: str | None = None """The file path to the chat template, or the template in single-line form for the specified model.""" chat_template_content_format: ChatTemplateContentFormatOption = "auto" """The format to render message content within a chat template. * "string" will render the content as a string. Example: `"Hello World"` * "openai" will render the content as a list of dictionaries, similar to OpenAI schema. Example: `[{"type": "text", "text": "Hello world!"}]`""" trust_request_chat_template: bool = False """Whether to trust the chat template provided in the request. If False, the server will always use the chat template specified by `--chat-template` or the ones from tokenizer.""" default_chat_template_kwargs: dict[str, Any] | None = None """Default keyword arguments to pass to the chat template renderer. These will be merged with request-level chat_template_kwargs, with request values taking precedence. Useful for setting default behavior for reasoning models. Example: '{"enable_thinking": false}' to disable thinking mode by default for Qwen3/DeepSeek models.""" response_role: str = "assistant" """The role name to return if `request.add_generation_prompt=true`.""" ssl_keyfile: str | None = None """The file path to the SSL key file.""" ssl_certfile: str | None = None """The file path to the SSL cert file.""" ssl_ca_certs: str | None = None """The CA certificates file.""" enable_ssl_refresh: bool = False """Refresh SSL Context when SSL certificate files change""" ssl_cert_reqs: int = int(ssl.CERT_NONE) """Whether client certificate is required (see stdlib ssl module's).""" ssl_ciphers: str | None = None """SSL cipher suites for HTTPS (TLS 1.2 and below only). Example: 'ECDHE-RSA-AES256-GCM-SHA384:ECDHE-RSA-CHACHA20-POLY1305'""" root_path: str | None = None """FastAPI root_path when app is behind a path based routing proxy.""" middleware: list[str] = field(default_factory=lambda: []) """Additional ASGI middleware to apply to the app. We accept multiple --middleware arguments. The value should be an import path. If a function is provided, vLLM will add it to the server using `@app.middleware('http')`. If a class is provided, vLLM will add it to the server using `app.add_middleware()`.""" return_tokens_as_token_ids: bool = False """When `--max-logprobs` is specified, represents single tokens as strings of the form 'token_id:{token_id}' so that tokens that are not JSON-encodable can be identified.""" disable_frontend_multiprocessing: bool = False """If specified, will run the OpenAI frontend server in the same process as the model serving engine.""" enable_request_id_headers: bool = False """If specified, API server will add X-Request-Id header to responses.""" enable_auto_tool_choice: bool = False """Enable auto tool choice for supported models. Use `--tool-call-parser` to specify which parser to use.""" exclude_tools_when_tool_choice_none: bool = False """If specified, exclude tool definitions in prompts when tool_choice='none'.""" tool_call_parser: str | None = None """Select the tool call parser depending on the model that you're using. This is used to parse the model-generated tool call into OpenAI API format. Required for `--enable-auto-tool-choice`. You can choose any option from the built-in parsers or register a plugin via `--tool-parser-plugin`.""" tool_parser_plugin: str = "" """Special the tool parser plugin write to parse the model-generated tool into OpenAI API format, the name register in this plugin can be used in `--tool-call-parser`.""" tool_server: str | None = None """Comma-separated list of host:port pairs (IPv4, IPv6, or hostname). Examples: 127.0.0.1:8000, [::1]:8000, localhost:1234. Or `demo` for demo purpose.""" log_config_file: str | None = envs.VLLM_LOGGING_CONFIG_PATH """Path to logging config JSON file for both vllm and uvicorn""" max_log_len: int | None = None """Max number of prompt characters or prompt ID numbers being printed in log. The default of None means unlimited.""" disable_fastapi_docs: bool = False """Disable FastAPI's OpenAPI schema, Swagger UI, and ReDoc endpoint.""" enable_prompt_tokens_details: bool = False """If set to True, enable prompt_tokens_details in usage.""" enable_server_load_tracking: bool = False """If set to True, enable tracking server_load_metrics in the app state.""" enable_force_include_usage: bool = False """If set to True, including usage on every request.""" enable_tokenizer_info_endpoint: bool = False """Enable the `/tokenizer_info` endpoint. May expose chat templates and other tokenizer configuration.""" enable_log_outputs: bool = False """If set to True, log model outputs (generations). Requires --enable-log-requests.""" enable_log_deltas: bool = True """If set to False, output deltas will not be logged. Relevant only if --enable-log-outputs is set. """ h11_max_incomplete_event_size: int = H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT """Maximum size (bytes) of an incomplete HTTP event (header or body) for h11 parser. Helps mitigate header abuse. Default: 4194304 (4 MB).""" h11_max_header_count: int = H11_MAX_HEADER_COUNT_DEFAULT """Maximum number of HTTP headers allowed in a request for h11 parser. Helps mitigate header abuse. Default: 256.""" log_error_stack: bool = envs.VLLM_SERVER_DEV_MODE """If set to True, log the stack trace of error responses""" tokens_only: bool = False """ If set to True, only enable the Tokens In<>Out endpoint. This is intended for use in a Disaggregated Everything setup. """ enable_offline_docs: bool = False """ Enable offline FastAPI documentation for air-gapped environments. Uses vendored static assets bundled with vLLM. """ @staticmethod def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: from vllm.engine.arg_utils import get_kwargs frontend_kwargs = get_kwargs(FrontendArgs) # Special case: allowed_origins, allowed_methods, allowed_headers all # need json.loads type # Should also remove nargs frontend_kwargs["allowed_origins"]["type"] = json.loads frontend_kwargs["allowed_methods"]["type"] = json.loads frontend_kwargs["allowed_headers"]["type"] = json.loads del frontend_kwargs["allowed_origins"]["nargs"] del frontend_kwargs["allowed_methods"]["nargs"] del frontend_kwargs["allowed_headers"]["nargs"] # Special case: default_chat_template_kwargs needs json.loads type frontend_kwargs["default_chat_template_kwargs"]["type"] = json.loads # Special case: LoRA modules need custom parser action and # optional_type(str) frontend_kwargs["lora_modules"]["type"] = optional_type(str) frontend_kwargs["lora_modules"]["action"] = LoRAParserAction # Special case: Middleware needs to append action frontend_kwargs["middleware"]["action"] = "append" frontend_kwargs["middleware"]["type"] = str if "nargs" in frontend_kwargs["middleware"]: del frontend_kwargs["middleware"]["nargs"] frontend_kwargs["middleware"]["default"] = [] # Special case: Tool call parser shows built-in options. valid_tool_parsers = list(ToolParserManager.list_registered()) parsers_str = ",".join(valid_tool_parsers) frontend_kwargs["tool_call_parser"]["metavar"] = ( f"{{{parsers_str}}} or name registered in --tool-parser-plugin" ) frontend_group = parser.add_argument_group( title="Frontend", description=FrontendArgs.__doc__, ) for key, value in frontend_kwargs.items(): frontend_group.add_argument(f"--{key.replace('_', '-')}", **value) return parser def make_arg_parser(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: """Create the CLI argument parser used by the OpenAI API server. We rely on the helper methods of `FrontendArgs` and `AsyncEngineArgs` to register all arguments instead of manually enumerating them here. This avoids code duplication and keeps the argument definitions in one place. """ parser.add_argument( "model_tag", type=str, nargs="?", help="The model tag to serve (optional if specified in config)", ) parser.add_argument( "--headless", action="store_true", default=False, help="Run in headless mode. See multi-node data parallel " "documentation for more details.", ) parser.add_argument( "--api-server-count", "-asc", type=int, default=None, help="How many API server processes to run. " "Defaults to data_parallel_size if not specified.", ) parser.add_argument( "--config", help="Read CLI options from a config file. " "Must be a YAML with the following options: " "https://docs.vllm.ai/en/latest/configuration/serve_args.html", ) parser = FrontendArgs.add_cli_args(parser) parser = AsyncEngineArgs.add_cli_args(parser) return parser def validate_parsed_serve_args(args: argparse.Namespace): """Quick checks for model serve args that raise prior to loading.""" if hasattr(args, "subparser") and args.subparser != "serve": return # Ensure that the chat template is valid; raises if it likely isn't validate_chat_template(args.chat_template) # Enable auto tool needs a tool call parser to be valid if args.enable_auto_tool_choice and not args.tool_call_parser: raise TypeError("Error: --enable-auto-tool-choice requires --tool-call-parser") if args.enable_log_outputs and not args.enable_log_requests: raise TypeError("Error: --enable-log-outputs requires --enable-log-requests") def create_parser_for_docs() -> FlexibleArgumentParser: parser_for_docs = FlexibleArgumentParser( prog="-m vllm.entrypoints.openai.api_server" ) return make_arg_parser(parser_for_docs)