infiniflow/ragflow · error · ValueError

{} not supported, should be string type

Error message

{} not supported, should be string type

What it means

check_string is a param validator used inside component check() methods: it raises ValueError when the param's runtime type is not exactly 'str' (bool/int/None etc. all fail). The message embeds the caller-provided description plus the offending value.

Source

Thrown at agent/component/base.py:256

                validation_dict = validation_json[default_section][variable]
                value = getattr(param_obj, variable)
                value_legal = False

                for op_type in validation_dict:
                    if self.func[op_type](value, validation_dict[op_type]):
                        value_legal = True
                        break

                if not value_legal:
                    raise ValueError("Please check runtime conf, {} = {} does not match user-parameter restriction".format(variable, value))

            elif variable in validation_json:
                self._validate_param(attr, validation_json)

    @staticmethod
    def check_string(param, description):
        if type(param).__name__ not in ["str"]:
            raise ValueError(description + " {} not supported, should be string type".format(param))

    @staticmethod
    def check_empty(param, description):
        if not param:
            raise ValueError(description + " does not support empty value.")

    @staticmethod
    def check_nonnegative_integer(param, description):
        if type(param).__name__ not in ["int", "long"] or param < 0:
            raise ValueError(description + " {} not supported, should be 0 or positive integer".format(param))

    @staticmethod
    def check_positive_integer(param, description):
        if type(param).__name__ not in ["int", "long"] or param <= 0:
            raise ValueError(description + " {} not supported, should be positive integer".format(param))

    @staticmethod
    def check_positive_number(param, description):

View on GitHub (pinned to 554fb1133a)

Solutions

1. Wrap the value in a string or quote it in YAML/JSON so it deserializes as str.
2. Provide the missing value if it is None because the field was never set.
3. In component code, gate check_string on the field being set if it is genuinely optional.
4. Validate types client-side before submitting the canvas/config.

Example fix

# before
conf = {"api_key": 12345}

# after
conf = {"api_key": "12345"}

def ensure_strings(conf, string_fields):
    for f in string_fields:
        if f in conf and conf[f] is not None:
            conf[f] = str(conf[f])
    return conf

def is_str(v) -> bool:
    return type(v).__name__ == 'str'  # exact match, excludes bool/int/None

try:
    param.check()
except ValueError as e:
    if 'should be string type' in str(e):
        field = parse_field(str(e))
        conf[field] = str(conf[field])
        param.update(conf); param.check()

When it happens

Trigger: A component check() calling check_string(param, description) where param is a non-string — e.g. a model id passed as int, None from an unset optional field, or a bool from a config form.

Common situations: YAML/JSON configs coercing values (unquoted yes/no -> bool, numeric-looking ids -> int); optional fields left as None but validated as required strings; API callers sending typed values where strings are expected.

Related errors

• {} not supported, should be 0 or positive integer
• {} not supported, should be positive integer
• {} not supported, should be positive numeric
• {} not supported, should be non-negative numeric
• {component_name}: {e}

AI-assisted analysis of infiniflow/ragflow@554fb1133a (2026-08-15). Data as JSON: /api/errors/4ae725c7f140a2ec. Report an issue: GitHub.