vllm.entrypoints.openai.cli_args

@config
@dataclass
class FrontendArgs:
    """Arguments for the OpenAI-compatible frontend server."""
    host: Optional[str] = None
    """Host name."""
    port: int = 8000
    """Port number."""
    uds: Optional[str] = None
    """Unix domain socket path. If set, host and port arguments are ignored."""
    uvicorn_log_level: Literal["debug", "info", "warning", "error", "critical",
                               "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: Optional[list[str]] = None
    """If provided, the server will require one of these keys to be presented in
    the header."""
    lora_modules: Optional[list[LoRAModulePath]] = 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: Optional[str] = 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!"}]`"""
    response_role: str = "assistant"
    """The role name to return if `request.add_generation_prompt=true`."""
    ssl_keyfile: Optional[str] = None
    """The file path to the SSL key file."""
    ssl_certfile: Optional[str] = None
    """The file path to the SSL cert file."""
    ssl_ca_certs: Optional[str] = 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)."""
    root_path: Optional[str] = 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.
    Caution: this hurts performance at high QPS."""
    enable_auto_tool_choice: bool = False
    """If specified, exclude tool definitions in prompts when
    tool_choice='none'."""
    exclude_tools_when_tool_choice_none: bool = False
    """Enable auto tool choice for supported models. Use `--tool-call-parser`
    to specify which parser to use."""
    tool_call_parser: Optional[str] = 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: Optional[str] = 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: Optional[str] = envs.VLLM_LOGGING_CONFIG_PATH
    """Path to logging config JSON file for both vllm and uvicorn"""
    max_log_len: Optional[int] = 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 /get_tokenizer_info endpoint. May expose chat
    templates and other tokenizer configuration."""
    enable_log_outputs: bool = False
    """If set to True, enable logging of model outputs (generations) 
    in addition to the input logging that is enabled by default."""

    @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: 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 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.tool_parsers.keys())
        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

class LoRAParserAction(argparse.Action):

    def __call__(
        self,
        parser: argparse.ArgumentParser,
        namespace: argparse.Namespace,
        values: Optional[Union[str, Sequence[str]]],
        option_string: Optional[str] = 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)